Oracle® Database

SQL Tuning Guide

12c Release 1 (12.1)

E49106-08

December 2014

Oracle Database SQL Tuning Guide, 12c Release 1 (12.1)

E49106-08

Copyright © 2014, Oracle and/or its affiliates. All rights reserved.

Primary Author: Lance Ashdown

Contributing Authors: Maria Colgan, Tom Kyte

Contributors: Pete Belknap, Ali Cakmak, Sunil Chakkappen, Immanuel Chan, Deba Chatterjee, Chris Chiappa, Dinesh Das, Leonidas Galanis, William Endress, Bruce Golbus, Katsumi Inoue, Kevin Jernigan, Shantanu Joshi, Adam Kociubes, Allison Lee, Sue Lee, David McDermid, Colin McGregor, Ted Persky, Ekrem Soylemez, Hong Su, Murali Thiyagarajah, Mark Townsend, Randy Urbano, Bharath Venkatakrishnan, Hailing Yu

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Contents

Preface

- Audience

- Documentation Accessibility

- Related Documents

- Conventions

Changes in This Release for Oracle Database SQL Tuning Guide

- Changes in Oracle Database 12c Release 1 (12.1.0.2)

- Changes in Oracle Database 12c Release 1 (12.1.0.1)

Part I SQL Performance Fundamentals

1 Introduction to SQL Tuning

- 1.1 About SQL Tuning

- 1.2 Purpose of SQL Tuning

- 1.3 Prerequisites for SQL Tuning

- 1.4 Tasks and Tools for SQL Tuning

- 1.4.1 SQL Tuning Tasks

- 1.4.2 SQL Tuning Tools

- 1.4.3 User Interfaces to SQL Tuning Tools

2 SQL Performance Methodology

- 2.1 Designing Your Application

- 2.1.1 Data Modeling

- 2.1.2 Writing Efficient Applications

- 2.2 Deploying Your Application

- 2.2.1 Deploying in a Test Environment

- 2.2.2 Rollout Strategies

Part II Query Optimizer Fundamentals

3 SQL Processing

- 3.1 About SQL Processing

- 3.1.1 SQL Parsing

- 3.1.2 SQL Optimization

- 3.1.3 SQL Row Source Generation

- 3.1.4 SQL Execution

- 3.2 How Oracle Database Processes DML

- 3.2.1 Read Consistency

- 3.2.2 Data Changes

- 3.3 How Oracle Database Processes DDL

4 Query Optimizer Concepts

- 4.1 Introduction to the Query Optimizer

- 4.1.1 Purpose of the Query Optimizer

- 4.1.2 Cost-Based Optimization

- 4.1.3 Execution Plans

- 4.2 About Optimizer Components

- 4.2.1 Query Transformer

- 4.2.2 Estimator

- 4.2.3 Plan Generator

- 4.3 About Automatic Tuning Optimizer

- 4.4 About Adaptive Query Optimization

- 4.4.1 Adaptive Plans

- 4.4.2 Adaptive Statistics

- 4.5 About Optimizer Management of SQL Plan Baselines

5 Query Transformations

- 5.1 OR Expansion

- 5.2 View Merging

- 5.2.1 Query Blocks in View Merging

- 5.2.2 Simple View Merging

- 5.2.3 Complex View Merging

- 5.3 Predicate Pushing

- 5.4 Subquery Unnesting

- 5.5 Query Rewrite with Materialized Views

- 5.6 Star Transformation

- 5.6.1 About Star Schemas

- 5.6.2 Purpose of Star Transformations

- 5.6.3 How Star Transformation Works

- 5.6.4 Controls for Star Transformation

- 5.6.5 Star Transformation: Scenario

- 5.6.6 Temporary Table Transformation: Scenario

- 5.7 In-Memory Aggregation

- 5.7.1 Purpose of In-Memory Aggregation

- 5.7.2 How In-Memory Aggregation Works

- 5.7.3 Controls for In-Memory Aggregation

- 5.7.4 In-Memory Aggregation: Scenario

- 5.7.5 In-Memory Aggregation: Example

- 5.8 Table Expansion

- 5.8.1 Purpose of Table Expansion

- 5.8.2 How Table Expansion Works

- 5.8.3 Table Expansion: Scenario

- 5.8.4 Table Expansion and Star Transformation: Scenario

- 5.9 Join Factorization

- 5.9.1 Purpose of Join Factorization

- 5.9.2 How Join Factorization Works

- 5.9.3 Factorization and Join Orders: Scenario

- 5.9.4 Factorization of Outer Joins: Scenario

Part III Query Execution Plans

6 Generating and Displaying Execution Plans

- 6.1 Introduction to Execution Plans

- 6.2 About Plan Generation and Display

- 6.2.1 About the Plan Explanation

- 6.2.2 Why Execution Plans Change

- 6.2.3 Minimizing Throw-Away

- 6.2.4 Looking Beyond Execution Plans

- 6.2.5 EXPLAIN PLAN Restrictions

- 6.2.6 The PLAN_TABLE Output Table

- 6.3 Generating Execution Plans

- 6.3.1 Identifying Statements for EXPLAIN PLAN

- 6.3.2 Specifying Different Tables for EXPLAIN PLAN

- 6.4 Displaying PLAN_TABLE Output

- 6.4.1 Displaying an Execution Plan: Example

- 6.4.2 Customizing PLAN_TABLE Output

7 Reading Execution Plans

- 7.1 Reading Execution Plans: Basic

- 7.2 Reading Execution Plans: Advanced

- 7.2.1 Reading Adaptive Plans

- 7.2.2 Viewing Parallel Execution with EXPLAIN PLAN

- 7.2.3 Viewing Bitmap Indexes with EXPLAIN PLAN

- 7.2.4 Viewing Result Cache with EXPLAIN PLAN

- 7.2.5 Viewing Partitioned Objects with EXPLAIN PLAN

- 7.2.6 PLAN_TABLE Columns

- 7.3 Execution Plan Reference

- 7.3.1 Execution Plan Views

- 7.3.2 PLAN_TABLE Columns

- 7.3.3 DBMS_XPLAN Program Units

Part IV SQL Operators

8 Optimizer Access Paths

- 8.1 Introduction to Access Paths

- 8.2 Table Access Paths

- 8.2.1 About Heap-Organized Table Access

- 8.2.2 Full Table Scans

- 8.2.3 Table Access by Rowid

- 8.2.4 Sample Table Scans

- 8.2.5 In-Memory Table Scans

- 8.3 B-Tree Index Access Paths

- 8.3.1 About B-Tree Index Access

- 8.3.2 Index Unique Scans

- 8.3.3 Index Range Scans

- 8.3.4 Index Full Scans

- 8.3.5 Index Fast Full Scans

- 8.3.6 Index Skip Scans

- 8.3.7 Index Join Scans

- 8.4 Bitmap Index Access Paths

- 8.4.1 About Bitmap Index Access

- 8.4.2 Bitmap Conversion to Rowid

- 8.4.3 Bitmap Index Single Value

- 8.4.4 Bitmap Index Range Scans

- 8.4.5 Bitmap Merge

- 8.5 Table Cluster Access Paths

- 8.5.1 Cluster Scans

- 8.5.2 Hash Scans

9 Joins

- 9.1 About Joins

- 9.1.1 Join Trees

- 9.1.2 How the Optimizer Executes Join Statements

- 9.1.3 How the Optimizer Chooses Execution Plans for Joins

- 9.2 Join Methods

- 9.2.1 Nested Loops Joins

- 9.2.2 Hash Joins

- 9.2.3 Sort Merge Joins

- 9.2.4 Cartesian Joins

- 9.3 Join Types

- 9.3.1 Inner Joins

- 9.3.2 Outer Joins

- 9.3.3 Semijoins

- 9.3.4 Antijoins

- 9.4 Join Optimizations

- 9.4.1 Bloom Filters

- 9.4.2 Partition-Wise Joins

Part V Optimizer Statistics

10 Optimizer Statistics Concepts

- 10.1 Introduction to Optimizer Statistics

- 10.2 About Optimizer Statistics Types

- 10.2.1 Table Statistics

- 10.2.2 Column Statistics

- 10.2.3 Index Statistics

- 10.2.4 Session-Specific Statistics for Global Temporary Tables

- 10.2.5 System Statistics

- 10.2.6 User-Defined Optimizer Statistics

- 10.3 How the Database Gathers Optimizer Statistics

- 10.3.1 DBMS_STATS Package

- 10.3.2 Dynamic Statistics

- 10.3.3 Online Statistics Gathering for Bulk Loads

- 10.4 When the Database Gathers Optimizer Statistics

- 10.4.1 SQL Plan Directives

- 10.4.2 When the Database Samples Data

- 10.4.3 How the Database Samples Data

11 Histograms

- 11.1 Purpose of Histograms

- 11.2 When Oracle Database Creates Histograms

- 11.3 Cardinality Algorithms When Using Histograms

- 11.3.1 Endpoint Numbers and Values

- 11.3.2 Popular and Nonpopular Values

- 11.3.3 Bucket Compression

- 11.4 Frequency Histograms

- 11.4.1 Criteria For Frequency Histograms

- 11.4.2 Generating a Frequency Histogram

- 11.4.3 Generating a Top Frequency Histogram

- 11.5 Height-Balanced Histograms (Legacy)

- 11.5.1 Criteria for Height-Balanced Histograms

- 11.5.2 Generating a Height-Balanced Histogram

- 11.6 Hybrid Histograms

- 11.6.1 How Endpoint Repeat Counts Work

- 11.6.2 Criteria for Hybrid Histograms

- 11.6.3 Generating a Hybrid Histogram

12 Managing Optimizer Statistics: Basic Topics

- 12.1 About Optimizer Statistics Collection

- 12.1.1 Purpose of Optimizer Statistics Collection

- 12.1.2 User Interfaces for Optimizer Statistics Management

- 12.2 Controlling Automatic Optimizer Statistics Collection

- 12.2.1 Controlling Automatic Optimizer Statistics Collection Using Cloud Control

- 12.2.2 Controlling Automatic Optimizer Statistics Collection from the Command Line

- 12.3 Setting Optimizer Statistics Preferences

- 12.3.1 About Optimizer Statistics Preferences

- 12.3.2 Setting Global Optimizer Statistics Preferences Using Cloud Control

- 12.3.3 Setting Object-Level Optimizer Statistics Preferences Using Cloud Control

- 12.3.4 Setting Optimizer Statistics Preferences from the Command Line

- 12.4 Gathering Optimizer Statistics Manually

- 12.4.1 About Manual Statistics Collection with DBMS_STATS

- 12.4.2 Guidelines for Gathering Optimizer Statistics Manually

- 12.4.3 Determining When Optimizer Statistics Are Stale

- 12.4.4 Gathering Schema and Table Statistics

- 12.4.5 Gathering Statistics for Fixed Objects

- 12.4.6 Gathering Statistics for Volatile Tables Using Dynamic Statistics

- 12.4.7 Gathering Optimizer Statistics Concurrently

- 12.4.8 Gathering Incremental Statistics on Partitioned Objects

- 12.5 Gathering System Statistics Manually

- 12.5.1 About Gathering System Statistics with DBMS_STATS

- 12.5.2 Guidelines for Gathering System Statistics

- 12.5.3 Gathering Workload Statistics

- 12.5.4 Gathering Noworkload Statistics

- 12.5.5 Deleting System Statistics

13 Managing Optimizer Statistics: Advanced Topics

- 13.1 Controlling Dynamic Statistics

- 13.1.1 About Dynamic Statistics Levels

- 13.1.2 Setting Dynamic Statistics Levels Manually

- 13.1.3 Disabling Dynamic Statistics

- 13.2 Publishing Pending Optimizer Statistics

- 13.2.1 User Interfaces for Publishing Optimizer Statistics

- 13.2.2 Managing Published and Pending Statistics

- 13.3 Managing Extended Statistics

- 13.3.1 Managing Column Group Statistics

- 13.3.2 Managing Expression Statistics

- 13.4 Locking and Unlocking Optimizer Statistics

- 13.4.1 Locking Statistics

- 13.4.2 Unlocking Statistics

- 13.5 Restoring Optimizer Statistics

- 13.5.1 Guidelines for Restoring Optimizer Statistics

- 13.5.2 Restrictions for Restoring Optimizer Statistics

- 13.5.3 Restoring Optimizer Statistics

- 13.6 Managing Optimizer Statistics Retention

- 13.6.1 Obtaining Optimizer Statistics History

- 13.6.2 Changing the Optimizer Statistics Retention Period

- 13.6.3 Purging Optimizer Statistics

- 13.7 Importing and Exporting Optimizer Statistics

- 13.7.1 About Transporting Optimizer Statistics

- 13.7.2 Transporting Optimizer Statistics to a Test Database

- 13.8 Running Statistics Gathering Functions in Reporting Mode

- 13.9 Reporting on Past Statistics Gathering Operations

- 13.10 Managing SQL Plan Directives

Part VI Optimizer Controls

14 Influencing the Optimizer

- 14.1 About Influencing the Optimizer

- 14.2 Influencing the Optimizer with Initialization Parameters

- 14.2.1 About Optimizer Initialization Parameters

- 14.2.2 Enabling Optimizer Features

- 14.2.3 Choosing an Optimizer Goal

- 14.2.4 Controlling Adaptive Optimization

- 14.3 Influencing the Optimizer with Hints

- 14.3.1 About Optimizer Hints

- 14.3.2 Guidelines for Join Order Hints

15 Controlling Cursor Sharing

- 15.1 About Bind Variables and Cursors

- 15.1.1 Bind Variable Peeking

- 15.1.2 SQL Sharing Criteria

- 15.1.3 Adaptive Cursor Sharing

- 15.1.4 Bind-Related Performance Views

- 15.2 Designing Applications for Cursor Sharing

- 15.3 Sharing Cursors for Existing Applications

- 15.3.1 How Similar Statements Can Share SQL Areas

- 15.3.2 When to Set CURSOR_SHARING to FORCE

Part VII Monitoring and Tracing SQL

16 Monitoring Database Operations

- 16.1 About Monitoring Database Operations

- 16.1.1 Purpose of Monitoring Database Operations

- 16.1.2 Database Operation Monitoring Concepts

- 16.1.3 User Interfaces for Database Operations Monitoring

- 16.1.4 Basic Tasks in Database Operations Monitoring

- 16.2 Enabling and Disabling Monitoring of Database Operations

- 16.2.1 Enabling Monitoring of Database Operations at the System Level

- 16.2.2 Enabling and Disabling Monitoring of Database Operations at the Statement Level

- 16.3 Creating a Database Operation

- 16.4 Reporting on Database Operations Using SQL Monitor

17 Gathering Diagnostic Data with SQL Test Case Builder

- 17.1 Purpose of SQL Test Case Builder

- 17.2 Concepts for SQL Test Case Builder

- 17.2.1 SQL Incidents

- 17.2.2 What SQL Test Case Builder Captures

- 17.2.3 Output of SQL Test Case Builder

- 17.3 User Interfaces for SQL Test Case Builder

- 17.3.1 Graphical Interface for SQL Test Case Builder

- 17.3.2 Command-Line Interface for SQL Test Case Builder

- 17.4 Running SQL Test Case Builder

18 Performing Application Tracing

- 18.1 Overview of End-to-End Application Tracing

- 18.1.1 Purpose of End-to-End Application Tracing

- 18.1.2 User Interfaces for End-to-End Application Tracing

- 18.2 Enabling Statistics Gathering for End-to-End Tracing

- 18.2.1 Enabling Statistics Gathering for a Client ID

- 18.2.2 Enabling Statistics Gathering for a Service, Module, and Action

- 18.3 Enabling End-to-End Application Tracing

- 18.3.1 Enabling Tracing for a Client Identifier

- 18.3.2 Enabling Tracing for a Service, Module, and Action

- 18.3.3 Enabling Tracing for a Session

- 18.3.4 Enabling Tracing for the Instance or Database

- 18.4 Generating Output Files Using SQL Trace and TKPROF

- 18.4.1 Step 1: Setting Initialization Parameters for Trace File Management

- 18.4.2 Step 2: Enabling the SQL Trace Facility

- 18.4.3 Step 3: Generating Output Files with TKPROF

- 18.4.4 Step 4: Storing SQL Trace Facility Statistics

- 18.5 Guidelines for Interpreting TKPROF Output

- 18.5.1 Guideline for Interpreting the Resolution of Statistics

- 18.5.2 Guideline for Recursive SQL Statements

- 18.5.3 Guideline for Deciding Which Statements to Tune

- 18.5.4 Guidelines for Avoiding Traps in TKPROF Interpretation

- Application Tracing Utilities

- TRCSESS

- TKPROF

- Views for Application Tracing

- Views Relevant for Trace Statistics

- Views Related to Enabling Tracing

Part VIII Automatic SQL Tuning

19 Managing SQL Tuning Sets

- 19.1 About SQL Tuning Sets

- 19.1.1 Purpose of SQL Tuning Sets

- 19.1.2 Concepts for SQL Tuning Sets

- 19.1.3 User Interfaces for SQL Tuning Sets

- 19.1.4 Basic Tasks for SQL Tuning Sets

- 19.2 Creating a SQL Tuning Set

- 19.3 Loading a SQL Tuning Set

- 19.4 Displaying the Contents of a SQL Tuning Set

- 19.5 Modifying a SQL Tuning Set

- 19.6 Transporting a SQL Tuning Set

- 19.6.1 About Transporting SQL Tuning Sets

- 19.6.2 Transporting SQL Tuning Sets with DBMS_SQLTUNE

- 19.7 Dropping a SQL Tuning Set

20 Analyzing SQL with SQL Tuning Advisor

- 20.1 About SQL Tuning Advisor

- 20.1.1 Purpose of SQL Tuning Advisor

- 20.1.2 SQL Tuning Advisor Architecture

- 20.1.3 Automatic Tuning Optimizer Concepts

- 20.2 Managing the Automatic SQL Tuning Task

- 20.2.1 About the Automatic SQL Tuning Task

- 20.2.2 Enabling and Disabling the Automatic SQL Tuning Task

- 20.2.3 Configuring the Automatic SQL Tuning Task

- 20.2.4 Viewing Automatic SQL Tuning Reports

- 20.3 Running SQL Tuning Advisor On Demand

- 20.3.1 About On-Demand SQL Tuning

- 20.3.2 Creating a SQL Tuning Task

- 20.3.3 Configuring a SQL Tuning Task

- 20.3.4 Executing a SQL Tuning Task

- 20.3.5 Monitoring a SQL Tuning Task

- 20.3.6 Displaying the Results of a SQL Tuning Task

21 Optimizing Access Paths with SQL Access Advisor

- 21.1 About SQL Access Advisor

- 21.1.1 Purpose of SQL Access Advisor

- 21.1.2 SQL Access Advisor Architecture

- 21.1.3 User Interfaces for SQL Access Advisor

- 21.2 Using SQL Access Advisor: Basic Tasks

- 21.2.1 Creating a SQL Tuning Set as Input for SQL Access Advisor

- 21.2.2 Populating a SQL Tuning Set with a User-Defined Workload

- 21.2.3 Creating and Configuring a SQL Access Advisor Task

- 21.2.4 Executing a SQL Access Advisor Task

- 21.2.5 Viewing SQL Access Advisor Task Results

- 21.2.6 Generating and Executing a Task Script

- 21.3 Performing a SQL Access Advisor Quick Tune

- 21.4 Using SQL Access Advisor: Advanced Tasks

- 21.4.1 Evaluating Existing Access Structures

- 21.4.2 Updating SQL Access Advisor Task Attributes

- 21.4.3 Creating and Using SQL Access Advisor Task Templates

- 21.4.4 Terminating SQL Access Advisor Task Execution

- 21.4.5 Deleting SQL Access Advisor Tasks

- 21.4.6 Marking SQL Access Advisor Recommendations

- 21.4.7 Modifying SQL Access Advisor Recommendations

- 21.5 SQL Access Advisor Examples

- 21.6 SQL Access Advisor Reference

- 21.6.1 Action Attributes in the DBA_ADVISOR_ACTIONS View

- 21.6.2 Categories for SQL Access Advisor Task Parameters

- 21.6.3 SQL Access Advisor Constants

Part IX SQL Controls

22 Managing SQL Profiles

- 22.1 About SQL Profiles

- 22.1.1 Purpose of SQL Profiles

- 22.1.2 Concepts for SQL Profiles

- 22.1.3 User Interfaces for SQL Profiles

- 22.1.4 Basic Tasks for SQL Profiles

- 22.2 Implementing a SQL Profile

- 22.2.1 About SQL Profile Implementation

- 22.2.2 Implementing a SQL Profile

- 22.3 Listing SQL Profiles

- 22.4 Altering a SQL Profile

- 22.5 Dropping a SQL Profile

- 22.6 Transporting a SQL Profile

23 Managing SQL Plan Baselines

- 23.1 About SQL Plan Management

- 23.1.1 Purpose of SQL Plan Management

- 23.1.2 Plan Capture

- 23.1.3 Plan Selection

- 23.1.4 Plan Evolution

- 23.1.5 Storage Architecture for SQL Plan Management

- 23.1.6 User Interfaces for SQL Plan Management

- 23.1.7 Basic Tasks in SQL Plan Management

- 23.2 Configuring SQL Plan Management

- 23.2.1 Configuring the Capture and Use of SQL Plan Baselines

- 23.2.2 Managing the SPM Evolve Advisor Task

- 23.3 Displaying Plans in a SQL Plan Baseline

- 23.4 Loading SQL Plan Baselines

- 23.4.1 Loading Plans from a SQL Tuning Set

- 23.4.2 Loading Plans from the Shared SQL Area

- 23.4.3 Loading Plans from a Staging Table

- 23.5 Evolving SQL Plan Baselines Manually

- 23.5.1 About the DBMS_SPM Evolve Functions

- 23.5.2 Managing an Evolve Task

- 23.6 Dropping SQL Plan Baselines

- 23.7 Managing the SQL Management Base

- 23.7.1 Changing the Disk Space Limit for the SMB

- 23.7.2 Changing the Plan Retention Policy in the SMB

24 Migrating Stored Outlines to SQL Plan Baselines

- 24.1 About Stored Outline Migration

- 24.1.1 Purpose of Stored Outline Migration

- 24.1.2 How Stored Outline Migration Works

- 24.1.3 User Interface for Stored Outline Migration

- 24.1.4 Basic Steps in Stored Outline Migration

- 24.2 Preparing for Stored Outline Migration

- 24.3 Migrating Outlines to Utilize SQL Plan Management Features

- 24.4 Migrating Outlines to Preserve Stored Outline Behavior

- 24.5 Performing Follow-Up Tasks After Stored Outline Migration

A Guidelines for Indexes and Table Clusters

- A.1 Guidelines for Tuning Index Performance

- A.1.1 Guidelines for Tuning the Logical Structure

- A.1.2 Guidelines for Using SQL Access Advisor

- A.1.3 Guidelines for Choosing Columns and Expressions to Index

- A.1.4 Guidelines for Choosing Composite Indexes

- A.1.5 Guidelines for Writing SQL Statements That Use Indexes

- A.1.6 Guidelines for Writing SQL Statements That Avoid Using Indexes

- A.1.7 Guidelines for Re-Creating Indexes

- A.1.8 Guidelines for Compacting Indexes

- A.1.9 Guidelines for Using Nonunique Indexes to Enforce Uniqueness

- A.1.10 Guidelines for Using Enabled Novalidated Constraints

- A.2 Guidelines for Using Function-Based Indexes for Performance

- A.3 Guidelines for Using Partitioned Indexes for Performance

- A.4 Guidelines for Using Index-Organized Tables for Performance

- A.5 Guidelines for Using Bitmap Indexes for Performance

- A.6 Guidelines for Using Bitmap Join Indexes for Performance

- A.7 Guidelines for Using Domain Indexes for Performance

- A.8 Guidelines for Using Table Clusters

- A.9 Guidelines for Using Hash Clusters for Performance

Glossary

Index

Preface

This manual explains how to tune Oracle SQL.

This preface contains the following topics:

- Audience

- Documentation Accessibility

- Related Documents

- Conventions

Audience

This document is intended for database administrators and application developers who perform the following tasks:

- Generating and interpreting SQL execution plans

- Managing optimizer statistics

- Influencing the optimizer through initialization parameters or SQL hints

- Controlling cursor sharing for SQL statements

- Monitoring SQL execution

- Performing application tracing

- Managing SQL tuning sets

- Using SQL Tuning Advisor or SQL Access Advisor

- Managing SQL profiles

- Managing SQL baselines

Documentation Accessibility

For information about Oracle's commitment to accessibility, visit the Oracle Accessibility Program website at http://www.oracle.com/pls/topic/lookup?ctx=acc&id=docacc.

Access to Oracle Support

Oracle customers that have purchased support have access to electronic support through My Oracle Support. For information, visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=info or visit http://www.oracle.com/pls/topic/lookup?ctx=acc&id=trs if you are hearing impaired.

Related Documents

This manual assumes that you are familiar with the following documents:

- Oracle Database Concepts

- Oracle Database SQL Language Reference

- Oracle Database Performance Tuning Guide

- Oracle Database Development Guide

To learn how to tune data warehouse environments, see Oracle Database Data Warehousing Guide.

Many examples in this book use the sample schemas, which are installed by default when you select the Basic Installation option with an Oracle Database. See Oracle Database Sample Schemas for information on how these schemas were created and how you can use them.

To learn about Oracle Database error messages, see Oracle Database Error Messages Reference. Oracle Database error message documentation is only available in HTML. If you are accessing the error message documentation on the Oracle Documentation CD, then you can browse the error messages by range. After you find the specific range, use your browser's find feature to locate the specific message. When connected to the Internet, you can search for a specific error message using the error message search feature of the Oracle online documentation.

Conventions

The following text conventions are used in this document:

Convention

Meaning

boldface

Boldface type indicates graphical user interface elements associated with an action, or terms defined in text or the glossary.

italic

Italic type indicates book titles, emphasis, or placeholder variables for which you supply particular values.

monospace

Monospace type indicates commands within a paragraph, URLs, code in examples, text that appears on the screen, or text that you enter.

Changes in This Release for Oracle Database SQL Tuning Guide

This preface contains:

- Changes in Oracle Database 12c Release 1 (12.1.0.2)

- Changes in Oracle Database 12c Release 1 (12.1.0.1)

Changes in Oracle Database 12c Release 1 (12.1.0.2)

Oracle Database SQL Tuning Guide for Oracle Database 12c Release 1 (12.1.0.2) has the following changes.

New Features

The following features are new in this release:

- In-memory aggregation
This optimization minimizes the join and GROUP BY processing required for each row when joining a single large table to multiple small tables, as in a star schema. VECTOR GROUP BY aggregation uses the infrastructure related to parallel query (PQ) processing, and blends it with CPU-efficient algorithms to maximize the performance and effectiveness of the initial aggregation performed before redistributing fact data.

See "In-Memory Aggregation".

- SQL Monitor support for adaptive plans
SQL Monitor supports adaptive plans in the following ways:

- Indicates whether a plan is adaptive, and show its current status: resolving or resolved.

- Provides a list that enables you to select the current, full, or final plans
See "Adaptive Plans" to learn more about adaptive plans, and "Reporting on Database Operations Using SQL Monitor" to learn more about SQL Monitor.

Changes in Oracle Database 12c Release 1 (12.1.0.1)

Oracle Database SQL Tuning Guide for Oracle Database 12c Release 1 (12.1) has the following changes.

New Features

The following features are new in this release:

- Adaptive SQL Plan Management (SPM)
The SPM Evolve Advisor is a task infrastructure that enables you to schedule an evolve task, rerun an evolve task, and generate persistent reports. The new automatic evolve task, SYS_AUTO_SPM_EVOLVE_TASK, runs in the default maintenance window. This task ranks all unaccepted plans and runs the evolve process for them. If the task finds a new plan that performs better than existing plan, the task automatically accepts the plan. You can also run evolution tasks manually using the DBMS_SPM package.

See "Managing the SPM Evolve Advisor Task".

- Adaptive query optimization
Adaptive query optimization is a set of capabilities that enable the optimizer to make run-time adjustments to execution plans and discover additional information that can lead to better statistics. The set of capabilities include:

- Adaptive plans
An adaptive plan has built-in options that enable the final plan for a statement to differ from the default plan. During the first execution, before a specific subplan becomes active, the optimizer makes a final decision about which option to use. The optimizer bases its choice on observations made during the execution up to this point. The ability of the optimizer to adapt plans can improve query performance.

See "Adaptive Plans".

- Automatic reoptimization
When using automatic reoptimization, the optimizer monitors the initial execution of a query. If the actual execution statistics vary significantly from the original plan statistics, then the optimizer records the execution statistics and uses them to choose a better plan the next time the statement executes. The database uses information obtained during automatic reoptimization to generate SQL plan directives automatically.

See "Automatic Reoptimization".

- SQL plan directives
In releases earlier than Oracle Database 12c, the database stored compilation and execution statistics in the shared SQL area, which is nonpersistent. Starting in this release, the database can use a SQL plan directive, which is additional information and instructions that the optimizer can use to generate a more optimal plan. The database stores SQL plan directives persistently in the SYSAUX tablespace. When generating an execution plan, the optimizer can use SQL plan directives to obtain more information about the objects accessed in the plan.

See "SQL Plan Directives".

- Dynamic statistics enhancements
In releases earlier than Oracle Database 12c, Oracle Database only used dynamic statistics (previously called dynamic sampling) when one or more of the tables in a query did not have optimizer statistics. Starting in this release, the optimizer automatically decides whether dynamic statistics are useful and which dynamic statistics level to use for all SQL statements. Dynamic statistics gathers are persistent and usable by other queries.

See "Dynamic Statistics".

- New types of histograms
This release introduces top frequency and hybrid histograms. If a column contains more than 254 distinct values, and if the top 254 most frequent values occupy more than 99% of the data, then the database creates a top frequency histogram using the top 254 most frequent values. By ignoring the nonpopular values, which are statistically insignificant, the database can produce a better quality histogram for highly popular values. A hybrid histogram is an enhanced height-based histogram that stores the exact frequency of each endpoint in the sample, and ensures that a value is never stored in multiple buckets.

Also, regular frequency histograms have been enhanced. The optimizer computes frequency histograms during NDV computation based on a full scan of the data rather than a small sample (when AUTO_SAMPLING is used). The enhanced frequency histograms ensure that even highly infrequent values are properly represented with accurate bucket counts within a histogram.

See Chapter 11, "Histograms."

- Monitoring database operations
Real-Time Database Operations Monitoring enables you to monitor long running database tasks such as batch jobs, scheduler jobs, and Extraction, Transformation, and Loading (ETL) jobs as a composite business operation. This feature tracks the progress of SQL and PL/SQL queries associated with the business operation being monitored. As a DBA or developer, you can define business operations for monitoring by explicitly specifying the start and end of the operation or implicitly with tags that identify the operation.

See "Monitoring Database Operations".

- Concurrent statistics gathering
You can concurrently gather optimizer statistics on multiple tables, table partitions, or table subpartitions. By fully utilizing multiprocessor environments, the database can reduce the overall time required to gather statistics. Oracle Scheduler and Advanced Queuing create and manage jobs to gather statistics concurrently. The scheduler decides how many jobs to execute concurrently, and how many to queue based on available system resources and the value of the JOB_QUEUE_PROCESSES initialization parameter.

See "Gathering Optimizer Statistics Concurrently".

- Reporting mode for DBMS_STATS statistics gathering functions
You can run the DBMS_STATS functions in reporting mode. In this mode, the optimizer does not actually gather statistics, but reports objects that would be processed if you were to use a specified statistics gathering function.

See "Running Statistics Gathering Functions in Reporting Mode".

- Reports on past statistics gathering operations
You can use DBMS_STATS functions to report on a specific statistics gathering operation or on operations that occurred during a specified time.

See "Reporting on Past Statistics Gathering Operations".

- Automatic column group creation
With column group statistics, the database gathers optimizer statistics on a group of columns treated as a unit. Starting in Oracle Database 12c, the database automatically determines which column groups are required in a specified workload or SQL tuning set, and then creates the column groups. Thus, for any specified workload, you no longer need to know which columns from each table must be grouped.

See "Detecting Useful Column Groups for a Specific Workload".

- Session-private statistics for global temporary tables
Starting in this release, global temporary tables have a different set of optimizer statistics for each session. Session-specific statistics improve performance and manageability of temporary tables because users no longer need to set statistics for a global temporary table in each session or rely on dynamic statistics. The possibility of errors in cardinality estimates for global temporary tables is lower, ensuring that the optimizer has the necessary information to determine an optimal execution plan.

See "Session-Specific Statistics for Global Temporary Tables".

- SQL Test Case Builder enhancements
SQL Test Case Builder can capture and replay actions and events that enable you to diagnose incidents that depend on certain dynamic and volatile factors. This capability is especially useful for parallel query and automatic memory management.

See Chapter 17, "Gathering Diagnostic Data with SQL Test Case Builder."

- Online statistics gathering for bulk loads
A bulk load is a CREATE TABLE AS SELECT or INSERT INTO ... SELECT operation. In releases earlier than Oracle Database 12c, you needed to manually gather statistics after a bulk load to avoid the possibility of a suboptimal execution plan caused by stale statistics. Starting in this release, Oracle Database gathers optimizer statistics automatically, which improves both performance and manageability.

See "Online Statistics Gathering for Bulk Loads".

- Reuse of synopses after partition maintenance operations
ALTER TABLE EXCHANGE is a common partition maintenance operation. During a partition exchange, the statistics of the partition and the table are also exchanged. A synopsis is a set of auxiliary statistics gathered on a partitioned table when the INCREMENTAL value is set to true. In releases earlier than Oracle Database 12c, you could not gather table-level synopses on a table. Thus, you could not gather table-level synopses on a table, exchange the table with a partition, and end up with synopses on the partition. You had to explicitly gather optimizer statistics in incremental mode to create the missing synopses. Starting in this release, you can gather table-level synopses on a table. When you exchange this table with a partition in an incremental mode table, the synopses are also exchanged.

See "Maintaining Incremental Statistics for Partition Maintenance Operations".

- Automatic updates of global statistics for tables with stale or locked partition statistics
Incremental statistics can automatically calculate global statistics for a partitioned table even if the partition or subpartition statistics are stale and locked.

See "Maintaining Incremental Statistics for Tables with Stale or Locked Partition Statistics".

- Cube query performance enhancements
These enhancements minimize CPU and memory consumption and reduce I/O for queries against cubes.

See Table 7-7, "OPERATION and OPTIONS Values Produced by EXPLAIN PLAN" to learn about the CUBE JOIN operation.

Deprecated Features

The following features are deprecated in this release, and may be desupported in a future release:

- Stored outlines
See Chapter 23, "Managing SQL Plan Baselines" for information about alternatives.

- The SIMILAR value for the CURSOR_SHARING initialization parameter
This value is deprecated. Use FORCE instead.

See "When to Set CURSOR_SHARING to FORCE".

Desupported Features

Some features previously described in this document are desupported in Oracle Database 12c. See Oracle Database Upgrade Guide for a list of desupported features.

Other Changes

The following are additional changes in the release:

- New tuning books
The Oracle Database 11g Oracle Database Performance Tuning Guide has been divided into two books for Oracle Database 12c:

- Oracle Database Performance Tuning Guide, which contains only topics that pertain to tuning the database

- Oracle Database SQL Tuning Guide, which contains only topics that pertain to tuning SQL

Part I

SQL Performance Fundamentals

This part contains the following chapters:

- Chapter 1, "Introduction to SQL Tuning"

- Chapter 2, "SQL Performance Methodology"

1 Introduction to SQL Tuning

This chapter provides a brief introduction to SQL tuning.

This chapter contains the following topics:

- About SQL Tuning

- Purpose of SQL Tuning

- Prerequisites for SQL Tuning

- Tasks and Tools for SQL Tuning

1.1 About SQL Tuning

SQL tuning is the iterative process of improving SQL statement performance to meet specific, measurable, and achievable goals. SQL tuning implies fixing problems in deployed applications. In contrast, application design sets the security and performance goals before deploying an application.

See Also:

- Chapter 2, "SQL Performance Methodology"

- "Designing Your Application" to learn how to design for SQL performance

1.2 Purpose of SQL Tuning

A SQL statement becomes a problem when it fails to perform according to a predetermined and measurable standard. After you have identified the problem, a typical tuning session has one of the following goals:

- Reduce user response time, which means decreasing the time between when a user issues a statement and receives a response

- Improve throughput, which means using the least amount of resources necessary to process all rows accessed by a statement

For a response time problem, consider an online book seller application that hangs for three minutes after a customer updates the shopping cart. Contrast with a three-minute parallel query in a data warehouse that consumes all of the database host CPU, preventing other queries from running. In each case, the user response time is three minutes, but the cause of the problem is different, and so is the tuning goal.

1.3 Prerequisites for SQL Tuning

If you are tuning SQL, then this manual assumes that you have the following knowledge and skills:

- Familiarity with database architecture
Database architecture is not the domain of administrators alone. As a developer, you want to develop applications in the least amount of time against an Oracle database, which requires exploiting the database architecture and features. For example, not understanding Oracle Database concurrency controls and multiversioning read consistency may make an application corrupt the integrity of the data, run slowly, and decrease scalability.

Oracle Database Concepts explains the basic relational data structures, transaction management, storage structures, and instance architecture of Oracle Database.

- Knowledge of SQL and PL/SQL
Because of the existence of GUI-based tools, it is possible to create applications and administer a database without knowing SQL. However, it is impossible to tune applications or a database without knowing SQL.

Oracle Database Concepts includes an introduction to Oracle SQL and PL/SQL. You must also have a working knowledge of Oracle Database SQL Language Reference, Oracle Database PL/SQL Language Reference, and Oracle Database PL/SQL Packages and Types Reference.

- Familiarity with database-provided SQL tuning tools
The database generates performance statistics, and provides SQL tuning tools that interpret these statistics.

Oracle Database 2 Day + Performance Tuning Guide provides an introduction to the principal SQL tuning tools.

1.4 Tasks and Tools for SQL Tuning

After you have identified the goal for a tuning session, for example, reducing user response time from three minutes to less than a second, the problem becomes how to accomplish this goal. The Oracle-recommended tuning methodology is covered in detail in Chapter 2, "SQL Performance Methodology."

1.4.1 SQL Tuning Tasks

The specifics of a tuning session depend on many factors, including whether you tune proactively or reactively. In proactive SQL tuning, you regularly use SQL Tuning Advisor to determine whether you can make SQL statements perform better. In reactive SQL tuning, you correct a SQL-related problem that a user has experienced.

Whether you tune proactively or reactively, a typical SQL tuning session involves all or most of the following tasks:

1. Identifying high-load SQL statements
Review past execution history to find the statements responsible for a large share of the application workload and system resources.

2. Gathering performance-related data
The optimizer statistics are crucial to SQL tuning. If these statistics do not exist or are no longer accurate, then the optimizer cannot generate the best plan. Other data relevant to SQL performance include the structure of tables and views that the statement accessed, and definitions of any indexes available to the statement.

3. Determining the causes of the problem
Typically, causes of SQL performance problems include:

- Inefficiently designed SQL statements
If a SQL statement is written so that it performs unnecessary work, then the optimizer cannot do much to improve its performance. Examples of inefficient design include

- Neglecting to add a join condition, which leads to a Cartesian join

- Using hints to specify a large table as the driving table in a join

- Specifying UNION instead of UNION ALL

- Making a subquery execute for every row in an outer query

- Suboptimal execution plans
The query optimizer (also called the optimizer) is internal software that determines which execution plan is most efficient. Sometimes the optimizer chooses a plan with a suboptimal access path, which is the means by which the database retrieves data from the database. For example, the plan for a query predicate with low selectivity may use a full table scan on a large table instead of an index.

You can compare the execution plan of an optimally performing SQL statement to the plan of the statement when it performs suboptimally. This comparison, along with information such as changes in data volumes, can help identify causes of performance degradation.

- Missing SQL access structures
Absence of SQL access structures, such as indexes and materialized views, is a typical reason for suboptimal SQL performance. The optimal set of access structures can improve SQL performance by orders of magnitude.

- Stale optimizer statistics
Statistics gathered by DBMS_STATS can become stale when the statistics maintenance operations, either automatic or manual, cannot keep up with the changes to the table data caused by DML. Because stale statistics on a table do not accurately reflect the table data, the optimizer can make decisions based on faulty information and generate suboptimal execution plans.

- Hardware problems
Suboptimal performance might be connected with memory, I/O, and CPU problems.

4. Defining the scope of the problem
The scope of the solution must match the scope of the problem. Consider a problem at the database level and a problem at the statement level. For example, the shared pool is too small, which causes cursors to age out quickly, which in turn causes many hard parses (see "Shared Pool Check"). Using an initialization parameter to increase the shared pool size fixes the problem at the database level and improves performance for all sessions. However, if a single SQL statement is not using a helpful index, then changing the optimizer initialization parameters for the entire database could harm overall performance. If a single SQL statement has a problem, then an appropriately scoped solution addresses just this problem with this statement.

5. Implementing corrective actions for suboptimally performing SQL statements
These actions vary depending on circumstances. For example, you might rewrite a SQL statement to be more efficient, avoiding unnecessary hard parsing by rewriting the statement to use bind variables. You might also use equijoins, remove functions from WHERE clauses, and break a complex SQL statement into multiple simple statements.

In some cases, you improve SQL performance not by rewriting the statement, but by restructuring schema objects. For example, you might index a new access path, or reorder columns in a concatenated index. You might also partition a table, introduce derived values, or even change the database design.

6. Preventing SQL performance regressions
To ensure optimal SQL performance, verify that execution plans continue to provide optimal performance, and choose better plans if they come available. You can achieve these goals using optimizer statistics, SQL profiles, and SQL plan baselines.

1.4.2 SQL Tuning Tools

SQL tuning tools fall into the categories of automated and manual. In this context, a tool is automated if the database itself can provide diagnosis, advice, or corrective actions. A manual tool requires you to perform all of these operations.

All tuning tools depend on the basic tools of the dynamic performance views, statistics, and metrics that the database instance collects. The database itself contains the data and metadata required to tune SQL statements.

1.4.2.1 Automated SQL Tuning Tools

Oracle Database provides several advisors relevant for SQL tuning. Additionally, SQL plan management is a mechanism that can prevent performance regressions and also help you to improve SQL performance.

All of the automated SQL tuning tools can use SQL tuning sets as input. A SQL tuning set (STS) is a database object that includes one or more SQL statements along with their execution statistics and execution context.

See Also:
"About SQL Tuning Sets"

1.4.2.1.1 Automatic Database Diagnostic Monitor (ADDM)

ADDM is self-diagnostic software built into Oracle Database. ADDM can automatically locate the root causes of performance problems, provide recommendations for correction, and quantify the expected benefits. ADDM also identifies areas where no action is necessary.

ADDM and other advisors use Automatic Workload Repository (AWR), which is an infrastructure that provides services to database components to collect, maintain, and use statistics. ADDM examines and analyzes statistics in AWR to determine possible performance problems, including high-load SQL.

For example, you can configure ADDM to run nightly. In the morning, you can examine the latest ADDM report to see what might have caused a problem and if there is a recommended fix. The report might show that a particular SELECT statement consumed a huge amount of CPU, and recommend that you run SQL Tuning Advisor.

See Also:

- Oracle Database 2 Day + Performance Tuning Guide

- Oracle Database Performance Tuning Guide

1.4.2.1.2 SQL Tuning Advisor

SQL Tuning Advisor is internal diagnostic software that identifies problematic SQL statements and recommends how to improve statement performance. When run during database maintenance windows as an automated maintenance task, SQL Tuning Advisor is known as Automatic SQL Tuning Advisor.

SQL Tuning Advisor takes one or more SQL statements as an input and invokes the Automatic Tuning Optimizer to perform SQL tuning on the statements. The advisor performs the following types of analysis:

- Checks for missing or stale statistics

- Builds SQL profiles
A SQL profile is a set of auxiliary information specific to a SQL statement. A SQL profile contains corrections for suboptimal optimizer estimates discovered during Automatic SQL Tuning. This information can improve optimizer estimates for cardinality, which is the number of rows that is estimated to be or actually is returned by an operation in an execution plan, and selectivity. These improved estimates lead the optimizer to select better plans.

- Explores whether a different access path can significantly improve performance

- Identifies SQL statements that lend themselves to suboptimal plans

The output is in the form of advice or recommendations, along with a rationale for each recommendation and its expected benefit. The recommendation relates to a collection of statistics on objects, creation of new indexes, restructuring of the SQL statement, or creation of a SQL profile. You can choose to accept the recommendations to complete the tuning of the SQL statements.

See Also:

- Oracle Database 2 Day + Performance Tuning Guide

- Oracle Database Performance Tuning Guide

1.4.2.1.3 SQL Access Advisor

SQL Access Advisor is internal diagnostic software that recommends which materialized views, indexes, and materialized view logs to create, drop, or retain.

SQL Access Advisor takes an actual workload as input, or the advisor can derive a hypothetical workload from the schema. SQL Access Advisor considers the trade-offs between space usage and query performance, and recommends the most cost-effective configuration of new and existing materialized views and indexes. The advisor also makes recommendations about partitioning.

See Also:

- "About SQL Access Advisor"

- Oracle Database 2 Day + Performance Tuning Guide

1.4.2.1.4 SQL Plan Management

SQL plan management is a preventative mechanism that enables the optimizer to automatically manage execution plans, ensuring that the database uses only known or verified plans. This mechanism can build a SQL plan baseline, which contains one or more accepted plans for each SQL statement. By using baselines, SQL plan management can prevent plan regressions from environmental changes, while permitting the optimizer to discover and use better plans.

See Also:
"About SQL Plan Management"

1.4.2.1.5 SQL Performance Analyzer

SQL Performance Analyzer determines the effect of a change on a SQL workload by identifying performance divergence for each SQL statement. System changes such as upgrading a database or adding an index may cause changes to execution plans, affecting SQL performance. By using SQL Performance Analyzer, you can accurately forecast the effect of system changes on SQL performance. Using this information, you can tune the database when SQL performance regresses, or validate and measure the gain when SQL performance improves.

See Also:
Oracle Database Testing Guide

1.4.2.2 Manual SQL Tuning Tools

In some situations, you may want to run manual tools in addition to the automated tools. Alternatively, you may not have access to the automated tools.

1.4.2.2.1 Execution Plans

Execution plans are the principal diagnostic tool in manual SQL tuning. For example, you can view plans to determine whether the optimizer selects the plan you expect, or identify the effect of creating an index on a table.

You can display execution plans in multiple ways. The following tools are the most commonly used:

- EXPLAIN PLAN
This SQL statement enables you to view the execution plan that the optimizer would use to execute a SQL statement without actually executing the statement. See Oracle Database SQL Language Reference.

- AUTOTRACE
The AUTOTRACE command in SQL*Plus generates the execution plan and statistics about the performance of a query. This command provides statistics such as disk reads and memory reads. See SQL*Plus User's Guide and Reference.

- V$SQL_PLAN and related views
These views contain information about executed SQL statements, and their execution plans, that are still in the shared pool. See Oracle Database Reference.

You can use the DBMS_XPLAN package methods to display the execution plan generated by the EXPLAIN PLAN command and query of V$SQL_PLAN.

1.4.2.2.2 Real-Time SQL Monitoring and Real-Time Database Operations

The Real-Time SQL Monitoring feature of Oracle Database enables you to monitor the performance of SQL statements while they are executing. By default, SQL monitoring starts automatically when a SQL statement runs in parallel, or when it has consumed at least 5 seconds of CPU or I/O time in a single execution.

A database operation is a set of database tasks defined by end users or application code, for example, a batch job or Extraction, Transformation, and Loading (ETL) processing. You can define, monitor, and report on database operations. Real-Time Database Operations provides the ability to monitor composite operations automatically. The database automatically monitors parallel queries, DML, and DDL statements as soon as execution begins.

Oracle Enterprise Manager Cloud Control (Cloud Control) provides easy-to-use SQL monitoring pages. Alternatively, you can monitor SQL-related statistics using the V$SQL_MONITOR and V$SQL_PLAN_MONITOR views. You can use these views with the following views to get more information about executions that you are monitoring:

- V$ACTIVE_SESSION_HISTORY

- V$SESSION

- V$SESSION_LONGOPS

- V$SQL

- V$SQL_PLAN

See Also:

- "About Monitoring Database Operations"

- Oracle Database Reference to learn about the V$ views

1.4.2.2.3 Application Tracing

A SQL trace file provides performance information on individual SQL statements: parse counts, physical and logical reads, misses on the library cache, and so on. You can use this information to diagnose SQL performance problems.

You can enable and disable SQL tracing for a specific session using the DBMS_MONITOR or DBMS_SESSION packages. Oracle Database implements tracing by generating a trace file for each server process when you enable the tracing mechanism.

Oracle Database provides the following command-line tools for analyzing trace files:

- TKPROF
This utility accepts as input a trace file produced by the SQL Trace facility, and then produces a formatted output file.

- trcsess
This utility consolidates trace output from multiple trace files based on criteria such as session ID, client ID, and service ID. After trcsess merges the trace information into a single output file, you can format the output file with TKPROF. trcsess is useful for consolidating the tracing of a particular session for performance or debugging purposes.

End-to-End Application Tracing simplifies the process of diagnosing performance problems in multitier environments. In these environments, the middle tier routes a request from an end client to different database sessions, making it difficult to track a client across database sessions. End-to-End application tracing uses a client ID to uniquely trace a specific end-client through all tiers to the database.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_MONITOR and DBMS_SESSION

1.4.2.2.4 Optimizer Hints

A hint is an instruction passed to the optimizer through comments in a SQL statement. Hints enable you to make decisions normally made automatically by the optimizer.

In a test or development environment, hints are useful for testing the performance of a specific access path. For example, you may know that a specific index is more selective for certain queries. In this case, you may use hints to instruct the optimizer to use a better execution plan, as in the following example:

SELECT /*+ INDEX (employees emp_department_ix) */
employee_id, department_id
FROM   employees
WHERE  department_id > 50;

See Also:
"Influencing the Optimizer with Hints"

1.4.3 User Interfaces to SQL Tuning Tools

You can access most tuning tools using Cloud Control, which is a system management tool that provides centralized management of a database environment. By combining a graphical console, Oracle Management Servers, Oracle Intelligent Agents, common services, and administrative tools, Cloud Control provides a comprehensive system management platform.

You can also access all SQL tuning tools using a command-line interface. For example, the DBMS_ADVISOR package is the command-line interface for SQL Tuning Advisor.

Oracle recommends Cloud Control as the best interface for database administration and tuning. In cases where the command-line interface better illustrates a particular concept or task, this manual uses command-line examples. However, in these cases the tuning tasks include a reference to the principal Cloud Control page associated with the task.

2 SQL Performance Methodology

This chapter describes the recommended methodology for SQL tuning. This chapter contains the following topics:

- Designing Your Application

- Deploying Your Application

2.1 Designing Your Application

This section contains the following topics:

- Data Modeling

- Writing Efficient Applications

2.1.1 Data Modeling

Data modeling is important to successful application design. You must perform this modeling in a way that quickly represents the business practices. Heated debates may occur about the correct data model. The important thing is to apply greatest modeling efforts to those entities affected by the most frequent business transactions.

In the modeling phase, there is a great temptation to spend too much time modeling the non-core data elements, which results in increased development lead times. Use of modeling tools can then rapidly generate schema definitions and can be useful when a fast prototype is required.

2.1.2 Writing Efficient Applications

In the design and architecture phase of any system development, care should be taken to ensure that the application developers understand SQL execution efficiency. To achieve this goal, the development environment must support the following characteristics:

- Good database connection management
Connecting to the database is an expensive operation that is highly unscalable. Therefore, a best practice is to minimize the number of concurrent connections to the database. A simple system, where a user connects at application initialization, is ideal. However, in a web-based or multitiered application in which application servers multiplex database connections to users, this approach can be difficult. With these types of applications, design them to pool database connections, and not reestablish connections for each user request.

- Good cursor usage and management
Maintaining user connections is equally important to minimizing the parsing activity on the system. Parsing is the process of interpreting a SQL statement and creating an execution plan for it. This process has many phases, including syntax checking, security checking, execution plan generation, and loading shared structures into the shared pool. There are two types of parse operations:

- Hard parsing
A SQL statement is submitted for the first time, and no match is found in the shared pool. Hard parses are the most resource-intensive and unscalable, because they perform all the operations involved in a parse.

- Soft parsing
A SQL statement is submitted for the first time, and a match is found in the shared pool. The match can be the result of previous execution by another user. The SQL statement is shared, which is optimal for performance. However, soft parses are not ideal, because they still require syntax and security checking, which consume system resources.

Because parsing should be minimized as much as possible, application developers should design their applications to parse SQL statements once and execute them many times. This is done through cursors. Experienced SQL programmers should be familiar with the concept of opening and re-executing cursors.

- Effective use of bind variables
Application developers must also ensure that SQL statements are shared within the shared pool. To achieve this goal, use bind variables to represent the parts of the query that change from execution to execution. If this is not done, then the SQL statement is likely to be parsed once and never re-used by other users. To ensure that SQL is shared, use bind variables and do not use string literals with SQL statements. For example:

Statement with string literals:

SELECT * FROM employees
WHERE last_name LIKE 'KING';

Statement with bind variables:

SELECT * FROM employees
WHERE last_name LIKE :1;

The following example shows the results of some tests on a simple OLTP application:

Test                         #Users Supported
No Parsing all statements           270
Soft Parsing all statements         150
Hard Parsing all statements          60
Re-Connecting for each Transaction   30

These tests were performed on a four-CPU computer. The differences increase as the number of CPUs on the system increase.

2.2 Deploying Your Application

This section contains the following topics:

- Deploying in a Test Environment

- Rollout Strategies

2.2.1 Deploying in a Test Environment

The testing process mainly consists of functional and stability testing. At some point in the process, performance testing is performed.

The following list describes some simple rules for performance testing an application. If correctly documented, then this list provides important information for the production application and the capacity planning process after the application has gone live.

- Use the Automatic Database Diagnostic Monitor (ADDM) and SQL Tuning Advisor for design validation

- Test with realistic data volumes and distributions
All testing must be done with fully populated tables. The test database should contain data representative of the production system in terms of data volume and cardinality between tables. All the production indexes should be built and the schema statistics should be populated correctly.

- Use the correct optimizer mode
Perform all testing with the optimizer mode that you plan to use in production.

- Test a single user performance
Test a single user on an idle or lightly-used database for acceptable performance. If a single user cannot achieve acceptable performance under ideal conditions, then multiple users cannot achieve acceptable performance under real conditions.

- Obtain and document plans for all SQL statements
Obtain an execution plan for each SQL statement. Use this process to verify that the optimizer is obtaining an optimal execution plan, and that the relative cost of the SQL statement is understood in terms of CPU time and physical I/Os. This process assists in identifying the heavy use transactions that require the most tuning and performance work in the future.

- Attempt multiuser testing
This process is difficult to perform accurately, because user workload and profiles might not be fully quantified. However, transactions performing DML statements should be tested to ensure that there are no locking conflicts or serialization problems.

- Test with the correct hardware configuration
Test with a configuration as close to the production system as possible. Using a realistic system is particularly important for network latencies, I/O subsystem bandwidth, and processor type and speed. Failing to use this approach may result in an incorrect analysis of potential performance problems.

- Measure steady state performance
When benchmarking, it is important to measure the performance under steady state conditions. Each benchmark run should have a ramp-up phase, where users are connected to the application and gradually start performing work on the application. This process allows for frequently cached data to be initialized into the cache and single execution operations—such as parsing—to be completed before the steady state condition. Likewise, after a benchmark run, a ramp-down period is useful so that the system frees resources, and users cease work and disconnect.

2.2.2 Rollout Strategies

When new applications are rolled out, two strategies are commonly adopted:

- Big Bang approach - all users migrate to the new system at once

- Trickle approach - users slowly migrate from existing systems to the new one

Both approaches have merits and disadvantages. The Big Bang approach relies on reliable testing of the application at the required scale, but has the advantage of minimal data conversion and synchronization with the old system, because it is simply switched off. The Trickle approach allows debugging of scalability issues as the workload increases, but might mean that data must be migrated to and from legacy systems as the transition takes place.

It is difficult to recommend one approach over the other, because each technique has associated risks that could lead to system outages as the transition takes place. Certainly, the Trickle approach allows profiling of real users as they are introduced to the new application, and allows the system to be reconfigured while only affecting the migrated users. This approach affects the work of the early adopters, but limits the load on support services. Thus, unscheduled outages only affect a small percentage of the user population.

The decision on how to roll out a new application is specific to each business. Any adopted approach has its own unique pressures and stresses. The more testing and knowledge that you derive from the testing process, the more you realize what is best for the rollout.

Part II

Query Optimizer Fundamentals

This part contains the following chapters:

- Chapter 3, "SQL Processing"

- Chapter 4, "Query Optimizer Concepts"

- Chapter 5, "Query Transformations"

3 SQL Processing

This chapter explains how Oracle Database processes SQL statements. Specifically, the section explains the way in which the database processes DDL statements to create objects, DML to modify data, and queries to retrieve data.

This chapter contains the following topics:

- About SQL Processing

- How Oracle Database Processes DML

- How Oracle Database Processes DDL

3.1 About SQL Processing

SQL processing is the parsing, optimization, row source generation, and execution of a SQL statement. Depending on the statement, the database may omit some of these stages. Figure 3-1 depicts the general stages of SQL processing.

Figure 3-1 Stages of SQL Processing

3.1.1 SQL Parsing

As shown in Figure 3-1, the first stage of SQL processing is parsing. This stage involves separating the pieces of a SQL statement into a data structure that other routines can process. The database parses a statement when instructed by the application, which means that only the application, and not the database itself, can reduce the number of parses.

When an application issues a SQL statement, the application makes a parse call to the database to prepare the statement for execution. The parse call opens or creates a cursor, which is a handle for the session-specific private SQL area that holds a parsed SQL statement and other processing information. The cursor and private SQL area are in the program global area (PGA).

During the parse call, the database performs the following checks:

- Syntax Check

- Semantic Check

- Shared Pool Check

The preceding checks identify the errors that can be found before statement execution. Some errors cannot be caught by parsing. For example, the database can encounter deadlocks or errors in data conversion only during statement execution.

See Also:
Oracle Database Concepts to learn about deadlocks

3.1.1.1 Syntax Check

Oracle Database must check each SQL statement for syntactic validity. A statement that breaks a rule for well-formed SQL syntax fails the check. For example, the following statement fails because the keyword FROM is misspelled as FORM:

SQL> SELECT * FORM employees;
SELECT * FORM employees
*
ERROR at line 1:
ORA-00923: FROM keyword not found where expected

3.1.1.2 Semantic Check

The semantics of a statement are its meaning. Thus, a semantic check determines whether a statement is meaningful, for example, whether the objects and columns in the statement exist. A syntactically correct statement can fail a semantic check, as shown in the following example of a query of a nonexistent table:

SQL> SELECT * FROM nonexistent_table;
SELECT * FROM nonexistent_table
*
ERROR at line 1:
ORA-00942: table or view does not exist

3.1.1.3 Shared Pool Check

During the parse, the database performs a shared pool check to determine whether it can skip resource-intensive steps of statement processing. To this end, the database uses a hashing algorithm to generate a hash value for every SQL statement. The statement hash value is the SQL ID shown in V$SQL.SQL_ID. This hash value is deterministic within a version of Oracle Database, so the same statement in a single instance or in different instances has the same SQL ID.

When a user submits a SQL statement, the database searches the shared SQL area to see if an existing parsed statement has the same hash value. The hash value of a SQL statement is distinct from the following values:

- Memory address for the statement
Oracle Database uses the SQL ID to perform a keyed read in a lookup table. In this way, the database obtains possible memory addresses of the statement.

- Hash value of an execution plan for the statement
A SQL statement can have multiple plans in the shared pool. Typically, each plan has a different hash value. If the same SQL ID has multiple plan hash values, then the database knows that multiple plans exist for this SQL ID.

Parse operations fall into the following categories, depending on the type of statement submitted and the result of the hash check:

- Hard parse
If Oracle Database cannot reuse existing code, then it must build a new executable version of the application code. This operation is known as a hard parse, or a library cache miss.

Note:
The database always perform a hard parse of DDL.

During the hard parse, the database accesses the library cache and data dictionary cache numerous times to check the data dictionary. When the database accesses these areas, it uses a serialization device called a latch on required objects so that their definition does not change. Latch contention increases statement execution time and decreases concurrency.

- Soft parse
A soft parse is any parse that is not a hard parse. If the submitted statement is the same as a reusable SQL statement in the shared pool, then Oracle Database reuses the existing code. This reuse of code is also called a library cache hit.

Soft parses can vary in how much work they perform. For example, configuring the session shared SQL area can sometimes reduce the amount of latching in the soft parses, making them "softer."

In general, a soft parse is preferable to a hard parse because the database skips the optimization and row source generation steps, proceeding straight to execution.

Figure 3-2 is a simplified representation of a shared pool check of an UPDATE statement in a dedicated server architecture.

Figure 3-2 Shared Pool Check

If a check determines that a statement in the shared pool has the same hash value, then the database performs semantic and environment checks to determine whether the statements have the same meaning. Identical syntax is not sufficient. For example, suppose two different users log in to the database and issue the following SQL statements:

CREATE TABLE my_table ( some_col INTEGER );
SELECT * FROM my_table;

The SELECT statements for the two users are syntactically identical, but two separate schema objects are named my_table. This semantic difference means that the second statement cannot reuse the code for the first statement.

Even if two statements are semantically identical, an environmental difference can force a hard parse. In this context, the optimizer environment is the totality of session settings that can affect execution plan generation, such as the work area size or optimizer settings (for example, the optimizer mode). Consider the following series of SQL statements executed by a single user:

ALTER SESSION SET OPTIMIZER_MODE=ALL_ROWS;
ALTER SYSTEM FLUSH SHARED_POOL;               # optimizer environment 1
SELECT * FROM sh.sales;

ALTER SESSION SET OPTIMIZER_MODE=FIRST_ROWS;  # optimizer environment 2
SELECT * FROM sh.sales;

ALTER SESSION SET SQL_TRACE=true;             # optimizer enviornment 3
SELECT * FROM sh.sales;

In the preceding example, the same SELECT statement is executed in three different optimizer environments. Consequently, the database creates three separate shared SQL areas for these statements and forces a hard parse of each statement.

See Also:

- Oracle Database Concepts to learn about private SQL areas and shared SQL areas

- Oracle Database Performance Tuning Guide to learn how to configure the shared pool

- Oracle Database Concepts to learn about latches

3.1.2 SQL Optimization

During the optimization stage, Oracle Database must perform a hard parse at least once for every unique DML statement and performs the optimization during this parse. The database never optimizes DDL unless it includes a DML component such as a subquery that requires optimization. Chapter 4, "Query Optimizer Concepts" explains the optimization process in more detail.

3.1.3 SQL Row Source Generation

The row source generator is software that receives the optimal execution plan from the optimizer and produces an iterative execution plan that is usable by the rest of the database. The iterative plan is a binary program that, when executed by the SQL engine, produces the result set.

The execution plan takes the form of a combination of steps. Each step returns a row set. The next step either uses the rows in this set, or the last step returns the rows to the application issuing the SQL statement.

A row source is a row set returned by a step in the execution plan along with a control structure that can iteratively process the rows. The row source can be a table, view, or result of a join or grouping operation.

The row source generator produces a row source tree, which is a collection of row sources. The row source tree shows the following information:

- An ordering of the tables referenced by the statement

- An access method for each table mentioned in the statement

- A join method for tables affected by join operations in the statement

- Data operations such as filter, sort, or aggregation

Example 3-1 shows the execution plan of a SELECT statement when AUTOTRACE is enabled. The statement selects the last name, job title, and department name for all employees whose last names begin with the letter A. The execution plan for this statement is the output of the row source generator.

Example 3-1 Execution Plan

SELECT e.last_name, j.job_title, d.department_name
FROM   hr.employees e, hr.departments d, hr.jobs j
WHERE  e.department_id = d.department_id
AND    e.job_id = j.job_id
AND    e.last_name LIKE 'A%';

Execution Plan
----------------------------------------------------------
Plan hash value: 975837011

---------------------------------------------------------------------------------------------
| Id  | Operation                     | Name        | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT              |             |     3 |   189 |     7  (15)| 00:00:01 |
|*  1 |  HASH JOIN                    |             |     3 |   189 |     7  (15)| 00:00:01 |
|*  2 |   HASH JOIN                   |             |     3 |   141 |     5  (20)| 00:00:01 |
|   3 |    TABLE ACCESS BY INDEX ROWID| EMPLOYEES   |     3 |    60 |     2   (0)| 00:00:01 |
|*  4 |     INDEX RANGE SCAN          | EMP_NAME_IX |     3 |       |     1   (0)| 00:00:01 |
|   5 |    TABLE ACCESS FULL          | JOBS        |    19 |   513 |     2   (0)| 00:00:01 |
|   6 |   TABLE ACCESS FULL           | DEPARTMENTS |    27 |   432 |     2   (0)| 00:00:01 |
---------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")
2 - access("E"."JOB_ID"="J"."JOB_ID")
4 - access("E"."LAST_NAME" LIKE 'A%')
filter("E"."LAST_NAME" LIKE 'A%')

3.1.4 SQL Execution

During execution, the SQL engine executes each row source in the tree produced by the row source generator. This step is the only mandatory step in DML processing.

Figure 3-3 is an execution tree, also called a parse tree, that shows the flow of row sources from one step to another in the plan in Example 3-1. In general, the order of the steps in execution is the reverse of the order in the plan, so you read the plan from the bottom up.

Each step in an execution plan has an ID number. The numbers in Figure 3-3 correspond to the Id column in the plan shown in Example 3-1. Initial spaces in the Operation column of the plan indicate hierarchical relationships. For example, if the name of an operation is preceded by two spaces, then this operation is a child of an operation preceded by one space. Operations preceded by one space are children of the SELECT statement itself.

Figure 3-3 Row Source Tree

In Figure 3-3, each node of the tree acts as a row source, which means that each step of the execution plan in Example 3-1 either retrieves rows from the database or accepts rows from one or more row sources as input. The SQL engine executes each row source as follows:

- Steps indicated by the black boxes physically retrieve data from an object in the database. These steps are the access paths, or techniques for retrieving data from the database.

- Step 6 uses a full table scan to retrieve all rows from the departments table.

- Step 5 uses a full table scan to retrieve all rows from the jobs table.

- Step 4 scans the emp_name_ix index in order, looking for each key that begins with the letter A and retrieving the corresponding rowid. For example, the rowid corresponding to Atkinson is AAAPzRAAFAAAABSAAe.

- Step 3 retrieves from the employees table the rows whose rowids were returned by Step 4. For example, the database uses rowid AAAPzRAAFAAAABSAAe to retrieve the row for Atkinson.

- Steps indicated by the clear boxes operate on row sources.

- Step 2 performs a hash join, accepting row sources from Steps 3 and 5, joining each row from the Step 5 row source to its corresponding row in Step 3, and returning the resulting rows to Step 1.
For example, the row for employee Atkinson is associated with the job name Stock Clerk.

- Step 1 performs another hash join, accepting row sources from Steps 2 and 6, joining each row from the Step 6 source to its corresponding row in Step 2, and returning the result to the client.
For example, the row for employee Atkinson is associated with the department named Shipping.

In some execution plans the steps are iterative and in others sequential. The hash join shown in Example 3-1 is sequential. The database completes the steps in their entirety based on the join order. The database starts with the index range scan of emp_name_ix. Using the rowids that it retrieves from the index, the database reads the matching rows in the employees table, and then scans the jobs table. After it retrieves the rows from the jobs table, the database performs the hash join.

During execution, the database reads the data from disk into memory if the data is not in memory. The database also takes out any locks and latches necessary to ensure data integrity and logs any changes made during the SQL execution. The final stage of processing a SQL statement is closing the cursor.

3.2 How Oracle Database Processes DML

Most DML statements have a query component. In a query, execution of a cursor places the results of the query into a set of rows called the result set.

Result set rows can be fetched either a row at a time or in groups. In the fetch stage, the database selects rows and, if requested by the query, orders the rows. Each successive fetch retrieves another row of the result until the last row has been fetched.

In general, the database cannot determine for certain the number of rows to be retrieved by a query until the last row is fetched. Oracle Database retrieves the data in response to fetch calls, so that the more rows the database reads, the more work it performs. For some queries the database returns the first row as quickly as possible, whereas for others it creates the entire result set before returning the first row.

3.2.1 Read Consistency

In general, a query retrieves data by using the Oracle Database read consistency mechanism. This mechanism, which uses undo data to show past versions of data, guarantees that all data blocks read by a query are consistent to a single point in time.

For an example of read consistency, suppose a query must read 100 data blocks in a full table scan. The query processes the first 10 blocks while DML in a different session modifies block 75. When the first session reaches block 75, it realizes the change and uses undo data to retrieve the old, unmodified version of the data and construct a noncurrent version of block 75 in memory.

See Also:
Oracle Database Concepts to learn about multiversion read consistency

3.2.2 Data Changes

DML statements that must change data use the read consistency mechanism to retrieve only the data that matched the search criteria when the modification began. Afterward, these statements retrieve the data blocks as they exist in their current state and make the required modifications. The database must perform other actions related to the modification of the data such as generating redo and undo data.

3.3 How Oracle Database Processes DDL

Oracle Database processes DDL differently from DML. For example, when you create a table, the database does not optimize the CREATE TABLE statement. Instead, Oracle Database parses the DDL statement and carries out the command.

The database processes DDL differently because it is a means of defining an object in the data dictionary. Typically, Oracle Database must parse and execute many recursive SQL statements to execute a DDL statement. Suppose you create a table as follows:

CREATE TABLE mytable (mycolumn INTEGER);

Typically, the database would run dozens of recursive statements to execute the preceding statement. The recursive SQL would perform actions such as the following:

- Issue a COMMIT before executing the CREATE TABLE statement

- Verify that user privileges are sufficient to create the table

- Determine which tablespace the table should reside in

- Ensure that the tablespace quota has not been exceeded

- Ensure that no object in the schema has the same name

- Insert rows that define the table into the data dictionary

- Issue a COMMIT if the DDL statement succeeded or a ROLLBACK if it did not

See Also:
Oracle Database Development Guide to learn about processing DDL, transaction control, and other types of statements

4 Query Optimizer Concepts

This chapter describes the most important concepts relating to the query optimizer. This chapter contains the following topics:

- Introduction to the Query Optimizer

- About Optimizer Components

- About Automatic Tuning Optimizer

- About Adaptive Query Optimization

- About Optimizer Management of SQL Plan Baselines

4.1 Introduction to the Query Optimizer

The query optimizer (called simply the optimizer) is built-in database software that determines the most efficient method for a SQL statement to access requested data.

This section contains the following topics:

- Purpose of the Query Optimizer

- Cost-Based Optimization

- Execution Plans

4.1.1 Purpose of the Query Optimizer

The optimizer attempts to generate the best execution plan for a SQL statement. The best execution plan is defined as the plan with the lowest cost among all considered candidate plans. The cost computation accounts for factors of query execution such as I/O, CPU, and communication.

The best method of execution depends on myriad conditions including how the query is written, the size of the data set, the layout of the data, and which access structures exist. The optimizer determines the best plan for a SQL statement by examining multiple access methods, such as full table scan or index scans, and different join methods such as nested loops and hash joins.

Because the database has many internal statistics and tools at its disposal, the optimizer is usually in a better position than the user to determine the best method of statement execution. For this reason, all SQL statements use the optimizer.

Consider a user who queries records for employees who are managers. If the database statistics indicate that 80% of employees are managers, then the optimizer may decide that a full table scan is most efficient. However, if statistics indicate that few employees are managers, then reading an index followed by a table access by rowid may be more efficient than a full table scan.

4.1.2 Cost-Based Optimization

Query optimization is the overall process of choosing the most efficient means of executing a SQL statement. SQL is a nonprocedural language, so the optimizer is free to merge, reorganize, and process in any order.

The database optimizes each SQL statement based on statistics collected about the accessed data. When generating execution plans, the optimizer considers different access paths and join methods. Factors considered by the optimizer include:

- System resources, which includes I/O, CPU, and memory

- Number of rows returned

- Size of the initial data sets

The cost is a number that represents the estimated resource usage for an execution plan. The optimizer assigns a cost to each possible plan, and then chooses the plan with the lowest cost. For this reason, the optimizer is sometimes called the cost-based optimizer (CBO) to contrast it with the legacy rule-based optimizer (RBO).

Note:
The optimizer may not make the same decisions from one version of Oracle Database to the next. In recent versions, the optimizer might make different decision because better information is available and more optimizer transformations are possible.

4.1.3 Execution Plans

An execution plan describes a recommended method of execution for a SQL statement. The plans shows the combination of the steps Oracle Database uses to execute a SQL statement. Each step either retrieves rows of data physically from the database or prepares them for the user issuing the statement.

In Figure 4-1, the optimizer generates two possible execution plans for an input SQL statement, uses statistics to calculate their costs, compares their costs, and chooses the plan with the lowest cost.

Figure 4-1 Execution Plans

4.1.3.1 Query Blocks

As shown in Figure 4-1, the input to the optimizer is a parsed representation of a SQL statement. Each SELECT block in the original SQL statement is represented internally by a query block. A query block can be a top-level statement, subquery, or unmerged view (see "View Merging").

In Example 4-1, the SQL statement consists of two query blocks. The subquery in parentheses is the inner query block. The outer query block, which is the rest of the SQL statement, retrieves names of employees in the departments whose IDs were supplied by the subquery.

Example 4-1 Query Blocks

SELECT first_name, last_name
FROM   hr.employees
WHERE  department_id
IN     (SELECT department_id
FROM   hr.departments
WHERE  location_id = 1800);

The query form determines how query blocks are interrelated.

See Also:
Oracle Database Concepts for an overview of SQL processing

4.1.3.2 Query Subplans

For each query block, the optimizer generates a query subplan. The database optimizes query blocks separately from the bottom up. Thus, the database optimizes the innermost query block first and generates a subplan for it, and then generates the outer query block representing the entire query.

The number of possible plans for a query block is proportional to the number of objects in the FROM clause. This number rises exponentially with the number of objects. For example, the possible plans for a join of five tables are significantly higher than the possible plans for a join of two tables.

4.1.3.3 Analogy for the Optimizer

One analogy for the optimizer is an online trip advisor. A cyclist wants to know the most efficient bicycle route from point A to point B. A query is like the directive "I need the most efficient route from point A to point B" or "I need the most efficient route from point A to point B by way of point C." The trip advisor uses an internal algorithm, which relies on factors such as speed and difficulty, to determine the most efficient route. The cyclist can influence the trip advisor's decision by using directives such as "I want to arrive as fast as possible" or "I want the easiest ride possible."

In this analogy, an execution plan is a possible route generated by the trip advisor. Internally, the advisor may divide the overall route into several subroutes (subplans), and calculate the efficiency for each subroute separately. For example, the trip advisor may estimate one subroute at 15 minutes with medium difficulty, an alternative subroute at 22 minutes with minimal difficulty, and so on.

The advisor picks the most efficient (lowest cost) overall route based on user-specified goals and the available statistics about roads and traffic conditions. The more accurate the statistics, the better the advice. For example, if the advisor is not frequently notified of traffic jams, road closures, and poor road conditions, then the recommended route may turn out to be inefficient (high cost).

4.2 About Optimizer Components

The optimizer contains three main components, which are shown in Figure 4-2.

Figure 4-2 Optimizer Components

A set of query blocks represents a parsed query, which is the input to the optimizer. The optimizer performs the following operations:

1. Query transformer
The optimizer determines whether it is helpful to change the form of the query so that the optimizer can generate a better execution plan. See "Query Transformer".

2. Estimator
The optimizer estimates the cost of each plan based on statistics in the data dictionary. See "Estimator".

3. Plan Generator
The optimizer compares the costs of plans and chooses the lowest-cost plan, known as the execution plan, to pass to the row source generator. See "Plan Generator".

4.2.1 Query Transformer

For some statements, the query transformer determines whether it is advantageous to rewrite the original SQL statement into a semantically equivalent SQL statement with a lower cost. When a viable alternative exists, the database calculates the cost of the alternatives separately and chooses the lowest-cost alternative. Chapter 5, "Query Transformations" describes the different types of optimizer transformations.

Figure 4-3 shows the query transformer rewriting an input query that uses OR into an output query that uses UNION ALL.

Figure 4-3 Query Transformer

See Also:
Chapter 5, "Query Transformations"

4.2.2 Estimator

The estimator is the component of the optimizer that determines the overall cost of a given execution plan. The estimator uses three different types of measures to achieve this goal:

- Selectivity
The percentage of rows in the row set that the query selects, with 0 meaning no rows and 1 meaning all rows. Selectivity is tied to a query predicate, such as WHERE last_name LIKE 'A%', or a combination of predicates. A predicate becomes more selective as the selectivity value approaches 0 and less selective (or more unselective) as the value approaches 1.

Note:
Selectivity is an internal calculation that is not visible in the execution plans.

- Cardinality
The cardinality is the estimated number of rows returned by each operation in an execution plan. This input, which is crucial to obtaining an optimal plan, is common to all cost functions. Cardinality can be derived from the table statistics collected by DBMS_STATS, or derived after accounting for effects from predicates (filter, join, and so on), DISTINCT or GROUP BY operations, and so on.

- Cost
This measure represents units of work or resource used. The query optimizer uses disk I/O, CPU usage, and memory usage as units of work.

As shown in Figure 4-4, if statistics are available, then the estimator uses them to compute the measures. The statistics improve the degree of accuracy of the measures.

Figure 4-4 Estimator

For the query shown in Example 4-1, the estimator uses selectivity, cardinality, and cost measures to produce its total cost estimate of 3:

----------------------------------------------------------------------------------
| Id| Operation                    |Name             |Rows|Bytes|Cost(%CPU)| Time|
----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT             |                 |  10|  250| 3 (0)| 00:00:01|
| 1 |  NESTED LOOPS                |                 |    |     |      |         |
| 2 |   NESTED LOOPS               |                 |  10|  250| 3 (0)| 00:00:01|
|*3 |    TABLE ACCESS FULL         |DEPARTMENTS      |   1|    7| 2 (0)| 00:00:01|
|*4 |    INDEX RANGE SCAN          |EMP_DEPARTMENT_IX|  10|     | 0 (0)| 00:00:01|
| 5 |   TABLE ACCESS BY INDEX ROWID|EMPLOYEES        |  10|  180| 1 (0)| 00:00:01|
----------------------------------------------------------------------------------

4.2.2.1 Selectivity

The selectivity represents a fraction of rows from a row set. The row set can be a base table, a view, or the result of a join. The selectivity is tied to a query predicate, such as last_name = 'Smith', or a combination of predicates, such as last_name = 'Smith' AND job_id = 'SH_CLERK'.

Note:
Selectivity is an internal calculation that is not visible in execution plans.

A predicate filters a specific number of rows from a row set. Thus, the selectivity of a predicate indicates how many rows pass the predicate test. Selectivity ranges from 0.0 to 1.0. A selectivity of 0.0 means that no rows are selected from a row set, whereas a selectivity of 1.0 means that all rows are selected. A predicate becomes more selective as the value approaches 0.0 and less selective (or more unselective) as the value approaches 1.0.

The optimizer estimates selectivity depending on whether statistics are available:

- Statistics not available
Depending on the value of the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter, the optimizer either uses dynamic statistics or an internal default value. The database uses different internal defaults depending on the predicate type. For example, the internal default for an equality predicate (last_name = 'Smith') is lower than for a range predicate (last_name > 'Smith') because an equality predicate is expected to return a smaller fraction of rows.

- Statistics available
When statistics are available, the estimator uses them to estimate selectivity. Assume there are 150 distinct employee last names. For an equality predicate last_name = 'Smith', selectivity is the reciprocal of the number n of distinct values of last_name, which in this example is .006 because the query selects rows that contain 1 out of 150 distinct values.

If a histogram exists on the last_name column, then the estimator uses the histogram instead of the number of distinct values. The histogram captures the distribution of different values in a column, so it yields better selectivity estimates, especially for columns that have data skew. See Chapter 11, "Histograms."

4.2.2.2 Cardinality

The cardinality is the estimated number of rows returned by each operation in an execution plan. For example, if the optimizer estimate for the number of rows returned by a full table scan is 100, then the cardinality for this operation is 100. The cardinality value appears in the Rows column of the execution plan.

The optimizer determines the cardinality for each operation based on a complex set of formulas that use both table and column level statistics, or dynamic statistics, as input. The optimizer uses one of the simplest formulas when a single equality predicate appears in a single-table query, with no histogram. In this case, the optimizer assumes a uniform distribution and calculates the cardinality for the query by dividing the total number of rows in the table by the number of distinct values in the column used in the WHERE clause predicate.

For example, user hr queries the employees table as follows:

SELECT first_name, last_name
FROM   employees
WHERE  salary='10200';

The employees table contains 107 rows. The current database statistics indicate that the number of distinct values in the salary column is 58. Thus, the optimizer calculates the cardinality of the result set as 2, using the formula 107/58=1.84.

Cardinality estimates must be as accurate as possible because they influence all aspects of the execution plan. Cardinality is important when the optimizer determines the cost of a join. For example, in a nested loops join of the employees and departments tables, the number of rows in employees determines how often the database must probe the departments table. Cardinality is also important for determining the cost of sorts.

4.2.2.3 Cost

The optimizer cost model accounts for the I/O, CPU, and network resources that a query is predicted to use. The cost is an internal numeric measure that represents the estimated resource usage for a plan. The lower the cost, the more efficient the plan.

The execution plan displays the cost of the entire plan, which is indicated on line 0, and each individual operation. For example, the following plan shows a cost of 14.

EXPLAINED SQL STATEMENT:
------------------------
SELECT prod_category, AVG(amount_sold) FROM   sales s, products p WHERE
p.prod_id = s.prod_id GROUP BY prod_category

Plan hash value: 4073170114

----------------------------------------------------------------------
| Id  | Operation                | Name                 | Cost (%CPU)|
----------------------------------------------------------------------
|   0 | SELECT STATEMENT         |                      |    14 (100)|
|   1 |  HASH GROUP BY           |                      |    14  (22)|
|   2 |   HASH JOIN              |                      |    13  (16)|
|   3 |    VIEW                  | index$_join$_002     |     7  (15)|
|   4 |     HASH JOIN            |                      |            |
|   5 |      INDEX FAST FULL SCAN| PRODUCTS_PK          |     4   (0)|
|   6 |      INDEX FAST FULL SCAN| PRODUCTS_PROD_CAT_IX |     4   (0)|
|   7 |    PARTITION RANGE ALL   |                      |     5   (0)|
|   8 |     TABLE ACCESS FULL    | SALES                |     5   (0)|
----------------------------------------------------------------------

The cost is an internal unit that you can use for plan comparisons. You cannot tune or change it.

The access path determines the number of units of work required to get data from a base table. The access path can be a table scan, a fast full index scan, or an index scan.

- Table scan or fast full index scan
During a table scan or fast full index scan, the database reads multiple blocks from disk in a single I/O. Therefore, the cost of the scan depends on the number of blocks to be scanned and the multiblock read count value.

- Index scan
The cost of an index scan depends on the levels in the B-tree, the number of index leaf blocks to be scanned, and the number of rows to be fetched using the rowid in the index keys. The cost of fetching rows using rowids depends on the index clustering factor.

The join cost represents the combination of the individual access costs of the two row sets being joined, plus the cost of the join operation.

4.2.3 Plan Generator

The plan generator explores various plans for a query block by trying out different access paths, join methods, and join orders. Many plans are possible because of the various combinations that the database can use to produce the same result. The optimizer picks the plan with the lowest cost.

Figure 4-5 shows the optimizer testing different plans for an input query.

Figure 4-5 Plan Generator

The following snippet from an optimizer trace file shows some computations that the optimizer performs:

GENERAL PLANS
***************************************
Considering cardinality-based initial join order.
Permutations for Starting Table :0
Join order[1]:  DEPARTMENTS[D]#0  EMPLOYEES[E]#1

***************
Now joining: EMPLOYEES[E]#1
***************
NL Join
Outer table: Card: 27.00  Cost: 2.01  Resp: 2.01  Degree: 1  Bytes: 16
Access path analysis for EMPLOYEES
. . .
Best NL cost: 13.17
. . .
SM Join
SM cost: 6.08
resc: 6.08 resc_io: 4.00 resc_cpu: 2501688
resp: 6.08 resp_io: 4.00 resp_cpu: 2501688
. . .
SM Join (with index on outer)
Access Path: index (FullScan)
. . .
HA Join
HA cost: 4.57
resc: 4.57 resc_io: 4.00 resc_cpu: 678154
resp: 4.57 resp_io: 4.00 resp_cpu: 678154
Best:: JoinMethod: Hash
Cost: 4.57  Degree: 1  Resp: 4.57  Card: 106.00 Bytes: 27
. . .

***********************
Join order[2]:  EMPLOYEES[E]#1  DEPARTMENTS[D]#0
. . .

***************
Now joining: DEPARTMENTS[D]#0
***************
. . .
HA Join
HA cost: 4.58
resc: 4.58 resc_io: 4.00 resc_cpu: 690054
resp: 4.58 resp_io: 4.00 resp_cpu: 690054
Join order aborted: cost > best plan cost
***********************

The trace file shows the optimizer first trying the departments table as the outer table in the join. The optimizer calculates the cost for three different join methods: nested loops join (NL), sort merge (SM), and hash join (HA). The optimizer picks the hash join as the most efficient method:

Best:: JoinMethod: Hash
Cost: 4.57  Degree: 1  Resp: 4.57  Card: 106.00 Bytes: 27

The optimizer then tries a different join order, using employees as the outer table. This join order costs more than the previous join order, so it is abandoned.

The optimizer uses an internal cutoff to reduce the number of plans it tries when finding the lowest-cost plan. The cutoff is based on the cost of the current best plan. If the current best cost is large, then the optimizer explores alternative plans to find a lower cost plan. If the current best cost is small, then the optimizer ends the search swiftly because further cost improvement is not significant.

4.3 About Automatic Tuning Optimizer

The optimizer performs different operations depending on how it is invoked. The database provides the following types of optimization:

- Normal optimization
The optimizer compiles the SQL and generates an execution plan. The normal mode generates a reasonable plan for most SQL statements. Under normal mode, the optimizer operates with strict time constraints, usually a fraction of a second, during which it must find an optimal plan.

- SQL Tuning Advisor optimization
When SQL Tuning Advisor invokes the optimizer, the optimizer is known as Automatic Tuning Optimizer. In this case, the optimizer performs additional analysis to further improve the plan produced in normal mode. The optimizer output is not an execution plan, but a series of actions, along with their rationale and expected benefit for producing a significantly better plan.

See Also:
Chapter 20, "Analyzing SQL with SQL Tuning Advisor"

4.4 About Adaptive Query Optimization

In Oracle Database, adaptive query optimization is a set of capabilities that enables the optimizer to make run-time adjustments to execution plans and discover additional information that can lead to better statistics. Adaptive optimization is helpful when existing statistics are not sufficient to generate an optimal plan.

The following graphic shows the feature set for adaptive query optimization:

4.4.1 Adaptive Plans

An adaptive plan enables the optimizer to defer the final plan decision for a statement until execution time. The ability of the optimizer to adapt a plan, based on information learned during execution, can greatly improve query performance.

Adaptive plans are useful because the optimizer occasionally picks a suboptimal default plan because of a cardinality misestimate. The ability to adapt the plan at run time based on actual execution statistics results in a more optimal final plan. After choosing the final plan, the optimizer uses it for subsequent executions, thus ensuring that the suboptimal plan is not reused.

See Also:

- "Introduction to Optimizer Statistics"

- "About SQL Tuning Advisor"

- "About SQL Plan Management"

4.4.1.1 How Adaptive Plans Work

An adaptive plan contains multiple predetermined subplans, and an optimizer statistics collector. A subplan is a portion of a plan that the optimizer can switch to as an alternative at run time. For example, a nested loops join could be switched to a hash join during execution. An optimizer statistics collector is a row source inserted into a plan at key points to collect run-time statistics. These statistics help the optimizer make a final decision between multiple subplans.

During statement execution, the statistics collector gathers information about the execution, and buffers some rows received by the subplan. Based on the information observed by the collector, the optimizer chooses a subplan. At this point, the collector stops collecting statistics and buffering rows, and permits rows to pass through instead. On subsequent executions of the child cursor, the optimizer continues to use the same plan unless the plan ages out of the cache, or a different optimizer feature (for example, adaptive cursor sharing or statistics feedback) invalidates the plan.

The database uses adaptive plans when OPTIMIZER_FEATURES_ENABLE is 12.1.0.1 or later, and the OPTIMIZER_ADAPTIVE_REPORTING_ONLY initialization parameter is set to the default of false (see "Controlling Adaptive Optimization").

4.4.1.2 Adaptive Plans: Join Method Example

Example 4-2 shows a join of the order_items and product_information tables. An adaptive plan for this statement shows two possible plans, one with a nested loops join and the other with a hash join.

Example 4-2 Join of order_items and product_information

SELECT product_name
FROM   order_items o, product_information p
WHERE  o.unit_price = 15
AND    quantity > 1
AND    p.product_id = o.product_id

A nested loops join is preferable if the database can avoid scanning a significant portion of product_information because its rows are filtered by the join predicate. If few rows are filtered, however, then scanning the right table in a hash join is preferable.

The following graphic shows the adaptive process. For the query in Example 4-2, the adaptive portion of the default plan contains two subplans, each of which uses a different join method. The optimizer automatically determines when each join method is optimal, depending on the cardinality of the left side of the join.

The statistics collector buffers enough rows coming from the order_items table to determine which join method to use. If the row count is below the threshold determined by the optimizer, then the optimizer chooses the nested loops join; otherwise, the optimizer chooses the hash join. In this case, the row count coming from the order_items table is above the threshold, so the optimizer chooses a hash join for the final plan, and disables buffering.

After the optimizer determines the final plan, DBMS_XPLAN.DISPLAY_CURSOR displays the hash join. The Note section of the execution plan indicates whether the plan is adaptive, as shown in the following sample plan:

----------------------------------------------------------------------------------------------------------------
|Id | Operation          | Name                |Starts|E-Rows|A-Rows|   A-Time   |Buffers|Reads|OMem|1Mem|O/1/M|
----------------------------------------------------------------------------------------------------------------
|  0| SELECT STATEMENT   |                     | 1 |   |  13 |00:00:00.10 |  21 |  17 |       |       |        |
|* 1|  HASH JOIN         |                     | 1 | 4 |  13 |00:00:00.10 |  21 |  17 |  2061K|  2061K|   1/0/0|
|* 2|   TABLE ACCESS FULL| ORDER_ITEMS         | 1 | 4 |  13 |00:00:00.07 |   5 |   4 |       |       |        |
|  3|   TABLE ACCESS FULL| PRODUCT_INFORMATION | 1 | 1 | 288 |00:00:00.03 |  16 |  13 |       |       |        |
----------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID")
2 - filter(("O"."UNIT_PRICE"=15 AND "QUANTITY">1))

PLAN_TABLE_OUTPUT
----------------------------------------------------------------------------------------------------------------
Note
-----
- this is an adaptive plan

See Also:

- "Controlling Adaptive Optimization"

- "Reading Execution Plans: Advanced" for an extended example showing an adaptive plan

4.4.1.3 Adaptive Plans: Parallel Distribution Methods

Typically, parallel execution requires data redistribution to perform operations such as parallel sorts, aggregations, and joins. Oracle Database can use many different data distributions methods. The database chooses the method based on the number of rows to be distributed and the number of parallel server processes in the operation.

For example, consider the following alternative cases:

- Many parallel server processes distribute few rows.
The database may choose the broadcast distribution method. In this case, each row in the result set is sent to each of the parallel server processes.

- Few parallel server processes distribute many rows.
If a data skew is encountered during the data redistribution, then it could adversely effect the performance of the statement. The database is more likely to pick a hash distribution to ensure that each parallel server process receives an equal number of rows.

The hybrid hash distribution technique is an adaptive parallel data distribution that does not decide the final data distribution method until execution time. The optimizer inserts statistic collectors in front of the parallel server processes on the producer side of the operation. If the actual number of rows is less than a threshold, defined as twice the degree of parallelism (DOP) chosen for the operation, then the data distribution method switches from hash to broadcast. Otherwise, the distribution method is a hash.

Figure 4-6 depicts a hybrid hash join between the departments and employees tables, with a query coordinator directing 8 PX server processes. A statistics collector is inserted in front of the parallel server processes scanning the departments table. The distribution method is based on the run-time statistics. In the example shown in Figure 4-6, the number of rows is below the threshold (8), which is twice the DOP (4), so the optimizer chooses a broadcast technique for the departments table.

Figure 4-6 Adaptive Parallel Query

Contrast the broadcast distribution example in Figure 4-6 with an example that returns a greater number of rows. In the following plan, the threshold is 8, or twice the specified DOP of 4. However, because the statistics collector (Step 10) discovers that the number of rows (27) is greater than the threshold (8), the optimizer chooses a hybrid hash distribution rather than a broadcast distribution.

EXPLAIN PLAN FOR
SELECT /*+ parallel(4) full(e) full(d) */ department_name, sum(salary)
FROM   employees e, departments d
WHERE  d.department_id=e.department_id
GROUP BY department_name;

Plan hash value: 2940813933
----------------------------------------------------------------------------------------------------------------
| Id| Operation                          | Name      | Rows|Bytes|Cost   |Time      |    TQ |IN-OUT|PQ Distrib |
----------------------------------------------------------------------------------------------------------------
|  0| SELECT STATEMENT                   |DEPARTMENTS|  27 | 621 | 6 (34)| 00:00:01 |       |      |           |
|  1|  PX COORDINATOR                    |           |     |     |       |          |       |      |           |
|  2|   PX SEND QC (RANDOM)              | :TQ10003  |  27 | 621 | 6 (34)| 00:00:01 | Q1,03 | P->S | QC (RAND) |
|  3|    HASH GROUP BY                   |           |  27 | 621 | 6 (34)| 00:00:01 | Q1,03 | PCWP |           |
|  4|     PX RECEIVE                     |           |  27 | 621 | 6 (34)| 00:00:01 | Q1,03 | PCWP |           |
|  5|      PX SEND HASH                  | :TQ10002  |  27 | 621 | 6 (34)| 00:00:01 | Q1,02 | P->P | HASH      |
|  6|       HASH GROUP BY                |           |  27 | 621 | 6 (34)| 00:00:01 | Q1,02 | PCWP |           |
|* 7|        HASH JOIN                   |           | 106 |2438 | 5 (20)| 00:00:01 | Q1,02 | PCWP |           |
|  8|         PX RECEIVE                 |           |  27 | 432 | 2  (0)| 00:00:01 | Q1,02 | PCWP |           |
|  9|          PX SEND HYBRID HASH       | :TQ10000  |  27 | 432 | 2  (0)| 00:00:01 | Q1,00 | P->P |HYBRID HASH|
| 10|           STATISTICS COLLECTOR     |           |     |     |       |          | Q1,00 | PCWC |           |
| 11|            PX BLOCK ITERATOR       |           |  27 | 432 | 2  (0)| 00:00:01 | Q1,00 | PCWC |           |
| 12|             TABLE ACCESS FULL      |DEPARTMENTS|  27 | 432 | 2  (0)| 00:00:01 | Q1,00 | PCWP |           |
| 13|         PX RECEIVE                 |           | 107 | 749 | 2  (0)| 00:00:01 | Q1,02 | PCWP |           |
| 14|          PX SEND HYBRID HASH (SKEW)| :TQ10001  | 107 | 749 | 2  (0)| 00:00:01 | Q1,01 | P->P |HYBRID HASH|
| 15|           PX BLOCK ITERATOR        |           | 107 | 749 | 2  (0)| 00:00:01 | Q1,01 | PCWC |           |
| 16|            TABLE ACCESS FULL       |EMPLOYEES  | 107 | 749 | 2  (0)| 00:00:01 | Q1,01 | PCWP |           |
----------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):---------------------------------------------------

7 - access("D"."DEPARTMENT_ID"="E"."DEPARTMENT_ID")

Note
-----
- Degree of Parallelism is 4 because of hint

32 rows selected.

See Also:
Oracle Database VLDB and Partitioning Guide to learn more about parallel data redistribution techniques

4.4.2 Adaptive Statistics

The quality of the plans that the optimizer generates depends on the quality of the statistics. Some query predicates become too complex to rely on base table statistics alone, so the optimizer augments these statistics with adaptive statistics.

The following topics describe types of adaptive statistics:

- Dynamic Statistics

- Automatic Reoptimization

- SQL Plan Directives

4.4.2.1 Dynamic Statistics

During the compilation of a SQL statement, the optimizer decides whether to use dynamic statistics by considering whether the available statistics are sufficient to generate an optimal execution plan. If the available statistics are insufficient, then the optimizer uses dynamic statistics to augment the statistics. One type of dynamic statistics is the information gathered by dynamic sampling. The optimizer can use dynamic statistics for table scans, index access, joins, and GROUP BY operations, thus improving the quality of optimizer decisions.

See Also:
"Dynamic Statistics" to learn more about dynamic statistics and optimizer statistics in general

4.4.2.2 Automatic Reoptimization

Whereas adaptive plans help decide between multiple subplans, they are not feasible for all kinds of plan changes. For example, a query with an inefficient join order might perform suboptimally, but adaptive plans do not support adapting the join order during execution. In these cases, the optimizer considers automatic reoptimization. In contrast to adaptive plans, automatic reoptimization changes a plan on subsequent executions after the initial execution.

At the end of the first execution of a SQL statement, the optimizer uses the information gathered during execution to determine whether automatic reoptimization is worthwhile. If execution informations differs significantly from optimizer estimates, then the optimizer looks for a replacement plan on the next execution. The optimizer uses the information gathered during the previous execution to help determine an alternative plan. The optimizer can reoptimize a query several times, each time learning more and further improving the plan.

See Also:
"Controlling Adaptive Optimization"

4.4.2.2.1 Reoptimization: Statistics Feedback

A form of reoptimization known as statistics feedback (formerly known as cardinality feedback) automatically improves plans for repeated queries that have cardinality misestimates. The optimizer can estimate cardinalities incorrectly for many reasons, such as missing statistics, inaccurate statistics, or complex predicates.

The basic process of reoptimization using statistics feedback is as follows:

1. During the first execution of a SQL statement, the optimizer generates an execution plan.
The optimizer may enable monitoring for statistics feedback for the shared SQL area in the following cases:

- Tables with no statistics

- Multiple conjunctive or disjunctive filter predicates on a table

- Predicates containing complex operators for which the optimizer cannot accurately compute selectivity estimates

At the end of execution, the optimizer compares its initial cardinality estimates to the actual number of rows returned by each operation in the plan during execution. If estimates differ significantly from actual cardinalities, then the optimizer stores the correct estimates for subsequent use. The optimizer also creates a SQL plan directive so that other SQL statements can benefit from the information obtained during this initial execution.

2. After the first execution, the optimizer disables monitoring for statistics feedback.

3. If the query executes again, then the optimizer uses the corrected cardinality estimates instead of its usual estimates.

Example 4-3 Statistics Feedback

This example shows how the database uses statistics feedback to adjust incorrect estimates.

1. The user oe runs the following query of the orders, order_items, and product_information tables:

SELECT o.order_id, v.product_name
FROM   orders o,
( SELECT order_id, product_name
FROM   order_items o, product_information p
WHERE  p.product_id = o.product_id
AND    list_price < 50
AND    min_price < 40 ) v
WHERE  o.order_id = v.order_id

2. Querying the plan in the cursor shows that the estimated rows (E-Rows) is far fewer than the actual rows (A-Rows).

Example 4-4 Actual Rows and Estimated Rows

------------------------------------------------------------------------------------------------------------
| Id| Operation             | Name                |Starts|E-Rows|A-Rows|   A-Time  |Buffers|OMem|1Mem|O/1/M|
------------------------------------------------------------------------------------------------------------
|  0| SELECT STATEMENT      |                     |     1|      |  269 |00:00:00.10|   1338|    |    |     |
|  1|  NESTED LOOPS         |                     |     1|     1|  269 |00:00:00.10|   1338|    |    |     |
|  2|   MERGE JOIN CARTESIAN|                     |     1|     4| 9135 |00:00:00.04|     33|    |    |     |
|* 3|    TABLE ACCESS FULL  | PRODUCT_INFORMATION |     1|     1|   87 |00:00:00.01|     32|    |    |     |
|  4|    BUFFER SORT        |                     |    87|   105| 9135 |00:00:00.01|      1|4096|4096|1/0/0|
|  5|     INDEX FULL SCAN   | ORDER_PK            |     1|   105|  105 |00:00:00.01|      1|    |    |     |
|* 6|   INDEX UNIQUE SCAN   | ORDER_ITEMS_UK      |  9135|     1|  269 |00:00:00.03|   1305|    |    |     |
------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50))
6 - access("O"."ORDER_ID"="ORDER_ID" AND "P"."PRODUCT_ID"="O"."PRODUCT_ID")

3. The user oe reruns the following query of the orders, order_items, and product_information tables:

SELECT o.order_id, v.product_name
FROM   orders o,
( SELECT order_id, product_name
FROM   order_items o, product_information p
WHERE  p.product_id = o.product_id
AND    list_price < 50
AND    min_price < 40 ) v
WHERE  o.order_id = v.order_id;

4. Querying the plan in the cursor shows that the optimizer used statistics feedback (shown in the Note) for the second execution, and also chose a different plan.

Example 4-5 Actual Rows and Estimated Rows

----------------------------------------------------------------------------------------------------------------
| Id| Operation              |Name            |Starts|E-Rows|A-Rows|   A-Time  |Buffers|Reads|OMem |1Mem |O/1/M|
----------------------------------------------------------------------------------------------------------------
|  0| SELECT STATEMENT       |                   |  1|      |   269|00:00:00.03|    60 |   1 |     |     |     |
|  1|  NESTED LOOPS          |                   |  1|   269|   269|00:00:00.03|    60 |   1 |     |     |     |
|* 2|   HASH JOIN            |                   |  1|   313|   269|00:00:00.03|    39 |   1 |1321K|1321K|1/0/0|
|* 3|    TABLE ACCESS FULL   |PRODUCT_INFORMATION|  1|    87|    87|00:00:00.01|    15 |   0 |     |     |     |
|  4|    INDEX FAST FULL SCAN|ORDER_ITEMS_UK     |  1|   665|   665|00:00:00.02|    24 |   1 |     |     |     |
|* 5|   INDEX UNIQUE SCAN    |ORDER_PK           |269|     1|   269|00:00:00.01|    21 |   0 |     |     |     |
----------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID")
3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50))
5 - access("O"."ORDER_ID"="ORDER_ID")

Note
-----
- statistics feedback used for this statement

In the preceding output, the estimated number of rows (269) matches the actual number of rows.

4.4.2.2.2 Reoptimization: Performance Feedback

Another form of reoptimization is performance feedback. This reoptimization helps improve the degree of parallelism automatically chosen for repeated SQL statements when PARALLEL_DEGREE_POLICY is set to ADAPTIVE.

The basic process of reoptimization using performance feedback is as follows:

1. During the first execution of a SQL statement, when PARALLEL_DEGREE_POLICY is set to ADAPTIVE, the optimizer determines whether to execute the statement in parallel, and if so, which degree of parallelism to use.
The optimizer chooses the degree of parallelism based on the estimated performance of the statement. Additional performance monitoring is enabled for all statements.

2. At the end of the initial execution, the optimizer compares the following:

- The degree of parallelism chosen by the optimizer

- The degree of parallelism computed based on the performance statistics (for example, the CPU time) gathered during the actual execution of the statement

If the two values vary significantly, then the database marks the statement for reparsing, and stores the initial execution statistics as feedback. This feedback helps better compute the degree of parallelism for subsequent executions.

3. If the query executes again, then the optimizer uses the performance statistics gathered during the initial execution to better determine a degree of parallelism for the statement.

Note:
Even if PARALLEL_DEGREE_POLICY is not set to ADAPTIVE, statistics feedback may influence the degree of parallelism chosen for a statement.

4.4.2.3 SQL Plan Directives

A SQL plan directive is additional information that the optimizer uses to generate a more optimal plan. For example, during query optimization, when deciding whether the table is a candidate for dynamic statistics, the database queries the statistics repository for directives on a table. If the query joins two tables that have a data skew in their join columns, a SQL plan directive can direct the optimizer to use dynamic statistics to obtain an accurate cardinality estimate.The optimizer collects SQL plan directives on query expressions rather than at the statement level. In this way, the optimizer can apply directives to multiple SQL statements. The database automatically maintains directives, and stores them in the SYSAUX tablespace. You can manage directives using the package DBMS_SPD.

See Also:

- "SQL Plan Directives"

- "Managing SQL Plan Directives"

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPD package

4.5 About Optimizer Management of SQL Plan Baselines

SQL plan management is a mechanism that enables the optimizer to automatically manage execution plans, ensuring that the database uses only known or verified plans (see Chapter 23, "Managing SQL Plan Baselines"). This mechanism can build a SQL plan baseline, which contains one or more accepted plans for each SQL statement.

The optimizer can access and manage the plan history and SQL plan baselines of SQL statements. This capability is central to the SQL plan management architecture. In SQL plan management, the optimizer has the following main objectives:

- Identify repeatable SQL statements

- Maintain plan history, and possibly SQL plan baselines, for a set of SQL statements

- Detect plans that are not in the plan history

- Detect potentially better plans that are not in the SQL plan baseline

The optimizer uses the normal cost-based search method.

See Also:
Chapter 23, "Managing SQL Plan Baselines"

5 Query Transformations

As explained in "Query Transformer," the optimizer employs several query transformation techniques. This chapter contains the following topics:

- OR Expansion

- View Merging

- Predicate Pushing

- Subquery Unnesting

- Query Rewrite with Materialized Views

- Star Transformation

- In-Memory Aggregation

- Table Expansion

- Join Factorization

5.1 OR Expansion

In OR expansion, the optimizer transforms a query with a WHERE clause containing OR operators into a query that uses the UNION ALL operator. The database can perform OR expansion for various reasons. For example, it may enable more efficient access paths or alternative join methods that avoid Cartesian products. As always, the optimizer performs the expansion only if the cost of the transformed statement is lower than the cost of the original statement.

In Example 5-1, user sh creates a concatenated index on the sales.prod_id and sales.promo_id columns, and then queries the sales table using an OR condition.

Example 5-1 OR Condition

CREATE INDEX sales_prod_promo_ind
ON sales(prod_id, promo_id);

SELECT *
FROM   sales
WHERE  promo_id=33
OR     prod_id=136;

In Example 5-1, because the promo_id=33 and prod_id=136 conditions could each take advantage of an index access path, the optimizer transforms the statement into the query in Example 5-2.

Example 5-2 UNION ALL Condition

SELECT *
FROM   sales
WHERE  prod_id=136
UNION ALL
SELECT *
FROM   sales
WHERE  promo_id=33
AND    LNNVL(prod_id=136);

For the transformed query in Example 5-2, the optimizer selects an execution plan that accesses the sales table using the index, and then assembles the result. The plan is shown in Example 5-3.

Example 5-3 Plan for Query of sales

----------------------------------------------------------------------------------
| Id| Operation                                   | Name                 | Rows  |
----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT                            |                      |       |
| 1 |  CONCATENATION                              |                      |       |
| 2 |   TABLE ACCESS BY GLOBAL INDEX ROWID BATCHED| SALES                |   710 |
| 3 |    INDEX RANGE SCAN                         | SALES_PROD_PROMO_IND |   710 |
| 4 |   PARTITION RANGE ALL                       |                      |   229K|
| 5 |    TABLE ACCESS FULL                        | SALES                |   229K|
----------------------------------------------------------------------------------

5.2 View Merging

In view merging, the optimizer merges the query block representing a view into the query block that contains it. View merging can improve plans by enabling the optimizer to consider additional join orders, access methods, and other transformations.

For example, after a view has been merged and several tables reside in one query block, a table inside a view may permit the optimizer to use join elimination to remove a table outside the view. For certain simple views in which merging always leads to a better plan, the optimizer automatically merges the view without considering cost. Otherwise, the optimizer uses cost to make the determination. The optimizer may choose not to merge a view for many reasons, including cost or validity restrictions.

If OPTIMIZER_SECURE_VIEW_MERGING is true (default), then Oracle Database performs checks to ensure that view merging and predicate pushing do not violate the security intentions of the view creator. To disable these additional security checks for a specific view, you can grant the MERGE VIEW privilege to a user for this view. To disable additional security checks for all views for a specific user, you can grant the MERGE ANY VIEW privilege to that user.

Note:
You can use hints to override view merging rejected because of cost or heuristics, but not validity.

This section contains the following topics:

- Query Blocks in View Merging

- Simple View Merging

- Complex View Merging

See Also:

- Oracle Database SQL Language Reference for more information about the MERGE ANY VIEW and MERGE VIEW privileges

- Oracle Database Reference for more information about the OPTIMIZER_SECURE_VIEW_MERGING initialization parameter

5.2.1 Query Blocks in View Merging

The optimizer represents each nested subquery or unmerged view by a separate query block. The database optimizes query blocks separately from the bottom up. Thus, the database optimizes the innermost query block first, generates the part of the plan for it, and then generates the plan for the outer query block, representing the entire query.

The parser expands each view referenced in a query into a separate query block. The block essentially represents the view definition, and thus the result of a view. One option for the optimizer is to analyze the view query block separately, generate a view subplan, and then process the rest of the query by using the view subplan to generate an overall execution plan. However, this technique may lead to a suboptimal execution plan because the view is optimized separately.

View merging can sometimes improve performance. As shown in Example 5-4, view merging merges the tables from the view into the outer query block, removing the inner query block. Thus, separate optimization of the view is not necessary.

5.2.2 Simple View Merging

In simple view merging, the optimizer merges select-project-join views. For example, a query of the employees table contains a subquery that joins the departments and locations tables.

Simple view merging frequently results in a more optimal plan because of the additional join orders and access paths available after the merge. A view may not be valid for simple view merging because:

- The view contains constructs not included in select-project-join views, including:

- GROUP BY

- DISTINCT

- Outer join

- MODEL

- CONNECT BY

- Set operators

- Aggregation

- The view appears on the right side of a semijoin or antijoin.

- The view contains subqueries in the SELECT list.

- The outer query block contains PL/SQL functions.

- The view participates in an outer join, and does not meet one of the several additional validity requirements that determine whether the view can be merged.

Example 5-4 Simple View Merging

The following query joins the hr.employees table with the dept_locs_v view, which returns the street address for each department. dept_locs_v is a join of the departments and locations tables.

SELECT e.first_name, e.last_name, dept_locs_v.street_address,
dept_locs_v.postal_code
FROM   employees e,
( SELECT d.department_id, d.department_name, l.street_address, l.postal_code
FROM   departments d, locations l
WHERE  d.location_id = l.location_id ) dept_locs_v
WHERE  dept_locs_v.department_id = e.department_id
AND    e.last_name = 'Smith';

The database can execute the preceding query by joining departments and locations to generate the rows of the view, and then joining this result to employees. Because the query contains the view dept_locs_v, and this view contains two tables, the optimizer must use one of the following join orders:

- employees, dept_locs_v (departments, locations)

- employees, dept_locs_v (locations, departments)

- dept_locs_v (departments, locations), employees

- dept_locs_v (locations, departments), employees

Join methods are also constrained. The index-based nested loops join is not feasible for join orders that begin with employees because no index exists on the column from this view. Without view merging, the optimizer generates the following execution plan:

-----------------------------------------------------------------
| Id  | Operation                    | Name        | Cost (%CPU)|
-----------------------------------------------------------------
|   0 | SELECT STATEMENT             |             |     7  (15)|
|*  1 |  HASH JOIN                   |             |     7  (15)|
|   2 |   TABLE ACCESS BY INDEX ROWID| EMPLOYEES   |     2   (0)|
|*  3 |    INDEX RANGE SCAN          | EMP_NAME_IX |     1   (0)|
|   4 |   VIEW                       |             |     5  (20)|
|*  5 |    HASH JOIN                 |             |     5  (20)|
|   6 |     TABLE ACCESS FULL        | LOCATIONS   |     2   (0)|
|   7 |     TABLE ACCESS FULL        | DEPARTMENTS |     2   (0)|
-----------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
1 - access("DEPT_LOCS_V"."DEPARTMENT_ID"="E"."DEPARTMENT_ID")
3 - access("E"."LAST_NAME"='Smith')
5 - access("D"."LOCATION_ID"="L"."LOCATION_ID")

View merging merges the tables from the view into the outer query block, removing the inner query block. After view merging, the query is as follows:

SELECT e.first_name, e.last_name, l.street_address, l.postal_code
FROM   employees e, departments d, locations l
WHERE  d.location_id = l.location_id
AND    d.department_id = e.department_id
AND    e.last_name = 'Smith';

Because all three tables appear in one query block, the optimizer can choose from the following six join orders:

- employees, departments, locations

- employees, locations, departments

- departments, employees, locations

- departments, locations, employees

- locations, employees, departments

- locations, departments, employees

The joins to employees and departments can now be index-based. After view merging, the optimizer chooses the following more efficient plan, which uses nested loops:

-------------------------------------------------------------------
| Id  | Operation                      | Name        | Cost (%CPU)|
-------------------------------------------------------------------
|   0 | SELECT STATEMENT               |             |     4   (0)|
|   1 |  NESTED LOOPS                  |             |            |
|   2 |   NESTED LOOPS                 |             |     4   (0)|
|   3 |    NESTED LOOPS                |             |     3   (0)|
|   4 |     TABLE ACCESS BY INDEX ROWID| EMPLOYEES   |     2   (0)|
|*  5 |      INDEX RANGE SCAN          | EMP_NAME_IX |     1   (0)|
|   6 |     TABLE ACCESS BY INDEX ROWID| DEPARTMENTS |     1   (0)|
|*  7 |      INDEX UNIQUE SCAN         | DEPT_ID_PK  |     0   (0)|
|*  8 |    INDEX UNIQUE SCAN           | LOC_ID_PK   |     0   (0)|
|   9 |   TABLE ACCESS BY INDEX ROWID  | LOCATIONS   |     1   (0)|
-------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
5 - access("E"."LAST_NAME"='Smith')
7 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")
8 - access("D"."LOCATION_ID"="L"."LOCATION_ID")

See Also:
The Oracle Optimizer blog at https://blogs.oracle.com/optimizer/ to learn about outer join view merging, which is a special case of simple view merging

5.2.3 Complex View Merging

In complex view merging, the optimizer merges views containing GROUP BY and DISTINCT views. Like simple view merging, complex merging enables the optimizer to consider additional join orders and access paths.

The optimizer can delay evaluation of GROUP BY or DISTINCT operations until after it has evaluated the joins. Delaying these operations can improve or worsen performance depending on the data characteristics. If the joins use filters, then delaying the operation until after joins can reduce the data set on which the operation is to be performed. Evaluating the operation early can reduce the amount of data to be processed by subsequent joins, or the joins could increase the amount of data to be processed by the operation. The optimizer uses cost to evaluate view merging and merges the view only when it is the lower cost option.

Aside from cost, the optimizer may be unable to perform complex view merging for the following reasons:

- The outer query tables do not have a rowid or unique column.

- The view appears in a CONNECT BY query block.

- The view contains GROUPING SETS, ROLLUP, or PIVOT clauses.

- The view or outer query block contains the MODEL clause.

Example 5-5 Complex View Joins with GROUP BY

The following view uses a GROUP BY clause:

CREATE VIEW cust_prod_totals_v AS
SELECT SUM(s.quantity_sold) total, s.cust_id, s.prod_id
FROM   sales s
GROUP BY s.cust_id, s.prod_id;

The following query finds all of the customers from the United States who have bought at least 100 fur-trimmed sweaters:

SELECT c.cust_id, c.cust_first_name, c.cust_last_name, c.cust_email
FROM   customers c, products p, cust_prod_totals_v
WHERE  c.country_id = 52790
AND    c.cust_id = cust_prod_totals_v.cust_id
AND    cust_prod_totals_v.total > 100
AND    cust_prod_totals_v.prod_id = p.prod_id
AND    p.prod_name = 'T3 Faux Fur-Trimmed Sweater';

The cust_prod_totals_v view is eligible for complex view merging. After merging, the query is as follows:

SELECT c.cust_id, cust_first_name, cust_last_name, cust_email
FROM   customers c, products p, sales s
WHERE  c.country_id = 52790
AND    c.cust_id = s.cust_id
AND    s.prod_id = p.prod_id
AND    p.prod_name = 'T3 Faux Fur-Trimmed Sweater'
GROUP BY s.cust_id, s.prod_id, p.rowid, c.rowid, c.cust_email, c.cust_last_name,
c.cust_first_name, c.cust_id
HAVING SUM(s.quantity_sold) > 100;

The transformed query is cheaper than the untransformed query, so the optimizer chooses to merge the view. In the untransformed query, the GROUP BY operator applies to the entire sales table in the view. In the transformed query, the joins to products and customers filter out a large portion of the rows from the sales table, so the GROUP BY operation is lower cost. The join is more expensive because the sales table has not been reduced, but it is not much more expensive because the GROUP BY operation does not reduce the size of the row set very much in the original query. If any of the preceding characteristics were to change, merging the view might no longer be lower cost. The final plan, which does not include a view, is as follows:

--------------------------------------------------------
| Id  | Operation             | Name      | Cost (%CPU)|
--------------------------------------------------------
|   0 | SELECT STATEMENT      |           |  2101  (18)|
|*  1 |  FILTER               |           |            |
|   2 |   HASH GROUP BY       |           |  2101  (18)|
|*  3 |    HASH JOIN          |           |  2099  (18)|
|*  4 |     HASH JOIN         |           |  1801  (19)|
|*  5 |      TABLE ACCESS FULL| PRODUCTS  |    96   (5)|
|   6 |      TABLE ACCESS FULL| SALES     |  1620  (15)|
|*  7 |     TABLE ACCESS FULL | CUSTOMERS |   296  (11)|
--------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter(SUM("QUANTITY_SOLD")>100)
3 - access("C"."CUST_ID"="CUST_ID")
4 - access("PROD_ID"="P"."PROD_ID")
5 - filter("P"."PROD_NAME"='T3 Faux Fur-Trimmed Sweater')
7 - filter("C"."COUNTRY_ID"='US')

Example 5-6 Complex View Joins with DISTINCT

The following query of the cust_prod_v view uses a DISTINCT operator:

SELECT c.cust_id, c.cust_first_name, c.cust_last_name, c.cust_email
FROM   customers c, products p,
( SELECT DISTINCT s.cust_id, s.prod_id
FROM   sales s) cust_prod_v
WHERE  c.country_id = 52790
AND    c.cust_id = cust_prod_v.cust_id
AND    cust_prod_v.prod_id = p.prod_id
AND    p.prod_name = 'T3 Faux Fur-Trimmed Sweater';

After determining that view merging produces a lower-cost plan, the optimizer rewrites the query into this equivalent query:

SELECT nwvw.cust_id, nwvw.cust_first_name, nwvw.cust_last_name, nwvw.cust_email
FROM   ( SELECT DISTINCT(c.rowid), p.rowid, s.prod_id, s.cust_id,
c.cust_first_name, c.cust_last_name, c.cust_email
FROM   customers c, products p, sales s
WHERE  c.country_id = 52790
AND    c.cust_id = s.cust_id
AND    s.prod_id = p.prod_id
AND    p.prod_name = 'T3 Faux Fur-Trimmed Sweater' ) nwvw;

The plan for the preceding query is as follows:

-------------------------------------------
| Id  | Operation             | Name      |
-------------------------------------------
|   0 | SELECT STATEMENT      |           |
|   1 |  VIEW                 | VM_NWVW_1 |
|   2 |   HASH UNIQUE         |           |
|*  3 |    HASH JOIN          |           |
|*  4 |     HASH JOIN         |           |
|*  5 |      TABLE ACCESS FULL| PRODUCTS  |
|   6 |      TABLE ACCESS FULL| SALES     |
|*  7 |     TABLE ACCESS FULL | CUSTOMERS |
-------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
3 - access("C"."CUST_ID"="S"."CUST_ID")
4 - access("S"."PROD_ID"="P"."PROD_ID")
5 - filter("P"."PROD_NAME"='T3 Faux Fur-Trimmed Sweater')
7 - filter("C"."COUNTRY_ID"='US')

The preceding plan contains a view named vm_nwvw_1, known as a projection view, even after view merging has occurred. Projection views appear in queries in which a DISTINCT view has been merged, or a GROUP BY view is merged into an outer query block that also contains GROUP BY, HAVING, or aggregates. In the latter case, the projection view contains the GROUP BY, HAVING, and aggregates from the original outer query block.

In the preceding example of a projection view, when the optimizer merges the view, it moves the DISTINCT operator to the outer query block, and then adds several additional columns to maintain semantic equivalence with the original query. Afterward, the query can select only the desired columns in the SELECT list of the outer query block. The optimization retains all of the benefits of view merging: all tables are in one query block, the optimizer can permute them as needed in the final join order, and the DISTINCT operation has been delayed until after all of the joins complete.

5.3 Predicate Pushing

In predicate pushing, the optimizer "pushes" the relevant predicates from the containing query block into the view query block. For views that are not merged, this technique improves the subplan of the unmerged view because the database can use the pushed-in predicates to access indexes or to use as filters.

For example, suppose you create a table hr.contract_workers as follows:

DROP TABLE contract_workers;
CREATE TABLE contract_workers AS (SELECT * FROM employees where 1=2);
INSERT INTO contract_workers VALUES (306, 'Bill', 'Jones', 'BJONES',
'555.555.2000', '07-JUN-02', 'AC_ACCOUNT', 8300, 0,205, 110);
INSERT INTO contract_workers VALUES (406, 'Jill', 'Ashworth', 'JASHWORTH',
'555.999.8181', '09-JUN-05', 'AC_ACCOUNT', 8300, 0,205, 50);
INSERT INTO contract_workers VALUES (506, 'Marcie', 'Lunsford', 'MLUNSFORD',
'555.888.2233', '22-JUL-01', 'AC_ACCOUNT', 8300, 0,205, 110);
COMMIT;
CREATE INDEX contract_workers_index ON contract_workers(department_id);

You create a view that references employees and contract_workers. The view is defined with a query that uses the UNION set operator, as follows:

CREATE VIEW all_employees_vw AS
( SELECT employee_id, last_name, job_id, commission_pct, department_id
FROM   employees )
UNION
( SELECT employee_id, last_name, job_id, commission_pct, department_id
FROM   contract_workers );

You then query the view as follows:

SELECT last_name
FROM   all_employees_vw
WHERE  department_id = 50;

Because the view is a UNION set query, the optimizer cannot merge the view's query into the accessing query block. Instead, the optimizer can transform the accessing statement by pushing its predicate, the WHERE clause condition department_id=50, into the view's UNION set query. The equivalent transformed query is as follows:

SELECT last_name
FROM   ( SELECT employee_id, last_name, job_id, commission_pct, department_id
FROM   employees
WHERE  department_id=50
UNION
SELECT employee_id, last_name, job_id, commission_pct, department_id
FROM   contract_workers
WHERE  department_id=50 );

The transformed query can now consider index access in each of the query blocks.

5.4 Subquery Unnesting

In subquery unnesting, the optimizer transforms a nested query into an equivalent join statement, and then optimizes the join. This transformation enables the optimizer to consider the subquery tables during access path, join method, and join order selection. The optimizer can perform this transformation only if the resulting join statement is guaranteed to return the same rows as the original statement, and if subqueries do not contain aggregate functions such as AVG.

For example, suppose you connect as user sh and execute the following query:

SELECT *
FROM   sales
WHERE  cust_id IN ( SELECT cust_id
FROM   customers );

Because the customers.cust_id column is a primary key, the optimizer can transform the complex query into the following join statement that is guaranteed to return the same data:

SELECT sales.*
FROM   sales, customers
WHERE  sales.cust_id = customers.cust_id;

If the optimizer cannot transform a complex statement into a join statement, it selects execution plans for the parent statement and the subquery as though they were separate statements. The optimizer then executes the subquery and uses the rows returned to execute the parent query. To improve execution speed of the overall execution plan, the optimizer orders the subplans efficiently.

5.5 Query Rewrite with Materialized Views

A materialized view is like a query with a result that the database materializes and stores in a table. When the optimizer finds a user query compatible with the query associated with a materialized view, then the database can rewrite the query in terms of the materialized view. This technique improves query execution because the database has precomputed most of the query result.

The optimizer looks for any materialized views that are compatible with the user query, and then selects one or more materialized views to rewrite the user query. The use of materialized views to rewrite a query is cost-based. That is, the optimizer does not rewrite the query when the plan generated without the materialized views has a lower cost than the plan generated with the materialized views.

Consider the following materialized view, cal_month_sales_mv, which aggregates the dollar amount sold each month:

CREATE MATERIALIZED VIEW cal_month_sales_mv
ENABLE QUERY REWRITE
AS
SELECT t.calendar_month_desc, SUM(s.amount_sold) AS dollars
FROM   sales s, times t
WHERE  s.time_id = t.time_id
GROUP BY t.calendar_month_desc;

Assume that sales number is around one million in a typical month. The view has the precomputed aggregates for the dollar amount sold for each month. Consider the following query, which asks for the sum of the amount sold for each month:

SELECT t.calendar_month_desc, SUM(s.amount_sold)
FROM   sales s, times t
WHERE  s.time_id = t.time_id
GROUP BY t.calendar_month_desc;

Without query rewrite, the database must access sales directly and compute the sum of the amount sold. This method involves reading many million rows from sales, which invariably increases query response time. The join also further slows query response because the database must compute the join on several million rows. With query rewrite, the optimizer transparently rewrites the query as follows:

SELECT calendar_month, dollars
FROM   cal_month_sales_mv;

See Also:
Oracle Database Data Warehousing Guide to learn more about query rewrite

5.6 Star Transformation

Star transformation is an optimizer transformation that avoids full table scans of fact tables in a star schema. This section contains the following topics:

- About Star Schemas

- Purpose of Star Transformations

- How Star Transformation Works

- Controls for Star Transformation

- Star Transformation: Scenario

- Temporary Table Transformation: Scenario

5.6.1 About Star Schemas

A star schema divides data into facts and dimensions. Facts are the measurements of an event such as a sale and are typically numbers. Dimensions are the categories that identify facts, such as date, location, and product.

A fact table has a composite key made up of the primary keys of the dimension tables of the schema. Dimension tables act as lookup or reference tables that enable you to choose values that constrain your queries.

Diagrams typically show a central fact table with lines joining it to the dimension tables, giving the appearance of a star. Figure 5-1 shows sales as the fact table and products, times, customers, and channels as the dimension tables.

Figure 5-1 Star Schema

See Also:
Oracle Database Data Warehousing Guide to learn more about star schemas

5.6.2 Purpose of Star Transformations

In joins of fact and dimension tables, a star transformation can avoid a full scan of a fact table by fetching only relevant rows from the fact table that join to the constraint dimension rows. When queries contain restrictive filter predicates on other columns of the dimension tables, the combination of filters can dramatically reduce the data set that the database processes from the fact table.

5.6.3 How Star Transformation Works

Star transformation adds subquery predicates, called bitmap semijoin predicates, corresponding to the constraint dimensions. The optimizer performs the transformation when indexes exist on the fact join columns. By driving bitmap AND and OR operations of key values supplied by the subqueries, the database only needs to retrieve relevant rows from the fact table. If the predicates on the dimension tables filter out significant data, then the transformation can be more efficient than a full table scan on the fact table.

After the database has retrieved the relevant rows from the fact table, the database may need to join these rows back to the dimension tables using the original predicates. The database can eliminate the join back of the dimension table when the following conditions are met:

- All the predicates on dimension tables are part of the semijoin subquery predicate.

- The columns selected from the subquery are unique.

- The dimension columns are not in the SELECT list, GROUP BY clause, and so on.

5.6.4 Controls for Star Transformation

The STAR_TRANSFORMATION_ENABLED initialization parameter controls star transformations. This parameter takes the following values:

- true
The optimizer performs the star transformation by identifying the fact and constraint dimension tables automatically. The optimizer performs the star transformation only if the cost of the transformed plan is lower than the alternatives. Also, the optimizer attempts temporary table transformation automatically whenever materialization improves performance (see "Temporary Table Transformation: Scenario").

- false (default)
The optimizer does not perform star transformations.

- TEMP_DISABLE
This value is identical to true except that the optimizer does not attempt temporary table transformation.

See Also:
Oracle Database Reference to learn about the STAR_TRANSFORMATION_ENABLED initialization parameter

5.6.5 Star Transformation: Scenario

In Example 5-7, the query finds the total Internet sales amount in all cities in California for quarters Q1 and Q2 of year 1999. In this example, sales is the fact table, and the other tables are dimension tables.

Example 5-7 Star Query

SELECT c.cust_city, t.calendar_quarter_desc, SUM(s.amount_sold) sales_amount
FROM   sales s, times t, customers c, channels ch
WHERE  s.time_id = t.time_id
AND    s.cust_id = c.cust_id
AND    s.channel_id = ch.channel_id
AND    c.cust_state_province = 'CA'
AND    ch.channel_desc = 'Internet'
AND    t.calendar_quarter_desc IN ('1999-01','1999-02')
GROUP BY c.cust_city, t.calendar_quarter_desc;

Sample output for Example 5-7 is as follows:

CUST_CITY                      CALENDA SALES_AMOUNT
------------------------------ ------- ------------
Montara                        1999-02      1618.01
Pala                           1999-01      3263.93
Cloverdale                     1999-01        52.64
Cloverdale                     1999-02       266.28
. . .

In Example 5-7, the sales table contains one row for every sale of a product, so it could conceivably contain billions of sales records. However, only a few products are sold to customers in California through the Internet for the specified quarters. Example 5-8 shows a star transformation of the query in Example 5-7. The transformation avoids a full table scan of sales.

Example 5-8 Star Transformation

SELECT c.cust_city, t.calendar_quarter_desc, SUM(s.amount_sold) sales_amount
FROM   sales s, times t, customers c
WHERE  s.time_id = t.time_id
AND    s.cust_id = c.cust_id
AND    c.cust_state_province = 'CA'
AND    t.calendar_quarter_desc IN ('1999-01','1999-02')
AND    s.time_id IN ( SELECT time_id
FROM   times
WHERE  calendar_quarter_desc IN('1999-01','1999-02') )
AND    s.cust_id IN ( SELECT cust_id
FROM   customers
WHERE  cust_state_province='CA' )
AND    s.channel_id IN ( SELECT channel_id
FROM   channels
WHERE  channel_desc = 'Internet' )
GROUP BY c.cust_city, t.calendar_quarter_desc;

Example 5-9 shows an edited version of the execution plan for the star transformation in Example 5-8.

Example 5-9 Partial Execution Plan for Star Transformation

----------------------------------------------------------------------------------
| Id  | Operation                         | Name
----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                  |
|   1 |  HASH GROUP BY                    |
|*  2 |   HASH JOIN                       |
|*  3 |    TABLE ACCESS FULL              | CUSTOMERS
|*  4 |    HASH JOIN                      |
|*  5 |     TABLE ACCESS FULL             | TIMES
|   6 |     VIEW                          | VW_ST_B1772830
|   7 |      NESTED LOOPS                 |
|   8 |       PARTITION RANGE SUBQUERY    |
|   9 |        BITMAP CONVERSION TO ROWIDS|
|  10 |         BITMAP AND                |
|  11 |          BITMAP MERGE             |
|  12 |           BITMAP KEY ITERATION    |
|  13 |            BUFFER SORT            |
|* 14 |             TABLE ACCESS FULL     | CHANNELS
|* 15 |            BITMAP INDEX RANGE SCAN| SALES_CHANNEL_BIX
|  16 |          BITMAP MERGE             |
|  17 |           BITMAP KEY ITERATION    |
|  18 |            BUFFER SORT            |
|* 19 |             TABLE ACCESS FULL     | TIMES
|* 20 |            BITMAP INDEX RANGE SCAN| SALES_TIME_BIX
|  21 |          BITMAP MERGE             |
|  22 |           BITMAP KEY ITERATION    |
|  23 |            BUFFER SORT            |
|* 24 |             TABLE ACCESS FULL     | CUSTOMERS
|* 25 |            BITMAP INDEX RANGE SCAN| SALES_CUST_BIX
|  26 |       TABLE ACCESS BY USER ROWID  | SALES
----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - access("ITEM_1"="C"."CUST_ID")
3 - filter("C"."CUST_STATE_PROVINCE"='CA')
4 - access("ITEM_2"="T"."TIME_ID")
5 - filter(("T"."CALENDAR_QUARTER_DESC"='1999-01'
OR "T"."CALENDAR_QUARTER_DESC"='1999-02'))
14 - filter("CH"."CHANNEL_DESC"='Internet')
15 - access("S"."CHANNEL_ID"="CH"."CHANNEL_ID")
19 - filter(("T"."CALENDAR_QUARTER_DESC"='1999-01'
OR "T"."CALENDAR_QUARTER_DESC"='1999-02'))
20 - access("S"."TIME_ID"="T"."TIME_ID")
24 - filter("C"."CUST_STATE_PROVINCE"='CA')
25 - access("S"."CUST_ID"="C"."CUST_ID")

Note
-----
- star transformation used for this statement

Line 26 of Example 5-9 shows that the sales table has an index access path instead of a full table scan. For each key value that results from the subqueries of channels (line 14), times (line 19), and customers (line 24), the database retrieves a bitmap from the indexes on the sales fact table (lines 15, 20, 25).

Each bit in the bitmap corresponds to a row in the fact table. The bit is set when the key value from the subquery is same as the value in the row of the fact table. For example, in the bitmap 101000... (the ellipses indicates that the values for the remaining rows are 0), rows 1 and 3 of the fact table have matching key values from the subquery.

The operations in lines 12, 17, and 22 iterate over the keys from the subqueries and retrieve the corresponding bitmaps. In Example 5-8, the customers subquery seeks the IDs of customers whose state or province is CA. Assume that the bitmap 101000... corresponds to the customer ID key value 103515 from the customers table subquery. Also assume that the customers subquery produces the key value 103516 with the bitmap 010000..., which means that only row 2 in sales has a matching key value from the subquery.

The database merges (using the OR operator) the bitmaps for each subquery (lines 11, 16, 21). In our customers example, the database produces a single bitmap 111000... for the customers subquery after merging the two bitmaps:

101000...   # bitmap corresponding to key 103515
010000...   # bitmap corresponding to key 103516
---------
111000...   # result of OR operation

In line 10 of Example 5-9, the database applies the AND operator to the merged bitmaps. Assume that after the database has performed all OR operations, the resulting bitmap for channels is 100000... If the database performs an AND operation on this bitmap and the bitmap from customers subquery, then the result is as follows:

100000...   # channels bitmap after all OR operations performed
111000...   # customers bitmap after all OR operations performed
---------
100000...   # bitmap result of AND operation for channels and customers

In line 9 of Example 5-9, the database generates the corresponding rowids of the final bitmap. The database retrieves rows from the sales fact table using the rowids (line 26). In our example, the database generate only one rowid, which corresponds to the first row, and thus fetches only a single row instead of scanning the entire sales table.

5.6.6 Temporary Table Transformation: Scenario

In Example 5-9, the optimizer does not join back the table channels to the sales table because it is not referenced outside and the channel_id is unique. If the optimizer cannot eliminate the join back, however, then the database stores the subquery results in a temporary table to avoid rescanning the dimension table for bitmap key generation and join back. Also, if the query runs in parallel, then the database materializes the results so that each parallel execution server can select the results from the temporary table instead of executing the subquery again.

In Example 5-10, the database materializes the results of the subquery on customers into a temporary table.

Example 5-10 Star Transformation Using Temporary Table

SELECT t1.c1 cust_city, t.calendar_quarter_desc calendar_quarter_desc,
SUM(s.amount_sold) sales_amount
FROM   sales s, sh.times t, sys_temp_0fd9d6621_e7e24 t1
WHERE  s.time_id=t.time_id
AND    s.cust_id=t1.c0
AND    (t.calendar_quarter_desc='1999-q1' OR t.calendar_quarter_desc='1999-q2')
AND    s.cust_id IN    ( SELECT t1.c0
FROM   sys_temp_0fd9d6621_e7e24 t1 )
AND    s.channel_id IN ( SELECT ch.channel_id
FROM   channels ch
WHERE  ch.channel_desc='internet' )
AND    s.time_id IN    ( SELECT t.time_id
FROM   times t
WHERE  t.calendar_quarter_desc='1999-q1'
OR     t.calendar_quarter_desc='1999-q2' )
GROUP BY t1.c1, t.calendar_quarter_desc

The optimizer replaces customers with the temporary table sys_temp_0fd9d6621_e7e24, and replaces references to columns cust_id and cust_city with the corresponding columns of the temporary table. The database creates the temporary table with two columns: (c0 NUMBER, c1 VARCHAR2(30)). These columns correspond to cust_id and cust_city of the customers table. The database populates the temporary table by executing the following query at the beginning of the execution of the previous query:

SELECT c.cust_id, c.cust_city FROM customers WHERE c.cust_state_province = 'CA'

Example 5-11 shows an edited version of the execution plan for the query in Example 5-10.

Example 5-11 Partial Execution Plan for Star Transformation Using Temporary Table

----------------------------------------------------------------------------------
| Id  | Operation                          | Name
----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                   |
|   1 |  TEMP TABLE TRANSFORMATION         |
|   2 |   LOAD AS SELECT                   |
|*  3 |    TABLE ACCESS FULL               | CUSTOMERS
|   4 |   HASH GROUP BY                    |
|*  5 |    HASH JOIN                       |
|   6 |     TABLE ACCESS FULL              | SYS_TEMP_0FD9D6613_C716F
|*  7 |     HASH JOIN                      |
|*  8 |      TABLE ACCESS FULL             | TIMES
|   9 |      VIEW                          | VW_ST_A3F94988
|  10 |       NESTED LOOPS                 |
|  11 |        PARTITION RANGE SUBQUERY    |
|  12 |         BITMAP CONVERSION TO ROWIDS|
|  13 |          BITMAP AND                |
|  14 |           BITMAP MERGE             |
|  15 |            BITMAP KEY ITERATION    |
|  16 |             BUFFER SORT            |
|* 17 |              TABLE ACCESS FULL     | CHANNELS
|* 18 |             BITMAP INDEX RANGE SCAN| SALES_CHANNEL_BIX
|  19 |           BITMAP MERGE             |
|  20 |            BITMAP KEY ITERATION    |
|  21 |             BUFFER SORT            |
|* 22 |              TABLE ACCESS FULL     | TIMES
|* 23 |             BITMAP INDEX RANGE SCAN| SALES_TIME_BIX
|  24 |           BITMAP MERGE             |
|  25 |            BITMAP KEY ITERATION    |
|  26 |             BUFFER SORT            |
|  27 |              TABLE ACCESS FULL     | SYS_TEMP_0FD9D6613_C716F
|* 28 |             BITMAP INDEX RANGE SCAN| SALES_CUST_BIX
|  29 |        TABLE ACCESS BY USER ROWID  | SALES
----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

3 - filter("C"."CUST_STATE_PROVINCE"='CA')
5 - access("ITEM_1"="C0")
7 - access("ITEM_2"="T"."TIME_ID")
8 - filter(("T"."CALENDAR_QUARTER_DESC"='1999-01' OR
"T"."CALENDAR_QUARTER_DESC"='1999-02'))
17 - filter("CH"."CHANNEL_DESC"='Internet')
18 - access("S"."CHANNEL_ID"="CH"."CHANNEL_ID")
22 - filter(("T"."CALENDAR_QUARTER_DESC"='1999-01' OR
"T"."CALENDAR_QUARTER_DESC"='1999-02'))
23 - access("S"."TIME_ID"="T"."TIME_ID")
28 - access("S"."CUST_ID"="C0")

Lines 1, 2, and 3 of the plan materialize the customers subquery into the temporary table. In line 6, the database scans the temporary table (instead of the subquery) to build the bitmap from the fact table. Line 27 scans the temporary table for joining back instead of scanning customers. The database does not need to apply the filter on customers on the temporary table because the filter is applied while materializing the temporary table.

5.7 In-Memory Aggregation

In-memory aggregation uses KEY VECTOR and VECTOR GROUP BY operations to optimize query blocks involving aggregation and joins from a single large table to multiple small tables, such as in a typical star query. These operations use efficient in-memory arrays for joins and aggregation, and are especially effective when the underlying tables are in-memory columnar tables.

This section contains the following topics:

- Purpose of In-Memory Aggregation

- How In-Memory Aggregation Works

- Controls for In-Memory Aggregation

- In-Memory Aggregation: Scenario

- In-Memory Aggregation: Example

5.7.1 Purpose of In-Memory Aggregation

VECTOR GROUP BY aggregation optimizes CPU usage, especially the CPU cache, to improve the performance of queries that aggregate the results of joins between small tables and a large table. To achieve better performance, the database accelerates the work up to and including the first aggregation, which is where the SQL engine must process the largest volume of rows.

5.7.2 How In-Memory Aggregation Works

A typical analytic query aggregates from a fact table, and joins the fact table to one or more dimensions. This type of query scans a large volume of data, with optional filtering, and performs a GROUP BY of between 1 and 40 columns.

VECTOR GROUP BY aggregation spends extra time processing the small tables up front to accelerate the per-row work performed on the large table. This optimization is possible because a typical analytic query distributes rows among processing stages:

1. Filtering tables and producing row sets

2. Joining row sets

3. Aggregating rows

The unit of work between stages is called a data flow operator (DFO). VECTOR GROUP BY aggregation uses a DFO for each dimension to create a key vector structure and temporary table. When aggregating measure columns from the fact table, the database uses this key vector to translate a fact join key to its dense grouping key. The late materialization step joins on the dense grouping keys to the temporary tables.

5.7.2.1 Key Vector

A key vector is a data structure that maps between dense join keys and dense grouping keys. A dense key is a numeric key that is stored as a native integer and has a range of values. A dense join key represents all join keys whose join columns come from a particular fact table or dimension. A dense grouping key represents all grouping keys whose grouping columns come from a particular fact table or dimension. A key vector enables fast lookups.

Example 5-12 Key Vector

Assume that the hr.locations tables has values for country_id as shown (only the first few results are shown):

SQL> SELECT country_id FROM locations;

CO
--
IT
IT
JP
JP
US
US
US
US
CA
CA
CN

A complex analytic query applies the filter WHERE country_id='US' to the locations table. A key vector for this filter might look like the following one-dimensional array:

0
0
0
0
1
1
1
1
0
0
0

In the preceding array, 1 is the dense grouping key for country_id='US'. The 0 values indicate rows in locations that do not match this filter. If a query uses the filter WHERE country_id IN ('US','JP'), then the array might look as follows, where 2 is the dense grouping key for JP and 1 is the dense grouping key for US:

0
0
2
2
1
1
1
1
0
0
0

5.7.2.2 Two Phases of In-Memory Aggregation

Typically, VECTOR GROUP BY aggregation processes an analytic query in the following phases:

1. Process each dimension sequentially as follows:

1. Find the unique dense grouping keys.

2. Create a key vector.

3. Create a temporary table.

Table 5-0 illustrates the steps in this phase, beginning with the scan of the dimension table in DFO 0, and ending with the creation of a temporary table. In the simplest form of parallel GROUP BY or join processing, the database processes each join or GROUP BY in its own DFO.

Figure 5-2 Phase 1 of In-Memory Aggregation

2. Process the fact table.

1. Process all the joins and aggregations using the key vectors created in the preceding phase.

2. Join back the results to each temporary table.

Table 5-0 illustrates phase 2 in a join of the fact table with two dimensions. In DFO 0, the database performs a full scan of the fact table, and then uses the key vectors for each dimension to filter out nonmatching rows. DFO 2 joins the results of DFO 0 with DFO 1. DFO 4 joins the result of DFO 2 with DFO 3.

Figure 5-3 Phase 2 of In-Memory Aggregation

5.7.3 Controls for In-Memory Aggregation

VECTOR GROUP BY aggregation does not involve any new SQL or public initialization parameters. You can use the following pairs of hints:

- Query block hints
VECTOR_TRANSFORM enables the vector transformation on the specified query block, regardless of costing. NO_VECTOR_TRANSFORM disables the vector transformation from engaging on the specified query block.

- Table hints
You can use the following pairs of hints:

- VECTOR_TRANSFORM_FACT includes the specified FROM expressions in the fact table generated by the vector transformation. NO_VECTOR_TRANSFORM_FACT excludes the specified FROM expressions from the fact table generated by the vector transformation.

- VECTOR_TRANSFORM_DIMS includes the specified FROM expressions in enabled dimensions generated by the vector transformation. NO_VECTOR_TRANSFORM_DIMS excludes the specified from expressions from enabled dimensions generated by the vector transformation.

See Also:
Oracle Database SQL Language Reference to learn more about the VECTOR_TRANSFORM_FACT and VECTOR_TRANSFORM_DIMS hints

5.7.4 In-Memory Aggregation: Scenario

This section gives a conceptual example of how VECTOR GROUP BY aggregation works. The scenario does not use the sample schema tables or show an actual execution plan.

This section contains the following topics:

- Sample Analytic Query of a Star Schema

- Step 1: Key Vector and Temporary Table Creation for geography Dimension

- Step 2: Key Vector and Temporary Table Creation for products Dimension

- Step 3: Key Vector Query Transformation

- Step 4: Row Filtering from Fact Table

- Step 5: Aggregation Using an Array

- Step 6: Join Back to Temporary Tables

5.7.4.1 Sample Analytic Query of a Star Schema

This sample star schema in this scenario contains the sales_online fact table and two dimension tables: geography and products. Each row in geography is uniquely identified by the geog_id column. Each row in products is uniquely identified by the prod_id column. Each row in sales_online is uniquely identified by the geog_id, prod_id, and amount sold.

Table 5-1 Sample Rows in geography Table

country

state

city

geog_id

USA

WA

seattle

2

USA

WA

spokane

3

USA

CA

SF

7

USA

CA

LA

8

Table 5-2 Sample Rows in products Table

manuf

category

subcategory

prod_id

Acme

sport

bike

4

Acme

sport

ball

3

Acme

electric

bulb

1

Acme

electric

switch

8

Table 5-3 Sample Rows in sales_online Table

prod_id

geog_id

amount

8

1

100

9

1

150

8

2

100

4

3

110

2

30

130

6

20

400

3

1

100

1

7

120

3

8

130

4

3

200

A manager asks the business question, "How many Acme products in each subcategory were sold online in Washington, and how many were sold in California?" To answer this question, an analytic query of the sales_online fact table joins the products and geography dimension tables as follows:

SELECT p.category, p.subcategory, g.country, g.state, SUM(s.amount)
FROM   sales_online s, products p, geography g
WHERE  s.geog_id = g.geog_id
AND    s.prod_id = p.prod_id
AND    g.state IN ('WA','CA')
AND    p.manuf = 'ACME'
GROUP BY category, subcategory, country, state

5.7.4.2 Step 1: Key Vector and Temporary Table Creation for geography Dimension

In the first phase of VECTOR GROUP BY aggregation for this query, the database creates a dense grouping key for each city/state combination for cities in the states of Washington or California. In Table 5-6, the 1 is the USA,WA grouping key, and the 2 is the USA,CA grouping key.

Table 5-4 Dense Grouping Key for geography

country

state

city

geog_id

dense_gr_key_geog

USA

WA

seattle

2

1

USA

WA

spokane

3

1

USA

CA

SF

7

2

USA

CA

LA

8

2

A key vector for the geography table looks like the array represented by the final column in Table 5-5. The values are the geography dense grouping keys. Thus, the key vector indicates which rows in sales_online meet the geography.state filter criteria (a sale made in the state of CA or WA) and which country/state group each row belongs to (either the USA,WA group or USA,CA group).

Table 5-5 Online Sales

prod_id

geog_id

amount

key vector for geography

8

1

100

0

9

1

150

0

8

2

100

1

4

3

110

1

2

30

130

0

6

20

400

0

3

1

100

0

1

7

120

2

3

8

130

2

4

3

200

1

Internally, the database creates a temporary table similar to the following:

CREATE TEMPORARY TABLE tt_geography AS
SELECT MAX(country), MAX(state), KEY_VECTOR_CREATE(...) dense_gr_key_geog
FROM   geography
WHERE  state IN ('WA','CA')
GROUP BY country, state

Table 5-6 shows rows in the tt_geography temporary table. The dense grouping key for the USA,WA combination is 1, and the dense grouping key for the USA,CA combination is 2.

Table 5-6 tt_geography

country

state

dense_gr_key_geog

USA

WA

1

USA

CA

2

5.7.4.3 Step 2: Key Vector and Temporary Table Creation for products Dimension

The database creates a dense grouping key for each distinct category/subcategory combination of an Acme product. For example, in Table 5-7, the 4 is dense grouping key for an Acme electric switch.

Table 5-7 Sample Rows in products Table

manuf

category

subcategory

prod_id

dense_gr_key_prod

Acme

sport

bike

4

1

Acme

sport

ball

3

2

Acme

electric

bulb

1

3

Acme

electric

switch

8

4

A key vector for the products table might look like the array represented by the final column in Table 5-8. The values represent the products dense grouping key. For example, the 4 represents the online sale of an Acme electric switch. Thus, the key vector indicates which rows in sales_online meet the products filter criteria (a sale of an Acme product).

Table 5-8 Key Vector

prod_id

geog_id

amount

key vector for products

8

1

100

4

9

1

150

0

8

2

100

4

4

3

110

1

2

30

130

0

6

20

400

0

3

1

100

2

1

7

120

3

3

8

130

2

4

3

200

1

Internally, the database creates a temporary table similar to the following:

CREATE TEMPORTARY TABLE tt_products AS
SELECT MAX(category), MAX(subcategory), KEY_VECTOR_CREATE(...) dense_gr_key_prod
FROM   products
WHERE  manuf = 'ACME'
GROUP BY category, subcategory

Table 5-9 shows rows in this temporary table.

Table 5-9 tt_products

category

subcategory

dense_gr_key_prod

sport

bike

1

sport

ball

2

electric

bulb

3

electric

switch

4

5.7.4.4 Step 3: Key Vector Query Transformation

The database now enters the phase of processing the fact table. The optimizer transforms the original query into the following equivalent query, which accesses the key vectors:

SELECT KEY_VECTOR_PROD(prod_id),
KEY_VECTOR_GEOG(geog_id),
SUM(amount)
FROM   sales_online
WHERE  KEY_VECTOR_PROD_FILTER(prod_id) IS NOT NULL
AND    KEY_VECTOR_GEOG_FILTER(geog_id) IS NOT NULL
GROUP BY KEY_VECTOR_PROD(prod_id), KEY_VECTOR_GEOG(geog_id)

The preceding transformation is not an exact rendition of the internal SQL, which is much more complicated, but a conceptual representation designed to illustrate the basic concept.

5.7.4.5 Step 4: Row Filtering from Fact Table

The goal in this phase is to obtain the amount sold for each combination of grouping keys. The database uses the key vectors to filter out unwanted rows from the fact table. In Table 5-10, the first three columns represent the sales_online table. The last two columns provide the dense grouping keys for the geography and products tables.

Table 5-10 Dense Grouping Keys for the sales_online Table

prod_id

geog_id

amount

dense_gr_key_prod

dense_gr_key_geog

7

1

100

4

9

1

150

8

2

100

4

1

4

3

110

1

1

2

30

130

6

20

400

3

1

100

2

1

7

120

3

2

3

8

130

2

2

4

3

200

1

1

As shown in Table 5-11, the database retrieves only those rows from sales_online with non-null values for both dense grouping keys, indicating rows that satisfy all the filtering criteria.

Table 5-11 Filtered Rows from sales_online Table

geog_id

prod_id

amount

dense_gr_key_prod

dense_gr_key_geog

2

8

100

4

1

3

4

110

1

1

3

4

200

1

1

7

1

120

3

2

8

3

130

2

2

5.7.4.6 Step 5: Aggregation Using an Array

The database uses a multidimensional array to perform the aggregation. In Table 5-12, the geography grouping keys are horizontal, and the products grouping keys are vertical. The database adds the values in the intersection of each dense grouping key combination. For example, for the intersection of the geography grouping key 1 and the products grouping key 1, the sum of 110 and 200 is 310.

Table 5-12 Aggregation Array

dgkp/dgkg

1

2

1

110,200

2

130

3

120

4

100

5.7.4.7 Step 6: Join Back to Temporary Tables

In the final stage of processing, the database uses the dense grouping keys to join back the rows to the temporary tables to obtain the names of the regions and categories. The results look as follows:

CATEGORY SUBCATEGORY COUNTRY STATE AMOUNT
-------- ----------- ------- ----- ------
electric bulb        USA     CA    120
electric switch      USA     WA    100
sport    ball        USA     CA    130
sport    bike        USA     WA    310

5.7.5 In-Memory Aggregation: Example

The following query of the sh tables answers the business question "How many products were sold in each category in each calendar year?"

SELECT t.calendar_year, p.prod_category, SUM(quantity_sold)
FROM   times t, products p, sales s
WHERE  t.time_id = s.time_id
AND    p.prod_id = s.prod_id
GROUP BY t.calendar_year, p.prod_category;

Example 5-13 shows the execution plan contained in the current cursor. Steps 4 and 8 show the creation of the key vectors for the dimension tables times and products. Steps 17 and 18 show the use of the previously created key vectors. Steps 3, 7, and 15 show the VECTOR GROUP BY operations.

Example 5-13 VECTOR GROUP BY Execution Plan

SQL_ID  0yxqj2nq8p9kt, child number 0
-------------------------------------
SELECT t.calendar_year, p.prod_category, SUM(quantity_sold) FROM
times t, products p, sales f WHERE  t.time_id = f.time_id AND
p.prod_id   = f.prod_id GROUP BY t.calendar_year, p.prod_category

Plan hash value: 2377225738

----------------------------------------------------------------------------------------------------------------
|Id | Operation                            | Name                      |Rows|Bytes|Cost(%CPU)|Time|Pstart|Pstop|
----------------------------------------------------------------------------------------------------------------
|  0| SELECT STATEMENT                     |                           |    |     |285 (100)|        |   |    |
|  1|  TEMP TABLE TRANSFORMATION           |                           |    |     |         |        |   |    |
|  2|   LOAD AS SELECT                     | SYS_TEMP_0FD9D6644_11CBE8 |    |     |         |        |   |    |
|  3|    VECTOR GROUP BY                   |                           |   5|  80 |  3 (100)|00:00:01|   |    |
|  4|     KEY VECTOR CREATE BUFFERED       | :KV0000                   |1826|29216|  3 (100)|00:00:01|   |    |
|  5|      TABLE ACCESS INMEMORY FULL      | TIMES                     |1826|21912|  1 (100)|00:00:01|   |    |
|  6|   LOAD AS SELECT                     | SYS_TEMP_0FD9D6645_11CBE8 |    |     |         |        |   |    |
|  7|    VECTOR GROUP BY                   |                           |   5| 125 |  1 (100)|00:00:01|   |    |
|  8|     KEY VECTOR CREATE BUFFERED       | :KV0001                   |  72| 1800|  1 (100)|00:00:01|   |    |
|  9|      TABLE ACCESS INMEMORY FULL      | PRODUCTS                  |  72| 1512|  0   (0)|        |   |    |
| 10|   HASH GROUP BY                      |                           |  18| 1440|282  (99)|00:00:01|   |    |
|*11|    HASH JOIN                         |                           |  18| 1440|281  (99)|00:00:01|   |    |
|*12|     HASH JOIN                        |                           |  18| 990 |278 (100)|00:00:01|   |    |
| 13|      TABLE ACCESS FULL               | SYS_TEMP_0FD9D6644_11CBE8 |   5|  80 |  2   (0)|00:00:01|   |    |
| 14|      VIEW                            | VW_VT_AF278325            |  18| 702 |276 (100)|00:00:01|   |    |
| 15|       VECTOR GROUP BY                |                           |  18| 414 |276 (100)|00:00:01|   |    |
| 16|        HASH GROUP BY                 |                           |  18| 414 |276 (100)|00:00:01|   |    |
| 17|         KEY VECTOR USE               | :KV0000                   |918K|  20M|276 (100)|00:00:01|   |    |
| 18|          KEY VECTOR USE              | :KV0001                   |918K|  16M|272 (100)|00:00:01|   |    |
| 19|           PARTITION RANGE ALL        |                           |918K|  13M|257 (100)|00:00:01|  1|  28|
| 20|            TABLE ACCESS INMEMORY FULL| SALES                     |918K|  13M|257 (100)|00:00:01|  1|  28|
| 21|     TABLE ACCESS FULL                | SYS_TEMP_0FD9D6645_11CBE8 |  5 |  125|  2   (0)|00:00:01|   |    |
----------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

11 - access("ITEM_10"=INTERNAL_FUNCTION("C0") AND "ITEM_11"="C2")
12 - access("ITEM_8"=INTERNAL_FUNCTION("C0") AND "ITEM_9"="C2")

Note
-----
- vector transformation used for this statement

45 rows selected.

5.8 Table Expansion

In table expansion, the optimizer generates a plan that uses indexes on the read-mostly portion of a partitioned table, but not on the active portion of the table. This section contains the following topics:

- Purpose of Table Expansion

- How Table Expansion Works

- Table Expansion: Scenario

- Table Expansion and Star Transformation: Scenario

5.8.1 Purpose of Table Expansion

Table expansion is useful because of the following facts:

- Index-based plans can improve performance dramatically.

- Index maintenance causes overhead to DML.

- In many databases, only a small portion of the data is actively updated through DML.

Table expansion takes advantage of index-based plans for tables that have high update volume. You can configure a table so that an index is only created on the read-mostly portion of the data, and does not suffer the overhead burden of index maintenance on the active portions of the data. Thus, table expansion reaps the benefit of improved performance without suffering the ill effects of index maintenance.

5.8.2 How Table Expansion Works

Table partitioning makes table expansion possible. If a local index exists on a partitioned table, then the optimizer can mark the index as unusable for specific partitions. In effect, some partitions are not indexed.

In table expansion, the optimizer transforms the query into a UNION ALL statement, with some subqueries accessing indexed partitions and other subqueries accessing unindexed partitions. The optimizer can choose the most efficient access method available for a partition, regardless of whether it exists for all of the partitions accessed in the query.

The optimizer does not always choose table expansion:

- Table expansion is cost-based.
While the database accesses each partition of the expanded table only once across all branches of the UNION ALL, any tables that the database joins to it are accessed in each branch.

- Semantic issues may render expansion invalid.
For example, a table appearing on the right side of an outer join is not valid for table expansion.

You can control table expansion with the hint EXPAND_TABLE hint. The hint overrides the cost-based decision, but not the semantic checks.

See Also:
"Influencing the Optimizer with Hints"

5.8.3 Table Expansion: Scenario

The optimizer keeps track of which partitions must be accessed from each table, based on predicates that appear in the query. Partition pruning enables the optimizer to use table expansion to generate more optimal plans.

Assumptions

This scenario assumes the following:

- You want to run a star query against the sh.sales table, which is range-partitioned on the time_id column.

- You want to disable indexes on specific partitions to see the benefits of table expansion.

To use table expansion:

1. Run the following query:

SELECT *
FROM   sales
WHERE  time_id >= TO_DATE('2000-01-01 00:00:00', 'SYYYY-MM-DD HH24:MI:SS')
AND    prod_id = 38;

2. Explain the plan by querying DBMS_EXPLAN:

SET LINESIZE 150
SET PAGESIZE 0
SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(format => 'BASIC,PARTITION'));

As shown in the Pstart and Pstop columns in the following plan, the optimizer determines from the filter that only 16 of the 28 partitions in the table must be accessed:

Plan hash value: 3087065703

-------------------------------------------------------------------------------
| Id| Operation                                  | Name          |Pstart|Pstop|
-------------------------------------------------------------------------------
| 0 | SELECT STATEMENT                           |               |      |    |
| 1 |  PARTITION RANGE ITERATOR                  |               |   13 | 28 |
| 2 |   TABLE ACCESS BY LOCAL INDEX ROWID BATCHED| SALES         |   13 | 28 |
| 3 |    BITMAP CONVERSION TO ROWIDS             |               |      |    |
|*4 |     BITMAP INDEX SINGLE VALUE              | SALES_PROD_BIX|   13 | 28 |
-------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

4 - access("PROD_ID"=38)

After the optimizer has determined the partitions to be accessed, it considers any index that is usable on all of those partitions. In the preceding plan, the optimizer chose to use the sales_prod_bix bitmap index.

3. Disable the index on the SALES_1995 partition of the sales table:

ALTER INDEX sales_prod_bix MODIFY PARTITION sales_1995 UNUSABLE;

The preceding DDL disables the index on partition 1, which contains all sales from before 1996.

Note:
You can obtain the partition information by querying the USER_IND_PARTITIONS view.

4. Execute the query of sales again, and then query DBMS_XPLAN to obtain the plan.
The output shows that the plan did not change:

Plan hash value: 3087065703

-------------------------------------------------------------------------------
| Id| Operation                                  | Name           |Pstart|Pstop
-------------------------------------------------------------------------------
| 0 | SELECT STATEMENT                           |                |      |    |
| 1 |  PARTITION RANGE ITERATOR                  |                |  13  | 28 |
| 2 |   TABLE ACCESS BY LOCAL INDEX ROWID BATCHED| SALES          |  13  | 28 |
| 3 |    BITMAP CONVERSION TO ROWIDS             |                |      |    |
|*4 |     BITMAP INDEX SINGLE VALUE              | SALES_PROD_BIX |  13  | 28 |
-------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

4 - access("PROD_ID"=38)

The plan is the same because the disabled index partition is not relevant to the query. If all partitions that the query accesses are indexed, then the database can answer the query using the index. Because the query only accesses partitions 16 through 28, disabling the index on partition 1 does not affect the plan.

5. Disable the indexes for partition 28 (SALES_Q4_2003), which is a partition that the query needs to access:

ALTER INDEX sales_prod_bix MODIFY PARTITION sales_q4_2003 UNUSABLE;
ALTER INDEX sales_time_bix MODIFY PARTITION sales_q4_2003 UNUSABLE;

By disabling the indexes on a partition that the query does need to access, the query can no longer use this index (without table expansion).

6. Query the plan using DBMS_EXPLAN.
As shown in the following plan, the optimizer does not use the index:

Plan hash value: 3087065703

-------------------------------------------------------------------------------
| Id| Operation                                  | Name           |Pstart|Pstop
-------------------------------------------------------------------------------
| 0 | SELECT STATEMENT                           |                |      |    |
| 1 |  PARTITION RANGE ITERATOR                  |                |  13  | 28 |
|*2 |   TABLE ACCESS FULL                        | SALES          |  13  | 28 |
-------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
2 - access("PROD_ID"=38)

In the preceding example, the query accesses 16 partitions. On 15 of these partitions, an index is available, but no index is available for the final partition. Because the optimizer has to choose one access path or the other, the optimizer cannot use the index on any of the partitions.

7. With table expansion, the optimizer rewrites the original query as follows:

SELECT *
FROM   sales
WHERE  time_id >= TO_DATE('2000-01-01 00:00:00', 'SYYYY-MM-DD HH24:MI:SS')
AND    time_id <  TO_DATE('2003-10-01 00:00:00', 'SYYYY-MM-DD HH24:MI:SS')
AND    prod_id = 38
UNION ALL
SELECT *
FROM   sales
WHERE  time_id >= TO_DATE('2003-10-01 00:00:00', 'SYYYY-MM-DD HH24:MI:SS')
AND    time_id < TO_DATE('2004-01-01 00:00:00', 'SYYYY-MM-DD HH24:MI:SS')
AND    prod_id = 38;

In the preceding query, the first query block in the UNION ALL accesses the partitions that are indexed, while the second query block accesses the partition that is not. The two subqueries enable the optimizer to choose to use the index in the first query block, if it is more optimal than using a table scan of all of the partitions that are accessed.

8. Query the plan using DBMS_EXPLAN.
The plan appears as follows:

Plan hash value: 2120767686

-------------------------------------------------------------------------------
| Id  | Operation                                   | Name       |Pstart|Pstop|
-------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                             |                |    |   |
|  1 |  VIEW                                        | VW_TE_2        |    |   |
|  2 |   UNION-ALL                                  |                |    |   |
|  3 |    PARTITION RANGE ITERATOR                  |                | 13 | 27|
|  4 |     TABLE ACCESS BY LOCAL INDEX ROWID BATCHED| SALES          | 13 | 27|
|  5 |      BITMAP CONVERSION TO ROWIDS             |                |    |   |
|* 6 |       BITMAP INDEX SINGLE VALUE              | SALES_PROD_BIX | 13 | 27|
|  7 |    PARTITION RANGE SINGLE                    |                | 28 | 28|
|* 8 |     TABLE ACCESS FULL                        | SALES          | 28 | 28|
-------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

6 - access("PROD_ID"=38)
8 - filter("PROD_ID"=38)

As shown in the preceding plan, the optimizer uses a UNION ALL for two query blocks (Step 2). The optimizer chooses an index to access partitions 13 to 27 in the first query block (Step 6). Because no index is available for partition 28, the optimizer chooses a full table scan in the second query block (Step 8).

5.8.4 Table Expansion and Star Transformation: Scenario

Star transformation enables specific types of queries to avoid accessing large portions of big fact tables (see "Star Transformation"). Star transformation requires defining several indexes, which in an actively updated table can have overhead. With table expansion, you can define indexes on only the inactive partitions so that the optimizer can consider star transformation on only the indexed portions of the table.

Assumptions

This scenario assumes the following:

- You query the same schema used in "Star Transformation: Scenario".

- The last partition of sales is actively being updated, as is often the case with time-partitioned tables.

- You want the optimizer to take advantage of table expansion.

To take advantage of table expansion in a star query:

1. Disable the indexes on the last partition as follows:

ALTER INDEX sales_channel_bix MODIFY PARTITION sales_q4_2003 UNUSABLE;
ALTER INDEX sales_cust_bix MODIFY PARTITION sales_q4_2003 UNUSABLE;

2. Execute the following star query:

SELECT t.calendar_quarter_desc, SUM(s.amount_sold) sales_amount
FROM   sales s, times t, customers c, channels ch
WHERE  s.time_id = t.time_id
AND    s.cust_id = c.cust_id
AND    s.channel_id = ch.channel_id
AND    c.cust_state_province = 'CA'
AND    ch.channel_desc = 'Internet'
AND    t.calendar_quarter_desc IN ('1999-01','1999-02')
GROUP BY t.calendar_quarter_desc;

3. Query the cursor using DBMS_XPLAN, which shows the following plan:

-------------------------------------------------------------------------------
| Id | Operation                          | Name              | Pstart| Pstop |
-------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                   |                   |       |       |
|  1 |  HASH GROUP BY                     |                   |       |       |
|  2 |   VIEW                             | VW_TE_14          |       |       |
|  3 |    UNION-ALL                       |                   |       |       |
|  4 |     HASH JOIN                      |                   |       |       |
|  5 |      TABLE ACCESS FULL             | TIMES             |       |       |
|  6 |      VIEW                          | VW_ST_1319B6D8    |       |       |
|  7 |       NESTED LOOPS                 |                   |       |       |
|  8 |        PARTITION RANGE SUBQUERY    |                   |KEY(SQ)|KEY(SQ)|
|  9 |         BITMAP CONVERSION TO ROWIDS|                   |       |       |
| 10 |          BITMAP AND                |                   |       |       |
| 11 |           BITMAP MERGE             |                   |       |       |
| 12 |            BITMAP KEY ITERATION    |                   |       |       |
| 13 |             BUFFER SORT            |                   |       |       |
| 14 |              TABLE ACCESS FULL     | CHANNELS          |       |       |
| 15 |             BITMAP INDEX RANGE SCAN| SALES_CHANNEL_BIX |KEY(SQ)|KEY(SQ)|
| 16 |           BITMAP MERGE             |                   |       |       |
| 17 |            BITMAP KEY ITERATION    |                   |       |       |
| 18 |             BUFFER SORT            |                   |       |       |
| 19 |              TABLE ACCESS FULL     | TIMES             |       |       |
| 20 |             BITMAP INDEX RANGE SCAN| SALES_TIME_BIX    |KEY(SQ)|KEY(SQ)|
| 21 |           BITMAP MERGE             |                   |       |       |
| 22 |            BITMAP KEY ITERATION    |                   |       |       |
| 23 |             BUFFER SORT            |                   |       |       |
| 24 |              TABLE ACCESS FULL     | CUSTOMERS         |       |       |
| 25 |             BITMAP INDEX RANGE SCAN| SALES_CUST_BIX    |KEY(SQ)|KEY(SQ)|
| 26 |        TABLE ACCESS BY USER ROWID  | SALES             | ROWID | ROWID |
| 27 |     NESTED LOOPS                   |                   |       |       |
| 28 |      NESTED LOOPS                  |                   |       |       |
| 29 |       NESTED LOOPS                 |                   |       |       |
| 30 |        NESTED LOOPS                |                   |       |       |
| 31 |         PARTITION RANGE SINGLE     |                   |    28 |    28 |
| 32 |          TABLE ACCESS FULL         | SALES             |    28 |    28 |
| 33 |         TABLE ACCESS BY INDEX ROWID| CHANNELS          |       |       |
| 34 |          INDEX UNIQUE SCAN         | CHANNELS_PK       |       |       |
| 35 |        TABLE ACCESS BY INDEX ROWID | CUSTOMERS         |       |       |
| 36 |         INDEX UNIQUE SCAN          | CUSTOMERS_PK      |       |       |
| 37 |       INDEX UNIQUE SCAN            | TIMES_PK          |       |       |
| 38 |      TABLE ACCESS BY INDEX ROWID   | TIMES             |       |       |
-------------------------------------------------------------------------------

The preceding plan uses table expansion. The UNION ALL branch that is accessing every partition except the last partition uses star transformation. Because the indexes on partition 28 are disabled, the database accesses the final partition using a full table scan.

5.9 Join Factorization

In the cost-based transformation known as join factorization, the optimizer can factorize common computations from branches of a UNION ALL query.

This section contains the following topics:

- Purpose of Join Factorization

- How Join Factorization Works

- Factorization and Join Orders: Scenario

- Factorization of Outer Joins: Scenario

5.9.1 Purpose of Join Factorization

UNION ALL queries are common in database applications, especially in data integration applications. Often, branches in a UNION ALL query refer to the same base tables. Without join factorization, the optimizer evaluates each branch of a UNION ALL query independently, which leads to repetitive processing, including data access and joins. Join factorization transformation can share common computations across the UNION ALL branches. Avoiding an extra scan of a large base table can lead to a huge performance improvement.

5.9.2 How Join Factorization Works

Join factorization can factorize multiple tables and from more than two UNION ALL branches. Join factorization is best explained through examples. Example 5-14 shows a query of four tables and two UNION ALL branches.

Example 5-14 UNION ALL Query

SELECT t1.c1, t2.c2
FROM   t1, t2, t3
WHERE  t1.c1 = t2.c1
AND    t1.c1 > 1
AND    t2.c2 = 2
AND    t2.c2 = t3.c2
UNION ALL
SELECT t1.c1, t2.c2
FROM   t1, t2, t4
WHERE  t1.c1 = t2.c1
AND    t1.c1 > 1
AND    t2.c3 = t4.c3

In Example 5-14, table t1 appears in both UNION ALL branches, as does the filter predicate t1.c1 > 1 and the join predicate t1.c1 = t2.c1. Nevertheless, without any transformation, the database must perform the scan and the filtering on table t1 twice, one time for each branch. Example 5-15 uses join factorization to transform the query in Example 5-14.

Example 5-15 Factorized Query

SELECT t1.c1, VW_JF_1.item_2
FROM   t1, (SELECT t2.c1 item_1, t2.c2 item_2
FROM   t2, t3
WHERE  t2.c2 = t3.c2
AND    t2.c2 = 2
UNION ALL
SELECT t2.c1 item_1, t2.c2 item_2
FROM   t2, t4
WHERE  t2.c3 = t4.c3) VW_JF_1
WHERE  t1.c1 = VW_JF_1.item_1
AND    t1.c1 > 1

In Example 5-15, table t1 is factorized. Thus, the database performs the table scan and the filtering on t1 only one time. If t1 is large, then this factorization avoids the huge performance cost of scanning and filtering t1 twice.

Note:
If the branches in a UNION ALL query have clauses that use the DISTINCT function, then join factorization is not valid.

5.9.3 Factorization and Join Orders: Scenario

A benefit of join factorization is that it can create more possibilities for join orders. In Example 5-16, view V is same as the query in Example 5-14.

Example 5-16 Query Involving Five Tables

SELECT *
FROM   t5, (SELECT t1.c1, t2.c2
FROM   t1, t2, t3
WHERE  t1.c1 = t2.c1
AND    t1.c1 > 1
AND    t2.c2 = 2
AND    t2.c2 = t3.c2
UNION ALL
SELECT t1.c1, t2.c2
FROM   t1, t2, t4
WHERE  t1.c1 = t2.c1
AND    t1.c1 > 1
AND    t2.c3 = t4.c3) V
WHERE  t5.c1 = V.c1

Before join factorization, the database must join t1, t2, and t3 before joining them with t5. But if join factorization factorizes t1 from view V, as shown in Example 5-17, then the database can join t1 with t5.

Example 5-17 Factorization of t1 from View V

SELECT *
FROM   t5, ( SELECT t1.c1, VW_JF_1.item_2
FROM   t1, (SELECT t2.c1 item_1, t2.c2 item_2
FROM   t2, t3
WHERE  t2.c2 = t3.c2
AND    t2.c2 = 2
UNION ALL
SELECT t2.c1 item_1, t2.c2 item_2
FROM   t2, t4
WHERE  t2.c3 = t4.c3) VW_JF_1
WHERE  t1.c1 = VW_JF_1.item_1
AND    t1.c1 > 1 )
WHERE  t5.c1 = V.c1

Example 5-18 shows the same query as Example 5-17, but with the view definition removed so that the factorization is easier to see.

Example 5-18 Factorization of t1 from View V

SELECT *
FROM   t5, (SELECT t1.c1, VW_JF_1.item_2
FROM   t1, VW_JF_1
WHERE  t1.c1 = VW_JF_1.item_1
AND    t1.c1 > 1)
WHERE  t5.c1 = V.c1

The query transformation in Example 5-17 opens up new join orders. However, join factorization imposes specific join orders. For example, in Example 5-17, tables t2 and t3 appear in the first branch of the UNION ALL query in view VW_JF_1. The database must join t2 with t3 before it can join with t1, which is not defined within the VW_JF_1 view. The imposed join order may not necessarily be the best join order. For this reason, the optimizer performs join factorization using the cost-based transformation framework. The optimizer calculate the cost of the plans with and without join factorization, and then chooses the cheapest plan.

5.9.4 Factorization of Outer Joins: Scenario

The database supports join factorization of outer joins, antijoins, and semijoins, but only for the right tables in such joins. For example, join factorization can transform the query in Example 5-19 by factorizing t2.

Example 5-19 Outer Join

SELECT t1.c2, t2.c2
FROM   t1, t2
WHERE  t1.c1 = t2.c1(+)
AND    t1.c1 = 1
UNION ALL
SELECT t1.c2, t2.c2
FROM   t1, t2
WHERE  t1.c1 = t2.c1(+)
AND    t1.c1 = 2

Example 5-20 shows the factorized query.

Example 5-20 Factorization of t2 from Outer Join

SELECT VW_JF_1.item_2, t2.c2
FROM   t2, (SELECT t1.c1 item_1, t1.c2 item_2
FROM   t1
WHERE  t1.c1 = 1
UNION ALL
SELECT t1.c1 item_1, t1.c2 item_2
FROM   t1
WHERE  t1.c1 = 2) VW_JF_1
WHERE  VW_JF_1.item_1 = t2.c1(+)

Part III

Query Execution Plans

This part contains the following chapters:

- Chapter 6, "Generating and Displaying Execution Plans"

- Chapter 7, "Reading Execution Plans"

6 Generating and Displaying Execution Plans

This chapter contains the following topics:

- Introduction to Execution Plans

- About Plan Generation and Display

- Generating Execution Plans

- Displaying PLAN_TABLE Output

6.1 Introduction to Execution Plans

The combination of the steps that Oracle Database uses to execute a statement is an execution plan. Each step either retrieves rows of data physically from the database or prepares them for the user issuing the statement. An execution plan includes an access path for each table that the statement accesses and an ordering of the tables (the join order) with the appropriate join method.

See Also:
Chapter 9, "Joins"

6.2 About Plan Generation and Display

The EXPLAIN PLAN statement displays execution plans that the optimizer chooses for SELECT, UPDATE, INSERT, and DELETE statements. This section contains the following topics:

- About the Plan Explanation

- Why Execution Plans Change

- Minimizing Throw-Away

- Looking Beyond Execution Plans

- EXPLAIN PLAN Restrictions

- The PLAN_TABLE Output Table

6.2.1 About the Plan Explanation

A statement execution plan is the sequence of operations that the database performs to run the statement. The row source tree is the core of the execution plan (see "SQL Row Source Generation"). The tree shows the following information:

- An ordering of the tables referenced by the statement

- An access method for each table mentioned in the statement

- A join method for tables affected by join operations in the statement

- Data operations like filter, sort, or aggregation

In addition to the row source tree, the plan table contains information about the following:

- Optimization, such as the cost and cardinality of each operation

- Partitioning, such as the set of accessed partitions

- Parallel execution, such as the distribution method of join inputs

The EXPLAIN PLAN results enables you to determine whether the optimizer selects a particular execution plan, such as a nested loops join. The results also help you to understand the optimizer decisions, such as why the optimizer chose a nested loops join instead of a hash join, and enables you to understand the performance of a query.

See Also:
Oracle Database SQL Language Reference to learn about the EXPLAIN PLAN statement

6.2.2 Why Execution Plans Change

Execution plans can and do change as the underlying optimizer inputs change. EXPLAIN PLAN output shows how the database would run the SQL statement when the statement was explained. This plan can differ from the actual execution plan a SQL statement uses because of differences in the execution environment and explain plan environment.

Note:
To avoid possible SQL performance regression that may result from execution plan changes, consider using SQL plan management.

Execution plans can differ because of the following:

- Different Schemas

- Different Costs

See Also:
"Managing SQL Plan Baselines"

6.2.2.1 Different Schemas

Schemas can differ for the following reasons:

- The execution and explain plan occur on different databases.

- The user explaining the statement is different from the user running the statement. Two users might be pointing to different objects in the same database, resulting in different execution plans.

- Schema changes (usually changes in indexes) between the two operations.

6.2.2.2 Different Costs

Even if the schemas are the same, the optimizer can choose different execution plans when the costs are different. Some factors that affect the costs include the following:

- Data volume and statistics

- Bind variable types and values

- Initialization parameters set globally or at session level

6.2.3 Minimizing Throw-Away

Examining an explain plan enables you to look for throw-away in cases such as the following:

- Full scans

- Unselective range scans

- Late predicate filters

- Wrong join order

- Late filter operations

In the plan shown in Example 6-1, the last step is a very unselective range scan that is executed 76563 times, accesses 11432983 rows, throws away 99% of them, and retains 76563 rows. Why access 11432983 rows to realize that only 76563 rows are needed?

Example 6-1 Looking for Throw-Away in an Explain Plan

Rows      Execution Plan
--------  ----------------------------------------------------
12  SORT AGGREGATE
2   SORT GROUP BY
76563    NESTED LOOPS
76575     NESTED LOOPS
19      TABLE ACCESS FULL CN_PAYRUNS_ALL
76570      TABLE ACCESS BY INDEX ROWID CN_POSTING_DETAILS_ALL
76570       INDEX RANGE SCAN (object id 178321)
76563     TABLE ACCESS BY INDEX ROWID CN_PAYMENT_WORKSHEETS_ALL
11432983      INDEX RANGE SCAN (object id 186024)

6.2.4 Looking Beyond Execution Plans

The execution plan operation alone cannot differentiate between well-tuned statements and those that perform poorly. For example, an EXPLAIN PLAN output that shows that a statement uses an index does not necessarily mean that the statement runs efficiently. Sometimes indexes are extremely inefficient. In this case, you should examine the following:

- The columns of the index being used

- Their selectivity (fraction of table being accessed)

It is best to use EXPLAIN PLAN to determine an access plan, and then later prove that it is the optimal plan through testing. When evaluating a plan, examine the statement's actual resource consumption.

6.2.4.1 Using V$SQL_PLAN Views

In addition to running the EXPLAIN PLAN command and displaying the plan, you can use the V$SQL_PLAN views to display the execution plan of a SQL statement:

After the statement has executed, you can display the plan by querying the V$SQL_PLAN view. V$SQL_PLAN contains the execution plan for every statement stored in the shared SQL area. Its definition is similar to the PLAN_TABLE. See "PLAN_TABLE Columns".

The advantage of V$SQL_PLAN over EXPLAIN PLAN is that you do not need to know the compilation environment that was used to execute a particular statement. For EXPLAIN PLAN, you would need to set up an identical environment to get the same plan when executing the statement.

The V$SQL_PLAN_STATISTICS view provides the actual execution statistics for every operation in the plan, such as the number of output rows and elapsed time. All statistics, except the number of output rows, are cumulative. For example, the statistics for a join operation also includes the statistics for its two inputs. The statistics in V$SQL_PLAN_STATISTICS are available for cursors that have been compiled with the STATISTICS_LEVEL initialization parameter set to ALL.

The V$SQL_PLAN_STATISTICS_ALL view enables side by side comparisons of the estimates that the optimizer provides for the number of rows and elapsed time. This view combines both V$SQL_PLAN and V$SQL_PLAN_STATISTICS information for every cursor.

See Also:

- Chapter 16, "Monitoring Database Operations" for information about the V$SQL_PLAN_MONITOR view

- Oracle Database Reference for more information about V$SQL_PLAN views

- Oracle Database Reference for information about the STATISTICS_LEVEL initialization parameter

6.2.5 EXPLAIN PLAN Restrictions

Oracle Database does not support EXPLAIN PLAN for statements performing implicit type conversion of date bind variables. With bind variables in general, the EXPLAIN PLAN output might not represent the real execution plan.

From the text of a SQL statement, TKPROF cannot determine the types of the bind variables. It assumes that the type is CHARACTER, and gives an error message otherwise. You can avoid this limitation by putting appropriate type conversions in the SQL statement.

See Also:
Chapter 18, "Performing Application Tracing"

6.2.6 The PLAN_TABLE Output Table

The PLAN_TABLE is automatically created as a public synonym to a global temporary table. This temporary table holds the output of EXPLAIN PLAN statements for all users. PLAN_TABLE is the default sample output table into which the EXPLAIN PLAN statement inserts rows describing execution plans. See "PLAN_TABLE Columns" for a description of the columns in the table.

While a PLAN_TABLE table is automatically set up for each user, you can use the SQL script catplan.sql to manually create the global temporary table and the PLAN_TABLE synonym. The name and location of this script depends on your operating system. On UNIX and Linux, the script is located in the $ORACLE_HOME/rdbms/admin directory.

For example, start a SQL*Plus session, connect with SYSDBA privileges, and run the script as follows:

@$ORACLE_HOME/rdbms/admin/catplan.sql

Oracle recommends that you drop and rebuild your local PLAN_TABLE table after upgrading the version of the database because the columns might change. This can cause scripts to fail or cause TKPROF to fail, if you are specifying the table.

If you do not want to use the name PLAN_TABLE, create a new synonym after running the catplan.sql script. For example:

CREATE OR REPLACE PUBLIC SYNONYM my_plan_table for plan_table$

6.3 Generating Execution Plans

The EXPLAIN PLAN statement enables you to examine the execution plan that the optimizer chose for a SQL statement. When the statement is issued, the optimizer chooses an execution plan and then inserts data describing the plan into a database table. Issue the EXPLAIN PLAN statement and then query the output table.

The basics of using the EXPLAIN PLAN statement are as follows:

- Use the SQL script CATPLAN.SQL to create a sample output table called PLAN_TABLE in your schema. See "The PLAN_TABLE Output Table".

- Include the EXPLAIN PLAN FOR clause before the SQL statement.

- After issuing the EXPLAIN PLAN statement, use a script or package provided by Oracle Database to display the most recent plan table output. See "Displaying PLAN_TABLE Output".

- The execution order in EXPLAIN PLAN output begins with the line that is the furthest indented to the right. The next step is the parent of that line. If two lines are indented equally, then the top line is normally executed first.

Notes:

- The EXPLAIN PLAN output tables in this chapter were displayed with the utlxpls.sql script.

- The steps in the EXPLAIN PLAN output in this chapter may be different on your system. The optimizer may choose different execution plans, depending on database configurations.

To explain a SQL statement, use the EXPLAIN PLAN FOR clause immediately before the statement. For example:

EXPLAIN PLAN FOR
SELECT last_name FROM employees;

This explains the plan into the PLAN_TABLE table. You can then select the execution plan from PLAN_TABLE. See "Displaying PLAN_TABLE Output".

6.3.1 Identifying Statements for EXPLAIN PLAN

With multiple statements, you can specify a statement identifier and use that to identify your specific execution plan. Before using SET STATEMENT ID, remove any existing rows for that statement ID.

In Example 6-2, st1 is specified as the statement identifier:

Example 6-2 Using EXPLAIN PLAN with the STATEMENT ID Clause

EXPLAIN PLAN
SET STATEMENT_ID = 'st1' FOR
SELECT last_name FROM employees;

6.3.2 Specifying Different Tables for EXPLAIN PLAN

You can specify the INTO clause to specify a different table.

Example 6-3 Using EXPLAIN PLAN with the INTO Clause

EXPLAIN PLAN
INTO my_plan_table FOR
SELECT last_name FROM employees;

You can specify a statement ID when using the INTO clause.

EXPLAIN PLAN
SET STATEMENT_ID = 'st1'
INTO my_plan_table FOR
SELECT last_name FROM employees;

See Also:
Oracle Database SQL Language Reference for a complete description of EXPLAIN PLAN syntax.

6.4 Displaying PLAN_TABLE Output

After you have explained the plan, use the following SQL scripts or PL/SQL package provided by Oracle Database to display the most recent plan table output:

- UTLXPLS.SQL
This script displays the plan table output for serial processing. Example 6-5, "EXPLAIN PLAN Output" is an example of the plan table output when using the UTLXPLS.SQL script.

- UTLXPLP.SQL
This script displays the plan table output including parallel execution columns.

- DBMS_XPLAN.DISPLAY table function
This function accepts options for displaying the plan table output. You can specify:

- A plan table name if you are using a table different than PLAN_TABLE

- A statement ID if you have set a statement ID with the EXPLAIN PLAN

- A format option that determines the level of detail: BASIC, SERIAL, TYPICAL, and ALL

Examples of using DBMS_XPLAN to display PLAN_TABLE output are:

SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY());

SELECT PLAN_TABLE_OUTPUT
FROM TABLE(DBMS_XPLAN.DISPLAY('MY_PLAN_TABLE', 'st1','TYPICAL'));

See Also:
Oracle Database PL/SQL Packages and Types Reference for more information about the DBMS_XPLAN package

6.4.1 Displaying an Execution Plan: Example

Example 6-4 uses EXPLAIN PLAN to examine a SQL statement that selects the employee_id, job_title, salary, and department_name for the employees whose IDs are less than 103.

Example 6-4 Using EXPLAIN PLAN

EXPLAIN PLAN FOR
SELECT e.employee_id, j.job_title, e.salary, d.department_name
FROM   employees e, jobs j, departments d
WHERE  e.employee_id < 103
AND    e.job_id = j.job_id
AND    e.department_id = d.department_id;

The resulting output table in Example 6-5 shows the execution plan chosen by the optimizer to execute the SQL statement in the example:

Example 6-5 EXPLAIN PLAN Output

-----------------------------------------------------------------------------------
| Id  | Operation                     |  Name        | Rows  | Bytes | Cost (%CPU)|
-----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT              |              |     3 |   189 |    10  (10)|
|   1 |  NESTED LOOPS                 |              |     3 |   189 |    10  (10)|
|   2 |   NESTED LOOPS                |              |     3 |   141 |     7  (15)|
|*  3 |    TABLE ACCESS FULL          | EMPLOYEES    |     3 |    60 |     4  (25)|
|   4 |    TABLE ACCESS BY INDEX ROWID| JOBS         |    19 |   513 |     2  (50)|
|*  5 |     INDEX UNIQUE SCAN         | JOB_ID_PK    |     1 |       |            |
|   6 |   TABLE ACCESS BY INDEX ROWID | DEPARTMENTS  |    27 |   432 |     2  (50)|
|*  7 |    INDEX UNIQUE SCAN          | DEPT_ID_PK   |     1 |       |            |
-----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
3 - filter("E"."EMPLOYEE_ID"<103)
5 - access("E"."JOB_ID"="J"."JOB_ID")
7 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID"

-------------------------------------------------------------------------------------------------
| Id  | Operation                       | Name          | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                |               |     3 |   189 |     8  (13)| 00:00:01 |
|   1 |  NESTED LOOPS                   |               |       |       |            |          |
|   2 |   NESTED LOOPS                  |               |     3 |   189 |     8  (13)| 00:00:01 |
|   3 |    MERGE JOIN                   |               |     3 |   141 |     5  (20)| 00:00:01 |
|   4 |     TABLE ACCESS BY INDEX ROWID | JOBS          |    19 |   513 |     2   (0)| 00:00:01 |
|   5 |      INDEX FULL SCAN            | JOB_ID_PK     |    19 |       |     1   (0)| 00:00:01 |
|*  6 |     SORT JOIN                   |               |     3 |    60 |     3  (34)| 00:00:01 |
|   7 |      TABLE ACCESS BY INDEX ROWID| EMPLOYEES     |     3 |    60 |     2   (0)| 00:00:01 |
|*  8 |       INDEX RANGE SCAN          | EMP_EMP_ID_PK |     3 |       |     1   (0)| 00:00:01 |
|*  9 |    INDEX UNIQUE SCAN            | DEPT_ID_PK    |     1 |       |     0   (0)| 00:00:01 |
|  10 |   TABLE ACCESS BY INDEX ROWID   | DEPARTMENTS   |     1 |    16 |     1   (0)| 00:00:01 |
-------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

6 - access("E"."JOB_ID"="J"."JOB_ID")
filter("E"."JOB_ID"="J"."JOB_ID")
8 - access("E"."EMPLOYEE_ID"<103)
9 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")

6.4.2 Customizing PLAN_TABLE Output

If you have specified a statement identifier, then you can write your own script to query the PLAN_TABLE. For example:

- Start with ID = 0 and given STATEMENT_ID.

- Use the CONNECT BY clause to walk the tree from parent to child, the join keys being STATEMENT_ID = PRIOR STATEMENT_ID and PARENT_ID = PRIOR ID.

- Use the pseudo-column LEVEL (associated with CONNECT BY) to indent the children.

SELECT cardinality "Rows",
lpad(' ',level-1)||operation||' '||options||' '||object_name "Plan"
FROM PLAN_TABLE
CONNECT BY prior id = parent_id
AND prior statement_id = statement_id
START WITH id = 0
AND statement_id = 'st1'
ORDER BY id;

Rows Plan
------- ----------------------------------------
SELECT STATEMENT
TABLE ACCESS FULL EMPLOYEES

The NULL in the Rows column indicates that the optimizer does not have any statistics on the table. Analyzing the table shows the following:

Rows Plan
------- ----------------------------------------
16957 SELECT STATEMENT
16957  TABLE ACCESS FULL EMPLOYEES

You can also select the COST. This is useful for comparing execution plans or for understanding why the optimizer chooses one execution plan over another.

Note:
These simplified examples are not valid for recursive SQL.

7 Reading Execution Plans

This chapter contains the following topics:

- Reading Execution Plans: Basic

- Reading Execution Plans: Advanced

- Execution Plan Reference

7.1 Reading Execution Plans: Basic

This section uses EXPLAIN PLAN examples to illustrate execution plans. The statement in Example 7-1 displays the execution plans.

Example 7-1 Statement to display the EXPLAIN PLAN

SELECT PLAN_TABLE_OUTPUT
FROM TABLE(DBMS_XPLAN.DISPLAY(NULL, 'statement_id','BASIC'));

Examples of the output from this statement are shown in Example 7-7 and Example 7-2.

Example 7-2 EXPLAIN PLAN for Statement ID ex_plan1

EXPLAIN PLAN
SET statement_id = 'ex_plan1' FOR
SELECT phone_number
FROM   employees
WHERE  phone_number LIKE '650%';

---------------------------------------
| Id  | Operation         | Name      |
---------------------------------------
|   0 | SELECT STATEMENT  |           |
|   1 |  TABLE ACCESS FULL| EMPLOYEES |
---------------------------------------

This plan shows execution of a SELECT statement. The table employees is accessed using a full table scan.

- Every row in the table employees is accessed, and the WHERE clause criteria is evaluated for every row.

- The SELECT statement returns the rows meeting the WHERE clause criteria.

Example 7-3 EXPLAIN PLAN for Statement ID ex_plan2

EXPLAIN PLAN
SET statement_id = 'ex_plan2' FOR
SELECT last_name
FROM   employees
WHERE  last_name LIKE 'Pe%';

SELECT PLAN_TABLE_OUTPUT
FROM TABLE(DBMS_XPLAN.DISPLAY(NULL, 'ex_plan2','BASIC'));

----------------------------------------
| Id  | Operation        | Name        |
----------------------------------------
|   0 | SELECT STATEMENT |             |
|   1 |  INDEX RANGE SCAN| EMP_NAME_IX |
----------------------------------------

This plan shows execution of a SELECT statement.

- The database range scans EMP_NAME_IX to evaluate the WHERE clause criteria.

- The SELECT statement returns rows satisfying the WHERE clause conditions.

7.2 Reading Execution Plans: Advanced

This section contains the following topics:

- Reading Adaptive Plans

- Viewing Parallel Execution with EXPLAIN PLAN

- Viewing Bitmap Indexes with EXPLAIN PLAN

- Viewing Result Cache with EXPLAIN PLAN

- Viewing Partitioned Objects with EXPLAIN PLAN

- PLAN_TABLE Columns

7.2.1 Reading Adaptive Plans

The adaptive optimizer is a feature of the optimizer that enables it to adapt plans based on run-time statistics (see "Adaptive Plans"). All adaptive mechanisms can execute a final plan for a statement that differs from the default plan.

An adaptive plan chooses among subplans during the current statement execution. In contrast, automatic reoptimization changes a plan only on executions that occur after the current statement execution.

You can determine whether the database used adaptive query optimization for a SQL statement based on the comments in the Notes section of plan. The comments indicate whether row sources are dynamic, or whether automatic reoptimization adapted a plan (see Table 7-8).

Assumptions

This tutorial assumes the following:

- The STATISTICS_LEVEL initialization parameter is set to ALL (see Oracle Database Reference to learn about the STATISTICS_LEVEL initialization parameter).

- The database uses the default settings for adaptive execution (see "Controlling Adaptive Optimization").

- As user oe, you want to issue the following separate queries:

SELECT o.order_id, v.product_name
FROM   orders o,
(  SELECT order_id, product_name
FROM   order_items o, product_information p
WHERE  p.product_id = o.product_id
AND    list_price < 50
AND    min_price < 40  ) v
WHERE  o.order_id = v.order_id

SELECT product_name
FROM   order_items o, product_information p
WHERE  o.unit_price = 15
AND    quantity > 1
AND    p.product_id = o.product_id

- Before executing each query, you want to query DBMS_XPLAN.DISPLAY_PLAN to see the default plan, that is, the plan that the optimizer chose before applying its adaptive mechanism.

- After executing each query, you want to query DBMS_XPLAN.DISPLAY_CURSOR to see the final plan and adaptive plan.

- SYS has granted oe the following privileges:

- GRANT SELECT ON V_$SESSION TO oe

- GRANT SELECT ON V_$SQL TO oe

- GRANT SELECT ON V_$SQL_PLAN TO oe

- GRANT SELECT ON V_$SQL_PLAN_STATISTICS_ALL TO oe

To see the results of adaptive optimization:

1. Start SQL*Plus, and then connect to the database as user oe.

2. Query orders.

For example, use the following statement:

SELECT o.order_id, v.product_name
FROM   orders o,
(  SELECT order_id, product_name
FROM   order_items o, product_information p
WHERE  p.product_id = o.product_id
AND    list_price < 50
AND    min_price < 40  ) v
WHERE  o.order_id = v.order_id;

3. View the plan in the cursor.

For example, run the following commands:

SET LINESIZE 165
SET PAGESIZE 0
SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'+ALLSTATS'));

Example 7-4 shows sample output, which has been reformatted to fit on the page. In this plan, the optimizer chooses a nested loops join. The original optimizer estimates are shown in the E-Rows column, whereas the actual statistics gathered during execution are shown in the A-Rows column. In the MERGE JOIN operation, the difference between the estimated and actual number of rows is significant.

Example 7-4 DBMS_XPLAN.DISPLAY_CURSOR Output

----------------------------------------------------------------------------------------------------------------
| Id  | Operation             | Name              |Starts|E-Rows|A-Rows| A-Time     |Buffers|OMem  |1Mem |O/1/M|
----------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT      |                     |   1|      |  269 |00:00:00.09 | 1338 |       |     |     |
|   1 |  NESTED LOOPS         |                     |   1|    1 |  269 |00:00:00.09 | 1338 |       |     |     |
|   2 |   MERGE JOIN CARTESIAN|                     |   1|    4 | 9135 |00:00:00.03 |   33 |       |     |     |
|*  3 |    TABLE ACCESS FULL  |PRODUCT_INFORMATION  |   1|    1 |   87 |00:00:00.01 |   32 |       |     |     |
|   4 |    BUFFER SORT        |                     |  87|  105 | 9135 |00:00:00.01 |    1 |  4096 | 4096|1/0/0|
|   5 |     INDEX FULL SCAN   | ORDER_PK            |   1|  105 |  105 |00:00:00.01 |    1 |       |     |     |
|*  6 |   INDEX UNIQUE SCAN   | ORDER_ITEMS_UK      |9135|    1 |  269 |00:00:00.03 | 1305 |       |     |     |
----------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50))
6 - access("O"."ORDER_ID"="ORDER_ID" AND "P"."PRODUCT_ID"="O"."PRODUCT_ID")

4. Run the same query of orders that you ran in Step 2.

5. View the execution plan in the cursor by using the same SELECT statement that you ran in Step 3.
Example 7-5 shows that the optimizer has chosen a different plan, using a hash join. The Note section shows that the optimizer used statistics feedback to adjust its cost estimates for the second execution of the query, thus illustrating automatic reoptimization.

Example 7-5 DBMS_XPLAN.DISPLAY_CURSOR Output

---------------------------------------------------------------------------------------------------
| Id  | Operation              | Name              |Starts|E-Rows|A-Rows|A-Time  |Buffers|Reads|OMem|1Mem|O/1/M|
----------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT       |                     |  1 |     | 269 | 00:00:00.02 | 60 | 1 |     |     |     |
|   1 |  NESTED LOOPS          |                     |  1 | 269 | 269 | 00:00:00.02 | 60 | 1 |     |     |     |
|*  2 |   HASH JOIN            |                     |  1 | 313 | 269 | 00:00:00.02 | 39 | 1 |1000K|1000K|1/0/0|
|*  3 |    TABLE ACCESS FULL   | PRODUCT_INFORMATION |  1 |  87 |  87 | 00:00:00.01 | 15 | 0 |     |     |     |
|   4 |    INDEX FAST FULL SCAN| ORDER_ITEMS_UK      |  1 | 665 | 665 | 00:00:00.01 | 24 | 1 |     |     |     |
|*  5 |   INDEX UNIQUE SCAN    | ORDER_PK            | 269|   1 | 269 | 00:00:00.01 | 21 | 0 |     |     |     |
----------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID")
3 - filter(("MIN_PRICE"<40 AND "LIST_PRICE"<50))
5 - access("O"."ORDER_ID"="ORDER_ID")

Note
-----
- statistics feedback used for this statement

6. Query V$SQL to verify the performance improvement.
The following query shows the performance of the two statements (sample output included).

SELECT CHILD_NUMBER, CPU_TIME, ELAPSED_TIME, BUFFER_GETS
FROM   V$SQL
WHERE  SQL_ID = 'gm2npz344xqn8';

CHILD_NUMBER   CPU_TIME ELAPSED_TIME BUFFER_GETS
------------ ---------- ------------ -----------
0      92006       131485        1831
1      12000        24156          60

The second statement executed, which is child number 1, used statistics feedback. CPU time, elapsed time, and buffer gets are all significantly lower.

7. Explain the plan for the query of order_items.
For example, use the following statement:

EXPLAIN PLAN FOR
SELECT product_name
FROM   order_items o, product_information p
WHERE  o.unit_price = 15
AND    quantity > 1
AND    p.product_id = o.product_id

8. View the plan in the plan table.
For example, run the following statement:

SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY);

Sample output appears below:

-------------------------------------------------------------------------------
|Id| Operation                 | Name             |Rows|Bytes|Cost (%CPU)|Time|
-------------------------------------------------------------------------------
| 0| SELECT STATEMENT             |                      |4|128|7 (0)|00:00:01|
| 1|  NESTED LOOPS                |                      | |   |     |        |
| 2|   NESTED LOOPS               |                      |4|128|7 (0)|00:00:01|
|*3|    TABLE ACCESS FULL         |ORDER_ITEMS           |4|48 |3 (0)|00:00:01|
|*4|    INDEX UNIQUE SCAN         |PRODUCT_INFORMATION_PK|1|   |0 (0)|00:00:01|
| 5|   TABLE ACCESS BY INDEX ROWID|PRODUCT_INFORMATION   |1|20 |1 (0)|00:00:01|
-------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

3 - filter("O"."UNIT_PRICE"=15 AND "QUANTITY">1)
4 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID")

In this plan, the optimizer chooses a nested loops join.

9. Run the query that you previously explained.
For example, use the following statement:

SELECT product_name
FROM   order_items o, product_information p
WHERE  o.unit_price = 15
AND    quantity > 1
AND    p.product_id = o.product_id

10. View the plan in the cursor.
For example, run the following commands:

SET LINESIZE 165
SET PAGESIZE 0
SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY(FORMAT=>'+ADAPTIVE'));

Sample output appears below:

-------------------------------------------------------------------------------
|Id | Operation                     | Name     |Rows|Bytes|Cost(%CPU)|Time    |
-------------------------------------------------------------------------------
|  0| SELECT STATEMENT              |                     |4|128|7(0)|00:00:01|
| *1|  HASH JOIN                    |                     |4|128|7(0)|00:00:01|
|- 2|   NESTED LOOPS                |                     | |   |    |        |
|- 3|    NESTED LOOPS               |                     | |128|7(0)|00:00:01|
|- 4|     STATISTICS COLLECTOR      |                     | |   |    |        |
| *5|      TABLE ACCESS FULL        | ORDER_ITEMS         |4| 48|3(0)|00:00:01|
|-*6|     INDEX UNIQUE SCAN         | PRODUCT_INFORMATI_PK|1|   |0(0)|00:00:01|
|- 7|    TABLE ACCESS BY INDEX ROWID| PRODUCT_INFORMATION |1| 20|1(0)|00:00:01|
|  8|   TABLE ACCESS FULL           | PRODUCT_INFORMATION |1| 20|1(0)|00:00:01|
-------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID")
5 - filter("O"."UNIT_PRICE"=15 AND "QUANTITY">1)
6 - access("P"."PRODUCT_ID"="O"."PRODUCT_ID")

Note
-----
- this is an adaptive plan (rows marked '-' are inactive)

Based on statistics collected at run time (Step 4), the optimizer chose a hash join rather than the nested loops join. The dashes (-) indicate the steps in the nested loops plan that the optimizer considered but do not ultimately choose. The switch illustrates the adaptive plan feature.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_XPLAN

7.2.2 Viewing Parallel Execution with EXPLAIN PLAN

Tuning a parallel query begins much like a non-parallel query tuning exercise by choosing the driving table. However, the rules governing the choice are different. In the non-parallel case, the best driving table is typically the one that produces fewest number of rows after limiting conditions are applied. The small number of rows are joined to larger tables using non-unique indexes.

For example, consider a table hierarchy consisting of customer, account, and transaction.

Figure 7-1 A Table Hierarchy

customer is the smallest table while transaction is the largest. A typical OLTP query might retrieve transaction information about a specific customer account. The query drives from the customer table. The goal in this case is to minimize logical I/O, which typically minimizes other critical resources including physical I/O and CPU time.

For parallel queries, the driving table is usually the largest table because the database can use parallel query. It would not be efficient to use parallel query in this case because only a few rows from each table are ultimately accessed. However, what if it were necessary to identify all customers who had transactions of a certain type last month? It would be more efficient to drive from the transaction table because no limiting conditions exist on the customer table. The database would join rows from the transaction table to the account table, and finally to the customer table. In this case, the indexes used on the account and customer table are probably highly selective primary key or unique indexes rather than non-unique indexes used in the first query. Because the transaction table is large and the column is not selective, it would be beneficial to use parallel query driving from the transaction table.

Parallel operations include the following:

- PARALLEL_TO_PARALLEL

- PARALLEL_TO_SERIAL
A PARALLEL_TO_SERIAL operation is always the step that occurs when the query coordinator consumes rows from a parallel operation. Another type of operation that does not occur in this query is a SERIAL operation. If these types of operations occur, then consider making them parallel operations to improve performance because they too are potential bottlenecks.

- PARALLEL_FROM_SERIAL

- PARALLEL_TO_PARALLEL
If the workloads in each step are relatively equivalent, then the PARALLEL_TO_PARALLEL operations generally produce the best performance.

- PARALLEL_COMBINED_WITH_CHILD

- PARALLEL_COMBINED_WITH_PARENT
A PARALLEL_COMBINED_WITH_PARENT operation occurs when the database performs the step simultaneously with the parent step.

If a parallel step produces many rows, then the QC may not be able to consume the rows as fast as they are produced. Little can be done to improve this situation.

See Also:
The OTHER_TAG column in "PLAN_TABLE Columns"

7.2.2.1 Viewing Parallel Queries with EXPLAIN PLAN

When using EXPLAIN PLAN with parallel queries, the database compiles and executes one parallel plan. This plan is derived from the serial plan by allocating row sources specific to the parallel support in the QC plan. The table queue row sources (PX Send and PX Receive), the granule iterator, and buffer sorts, required by the two parallel execution server set PQ model, are directly inserted into the parallel plan. This plan is the same plan for all parallel execution servers when executed in parallel or for the QC when executed serially.

Example 7-6 is a simple query for illustrating an EXPLAIN PLAN for a parallel query.

Example 7-6 Parallel Query Explain Plan

CREATE TABLE emp2 AS SELECT * FROM employees;

ALTER TABLE emp2 PARALLEL 2;

EXPLAIN PLAN FOR
SELECT SUM(salary)
FROM   emp2
GROUP BY department_id;

SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY());

--------------------------------------------------------------------------------------------------------
| Id  | Operation                | Name     | Rows  | Bytes | Cost (%CPU) |    TQ  |IN-OUT| PQ Distrib |
--------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT         |          |   107 |  2782 |     3 (34)  |        |      |            |
|   1 |  PX COORDINATOR          |          |       |       |             |        |      |            |
|   2 |   PX SEND QC (RANDOM)    | :TQ10001 |   107 |  2782 |     3 (34)  |  Q1,01 | P->S | QC (RAND)  |
|   3 |    HASH GROUP BY         |          |   107 |  2782 |     3 (34)  |  Q1,01 | PCWP |            |
|   4 |     PX RECEIVE           |          |   107 |  2782 |     3 (34)  |  Q1,01 | PCWP |            |
|   5 |      PX SEND HASH        | :TQ10000 |   107 |  2782 |     3 (34)  |  Q1,00 | P->P | HASH       |
|   6 |       HASH GROUP BY      |          |   107 |  2782 |     3 (34)  |  Q1,00 | PCWP |            |
|   7 |        PX BLOCK ITERATOR |          |   107 |  2782 |     2 (0)   |  Q1,00 | PCWP |            |
|   8 |         TABLE ACCESS FULL| EMP2     |   107 |  2782 |     2 (0)   |  Q1,00 | PCWP |            |
--------------------------------------------------------------------------------------------------------

One set of parallel execution servers scans EMP2 in parallel, while the second set performs the aggregation for the GROUP BY operation. The PX BLOCK ITERATOR row source represents the splitting up of the table EMP2 into pieces to divide the scan workload between the parallel execution servers. The PX SEND and PX RECEIVE row sources represent the pipe that connects the two sets of parallel execution servers as rows flow up from the parallel scan, get repartitioned through the HASH table queue, and then read by and aggregated on the top set. The PX SEND QC row source represents the aggregated values being sent to the QC in random (RAND) order. The PX COORDINATOR row source represents the QC or Query Coordinator which controls and schedules the parallel plan appearing below it in the plan tree.

7.2.3 Viewing Bitmap Indexes with EXPLAIN PLAN

Index row sources using bitmap indexes appear in the EXPLAIN PLAN output with the word BITMAP indicating the type of the index. Consider the sample query and plan in Example 7-7.

Example 7-7 EXPLAIN PLAN with Bitmap Indexes

EXPLAIN PLAN FOR

SELECT *
FROM   t
WHERE  c1 = 2
AND    c2 <> 6
OR     c3 BETWEEN 10 AND 20;

SELECT STATEMENT
TABLE ACCESS T BY INDEX ROWID
BITMAP CONVERSION TO ROWID
BITMAP OR
BITMAP MINUS
BITMAP MINUS
BITMAP INDEX C1_IND SINGLE VALUE
BITMAP INDEX C2_IND SINGLE VALUE
BITMAP INDEX C2_IND SINGLE VALUE
BITMAP MERGE
BITMAP INDEX C3_IND RANGE SCAN

In this example, the predicate c1=2 yields a bitmap from which a subtraction can take place. From this bitmap, the bits in the bitmap for c2 = 6 are subtracted. Also, the bits in the bitmap for c2 IS NULL are subtracted, explaining why there are two MINUS row sources in the plan. The NULL subtraction is necessary for semantic correctness unless the column has a NOT NULL constraint. The TO ROWIDS option generates the rowids necessary for the table access.

Note:
Queries using bitmap join index indicate the bitmap join index access path. The operation for bitmap join index is the same as bitmap index.

7.2.4 Viewing Result Cache with EXPLAIN PLAN

When your query contains the result_cache hint, the ResultCache operator is inserted into the execution plan.

For example, consider the following query:

SELECT /*+ result_cache */ deptno, avg(sal)
FROM   emp
GROUP BY deptno;

To view the EXPLAIN PLAN for this query, use the following command:

EXPLAIN PLAN FOR
SELECT /*+ result_cache */ deptno, avg(sal)
FROM emp
GROUP BY deptno;

SELECT PLAN_TABLE_OUTPUT FROM TABLE (DBMS_XPLAN.DISPLAY());

The EXPLAIN PLAN output for this query should look similar to the following:

---------------------------------------------------------------------------------------------
| Id  | Operation           | Name                        |Rows |Bytes |Cost(%CPU)|Time     |
---------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT    |                             | 11  | 77   |  4 (25)  | 00:00:01|
|   1 |  RESULT CACHE       |b06ppfz9pxzstbttpbqyqnfbmy   |     |      |          |         |
|   2 |   HASH GROUP BY     |                             | 11  | 77   |  4 (25)  | 00:00:01|
|   3 |    TABLE ACCESS FULL| EMP                         |107  | 749  |  3 (0)   | 00:00:01|
---------------------------------------------------------------------------------------------

In this EXPLAIN PLAN, the ResultCache operator is identified by its CacheId, which is b06ppfz9pxzstbttpbqyqnfbmy. You can now run a query on the V$RESULT_CACHE_OBJECTS view by using this CacheId.

7.2.5 Viewing Partitioned Objects with EXPLAIN PLAN

Use EXPLAIN PLAN to see how Oracle Database accesses partitioned objects for specific queries.

Partitions accessed after pruning are shown in the PARTITION START and PARTITION STOP columns. The row source name for the range partition is PARTITION RANGE. For hash partitions, the row source name is PARTITION HASH.

A join is implemented using partial partition-wise join if the DISTRIBUTION column of the plan table of one of the joined tables contains PARTITION(KEY). Partial partition-wise join is possible if one of the joined tables is partitioned on its join column and the table is parallelized.

A join is implemented using full partition-wise join if the partition row source appears before the join row source in the EXPLAIN PLAN output. Full partition-wise joins are possible only if both joined tables are equi-partitioned on their respective join columns. Examples of execution plans for several types of partitioning follow.

7.2.5.1 Examples of Displaying Range and Hash Partitioning with EXPLAIN PLAN

Consider the following table, emp_range, partitioned by range on hire_date to illustrate how pruning is displayed. Assume that the tables employees and departments from the Oracle Database sample schema exist.

CREATE TABLE emp_range
PARTITION BY RANGE(hire_date)
(
PARTITION emp_p1 VALUES LESS THAN (TO_DATE('1-JAN-1992','DD-MON-YYYY')),
PARTITION emp_p2 VALUES LESS THAN (TO_DATE('1-JAN-1994','DD-MON-YYYY')),
PARTITION emp_p3 VALUES LESS THAN (TO_DATE('1-JAN-1996','DD-MON-YYYY')),
PARTITION emp_p4 VALUES LESS THAN (TO_DATE('1-JAN-1998','DD-MON-YYYY')),
PARTITION emp_p5 VALUES LESS THAN (TO_DATE('1-JAN-2001','DD-MON-YYYY'))
)
AS SELECT * FROM employees;

For the first example, consider the following statement:

EXPLAIN PLAN FOR
SELECT * FROM emp_range;

Oracle Database displays something similar to the following:

---------------------------------------------------------------------------------
| Id  | Operation           | Name      | Rows  | Bytes | Cost  | Pstart| Pstop |
---------------------------------------------------------------------------------
|   0 | SELECT STATEMENT    |           |   105 | 13965 |     2 |       |       |
|   1 |  PARTITION RANGE ALL|           |   105 | 13965 |     2 |     1 |     5 |
|   2 |   TABLE ACCESS FULL | EMP_RANGE |   105 | 13965 |     2 |     1 |     5 |
---------------------------------------------------------------------------------

The database creates a partition row source on top of the table access row source. It iterates over the set of partitions to be accessed. In this example, the partition iterator covers all partitions (option ALL), because a predicate was not used for pruning. The PARTITION_START and PARTITION_STOP columns of the PLAN_TABLE show access to all partitions from 1 to 5.

For the next example, consider the following statement:

EXPLAIN PLAN FOR
SELECT *
FROM   emp_range
WHERE  hire_date >= TO_DATE('1-JAN-1996','DD-MON-YYYY');

--------------------------------------------------------------------------------------
| Id  | Operation                | Name      | Rows  | Bytes | Cost  | Pstart| Pstop |
--------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT         |           |     3 |   399 |     2 |       |       |
|   1 |  PARTITION RANGE ITERATOR|           |     3 |   399 |     2 |     4 |     5 |
|*  2 |   TABLE ACCESS FULL      | EMP_RANGE |     3 |   399 |     2 |     4 |     5 |
--------------------------------------------------------------------------------------

In the previous example, the partition row source iterates from partition 4 to 5 because the database prunes the other partitions using a predicate on hire_date.

Finally, consider the following statement:

EXPLAIN PLAN FOR
SELECT *
FROM   emp_range
WHERE  hire_date < TO_DATE('1-JAN-1992','DD-MON-YYYY');

------------------------------------------------------------------------------------
| Id  | Operation              | Name      | Rows  | Bytes | Cost  | Pstart| Pstop |
------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT       |           |     1 |   133 |     2 |       |       |
|   1 |  PARTITION RANGE SINGLE|           |     1 |   133 |     2 |     1 |     1 |
|*  2 |   TABLE ACCESS FULL    | EMP_RANGE |     1 |   133 |     2 |     1 |     1 |
------------------------------------------------------------------------------------

In the previous example, only partition 1 is accessed and known at compile time; thus, there is no need for a partition row source.

7.2.5.1.1 Plans for Hash Partitioning

Oracle Database displays the same information for hash partitioned objects, except the partition row source name is PARTITION HASH instead of PARTITION RANGE. Also, with hash partitioning, pruning is only possible using equality or IN-list predicates.

7.2.5.2 Examples of Pruning Information with Composite Partitioned Objects

To illustrate how Oracle Database displays pruning information for composite partitioned objects, consider the table emp_comp that is range partitioned on hiredate and subpartitioned by hash on deptno.

CREATE TABLE emp_comp PARTITION BY RANGE(hire_date)
SUBPARTITION BY HASH(department_id) SUBPARTITIONS 3
(
PARTITION emp_p1 VALUES LESS THAN (TO_DATE('1-JAN-1992','DD-MON-YYYY')),
PARTITION emp_p2 VALUES LESS THAN (TO_DATE('1-JAN-1994','DD-MON-YYYY')),
PARTITION emp_p3 VALUES LESS THAN (TO_DATE('1-JAN-1996','DD-MON-YYYY')),
PARTITION emp_p4 VALUES LESS THAN (TO_DATE('1-JAN-1998','DD-MON-YYYY')),
PARTITION emp_p5 VALUES LESS THAN (TO_DATE('1-JAN-2001','DD-MON-YYYY'))
)
AS SELECT * FROM employees;

For the first example, consider the following statement:

EXPLAIN PLAN FOR
SELECT * FROM emp_comp;

--------------------------------------------------------------------------------
| Id  | Operation           | Name     | Rows  | Bytes | Cost  | Pstart| Pstop |
--------------------------------------------------------------------------------
|   0 | SELECT STATEMENT    |          | 10120 |  1314K|    78 |       |       |
|   1 |  PARTITION RANGE ALL|          | 10120 |  1314K|    78 |     1 |     5 |
|   2 |   PARTITION HASH ALL|          | 10120 |  1314K|    78 |     1 |     3 |
|   3 |    TABLE ACCESS FULL| EMP_COMP | 10120 |  1314K|    78 |     1 |    15 |
--------------------------------------------------------------------------------

This example shows the plan when Oracle Database accesses all subpartitions of all partitions of a composite object. The database uses two partition row sources for this purpose: a range partition row source to iterate over the partitions and a hash partition row source to iterate over the subpartitions of each accessed partition.

In the following example, the range partition row source iterates from partition 1 to 5, because the database performs no pruning. Within each partition, the hash partition row source iterates over subpartitions 1 to 3 of the current partition. As a result, the table access row source accesses subpartitions 1 to 15. In other words, it accesses all subpartitions of the composite object.

EXPLAIN PLAN FOR
SELECT *
FROM   emp_comp
WHERE  hire_date = TO_DATE('15-FEB-1998', 'DD-MON-YYYY');

-----------------------------------------------------------------------------------
| Id  | Operation              | Name     | Rows  | Bytes | Cost  | Pstart| Pstop |
-----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT       |          |    20 |  2660 |    17 |       |       |
|   1 |  PARTITION RANGE SINGLE|          |    20 |  2660 |    17 |     5 |     5 |
|   2 |   PARTITION HASH ALL   |          |    20 |  2660 |    17 |     1 |     3 |
|*  3 |    TABLE ACCESS FULL   | EMP_COMP |    20 |  2660 |    17 |    13 |    15 |
-----------------------------------------------------------------------------------

In the previous example, only the last partition, partition 5, is accessed. This partition is known at compile time, so the database does not need to show it in the plan. The hash partition row source shows accessing of all subpartitions within that partition; that is, subpartitions 1 to 3, which translates into subpartitions 13 to 15 of the emp_comp table.

Now consider the following statement:

EXPLAIN PLAN FOR
SELECT *
FROM   emp_comp
WHERE  department_id = 20;

-----------------------------------------------------------------------------------
| Id  | Operation              | Name     | Rows  | Bytes | Cost  | Pstart| Pstop |
-----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT       |          |   101 | 13433 |    78 |       |       |
|   1 |  PARTITION RANGE ALL   |          |   101 | 13433 |    78 |     1 |     5 |
|   2 |   PARTITION HASH SINGLE|          |   101 | 13433 |    78 |     3 |     3 |
|*  3 |    TABLE ACCESS FULL   | EMP_COMP |   101 | 13433 |    78 |       |       |
-----------------------------------------------------------------------------------

In the previous example, the predicate deptno = 20 enables pruning on the hash dimension within each partition, so Oracle Database only needs to access a single subpartition. The number of that subpartition is known at compile time, so the hash partition row source is not needed.

Finally, consider the following statement:

VARIABLE dno NUMBER;
EXPLAIN PLAN FOR
SELECT *
FROM   emp_comp
WHERE  department_id = :dno;

-----------------------------------------------------------------------------------
| Id  | Operation              | Name     | Rows  | Bytes | Cost  | Pstart| Pstop |
-----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT       |          |   101 | 13433 |    78 |       |       |
|   1 |  PARTITION RANGE ALL   |          |   101 | 13433 |    78 |     1 |     5 |
|   2 |   PARTITION HASH SINGLE|          |   101 | 13433 |    78 |   KEY |   KEY |
|*  3 |    TABLE ACCESS FULL   | EMP_COMP |   101 | 13433 |    78 |       |       |
-----------------------------------------------------------------------------------

The last two examples are the same, except that department_id = :dno replaces deptno = 20. In this last case, the subpartition number is unknown at compile time, and a hash partition row source is allocated. The option is SINGLE for that row source, because Oracle Database accesses only one subpartition within each partition. The PARTITION_START and PARTITION_STOP is set to KEY, which means that Oracle Database determines the number of subpartitions at run time.

7.2.5.3 Examples of Partial Partition-Wise Joins

In the following example, emp_range_did is joined on the partitioning column department_id and is parallelized. The database can use a partial partition-wise join because the dept2 table is not partitioned. Oracle Database dynamically partitions the dept2 table before the join.

Example 7-8 Partial Partition-Wise Join with Range Partition

CREATE TABLE dept2 AS SELECT * FROM departments;
ALTER TABLE dept2 PARALLEL 2;

CREATE TABLE emp_range_did PARTITION BY RANGE(department_id)
(PARTITION emp_p1 VALUES LESS THAN (150),
PARTITION emp_p5 VALUES LESS THAN (MAXVALUE) )
AS SELECT * FROM employees;

ALTER TABLE emp_range_did PARALLEL 2;

EXPLAIN PLAN FOR
SELECT /*+ PQ_DISTRIBUTE(d NONE PARTITION) ORDERED */ e.last_name,
d.department_name
FROM   emp_range_did e, dept2 d
WHERE  e.department_id = d.department_id ;

-------------------------------------------------------------------------------------------------------------
| Id| Operation                    |Name         |Rows | Bytes |Cost|Pstart|Pstop|   TQ  |IN-OUT|PQ Distrib |
-------------------------------------------------------------------------------------------------------------
|  0| SELECT STATEMENT             |             | 284 | 16188 |  6 |      |     |       |      |
|  1|  PX COORDINATOR              |             |     |       |    |      |     |       |      |           |
|  2|   PX SEND QC (RANDOM)        |:TQ10001     | 284 | 16188 |  6 |      |     | Q1,01 | P->S | QC (RAND) |
|* 3|    HASH JOIN                 |             | 284 | 16188 |  6 |      |     | Q1,01 | PCWP |           |
|  4|     PX PARTITION RANGE ALL   |             | 284 |  7668 |  2 |    1 |   2 | Q1,01 | PCWC |           |
|  5|      TABLE ACCESS FULL       |EMP_RANGE_DID| 284 |  7668 |  2 |    1 |   2 | Q1,01 | PCWP |           |
|  6|     BUFFER SORT              |             |     |       |    |      |     | Q1,01 | PCWC |           |
|  7|      PX RECEIVE              |             |  21 |   630 |  2 |      |     | Q1,01 | PCWP |           |
|  8|       PX SEND PARTITION (KEY)|:TQ10000     |  21 |   630 |  2 |      |     |       | S->P |PART (KEY) |
|  9|        TABLE ACCESS FULL     |DEPT2        |  21 |   630 |  2 |      |     |       |      |           |
------------------------------------------------------------------------------------------------------------

The execution plan shows that the table dept2 is scanned serially and all rows with the same partitioning column value of emp_range_did (department_id) are sent through a PART (KEY), or partition key, table queue to the same parallel execution server doing the partial partition-wise join.

In the following example, emp_comp is joined on the partitioning column and is parallelized, enabling use of a partial partition-wise join because dept2 is not partitioned. The database dynamically partitions dept2 before the join.

Example 7-9 Partial Partition-Wise Join with Composite Partition

ALTER TABLE emp_comp PARALLEL 2;

EXPLAIN PLAN FOR
SELECT /*+ PQ_DISTRIBUTE(d NONE PARTITION) ORDERED */ e.last_name,
d.department_name
FROM   emp_comp e, dept2 d
WHERE  e.department_id = d.department_id;

SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY());

-------------------------------------------------------------------------------------------------------------
| Id | Operation                   | Name    | Rows | Bytes | Cost |Pstart|Pstop|    TQ  |IN-OUT| PQ Distrib |
-------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT            |         |  445 | 17800 |    5 |      |     |        |      |           |
|  1 |  PX COORDINATOR             |         |      |       |      |      |     |        |      |           |
|  2 |   PX SEND QC (RANDOM)       |:TQ10001 |  445 | 17800 |    5 |      |     |  Q1,01 | P->S | QC (RAND) |
|* 3 |    HASH JOIN                |         |  445 | 17800 |    5 |      |     |  Q1,01 | PCWP |           |
|  4 |     PX PARTITION RANGE ALL  |         |  107 |  1070 |    3 |    1 |   5 |  Q1,01 | PCWC |           |
|  5 |      PX PARTITION HASH ALL  |         |  107 |  1070 |    3 |    1 |   3 |  Q1,01 | PCWC |           |
|  6 |       TABLE ACCESS FULL     |EMP_COMP |  107 |  1070 |    3 |    1 |  15 |  Q1,01 | PCWP |           |
|  7 |     PX RECEIVE              |         |   21 |   630 |    1 |      |     |  Q1,01 | PCWP |           |
|  8 |      PX SEND PARTITION (KEY)|:TQ10000 |   21 |   630 |    1 |      |     |  Q1,00 | P->P |PART (KEY) |
|  9 |       PX BLOCK ITERATOR     |         |   21 |   630 |    1 |      |     |  Q1,00 | PCWC |           |
| 10 |        TABLE ACCESS FULL    |DEPT2    |   21 |   630 |    1 |      |     |  Q1,00 | PCWP |           |
-------------------------------------------------------------------------------------------------------------

The plan shows that the optimizer selects partial partition-wise join from one of two columns. The PX SEND node type is PARTITION(KEY) and the PQ Distrib column contains the text PART (KEY), or partition key. This implies that the table dept2 is re-partitioned based on the join column department_id to be sent to the parallel execution servers executing the scan of EMP_COMP and the join.

In both Example 7-8 and Example 7-9, the PQ_DISTRIBUTE hint explicitly forces a partial partition-wise join because the query optimizer could have chosen a different plan based on cost in this query.

7.2.5.4 Examples of Full Partition-wise Joins

In the following example, emp_comp and dept_hash are joined on their hash partitioning columns, enabling use of a full partition-wise join. The PARTITION HASH row source appears on top of the join row source in the plan table output.

The PX PARTITION HASH row source appears on top of the join row source in the plan table output while the PX PARTITION RANGE row source appears over the scan of emp_comp. Each parallel execution server performs the join of an entire hash partition of emp_comp with an entire partition of dept_hash.

Example 7-10 Full Partition-Wise Join

CREATE TABLE dept_hash
PARTITION BY HASH(department_id)
PARTITIONS 3
PARALLEL 2
AS SELECT * FROM departments;

EXPLAIN PLAN FOR
SELECT /*+ PQ_DISTRIBUTE(e NONE NONE) ORDERED */ e.last_name,
d.department_name
FROM   emp_comp e, dept_hash d
WHERE  e.department_id = d.department_id;

-------------------------------------------------------------------------------------------------------------
| Id | Operation                  | Name      | Rows |Bytes |Cost |Pstart|Pstop |   TQ  |IN-OUT| PQ Distrib |
-------------------------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT           |           |  106 | 2544 |   8 |      |      |       |      |            |
|  1 |  PX COORDINATOR            |           |      |      |     |      |      |       |      |            |
|  2 |   PX SEND QC (RANDOM)      | :TQ10000  |  106 | 2544 |   8 |      |      | Q1,00 | P->S | QC (RAND)  |
|  3 |    PX PARTITION HASH ALL   |           |  106 | 2544 |   8 |    1 |    3 | Q1,00 | PCWC |            |
|* 4 |     HASH JOIN              |           |  106 | 2544 |   8 |      |      | Q1,00 | PCWP |            |
|  5 |      PX PARTITION RANGE ALL|           |  107 | 1070 |   3 |    1 |    5 | Q1,00 | PCWC |            |
|  6 |       TABLE ACCESS FULL    | EMP_COMP  |  107 | 1070 |   3 |    1 |   15 | Q1,00 | PCWP |            |
|  7 |      TABLE ACCESS FULL     | DEPT_HASH |   27 |  378 |   4 |    1 |    3 | Q1,00 | PCWP |            |
-------------------------------------------------------------------------------------------------------------

7.2.5.5 Examples of INLIST ITERATOR and EXPLAIN PLAN

An INLIST ITERATOR operation appears in the EXPLAIN PLAN output if an index implements an IN-list predicate. For example:

SELECT * FROM emp WHERE empno IN (7876, 7900, 7902);

The EXPLAIN PLAN output appears as follows:

OPERATION          OPTIONS           OBJECT_NAME
----------------   ---------------   --------------
SELECT STATEMENT
INLIST ITERATOR
TABLE ACCESS       BY ROWID          EMP
INDEX              RANGE SCAN        EMP_EMPNO

The INLIST ITERATOR operation iterates over the next operation in the plan for each value in the IN-list predicate. The following sections describe the three possible types of IN-list columns for partitioned tables and indexes.

7.2.5.5.1 When the IN-List Column is an Index Column

If the IN-list column empno is an index column but not a partition column, then the plan is as follows (the IN-list operator appears before the table operation but after the partition operation):

OPERATION        OPTIONS              OBJECT_NAME PARTITION_START PARTITION_STOP
---------------- ------------         ----------- --------------- --------------
SELECT STATEMENT
PARTITION RANGE  ALL                               KEY(INLIST)     KEY(INLIST)
INLIST ITERATOR
TABLE ACCESS     BY LOCAL INDEX ROWID EMP          KEY(INLIST)     KEY(INLIST)
INDEX            RANGE SCAN           EMP_EMPNO    KEY(INLIST)     KEY(INLIST)

The KEY(INLIST) designation for the partition start and stop keys specifies that an IN-list predicate appears on the index start and stop keys.

7.2.5.5.2 When the IN-List Column is an Index and a Partition Column

If empno is an indexed and a partition column, then the plan contains an INLIST ITERATOR operation before the partition operation:

OPERATION        OPTIONS              OBJECT_NAME PARTITION_START PARTITION_STOP
---------------- ------------         ----------- --------------- --------------
SELECT STATEMENT
INLIST ITERATOR
PARTITION RANGE  ITERATOR                         KEY(INLIST)     KEY(INLIST)
TABLE ACCESS     BY LOCAL INDEX ROWID EMP         KEY(INLIST)     KEY(INLIST)
INDEX            RANGE SCAN           EMP_EMPNO   KEY(INLIST)     KEY(INLIST)

7.2.5.5.3 When the IN-List Column is a Partition Column

If empno is a partition column and no indexes exist, then no INLIST ITERATOR operation is allocated:

OPERATION         OPTIONS        OBJECT_NAME   PARTITION_START   PARTITION_STOP
----------------  ------------   -----------   ---------------   --------------
SELECT STATEMENT
PARTITION RANGE   INLIST                       KEY(INLIST)       KEY(INLIST)
TABLE ACCESS      FULL           EMP           KEY(INLIST)       KEY(INLIST)

If emp_empno is a bitmap index, then the plan is as follows:

OPERATION          OPTIONS           OBJECT_NAME
----------------   ---------------   --------------
SELECT STATEMENT
INLIST ITERATOR
TABLE ACCESS       BY INDEX ROWID    EMP
BITMAP CONVERSION  TO ROWIDS
BITMAP INDEX       SINGLE VALUE      EMP_EMPNO

7.2.5.6 Example of Domain Indexes and EXPLAIN PLAN

You can also use EXPLAIN PLAN to derive user-defined CPU and I/O costs for domain indexes. EXPLAIN PLAN displays these statistics in the OTHER column of PLAN_TABLE.

For example, assume table emp has user-defined operator CONTAINS with a domain index emp_resume on the resume column, and the index type of emp_resume supports the operator CONTAINS. You explain the plan for the following query:

SELECT * FROM emp WHERE CONTAINS(resume, 'Oracle') = 1

The database could display the following plan:

OPERATION            OPTIONS      OBJECT_NAME     OTHER
-----------------    -----------  ------------    ----------------
SELECT STATEMENT
TABLE ACCESS         BY ROWID     EMP
DOMAIN INDEX                      EMP_RESUME      CPU: 300, I/O: 4

7.2.6 PLAN_TABLE Columns

The PLAN_TABLE used by the EXPLAIN PLAN statement contains the columns listed in Table 7-1.

Table 7-1 PLAN_TABLE Columns

Column

Type

Description

STATEMENT_ID

VARCHAR2(30)

Value of the optional STATEMENT_ID parameter specified in the EXPLAIN PLAN statement.

PLAN_ID

NUMBER

Unique identifier of a plan in the database.

TIMESTAMP

DATE

Date and time when the EXPLAIN PLAN statement was generated.

REMARKS

VARCHAR2(80)

Any comment (of up to 80 bytes) you want to associate with each step of the explained plan. This column indicates whether the database used an outline or SQL profile for the query.

If you need to add or change a remark on any row of the PLAN_TABLE, then use the UPDATE statement to modify the rows of the PLAN_TABLE.

OPERATION

VARCHAR2(30)

Name of the internal operation performed in this step. In the first row generated for a statement, the column contains one of the following values:

- DELETE STATEMENT

- INSERT STATEMENT

- SELECT STATEMENT

- UPDATE STATEMENT

See Table 7-3 for more information about values for this column.

OPTIONS

VARCHAR2(225)

A variation on the operation described in the OPERATION column.

See Table 7-3 for more information about values for this column.

OBJECT_NODE

VARCHAR2(128)

Name of the database link used to reference the object (a table name or view name). For local queries using parallel execution, this column describes the order in which the database consumes output from operations.

OBJECT_OWNER

VARCHAR2(30)

Name of the user who owns the schema containing the table or index.

OBJECT_NAME

VARCHAR2(30)

Name of the table or index.

OBJECT_ALIAS

VARCHAR2(65)

Unique alias of a table or view in a SQL statement. For indexes, it is the object alias of the underlying table.

OBJECT_INSTANCE

NUMERIC

Number corresponding to the ordinal position of the object as it appears in the original statement. The numbering proceeds from left to right, outer to inner for the original statement text. View expansion results in unpredictable numbers.

OBJECT_TYPE

VARCHAR2(30)

Modifier that provides descriptive information about the object; for example, NON-UNIQUE for indexes.

OPTIMIZER

VARCHAR2(255)

Current mode of the optimizer.

SEARCH_COLUMNS

NUMBERIC

Not currently used.

ID

NUMERIC

A number assigned to each step in the execution plan.

PARENT_ID

NUMERIC

The ID of the next execution step that operates on the output of the ID step.

DEPTH

NUMERIC

Depth of the operation in the row source tree that the plan represents. You can use the value to indent the rows in a plan table report.

POSITION

NUMERIC

For the first row of output, this indicates the optimizer's estimated cost of executing the statement. For the other rows, it indicates the position relative to the other children of the same parent.

COST

NUMERIC

Cost of the operation as estimated by the optimizer's query approach. Cost is not determined for table access operations. The value of this column does not have any particular unit of measurement; it is a weighted value used to compare costs of execution plans. The value of this column is a function of the CPU_COST and IO_COST columns.

CARDINALITY

NUMERIC

Estimate by the query optimization approach of the number of rows that the operation accessed.

BYTES

NUMERIC

Estimate by the query optimization approach of the number of bytes that the operation accessed.

OTHER_TAG

VARCHAR2(255)

Describes the contents of the OTHER column. Values are:

- SERIAL (blank): Serial execution. Currently, SQL is not loaded in the OTHER column for this case.

- SERIAL_FROM_REMOTE (S -> R): Serial execution at a remote site.

- PARALLEL_FROM_SERIAL (S -> P): Serial execution. Output of step is partitioned or broadcast to parallel execution servers.

- PARALLEL_TO_SERIAL (P -> S): Parallel execution. Output of step is returned to serial QC process.

- PARALLEL_TO_PARALLEL (P -> P): Parallel execution. Output of step is repartitioned to second set of parallel execution servers.

- PARALLEL_COMBINED_WITH_PARENT (PWP): Parallel execution; Output of step goes to next step in same parallel process. No interprocess communication to parent.

- PARALLEL_COMBINED_WITH_CHILD (PWC): Parallel execution. Input of step comes from prior step in same parallel process. No interprocess communication from child.

PARTITION_START

VARCHAR2(255)

Start partition of a range of accessed partitions. It can take one of the following values:

n indicates that the start partition has been identified by the SQL compiler, and its partition number is given by n.

KEY indicates that the start partition is identified at run time from partitioning key values.

ROW REMOVE_LOCATION indicates that the database computes the start partition (same as the stop partition) at run time from the location of each retrieved record. The record location is obtained by a user or from a global index.

INVALID indicates that the range of accessed partitions is empty.

PARTITION_STOP

VARCHAR2(255)

Stop partition of a range of accessed partitions. It can take one of the following values:

n indicates that the stop partition has been identified by the SQL compiler, and its partition number is given by n.

KEY indicates that the stop partition is identified at run time from partitioning key values.

ROW REMOVE_LOCATION indicates that the database computes the stop partition (same as the start partition) at run time from the location of each retrieved record. The record location is obtained by a user or from a global index.

INVALID indicates that the range of accessed partitions is empty.

PARTITION_ID

NUMERIC

Step that has computed the pair of values of the PARTITION_START and PARTITION_STOP columns.

OTHER

LONG

Other information that is specific to the execution step that a user might find useful. See the OTHER_TAG column.

DISTRIBUTION

VARCHAR2(30)

Method used to distribute rows from producer query servers to consumer query servers.

See Table 7-2 for more information about the possible values for this column. For more information about consumer and producer query servers, see Oracle Database Data Warehousing Guide.

CPU_COST

NUMERIC

CPU cost of the operation as estimated by the query optimizer's approach. The value of this column is proportional to the number of machine cycles required for the operation. For statements that use the rule-based approach, this column is null.

IO_COST

NUMERIC

I/O cost of the operation as estimated by the query optimizer's approach. The value of this column is proportional to the number of data blocks read by the operation. For statements that use the rule-based approach, this column is null.

TEMP_SPACE

NUMERIC

Temporary space, in bytes, that the operation uses as estimated by the query optimizer's approach. For statements that use the rule-based approach, or for operations that do not use any temporary space, this column is null.

ACCESS_PREDICATES

VARCHAR2(4000)

Predicates used to locate rows in an access structure. For example, start or stop predicates for an index range scan.

FILTER_PREDICATES

VARCHAR2(4000)

Predicates used to filter rows before producing them.

PROJECTION

VARCHAR2(4000)

Expressions produced by the operation.

TIME

NUMBER(20,2)

Elapsed time in seconds of the operation as estimated by query optimization. For statements that use the rule-based approach, this column is null. The DBMS_XPLAN.DISPLAY_PLAN out, the time is in the HH:MM:SS format.

QBLOCK_NAME

VARCHAR2(30)

Name of the query block, either system-generated or defined by the user with the QB_NAME hint.

Table 7-2 describes the values that can appear in the DISTRIBUTION column:

Table 7-2 Values of DISTRIBUTION Column of the PLAN_TABLE

DISTRIBUTION Text

Interpretation

PARTITION (ROWID)

Maps rows to query servers based on the partitioning of a table or index using the rowid of the row to UPDATE/DELETE.

PARTITION (KEY)

Maps rows to query servers based on the partitioning of a table or index using a set of columns. Used for partial partition-wise join, PARALLEL INSERT, CREATE TABLE AS SELECT of a partitioned table, and CREATE PARTITIONED GLOBAL INDEX.

HASH

Maps rows to query servers using a hash function on the join key. Used for PARALLEL JOIN or PARALLEL GROUP BY.

RANGE

Maps rows to query servers using ranges of the sort key. Used when the statement contains an ORDER BY clause.

ROUND-ROBIN

Randomly maps rows to query servers.

BROADCAST

Broadcasts the rows of the entire table to each query server. Used for a parallel join when one table is very small compared to the other.

QC (ORDER)

The QC consumes the input in order, from the first to the last query server. Used when the statement contains an ORDER BY clause.

QC (RANDOM)

The QC consumes the input randomly. Used when the statement does not have an ORDER BY clause.

Table 7-3 lists each combination of OPERATION and OPTIONS produced by the EXPLAIN PLAN statement and its meaning within an execution plan.

Table 7-3 OPERATION and OPTIONS Values Produced by EXPLAIN PLAN

Operation

Option

Description

AND-EQUAL

Operation accepting multiple sets of rowids, returning the intersection of the sets, eliminating duplicates. Used for the single-column indexes access path.

BITMAP

CONVERSION

TO ROWIDS converts bitmap representations to actual rowids that you can use to access the table.

FROM ROWIDS converts the rowids to a bitmap representation.

COUNT returns the number of rowids if the actual values are not needed.

BITMAP

INDEX

SINGLE VALUE looks up the bitmap for a single key value in the index.

RANGE SCAN retrieves bitmaps for a key value range.

FULL SCAN performs a full scan of a bitmap index if there is no start or stop key.

BITMAP

MERGE

Merges several bitmaps resulting from a range scan into one bitmap.

BITMAP

MINUS

Subtracts bits of one bitmap from another. Row source is used for negated predicates. Use this option only if there are nonnegated predicates yielding a bitmap from which the subtraction can take place. An example appears in "Viewing Bitmap Indexes with EXPLAIN PLAN".

BITMAP

OR

Computes the bitwise OR of two bitmaps.

BITMAP

AND

Computes the bitwise AND of two bitmaps.

BITMAP

KEY ITERATION

Takes each row from a table row source and finds the corresponding bitmap from a bitmap index. This set of bitmaps are then merged into one bitmap in a following BITMAP MERGE operation.

CONNECT BY

Retrieves rows in hierarchical order for a query containing a CONNECT BY clause.

CONCATENATION

Operation accepting multiple sets of rows returning the union-all of the sets.

COUNT

Operation counting the number of rows selected from a table.

COUNT

STOPKEY

Count operation where the number of rows returned is limited by the ROWNUM expression in the WHERE clause.

CUBE SCAN

Uses inner joins for all cube access.

CUBE SCAN

PARTIAL OUTER

Uses an outer join for at least one dimension, and inner joins for the other dimensions.

CUBE SCAN

OUTER

Uses outer joins for all cube access.

DOMAIN INDEX

Retrieval of one or more rowids from a domain index. The options column contain information supplied by a user-defined domain index cost function, if any.

FILTER

Operation accepting a set of rows, eliminates some of them, and returns the rest.

FIRST ROW

Retrieval of only the first row selected by a query.

FOR UPDATE

Operation retrieving and locking the rows selected by a query containing a FOR UPDATE clause.

HASH

GROUP BY

Operation hashing a set of rows into groups for a query with a GROUP BY clause.

HASH

GROUP BY PIVOT

Operation hashing a set of rows into groups for a query with a GROUP BY clause. The PIVOT option indicates a pivot-specific optimization for the HASH GROUP BY operator.

HASH JOIN

(These are join operations.)

Operation joining two sets of rows and returning the result. This join method is useful for joining large data sets of data (DSS, Batch). The join condition is an efficient way of accessing the second table.

Query optimizer uses the smaller of the two tables/data sources to build a hash table on the join key in memory. Then it scans the larger table, probing the hash table to find the joined rows.

HASH JOIN

ANTI

Hash (left) antijoin

HASH JOIN

SEMI

Hash (left) semijoin

HASH JOIN

RIGHT ANTI

Hash right antijoin

HASH JOIN

RIGHT SEMI

Hash right semijoin

HASH JOIN

OUTER

Hash (left) outer join

HASH JOIN

RIGHT OUTER

Hash right outer join

INDEX

(These are access methods.)

UNIQUE SCAN

Retrieval of a single rowid from an index.

INDEX

RANGE SCAN

Retrieval of one or more rowids from an index. Indexed values are scanned in ascending order.

INDEX

RANGE SCAN DESCENDING

Retrieval of one or more rowids from an index. Indexed values are scanned in descending order.

INDEX

FULL SCAN

Retrieval of all rowids from an index when there is no start or stop key. Indexed values are scanned in ascending order.

INDEX

FULL SCAN DESCENDING

Retrieval of all rowids from an index when there is no start or stop key. Indexed values are scanned in descending order.

INDEX

FAST FULL SCAN

Retrieval of all rowids (and column values) using multiblock reads. No sorting order can be defined. Compares to a full table scan on only the indexed columns. Only available with the cost based optimizer.

INDEX

SKIP SCAN

Retrieval of rowids from a concatenated index without using the leading column(s) in the index. Only available with the cost based optimizer.

INLIST ITERATOR

Iterates over the next operation in the plan for each value in the IN-list predicate.

INTERSECTION

Operation accepting two sets of rows and returning the intersection of the sets, eliminating duplicates.

MERGE JOIN

(These are join operations.)

Operation accepting two sets of rows, each sorted by a value, combining each row from one set with the matching rows from the other, and returning the result.

MERGE JOIN

OUTER

Merge join operation to perform an outer join statement.

MERGE JOIN

ANTI

Merge antijoin.

MERGE JOIN

SEMI

Merge semijoin.

MERGE JOIN

CARTESIAN

Can result from 1 or more of the tables not having any join conditions to any other tables in the statement. Can occur even with a join and it may not be flagged as CARTESIAN in the plan.

CONNECT BY

Retrieval of rows in hierarchical order for a query containing a CONNECT BY clause.

MAT_VIEW REWITE ACCESS

(These are access methods.)

FULL

Retrieval of all rows from a materialized view.

MAT_VIEW REWITE ACCESS

SAMPLE

Retrieval of sampled rows from a materialized view.

MAT_VIEW REWITE ACCESS

CLUSTER

Retrieval of rows from a materialized view based on a value of an indexed cluster key.

MAT_VIEW REWITE ACCESS

HASH

Retrieval of rows from materialized view based on hash cluster key value.

MAT_VIEW REWITE ACCESS

BY ROWID RANGE

Retrieval of rows from a materialized view based on a rowid range.

MAT_VIEW REWITE ACCESS

SAMPLE BY ROWID RANGE

Retrieval of sampled rows from a materialized view based on a rowid range.

MAT_VIEW REWITE ACCESS

BY USER ROWID

If the materialized view rows are located using user-supplied rowids.

MAT_VIEW REWITE ACCESS

BY INDEX ROWID

If the materialized view is nonpartitioned and rows are located using index(es).

MAT_VIEW REWITE ACCESS

BY GLOBAL INDEX ROWID

If the materialized view is partitioned and rows are located using only global indexes.

MAT_VIEW REWITE ACCESS

BY LOCAL INDEX ROWID

If the materialized view is partitioned and rows are located using one or more local indexes and possibly some global indexes.

Partition Boundaries:

The partition boundaries might have been computed by:

A previous PARTITION step, in which case the PARTITION_START and PARTITION_STOP column values replicate the values present in the PARTITION step, and the PARTITION_ID contains the ID of the PARTITION step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, INVALID.

The MAT_VIEW REWRITE ACCESS or INDEX step itself, in which case the PARTITION_ID contains the ID of the step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, ROW REMOVE_LOCATION (MAT_VIEW REWRITE ACCESS only), and INVALID.

MINUS

Operation accepting two sets of rows and returning rows appearing in the first set but not in the second, eliminating duplicates.

NESTED LOOPS

(These are join operations.)

Operation accepting two sets of rows, an outer set and an inner set. Oracle Database compares each row of the outer set with each row of the inner set, returning rows that satisfy a condition. This join method is useful for joining small subsets of data (OLTP). The join condition is an efficient way of accessing the second table.

NESTED LOOPS

OUTER

Nested loops operation to perform an outer join statement.

PARTITION

Iterates over the next operation in the plan for each partition in the range given by the PARTITION_START and PARTITION_STOP columns. PARTITION describes partition boundaries applicable to a single partitioned object (table or index) or to a set of equi-partitioned objects (a partitioned table and its local indexes). The partition boundaries are provided by the values of PARTITION_START and PARTITION_STOP of the PARTITION. Refer to Table 7-1 for valid values of partition start and stop.

PARTITION

SINGLE

Access one partition.

PARTITION

ITERATOR

Access many partitions (a subset).

PARTITION

ALL

Access all partitions.

PARTITION

INLIST

Similar to iterator, but based on an IN-list predicate.

PARTITION

INVALID

Indicates that the partition set to be accessed is empty.

PX ITERATOR

BLOCK, CHUNK

Implements the division of an object into block or chunk ranges among a set of parallel execution servers.

PX COORDINATOR

Implements the query coordinator that controls, schedules, and executes the parallel plan below it using parallel execution servers. It also represents a serialization point, as the end of the part of the plan executed in parallel and always has a PX SEND QC operation below it.

PX PARTITION

Same semantics as the regular PARTITION operation except that it appears in a parallel plan.

PX RECEIVE

Shows the consumer/receiver parallel execution node reading repartitioned data from a send/producer (QC or parallel execution server) executing on a PX SEND node. This information was formerly displayed into the DISTRIBUTION column. See Table 7-2, "Values of DISTRIBUTION Column of the PLAN_TABLE".

PX SEND

QC (RANDOM), HASH, RANGE

Implements the distribution method taking place between two parallel execution servers. Shows the boundary between two sets and how data is repartitioned on the send/producer side (QC or side. This information was formerly displayed into the DISTRIBUTION column. See Table 7-2, "Values of DISTRIBUTION Column of the PLAN_TABLE".

REMOTE

Retrieval of data from a remote database.

SEQUENCE

Operation involving accessing values of a sequence.

SORT

AGGREGATE

Retrieval of a single row that is the result of applying a group function to a group of selected rows.

SORT

UNIQUE

Operation sorting a set of rows to eliminate duplicates.

SORT

GROUP BY

Operation sorting a set of rows into groups for a query with a GROUP BY clause.

SORT

GROUP BY PIVOT

Operation sorting a set of rows into groups for a query with a GROUP BY clause. The PIVOT option indicates a pivot-specific optimization for the SORT GROUP BY operator.

SORT

JOIN

Operation sorting a set of rows before a merge-join.

SORT

ORDER BY

Operation sorting a set of rows for a query with an ORDER BY clause.

TABLE ACCESS

(These are access methods.)

FULL

Retrieval of all rows from a table.

TABLE ACCESS

SAMPLE

Retrieval of sampled rows from a table.

TABLE ACCESS

CLUSTER

Retrieval of rows from a table based on a value of an indexed cluster key.

TABLE ACCESS

HASH

Retrieval of rows from table based on hash cluster key value.

TABLE ACCESS

BY ROWID RANGE

Retrieval of rows from a table based on a rowid range.

TABLE ACCESS

SAMPLE BY ROWID RANGE

Retrieval of sampled rows from a table based on a rowid range.

TABLE ACCESS

BY USER ROWID

If the table rows are located using user-supplied rowids.

TABLE ACCESS

BY INDEX ROWID

If the table is nonpartitioned and rows are located using index(es).

TABLE ACCESS

BY GLOBAL INDEX ROWID

If the table is partitioned and rows are located using only global indexes.

TABLE ACCESS

BY LOCAL INDEX ROWID

If the table is partitioned and rows are located using one or more local indexes and possibly some global indexes.

Partition Boundaries:

The partition boundaries might have been computed by:

A previous PARTITION step, in which case the PARTITION_START and PARTITION_STOP column values replicate the values present in the PARTITION step, and the PARTITION_ID contains the ID of the PARTITION step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, INVALID.

The TABLE ACCESS or INDEX step itself, in which case the PARTITION_ID contains the ID of the step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, ROW REMOVE_LOCATION (TABLE ACCESS only), and INVALID.

TRANSPOSE

Operation evaluating a PIVOT operation by transposing the results of GROUP BY to produce the final pivoted data.

UNION

Operation accepting two sets of rows and returns the union of the sets, eliminating duplicates.

UNPIVOT

Operation that rotates data from columns into rows.

VIEW

Operation performing a view's query and then returning the resulting rows to another operation.

See Also:
Oracle Database Reference for more information about PLAN_TABLE

7.3 Execution Plan Reference

This section contains the following topics:

- Execution Plan Views

- PLAN_TABLE Columns

- DBMS_XPLAN Program Units

7.3.1 Execution Plan Views

The following dynamic performance and data dictionary views provide information on execution plans.

Table 7-4 Execution Plan Views

View

Description

V$SQL_SHARED_CURSOR

Explains why a particular child cursor is not shared with existing child cursors. Each column identifies a specific reason why the cursor cannot be shared.

The USE_FEEDBACK_STATS column shows whether a child cursor fails to match because of reoptimization.

V$SQL_PLAN

Includes a superset of all rows appearing in all final plans. PLAN_LINE_ID is consecutively numbered, but for a single final plan, the IDs may not be consecutive.

V$SQL_PLAN_STATISTICS_ALL

Contains memory usage statistics for row sources that use SQL memory (sort or hash join). This view concatenates information in V$SQL_PLAN with execution statistics from V$SQL_PLAN_STATISTICS and V$SQL_WORKAREA.

7.3.2 PLAN_TABLE Columns

The PLAN_TABLE used by the EXPLAIN PLAN statement contains the columns listed in Table 7-5.

Table 7-5 PLAN_TABLE Columns

Column

Type

Description

STATEMENT_ID

VARCHAR2(30)

Value of the optional STATEMENT_ID parameter specified in the EXPLAIN PLAN statement.

PLAN_ID

NUMBER

Unique identifier of a plan in the database.

TIMESTAMP

DATE

Date and time when the EXPLAIN PLAN statement was generated.

REMARKS

VARCHAR2(80)

Any comment (of up to 80 bytes) you want to associate with each step of the explained plan. This column indicates whether the database used an outline or SQL profile for the query.

If you need to add or change a remark on any row of the PLAN_TABLE, then use the UPDATE statement to modify the rows of the PLAN_TABLE.

OPERATION

VARCHAR2(30)

Name of the internal operation performed in this step. In the first row generated for a statement, the column contains one of the following values:

- DELETE STATEMENT

- INSERT STATEMENT

- SELECT STATEMENT

- UPDATE STATEMENT

See Table 7-6 for more information about values for this column.

OPTIONS

VARCHAR2(225)

A variation on the operation that the OPERATION column describes.

See Table 7-6 for more information about values for this column.

OBJECT_NODE

VARCHAR2(128)

Name of the database link used to reference the object (a table name or view name). For local queries using parallel execution, this column describes the order in which the database consumes output from operations.

OBJECT_OWNER

VARCHAR2(30)

Name of the user who owns the schema containing the table or index.

OBJECT_NAME

VARCHAR2(30)

Name of the table or index.

OBJECT_ALIAS

VARCHAR2(65)

Unique alias of a table or view in a SQL statement. For indexes, it is the object alias of the underlying table.

OBJECT_INSTANCE

NUMERIC

Number corresponding to the ordinal position of the object as it appears in the original statement. The numbering proceeds from left to right, outer to inner for the original statement text. View expansion results in unpredictable numbers.

OBJECT_TYPE

VARCHAR2(30)

Modifier that provides descriptive information about the object; for example, NON-UNIQUE for indexes.

OPTIMIZER

VARCHAR2(255)

Current mode of the optimizer.

SEARCH_COLUMNS

NUMBERIC

Not currently used.

ID

NUMERIC

A number assigned to each step in the execution plan.

PARENT_ID

NUMERIC

The ID of the next execution step that operates on the output of the ID step.

DEPTH

NUMERIC

Depth of the operation in the row source tree that the plan represents. You can use this value to indent the rows in a plan table report.

POSITION

NUMERIC

For the first row of output, this indicates the optimizer's estimated cost of executing the statement. For the other rows, it indicates the position relative to the other children of the same parent.

COST

NUMERIC

Cost of the operation as estimated by the optimizer's query approach. Cost is not determined for table access operations. The value of this column does not have any particular unit of measurement; it is a weighted value used to compare costs of execution plans. The value of this column is a function of the CPU_COST and IO_COST columns.

CARDINALITY

NUMERIC

Estimate by the query optimization approach of the number of rows that the operation accessed.

BYTES

NUMERIC

Estimate by the query optimization approach of the number of bytes that the operation accessed.

OTHER_TAG

VARCHAR2(255)

Describes the contents of the OTHER column. Values are:

- SERIAL (blank): Serial execution. Currently, SQL is not loaded in the OTHER column for this case.

- SERIAL_FROM_REMOTE (S -> R): Serial execution at a remote site.

- PARALLEL_FROM_SERIAL (S -> P): Serial execution. Output of step is partitioned or broadcast to parallel execution servers.

- PARALLEL_TO_SERIAL (P -> S): Parallel execution. Output of step is returned to serial QC process.

- PARALLEL_TO_PARALLEL (P -> P): Parallel execution. Output of step is repartitioned to second set of parallel execution servers.

- PARALLEL_COMBINED_WITH_PARENT (PWP): Parallel execution; Output of step goes to next step in same parallel process. No interprocess communication to parent.

- PARALLEL_COMBINED_WITH_CHILD (PWC): Parallel execution. Input of step comes from prior step in same parallel process. No interprocess communication from child.

PARTITION_START

VARCHAR2(255)

Start partition of a range of accessed partitions. It can take one of the following values:

n indicates that the start partition has been identified by the SQL compiler, and its partition number is given by n.

KEY indicates that the start partition is identified at run time from partitioning key values.

ROW REMOVE_LOCATION indicates that the database computes the start partition (same as the stop partition) at run time from the location of each retrieved record. The record location is obtained by a user or from a global index.

INVALID indicates that the range of accessed partitions is empty.

PARTITION_STOP

VARCHAR2(255)

Stop partition of a range of accessed partitions. It can take one of the following values:

n indicates that the stop partition has been identified by the SQL compiler, and its partition number is given by n.

KEY indicates that the stop partition is identified at run time from partitioning key values.

ROW REMOVE_LOCATION indicates that the database computes the stop partition (same as the start partition) at run time from the location of each retrieved record. The record location is obtained by a user or from a global index.

INVALID indicates that the range of accessed partitions is empty.

PARTITION_ID

NUMERIC

Step that has computed the pair of values of the PARTITION_START and PARTITION_STOP columns.

OTHER

LONG

Other information that is specific to the execution step that a user might find useful. See the OTHER_TAG column.

DISTRIBUTION

VARCHAR2(30)

Method used to distribute rows from producer query servers to consumer query servers.

See Table 7-6 for more information about the possible values for this column. For more information about consumer and producer query servers, see Oracle Database Data Warehousing Guide.

CPU_COST

NUMERIC

CPU cost of the operation as estimated by the query optimizer's approach. The value of this column is proportional to the number of machine cycles required for the operation. For statements that use the rule-based approach, this column is null.

IO_COST

NUMERIC

I/O cost of the operation as estimated by the query optimizer's approach. The value of this column is proportional to the number of data blocks read by the operation. For statements that use the rule-based approach, this column is null.

TEMP_SPACE

NUMERIC

Temporary space, in bytes, used by the operation as estimated by the query optimizer's approach. For statements that use the rule-based approach, or for operations that do not use any temporary space, this column is null.

ACCESS_PREDICATES

VARCHAR2(4000)

Predicates used to locate rows in an access structure. For example, start or stop predicates for an index range scan.

FILTER_PREDICATES

VARCHAR2(4000)

Predicates used to filter rows before producing them.

PROJECTION

VARCHAR2(4000)

Expressions produced by the operation.

TIME

NUMBER(20,2)

Elapsed time in seconds of the operation as estimated by query optimization. For statements that use the rule-based approach, this column is null.

QBLOCK_NAME

VARCHAR2(30)

Name of the query block, either system-generated or defined by the user with the QB_NAME hint.

Table 7-6 describes the values that can appear in the DISTRIBUTION column:

Table 7-6 Values of DISTRIBUTION Column of the PLAN_TABLE

DISTRIBUTION Text

Interpretation

PARTITION (ROWID)

Maps rows to query servers based on the partitioning of a table or index using the rowid of the row to UPDATE/DELETE.

PARTITION (KEY)

Maps rows to query servers based on the partitioning of a table or index using a set of columns. Used for partial partition-wise join, PARALLEL INSERT, CREATE TABLE AS SELECT of a partitioned table, and CREATE PARTITIONED GLOBAL INDEX.

HASH

Maps rows to query servers using a hash function on the join key. Used for PARALLEL JOIN or PARALLEL GROUP BY.

RANGE

Maps rows to query servers using ranges of the sort key. Used when the statement contains an ORDER BY clause.

ROUND-ROBIN

Randomly maps rows to query servers.

BROADCAST

Broadcasts the rows of the entire table to each query server. Used for a parallel join when one table is very small compared to the other.

QC (ORDER)

The QC consumes the input in order, from the first to the last query server. Used when the statement contains an ORDER BY clause.

QC (RANDOM)

The QC consumes the input randomly. Used when the statement does not have an ORDER BY clause.

Table 7-7 lists each combination of OPERATION and OPTIONS produced by the EXPLAIN PLAN statement and its meaning within an execution plan.

Table 7-7 OPERATION and OPTIONS Values Produced by EXPLAIN PLAN

Operation

Option

Description

AND-EQUAL

Operation accepting multiple sets of rowids, returning the intersection of the sets, eliminating duplicates. Used for the single-column indexes access path.

BITMAP

CONVERSION

TO ROWIDS converts bitmap representations to actual rowids that you can use to access the table.

FROM ROWIDS converts the rowids to a bitmap representation.

COUNT returns the number of rowids if the actual values are not needed.

BITMAP

INDEX

SINGLE VALUE looks up the bitmap for a single key value in the index.

RANGE SCAN retrieves bitmaps for a key value range.

FULL SCAN performs a full scan of a bitmap index if there is no start or stop key.

BITMAP

MERGE

Merges several bitmaps resulting from a range scan into one bitmap.

BITMAP

MINUS

Subtracts bits of one bitmap from another. Row source is used for negated predicates. This option is usable only if there are nonnegated predicates yielding a bitmap from which the subtraction can take place.

BITMAP

OR

Computes the bitwise OR of two bitmaps.

BITMAP

AND

Computes the bitwise AND of two bitmaps.

BITMAP

KEY ITERATION

Takes each row from a table row source and finds the corresponding bitmap from a bitmap index. This set of bitmaps are then merged into one bitmap in a following BITMAP MERGE operation.

CONNECT BY

Retrieves rows in hierarchical order for a query containing a CONNECT BY clause.

CONCATENATION

Operation accepting multiple sets of rows returning the union-all of the sets.

COUNT

Operation counting the number of rows selected from a table.

COUNT

STOPKEY

Count operation where the number of rows returned is limited by the ROWNUM expression in the WHERE clause.

CUBE JOIN

Joins a table or view on the left and a cube on the right.

See Oracle Database SQL Language Reference to learn about the NO_USE_CUBE and USE_CUBE hints.

CUBE JOIN

ANTI

Uses an antijoin for a table or view on the left and a cube on the right.

CUBE JOIN

ANTI SNA

Uses an antijoin (single-sided null aware) for a table or view on the left and a cube on the right. The join column on the right (cube side) is NOT NULL.

CUBE JOIN

OUTER

Uses an outer join for a table or view on the left and a cube on the right.

CUBE JOIN

RIGHT SEMI

Uses a right semijoin for a table or view on the left and a cube on the right.

CUBE SCAN

Uses inner joins for all cube access.

CUBE SCAN

PARTIAL OUTER

Uses an outer join for at least one dimension, and inner joins for the other dimensions.

CUBE SCAN

OUTER

Uses outer joins for all cube access.

DOMAIN INDEX

Retrieval of one or more rowids from a domain index. The options column contain information supplied by a user-defined domain index cost function, if any.

FILTER

Operation accepting a set of rows, eliminates some of them, and returns the rest.

FIRST ROW

Retrieval of only the first row selected by a query.

FOR UPDATE

Operation retrieving and locking the rows selected by a query containing a FOR UPDATE clause.

HASH

GROUP BY

Operation hashing a set of rows into groups for a query with a GROUP BY clause.

HASH

GROUP BY PIVOT

Operation hashing a set of rows into groups for a query with a GROUP BY clause. The PIVOT option indicates a pivot-specific optimization for the HASH GROUP BY operator.

HASH JOIN

(These are join operations.)

Operation joining two sets of rows and returning the result. This join method is useful for joining large data sets of data (DSS, Batch). The join condition is an efficient way of accessing the second table.

Query optimizer uses the smaller of the two tables/data sources to build a hash table on the join key in memory. Then it scans the larger table, probing the hash table to find the joined rows.

HASH JOIN

ANTI

Hash (left) antijoin

HASH JOIN

SEMI

Hash (left) semijoin

HASH JOIN

RIGHT ANTI

Hash right antijoin

HASH JOIN

RIGHT SEMI

Hash right semijoin

HASH JOIN

OUTER

Hash (left) outer join

HASH JOIN

RIGHT OUTER

Hash right outer join

INDEX

(These are access methods.)

UNIQUE SCAN

Retrieval of a single rowid from an index.

INDEX

RANGE SCAN

Retrieval of one or more rowids from an index. Indexed values are scanned in ascending order.

INDEX

RANGE SCAN DESCENDING

Retrieval of one or more rowids from an index. Indexed values are scanned in descending order.

INDEX

FULL SCAN

Retrieval of all rowids from an index when there is no start or stop key. Indexed values are scanned in ascending order.

INDEX

FULL SCAN DESCENDING

Retrieval of all rowids from an index when there is no start or stop key. Indexed values are scanned in descending order.

INDEX

FAST FULL SCAN

Retrieval of all rowids (and column values) using multiblock reads. No sorting order can be defined. Compares to a full table scan on only the indexed columns. Only available with the cost based optimizer.

INDEX

SKIP SCAN

Retrieval of rowids from a concatenated index without using the leading column(s) in the index. Only available with the cost based optimizer.

INLIST ITERATOR

Iterates over the next operation in the plan for each value in the IN-list predicate.

INTERSECTION

Operation accepting two sets of rows and returning the intersection of the sets, eliminating duplicates.

MERGE JOIN

(These are join operations.)

Operation accepting two sets of rows, each sorted by a value, combining each row from one set with the matching rows from the other, and returning the result.

MERGE JOIN

OUTER

Merge join operation to perform an outer join statement.

MERGE JOIN

ANTI

Merge antijoin.

MERGE JOIN

SEMI

Merge semijoin.

MERGE JOIN

CARTESIAN

Can result from 1 or more of the tables not having any join conditions to any other tables in the statement. Can occur even with a join and it may not be flagged as CARTESIAN in the plan.

CONNECT BY

Retrieval of rows in hierarchical order for a query containing a CONNECT BY clause.

MAT_VIEW REWITE ACCESS

(These are access methods.)

FULL

Retrieval of all rows from a materialized view.

MAT_VIEW REWITE ACCESS

SAMPLE

Retrieval of sampled rows from a materialized view.

MAT_VIEW REWITE ACCESS

CLUSTER

Retrieval of rows from a materialized view based on a value of an indexed cluster key.

MAT_VIEW REWITE ACCESS

HASH

Retrieval of rows from materialized view based on hash cluster key value.

MAT_VIEW REWITE ACCESS

BY ROWID RANGE

Retrieval of rows from a materialized view based on a rowid range.

MAT_VIEW REWITE ACCESS

SAMPLE BY ROWID RANGE

Retrieval of sampled rows from a materialized view based on a rowid range.

MAT_VIEW REWITE ACCESS

BY USER ROWID

If the materialized view rows are located using user-supplied rowids.

MAT_VIEW REWITE ACCESS

BY INDEX ROWID

If the materialized view is nonpartitioned and rows are located using index(es).

MAT_VIEW REWITE ACCESS

BY GLOBAL INDEX ROWID

If the materialized view is partitioned and rows are located using only global indexes.

MAT_VIEW REWITE ACCESS

BY LOCAL INDEX ROWID

If the materialized view is partitioned and rows are located using one or more local indexes and possibly some global indexes.

Partition Boundaries:

The partition boundaries might have been computed by:

A previous PARTITION step, in which case the PARTITION_START and PARTITION_STOP column values replicate the values present in the PARTITION step, and the PARTITION_ID contains the ID of the PARTITION step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, INVALID.

The MAT_VIEW REWRITE ACCESS or INDEX step itself, in which case the PARTITION_ID contains the ID of the step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, ROW REMOVE_LOCATION (MAT_VIEW REWRITE ACCESS only), and INVALID.

MINUS

Operation accepting two sets of rows and returning rows appearing in the first set but not in the second, eliminating duplicates.

NESTED LOOPS

(These are join operations.)

Operation accepting two sets of rows, an outer set and an inner set. Oracle Database compares each row of the outer set with each row of the inner set, returning rows that satisfy a condition. This join method is useful for joining small subsets of data (OLTP). The join condition is an efficient way of accessing the second table.

NESTED LOOPS

OUTER

Nested loops operation to perform an outer join statement.

PARTITION

Iterates over the next operation in the plan for each partition in the range given by the PARTITION_START and PARTITION_STOP columns. PARTITION describes partition boundaries applicable to a single partitioned object (table or index) or to a set of equipartitioned objects (a partitioned table and its local indexes). The partition boundaries are provided by the values of PARTITION_START and PARTITION_STOP of the PARTITION. Refer to Table 7-4 for valid values of partition start and stop.

PARTITION

SINGLE

Access one partition.

PARTITION

ITERATOR

Access many partitions (a subset).

PARTITION

ALL

Access all partitions.

PARTITION

INLIST

Similar to iterator, but based on an IN-list predicate.

PARTITION

INVALID

Indicates that the partition set to be accessed is empty.

PX ITERATOR

BLOCK, CHUNK

Implements the division of an object into block or chunk ranges among a set of parallel execution servers.

PX COORDINATOR

Implements the Query Coordinator which controls, schedules, and executes the parallel plan below it using parallel execution servers. It also represents a serialization point, as the end of the part of the plan executed in parallel and always has a PX SEND QC operation below it.

PX PARTITION

Same semantics as the regular PARTITION operation except that it appears in a parallel plan.

PX RECEIVE

Shows the consumer/receiver parallel execution node reading repartitioned data from a send/producer (QC or parallel execution server) executing on a PX SEND node. This information was formerly displayed into the DISTRIBUTION column. See Table 7-5.

PX SEND

QC (RANDOM), HASH, RANGE

Implements the distribution method taking place between two sets of parallel execution servers. Shows the boundary between two sets and how data is repartitioned on the send/producer side (QC or side. This information was formerly displayed into the DISTRIBUTION column. See Table 7-5.

REMOTE

Retrieval of data from a remote database.

SEQUENCE

Operation involving accessing values of a sequence.

SORT

AGGREGATE

Retrieval of a single row that is the result of applying a group function to a group of selected rows.

SORT

UNIQUE

Operation sorting a set of rows to eliminate duplicates.

SORT

GROUP BY

Operation sorting a set of rows into groups for a query with a GROUP BY clause.

SORT

GROUP BY PIVOT

Operation sorting a set of rows into groups for a query with a GROUP BY clause. The PIVOT option indicates a pivot-specific optimization for the SORT GROUP BY operator.

SORT

JOIN

Operation sorting a set of rows before a merge-join.

SORT

ORDER BY

Operation sorting a set of rows for a query with an ORDER BY clause.

TABLE ACCESS

(These are access methods.)

FULL

Retrieval of all rows from a table.

TABLE ACCESS

SAMPLE

Retrieval of sampled rows from a table.

TABLE ACCESS

CLUSTER

Retrieval of rows from a table based on a value of an indexed cluster key.

TABLE ACCESS

HASH

Retrieval of rows from table based on hash cluster key value.

TABLE ACCESS

BY ROWID RANGE

Retrieval of rows from a table based on a rowid range.

TABLE ACCESS

SAMPLE BY ROWID RANGE

Retrieval of sampled rows from a table based on a rowid range.

TABLE ACCESS

BY USER ROWID

If the table rows are located using user-supplied rowids.

TABLE ACCESS

BY INDEX ROWID

If the table is nonpartitioned and rows are located using index(es).

TABLE ACCESS

BY GLOBAL INDEX ROWID

If the table is partitioned and rows are located using only global indexes.

TABLE ACCESS

BY LOCAL INDEX ROWID

If the table is partitioned and rows are located using one or more local indexes and possibly some global indexes.

Partition Boundaries:

The partition boundaries might have been computed by:

A previous PARTITION step, in which case the PARTITION_START and PARTITION_STOP column values replicate the values present in the PARTITION step, and the PARTITION_ID contains the ID of the PARTITION step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, INVALID.

The TABLE ACCESS or INDEX step itself, in which case the PARTITION_ID contains the ID of the step. Possible values for PARTITION_START and PARTITION_STOP are NUMBER(n), KEY, ROW REMOVE_LOCATION (TABLE ACCESS only), and INVALID.

TRANSPOSE

Operation evaluating a PIVOT operation by transposing the results of GROUP BY to produce the final pivoted data.

UNION

Operation accepting two sets of rows and returns the union of the sets, eliminating duplicates.

UNPIVOT

Operation that rotates data from columns into rows.

VIEW

Operation performing a view's query and then returning the resulting rows to another operation.

See Also:
Oracle Database Reference for more information about PLAN_TABLE

7.3.3 DBMS_XPLAN Program Units

Table 7-8 provides notes on DBMS_XPLAN functions and parameters that are relevant for accessing adapted plans. See Oracle Database PL/SQL Packages and Types Reference for complete reference information.

Table 7-8 DBMS_XPLAN Functions and Parameters Relevant for Adaptive Queries

Functions

Notes

DISPLAY_PLAN

The FORMAT argument supports the modifier ADAPTIVE.

When you specify ADAPTIVE, the output includes the default plan. For each dynamic subplan, the plan shows a list of the row sources from the original that may be replaced, and the row sources that would replace them.

If the format argument specifies the outline display, then the function displays the hints for each option in the dynamic subplan. If the plan is not an adaptive plan, then the function displays the default plan.When you do not specify ADAPTIVE, the plan is shown as-is, but with additional comments in the Note section that show any row sources that are dynamic.

DISPLAY_CURSOR

The FORMAT argument supports the modifier ADAPTIVE.

When you specify ADAPTIVE, the output includes:

- The final plan. If the execution has not completed, then the output shows the current plan. This section also includes notes about run-time optimizations that affect the plan.

- Recommended plan. In reporting mode, the output includes the plan that would be chosen based on execution statistics.

- Dynamic plan. The output summarizes the portions of the plan that differ from the default plan chosen by the optimizer.

- Reoptimization. The output displays the plan that would be chosen on a subsequent execution because of reoptimization.

Part IV

SQL Operators

A row source is a set of rows returned by a step in the execution plan. A SQL operator acts on a row source. A unary operator acts on one input, as with access paths. A binary operator acts on two outputs, as with joins.

This part contains the following chapters:

- Chapter 8, "Optimizer Access Paths"

- Chapter 9, "Joins"

8 Optimizer Access Paths

This chapter contains the following topics:

- Introduction to Access Paths

- Table Access Paths

- B-Tree Index Access Paths

- Bitmap Index Access Paths

- Table Cluster Access Paths

8.1 Introduction to Access Paths

A row source is a set of rows returned by a step in an execution plan. A row source can be a table, view, or result of a join or grouping operation.

A unary operation such as an access path, which is a technique used by a query to retrieve rows from a row source, accepts a single row source as input. For example, a full table scan is the retrieval of rows of a single row source. In contrast, a join operation is binary and receives inputs from two row sources (see Chapter 9, "Joins").

The database uses different access paths for different relational data structures (see Oracle Database Concepts for an overview of these structures). Table 8-1 summarizes common access paths for the major data structures.

Table 8-1 Data Structures and Access Paths

Access Path

Heap-Organized Tables

B-Tree Indexes and IOTs

Bitmap Indexes

Table Clusters

Full Table Scans

x

Table Access by Rowid

x

Sample Table Scans

x

Index Unique Scans

x

Index Range Scans

x

Index Full Scans

x

Index Fast Full Scans

x

Index Skip Scans

x

Index Join Scans

x

Bitmap Index Single Value

x

Bitmap Index Range Scans

x

Bitmap Merge

x

Bitmap Index Range Scans

x

Cluster Scans

x

Hash Scans

x

As explained in "Cost-Based Optimization," the optimizer considers different possible execution plans, and then assigns each plan a cost. The optimizer chooses the plan with the lowest cost. In general, index access paths are more efficient for statements that retrieve a small subset of table rows, whereas full table scans are more efficient when accessing a large portion of a table.

8.2 Table Access Paths

A table is the basic unit of data organization in an Oracle database. Relational tables are the most common table type. Relational tables have with the following organizational characteristics:

- A heap-organized table does not store rows in any particular order.

- An index-organized table orders rows according to the primary key values.

- An external table is a read-only table whose metadata is stored in the database but whose data is stored outside the database.

This section explains optimizer access paths for heap-organized tables, and contains the following topics:

- About Heap-Organized Table Access

- Full Table Scans

- Table Access by Rowid

- Sample Table Scans

- In-Memory Table Scans

See Also:

- Oracle Database Concepts for an overview of tables

- Oracle Database Administrator's Guide to learn how to manage tables

8.2.1 About Heap-Organized Table Access

By default, a table is organized as a heap, which means that the database places rows where they fit best rather than in a user-specified order. As users add rows, the database places the rows in the first available free space in the data segment. Rows are not guaranteed to be retrieved in the order in which they were inserted.

8.2.1.1 Row Storage in Data Blocks and Segments: A Primer

The database stores rows in data blocks. In tables, the database can write a row anywhere in the bottom part of the block. Oracle Database uses the block overhead, which contains the row directory and table directory, to manage the block itself.

An extent is made up of logically contiguous data blocks. The blocks may not be physically contiguous on disk. A segment is a set of extents that contains all the data for a logical storage structure within a tablespace. For example, Oracle Database allocates one or more extents to form the data segment for a table. The database also allocates one or more extents to form the index segment for a table.

By default, the database uses automatic segment space management (ASSM) for permanent, locally managed tablespaces. When a session first inserts data into a table, the database formats a bitmap block. The bitmap tracks the blocks in the segment. The database uses the bitmap to find free blocks and then formats each block before writing to it. ASSM spread out inserts among blocks to avoid concurrency issues.

The high water mark (HWM) is the point in a segment beyond which data blocks are unformatted and have never been used. Below the HWM, a block may be formatted and written to, formatted and empty, or unformatted. The low high water mark (low HWM) marks the point below which all blocks are known to be formatted because they either contain data or formerly contained data.

During a full table scan, the database reads all blocks up to the low HWM, which are known to be formatted, and then reads the segment bitmap to determine which blocks between the HWM and low HWM are formatted and safe to read. The database knows not to read past the HWM because these blocks are unformatted.

See Also:
Oracle Database Concepts to learn about data block storage

8.2.1.2 Importance of Rowids for Row Access

Every row in a heap-organized table has a rowid unique to this table that corresponds to the physical address of a row piece. A rowid is a 10-byte physical address of a row.

The rowid points to a specific file, block, and row number. For example, in the rowid AAAPecAAFAAAABSAAA, the final AAA represents the row number. The row number is an index into a row directory entry. The row directory entry contains a pointer to the location of the row on the block.

The database can sometimes move a row in the bottom part of the block. For example, if row movement is enabled, then the row can move because of partition key updates, Flashback Table operations, shrink table operations, and so on. If the database moves a row within a block, then the database updates the row directory entry to modify the pointer. The rowid stays constant.

Oracle Database uses rowids internally for the construction of indexes. For example, each key in a B-tree index is associated with a rowid that points to the address of the associated row. Physical rowids provide the fastest possible access to a table row, enabling the database to retrieve a row in as little as a single I/O.

See Also:
Oracle Database Concepts to learn about rowids

8.2.1.3 Direct Path Reads

In a direct path read, the database reads buffers from disk directly into the PGA, bypassing the SGA entirely. Figure 8-1 shows the difference between scattered and sequential reads, which store buffers in the SGA, and direct path reads.

Figure 8-1 Direct Path Reads

Situations in which Oracle Database may perform direct path reads include:

- Execution of a CREATE TABLE AS SELECT statement

- Execution of an ALTER REBUILD or ALTER MOVE statement

- Reads from a temporary tablespace

- Parallel queries

- Reads from a LOB segment

See Also:
Oracle Database Performance Tuning Guide to learn about wait events for direct path reads

8.2.2 Full Table Scans

A full table scan reads all rows from a table, and then filters out those rows that do not meet the selection criteria.

8.2.2.1 When the Optimizer Considers a Full Table Scan

In general, the optimizer chooses a full table scan when it cannot use a different access path, or another usable access path is higher cost. Typical reasons for choosing a full table scan include the following:

- No index exists.
If no index exists, then the optimizer uses a full table scan.

- The query predicate applies a function to the indexed column.
Unless the index is a function-based index (see "Guidelines for Using Function-Based Indexes for Performance"), the database indexes the values of the column, not the values of the column with the function applied. A typical application-level mistake is to index a character column, such as char_col, and then query the column using syntax such as WHERE char_col=1. The database implicitly applies a TO_NUMBER function to the constant number 1, which prevents use of the index.

- A SELECT COUNT(*) query is issued, and an index exists, but the indexed column contains nulls.
The optimizer cannot use the index to count the number of table rows because the index cannot contain null entries (see "B-Tree Indexes and Nulls").

- The query predicate does not use the leading edge of a B-tree index.
For example, an index might exist on employees(first_name,last_name). If a user issues a query with the predicate WHERE last_name='KING', then the optimizer may not choose an index because column first_name is not in the predicate. However, in this situation the optimizer may choose to use an index skip scan (see "Index Skip Scans").

- The query is unselective.
If the optimizer determines that the query requires most of the blocks in the table, then it uses a full table scan, even though indexes are available. Full table scans can use larger I/O calls. Making fewer large I/O calls is cheaper than making many smaller calls.

- The table statistics are stale.
For example, a table was small, but now has grown large. If the table statistics are stale and do not reflect the current size of the table, then the optimizer does not know that an index is now most efficient than a full table scan. See "Introduction to Optimizer Statistics."

- The table is small.
If a table contains fewer than n blocks under the high water mark, where n equals the setting for the DB_FILE_MULTIBLOCK_READ_COUNT initialization parameter, then a full table scan may be cheaper than an index range scan. The scan may be less expensive regardless of the fraction of tables being accessed or indexes present.

- The table has a high degree of parallelism.
A high degree of parallelism for a table skews the optimizer toward full table scans over range scans. Query the value in the ALL_TABLES.DEGREE column in for the table to determine the degree of parallelism.

- The query uses a full table scan hint.
The hint FULL(table alias) instructs the optimizer to use a full table scan.

See Also:
"Influencing the Optimizer with Hints"

8.2.2.2 How a Full Table Scan Works

In a full table scan, the database sequentially reads every formatted block under the high water mark. The database reads each block only once. The following graphic depicts a scan of a table segment, showing how the scan skips unformatted blocks below the high water mark.

Because the blocks are adjacent, the database can speed up the scan by making I/O calls larger than a single block, known as a multiblock read. The size of a read call ranges from one block to the number of blocks specified by the DB_FILE_MULTIBLOCK_READ_COUNT initialization parameter. For example, setting this parameter to 4 instructs the database to read up to 4 blocks in a single call.

The algorithms for caching blocks during full table scans are complex. For example, the database caches blocks differently depending on whether tables are small or large.

See Also:

- Table 14-1, "Initialization Parameters That Control Optimizer Behavior"

- Oracle Database Concepts for an overview of the default caching mode

- Oracle Database Reference to learn about the DB_FILE_MULTIBLOCK_READ_COUNT initialization parameter

8.2.2.3 Full Table Scan: Example

The following statement queries salaries over 4000 in the hr.employees table:

SELECT salary
FROM   hr.employees
WHERE  salary > 4000;

Example 8-1 retrieves the plan using the DBMS_XPLAN.DISPLAY_CURSOR function. Because no index exists on the salary column, the optimizer cannot use an index range scan, and so uses a full table scan.

Example 8-1 Full Table Scan

SQL_ID  54c20f3udfnws, child number 0
-------------------------------------
select salary from hr.employees where salary > 4000

Plan hash value: 3476115102

-------------------------------------------------------------------------------
| Id  | Operation         | Name      | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------
|   0 | SELECT STATEMENT  |           |       |       |     3 (100)|          |
|*  1 |  TABLE ACCESS FULL| EMPLOYEES |    98 |  6762 |     3   (0)| 00:00:01 |
-------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - filter("SALARY">4000)

8.2.3 Table Access by Rowid

A rowid is an internal representation of the storage location of data. The rowid of a row specifies the data file and data block containing the row and the location of the row in that block. Locating a row by specifying its rowid is the fastest way to retrieve a single row because it specifies the exact location of the row in the database.

Note:
Rowids can change between versions. Accessing data based on position is not recommended because rows can move. To learn more about rowids, see Oracle Database Development Guide.

8.2.3.1 When the Optimizer Chooses Table Access by Rowid

In most cases, the database accesses a table by rowid after a scan of one or more indexes. However, table access by rowid need not follow every index scan. If the index contains all needed columns, then access by rowid might not occur (see "Index Fast Full Scans").

8.2.3.2 How Table Access by Rowid Works

To access a table by rowid, the database performs the following steps:

1. Obtains the rowids of the selected rows, either from the statement WHERE clause or through an index scan of one or more indexes
Table access may be needed for columns in the statement not present in the index.

2. Locates each selected row in the table based on its rowid

8.2.3.3 Table Access by Rowid: Example

Assume run the following query:

SELECT *
FROM   employees
WHERE  employee_id > 190;

Step 2 of the following plan shows a range scan of the emp_emp_id_pk index on the hr.employees table. The database uses the rowids obtained from the index to find the corresponding rows from the employees table, and then retrieve them. The BATCHED access shown in Step 1 means that the database retrieves a few rowids from the index, and then attempts to access rows in block order to improve the clustering and reduce the number of times that the database must access a block.

----------------------------------------------------------------------------------
|Id| Operation                           | Name      |Rows|Bytes|Cost (%CPU)|Time|
----------------------------------------------------------------------------------
| 0| SELECT STATEMENT                    |             |  |     |2 (100)|        |
| 1|  TABLE ACCESS BY INDEX ROWID BATCHED|EMPLOYEES    |16| 1104|2   (0)|00:00:01|
|*2|   INDEX RANGE SCAN                  |EMP_EMP_ID_PK|16|     |1   (0)|00:00:01|
----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - access("EMPLOYEE_ID">190)

8.2.4 Sample Table Scans

A sample table scan retrieves a random sample of data from a simple table or a complex SELECT statement, such as a statement involving joins and views.

8.2.4.1 When the Optimizer Chooses a Sample Table Scan

The database uses a sample table scan when a statement FROM clause includes either of the following clauses:

- SAMPLE (sample_percent)
The database reads a specified percentage of rows in the table to perform a sample table scan.

- SAMPLE BLOCK (sample_percent)
The database reads a specified percentage of table blocks to perform a sample table scan.

The sample_percent specifies the percentage of the total row or block count to include in the sample. The value must be in the range .000001 up to, but not including, 100. This percentage indicates the probability of each row, or each cluster of rows in block sampling, being selected for the sample. It does not mean that the database retrieves exactly sample_percent of the rows.

Note:
Block sampling is possible only during full table scans or index fast full scans. If a more efficient execution path exists, then the database does not sample blocks. To guarantee block sampling for a specific table or index, use the FULL or INDEX_FFS hint.

See Also:

- "Influencing the Optimizer with Hints"

- Oracle Database SQL Language Reference to learn about the SAMPLE clause

8.2.4.2 Sample Table Scans: Example

Example 8-2 uses a sample table scan to access 1% of the employees table, sampling by blocks instead of rows.

Example 8-2 Sample Table Scan

SELECT * FROM hr.employees SAMPLE BLOCK (1);

The EXPLAIN PLAN output for this statement might look as follows:

-------------------------------------------------------------------------
| Id  | Operation            |  Name       | Rows  | Bytes | Cost (%CPU)|
-------------------------------------------------------------------------
|   0 | SELECT STATEMENT     |             |     1 |    68 |     3  (34)|
|   1 |  TABLE ACCESS SAMPLE | EMPLOYEES   |     1 |    68 |     3  (34)|
-------------------------------------------------------------------------

8.2.5 In-Memory Table Scans

Starting in Oracle Database 12c Release 1 (12.1.0.2), an in-memory scan retrieves some or all rows from the In-Memory Column Store (IM column store). The IM column store is an optional SGA area that stores copies of tables and partitions in a special columnar format optimized for rapid scans.

See Also:

- Oracle Database Concepts for an overview of the IM column store

- Oracle Database Administrator's Guide to learn how to enable the IM column store

8.2.5.1 When the Optimizer Chooses an In-Memory Table Scan

Starting in Oracle Database 12c Release 1 (12.1.0.2), the optimizer cost model is fully aware of the content of the IM column store. When a user executes a query that references a table in the IM column store, the optimizer calculates the cost of all possible access methods—including the in-memory table scan—and selects the access method with the lowest cost.

8.2.5.2 In-Memory Query Controls

The following database initialization parameters affect the in-memory features:

- INMEMORY_QUERY
This parameter enables or disables in-memory queries for the database at the session or system level. This parameter is helpful when you want to test workloads with and without the use of the IM column store.

- OPTIMIZER_INMEMORY_AWARE
This parameter enables (TRUE) or disables (FALSE) all of the in-memory enhancements made to the optimizer cost model, table expansion, bloom filters, and so on. Setting the parameter to FALSE causes the optimizer to ignore the in-memory property of tables during the optimization of SQL statements.

- OPTIMIZER_FEATURES_ENABLE
When set to values lower than 12.1.0.2, this parameter has the same effect as setting OPTIMIZER_INMEMORY_AWARE to FALSE.

To enable or disable in-memory queries, you can specify the INMEMORY or NO_INMEMORY hints, which are the per-query equivalent of the INMEMORY_QUERY initialization parameter. If a SQL statement uses the INMEMORY hint, but the object it references is not already loaded in the IM column store, then the database does not wait for the object to be populated in the IM column store before executing the statement. However, initial access of the object triggers the object population in the IM column store.

See Also:

- Oracle Database Reference to learn more about the INMEMORY_QUERY, OPTIMIZER_INMEMORY_AWARE, and OPTIMIZER_FEATURES_ENABLE initialization parameters

- Oracle Database SQL Language Reference to learn more about the INMEMORY hints

8.2.5.3 In-Memory Table Scans: Example

Example 8-3 shows a query of the oe.product_information table, which has been altered with the INMEMORY HIGH option.

Example 8-3 In-Memory Table Scan

SELECT *
FROM   oe.product_information
WHERE  list_price > 10
ORDER BY product_id

The plan for this statement might look as follows, with the INMEMORY keyword in Step 2 indicating that some or all of the object was accessed from the IM column store:

SQL> SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR);

SQL_ID  2mb4h57x8pabw, child number 0
-------------------------------------
select * from oe.product_information where list_price > 10 order byproduct_id

Plan hash value: 2256295385

---------------------------------------------------------------------------------------------
|Id | Operation                   | Name                |Rows|Bytes |TempSpc|Cost(%CPU)|Time|
---------------------------------------------------------------------------------------------
|  0| SELECT STATEMENT            |                     |    |      |     |21 (100)|        |
|  1|  SORT ORDER BY              |                     | 285| 62415|82000|21   (5)|00:00:01|
|* 2|   TABLE ACCESS INMEMORY FULL| PRODUCT_INFORMATION | 285| 62415|     | 5   (0)|00:00:01|
---------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - inmemory("LIST_PRICE">10)
filter("LIST_PRICE">10)

8.3 B-Tree Index Access Paths

An index is an optional structure, associated with a table or table cluster, that can sometimes speed data access. By creating an index on one or more columns of a table, you gain the ability in some cases to retrieve a small set of randomly distributed rows from the table. Indexes are one of many means of reducing disk I/O.

This section contains the following topics:

- About B-Tree Index Access

- Index Unique Scans

- Index Range Scans

- Index Full Scans

- Index Fast Full Scans

- Index Skip Scans

- Index Join Scans

See Also:

- Oracle Database Concepts for an overview of indexes

- Oracle Database Administrator's Guide to learn how to manage indexes

8.3.1 About B-Tree Index Access

B-trees, short for balanced trees, are the most common type of database index. A B-tree index is an ordered list of values divided into ranges. By associating a key with a row or range of rows, B-trees provide excellent retrieval performance for a wide range of queries, including exact match and range searches.

Figure 8-2 illustrates the logical structure of a B-tree index. A B-tree index has two types of blocks: branch blocks for searching and leaf blocks that store values. Branch blocks store the minimum key prefix needed to make a branching decision between two keys. The leaf blocks contain every indexed data value and a corresponding rowid used to locate the actual row. Each index entry is sorted by (key, rowid). Within a leaf block, a key and rowid is linked to its left and right sibling entries. The leaf blocks themselves are also doubly linked.

Figure 8-2 B-Tree Index Structure

8.3.1.1 How Index Storage Affects Index Scans

Figure 8-2 shows the leaf blocks as adjacent to each other, so that the 1-10 block is next to and before the 11-19 block. This arrangement illustrates the linked lists that connect the index entries. However, index blocks need not be stored in order within an index segment. For example, the 246-250 block could appear anywhere in the segment, including directly before the 1-10 block. Because blocks can appear anywhere in the segment, ordered index scans must perform single-block I/O. The database must read a block to determine which block it must read next.

Figure 8-2 shows the index entries within an index block stored sequentially. This is true at a high level. At a low level, the index entries in the index block body are stored in a heap, just like table rows. For example, if the value 10 is inserted first into a table, then the index entry with key 10 might be inserted at the bottom of the index block, and if 0 is inserted next into the table, then the index entry for key 0 might be inserted on top of the entry for 10, and so on. Thus, the index entries in the block body are not stored in key order. However, within the index block, the row header stores records in key order. For example, the first record in the header points to the index entry with key 0, and so on sequentially up to the record that points to the index entry with key 10. Thus, index scans can read the row header to determine where to begin and end range scans, avoiding the necessity of reading every entry in the block.

See Also:
Oracle Database Concepts to learn about index blocks

8.3.1.2 Unique and Nonunique Indexes

Figure 8-2 shows a nonunique index. In a nonunique index, the database stores the rowid by appending it to the key as an extra column with a length byte to make the key unique. For example, the first index key in Figure 8-2 is the pair 0,rowid and not simply 0. The database sorts the data by index key values and then by rowid ascending. For example, the entries are sorted as follows:

0,AAAPvCAAFAAAAFaAAa
0,AAAPvCAAFAAAAFaAAg
0,AAAPvCAAFAAAAFaAAl
2,AAAPvCAAFAAAAFaAAm

In a unique index, the index key does not include the rowid. The database sorts the data only by the index key values, such as 0, 1, 2, and so on.

See Also:
Oracle Database Concepts for an overview of unique and nonunique indexes

8.3.1.3 B-Tree Indexes and Nulls

B-tree indexes never store completely null keys, which is important for how the optimizer chooses access paths. A consequence of this rule is that single-column B-tree indexes never store nulls.

An example helps illustrate. The hr.employees table has a primary key index on employee_id, and a unique index on department_id. The department_id column can contain nulls, making it a nullable column, but the employee_id column cannot.

SQL> SELECT COUNT(*) FROM employees WHERE department_id IS NULL;

COUNT(*)
----------
1

SQL> SELECT COUNT(*) FROM employees WHERE employee_id IS NULL;

COUNT(*)
----------
0

The following example shows that the optimizer chooses a full table scan for a query of all department IDs in hr.employees. The optimizer cannot use the index on employees.department_id because the index is not guaranteed to include entries for every row in the table.

SQL> EXPLAIN PLAN FOR SELECT department_id FROM employees;

Explained.

SQL> SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY());

PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------
Plan hash value: 3476115102

-------------------------------------------------------------------------------
| Id  | Operation         | Name      | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------
|   0 | SELECT STATEMENT  |           |   107 |   321 |     2   (0)| 00:00:01 |
|   1 |  TABLE ACCESS FULL| EMPLOYEES |   107 |   321 |     2   (0)| 00:00:01 |
-------------------------------------------------------------------------------

8 rows selected.

The following example shows the optimizer can use the index on department_id for a query of a specific department ID because all non-null rows are indexed.

SQL> EXPLAIN PLAN FOR SELECT department_id FROM employees WHERE department_id=10;

Explained.

SQL> SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY());

PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------
Plan hash value: 67425611

--------------------------------------------------------------------------------
| Id| Operation        | Name              | Rows |Bytes| Cost (%CPU)| Time    |
--------------------------------------------------------------------------------

PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------
| 0 | SELECT STATEMENT |                   |    1 |   3 |     1   (0)| 00:0 0:01 |
|*1 |  INDEX RANGE SCAN| EMP_DEPARTMENT_IX |    1 |   3 |     1   (0)| 00:0 0:01 |
---------------------------------------------------------------------------------

Predicate Information (identified by operation id):

PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------
1 - access("DEPARTMENT_ID"=10)

The following example shows that the optimizer chooses an index scan when the predicate excludes null values:

SQL> EXPLAIN PLAN FOR SELECT department_id FROM employees
WHERE department_id IS NOT NULL;

Explained.

SQL> SELECT PLAN_TABLE_OUTPUT FROM TABLE(DBMS_XPLAN.DISPLAY());

PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------
Plan hash value: 1590637672

--------------------------------------------------------------------------------
| Id| Operation        | Name              | Rows|Bytes| Cost (%CPU)| Time     |
--------------------------------------------------------------------------------
| 0 | SELECT STATEMENT |                   | 106 | 318 |    1   (0)| 00:0 0:01 |
|*1 |  INDEX FULL SCAN | EMP_DEPARTMENT_IX | 106 | 318 |    1   (0)| 00:0 0:01 |
--------------------------------------------------------------------------------

Predicate Information (identified by operation id):

PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------

1 - filter("DEPARTMENT_ID" IS NOT NULL)

8.3.2 Index Unique Scans

An index unique scan returns at most 1 rowid.

8.3.2.1 When the Optimizer Considers Index Unique Scans

The database performs a unique scan when the following conditions apply:

- A query predicate references all of the columns in a unique index key using an equality operator, such as WHERE prod_id=10.

- A SQL statement contains an equality predicate on a column referenced in an index created with the CREATE UNIQUE INDEX statement.

A unique or primary key constraint is insufficient by itself to produce an index unique scan. Consider the following example, which creates a primary key constraint on a column with a non-unique index, resulting in an index range scan rather than an index unique scan:

CREATE TABLE t_table(numcol INT);
CREATE INDEX t_table_idx ON t_table(numcol);
ALTER TABLE t_table ADD CONSTRAINT t_table_pk PRIMARY KEY(numcol);
SET AUTOTRACE TRACEONLY EXPLAIN
SELECT * FROM t_table WHERE numcol = 1;

Execution Plan
----------------------------------------------------------
Plan hash value: 868081059

--------------------------------------------------------------------------------
| Id  | Operation        | Name        | Rows  | Bytes | Cost (%CPU)| Time     |
--------------------------------------------------------------------------------
|   0 | SELECT STATEMENT |             |     1 |    13 |     1   (0)| 00:00:01 |
|*  1 |  INDEX RANGE SCAN| T_TABLE_IDX |     1 |    13 |     1   (0)| 00:00:01 |
--------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - access("NUMCOL"=1)

You can use the INDEX(alias index_name) hint to specify the index to use, but not a specific type of index access path.

See Also:

- Oracle Database Concepts for more details on index structures and for detailed information on how a B-tree is searched

- Oracle Database SQL Language Reference to learn more about the INDEX hint

8.3.2.2 How Index Unique Scans Work

The scan searches the index in order for the specified key. An index unique scan stops processing as soon as it finds the first record because no second record is possible. The database obtains the rowid from the index entry, and then retrieves the row specified by the rowid.

Figure 8-3 illustrates an index unique scan. The statement requests the record for product ID 19 in the prod_id column, which has a primary key index.

Figure 8-3 Index Unique Scan

8.3.2.3 Index Unique Scans: Example

The following statement queries the record for product 19 in the sh.products table:

SELECT *
FROM   sh.products
WHERE  prod_id=19;

Example 8-4 retrieves the plan using the DBMS_XPLAN.DISPLAY_CURSOR function. Because a primary key index exists on products.prod_id, and the WHERE clause references all of the columns using an equality operator, the optimizer chooses a unique scan.

Example 8-4 Index Unique Scan

SQL_ID  3ptq5tsd5vb3d, child number 0
-------------------------------------
select * from sh.products where prod_id = 19

Plan hash value: 4047888317

----------------------------------------------------------------------------------
| Id | Operation                   | Name        |Rows |Bytes|Cost (%CPU)|Time   |
----------------------------------------------------------------------------------
|  0 | SELECT STATEMENT            |             |     |     |  1 (100)|         |
|  1 |  TABLE ACCESS BY INDEX ROWID| PRODUCTS    |   1 | 173 |  1   (0)| 00:00:01|
|* 2 |   INDEX UNIQUE SCAN         | PRODUCTS_PK |   1 |     |  0   (0)|         |
----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - access("PROD_ID"=19)

8.3.3 Index Range Scans

An index range scan is an ordered scan of values. The range in the scan can be bounded on both sides, or unbounded on one or both sides. The optimizer typically chooses a range scan for selective queries (see "Selectivity").

By default, the database stores indexes in ascending order, and scans them in the same order. For example, a query with the predicate department_id >= 20 uses a range scan to return rows sorted by index keys 20, 30, 40, and so on. If multiple index entries have identical keys, then the database returns them in ascending order by rowid, so that 0,AAAPvCAAFAAAAFaAAa is followed by 0,AAAPvCAAFAAAAFaAAg, and so on.

An index range scan descending is identical to an index range scan except that the database returns rows in descending order. Usually, the database uses a descending scan when ordering data in a descending order, or when seeking a value less than a specified value.

8.3.3.1 When the Optimizer Considers Index Range Scans

The optimizer considers index range scans in the following circumstances:

- One or more leading columns of an index are specified in conditions. A condition specifies a combination of one or more expressions and logical (Boolean) operators and returns a value of TRUE, FALSE, or UNKNOWN. Examples of conditions include:

- department_id = :id

- department_id < :id

- department_id > :id

- AND combination of the preceding conditions for leading columns in the index, such as department_id > :low AND department_id < :hi.

Note:
For the optimizer to consider a range scan, wild-card searches of the form col1 LIKE '%ASD' must not be in a leading position.

- 0, 1, or more values are possible for an index key.

Tip:
If you require sorted data, then use the ORDER BY clause, and do not rely on an index. If an index can satisfy an ORDER BY clause, then the optimizer uses this option and avoids a sort.

The optimizer considers an index range scan descending when an index can satisfy an ORDER BY DESCENDING clause.

If the optimizer chooses a full table scan or another index, then a hint may be required to force this access path. The INDEX(tbl_alias ix_name) and INDEX_DESC(tbl_alias ix_name) hints instruct the optimizer to use a specific index.

See Also:
Oracle Database SQL Language Reference to learn more about the INDEX and INDEX_DESC hints

8.3.3.2 How Index Range Scans Work

In general, the process is as follows:

1. Read the root block.

2. Read the branch block.

3. Alternate the following steps until all data is retrieved:

1. Read a leaf block to obtain a rowid.

2. Read a table block to retrieve a row.

Note:
In some cases, an index scan reads a set of index blocks, sorts the rowids, and then reads a set of table blocks.

Thus, to scan the index, the database moves backward or forward through the leaf blocks. For example, a scan for IDs between 20 and 40 locates the first leaf block that has the lowest key value that is 20 or greater. The scan proceeds horizontally through the linked list of leaf nodes until it finds a value greater than 40, and then stops.

Figure 8-4 illustrates an index range scan using ascending order. A statement requests the employees records with the value 20 in the department_id column, which has a nonunique index. In this example, 2 index entries for department 20 exist.

Figure 8-4 Index Range Scan

8.3.3.3 Index Range Scan: Example

The following statement queries the records for employees in department 20 with salaries greater than 1000:

SELECT *
FROM   employees
WHERE  department_id = 20
AND    salary > 1000;

Example 8-5 retrieves the plan using the DBMS_XPLAN.DISPLAY_CURSOR function. This query is highly selective, so the query uses the index on the department_id column. The database scans the index, fetches the records from the employees table, and then applies the salary > 1000 filter to these fetched records to generate the result.

Example 8-5 Index Range Scan

SQL_ID  brt5abvbxw9tq, child number 0
-------------------------------------
SELECT * FROM   employees WHERE  department_id = 20 AND    salary > 1000

Plan hash value: 2799965532

---------------------------------------------------------------------------------------------
| Id  | Operation                           | Name             |Rows|Bytes|Cost(%CPU)| Time |
---------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT                    |                   |    |     | 2 (100)|        |
|* 1 |  TABLE ACCESS BY INDEX ROWID BATCHED| EMPLOYEES         |  2 | 138 | 2   (0)|00:00:01|
|* 2 |   INDEX RANGE SCAN                  | EMP_DEPARTMENT_IX |  2 |     | 1   (0)|00:00:01|
---------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - filter("SALARY">1000)
2 - access("DEPARTMENT_ID"=20)

8.3.3.4 Index Range Scan Descending: Example

The following statement queries the records for employees in department 20 in descending order:

SELECT *
FROM   employees
WHERE  department_id < 20
ORDER BY department_id DESC;

Example 8-6 retrieves the plan using the DBMS_XPLAN.DISPLAY_CURSOR function. This query is selective, so the query uses the index on the department_id column. The database locates the first index leaf block that contains the highest key value that is 20 or less. The scan then proceeds horizontally to the left through the linked list of leaf nodes. The database obtains the rowid from each index entry, and then retrieves the row specified by the rowid.

Example 8-6 Index Range Scan Descending

SQL_ID  8182ndfj1ttj6, child number 0
-------------------------------------
SELECT * FROM   employees WHERE  department_id < 20 ORDER BY department_id DESC

Plan hash value: 1681890450

---------------------------------------------------------------------------------------------
| Id | Operation                    | Name              | Rows|Bytes| Cost (%CPU)| Time     |
---------------------------------------------------------------------------------------------
|  0 | SELECT STATEMENT             |                   |     |     |     2 (100)|          |
|  1 |  TABLE ACCESS BY INDEX ROWID | EMPLOYEES         |   2 | 138 |     2   (0)| 00:00:01 |
|* 2 |   INDEX RANGE SCAN DESCENDING| EMP_DEPARTMENT_IX |   2 |     |     1   (0)| 00:00:01 |
---------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - access("DEPARTMENT_ID"<20)

8.3.4 Index Full Scans

An index full scan reads the entire index in order. An index full scan can eliminate a separate sorting operation because the data in the index is ordered by index key.

8.3.4.1 When the Optimizer Considers Index Full Scans

Situations in which the optimizer considers an index full scan include:

- A predicate references a column in the index. This column need not be the leading column.

- No predicate is specified, but all of the following conditions are met:

- All columns in the table and in the query are in the index.

- At least one indexed column is not null.

- A query includes an ORDER BY on indexed non-nullable columns.

8.3.4.2 How Index Full Scans Work

The database reads the root block, and then navigates down the left hand side of the index (or right if doing a descending full scan) until it reaches a leaf block. The database then reads across the bottom of the index, one block at a time, in sorted order. The scan uses single-block I/O rather than multiblock I/O.

Figure 8-5 illustrates an index full scan. A statement requests the departments records ordered by department_id.

Figure 8-5 Index Full Scan

8.3.4.3 Index Full Scans: Example

The following statement queries the ID and name for departments in order of department ID:

SELECT department_id, department_name
FROM   departments
ORDER BY department_id;

Example 8-7 retrieves the plan using the DBMS_XPLAN.DISPLAY_CURSOR function. The database locates the first index leaf block, and then proceeds horizontally to the right through the linked list of leaf nodes. For each index entry, the database obtains the rowid from the entry, and then retrieves the table row specified by the rowid. In this way, the database avoids a separate operation to sort the retrieved rows.

Example 8-7 Index Full Scan

SQL_ID  94t4a20h8what, child number 0
-------------------------------------
select department_id, department_name from departments order by department_id

Plan hash value: 4179022242

-------------------------------------------------------------------------------------------
| Id  | Operation                   | Name        | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT            |             |       |       |     2 (100)|          |
|   1 |  TABLE ACCESS BY INDEX ROWID| DEPARTMENTS |    27 |   432 |     2   (0)| 00:00:01 |
|   2 |   INDEX FULL SCAN           | DEPT_ID_PK  |    27 |       |     1   (0)| 00:00:01 |
-------------------------------------------------------------------------------------------

8.3.5 Index Fast Full Scans

An index fast full scan reads the index blocks in unsorted order, as they exist on disk. This scan does not use the index to probe the table, but reads the index instead of the table, essentially using the index itself as a table.

8.3.5.1 When the Optimizer Considers Index Fast Full Scans

The optimizer considers this scan when a query only accesses attributes in the index. The INDEX_FFS(table_name index_name) hint forces a fast full index scan.

Note:
Unlike a full scan, a fast full scan cannot eliminate a sort operation because it does not read the index in order.

See Also:
Oracle Database SQL Language Reference to learn more about the INDEX hint

8.3.5.2 How Index Fast Full Scans Work

The database uses multiblock I/O to read the root block and all of the leaf and branch blocks. The databases ignores the branch and root blocks and reads the index entries on the leaf blocks.

8.3.5.3 Index Fast Full Scans: Example

The following statement queries the ID and name for departments in order of department ID:

SELECT /*+ INDEX_FFS(departments dept_id_pk) */ COUNT(*)
FROM   departments;

Example 8-8 retrieves the plan using the DBMS_XPLAN.DISPLAY_CURSOR function.

Example 8-8 Index Fast Full Scan

SQL_ID  fu0k5nvx7sftm, child number 0
-------------------------------------
select /*+ index_ffs(departments dept_id_pk) */ count(*) from departments

Plan hash value: 3940160378

----------------------------------------------------------------------------
| Id  | Operation             | Name       | Rows  | Cost (%CPU)| Time     |
----------------------------------------------------------------------------
|   0 | SELECT STATEMENT      |            |       |     2 (100)|          |
|   1 |  SORT AGGREGATE       |            |     1 |            |          |
|   2 |   INDEX FAST FULL SCAN| DEPT_ID_PK |    27 |     2   (0)| 00:00:01 |
----------------------------------------------------------------------------

8.3.6 Index Skip Scans

An index skip scan occurs when the initial column of a composite index is "skipped" or not specified in the query.

See Also:
Oracle Database Concepts

8.3.6.1 When the Optimizer Considers Index Skips Scans

The optimizer considers a skip scan when the following criteria are met:

- The leading column of a composite index is not specified in the query predicate.
For example, if the composite index key is (cust_gender,cust_email), then the query predicate does not reference the cust_gender column.

- Few distinct values exist in the leading column of the composite index, but many distinct values exist in the nonleading key of the index.
For example, if the composite index key is (cust_gender,cust_email), then the cust_gender column has only two distinct values, but cust_email has thousands.

Often, skip scanning index blocks is faster than scanning table blocks, and faster than performing full index scans.

8.3.6.2 How Index Skip Scans Work

An index skip scan logically splits a composite index into smaller subindexes. The number of distinct values in the leading columns of the index determines the number of logical subindexes. The lower the number, the fewer logical subindexes the optimizer must create, and the more efficient the scan becomes. The scan reads each logical index separately, and "skips" index blocks that do not meet the filter condition on the non-leading column.

8.3.6.3 Index Skip Scans: Example

The customers table contains a column cust_gender whose values are either M or F. You create a composite index on the columns (cust_gender, cust_email) as follows:

CREATE INDEX customers_gender_email
ON sh.customers (cust_gender, cust_email);

Conceptually, a portion of the index might look as in Example 8-9, with the gender value of F or M as the leading edge of the index.

Example 8-9 Composite Index Entries

. . .
F,Wolf@company.example.com,rowid
F,Wolsey@company.example.com,rowid
F,Wood@company.example.com,rowid
F,Woodman@company.example.com,rowid
F,Yang@company.example.com,rowid
F,Zimmerman@company.example.com,rowid
M,Abbassi@company.example.com,rowid
M,Abbey@company.example.com,rowid
. . .

You run the following query for a customer in the sh.customers table:

SELECT *
FROM   sh.customers
WHERE  cust_email = 'Abbey@company.example.com';

The database can use a skip scan of the customers_gender_email index even though cust_gender is not specified in the WHERE clause. In Example 8-9, the leading column cust_gender has two possible values. The database logically splits the index into two. One subindex has the key F, with entries in the following form:

F,Wolf@company.example.com,rowid
F,Wolsey@company.example.com,rowid
F,Wood@company.example.com,rowid
F,Woodman@company.example.com,rowid
F,Yang@company.example.com,rowid
F,Zimmerman@company.example.com,rowid

The second subindex has the key M, with entries in the following form:

M,Abbassi@company.example.com,rowid
M,Abbey@company.example.com,rowid

When searching for the record for the customer whose email is Abbey@company.com, the database searches the subindex with the leading value F first, and then searches the subindex with the leading value M. Conceptually, the database processes the query as follows:

( SELECT *
FROM   sh.customers
WHERE  cust_gender = 'F'
AND    cust_email = 'Abbey@company.com' )
UNION ALL
( SELECT *
FROM   sh.customers
WHERE  cust_gender = 'M'
AND    cust_email = 'Abbey@company.com' )

The plan for the query is as follows:

SQL_ID  d7a6xurcnx2dj, child number 0
-------------------------------------
SELECT * FROM   sh.customers WHERE  cust_email = 'Abbey@company.example.com'

Plan hash value: 797907791

---------------------------------------------------------------------------------------------
|Id| Operation                          | Name                   |Rows|Bytes|Cost(%CPU)|Time|
---------------------------------------------------------------------------------------------
| 0|SELECT STATEMENT                    |                         |  |    |10 (100)|        |
| 1| TABLE ACCESS BY INDEX ROWID BATCHED|CUSTOMERS                |33|6237|   10(0)|00:00:01|
|*2|  INDEX SKIP SCAN                   |CUSTOMERS_GENDER_EMAIL_IX|33|    |    4(0)|00:00:01|
---------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - access("CUST_EMAIL"='Abbey@company.example.com')
filter("CUST_EMAIL"='Abbey@company.example.com')

See Also:
Oracle Database Concepts to learn more about skip scans

8.3.7 Index Join Scans

An index join scan is a hash join of multiple indexes that together return all columns requested by a query. The database does not need to access the table because all data is retrieved from the indexes.

8.3.7.1 When the Optimizer Considers Index Join Scans

The optimizer considers an index join in the following circumstances:

- A hash join of multiple indexes retrieves all data requested by the query, without requiring table access.

- The cost of retrieving rows from the table is higher than reading the indexes without retrieving rows from the table. An index join is often expensive. For example, when scanning two indexes and joining them, it is often less costly to choose the most selective index, and then probe the table.

You can specify an index join with the INDEX_JOIN(table_name) hint.

See Also:
Oracle Database SQL Language Reference

8.3.7.2 How Index Join Scans Work

An index join involves scanning multiple indexes, and then using a hash join on the rowids obtained from these scans to return the rows. Table access is always avoided. For example, the process for joining two indexes on a single table is as follows:

1. Scan the first index to retrieve rowids.

2. Scan the second index to retrieve rowids.

3. Perform a hash join by rowid to obtain the rows.

8.3.7.3 Index Join Scans: Example

The following statement queries the last name and email for employees whose last name begins with A, specifying an index join:

SELECT /*+ INDEX_JOIN(employees) */ last_name, email
FROM   employees
WHERE  last_name like 'A%';

Separate indexes exist on the (last_name,first_name) and email columns. Part of the emp_name_ix index might look as follows:

Banda,Amit,AAAVgdAALAAAABSABD
Bates,Elizabeth,AAAVgdAALAAAABSABI
Bell,Sarah,AAAVgdAALAAAABSABc
Bernstein,David,AAAVgdAALAAAABSAAz
Bissot,Laura,AAAVgdAALAAAABSAAd
Bloom,Harrison,AAAVgdAALAAAABSABF
Bull,Alexis,AAAVgdAALAAAABSABV

The first part of the emp_email_uk index might look as follows:

ABANDA,AAAVgdAALAAAABSABD
ABULL,AAAVgdAALAAAABSABV
ACABRIO,AAAVgdAALAAAABSABX
AERRAZUR,AAAVgdAALAAAABSAAv
AFRIPP,AAAVgdAALAAAABSAAV
AHUNOLD,AAAVgdAALAAAABSAAD
AHUTTON,AAAVgdAALAAAABSABL

Example 8-10 retrieves the plan using the DBMS_XPLAN.DISPLAY_CURSOR function. The database retrieves all rowids in the emp_email_uk index, and then retrieves rowids in emp_name_ix for last names that begin with A. The database uses a hash join to search both sets of rowids for matches. For example, rowid AAAVgdAALAAAABSABD occurs in both sets of rowids, so the database probes the employees table for the record corresponding to this rowid.

Example 8-10 Index Join Scan

SQL_ID  d2djchyc9hmrz, child number 0
-------------------------------------
SELECT /*+ INDEX_JOIN(employees) */ last_name, email FROM   employees
WHERE  last_name like 'A%'

Plan hash value: 3719800892
-------------------------------------------------------------------------------------------
| Id  | Operation              | Name             | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT       |                  |       |       |     3 (100)|          |
|*  1 |  VIEW                  | index$_join$_001 |     3 |    48 |     3  (34)| 00:00:01 |
|*  2 |   HASH JOIN            |                  |       |       |            |          |
|*  3 |    INDEX RANGE SCAN    | EMP_NAME_IX      |     3 |    48 |     1   (0)| 00:00:01 |
|   4 |    INDEX FAST FULL SCAN| EMP_EMAIL_UK     |     3 |    48 |     1   (0)| 00:00:01 |
-------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - filter("LAST_NAME" LIKE 'A%')
2 - access(ROWID=ROWID)
3 - access("LAST_NAME" LIKE 'A%')

8.4 Bitmap Index Access Paths

This section explains how bitmap indexes, and describes some of the more common bitmap index access paths:

- About Bitmap Index Access

- Bitmap Conversion to Rowid

- Bitmap Index Single Value

- Bitmap Index Range Scans

- Bitmap Merge

8.4.1 About Bitmap Index Access

In a conventional B-tree index, one entry points to a single row. In a bitmap index, the indexed data combined with the rowid range is the key. The database stores at least one bitmap for each index key. Each value in the bitmap, which is a series of 1 and 0 values, points to a row within a rowid range. Thus, in a bitmap index, one entry points to multiple rows.

Table 8-2 shows the differences among types of index entries.

Table 8-2 Index Entries for B-Trees and Bitmaps

Index Entry

Key

Data

Example

Unique B-tree

Indexed data only

Rowid

In an entry of the index on the employees.employee_id column, employee ID 101 is the key, and the rowid AAAPvCAAFAAAAFaAAa is the data:

101,AAAPvCAAFAAAAFaAAa

Nonunique B-tree

Indexed data combined with rowid

None

In an entry of the index on the employees.last_name column, the name and rowid combination Smith,AAAPvCAAFAAAAFaAAa is the key, and there is no data:

Smith,AAAPvCAAFAAAAFaAAa

Bitmap

Indexed data combined with rowid range

Bitmap

In an entry of the index on the customers.cust_gender column, the M,low-rowid,high-rowid part is the key, and the series of 1 and 0 values is the data:

M,low-rowid,high-rowid,1000101010101010

The database stores a bitmap index in a B-tree structure (see "Bitmap Storage"). The database can search the B-tree quickly on the first part of the key, which is the set of attributes on which the index is defined, and then obtain the corresponding rowid range and bitmap.

See Also:

- Oracle Database Concepts for an overview of bitmap indexes

- Oracle Database Data Warehousing Guide for more information about bitmap indexes

8.4.1.1 Purpose of Bitmap Indexes

Bitmap indexes are suitable for low cardinality data that is infrequently modified. Data has low cardinality when the number of distinct values in a column is low in relation to the total number of rows.

Through compression techniques, these indexes can generate many rowids with minimal I/O. Bitmap indexes provide especially useful access paths in queries that contain the following:

- Multiple conditions in the WHERE clause
Before the table itself is accessed, the database filters out rows that satisfy some, but not all, conditions.

- AND and OR operations on low cardinality columns
Combining bitmap indexes on low cardinality columns makes these operations more efficient. The database can combine bitmaps from bitmap indexes very quickly. For example, if bitmap indexes exist on the cust_gender and cust_marital_status columns of customers, then these indexes can enormously improve the performance of the following query:

SELECT *
FROM   customers
WHERE  cust_gender = 'M'
AND    cust_marital_status = 'single'

- The COUNT function
The database can scan the index without needing to scan the table.

- Predicates that select for null values
Unlike B-tree indexes, bitmap indexes can contain nulls. Queries that count the number of nulls in a column can use the index without needing to scan the table.

See Also:
Oracle Database SQL Language Reference to learn about the COUNT function

8.4.1.2 Bitmaps and Rowids

For a particular value in a bitmap, the value is 1 if the row values match the bitmap condition, and 0 if it does not. Based on these values, the database uses an internal algorithm to map bitmaps onto rowids.

The bitmap entry contains the indexed value, the rowid range (start and end rowids), and a bitmap. Each 0 or 1 value in the bitmap is an offset into the rowid range, and maps to a potential row in the table, even if the row does not exist. Because the number of possible rows in a block is predetermined, the database can use the range endpoints to determine the rowid of an arbitrary row in the range.

Note:
The Hakan factor is an optimization used by the bitmap index algorithms to limit the number of rows that Oracle Database assumes can be stored in a single block. By artificially limiting the number of rows, the database reduces the size of the bitmaps.

Table 8-3 shows part of a sample bitmap for the sh.customers.cust_marital_status column, which is nullable. The actual index has 12 distinct values. Only 3 are shown in the sample: null, married, and single.

Table 8-3 Bitmap Index Entries

Column Value

Start Rowid in Range

End Rowid in Range

1st Row in Range

2nd Row in Range

3rd Row in Range

4th Row in Range

5th Row in Range

6th Row in Range

AAA ...

CCC ...

0

0

0

0

0

1

married

AAA ...

CCC ...

1

0

1

1

1

0

single

AAA ...

CCC ...

0

1

0

0

0

0

single

DDD ...

EEE ...

1

0

1

0

1

1

As shown in Table 8-3, bitmap indexes can include keys that consist entirely of null values, unlike B-tree indexes. In Table 8-3, the null has a value of 1 for the 6th row in the range, which means that the cust_marital_status value is null for the 6th row in the range. Indexing nulls can be useful for some SQL statements, such as queries with the aggregate function COUNT.

See Also:
Oracle Database Concepts to learn about rowid formats

8.4.1.3 Bitmap Join Indexes

A bitmap join index is a bitmap index for the join of two or more tables. The optimizer can use a bitmap join index to reduce or eliminate the volume of data that must be joined during plan execution. Bitmap join indexes can be much more efficient in storage than materialized join views.

The following example creates a bitmap index on the sh.sales and sh.customers tables:

CREATE BITMAP INDEX cust_sales_bji ON sales(c.cust_city)
FROM sales s, customers c
WHERE c.cust_id = s.cust_id LOCAL;

The FROM and WHERE clause in the preceding CREATE statement represent the join condition between the tables. The customers.cust_city column is the index key.

Each key value in the index represents a possible city in the customers table. Conceptually, key values for the index might look as follows, with one bitmap associated with each key value:

San Francisco   0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 . . .
San Mateo       0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 . . .
Smithville      1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 . . .
.
.
.

Each bit in a bitmap corresponds to one row in the sales table. In the Smithville key, the value 1 means that the first row in the sales table corresponds to a product sold to a Smithville customer, whereas the value 0 means that the second row corresponds to a product not sold to a Smithville customer.

Consider the following query of the number of separate sales to Smithville customers:

SELECT COUNT (*)
FROM sales s, customers c
WHERE c.cust_id = s.cust_id
AND c.cust_city = 'Smithville';

The following plan shows that the database reads the Smithville bitmap to derive the number of Smithville sales (Step 4), thereby avoiding the necessity of joining customers and sales to obtain the results.

SQL_ID  57s100mh142wy, child number 0
-------------------------------------
SELECT COUNT (*) FROM sales s, customers c WHERE c.cust_id = s.cust_id AND c.cust_city =
'Smithville'

Plan hash value: 3663491772

---------------------------------------------------------------------------------------------
|Id| Operation                    | Name         | Rows|Bytes|Cost (%CPU)| Time|Pstart|Pstop|
---------------------------------------------------------------------------------------------
| 0| SELECT STATEMENT             |                |     |    |29 (100)|          |   |    |
| 1|  SORT AGGREGATE              |                |   1 |   5|        |          |   |    |
| 2|   PARTITION RANGE ALL        |                | 1708|8540|29   (0)| 00:00:01 | 1 | 28 |
| 3|    BITMAP CONVERSION COUNT   |                | 1708|8540|29   (0)| 00:00:01 |   |    |
|*4|     BITMAP INDEX SINGLE VALUE| CUST_SALES_BJI |     |    |        |          | 1 | 28 |
---------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

4 - access("S"."SYS_NC00008$"='Smithville')

See Also:
Oracle Database Concepts to learn about the CREATE INDEX statement

8.4.1.4 Bitmap Storage

A bitmap index resides in a B-tree structure, using branch blocks and leaf blocks just as in a B-tree. For example, if the customers.cust_marital_status column has 12 distinct values, then one branch block might point to the keys married,rowid-range and single,rowid-range, another branch block might point to the widowed,rowid-range key, and so on. Alternatively, a single branch block could point to a leaf block containing all 12 distinct keys.

Each indexed column value may have one or more bitmap pieces, each with its own rowid range occupying a contiguous set of rows in one or more extents. The database can use a bitmap piece to break up an index entry that is large relative to the size of a block. For example, the database could break a single index entry into three pieces, with the first two pieces in separate blocks in the same extent, and the last piece in a separate block in a different extent.

8.4.2 Bitmap Conversion to Rowid

A bitmap conversion translates between an entry in the bitmap and a row in a table. The conversion can go from entry to row (TO ROWID), or from row to entry (FROM ROWID).

8.4.2.1 When the Optimizer Chooses Bitmap Conversion to Rowid

The optimizer uses a conversion whenever it retrieves a row from a table using a bitmap index entry.

8.4.2.2 How Bitmap Conversion to Rowid Works

Table 8-3 represents the bitmap conceptually as a table with customers row numbers as columns and cust_marital_status values as rows. Each field in Table 8-3 has the value 1 or 0, and represents a column value in a row. Conceptually, the bitmap conversion uses an internal algorithm that says, "Field F in the bitmap corresponds to the Nth row of the Mth block of the table," or "The Nth row of the Mth block in the table corresponds to field F in the bitmap."

8.4.2.3 Bitmap Conversion to Rowid: Example

A query of the sh.customers table selects the names of all customers born before 1918:

SELECT cust_last_name, cust_first_name
FROM   customers
WHERE  cust_year_of_birth < 1918;

The following plan shows that the database uses a range scan to find all key values less than 1918 (Step 3), converts the 1 values in the bitmap to rowids (Step 2), and then uses the rowids to obtain the rows from the customers table (Step 1):

---------------------------------------------------------------------------------------------
|Id| Operation                           | Name             |Rows|Bytes|Cost(%CPU)| Time    |
---------------------------------------------------------------------------------------------
| 0| SELECT STATEMENT                    |                  |    |     |421 (100)|          |
| 1|  TABLE ACCESS BY INDEX ROWID BATCHED| CUSTOMERS        |3604|68476|421   (1)| 00:00:01 |
| 2|   BITMAP CONVERSION TO ROWIDS       |                  |    |     |         |          |
|*3|    BITMAP INDEX RANGE SCAN          | CUSTOMERS_YOB_BIX|    |     |         |          |
---------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

3 - access("CUST_YEAR_OF_BIRTH"<1918)
filter("CUST_YEAR_OF_BIRTH"<1918)

8.4.3 Bitmap Index Single Value

This type of access path uses a bitmap index to look up a single key value.

8.4.3.1 When the Optimizer Considers Bitmap Index Single Value

The optimizer considers this access path when the predicate contains an equality operator.

8.4.3.2 How Bitmap Index Single Value Works

The query scans a single bitmap for positions containing a 1 value. The database converts the 1 values into rowids, and then uses the rowids to find the rows.

8.4.3.3 Bitmap Index Single Value: Example

A query of the sh.customers table selects all widowed customers:

SELECT *
FROM   customers
WHERE  cust_marital_status = 'Widowed'

The following plan shows that the database reads the entry with the Widowed key in the customers bitmap index (Step 3), converts the 1 values in the bitmap to rowids (Step 2), and then uses the rowids to obtain the rows from the customers table (Step 1):

SQL_ID  ff5an2xsn086h, child number 0
-------------------------------------
SELECT * FROM customers WHERE cust_marital_status = 'Widowed'

Plan hash value: 2579015045

---------------------------------------------------------------------------------------------
|Id| Operation                           | Name                |Rows|Bytes|Cost (%CPU)| Time|
---------------------------------------------------------------------------------------------
| 0| SELECT STATEMENT                    |                     |    |    |412 (100)|        |
| 1|  TABLE ACCESS BY INDEX ROWID BATCHED|CUSTOMERS            |3461|638K|412   (2)|00:00:01|
| 2|   BITMAP CONVERSION TO ROWIDS       |                     |    |    |         |        |
|*3|    BITMAP INDEX SINGLE VALUE        |CUSTOMERS_MARITAL_BIX|    |    |         |        |
---------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

3 - access("CUST_MARITAL_STATUS"='Widowed')

8.4.4 Bitmap Index Range Scans

This type of access path uses a bitmap index to look up a range of values.

8.4.4.1 When the Optimizer Considers Bitmap Index Range Scans

The optimizer considers this access path when the predicate selects a range of values (see "Index Range Scans").

8.4.4.2 How Bitmap Index Range Scans Work

This scan works similarly to a B-tree range scan (see "Index Range Scans").

8.4.4.3 Bitmap Index Range Scans: Example

A query of the sh.customers table selects the names of customers born before 1918:

SELECT cust_last_name, cust_first_name
FROM   customers
WHERE  cust_year_of_birth < 1918

The following plan shows that the database obtains all bitmaps for cust_year_of_birth keys lower than 1918 (Step 3), converts the bitmaps to rowids (Step 2), and then fetches the rows (Step 1):

SQL_ID  672z2h9rawyjg, child number 0
-------------------------------------
SELECT cust_last_name, cust_first_name FROM   customers WHERE
cust_year_of_birth < 1918

Plan hash value: 4198466611

---------------------------------------------------------------------------------------------
|Id| Operation                           | Name              |Rows|Bytes|Cost(%CPU)|Time    |
---------------------------------------------------------------------------------------------
| 0| SELECT STATEMENT                    |                   |    |     |421 (100)|         |
| 1|  TABLE ACCESS BY INDEX ROWID BATCHED| CUSTOMERS         |3604|68476|421   (1)|00:00:01 |
| 2|   BITMAP CONVERSION TO ROWIDS       |                   |    |     |         |         |
|*3|    BITMAP INDEX RANGE SCAN          | CUSTOMERS_YOB_BIX |    |     |         |         |
---------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

3 - access("CUST_YEAR_OF_BIRTH"<1918)
filter("CUST_YEAR_OF_BIRTH"<1918)

8.4.5 Bitmap Merge

This access path merges multiple bitmaps together, and returns a single bitmap as a result.

8.4.5.1 When the Optimizer Considers Bitmap Merge

The optimizer typically uses a bitmap merge to combine bitmaps generated from an index range scan.

8.4.5.2 How Bitmap Merge Works

A merge uses an OR operation between two bitmaps. The resulting bitmap selects all rows from the first bitmap, plus all rows from every subsequent bitmap.

The following example shows sample bitmaps for three customers.cust_year_of_birth keys: 1917, 1916, and 1915. If any position in any bitmap has a 1, then the merged bitmap has a 1 in the same position. Otherwise, the merged bitmap has a 0.

1917     1 0 1 0 0 0 0 0 0 0 0 0 0 1
1916     0 1 0 0 0 0 0 0 0 0 0 0 0 0
1915     0 0 0 0 0 0 0 0 1 0 0 0 0 0
------------------------------------
merged:  1 1 1 0 0 0 0 0 1 0 0 0 0 1

8.4.5.3 Bitmap Index Single Value: Example

A query of the sh.customers table selects the names of female customers born before 1918:

SELECT cust_last_name, cust_first_name
FROM   customers
WHERE  cust_gender = 'F'
AND    cust_year_of_birth < 1918

The following plan shows that the database obtains all bitmaps for cust_year_of_birth keys lower than 1918 (Step 6), and then merges these bitmaps to create a single bitmap (Step 5), obtains a single bitmap for the cust_gender key of F (Step 4), and then performs an AND operation on these two bitmaps to generate a single bitmap that contains 1 values for the desired rows (Step 3):

SQL_ID  1xf59h179zdg2, child number 0
-------------------------------------
select cust_last_name, cust_first_name from customers where cust_gender
= 'F' and cust_year_of_birth < 1918

Plan hash value: 49820847

---------------------------------------------------------------------------------------------
|Id| Operation                           | Name               |Rows|Bytes|Cost(%CPU)| Time  |
---------------------------------------------------------------------------------------------
| 0| SELECT STATEMENT                    |                    |    |     |288 (100)|        |
| 1|  TABLE ACCESS BY INDEX ROWID BATCHED| CUSTOMERS          |1802|37842|288   (1)|00:00:01|
| 2|   BITMAP CONVERSION TO ROWIDS       |                    |    |     |         |        |
| 3|    BITMAP AND                       |                    |    |     |         |        |
|*4|     BITMAP INDEX SINGLE VALUE       |CUSTOMERS_GENDER_BIX|    |     |         |        |
| 5|     BITMAP MERGE                    |                    |    |     |         |        |
|*6|      BITMAP INDEX RANGE SCAN        |CUSTOMERS_YOB_BIX   |    |     |         |        |
---------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

4 - access("CUST_GENDER"='F')
6 - access("CUST_YEAR_OF_BIRTH"<1918)
filter("CUST_YEAR_OF_BIRTH"<1918)

8.5 Table Cluster Access Paths

Oracle Database Concepts explains table clusters in depth. This section briefly discusses access paths for table clusters.

8.5.1 Cluster Scans

An index cluster is a table cluster that uses an index to locate data. The cluster index is a B-tree index on the cluster key. A cluster scan retrieves all rows that have the same cluster key value from a table stored in an indexed cluster.

8.5.1.1 When the Optimizer Considers Cluster Scans

The database considers a cluster scan when a query accesses a table in an indexed cluster.

8.5.1.2 How Cluster Scans Work

In an indexed cluster, the database stores all rows with the same cluster key value in the same data block. For example, if the hr.employees2 and hr.departments2 tables are clustered in emp_dept_cluster, and if the cluster key is department_id, then the database stores all employees in department 10 in the same block, all employees in department 20 in the same block, and so on.

The B-tree cluster index associates the cluster key value with the database block address (DBA) of the block containing the data. For example, the index entry for key 30 shows the address of the block that contains rows for employees in department 30:

30,AADAAAA9d

When a user requests rows in the cluster, the database scans the index to obtain the DBAs of the blocks containing the rows. Oracle Database then locates the rows based on these DBAs.

8.5.1.3 Cluster Scans: Example

As user hr, you create a table cluster, cluster index, and tables in the cluster as follows:

CREATE CLUSTER employees_departments_cluster
(department_id NUMBER(4)) SIZE 512;

CREATE INDEX idx_emp_dept_cluster
ON CLUSTER employees_departments_cluster;

CREATE TABLE employees2
CLUSTER employees_departments_cluster (department_id)
AS SELECT * FROM employees;
CREATE TABLE departments2
CLUSTER employees_departments_cluster (department_id)
AS SELECT * FROM departments;

You query the employees in department 30 as follows:

SELECT *
FROM   employees2
WHERE  department_id = 30

To perform the scan, Oracle Database first obtains the rowid of the row describing department 30 by scanning the cluster index (Step 2). Oracle Database then locates the rows in employees using this rowid (Step 1).

SQL_ID  b7xk1jzuwdc6t, child number 0
-------------------------------------
SELECT * FROM employees2 WHERE department_id = 30

Plan hash value: 49826199

----------------------------------------------------------------------------------
|Id| Operation            | Name               |Rows|Bytes|Cost (%CPU)| Time     |
----------------------------------------------------------------------------------
| 0| SELECT STATEMENT     |                    |    |     |    2 (100)|          |
| 1|  TABLE ACCESS CLUSTER| EMPLOYEES2         |  6 | 798 |    2   (0)| 00:00:01 |
|*2|   INDEX UNIQUE SCAN  |IDX_EMP_DEPT_CLUSTER|  1 |     |    1   (0)| 00:00:01 |
----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - access("DEPARTMENT_ID"=30)

See Also:
Oracle Database Concepts to learn about indexed clusters

8.5.2 Hash Scans

A hash cluster is like an indexed cluster, except the index key is replaced with a hash function. No separate cluster index exists. In a hash cluster, the data is the index. The database uses a hash scan to locate rows in a hash cluster based on a hash value.

8.5.2.1 When the Optimizer Considers a Hash Scan

The database considers a hash scan when a query accesses a table in a hash cluster.

8.5.2.2 How a Cluster Scan Works

In a hash cluster, all rows with the same hash value are stored in the same data block. To perform a hash scan, Oracle Database first obtains the hash value by applying a hash function to a cluster key value specified by the statement. Oracle Database then scans the data blocks containing rows with that hash value.

8.5.2.3 Cluster Scan: Example

You create a hash cluster and tables in the cluster as follows:

CREATE CLUSTER employees_departments_cluster
(department_id NUMBER(4)) SIZE 8192 HASHKEYS 100;

CREATE TABLE employees2
CLUSTER employees_departments_cluster (department_id)
AS SELECT * FROM employees;

CREATE TABLE departments2
CLUSTER employees_departments_cluster (department_id)
AS SELECT * FROM departments;

You query the employees in department 30 as follows:

SELECT *
FROM   employees2
WHERE  department_id = 30

To perform a hash scan, Oracle Database first obtains the hash value by applying a hash function to the key value 30, and then uses this hash value to scan the data blocks and retrieve the rows (Step 1).

SQL_ID  919x7hyyxr6p4, child number 0
-------------------------------------
SELECT * FROM employees2 WHERE department_id = 30

Plan hash value: 2399378016

----------------------------------------------------------------
| Id  | Operation         | Name       | Rows  | Bytes | Cost  |
----------------------------------------------------------------
|   0 | SELECT STATEMENT  |            |       |       |     1 |
|*  1 |  TABLE ACCESS HASH| EMPLOYEES2 |    10 |  1330 |       |
----------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - access("DEPARTMENT_ID"=30)

See Also:
Oracle Database Concepts to learn about hash clusters

9 Joins

This chapter contains the following topics:

- About Joins

- Join Methods

- Join Types

- Join Optimizations

9.1 About Joins

A join combines the output from exactly two row sources, such as tables or views, and returns one row source. The returned row source is the data set.

A join is characterized by multiple tables in the WHERE (non-ANSI) or FROM ... JOIN (ANSI) clause of a SQL statement. Whenever multiple tables exist in the FROM clause, Oracle Database performs a join.

A join condition compares two row sources using an expression. The join condition defines the relationship between the tables. If the statement does not specify a join condition, then the database performs a Cartesian join (see "Cartesian Joins"), matching every row in one table with every row in the other table.

See Also:
Oracle Database SQL Language Reference for a concise discussion of joins in Oracle SQL

9.1.1 Join Trees

Typically, a join tree is represented as an upside-down tree structure. As shown in Figure 9-1, table1 is the left table, and table2 is the right table. The optimizer processes the join from left to right. For example, if this graphic depicted a nested loops join, then table1 is the outer loop, and table2 is the inner loop.

Figure 9-1 Join Tree

The input of a join can be the result set from a previous join. If a join tree includes more than two branches, then the most common tree type is the left deep tree, which is illustrated in Figure 9-2. A left deep tree is a join tree in which every join has an input from a previous join, and this input is always on the left.

Figure 9-2 Left Deep Join Tree

A less common type of join tree is a right join tree, shown in Figure 9-3, in which every join has an input from a previous join, and this input is always on the right.

Figure 9-3 Right Deep Join Tree

Some join trees are hybrids of left and right trees, so that some joins have a right input from a previous join, and some joins have a left input from a previous join. Figure 9-4 gives an example of this type of tree.

Figure 9-4 Hybrid Left and Right Join Tree

In yet another variation, both inputs of the join are the results of a previous join.

9.1.2 How the Optimizer Executes Join Statements

The database joins pairs of row sources. When multiple tables exist in the FROM clause, the optimizer must determine which join operation is most efficient for each pair. The optimizer must make the following interrelated decisions:

- Access paths
As for simple statements, the optimizer must choose an access path to retrieve data from each table in the join statement. For example, the optimizer might choose between a full table scan or an index scan. See Chapter 8, "Optimizer Access Paths."

- Join methods
To join each pair of row sources, Oracle Database must decide how to do it. The "how" is the join method. The possible join methods are nested loop, sort merge, and hash joins. A Cartesian join requires one of the preceding join methods. Each join method has specific situations in which it is more suitable than the others. See "Join Methods."

- Join types
The join condition determines the join type. For example, an inner join retrieves only rows that match the join condition. An outer join retrieves rows that do not match the join condition. See "Join Types."

- Join order
To execute a statement that joins more than two tables, Oracle Database joins two tables and then joins the resulting row source to the next table. This process continues until all tables are joined into the result. For example, the database joins two tables, and then joins the result to a third table, and then joins this result to a fourth table, and so on.

9.1.3 How the Optimizer Chooses Execution Plans for Joins

When choosing an execution plan, the optimizer considers the following factors:

- The optimizer first determines whether joining two or more tables results in a row source containing at most one row.
The optimizer recognizes such situations based on UNIQUE and PRIMARY KEY constraints on the tables. If such a situation exists, then the optimizer places these tables first in the join order. The optimizer then optimizes the join of the remaining set of tables.

- For join statements with outer join conditions, the table with the outer join operator typically comes after the other table in the condition in the join order.
In general, the optimizer does not consider join orders that violate this guideline, although the optimizer overrides this ordering condition in certain circumstances. Similarly, when a subquery has been converted into an antijoin or semijoin, the tables from the subquery must come after those tables in the outer query block to which they were connected or correlated. However, hash antijoins and semijoins are able to override this ordering condition in certain circumstances.

The optimizer generates a set of execution plans, according to possible join orders, join methods, and available access paths. The optimizer then estimates the cost of each plan and chooses the one with the lowest cost.

The optimizer estimates the cost of a query plan by computing the estimated I/Os to be performed by the query plan and the estimated CPU required by the plan. These I/Os have specific costs associated with them: one cost for a single block I/O, and another cost for multiblock I/Os. Also, different functions and expressions have CPU costs associated with them. The optimizer determines the total cost of a query plan using these metrics. These metrics may be influenced by many initialization parameter and session settings at compile time, such as the DB_FILE_MULTI_BLOCK_READ_COUNT setting, system statistics, and so on.

For example, the optimizer estimates costs in the following ways:

- The cost of a nested loops join depends on the cost of reading each selected row of the outer table and each of its matching rows of the inner table into memory. The optimizer estimates these costs using statistics in the data dictionary (see "Introduction to Optimizer Statistics").

- The cost of a sort merge join depends largely on the cost of reading all the sources into memory and sorting them.

- The cost of a hash join largely depends on the cost of building a hash table on one of the input sides to the join and using the rows from the other side of the join to probe it.

See Also:

- Chapter 14, "Influencing the Optimizer" for more information about optimizer hints

- Oracle Database Reference to learn about DB_FILE_MULTIBLOCK_READ_COUNT

9.2 Join Methods

A join method is the mechanism for joining two row sources. Depending on the statistics, the optimizer chooses the method with the lowest estimated cost.

As shown in Figure 9-5, each join method has two children: the driving (also called outer) row source and the driven-to (also called inner) row source.

Figure 9-5 Join Method

This section contains the following topics:

- Nested Loops Joins

- Hash Joins

- Sort Merge Joins

- Cartesian Joins

9.2.1 Nested Loops Joins

A nested loop joins an outer data set to an inner data set. For each row in the outer data set that matches the single-table predicates, the database retrieves all rows in the inner data set that satisfy the join predicate. If an index is available, then the database can use it to access the inner data set by rowid.

This section contains the following topics:

- When the Optimizer Considers Nested Loops Joins

- How Nested Loop Joins Work

- Nested Nested Loops

- Current Implementation for Nested Loops Joins

- Original Implementation for Nested Loops Joins

- Nested Loops Controls

9.2.1.1 When the Optimizer Considers Nested Loops Joins

Nested loops joins are useful when the following conditions are true:

- The database joins small subsets of data, or the database joins large sets of data with the optimizer mode set to FIRST_ROWS (see Table 14-1).

Note:
The number of rows expected from the join is what drives the optimizer decision, not the size of the underlying tables. For example, a query might join two tables of a billion rows each, but because of the filters the optimizer expects data sets of 5 rows each.

- The join condition is an efficient method of accessing the inner table.

In general, nested loops joins work best on small tables with indexes on the join conditions. If a row source has only one row, as with an equality lookup on a primary key value (for example, WHERE employee_id=101), then the join is a simple lookup. The optimizer always tries to put the smallest row source first, making it the driving table.

Various factors enter into the optimizer decision to use nested loops. For example, the database may read several rows from the outer row source in a batch. Based on the number of rows retrieved, the optimizer may choose either a nested loop or a hash join to the inner row source (see "Adaptive Plans"). For example, if a query joins departments to driving table employees, and if the predicate specifies a value in employees.last_name, then the database might read enough entries in the index on last_name to determine whether an internal threshold is passed. If the threshold is not passed, then the optimizer picks a nested loop join to departments, and if the threshold is passed, then the database performs a hash join, which means reading the rest of employees, hashing it into memory, and then joining to departments.

If the access path for the inner loop is not dependent on the outer loop, then the result can be a Cartesian product: for every iteration of the outer loop, the inner loop produces the same set of rows. To avoid this problem, use other join methods to join two independent row sources.

9.2.1.2 How Nested Loop Joins Work

Think of a nested loop as two nested for loops. For example, if a query joins employees and departments, then a nested loop in pseudocode might be:

FOR erow IN (select * from employees where X=Y) LOOP
FOR drow IN (select * from departments where erow is matched) LOOP
output values from erow and drow
END LOOP
END LOOP

The inner loop is executed for every row of the outer loop. The employees table is the "outer" data set because it is in the exterior for loop. The outer table is sometimes called a driving table. The departments table is the "inner" data set because it is in the interior for loop.

A nested loops join involves the following basic steps:

1. The optimizer determines the driving row source and designates it as the outer loop.
The outer loop produces a set of rows for driving the join condition. The row source can be a table accessed using an index scan, a full table scan, or any other operation that generates rows.

2. The optimizer designates the other row source as the inner loop.
The outer loop appears before the inner loop in the execution plan, as follows:

NESTED LOOPS
outer_loop
inner_loop

3. For every fetch request from the client, the basic process is as follows:

1. Fetch a row from the outer row source

2. Probe the inner row source to find rows that match the predicate criteria

3. Repeat the preceding steps until all rows are obtained by the fetch request

Sometimes the database sorts rowids to obtain a more efficient buffer access pattern.

9.2.1.3 Nested Nested Loops

The outer loop of a nested loop can itself be a row source generated by a different nested loop. The database can nest two or more outer loops to join as many tables as needed. Each loop is a data access method.

The following template shows how the database iterates through three nested loops:

SELECT STATEMENT
NESTED LOOPS 3
NESTED LOOPS 2          - Row source becomes OUTER LOOP 3.1
NESTED LOOPS 1        - Row source becomes OUTER LOOP 2.1
OUTER LOOP 1.1
INNER LOOP 1.2
INNER LOOP 2.2
INNER LOOP 3.2

The database orders the loops as follows:

1. The database iterates through NESTED LOOPS 1:

NESTED LOOPS 1
OUTER LOOP 1.1
INNER LOOP 1.2

The output of NESTED LOOP 1 is a row source.

2. The database iterates through NESTED LOOPS 2, using the row source generated by NESTED LOOPS 1 as its outer loop:

NESTED LOOPS 2
OUTER LOOP 2.1         - Row source generated by NESTED LOOPS 1
INNER LOOP 2.2

The output of NESTED LOOPS 2 is another row source.

3. The database iterates through NESTED LOOPS 3, using the row source generated by NESTED LOOPS 2 as its outer loop:

NESTED LOOPS 3
OUTER LOOP 3.1         - Row source generated by NESTED LOOPS 2
INNER LOOP 3.2

Example 9-1 Nested Nested Loops Join

Suppose you join the employees and departments tables as follows:

SELECT /*+ ORDERED USE_NL(d) */ e.last_name, e.first_name, d.department_name
FROM   employees e, departments d
WHERE  e.department_id=d.department_id
AND    e.last_name like 'A%';

The plan reveals that the optimizer chose two nested loops (Step 1 and Step 2) to access the data:

SQL_ID  ahuavfcv4tnz4, child number 0
-------------------------------------
SELECT /*+ ORDERED USE_NL(d) */ e.last_name, d.department_name FROM
employees e, departments d WHERE  e.department_id=d.department_id AND
e.last_name like 'A%'

Plan hash value: 1667998133

----------------------------------------------------------------------------------
|Id| Operation                             |Name      |Rows|Bytes|Cost(%CPU)|Time|
----------------------------------------------------------------------------------
| 0| SELECT STATEMENT                      |             |  |   |5 (100)|        |
| 1|  NESTED LOOPS                         |             |  |   |       |        |
| 2|   NESTED LOOPS                        |             | 3|102|5   (0)|00:00:01|
| 3|    TABLE ACCESS BY INDEX ROWID BATCHED| EMPLOYEES   | 3| 54|2   (0)|00:00:01|
|*4|     INDEX RANGE SCAN                  | EMP_NAME_IX | 3|   |1   (0)|00:00:01|
|*5|    INDEX UNIQUE SCAN                  | DEPT_ID_PK  | 1|   |0   (0)|        |
| 6|   TABLE ACCESS BY INDEX ROWID         | DEPARTMENTS | 1| 16|1   (0)|00:00:01|
----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

4 - access("E"."LAST_NAME" LIKE 'A%')
filter("E"."LAST_NAME" LIKE 'A%')
5 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")

In this example, the basic process is as follows:

1. The database begins iterating through the inner nested loop (Step 2) as follows:

1. The database searches the emp_name_ix for the rowids for all last names that begins with A (Step 4).
For example:

Abel,employees_rowid
Ande,employees_rowid
Atkinson,employees_rowid
Austin,employees_rowid

2. Using the rowids from the previous step, the database retrieves a batch of rows from the employees table (Step 3). For example:

Abel,Ellen,80
Abel,John,50

These rows become the outer row source for the innermost nested loop.

The batch step is typically part of adaptive execution plans. To determine whether a nested loop is better than a hash join, the optimizer needs to determine many rows come back from the row source. If too many rows are returned, then the optimizer switches to a different join method.

3. For each row in the outer row source, the database scans the dept_id_pk index to obtain the rowid in departments of the matching department ID (Step 5), and joins it to the employees rows. For example:

Abel,Ellen,80,departments_rowid
Ande,Sundar,80,departments_rowid
Atkinson,Mozhe,50,departments_rowid
Austin,David,60,departments_rowid

These rows become the outer row source for the outer nested loop (Step 1).

2. The database iterates through the outer nested loop as follows:

1. The database reads the first row in outer row source.
For example:

Abel,Ellen,80,departments_rowid

2. The database uses the departments rowid to retrieve the corresponding row from departments (Step 6), and then joins the result to obtain the requested values (Step 1).
For example:

Abel,Ellen,80,Sales

3. The database reads the next row in the outer row source, uses the departments rowid to retrieve the corresponding row from departments (Step 6), and iterates through the loop until all rows are retrieved.
The result set has the following form:

Abel,Ellen,80,Sales
Ande,Sundar,80,Sales
Atkinson,Mozhe,50,Shipping
Austin,David,60,IT

9.2.1.4 Current Implementation for Nested Loops Joins

Oracle Database 11g introduced a new implementation for nested loops that reduces overall latency for physical I/O. When an index or a table block is not in the buffer cache and is needed to process the join, a physical I/O is required. The database can batch multiple physical I/O requests and process them using a vector I/O instead of one at a time. A vector is an array. The database obtains a set of rowids, and then sends them in an array to the operating system, which performs the read.

As part of the new implementation, two NESTED LOOPS join row sources might appear in the execution plan where only one would have appeared in prior releases. In such cases, Oracle Database allocates one NESTED LOOPS join row source to join the values from the table on the outer side of the join with the index on the inner side. A second row source is allocated to join the result of the first join, which includes the rowids stored in the index, with the table on the inner side of the join.

Consider the query in "Original Implementation for Nested Loops Joins". In the current implementation, the execution plan for this query might be as follows:

------------------------------------------------------------------------------------------------
| Id  | Operation                    | Name              | Rows  | Bytes | Cost(%CPU)| Time      |
-------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |                   |    19 |   722 |     3   (0)| 00:00:01 |
|   1 |  NESTED LOOPS                |                   |       |       |            |          |
|   2 |   NESTED LOOPS               |                   |    19 |   722 |     3   (0)| 00:00:01 |
|*  3 |    TABLE ACCESS FULL         | DEPARTMENTS       |     2 |    32 |     2   (0)| 00:00:01 |
|*  4 |    INDEX RANGE SCAN          | EMP_DEPARTMENT_IX |    10 |       |     0   (0)| 00:00:01 |
|   5 |   TABLE ACCESS BY INDEX ROWID| EMPLOYEES         |    10 |   220 |     1   (0)| 00:00:01 |
-------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
3 - filter("D"."DEPARTMENT_NAME"='Marketing' OR "D"."DEPARTMENT_NAME"='Sales')
4 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")

In this case, rows from the hr.departments table form the outer row source (Step 3) of the inner nested loop (Step 2). The index emp_department_ix is the inner row source (Step 4) of the inner nested loop. The results of the inner nested loop form the outer row source (Row 2) of the outer nested loop (Row 1). The hr.employees table is the outer row source (Row 5) of the outer nested loop.

For each fetch request, the basic process is as follows:

1. The database iterates through the inner nested loop (Step 2) to obtain the rows requested in the fetch:

1. The database reads the first row of departments to obtain the department IDs for departments named Marketing or Sales (Step 3). For example:

Marketing,20

This row set is the outer loop. The database caches the data in the PGA.

2. The database scans emp_department_ix, which is an index on the employees table, to find employees rowids that correspond to this department ID (Step 4), and then joins the result (Step 2).
The result set has the following form:

Marketing,20,employees_rowid
Marketing,20,employees_rowid
Marketing,20,employees_rowid

3. The database reads the next row of departments, scans emp_department_ix to find employees rowids that correspond to this department ID, and then iterates through the loop until the client request is satisfied.
In this example, the database only iterates through the outer loop twice because only two rows from departments satisfy the predicate filter. Conceptually, the result set has the following form:

Marketing,20,employees_rowid
Marketing,20,employees_rowid
Marketing,20,employees_rowid
.
.
.
Sales,80,employees_rowid
Sales,80,employees_rowid
Sales,80,employees_rowid
.
.
.

These rows become the outer row source for the outer nested loop (Step 1). This row set is cached in the PGA.

2. The database organizes the rowids obtained in the previous step so that it can more efficiently access them in the cache.

3. The database begins iterating through the outer nested loop as follows:

1. The database retrieves the first row from the row set obtained in the previous step, as in the following example:

Marketing,20,employees_rowid

2. Using the rowid, the database retrieves a row from employees to obtain the requested values (Step 1), as in the following example:

Michael,Hartstein,13000,Marketing

3. The database retrieves the next row from the row set, uses the rowid to probe employees for the matching row, and iterates through the loop until all rows are retrieved.
The result set has the following form:

Michael,Hartstein,13000,Marketing
Pat,Fay,6000,Marketing
John,Russell,14000,Sales
Karen,Partners,13500,Sales
Alberto,Errazuriz,12000,Sales
.
.
.

In some cases, a second join row source is not allocated, and the execution plan looks the same as it did before Oracle Database 11g. The following list describes such cases:

- All of the columns needed from the inner side of the join are present in the index, and there is no table access required. In this case, Oracle Database allocates only one join row source.

- The order of the rows returned might be different from the order returned in releases earlier than Oracle Database 12c. Thus, when Oracle Database tries to preserve a specific ordering of the rows, for example to eliminate the need for an ORDER BY sort, Oracle Database might use the original implementation for nested loops joins.

- The OPTIMIZER_FEATURES_ENABLE initialization parameter is set to a release before Oracle Database 11g. In this case, Oracle Database uses the original implementation for nested loops joins.

9.2.1.5 Original Implementation for Nested Loops Joins

In the current release, both the new and original implementation are possible. For an example of the original implementation, consider the following join of the hr.employees and hr.departments tables:

SELECT e.first_name, e.last_name, e.salary, d.department_name
FROM   hr.employees e, hr.departments d
WHERE  d.department_name IN ('Marketing', 'Sales')
AND    e.department_id = d.department_id;

In releases before Oracle Database 11g, the execution plan for this query might appear as follows:

-------------------------------------------------------------------------------------------------
| Id  | Operation                   | Name              | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT            |                   |    19 |   722 |     3  (0)| 00:00:01 |
|   1 |  TABLE ACCESS BY INDEX ROWID| EMPLOYEES         |    10 |   220 |     1  (0)| 00:00:01 |
|   2 |   NESTED LOOPS              |                   |    19 |   722 |     3  (0)| 00:00:01 |
|*  3 |    TABLE ACCESS FULL        | DEPARTMENTS       |     2 |    32 |     2  (0)| 00:00:01 |
|*  4 |    INDEX RANGE SCAN         | EMP_DEPARTMENT_IX |    10 |       |     0  (0)| 00:00:01 |
-------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
3 - filter("D"."DEPARTMENT_NAME"='Marketing' OR "D"."DEPARTMENT_NAME"='Sales')
4 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")

For each fetch request, the basic process is as follows:

1. The database iterates through the loop to obtain the rows requested in the fetch:

1. The database reads the first row of departments to obtain the department IDs for departments named Marketing or Sales (Step 3). For example:

Marketing,20

This row set is the outer loop. The database caches the row in the PGA.

2. The database scans emp_department_ix, which is an index on the employees.department_id column, to find employees rowids that correspond to this department ID (Step 4), and then joins the result (Step 2).
Conceptually, the result set has the following form:

Marketing,20,employees_rowid
Marketing,20,employees_rowid
Marketing,20,employees_rowid

3. The database reads the next row of departments, scans emp_department_ix to find employees rowids that correspond to this department ID, and iterates through the loop until the client request is satisfied.
In this example, the database only iterates through the outer loop twice because only two rows from departments satisfy the predicate filter. Conceptually, the result set has the following form:

Marketing,20,employees_rowid
Marketing,20,employees_rowid
Marketing,20,employees_rowid
.
.
.
Sales,80,employees_rowid
Sales,80,employees_rowid
Sales,80,employees_rowid
.
.
.

2. Depending on the circumstances, the database may organize the cached rowids obtained in the previous step so that it can more efficiently access them.

3. For each employees rowid in the result set generated by the nested loop, the database retrieves a row from employees to obtain the requested values (Step 1).
Thus, the basic process is to read a rowid and retrieve the matching employees row, read the next rowid and retrieve the matching employees row, and so on. Conceptually, the result set has the following form:

Michael,Hartstein,13000,Marketing
Pat,Fay,6000,Marketing
John,Russell,14000,Sales
Karen,Partners,13500,Sales
Alberto,Errazuriz,12000,Sales
.
.
.

9.2.1.6 Nested Loops Controls

For some SQL examples, the data is small enough for the optimizer to prefer full table scans and hash joins. However, you can add a USE_NL to instruct the optimizer to change the join method to nested loops. This hint instructs the optimizer to join each specified table to another row source with a nested loops join, using the specified table as the inner table.

The related hint USE_NL_WITH_INDEX ( table index ) hint instructs the optimizer to join the specified table to another row source with a nested loops join using the specified table as the inner table. The index is optional. If no index is specified, then the nested loops join uses an index with at least one join predicate as the index key.

Example 9-2 Nested Loops Hint

Assume that the optimizer chooses a hash join for the following query:

SELECT e.last_name, d.department_name
FROM   employees e, departments d
WHERE  e.department_id=d.department_id;

The plan looks as follows:

----------------------------------------------------------------------------------
| Id  | Operation          | Name        | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |             |       |       |     5 (100)|          |
|*  1 |  HASH JOIN         |             |   106 |  2862 |     5  (20)| 00:00:01 |
|   2 |   TABLE ACCESS FULL| DEPARTMENTS |    27 |   432 |     2   (0)| 00:00:01 |
|   3 |   TABLE ACCESS FULL| EMPLOYEES   |   107 |  1177 |     2   (0)| 00:00:01 |
----------------------------------------------------------------------------------

To force a nested loops join using departments as the inner table, add the USE_NL hint as in the following query:

SELECT /*+ ORDERED USE_NL(d) */ e.last_name, d.department_name
FROM   employees e, departments d
WHERE  e.department_id=d.department_id;

The plan looks as follows:

----------------------------------------------------------------------------------
| Id  | Operation          | Name        | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |             |       |       |    34 (100)|          |
|   1 |  NESTED LOOPS      |             |   106 |  2862 |    34   (3)| 00:00:01 |
|   2 |   TABLE ACCESS FULL| EMPLOYEES   |   107 |  1177 |     2   (0)| 00:00:01 |
|*  3 |   TABLE ACCESS FULL| DEPARTMENTS |     1 |    16 |     0   (0)|          |
----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

3 - filter("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")

The database obtains the result set as follows:

1. In the nested loop, the database reads employees to obtain the last name and department ID for an employee (Step 2). For example:

De Haan,90

2. For the row obtained in the previous step, the database scans departments to find the department name that matches the employees department ID (Step 3), and joins the result (Step 1). For example:

De Haan,Executive

3. The database retrieves the next row in employees, retrieves the matching row from departments, and then repeats this process until all rows are retrieved.
The result set has the following form:

De Haan,Executive
Kochnar,Executive
Baer,Public Relations
King,Executive
.
.
.

See Also:

- "Guidelines for Join Order Hints" to learn more about the USE_NL hint

- Oracle Database SQL Language Reference to learn about the USE_NL hint

9.2.2 Hash Joins

The database uses a hash join to join larger data sets. The optimizer uses the smaller of two data sets to build a hash table on the join key in memory, using a deterministic hash function to specify the location in the hash table in which to store each row. The database then scans the larger data set, probing the hash table to find the rows that meet the join condition.

9.2.2.1 When the Optimizer Considers Hash Joins

In general, the optimizer considers a hash join when the following conditions are true:

- A relatively large amount of data must be joined, or a large fraction of a small table must be joined.

- The join is an equijoin.

A hash join is most cost effective when the smaller data set fits in memory. In this case, the cost is limited to a single read pass over the two data sets.

Because the hash table is in the PGA, Oracle Database can access rows without latching them. This technique reduces logical I/O by avoiding the necessity of repeatedly latching and reading blocks in the database buffer cache.

If the data sets do not fit in memory, then the database partitions the row sources, and the join proceeds partition by partition. This can use a lot of sort area memory, and I/O to the temporary tablespace. This method can still be the most cost effective, especially when parallel query servers are used.

9.2.2.2 How Hash Joins Work

A hashing algorithm takes a set of inputs and applies a deterministic hash function to generate a hash value between 1 and n, where n is the size of the hash table. In a hash join, the input values are the join keys. The output values are indexes (slots) in an array, which is the hash table.

9.2.2.2.1 Hash Tables

To illustrate a hash table, assume that the database hashes hr.departments in a join of departments and employees. The join key column is department_id. The first 5 rows of departments are as follows:

SQL> select * from departments where rownum < 6;

DEPARTMENT_ID DEPARTMENT_NAME                MANAGER_ID LOCATION_ID
------------- ------------------------------ ---------- -----------
10 Administration                        200        1700
20 Marketing                             201        1800
30 Purchasing                            114        1700
40 Human Resources                       203        2400
50 Shipping                              121        1500

The database applies the hash function to each department_id in the table, generating a hash value for each. For this illustration, the hash table has 5 slots (it could have more or less). Because n is 5, the possible hash values range from 1 to 5. The hash functions might generate the following values for the department IDs:

f(10) = 4
f(20) = 1
f(30) = 4
f(40) = 2
f(50) = 5

Note that the hash function happens to generate the same hash value of 4 for departments 10 and 30. This is known as a hash collision. In this case, the database puts the records for departments 10 and 30 in the same slot, using a linked list. Conceptually, the hash table looks as follows:

1    20,Marketing,201,1800
2    40,Human Resources,203,2400
3
4    10,Administration,200,1700 -> 30,Purchasing,114,1700
5    50,Shipping,121,1500

9.2.2.2.2 Hash Join: Basic Steps

A hash join of two row sources uses the following basic steps:

1. The database performs a full scan of the smaller data set, and then applies a hash function to the join key in each row to build a hash table in the PGA.
In pseudocode, the algorithm might look as follows:

FOR small_table_row IN (SELECT * FROM small_table)
LOOP
slot_number := HASH(small_table_row.join_key);
INSERT_HASH_TABLE(slot_number,small_table_row);
END LOOP;

2. The database probes the second data set, using whichever access mechanism has the lowest cost.
Typically, the database performs a full scan of both the smaller and larger data set. The algorithm in pseudocode might look as follows:

FOR large_table_row IN (SELECT * FROM large_table)
LOOP
slot_number := HASH(large_table_row.join_key);
small_table_row = LOOKUP_HASH_TABLE(slot_number,large_table_row.join_key);
IF small_table_row FOUND
THEN
output small_table_row + large_table_row;
END IF;
END LOOP;

For each row retrieved from the larger data set, the database does the following:

1. Applies the same hash function to the join column or columns to calculate the number of the relevant slot in the hash table.
For example, to probe the hash table for department ID 30, the database applies the hash function to 30, which generates the hash value 4.

2. Probes the hash table to determine whether rows exists in the slot.
If no rows exist, then the database processes the next row in the larger data set. If rows exist, then the database proceeds to the next step.

3. Checks the join column or columns for a match. If a match occurs, then the database either reports the rows or passes them to the next step in the plan, and then processes the next row in the larger data set.
If multiple rows exist in the hash table slot, the database walks through the linked list of rows, checking each one. For example, if department 30 hashes to slot 4, then the database checks each row until it finds 30.

Example 9-3 Hash Joins

An application queries the oe.orders and oe.order_items tables, joining on the order_id column.

SELECT o.customer_id, l.unit_price * l.quantity
FROM   orders o, order_items l
WHERE  l.order_id = o.order_id;

The execution plan is as follows:

--------------------------------------------------------------------------
| Id  | Operation            |  Name        | Rows  | Bytes | Cost (%CPU)|
--------------------------------------------------------------------------
|   0 | SELECT STATEMENT     |              |   665 | 13300 |     8  (25)|
|*  1 |  HASH JOIN           |              |   665 | 13300 |     8  (25)|
|   2 |   TABLE ACCESS FULL  | ORDERS       |   105 |   840 |     4  (25)|
|   3 |   TABLE ACCESS FULL  | ORDER_ITEMS  |   665 |  7980 |     4  (25)|
--------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
1 - access("L"."ORDER_ID"="O"."ORDER_ID")

Because the orders table is small relative to the order_items table, which is 6 times larger, the database hashes orders. In a hash join, the data set for the hash table always appears first in the list of operations (Step 2). In Step 3, the database performs a full scan of the larger order_items later, probing the hash table for each row.

9.2.2.3 How Hash Joins Work When the Hash Table Does Not Fit in the PGA

The database must use a different technique when the hash table does not fit entirely in the PGA. In this case, the database uses a temporary space to hold portions (called partitions) of the hash table, and sometimes portions of the larger table that probes the hash table.

The basic process is as follows:

1. The database performs a full scan of the smaller data set, and then builds an array of hash buckets in both the PGA and on disk.
When the PGA hash area fills up, the database finds the largest partition within the hash table and writes it to temporary space on disk. The database stores any new row that belongs to this on-disk partition on disk, and all other rows in the PGA. Thus, part of the hash table is in memory and part of it on disk.

2. The database takes a first pass at reading the other data set.
For each row, the database does the following:

1. Applies the same hash function to the join column or columns to calculate the number of the relevant hash bucket.

2. Probes the hash table to determine whether rows exist in the bucket in memory.
If the hashed value points to a row in memory, then the database completes the join and returns the row. If the value points to a hash partition on disk, however, then the database stores this row in the temporary tablespace, using the same partitioning scheme used for the original data set.

3. The database reads each on-disk temporary partition one by one

4. The database joins each partition row to the row in the corresponding on-disk temporary partition.

9.2.2.4 Hash Join Controls

The USE_HASH hint instructs the optimizer to use a hash join when joining two tables together. See "Guidelines for Join Order Hints".

9.2.3 Sort Merge Joins

A sort merge join is a variation on a nested loops join. The database sorts two data sets (the SORT JOIN operations), if they are not already sorted. For each row in the first data set, the database probes the second data set for matching rows and joins them (the MERGE JOIN operation), basing its start position on the match made in the previous iteration:

9.2.3.1 When the Optimizer Considers Sort Merge Joins

A hash join requires one hash table and one probe of this table, whereas a sort merge join requires two sorts. The optimizer may choose a sort merge join over a hash join for joining large amounts of data when any of the following conditions is true:

- The join condition between two tables is not an equijoin, that is, uses an inequality condition such as <, <=, >, or >=.
In contrast to sort merges, hash joins require an equality condition.

- Because of sorts required by other operations, the optimizer finds it cheaper to use a sort merge.
If an index exists, then the database can avoid sorting the first data set. However, the database always sorts the second data set, regardless of indexes.

A sort merge has the same advantage over a nested loops join as the hash join: the database accesses rows in the PGA rather than the SGA, reducing logical I/O by avoiding the necessity of repeatedly latching and reading blocks in the database buffer cache. In general, hash joins perform better than sort merge joins because sorting is expensive. However, sort merge joins offer the following advantages over a hash join:

- After the initial sort, the merge phase is optimized, resulting in faster generation of output rows.

- A sort merge can be more cost-effective than a hash join when the hash table does not fit completely in memory.
A hash join with insufficient memory requires both the hash table and the other data set to be copied to disk. In this case, the database may have to read from disk multiple times. In a sort merge, if memory cannot hold the two data sets, then the database writes them both to disk, but reads each data set no more than once.

9.2.3.2 How Sort Merge Joins Work

As in a nested loops join, a sort merge join reads two data sets, but sorts them when they are not already sorted. For each row in the first data set, the database finds a starting row in the second data set, and then reads the second data set until it finds a nonmatching row. In pseudocode, the high-level algorithm might look as follows:

READ data_set_1 SORT BY JOIN KEY TO temp_ds1
READ data_set_2 SORT BY JOIN KEY TO temp_ds2

READ ds1_row FROM temp_ds1
READ ds2_row FROM temp_ds2

WHILE NOT eof ON temp_ds1,temp_ds2
LOOP
IF ( temp_ds1.key = temp_ds2.key ) OUTPUT JOIN ds1_row,ds2_row
ELSIF ( temp_ds1.key <= temp_ds2.key ) READ ds1_row FROM temp_ds1
ELSIF ( temp_ds1.key => temp_ds2.key ) READ ds2_row FROM temp_ds2
END LOOP

For example, the database sorts the first data set as follows:

10,20,30,40,50,60,70

The database sorts the second data set as follows:

20,20,40,40,40,40,40,60,70,70

The database begins by reading 10 in the first data set, and then starts at the beginning of data set 2:

20 too high, stop, get next ds1_row

The database proceeds to the second row of data set 1 (20). The database proceeds through the second data set as follows:

20 match, proceed
20 match, proceed
40 too high, stop, get next ds1_row

The database gets the next row in data set 1, which is 30. The database starts at the number of its last match, which was 20, and then walks through data set 2 looking for a match:

20 too low, proceed
20 too low, proceed
40 too high, stop, get next ds1_row

The database gets the next row in data set 1, which is 40. The database starts at the number of its last match, which was 20, and then proceeds through data set 2 looking for a match:

20 too low, proceed
20 too low, proceed
40 match, proceed
40 match, proceed
40 match, proceed
40 match, proceed
40 match, proceed
60 too high, stop, get next ds1_row

As the database proceeds through data set 1, the database does not need to read every row in data set 2. This is an advantage over a nested loops join.

Example 9-4 Sort Merge Join Using Index

The following query joins the employees and departments tables on the department_id column, ordering the rows on department_id as follows:

SELECT e.employee_id, e.last_name, e.first_name, e.department_id,
d.department_name
FROM   employees e, departments d
WHERE  e.department_id = d.department_id
ORDER BY department_id;

A query of DBMS_XPLAN.DISPLAY_CURSOR shows that the plan uses a sort merge join:

----------------------------------------------------------------------------------
|Id | Operation                    | Name        | Rows|Bytes |Cost (%CPU)| Time |
----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT             |             |     |      | 5(100)|          |
| 1 |  MERGE JOIN                  |             | 106 | 4028 | 5 (20)| 00:00:01 |
| 2 |   TABLE ACCESS BY INDEX ROWID| DEPARTMENTS |  27 |  432 | 2  (0)| 00:00:01 |
| 3 |    INDEX FULL SCAN           | DEPT_ID_PK  |  27 |      | 1  (0)| 00:00:01 |
|*4 |   SORT JOIN                  |             | 107 | 2354 | 3 (34)| 00:00:01 |
| 5 |    TABLE ACCESS FULL         | EMPLOYEES   | 107 | 2354 | 2  (0)| 00:00:01 |
----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

4 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")
filter("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")

The two data sets are the departments table and the employees table. Because an index orders the departments table by department_id, the database can read this index and avoid a sort (Step 3). The database only needs to sort the employees table (Step 4), which is the most CPU-intensive operation.

Example 9-5 Sort Merge Join Without an Index

You join the employees and departments tables on the department_id column, ordering the rows on department_id as follows. In this example, you specify NO_INDEX and USE_MERGE to force the optimizer to choose a sort merge:

SELECT /*+ USE_MERGE(d e) NO_INDEX(d) */ e.employee_id, e.last_name, e.first_name,
e.department_id, d.department_name
FROM   employees e, departments d
WHERE  e.department_id = d.department_id
ORDER BY department_id;

A query of DBMS_XPLAN.DISPLAY_CURSOR shows that the plan uses a sort merge join:

----------------------------------------------------------------------------------
| Id  | Operation           | Name        | Rows  | Bytes | Cost (%CPU)| Time    |
----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT    |             |       |       |     6 (100)|         |
|   1 |  MERGE JOIN         |             |   106 |  9540 |     6  (34)| 00:00:01|
|   2 |   SORT JOIN         |             |    27 |   567 |     3  (34)| 00:00:01|
|   3 |    TABLE ACCESS FULL| DEPARTMENTS |    27 |   567 |     2   (0)| 00:00:01|
|*  4 |   SORT JOIN         |             |   107 |  7383 |     3  (34)| 00:00:01|
|   5 |    TABLE ACCESS FULL| EMPLOYEES   |   107 |  7383 |     2   (0)| 00:00:01|
----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

4 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")
filter("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")

Because the departments.department_id index is ignored, the optimizer performs a sort, which increases the combined cost of Step 2 and Step 3 by 67% (from 3 to 5).

9.2.3.3 Sort Merge Join Controls

The USE_MERGE hint instructs the optimizer to use a sort merge join. In some situations it may make sense to override the optimizer with the USE_MERGE hint. For example, the optimizer can choose a full scan on a table and avoid a sort operation in a query. However, there is an increased cost because a large table is accessed through an index and single block reads, as opposed to faster access through a full table scan.

See Also:
Oracle Database SQL Language Reference to learn about the USE_MERGE hint

9.2.4 Cartesian Joins

The database uses a Cartesian join when one or more of the tables does not have any join conditions to any other tables in the statement. The optimizer joins every row from one data source with every row from the other data source, creating the Cartesian product of the two sets. Therefore, the total number of rows resulting from the join is calculated using the following formula, where rs1 is the number of rows in first row set and rs2 is the number of rows in the second row set:

rs1 X rs2 = total rows in result set

9.2.4.1 When the Optimizer Considers Cartesian Joins

The optimizer uses a Cartesian join for two row sources in any of the following circumstances:

- No join condition exists.
In some cases, the optimizer could pick up a common filter condition between the two tables as a possible join condition.

Note:
If a Cartesian join appears in a query plan, it could be caused by an inadvertently omitted join condition. In general, if a query joins n tables, then n-1 join conditions are required to avoid a Cartesian join.

- A Cartesian join is an efficient method.
For example, the optimizer may decide to generate a Cartesian product of two very small tables that are both joined to the same large table.

- The ORDERED hint specifies a table before its join table is specified.

9.2.4.2 How Cartesian Joins Work

At a high level, the algorithm for a Cartesian join looks as follows, where ds1 is typically the smaller data set, and ds2 is the larger data set:

FOR ds1_row IN ds1 LOOP
FOR ds2_row IN ds2 LOOP
output ds1_row and ds2_row
END LOOP
END LOOP

Example 9-6 Cartesian Join

In this example, a user intends to perform an inner join of the employees and departments tables, but accidentally leaves off the join condition:

SELECT e.last_name, d.department_name
FROM   employees e, departments d

The execution plan is as follows:

----------------------------------------------------------------------------------
| Id  | Operation              | Name        | Rows  | Bytes |Cost (%CPU)| Time  |
----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT       |             |       |       |11 (100)|          |
|   1 |  MERGE JOIN CARTESIAN  |             |  2889 | 57780 |11   (0)| 00:00:01 |
|   2 |   TABLE ACCESS FULL    | DEPARTMENTS |    27 |   324 | 2   (0)| 00:00:01 |
|   3 |   BUFFER SORT          |             |   107 |   856 | 9   (0)| 00:00:01 |
|   4 |    INDEX FAST FULL SCAN| EMP_NAME_IX |   107 |   856 | 0   (0)|          |
----------------------------------------------------------------------------------

In Step 1 of the preceding plan, the CARTESIAN keyword indicates the presence of a Cartesian join. The number of rows (2889) is the product of 27 and 107.

In Step 3, the BUFFER SORT operation indicates that the database is copying the data blocks obtained by the scan of emp_name_ix from the SGA to the PGA. This strategy avoids multiple scans of the same blocks in the database buffer cache, which would generate many logical reads and permit resource contention.

9.2.4.3 Cartesian Join Controls

The ORDERED hint instructs the optimizer to join tables in the order in which they appear in the FROM clause. By forcing a join between two row sources that have no direct connection, the optimizer must perform a Cartesian join.

Example 9-7 ORDERED Hint

In the following example, the ORDERED hint instructs the optimizer to join employees and locations, but no join condition connects these two row sources:

SELECT /*+ORDERED*/ e.last_name, d.department_name, l.country_id, l.state_province
FROM   employees e, locations l, departments d
WHERE  e.department_id = d.department_id
AND    d.location_id = l.location_id

The following execution plan shows a Cartesian product (Step 3) between locations (Step 6) and employees (Step 4), which is then joined to the departments table (Step 2):

----------------------------------------------------------------------------------
| Id  | Operation             | Name        | Rows  | Bytes |Cost (%CPU)|Time    |
----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT      |             |       |       | 37 (100)|          |
|*  1 |  HASH JOIN            |             |   106 |  4664 | 37   (6)| 00:00:01 |
|   2 |   TABLE ACCESS FULL   | DEPARTMENTS |    27 |   513 |  2   (0)| 00:00:01 |
|   3 |   MERGE JOIN CARTESIAN|             |  2461 | 61525 | 34   (3)| 00:00:01 |
|   4 |    TABLE ACCESS FULL  | EMPLOYEES   |   107 |  1177 |  2   (0)| 00:00:01 |
|   5 |    BUFFER SORT        |             |    23 |   322 | 32   (4)| 00:00:01 |
|   6 |     TABLE ACCESS FULL | LOCATIONS   |    23 |   322 |  0   (0)|          |
----------------------------------------------------------------------------------

See Also:
Oracle Database SQL Language Reference to learn about the ORDERED hint

9.3 Join Types

A join type is determined by the type of join condition. This section contains the following topics:

- Inner Joins

- Outer Joins

- Semijoins

- Antijoins

9.3.1 Inner Joins

An inner join (sometimes called a simple join) is a join that returns only rows that satisfy the join condition. Inner joins are either equijoins or nonequijoins.

9.3.1.1 Equijoins

An equijoin is an inner join whose join condition contains an equality operator. The following example is an equijoin because the join condition contains only an equality operator:

SELECT e.employee_id, e.last_name, d.department_name
FROM   employees e, departments d
WHERE  e.department_id=d.department_id;

In the preceding query, the join condition is e.department_id=d.department_id. If a row in the employees table has a department ID that matches the value in a row in the departments table, then the database returns the joined result; otherwise, the database does not return a result.

9.3.1.2 Nonequijoins

A nonequijoin is an inner join whose join condition contains an operator that is not an equality operator. The following query lists all employees whose hire date occurred when employee 176 (who is listed in job_history because he changed jobs in 2007) was working at the company:

SELECT e.employee_id, e.first_name, e.last_name, e.hire_date
FROM   employees e, job_history h
WHERE  h.employee_id = 176
AND    e.hire_date BETWEEN h.start_date AND h.end_date;

In the preceding example, the condition joining employees and job_history does not contain an equality operator, so it is a nonequijoin. Nonequijoins are relatively rare.

Note that a hash join requires at least a partial equijoin. The following SQL script contains an equality join condition (e1.empno = e2.empno) and a nonequality condition:

SET AUTOTRACE TRACEONLY EXPLAIN
SELECT *
FROM   scott.emp e1 JOIN scott.emp e2
ON     ( e1.empno = e2.empno
AND      e1.hiredate BETWEEN e2.hiredate-1 AND e2.hiredate+1 )

The optimizer chooses a hash join for the preceding query, as shown in the following plan:

Execution Plan
----------------------------------------------------------
Plan hash value: 3638257876

---------------------------------------------------------------------------
| Id  | Operation          | Name | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |      |     1 |   174 |     5  (20)| 00:00:01 |
|*  1 |  HASH JOIN         |      |     1 |   174 |     5  (20)| 00:00:01 |
|   2 |   TABLE ACCESS FULL| EMP  |    14 |  1218 |     2   (0)| 00:00:01 |
|   3 |   TABLE ACCESS FULL| EMP  |    14 |  1218 |     2   (0)| 00:00:01 |
---------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - access("E1"."EMPNO"="E2"."EMPNO")
filter("E1"."HIREDATE">=INTERNAL_FUNCTION("E2"."HIREDATE")-1 AND
"E1"."HIREDATE"<=INTERNAL_FUNCTION("E2"."HIREDATE")+1)

9.3.2 Outer Joins

An outer join returns all rows that satisfy the join condition and also returns some or all of those rows from one table for which no rows from the other satisfy the join condition. Thus, an outer join extends the result of a simple join.

In ANSI syntax, the OUTER JOIN clause specifies an outer join. In the FROM clause, the left table appears to the left of the OUTER JOIN keywords, and the right table appears to the right of these keywords. The left table is also called the outer table, and the right table is also called the inner table. For example, in the following statement the employees table is the left or outer table:

SELECT employee_id, last_name, first_name
FROM   employees LEFT OUTER JOIN departments
ON     (employees.department_id=departments.departments_id);

Outer joins require the outer joined table to be the driving table. In the preceding example, employees is the driving table, and departments is the driven-to table.

This section contains the following topics:

- Nested Loop Outer Joins

- Hash Join Outer Joins

- Sort Merge Outer Joins

- Full Outer Joins

- Multiple Tables on the Left of an Outer Join

9.3.2.1 Nested Loop Outer Joins

The database uses this operation to loop through an outer join between two tables. The outer join returns the outer (preserved) table rows, even when no corresponding rows are in the inner (optional) table.

In a standard nested loop, the optimizer chooses the order of tables—which is the driving table and which the driven table—based on the cost. However, in a nested loop outer join, the join condition determines the order of tables. The database uses the outer, row-preserved table to drive to the inner table.

The optimizer uses nested loops joins to process an outer join in the following circumstances:

- It is possible to drive from the outer table to the inner table.

- Data volume is low enough to make the nested loop method efficient.

For an example of a nested loop outer join, you can add the USE_NL hint to Example 9-8 to instruct the optimizer to use a nested loop. For example:

SELECT /*+ USE_NL(c o) */ cust_last_name,
SUM(NVL2(o.customer_id,0,1)) "Count"
FROM   customers c, orders o
WHERE  c.credit_limit > 1000
AND    c.customer_id = o.customer_id(+)
GROUP BY cust_last_name;

9.3.2.2 Hash Join Outer Joins

The optimizer uses hash joins for processing an outer join when either of the following conditions is met:

- The data volume is large enough to make the hash join method efficient.

- It is not possible to drive from the outer table to the inner table.

The cost determines the order of tables. The outer table, including preserved rows, may be used to build the hash table, or it may be used to probe the hash table.

Example 9-8 shows a typical hash join outer join query, and its execution plan. In this example, all the customers with credit limits greater than 1000 are queried. An outer join is needed so that the query captures customers who have no orders.

Example 9-8 Hash Join Outer Joins

SELECT cust_last_name, SUM(NVL2(o.customer_id,0,1)) "Count"
FROM   customers c, orders o
WHERE  c.credit_limit > 1000
AND    c.customer_id = o.customer_id(+)
GROUP BY cust_last_name;

---------------------------------------------------------------------------------
| Id  | Operation           | Name      | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------

PLAN_TABLE_OUTPUT
---------------------------------------------------------------------------------
|   0 | SELECT STATEMENT    |           |       |       |     7 (100)|          |
|   1 |  HASH GROUP BY      |           |   168 |  3192 |     7  (29)| 00:00:01 |
|*  2 |   HASH JOIN OUTER   |           |   318 |  6042 |     6  (17)| 00:00:01 |
|*  3 |    TABLE ACCESS FULL| CUSTOMERS |   260 |  3900 |     3   (0)| 00:00:01 |
|*  4 |    TABLE ACCESS FULL| ORDERS    |   105 |   420 |     2   (0)| 00:00:01 |
---------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - access("C"."CUSTOMER_ID"="O"."CUSTOMER_ID")

PLAN_TABLE_OUTPUT
----------------------------------------------------------------------------------
3 - filter("C"."CREDIT_LIMIT">1000)
4 - filter("O"."CUSTOMER_ID">0)

The query looks for customers which satisfy various conditions. An outer join returns NULL for the inner table columns along with the outer (preserved) table rows when it does not find any corresponding rows in the inner table. This operation finds all the customers rows that do not have any orders rows.

In this case, the outer join condition is the following:

customers.customer_id = orders.customer_id(+)

The components of this condition represent the following:

- The outer table is customers.

- The inner table is orders.

- The join preserves the customers rows, including those rows without a corresponding row in orders.

You could use a NOT EXISTS subquery to return the rows. However, because you are querying all the rows in the table, the hash join performs better (unless the NOT EXISTS subquery is not nested).

In Example 9-9, the outer join is to a multitable view. The optimizer cannot drive into the view like in a normal join or push the predicates, so it builds the entire row set of the view.

Example 9-9 Outer Join to a Multitable View

SELECT c.cust_last_name, sum(revenue)
FROM   customers c, v_orders o
WHERE  c.credit_limit > 2000
AND    o.customer_id(+) = c.customer_id
GROUP BY c.cust_last_name;

----------------------------------------------------------------------------
| Id  | Operation              |  Name        | Rows  | Bytes | Cost (%CPU)|
----------------------------------------------------------------------------
|   0 | SELECT STATEMENT       |              |   144 |  4608 |    16  (32)|
|   1 |  HASH GROUP BY         |              |   144 |  4608 |    16  (32)|
|*  2 |   HASH JOIN OUTER      |              |   663 | 21216 |    15  (27)|
|*  3 |    TABLE ACCESS FULL   | CUSTOMERS    |   195 |  2925 |     6  (17)|
|   4 |    VIEW                | V_ORDERS     |   665 | 11305 |            |
|   5 |     HASH GROUP BY      |              |   665 | 15960 |     9  (34)|
|*  6 |      HASH JOIN         |              |   665 | 15960 |     8  (25)|
|*  7 |       TABLE ACCESS FULL| ORDERS       |   105 |   840 |     4  (25)|
|   8 |       TABLE ACCESS FULL| ORDER_ITEMS  |   665 | 10640 |     4  (25)|
----------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
2 - access("O"."CUSTOMER_ID"(+)="C"."CUSTOMER_ID")
3 - filter("C"."CREDIT_LIMIT">2000)
6 - access("O"."ORDER_ID"="L"."ORDER_ID")
7 - filter("O"."CUSTOMER_ID">0)

The view definition is as follows:

CREATE OR REPLACE view v_orders AS
SELECT l.product_id, SUM(l.quantity*unit_price) revenue,
o.order_id, o.customer_id
FROM   orders o, order_items l
WHERE  o.order_id = l.order_id
GROUP BY l.product_id, o.order_id, o.customer_id;

9.3.2.3 Sort Merge Outer Joins

When an outer join cannot drive from the outer (preserved) table to the inner (optional) table, it cannot use a hash join or nested loops joins. In this case, it uses the sort merge outer join.

The optimizer uses sort merge for an outer join in the following cases:

- A nested loops join is inefficient. A nested loops join can be inefficient because of data volumes.

- The optimizer finds it is cheaper to use a sort merge over a hash join because of sorts required by other operations.

9.3.2.4 Full Outer Joins

A full outer join is a combination of the left and right outer joins. In addition to the inner join, rows from both tables that have not been returned in the result of the inner join are preserved and extended with nulls. In other words, full outer joins join tables together, yet show rows with no corresponding rows in the joined tables.

Example 9-10 retrieves all departments and all employees in each department, but also includes:

- Any employees without departments

- Any departments without employees

Example 9-10 Full Outer Join

SELECT d.department_id, e.employee_id
FROM   employees e FULL OUTER JOIN departments d
ON     e.department_id = d.department_id
ORDER BY d.department_id;

The statement produces the following output:

DEPARTMENT_ID EMPLOYEE_ID
------------- -----------
10         200
20         201
20         202
30         114
30         115
30         116
...
270
280
178
207

125 rows selected.

Starting with Oracle Database 11g, Oracle Database automatically uses a native execution method based on a hash join for executing full outer joins whenever possible. When the database uses the new method to execute a full outer join, the execution plan for the query contains HASH JOIN FULL OUTER. Example 9-11 shows the execution plan for the query in Example 9-10.

Example 9-11 Execution Plan for a Full Outer Join

---------------------------------------------------------------------------------------
| Id  | Operation               | Name       | Rows  | Bytes | Cost (%CPU)| Time      |
---------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT        |            |   122 |  4758 |     6  (34)| 00:0 0:01 |
|   1 |  SORT ORDER BY          |            |   122 |  4758 |     6  (34)| 00:0 0:01 |
|   2 |   VIEW                  | VW_FOJ_0   |   122 |  4758 |     5  (20)| 00:0 0:01 |
|*  3 |    HASH JOIN FULL OUTER |            |   122 |  1342 |     5  (20)| 00:0 0:01 |
|   4 |     INDEX FAST FULL SCAN| DEPT_ID_PK |    27 |   108 |     2   (0)| 00:0 0:01 |
|   5 |     TABLE ACCESS FULL   | EMPLOYEES  |   107 |   749 |     2   (0)| 00:0 0:01 |
---------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
3 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")

HASH JOIN FULL OUTER is included in the plan (Step 3), indicating that the query uses the hash full outer join execution method. Typically, when the full outer join condition between two tables is an equijoin, the hash full outer join execution method is possible, and Oracle Database uses it automatically.

To instruct the optimizer to consider using the hash full outer join execution method, apply the NATIVE_FULL_OUTER_JOIN hint. To instruct the optimizer not to consider using the hash full outer join execution method, apply the NO_NATIVE_FULL_OUTER_JOIN hint. The NO_NATIVE_FULL_OUTER_JOIN hint instructs the optimizer to exclude the native execution method when joining each specified table. Instead, the full outer join is executed as a union of left outer join and an antijoin.

9.3.2.5 Multiple Tables on the Left of an Outer Join

In Oracle Database 12c, multiple tables may exist on the left of an outer-joined table. This enhancement enables Oracle Database to merge a view that contains multiple tables and appears on the left of outer join.

In releases before Oracle Database 12c, a query such as the following was invalid, and would trigger an ORA-01417 error message:

SELECT t1.d, t3.c
FROM   t1, t2, t3
WHERE  t1.z = t2.z
AND    t1.x = t3.x (+)
AND    t2.y = t3.y (+);

Starting in Oracle Database 12c, the preceding query is valid.

9.3.3 Semijoins

A semijoin is a join between two data sets that returns a row from the first set when a matching row exists in the subquery data set. The database stops processing the second data set at the first match. Thus, optimization does not duplicate rows from the first data set when multiple rows in the second data set satisfy the subquery criteria.

Note:
Semijoins and antijoins are considered join types even though the SQL constructs that cause them are subqueries. They are internal algorithms that the optimizer uses to flatten subquery constructs so that they can be resolved in a join-like way.

9.3.3.1 When the Optimizer Considers Semijoins

A semijoin avoids returning a huge number of rows when a query only needs to determine whether a match exists. With large data sets, this optimization can result in significant time savings over a nested loops join that must loop through every record returned by the inner query for every row in the outer query. The optimizer can apply the semijoin optimization to nested loops joins, hash joins, and sort merge joins.

The optimizer may choose a semijoin in the following circumstances:

- The statement uses either an IN or EXISTS clause.

- The statement contains a subquery in the IN or EXISTS clause.

- The IN or EXISTS clause is not contained inside an OR branch.

9.3.3.2 How Semijoins Work

The semijoin optimization is implemented differently depending on what type of join is used. The following pseudocode shows a semijoin for a nested loops join:

FOR ds1_row IN ds1 LOOP
match := false;
FOR ds2_row IN ds2 LOOP
IF (ds1_row matches ds2_row) THEN
match := true;
EXIT -- stop processing second data set when a match is found
END IF
END LOOP
IF (match = true) THEN
RETURN ds1_row
END IF
END LOOP

In the preceding pseudocode, ds1 is the first data set, and ds2 is the subquery data set. The code obtains the first row from the first data set, and then loops through the subquery data set looking for a match. The code exits the inner loop as soon as it finds a match, and then begins processing the next row in the first data set.

Example 9-12 Semijoin Using WHERE EXISTS

The following query uses a WHERE EXISTS clause to list only the departments that contain employees:

SELECT department_id, department_name
FROM   departments
WHERE EXISTS (SELECT 1
FROM   employees
WHERE  employees.department_id = departments.department_id)

The execution plan reveals a NESTED LOOPS SEMI operation in Step 1:

----------------------------------------------------------------------------------
| Id| Operation          | Name              |Rows| Bytes |Cost (%CPU)| Time     |
----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT   |                   |    |       |    2 (100)|          |
| 1 |  NESTED LOOPS SEMI |                   | 11 |   209 |    2   (0)| 00:00:01 |
| 2 |   TABLE ACCESS FULL| DEPARTMENTS       | 27 |   432 |    2   (0)| 00:00:01 |
|*3 |   INDEX RANGE SCAN | EMP_DEPARTMENT_IX | 44 |   132 |    0   (0)|          |
----------------------------------------------------------------------------------

For each row in departments, which forms the outer loop, the database obtains the department ID, and then probes the employees.department_id index for matching entries. Conceptually, the index looks as follows:

10,rowid
10,rowid
10,rowid
10,rowid
30,rowid
30,rowid
30,rowid
...

If the first entry in the departments table is department 30, then the database performs a range scan of the index until it finds the first 30 entry, at which point it stops reading the index and returns the matching row from departments. If the next row in the outer loop is department 20, then the database scans the index for a 20 entry, and not finding any matches, performs the next iteration of the outer loop. The database proceeds in this way until all matching rows are returned.

Example 9-13 Semijoin Using IN

The following query uses a IN clause to list only the departments that contain employees:

SELECT department_id, department_name
FROM   departments
WHERE  department_id IN
(SELECT department_id
FROM   employees);

The execution plan reveals a NESTED LOOPS SEMI operation in Step 1:

----------------------------------------------------------------------------------
| Id| Operation          | Name              |Rows| Bytes |Cost (%CPU)| Time     |
----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT   |                   |    |       |    2 (100)|          |
| 1 |  NESTED LOOPS SEMI |                   | 11 |   209 |    2   (0)| 00:00:01 |
| 2 |   TABLE ACCESS FULL| DEPARTMENTS       | 27 |   432 |    2   (0)| 00:00:01 |
|*3 |   INDEX RANGE SCAN | EMP_DEPARTMENT_IX | 44 |   132 |    0   (0)|          |
----------------------------------------------------------------------------------

The plan is identical to the plan in Example 9-12, "Semijoin Using WHERE EXISTS".

9.3.4 Antijoins

An antijoin is a join between two data sets that returns a row from the first set when a matching row does not exist in the subquery data set. Like a semijoin, an antijoin stops processing the subquery data set when the first match is found. Unlike a semijoin, the antijoin only returns a row when no match is found.

9.3.4.1 When the Optimizer Considers Antijoins

An antijoin avoids unnecessary processing when a query only needs to return a row when a match does not exist. With large data sets, this optimization can result in significant time savings over a nested loops join that must loop through every record returned by the inner query for every row in the outer query. The optimizer can apply the antijoin optimization to nested loops joins, hash joins, and sort merge joins.

The optimizer may choose an antijoin in the following circumstances:

- The statement uses either the NOT IN or NOT EXISTS clause.

- The statement has a subquery in the NOT IN or NOT EXISTS clause.

- The NOT IN or NOT EXISTS clause is not contained inside an OR branch.

- The statement performs an outer join and applies an IS NULL condition to a join column, as in the following example:

SET AUTOTRACE TRACEONLY EXPLAIN
SELECT emp.*
FROM   emp, dept
WHERE  emp.deptno = dept.deptno(+)
AND    dept.deptno IS NULL

Execution Plan
----------------------------------------------------------
Plan hash value: 1543991079

---------------------------------------------------------------------------
| Id  | Operation          | Name | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |      |    14 |  1400 |     5  (20)| 00:00:01 |
|*  1 |  HASH JOIN ANTI    |      |    14 |  1400 |     5  (20)| 00:00:01 |
|   2 |   TABLE ACCESS FULL| EMP  |    14 |  1218 |     2   (0)| 00:00:01 |
|   3 |   TABLE ACCESS FULL| DEPT |     4 |    52 |     2   (0)| 00:00:01 |
---------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - access("EMP"."DEPTNO"="DEPT"."DEPTNO")

Note
-----
- dynamic statistics used: dynamic sampling (level=2)

9.3.4.2 How Antijoins Work

The antijoin optimization is implemented differently depending on what type of join is used. The following pseudocode shows an antijoin for a nested loops join:

FOR ds1_row IN ds1 LOOP
match := true;
FOR ds2_row IN ds2 LOOP
IF (ds1_row matches ds2_row) THEN
match := false;
EXIT -- stop processing second data set when a match is found
END IF
END LOOP
IF (match = true) THEN
RETURN ds1_row
END IF
END LOOP

In the preceding pseudocode, ds1 is the first data set, and ds2 is the second data set. The code obtains the first row from the first data set, and then loops through the second data set looking for a match. The code exits the inner loop as soon as it finds a match, and begins processing the next row in the first data set.

Example 9-14 Semijoin Using WHERE EXISTS

The following query uses a WHERE EXISTS clause to list only the departments that contain employees:

SELECT department_id, department_name
FROM   departments
WHERE EXISTS (SELECT 1
FROM   employees
WHERE  employees.department_id = departments.department_id)

The execution plan reveals a NESTED LOOPS SEMI operation in Step 1:

----------------------------------------------------------------------------------
| Id| Operation          | Name              |Rows| Bytes |Cost (%CPU)| Time     |
----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT   |                   |    |       |    2 (100)|          |
| 1 |  NESTED LOOPS SEMI |                   | 11 |   209 |    2   (0)| 00:00:01 |
| 2 |   TABLE ACCESS FULL| DEPARTMENTS       | 27 |   432 |    2   (0)| 00:00:01 |
|*3 |   INDEX RANGE SCAN | EMP_DEPARTMENT_IX | 44 |   132 |    0   (0)|          |
----------------------------------------------------------------------------------

For each row in departments, which forms the outer loop, the database obtains the department ID, and then probes the employees.department_id index for matching entries. Conceptually, the index looks as follows:

10,rowid
10,rowid
10,rowid
10,rowid
30,rowid
30,rowid
30,rowid
...

If the first record in the departments table is department 30, then the database performs a range scan of the index until it finds the first 30 entry, at which point it stops reading the index and returns the matching row from departments. If the next row in the outer loop is department 20, then the database scans the index for a 20 entry, and not finding any matches, performs the next iteration of the outer loop. The database proceeds in this way until all matching rows are returned.

9.3.4.3 How Antijoins Handle Nulls

For semijoins, IN and EXISTS are functionally equivalent. However, NOT IN and NOT EXISTS are not functionally equivalent. The difference is because of nulls. If a null value is returned to a NOT IN operator, then the statement returns no records. To see why, consider the following WHERE clause:

WHERE department_id NOT IN (null, 10, 20)

The database tests the preceding expression as follows:

WHERE (department_id != null) AND (department_id != 10) AND (department_id != 20)

For the entire expression to be true, each individual condition must be true. However, a null value cannot be compared to another value, so the department_id !=null condition cannot be true, and thus the whole expression cannot be true. The following techniques enable a statement to return records even when nulls are returned to the NOT IN operator:

- Apply an NVL function to the columns returned by the subquery.

- Add an IS NOT NULL predicate to the subquery.

- Implement NOT NULL constraints.

In contrast to NOT IN, the NOT EXISTS clause only considers predicates that return the existence of a match, and ignores any row that does not match or could not be determined because of nulls. If at least one row in the subquery matches the row from the outer query, then NOT EXISTS returns false. If no tuples match, then NOT EXISTS returns true. The presence of nulls in the subquery does not affect the search for matching records.

In releases earlier than Oracle Database 11g, the optimizer could not use an antijoin optimization when nulls could be returned by a subquery. However, starting in Oracle Database 11g, the ANTI NA (and ANTI SNA) optimizations described in the following sections enable the optimizer to use an antijoin even when nulls are possible.

Example 9-15 Antijoin Using NOT IN

Suppose that a user issues the following query with a NOT IN clause to list the departments that contain no employees:

SELECT department_id, department_name
FROM   departments
WHERE  department_id NOT IN
(SELECT department_id
FROM   employees);

The preceding query returns no rows even though several departments contain no employees. This result, which was not intended by the user, occurs because the employees.department_id column is nullable.

The execution plan reveals a NESTED LOOPS ANTI SNA operation in Step 2:

----------------------------------------------------------------------------------
| Id| Operation              | Name              |Rows|Bytes| Cost (%CPU) | Time |
----------------------------------------------------------------------------------
| 0 | SELECT STATEMENT       |                   |    |     |  4 (100)|          |
|*1 |  FILTER                |                   |    |     |         |          |
| 2 |   NESTED LOOPS ANTI SNA|                   | 17 | 323 |  4  (50)| 00:00:01 |
| 3 |    TABLE ACCESS FULL   | DEPARTMENTS       | 27 | 432 |  2   (0)| 00:00:01 |
|*4 |    INDEX RANGE SCAN    | EMP_DEPARTMENT_IX | 41 | 123 |  0   (0)|          |
|*5 |   TABLE ACCESS FULL    | EMPLOYEES         |  1 |   3 |  2   (0)| 00:00:01 |
----------------------------------------------------------------------------------

PLAN_TABLE_OUTPUT
----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
1 - filter( IS NULL)
4 - access("DEPARTMENT_ID"="DEPARTMENT_ID")
5 - filter("DEPARTMENT_ID" IS NULL)

The ANTI SNA stands for "single null-aware antijoin." ANTI NA stands for "null-aware antijoin." The null-aware operation enables the optimizer to use the semijoin optimization even on a nullable column. In releases earlier than Oracle Database 11g, the database could not perform antijoins on NOT IN queries when nulls were possible.

Suppose that the user rewrites the query by applying an IS NOT NULL condition to the subquery:

SELECT department_id, department_name
FROM   departments
WHERE  department_id NOT IN
(SELECT department_id
FROM   employees
WHERE  department_id IS NOT NULL);

The preceding query returns 16 rows, which is the expected result. Step 1 in the plan shows a standard NESTED LOOPS ANTI join instead of an ANTI NA or ANTI SNA join because the subquery cannot returns nulls:

----------------------------------------------------------------------------------
| Id | Operation          | Name              | Rows| Bytes | Cost (%CPU)| Time  |
----------------------------------------------------------------------------------
|  0 | SELECT STATEMENT   |                   |     |       |  2 (100)|          |
|  1 |  NESTED LOOPS ANTI |                   |  17 |   323 |  2   (0)| 00:00:01 |
|  2 |   TABLE ACCESS FULL| DEPARTMENTS       |  27 |   432 |  2   (0)| 00:00:01 |
|* 3 |   INDEX RANGE SCAN | EMP_DEPARTMENT_IX |  41 |   123 |  0   (0)|          |
----------------------------------------------------------------------------------

PLAN_TABLE_OUTPUT
----------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------

3 - access("DEPARTMENT_ID"="DEPARTMENT_ID")
filter("DEPARTMENT_ID" IS NOT NULL)

Example 9-16 Antijoin Using NOT EXISTS

Suppose that a user issues the following query with a NOT EXISTS clause to list the departments that contain no employees:

SELECT department_id, department_name
FROM   departments d
WHERE  NOT EXISTS
(SELECT null
FROM   employees e
WHERE  e.department_id = d.department_id)

The preceding query avoids the null problem for NOT IN clauses. Thus, even though employees.department_id column is nullable, the statement returns the desired result.

Step 1 of the execution plan reveals a NESTED LOOPS ANTI operation, not the ANTI NA variant, which was necessary for NOT IN when nulls were possible:

----------------------------------------------------------------------------------
| Id  | Operation          | Name              | Rows  | Bytes | Cost (%CPU)|Time|
----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |                   |       |       | 2 (100)|        |
|   1 |  NESTED LOOPS ANTI |                   |    17 |   323 | 2   (0)|00:00:01|
|   2 |   TABLE ACCESS FULL| DEPARTMENTS       |    27 |   432 | 2   (0)|00:00:01|
|*  3 |   INDEX RANGE SCAN | EMP_DEPARTMENT_IX |    41 |   123 | 0   (0)|        |
----------------------------------------------------------------------------------

PLAN_TABLE_OUTPUT
----------------------------------------------------------------------------------
Predicate Information (identified by operation id):
---------------------------------------------------

3 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")

9.4 Join Optimizations

This section describes common join optimizations:

- Bloom Filters

- Partition-Wise Joins

9.4.1 Bloom Filters

A Bloom filter, named after its creator Burton Bloom, is a low-memory data structure that tests membership in a set. A Bloom filter correctly indicates when an element is not in a set, but can incorrectly indicate when an element is in a set. Thus, false negatives are impossible but false positives are possible.

9.4.1.1 Purpose of Bloom Filters

Bloom filters are especially useful when the amount of memory needed to store the filter is small relative to the amount of data in the data set, and when most data is expected to fail the membership test.

Oracle Database uses Bloom filters to various specific goals, including the following:

- Reduce the amount of data transferred to slave processes in a parallel query, especially when the database discards most rows because they do not fulfill a join condition

- Eliminate unneeded partitions when building a partition access list in a join, known as partition pruning

- Test whether data exists in the server result cache, thereby avoiding a disk read

- Filter members in Exadata cells, especially when joining a large fact table and small dimension tables in a star schema

Bloom filters can occur in both parallel and serial processing.

9.4.1.2 How Bloom Filters Work

A Bloom filter uses an array of bits to indicate inclusion in a set. For example, 8 elements (an arbitrary number used for this example) in an array are initially set to 0:

e1 e2 e3 e4 e5 e6 e7 e8
0  0  0  0  0  0  0  0

This array represents a set. To represent an input value i in this array, three separate hash functions (an arbitrary number used for this example) are applied to i, each generating a hash value between 1 and 8:

f1(i) = h1
f2(i) = h2
f3(i) = h3

For example, to store the value 17 in this array, the hash functions set i to 17, and then return the following hash values:

f1(17) = 5
f2(17) = 3
f3(17) = 5

In the preceding example, two of the hash functions happened to return the same value of 5, known as a hash collision. Because the distinct hash values are 5 and 3, the 5th and 3rd elements in the array are set to 1:

e1 e2 e3 e4 e5 e6 e7 e8
0  0  1  0  1  0  0  0

Testing the membership of 17 in the set reverses the process. To test whether the set excludes the value 17, element 3 or element 5 must contain a 0. If a 0 is present in either element, then the set cannot contain 17. No false negatives are possible.

To test whether the set includes 17, both element 3 and element 5 must contain 1 values. However, if the test indicates a 1 for both elements, then it is still possible for the set not to include 17. False positives are possible. For example, the following array might represent the value 22, which also has a 1 for both element 3 and element 5:

e1 e2 e3 e4 e5 e6 e7 e8
1  0  1  0  1  0  0  0

9.4.1.3 Bloom Filter Controls

The optimizer automatically determines whether to use Bloom filters. To override optimizer decisions, use the hints PX_JOIN_FILTER and NO_PX_JOIN_FILTER.

See Also:
Oracle Database SQL Language Reference to learn more about the bloom filter hints

9.4.1.4 Bloom Filter Metadata

The following dynamic performance views contain metadata about Bloom filters:

- V$SQL_JOIN_FILTER
This view shows the number of rows filtered out (FILTERED column) and tested (PROBED column) by an active Bloom filter.

- V$PQ_TQSTAT
This view displays the number of rows processed through each parallel execution server at each stage of the execution tree. You can use it to monitor how much Bloom filters have reduced data transfer among parallel processes.

In an execution plan, a Bloom filter is indicated by keywords JOIN FILTER in the Operation column, and the prefix :BF in the Name column, as in the 9th step of the following plan snippet:

----------------------------------------------------------------------------
| Id  | Operation                  | Name     |    TQ  |IN-OUT| PQ Distrib |
----------------------------------------------------------------------------
...
|   9 |      JOIN FILTER CREATE    | :BF0000  |  Q1,03 | PCWP |            |

In the Predicate Information section of the plan, filters that contain functions beginning with the string SYS_OP_BLOOM_FILTER indicate use of a Bloom filter.

9.4.1.5 Bloom Filters: Scenario

The following parallel query joins the sales fact table to the products and times dimension tables, and filters on fiscal week 18:

SELECT /*+ parallel(s) */ p.prod_name, s.quantity_sold
FROM   sh.sales s, sh.products p, sh.times t
WHERE  s.prod_id = p.prod_id
AND    s.time_id = t.time_id
AND    t.fiscal_week_number = 18;

Querying DBMS_XPLAN.DISPLAY_CURSOR provides the following output:

SELECT * FROM
TABLE(DBMS_XPLAN.DISPLAY_CURSOR(format => 'BASIC,+PARALLEL,+PREDICATE'));

EXPLAINED SQL STATEMENT:
------------------------
SELECT /*+ parallel(s) */ p.prod_name, s.quantity_sold FROM sh.sales s,
sh.products p, sh.times t WHERE s.prod_id = p.prod_id AND s.time_id =
t.time_id AND t.fiscal_week_number = 18

Plan hash value: 1183628457

----------------------------------------------------------------------------
| Id  | Operation                  | Name     |    TQ  |IN-OUT| PQ Distrib |
----------------------------------------------------------------------------
|   0 | SELECT STATEMENT           |          |        |      |            |
|   1 |  PX COORDINATOR            |          |        |      |            |
|   2 |   PX SEND QC (RANDOM)      | :TQ10003 |  Q1,03 | P->S | QC (RAND)  |
|*  3 |    HASH JOIN BUFFERED      |          |  Q1,03 | PCWP |            |
|   4 |     PX RECEIVE             |          |  Q1,03 | PCWP |            |
|   5 |      PX SEND BROADCAST     | :TQ10001 |  Q1,01 | S->P | BROADCAST  |
|   6 |       PX SELECTOR          |          |  Q1,01 | SCWC |            |
|   7 |        TABLE ACCESS FULL   | PRODUCTS |  Q1,01 | SCWP |            |
|*  8 |     HASH JOIN              |          |  Q1,03 | PCWP |            |
|   9 |      JOIN FILTER CREATE    | :BF0000  |  Q1,03 | PCWP |            |
|  10 |       BUFFER SORT          |          |  Q1,03 | PCWC |            |
|  11 |        PX RECEIVE          |          |  Q1,03 | PCWP |            |
|  12 |         PX SEND HYBRID HASH| :TQ10000 |        | S->P | HYBRID HASH|
|* 13 |          TABLE ACCESS FULL | TIMES    |        |      |            |
|  14 |      PX RECEIVE            |          |  Q1,03 | PCWP |            |
|  15 |       PX SEND HYBRID HASH  | :TQ10002 |  Q1,02 | P->P | HYBRID HASH|
|  16 |        JOIN FILTER USE     | :BF0000  |  Q1,02 | PCWP |            |
|  17 |         PX BLOCK ITERATOR  |          |  Q1,02 | PCWC |            |
|* 18 |          TABLE ACCESS FULL | SALES    |  Q1,02 | PCWP |            |
----------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

3 - access("S"."PROD_ID"="P"."PROD_ID")
8 - access("S"."TIME_ID"="T"."TIME_ID")
13 - filter("T"."FISCAL_WEEK_NUMBER"=18)
18 - access(:Z>=:Z AND :Z<=:Z)
filter(SYS_OP_BLOOM_FILTER(:BF0000,"S"."TIME_ID"))

A single server process scans the times table (Step 13), and then uses a hybrid hash distribution method to send the rows to the parallel execution servers (Step 12). The processes in set Q1,03 create a bloom filter (Step 9). The processes in set Q1,02 scan sales in parallel (Step 18), and then use the Bloom filter to discard rows from sales (Step 16) before sending them on to set Q1,03 using hybrid hash distribution (Step 15). The processes in set Q1,03 hash join the times rows to the filtered sales rows (Step 8). The processes in set Q1,01 scan products (Step 7), and then send the rows to Q1,03 (Step 5). Finally, the processes in Q1,03 join the products rows to the rows generated by the previous hash join (Step 3).

The basic process looks as follows:

9.4.2 Partition-Wise Joins

A partition-wise join is a join optimization that divides a large join of two tables, one of which must be partitioned on the join key, into several smaller joins. Partition-wise joins are either of the following:

- Full partition-wise join
Both tables must be equipartitioned on their join keys, or use reference partitioning (that is, be related by referential constraints). The database divides a large join into smaller joins between two partitions from the two joined tables.

- Partial partition-wise joins
Only one table is partitioned on the join key. The other table may or may not be partitioned.

See Also:
Oracle Database VLDB and Partitioning Guide explains partition-wise joins in detail

9.4.2.1 Purpose of Partition-Wise Joins

Partition-wise joins reduce query response time by minimizing the amount of data exchanged among parallel execution servers when joins execute in parallel. This technique significantly reduces response time and improves the use of CPU and memory. In Oracle Real Application Clusters (Oracle RAC) environments, partition-wise joins also avoid or at least limit the data traffic over the interconnect, which is the key to achieving good scalability for massive join operations.

9.4.2.2 How Partition-Wise Joins Work

When the database serially joins two partitioned tables without using a partition-wise join, a single server process performs the join, as shown in Figure 9-6. In this example, the join is not partition-wise because the server process joins every partition of table t1 to every partition of table t2.

Figure 9-6 Join That Is Not Partition-Wise

9.4.2.2.1 How a Full Partition-Wise Join Works

Figure 9-7 shows a full partition-wise join performed in parallel (it can also be performed in serial). In this case, the granule of parallelism is a partition. Each parallel execution server joins the partitions in pairs. For example, the first parallel execution server joins the first partition of t1 to the first partition of t2. The parallel execution coordinator then assembles the result.

Figure 9-7 Full Partition-Wise Join in Parallel

A full partition-wise join can also join partitions to subpartitions, which is useful when the tables use different partitioning methods. For example, customers is partitioned by hash, but sales is partitioned by range. If you subpartition sales by hash, then the database can perform a full partition-wise join between the hash partitions of the customers and the hash subpartitions of sales.

In the execution plan, the presence of a partition operation before the join signals the presence of a full partition-wise join, as in the following snippet:

|   8 |         PX PARTITION HASH ALL|
|*  9 |          HASH JOIN           |

See Also:
Oracle Database VLDB and Partitioning Guide explains full partition-wise joins in detail, and includes several examples

9.4.2.2.2 How a Partial Partition-Wise Join Works

In contrast, the example in Figure 9-8 shows a partial partition-wise join between t1, which is partitioned, and t2, which is not partitioned. Partial partition-wise joins, unlike their full partition-wise counterpart, must execute in parallel.

Because t2 is not partitioned, a set of parallel execution servers must generate partitions from t2 as needed. A different set of parallel execution servers then joins the t1 partitions to the dynamically generated partitions. The parallel execution coordinator assembles the result.

Figure 9-8 Partial Partition-Wise Join

In the execution plan, the operation PX SEND PARTITION (KEY) signals a partial partition-wise join, as in the following snippet:

|  11 |            PX SEND PARTITION (KEY)    |

See Also:
Oracle Database VLDB and Partitioning Guide explains full partition-wise joins in detail, and includes several examples

Part V

Optimizer Statistics

This part contains the following chapters:

- Chapter 10, "Optimizer Statistics Concepts"

- Chapter 11, "Histograms"

- Chapter 12, "Managing Optimizer Statistics: Basic Topics"

- Chapter 13, "Managing Optimizer Statistics: Advanced Topics"

10 Optimizer Statistics Concepts

This chapter explains basic concepts relating to optimizer statistics.

This chapter includes the following topics:

- Introduction to Optimizer Statistics

- About Optimizer Statistics Types

- How the Database Gathers Optimizer Statistics

- When the Database Gathers Optimizer Statistics

See Also:

- Chapter 4, "Query Optimizer Concepts"

- Chapter 11, "Histograms"

- Chapter 12, "Managing Optimizer Statistics: Basic Topics"

- Chapter 13, "Managing Optimizer Statistics: Advanced Topics"

10.1 Introduction to Optimizer Statistics

Oracle Database optimizer statistics describe details about the database and its objects. The optimizer cost model relies on statistics collected about the objects involved in a query, and the database and host where the query runs. Statistics are critical to the optimizer's ability to pick the best execution plan for a SQL statement.

Optimizer statistics include the following:

- Table statistics

- Number of rows

- Number of blocks

- Average row length

- Column statistics

- Number of distinct values (NDV) in a column

- Number of nulls in a column

- Data distribution (histogram)

- Extended statistics

- Index statistics

- Number of leaf blocks

- Number of levels

- Index clustering factor

- System statistics

- I/O performance and utilization

- CPU performance and utilization

As shown in Figure 10-1, the database stores optimizer statistics for tables, columns, indexes, and the system in the data dictionary. You can access these statistics using data dictionary views.

Note:
The optimizer statistics are different from the performance statistics visible through V$ views.

Figure 10-1 Optimizer Statistics

10.2 About Optimizer Statistics Types

The optimizer collects statistics on different types of database objects and characteristics of the database environment. This section contains the following topics:

- Table Statistics

- Column Statistics

- Index Statistics

- Session-Specific Statistics for Global Temporary Tables

- System Statistics

- User-Defined Optimizer Statistics

10.2.1 Table Statistics

In Oracle Database, table statistics include information about rows and blocks. The optimizer uses these statistics to determine the cost of table scans and table joins. DBMS_STATS can gather statistics for both permanent and temporary tables.

The database tracks all relevant statistics about permanent tables. DBMS_STATS.GATHER_TABLE_STATS commits before gathering statistics on permanent tables. For example, table statistics stored in DBA_TAB_STATISTICS track the following:

- Number of rows and average row length
The database uses the row count stored in DBA_TAB_STATISTICS when determining cardinality.

- Number of data blocks
The optimizer uses the number of data blocks with the DB_FILE_MULTIBLOCK_READ_COUNT initialization parameter to determine the base table access cost.

Example 10-1 Table Statistics

This example queries some table statistics for the sh.customers table.

sys@PROD> SELECT NUM_ROWS, AVG_ROW_LEN, BLOCKS, LAST_ANALYZED
2  FROM   DBA_TAB_STATISTICS
3  WHERE  OWNER='SH'
4  AND    TABLE_NAME='CUSTOMERS';

NUM_ROWS AVG_ROW_LEN     BLOCKS LAST_ANAL
---------- ----------- ---------- ---------
55500         181       1486 14-JUN-10

See Also:

- "About Optimizer Initialization Parameters"

- "Gathering Schema and Table Statistics"

- Oracle Database Reference for a description of the DBA_TAB_STATISTICS view and the DB_FILE_MULTIBLOCK_READ_COUNT initialization parameter

10.2.2 Column Statistics

Column statistics track information about column values and data distribution. The optimizer uses these statistics to generate accurate cardinality estimates and make better decisions about index usage, join orders, join methods, and so on.

For example, index statistics in DBA_TAB_COL_STATISTICS track the following:

- Number of distinct values (NDV)

- Number of nulls

- High and low values

- Histogram-related information (see "Histograms")

The optimizer can use extended statistics, which are a special type of column statistics. These statistics are useful for informing the optimizer of logical relationships among columns.

See Also:

- "About Statistics on Column Groups"

- Oracle Database Reference for a description of the DBA_TAB_COL_STATISTICS view

10.2.3 Index Statistics

The index statistics include information about the number of index levels, the number of index blocks, and the relationship between the index and the data blocks. The optimizer uses these statistics to determine the cost of index scans.

For example, index statistics stored in the DBA_IND_STATISTICS view track the following:

- Levels
The BLEVEL column shows the number of blocks required to go from the root block to a leaf block. A B-tree index has two types of blocks: branch blocks for searching and leaf blocks that store values. See Oracle Database Concepts for a conceptual overview of B-tree indexes.

- Distinct keys
This columns tracks the number of distinct indexed values. If a unique constraint is defined, and if no NOT NULL constraint is defined, then this value equals the number of non-null values.

- Average number of leaf blocks for each distinct indexed key

- Average number of data blocks pointed to by each distinct indexed key

Example 10-2 Index Statistics

This example queries some index statistics for the cust_lname_ix and customers_pk indexes on the sh.customers table (sample output included):

SELECT INDEX_NAME, BLEVEL, LEAF_BLOCKS AS "LEAFBLK", DISTINCT_KEYS AS "DIST_KEY",
AVG_LEAF_BLOCKS_PER_KEY AS "LEAFBLK_PER_KEY",
AVG_DATA_BLOCKS_PER_KEY AS "DATABLK_PER_KEY"
FROM   DBA_IND_STATISTICS
WHERE  OWNER = 'SH'
AND    INDEX_NAME IN ('CUST_LNAME_IX','CUSTOMERS_PK');

INDEX_NAME     BLEVEL LEAFBLK DIST_KEY LEAFBLK_PER_KEY DATABLK_PER_KEY
-------------- ------ ------- -------- --------------- ---------------
CUSTOMERS_PK        1     115    55500               1               1
CUST_LNAME_IX       1     141      908               1              10

See Also:
Oracle Database Reference for a description of the DBA_IND_STATISTICS view

10.2.3.1 Index Clustering Factor

For a B-tree index, the index clustering factor measures the physical grouping of rows in relation to an index value, such as last name (see Oracle Database Concepts for an overview).

A clustering factor that is close to the number of blocks in a table indicates that the rows are physically ordered in the table blocks by the index key. If the database performs a full table scan, then the database tends to retrieve the rows as they are stored on disk sorted by the index key. A clustering factor that is close to the number of rows indicates that the rows are scattered randomly across the database blocks in relation to the index key. If the database performs a full table scan, then the database would not retrieve rows in any sorted order by this index key.

The index clustering factor helps the optimizer decide whether an index scan or full table scan is more efficient for certain queries. A low clustering factor indicates an efficient index scan.

The clustering factor is a property of a specific index, not a table. If multiple indexes exist on a table, then the clustering factor for one index might be small while the factor for another index is large. An attempt to reorganize the table to improve the clustering factor for one index may degrade the clustering factor of the other index.

Example 10-3 Index Clustering Factor

This example shows how the optimizer uses the index clustering factor to determine whether using an index is more effective than a full table scan.

1. Start SQL*Plus and connect to a database as sh, and then query the number of rows and blocks in the sh.customers table (sample output included):

SELECT  table_name, num_rows, blocks
FROM    user_tables
WHERE   table_name='CUSTOMERS';

TABLE_NAME                       NUM_ROWS     BLOCKS
------------------------------ ---------- ----------
CUSTOMERS                           55500       1486

2. Create an index on the customers.cust_last_name column.
For example, execute the following statement:

CREATE INDEX CUSTOMERS_LAST_NAME_IDX ON customers(cust_last_name);

3. Query the index clustering factor of the newly created index.
The following query shows that the customers_last_name_idx index has a high clustering factor because the clustering factor is significantly more than the number of blocks in the table:

SELECT index_name, blevel, leaf_blocks, clustering_factor
FROM   user_indexes
WHERE  table_name='CUSTOMERS'
AND    index_name= 'CUSTOMERS_LAST_NAME_IDX';

INDEX_NAME                         BLEVEL LEAF_BLOCKS CLUSTERING_FACTOR
------------------------------ ---------- ----------- -----------------
CUSTOMERS_LAST_NAME_IDX                 1         141              9859

4. Create a new copy of the customers table, with rows ordered by cust_last_name.
For example, execute the following statements:

DROP TABLE customers3 PURGE;
CREATE TABLE customers3 AS
SELECT *
FROM   customers
ORDER BY cust_last_name;

5. Gather statistics on the customers3 table.
For example, execute the GATHER_TABLE_STATS procedure as follows:

EXEC DBMS_STATS.GATHER_TABLE_STATS(null,'CUSTOMERS3');

6. Query the number of rows and blocks in the customers3 table .
For example, enter the following query (sample output included):

SELECT    TABLE_NAME, NUM_ROWS, BLOCKS
FROM      USER_TABLES
WHERE     TABLE_NAME='CUSTOMERS3';

TABLE_NAME                       NUM_ROWS     BLOCKS
------------------------------ ---------- ----------
CUSTOMERS3                          55500       1485

7. Create an index on the cust_last_name column of customers3.
For example, execute the following statement:

CREATE INDEX CUSTOMERS3_LAST_NAME_IDX ON customers3(cust_last_name);

8. Query the index clustering factor of the customers3_last_name_idx index.
The following query shows that the customers3_last_name_idx index has a lower clustering factor:

SELECT INDEX_NAME, BLEVEL, LEAF_BLOCKS, CLUSTERING_FACTOR
FROM   USER_INDEXES
WHERE  TABLE_NAME = 'CUSTOMERS3'
AND    INDEX_NAME = 'CUSTOMERS3_LAST_NAME_IDX';

INDEX_NAME                         BLEVEL LEAF_BLOCKS CLUSTERING_FACTOR
------------------------------ ---------- ----------- -----------------
CUSTOMERS3_LAST_NAME_IDX                1         141              1455

The table customers3 has the same data as the original customers table, but the index on customers3 has a much lower clustering factor because the data in the table is ordered by the cust_last_name. The clustering factor is now about 10 times the number of blocks instead of 70 times.

9. Query the customers table.
For example, execute the following query (sample output included):

SELECT cust_first_name, cust_last_name
FROM   customers
WHERE  cust_last_name BETWEEN 'Puleo' AND 'Quinn';

CUST_FIRST_NAME      CUST_LAST_NAME
-------------------- ----------------------------------------
Vida                 Puleo
Harriett             Quinlan
Madeleine            Quinn
Caresse              Puleo

10. Display the cursor for the query.
For example, execute the following query (partial sample output included):

SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR());

-------------------------------------------------------------------------------
| Id | Operation                 | Name      | Rows | Bytes | Cost (%CPU)|Time|
-------------------------------------------------------------------------------
|   0| SELECT STATEMENT          |           |      |     | 405 (100)|        |
|*  1|  TABLE ACCESS STORAGE FULL| CUSTOMERS |  2335|35025| 405   (1)|00:00:01|
-------------------------------------------------------------------------------

The preceding plan shows that the optimizer did not use the index on the original customers tables.

11. Query the customers3 table.
For example, execute the following query (sample output included):

SELECT cust_first_name, cust_last_name
FROM   customers3
WHERE  cust_last_name BETWEEN 'Puleo' AND 'Quinn';

CUST_FIRST_NAME      CUST_LAST_NAME
-------------------- ----------------------------------------
Vida                 Puleo
Harriett             Quinlan
Madeleine            Quinn
Caresse              Puleo

12. Display the cursor for the query.
For example, execute the following query (partial sample output included):

SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR());

-------------------------------------------------------------------------------
|Id| Operation                   | Name            |Rows|Bytes|Cost(%CPU)|Time|
-------------------------------------------------------------------------------
| 0| SELECT STATEMENT            |                  |    |     |69(100)|      |
| 1|  TABLE ACCESS BY INDEX ROWID|CUSTOMERS3        |2335|35025|69(0)|00:00:01|
|*2|   INDEX RANGE SCAN          |CUSTOMERS3_LAST_NAME_IDX|2335| 7(0)|00:00:01|
-------------------------------------------------------------------------------

The result set is the same, but the optimizer chooses the index. The plan cost is much less than the cost of the plan used on the original customers table.

13. Query customers with a hint that forces the optimizer to use the index.
For example, execute the following query (partial sample output included):

SELECT /*+ index (Customers CUSTOMERS_LAST_NAME_IDX) */ cust_first_name,
cust_last_name
FROM   customers
WHERE  cust_last_name BETWEEN 'Puleo' and 'Quinn';

CUST_FIRST_NAME      CUST_LAST_NAME
-------------------- ----------------------------------------
Vida                 Puleo
Caresse              Puleo
Harriett             Quinlan
Madeleine            Quinn

14. Display the cursor for the query.
For example, execute the following query (partial sample output included):

SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR());

-------------------------------------------------------------------------------
| Id  | Operation                | Name           |Rows|Bytes|Cost (%CPU)|Time|
-------------------------------------------------------------------------------
| 0| SELECT STATEMENT            |                  |   |     |422(100)|      |
| 1|  TABLE ACCESS BY INDEX ROWID|CUSTOMERS         |335|35025|422(0)|00:00:01|
|*2|   INDEX RANGE SCAN          |CUSTOMERS_LAST_NAME_IDX|2335| |7(0)|00:00:01|
-------------------------------------------------------------------------------

The preceding plan shows that the cost of using the index on customers is higher than the cost of a full table scan. Thus, using an index does not necessarily improve performance. The index clustering factor is an effective measure of whether an using an index is more effective than a full table scan.

10.2.3.1.1 Example: Effect of Index Clustering Factor on Cost

To illustrate how the index clustering factor can influence the cost of table access, consider the following scenario:

- A table contains 9 rows that are stored in 3 data blocks.

- The col1 column currently stores the values A, B, and C.

- A nonunique index named col1_idx exists on col1 for this table.

Assume that the rows are stored in the blocks as shown in Example 10-4.

Example 10-4 Collocated Data

Block 1       Block 2        Block 3
-------       -------        -------
A  A  A       B  B  B        C  C  C

In Example 10-4, the index clustering factor for col1_idx is low. The rows that have the same indexed column values for col1 are in the same data blocks in the table. Thus, the cost of using an index range scan to return all rows with value A is low because only one block in the table must be read.

Assume that the same rows are scattered across data blocks as shown in Example 10-5.

Example 10-5 Scattered Data

Block 1       Block 2        Block 3
-------       -------        -------
A  B  C       A  C  B        B  A  C

In Example 10-5, the index clustering factor for col1_idx is higher. The database must read all three blocks in the table to retrieve all rows with the value A in col1.

See Also:
Oracle Database Reference for a description of the DBA_INDEXES view

10.2.4 Session-Specific Statistics for Global Temporary Tables

A global temporary table is a special table that stores intermediate session-private data for a specific duration. The ON COMMIT clause of CREATE GLOBAL TEMPORARY TABLE indicates whether the table is transaction-specific (DELETE ROWS) or session-specific (PRESERVE ROWS). Thus, a temporary table holds intermediate result sets for the duration of either a transaction or a session.

When you create a global temporary table, you create a definition that is visible to all sessions. No physical storage is allocated. When a session first puts data into the table, the database allocates storage space. The data in the temporary table is only visible to the current session.

10.2.4.1 Shared and Session-Specific Statistics for Global Temporary Tables

In releases before Oracle Database 12c, the database did not maintain statistics for global temporary tables and non-global temporary tables differently. The database maintained one version of the statistics shared by all sessions, even though data in different sessions could differ.

Starting in Oracle Database 12c, you can set the table-level preference GLOBAL_TEMP_TABLE_STATS to make statistics on a global temporary table shared or session-specific. When set to session-specific, you can gather statistics for a global temporary table in one session, and then use the statistics for this session only. Meanwhile, users can continue to maintain a shared version of the statistics. During optimization, the optimizer first checks whether a global temporary table has session-specific statistics. If yes, the optimizer uses them. Otherwise, the optimizer uses shared statistics if they exist.

Session-specific statistics have the following characteristics:

- Dictionary views that track statistics show both the shared statistics and the session-specific statistics in the current session.
The views are DBA_TAB_STATISTICS, DBA_IND_STATISTICS, DBA_TAB_HISTOGRAMS, and DBA_TAB_COL_STATISTICS (each view has a corresponding USER_ and ALL_ version). The SCOPE column shows whether statistics are session-specific or shared.

- Other sessions do not share the cursor using the session-specific statistics.
Different sessions can share the cursor using shared statistics, as in releases earlier than Oracle Database 12c. The same session can share the cursor using session-specific statistics.

- Pending statistics are not supported for session-specific statistics.

- When the GLOBAL_TEMP_TABLE_STATS preference is set to SESSION, by default GATHER_TABLE_STATS immediately invalidates previous cursors compiled in the same session. However, this procedure does not invalidate cursors compiled in other sessions.

10.2.4.2 Effect of DBMS_STATS on Transaction-Specific Temporary Tables

DBMS_STATS commits changes to session-specific global temporary tables, but not to transaction-specific global temporary tables. Before Oracle Database 12c, running DBMS_STATS.GATHER_TABLE_STATS on a transaction-specific temporary table (ON COMMIT DELETE ROWS) would delete all rows, making the statistics show the table as empty. Starting in Oracle Database 12c, the following procedures do not commit for transaction-specific temporary tables, so that rows in these tables are not deleted:

- GATHER_TABLE_STATS

- DELETE_TABLE_STATS

- DELETE_COLUMN_STATS

- DELETE_INDEX_STATS

- SET_TABLE_STATS

- SET_COLUMN_STATS

- SET_INDEX_STATS

- GET_TABLE_STATS

- GET_COLUMN_STATS

- GET_INDEX_STATS

The preceding program units observe the GLOBAL_TEMP_TABLE_STATS preference. For example, if the table preference is set to SESSION, then SET_TABLE_STATS sets the session statistics, and GATHER_TABLE_STATS preserves all rows in a transaction-specific temporary table. If the table preference is set to SHARED, then SET_TABLE_STATS sets the shared statistics, and GATHER_TABLE_STATS deletes all rows from a transaction-specific temporary table.

See Also:

- "Gathering Schema and Table Statistics"

- Oracle Database Concepts to learn about global temporary tables

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure

10.2.5 System Statistics

The system statistics describe hardware characteristics such as I/O and CPU performance and utilization. System statistics enable the query optimizer to more accurately estimate I/O and CPU costs when choosing execution plans.

The database does not invalidate previously parsed SQL statements when updating system statistics. The database parses all new SQL statements using new statistics.

See Also:
"Gathering System Statistics Manually"

10.2.6 User-Defined Optimizer Statistics

The extensible optimizer enables authors of user-defined functions and indexes to create statistics collection, selectivity, and cost functions for the optimizer to use when choosing a execution plan. The optimizer cost model is extended to integrate information supplied by the user to assess CPU and the I/O cost.

Statistics types act as interfaces for user-defined functions that influence the choice of execution plan by the optimizer. However, to use a statistics type, the optimizer requires a mechanism to bind the type to a database object such as a column, standalone function, object type, index, indextype, or package. The SQL statement ASSOCIATE STATISTICS creates this association.

Functions for user-defined statistics are relevant for columns that use both standard SQL data types and object types, and for domain indexes. When you associate a statistics type with a column or domain index, the database calls the statistics collection method in the statistics type whenever DBMS_STATS gathers statistics for database objects.

See Also:

- "Gathering Schema and Table Statistics"

- Oracle Database Data Cartridge Developer's Guide to learn about the extensible optimizer and user-defined statistics

10.3 How the Database Gathers Optimizer Statistics

Oracle Database provides several mechanisms to gather statistics. This section contains the following topics:

- DBMS_STATS Package

- Dynamic Statistics

- Online Statistics Gathering for Bulk Loads

See Also:

- "Controlling Automatic Optimizer Statistics Collection" or "Gathering Optimizer Statistics Manually"

- "Locking and Unlocking Optimizer Statistics"

10.3.1 DBMS_STATS Package

The DBMS_STATS PL/SQL package collects and manages optimizer statistics. This package enables you to control what and how statistics are collected, including the degree of parallelism for statistics collection, sampling methods, granularity of statistics collection in partitioned tables, and so on.

Note:
Do not use the COMPUTE and ESTIMATE clauses of the ANALYZE statement to collect optimizer statistics. These clauses have been deprecated. Instead, use DBMS_STATS.

Statistics gathered with the DBMS_STATS package are required for the creation of accurate execution plans. For example, table statistics gathered by DBMS_STATS include the number of rows, number of blocks, and average row length.

By default, Oracle Database uses automatic optimizer statistics collection. In this case, the database automatically runs DBMS_STATS to collect optimizer statistics for all schema objects for which statistics are missing or stale. The process eliminates many manual tasks associated with managing the optimizer, and significantly reduces the risks of generating suboptimal execution plans because of missing or stale statistics. You can also update and manage optimizer statistics by manually executing DBMS_STATS.

See Also:

- "Controlling Automatic Optimizer Statistics Collection"

- "Gathering Optimizer Statistics Manually"

- Oracle Database Administrator's Guide to learn more about automated maintenance tasks

- Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_STATS

10.3.2 Dynamic Statistics

By default, when optimizer statistics are missing, stale, or insufficient, the database automatically gathers dynamic statistics during a parse. The database uses recursive SQL to scan a small random sample of table blocks.

Note:
Dynamic statistics augment statistics rather than providing an alternative to them.

Dynamic statistics can supplement statistics such as table and index block counts, table and join cardinalities (estimated number of rows), join column statistics, and GROUP BY statistics. This information helps the optimizer improve plans by making better estimates for predicate selectivity.

Dynamic statistics are beneficial in the following situations:

- An execution plan is suboptimal because of complex predicates.

- The sampling time is a small fraction of total execution time for the query.

- The query is executed many times so that the sampling time is amortized.

See Also:

- "When the Database Samples Data"

- "Guideline for Accurate Statistics"

- "Controlling Dynamic Statistics"

10.3.3 Online Statistics Gathering for Bulk Loads

Starting in Oracle Database 12c, the database can gather table statistics automatically during the following types of bulk load operations:

- CREATE TABLE AS SELECT

- INSERT INTO ... SELECT into an empty table using a direct path insert

Note:
By default, a parallel insert uses a direct path insert. You can force a direct path insert by using the /*+APPEND */ hint.

This section contains the following topics:

- Purpose of Online Statistics Gathering for Bulk Loads

- Global Statistics During Inserts into Empty Partitioned Tables

- Index Statistics and Histograms During Bulk Loads

- Restrictions for Online Statistics Gathering for Bulk Loads

- Hints for Online Statistics Gathering for Bulk Loads

See Also:
Oracle Database Data Warehousing Guide to learn more about bulk loads

10.3.3.1 Purpose of Online Statistics Gathering for Bulk Loads

Data warehouses typically load large amounts of data into the database. For example, a sales data warehouse might load sales data nightly.

In releases earlier than Oracle Database 12c, to avoid the possibility of a suboptimal plan caused by stale statistics, you needed to gather statistics manually after a bulk load. The ability to gather statistics automatically during bulk loads has the following benefits:

- Improved performance
Gathering statistics during the load avoids an additional table scan to gather table statistics.

- Improved manageability
No user intervention is required to gather statistics after a bulk load.

10.3.3.2 Global Statistics During Inserts into Empty Partitioned Tables

When inserting rows into an empty partitioned table, the database gathers global statistics during the insert. For example, if you run INSERT INTO sales SELECT, and if sales is an empty partitioned table, then the database gathers global statistics for sales, but does not gather partition-level statistics.

If you insert rows into a empty partitioned table using extended syntax, and if the specified partition or subpartition is empty, then the database gathers the statistics on the specified partition or subpartition during the insert. No global level statistics are gathered. For example, if you run INSERT INTO sales PARTITION (sales_q4_2000) SELECT, and if partition sales_q4_2000 is empty before the insert (other partitions need not be empty), then the database gathers statistics during the insert. Moreover, if the INCREMENTAL preference is enabled for sales, then the database also gathers synopses for sales_q4_2000. Statistics are immediately available after the INSERT statement. However, if you roll back the transaction, then the database automatically deletes statistics gathered during the bulk load.

See Also:
"How to Enable Incremental Statistics Maintenance"

10.3.3.3 Index Statistics and Histograms During Bulk Loads

While gathering online statistics, the database does not gather index statistics or create histograms. If these statistics are required, then Oracle recommends running DBMS_STATS.GATHER_TABLE_STATS with the options parameter set to GATHER AUTO after the bulk load. For example, the following command gathers statistics for the bulk-loaded sh_ctas table:

EXEC DBMS_STATS.GATHER_TABLE_STATS( user, 'SH_CTAS', options => 'GATHER AUTO' );

The preceding example only gathers missing or stale statistics. The database does not gather table and basic column statistics collected during the bulk load.

Note:
You can set the table preference options to GATHER AUTO on the tables that you plan to bulk load. In this way, you need not explicitly set the options parameter when running GATHER_TABLE_STATS.

See Also:
"Gathering Schema and Table Statistics"

10.3.3.4 Restrictions for Online Statistics Gathering for Bulk Loads

Currently, statistics gathering does not occur automatically for bulk loads when any of the following conditions apply to the target table, partition, or subpartition:

- It is not empty, and you perform an INSERT INTO ... SELECT.
In this case, an OPTIMIZER STATISTICS GATHERING row source appears in the plan, but this row source is only a pass-through. The database does not actually gather optimizer statistics.

Note:
The DBA_TAB_COL_STATISTICS.NOTES column is set to STATS_ON_LOAD by a bulk load into an empty table. However, subsequent bulk loads into the non-empty table do not reset the NOTES column. One method for determining whether the database gathered statistics is to execute DBMS_STATS.FLUSH_DATABASE_MONITORING_INFO, and then query USER_TAB_MODIFICATIONS.INSERTS. If the query returns a row indicating the number of rows loaded, then the statistics were not gathered automatically during the most recent bulk load.

- It is in an Oracle-owned schema such as SYS.

- It is a nested table.

- It is an index-organized table (IOT).

- It is an external table.

- It is a global temporary table defined as ON COMMIT DELETE ROWS.

- It has virtual columns.

- It has a PUBLISH preference set to FALSE.

- Its statistics are locked.

- It is partitioned, INCREMENTAL is set to true, and extended syntax is not used.
For example, assume that you execute DBMS_STATS.SET_TABLE_PREFS(null, 'sales', incremental', 'true'). In this case, the database does not gather statistics for INSERT INTO sales SELECT, even when sales is empty. However, the database does gather statistics automatically for INSERT INTO sales PARTITION (sales_q4_2000) SELECT.

- It is loaded using a multitable insert statement.

See Also:
"Gathering Schema and Table Statistics"

10.3.3.5 Hints for Online Statistics Gathering for Bulk Loads

By default, the database gathers statistics during bulk loads. You can disable the feature at the statement level by using the NO_GATHER_OPTIMIZER_STATISTICS hint, and enable the feature at the statement level by using the GATHER_OPTIMIZER_STATISTICS hint. For example, the following statement disables online statistics gathering for bulk loads:

CREATE TABLE employees2 AS
SELECT /*+NO_GATHER_OPTIMIZER_STATISTICS */* FROM employees

See Also:
Oracle Database SQL Language Reference to learn about the GATHER_OPTIMIZER_STATISTICS and NO_GATHER_OPTIMIZER_STATISTICS hints

10.4 When the Database Gathers Optimizer Statistics

The database collects optimizer statistics at various times and from various sources. The database uses the following sources:

- DBMS_STATS execution, automatic or manual
This PL/SQL package is the primary means of gathering optimizer statistics. See Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure.

- SQL compilation
During SQL compilation, the database can augment the statistics previously gathered by DBMS_STATS. In this stage, the database runs additional queries to obtain more accurate information on how many rows in the tables satisfy the WHERE clause predicates in the SQL statement (see "When the Database Samples Data").

- SQL execution
During execution, the database can further augment previously gathered statistics. In this stage, Oracle Database collects the number of rows produced by every row source during the execution of a SQL statement. At the end of execution, the optimizer determines whether the estimated number of rows is inaccurate enough to warrant reparsing at the next statement execution. If the cursor is marked for reparsing, then the optimizer uses actual row counts from the previous execution instead of estimates.

- SQL profiles
A SQL profile is a collection of auxiliary statistics on a query. The profile stores these supplemental statistics in the data dictionary. The optimizer uses SQL profiles during optimization to determine the most optimal plan (see "About SQL Profiles").

The database stores optimizer statistics in the data dictionary and updates or replaces them as needed. You can query statistics in data dictionary views.

This section contains the following topics:

- SQL Plan Directives

- When the Database Samples Data

- How the Database Samples Data

10.4.1 SQL Plan Directives

A SQL plan directive is additional information and instructions that the optimizer can use to generate a more optimal plan. For example, a SQL plan directive can instruct the optimizer to record a missing extension.

10.4.1.1 About SQL Plan Directives

During SQL execution, if a cardinality misestimate occurs, then the database creates SQL plan directives. During SQL compilation, the optimizer examines the query corresponding to the directive to determine whether missing extensions or histograms exist (see "Managing Extended Statistics"). The optimizer records any missing extensions. Subsequent DBMS_STATS calls collect statistics for the extensions.

The optimizer uses dynamic statistics whenever it does not have sufficient statistics corresponding to the directive. For example, the optimizer gathers dynamic statistics until the creation of column group statistics, and also after this point when misestimates occur. Currently, the optimizer monitors only column groups. The optimizer does not create an extension on expressions.

SQL plan directives are not tied to a specific SQL statement or SQL ID. The optimizer can use directives for statements that are nearly identical because directives are defined on a query expression. For example, directives can help the optimizer with queries that use similar patterns, such as queries that are identical except for a select list item.

The database automatically manages SQL plan directives. The database initially creates directives in the shared pool. The database periodically writes the directives to the SYSAUX tablespace. You can manage directives with the APIs available in the DBMS_SPD package.

See Also:
"About Statistics on Column Groups"

10.4.1.2 How the Optimizer Uses SQL Plan Directives: Example

This example shows how the database automatically creates and uses SQL plan directives for SQL statements.

Assumptions

You plan to run queries against the sh schema, and you have privileges on this schema and on data dictionary and V$ views.

To see how the database uses a SQL plan directive:

1. Query the sh.customers table.

SELECT /*+gather_plan_statistics*/ *
FROM   customers
WHERE  cust_state_province='CA'
AND    country_id='US';

The gather_plan_statistics hint shows the actual number of rows returned from each operation in the plan. Thus, you can compare the optimizer estimates with the actual number of rows returned.

2. Query the plan for the preceding query.
Example 10-6 shows the execution plan (sample output included).

Example 10-6 Execution Plan

SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST'));

PLAN_TABLE_OUTPUT
-------------------------------------
SQL_ID  b74nw722wjvy3, child number 0
-------------------------------------
select /*+gather_plan_statistics*/ * from customers where
CUST_STATE_PROVINCE='CA' and country_id='US'

Plan hash value: 1683234692

--------------------------------------------------------------------------------------------------
| Id  | Operation         | Name      | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  |
--------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT  |           |      1 |        |     29 |00:00:00.01 |      17 |     14 |
|*  1 |  TABLE ACCESS FULL| CUSTOMERS |      1 |      8 |     29 |00:00:00.01 |      17 |     14 |
--------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US'))

The actual number of rows (A-Rows) returned by each operation in the plan varies greatly from the estimates (E-Rows). This statement is a candidate for automatic reoptimization (see "Automatic Reoptimization").

3. Check whether the customers query can be reoptimized.
The following statement queries the V$SQL.IS_REOPTIMIZABLE value (sample output included):

SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE
FROM   V$SQL
WHERE  SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%';

SQL_ID        CHILD_NUMBER SQL_TEXT    I
------------- ------------ ----------- -
b74nw722wjvy3            0 select /*+g Y
ather_plan_
statistics*
/ * from cu
stomers whe
re CUST_STA
TE_PROVINCE
='CA' and c
ountry_id='
US'

The IS_REOPTIMIZABLE column is marked Y, so the database will perform a hard parse of the customers query on the next execution. The optimizer uses the execution statistics from this initial execution to determine the plan. The database persists the information learned from reoptimization as a SQL plan directive.

4. Display the directives for the sh schema.
Example 10-7 uses DBMS_SPD to write the SQL plan directives to disk, and then shows the directives for the sh schema only.

Example 10-7 Displaying SQL Plan Directives for the sh Schema

EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE;

SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME,
o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON
FROM   DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o
WHERE  d.DIRECTIVE_ID=o.DIRECTIVE_ID
AND    o.OWNER IN ('SH')
ORDER BY 1,2,3,4,5;

DIR_ID                  OWNER OBJECT_NAME   COL_NAME    OBJECT TYPE             STATE REASON
----------------------- ----- ------------- ----------- ------ ---------------- ------ ------------------------
1484026771529551585     SH    CUSTOMERS     COUNTRY_ID  COLUMN DYNAMIC_SAMPLING USABLE SINGLE TABLE CARDINALITY
MISESTIMATE
1484026771529551585     SH    CUSTOMERS     CUST_STATE_ COLUMN DYNAMIC_SAMPLING USABLE SINGLE TABLE CARDINALITY
PROVINCE                                   MISESTIMATE
1484026771529551585     SH    CUSTOMERS                 TABLE  DYNAMIC_SAMPLING USABLE SINGLE TABLE CARDINALITY
MISESTIMATE

Initially, the database stores SQL plan directives in memory, and then writes them to disk every 15 minutes. Thus, the preceding example calls DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE to force the database to write the directives to the SYSAUX tablespace.

Monitor directives using the views DBA_SQL_PLAN_DIRECTIVES and DBA_SQL_PLAN_DIR_OBJECTS. Three entries appear in the views, one for the customers table itself, and one for each of the correlated columns. Because the customers query has the IS_REOPTIMIZABLE value of Y, if you reexecute the statement, then the database will hard parse it again, and then generate a plan based on the previous execution statistics.

5. Query the customers table again.
For example, enter the following statement:

SELECT /*+gather_plan_statistics*/ *
FROM   customers
WHERE  cust_state_province='CA'
AND    country_id='US';

6. Query the plan in the cursor.
Example 10-8 shows the execution plan (sample output included).

Example 10-8 Execution Plan

SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST'));

PLAN_TABLE_OUTPUT
-------------------------------------
SQL_ID  b74nw722wjvy3, child number 1
-------------------------------------
select /*+gather_plan_statistics*/ * from customers where
CUST_STATE_PROVINCE='CA' and country_id='US'

Plan hash value: 1683234692

-----------------------------------------------------------------------------------------
| Id  | Operation         | Name      | Starts | E-Rows | A-Rows |   A-Time   | Buffers |
-----------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT  |           |      1 |        |     29 |00:00:00.01 |      17 |
|*  1 |  TABLE ACCESS FULL| CUSTOMERS |      1 |     29 |     29 |00:00:00.01 |      17 |
-----------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US'))

Note
-----
- cardinality feedback used for this statement

The Note section indicates that the database used reoptimization for this statement. The estimated number of rows (E-Rows) is now correct. The SQL plan directive has not been used yet.

7. Query the cursors for the customers query.
For example, run the following query (sample output included):

SELECT SQL_ID, CHILD_NUMBER, SQL_TEXT, IS_REOPTIMIZABLE
FROM   V$SQL
WHERE  SQL_TEXT LIKE 'SELECT /*+gather_plan_statistics*/%';

SQL_ID        CHILD_NUMBER SQL_TEXT    I
------------- ------------ ----------- -
b74nw722wjvy3            0 select /*+g Y
ather_plan_
statistics*
/ * from cu
stomers whe
re CUST_STA
TE_PROVINCE
='CA' and c
ountry_id='
US'

b74nw722wjvy3            1 select /*+g N
ather_plan_
statistics*
/ * from cu
stomers whe
re CUST_STA
TE_PROVINCE
='CA' and c
ountry_id='
US'

A new plan exists for the customers query, and also a new child cursor.

8. Confirm that a SQL plan directive exists and is usable for other statements.
For example, run the following query, which is similar but not identical to the original customers query (the state is MA instead of CA):

SELECT /*+gather_plan_statistics*/ CUST_EMAIL
FROM   CUSTOMERS
WHERE  CUST_STATE_PROVINCE='MA'
AND    COUNTRY_ID='US';

9. Query the plan in the cursor.
The following statement queries the cursor (sample output included).:

SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST'));

PLAN_TABLE_OUTPUT
-------------------------------------
SQL_ID  3tk6hj3nkcs2u, child number 0
-------------------------------------
Select /*+gather_plan_statistics*/ cust_email From   customers Where
cust_state_province='MA' And    country_id='US'

Plan hash value: 1683234692

-------------------------------------------------------------------------------
|Id | Operation         | Name      | Starts|E-Rows|A-Rows| A-Time    |Buffers|
-------------------------------------------------------------------------------
| 0 | SELECT STATEMENT  |           |     1 |      |    2 |00:00:00.01|    16 |
|*1 |  TABLE ACCESS FULL| CUSTOMERS |     1 |     2|    2 |00:00:00.01|    16 |
-----------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - filter(("CUST_STATE_PROVINCE"='MA' AND "COUNTRY_ID"='US'))

Note
-----
- dynamic sampling used for this statement (level=2)
- 1 Sql Plan Directive used for this statement

The Note section of the plan shows that the optimizer used the SQL directive for this statement, and also used dynamic statistics.

See Also:

- "Managing SQL Plan Directives"

- Oracle Database Reference to learn about DBA_SQL_PLAN_DIRECTIVES, V$SQL, and other database views

- Oracle Database Reference to learn about DBMS_SPD

10.4.1.3 How the Optimizer Uses Extensions and SQL Plan Directives: Example

This example is a continuation of "How the Optimizer Uses SQL Plan Directives: Example". The example shows how the database uses a SQL plan directive until the optimizer verifies that an extension exists and the statistics are applicable. At this point, the directive changes its status to SUPERSEDED. Subsequent compilations use the statistics instead of the directive.

Assumptions

This example assumes you have already followed the steps in "How the Optimizer Uses SQL Plan Directives: Example".

To see how the optimizer uses an extension and SQL plan directive:

1. Gather statistics for the sh.customers table.
For example, execute the following PL/SQL program:

BEGIN
DBMS_STATS.GATHER_TABLE_STATS('SH','CUSTOMERS');
END;
/

2. Check whether an extension exists on the customers table.
For example, execute the following query (sample output included):

SELECT TABLE_NAME, EXTENSION_NAME, EXTENSION
FROM   DBA_STAT_EXTENSIONS
WHERE  OWNER='SH'
AND    TABLE_NAME='CUSTOMERS';

TABLE_NAM EXTENSION_NAME                 EXTENSION
--------- ------------------------------ -----------------------------------
CUSTOMERS SYS_STU#S#WF25Z#QAHIHE#MOFFMM_ ("CUST_STATE_PROVINCE","COUNTRY_ID")

The preceding output indicates that a column group extension exists on the cust_state_province and country_id columns.

3. Query the state of the SQL plan directive.
Example 10-9 queries the data dictionary for information about the directive.

Example 10-9 Display Directives for sh Schema

EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE;

SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME,
o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON
FROM   DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o
WHERE  d.DIRECTIVE_ID=o.DIRECTIVE_ID
AND    o.OWNER IN ('SH')
ORDER BY 1,2,3,4,5;

DIR_ID              OWN OBJECT_NA COL_NAME   OBJECT  TYPE             STATE         REASON
------------------- --- --------- ---------- ------- ---------------- ------------- --------------------
1484026771529551585  SH CUSTOMERS COUNTRY_ID  COLUMN DYNAMIC_SAMPLING USABLE        SINGLE TABLE CARDINALITY
MISESTIMATE
1484026771529551585  SH CUSTOMERS CUST_STATE_ COLUMN DYNAMIC_SAMPLING USABLE        SINGLE TABLE CARDINALITY
PROVINCE                                          MISESTIMATE
1484026771529551585  SH CUSTOMERS              TABLE DYNAMIC_SAMPLING USABLE        SINGLE TABLE CARDINALITY
MISESTIMATE

Although column group statistics exist, the directive has a state of USABLE because the database has not yet recompiled the statement. During the next compilation, the optimizer verifies that the statistics are applicable. If they are applicable, then the status of the directive changes to SUPERSEDED. Subsequent compilations use the statistics instead of the directive.

4. Query the sh.customers table.

SELECT /*+gather_plan_statistics*/ *
FROM   customers
WHERE  cust_state_province='CA'
AND    country_id='US';

5. Query the plan in the cursor.
Example 10-10 shows the execution plan (sample output included).

Example 10-10 Execution Plan

SQL> SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST'));

PLAN_TABLE_OUTPUT
-------------------------------------
SQL_ID  b74nw722wjvy3, child number 0
-------------------------------------
select /*+gather_plan_statistics*/ * from customers where
CUST_STATE_PROVINCE='CA' and country_id='US'

Plan hash value: 1683234692

-----------------------------------------------------------------------------------------
| Id  | Operation         | Name      | Starts | E-Rows | A-Rows |   A-Time   | Buffers |
-----------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT  |           |      1 |        |     29 |00:00:00.01 |      16 |
|*  1 |  TABLE ACCESS FULL| CUSTOMERS |      1 |     29 |     29 |00:00:00.01 |      16 |
-----------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US'))

Note
-----
- dynamic sampling used for this statement (level=2)
- 1 Sql Plan Directive used for this statement

The Note section shows that the optimizer used the directive and not the extended statistics. During the compilation, the database verified the extended statistics.

6. Query the state of the SQL plan directive.
Example 10-11 queries the data dictionary for information about the directive.

Example 10-11 Display Directives for sh Schema

EXEC DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE;

SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME,
o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON
FROM   DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o
WHERE  d.DIRECTIVE_ID=o.DIRECTIVE_ID
AND    o.OWNER IN ('SH')
ORDER BY 1,2,3,4,5;

DIR_ID                 OWN OBJECT_NAME   COL_NAME   OBJECT  TYPE             STATE     REASON
-------------------    --- -----------   ---------- ------- ---------------- --------- ------------------------
1484026771529551585     SH   CUSTOMERS   COUNTRY_ID  COLUMN DYNAMIC_SAMPLING SUPERSEDED SINGLE TABLE CARDINALITY
MISESTIMATE
1484026771529551585     SH   CUSTOMERS   CUST_STATE_ COLUMN DYNAMIC_SAMPLING SUPERSEDED SINGLE TABLE CARDINALITY
PROVINCE                                       MISESTIMATE
1484026771529551585     SH   CUSTOMERS                TABLE DYNAMIC_SAMPLING SUPERSEDED SINGLE TABLE CARDINALITY
MISESTIMATE

The state of the directive, which has changed to SUPERSEDED, indicates that the corresponding column or groups have an extension or histogram, or that another SQL plan directive exists that can be used for the directive.

7. Query the sh.customers table again, using a slightly different form of the statement.
For example, run the following query:

SELECT /*+gather_plan_statistics*/ /* force reparse */ *
FROM   customers
WHERE  cust_state_province='CA'
AND    country_id='US';

If the cursor is in the shared SQL area, then the database typically shares the cursor. To force a reparse, this step changes the SQL text slightly by adding a comment.

8. Query the plan in the cursor.
Example 10-12 shows the execution plan (sample output included).

Example 10-12 Execution Plan

SQL> SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR(FORMAT=>'ALLSTATS LAST'));

PLAN_TABLE_OUTPUT
-------------------------------------
SQL_ID  b74nw722wjvy3, child number 0
-------------------------------------
select /*+gather_plan_statistics*/ * from customers where
CUST_STATE_PROVINCE='CA' and country_id='US'

Plan hash value: 1683234692

-----------------------------------------------------------------------------------------
| Id  | Operation         | Name      | Starts | E-Rows | A-Rows |   A-Time   | Buffers |
-----------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT  |           |      1 |        |     29 |00:00:00.01 |      17 |
|*  1 |  TABLE ACCESS FULL| CUSTOMERS |      1 |     29 |     29 |00:00:00.01 |      17 |
-----------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - filter(("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"='US'))

19 rows selected.

The absence of a Note shows that the optimizer used the extended statistics instead of the SQL plan directive. If the directive is not used for 53 weeks, then the database automatically purges it.

See Also:

- "Managing SQL Plan Directives"

- Oracle Database Reference to learn about DBA_SQL_PLAN_DIRECTIVES, V$SQL, and other database views

- Oracle Database Reference to learn about DBMS_SPD

10.4.2 When the Database Samples Data

In releases earlier than Oracle Database 12c, the database always gathered dynamic statistics (formerly called dynamic sampling) during optimization, and only when a table in the query had no statistics. Starting in Oracle Database 12c, the optimizer automatically decides whether dynamic statistics are useful and which statistics level to use for all SQL statements.

The primary factor in the decision to use dynamic statistics is whether available statistics are sufficient to generate an optimal plan. If statistics are insufficient, then the optimizer uses dynamic statistics.

Automatic dynamic statistics are enabled when any of the following conditions is true:

- The OPTIMIZER_DYNAMIC_SAMPLING initialization parameter uses its default value, which means that it is not explicitly set.

- The dynamic statistics level is set to 11 either through the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter or a SQL hint (see "Controlling Dynamic Statistics").

In general, the optimizer uses default statistics rather than dynamic statistics to compute statistics needed during optimizations on tables, indexes, and columns. The optimizer decides whether to use dynamic statistics based on several factors. For example, the database uses automatic dynamic statistics in the following situations:

- The SQL statement uses parallel execution.

- A SQL plan directive exists.

- The SQL statement is known to the database, which means that it was captured in SQL Plan Management or Automatic Workload Repository, or is currently in the shared SQL area.

Figure 10-2 illustrates the process of gathering dynamic statistics.

Figure 10-2 Dynamic Statistics

As shown in Figure 10-2, the optimizer automatically gathers dynamic statistics in the following cases:

- Missing statistics
When tables in a query have no statistics, the optimizer gathers basic statistics on these tables before optimization. Statistics can be missing because the application creates new objects without a follow-up call to DBMS_STATS to gather statistics, or because statistics were locked on an object before statistics were gathered.

In this case, the statistics are not as high-quality or as complete as the statistics gathered using the DBMS_STATS package. This trade-off is made to limit the impact on the compile time of the statement.

- Stale statistics
Statistics gathered by DBMS_STATS can become out-of-date. Typically, statistics are stale when 10% or more of the rows in the table have changed since the last time statistics were gathered.

For an example of the problem posed by stale statistics, consider a sales table that includes the sales date. After an application inserts new rows, the maximum statistics on the sales date column becomes stale because new rows have a higher sales date than the maximum value seen during the last statistics gathering. For any query that fetches the most recently added sales data, the optimizer assumes that table access will return very few or no rows, which leads to the selection of a suboptimal access path to the sales table (for example, the index on the sales date column), a suboptimal join method (typically a cartesian product), or an inefficient join order. This is commonly known as the out-of-range condition: the value specified in the predicate on the sales date column is outside the column statistics value domain.

- Insufficient statistics
Statistics can be insufficient whenever the optimizer estimates the selectivity of predicates (filter or join) or the GROUP BY clause without taking into account correlation between columns, skew in the column data distribution, statistics on expressions, and so on.

Extended statistics help the optimizer obtain accurate quality cardinality estimates for complex predicate expressions (see "About Statistics on Column Groups"). The optimizer can use dynamic statistics to compensate for the lack of extended statistics or when it cannot use extended statistics, for example, for non-equality predicates.

Note:
The database does not use dynamic statistics for queries that contain the AS OF clause.

See Also:
Oracle Database Reference to learn about the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter

10.4.3 How the Database Samples Data

At the beginning of optimization, when deciding whether a table is a candidate for dynamic statistics, the optimizer checks for the existence of persistent SQL plan directives on the table (see Figure 10-2). For each directive, the optimizer registers a statistics expression that the optimizer computes when it must determine the selectivity of a predicate involving the table.

When sampling is necessary, the database must determine the sample size (see Figure 10-2). Starting in Oracle Database 12c, if the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter is not explicitly set to a value other than 11, then the optimizer automatically decides whether to use dynamic statistics and which level to use.

In Figure 10-2, the database issues a recursive SQL statement to scan a small random sample of the table blocks. The database applies the relevant single-table predicates and joins to estimate predicate selectivities.

The database persists the results of dynamic statistics as sharable statistics. The database can share the results during the SQL compilation of one query with recompilations of the same query. The database can also reuse the results for queries that have the same patterns. If no rows have been inserted, deleted, or updated in the table being sampled, then the use of dynamic statistics is repeatable.

See Also:

- "Controlling Dynamic Statistics" to learn how to set the dynamic statistics level

- Oracle Database Reference for details about the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter

11 Histograms

A histogram is a special type of column statistic that provides more detailed information about the data distribution in a table column. A histogram sorts values into "buckets," as you might sort coins into buckets.

Based on the NDV and the distribution of the data, the database chooses the type of histogram to create. (In some cases, when creating a histogram, the database samples an internally predetermined number of rows.) The types of histograms are as follows:

- Frequency histograms and top frequency histograms

- Height-Balanced histograms (legacy)

- Hybrid histograms

This section contains the following topics:

- Purpose of Histograms

- When Oracle Database Creates Histograms

- Cardinality Algorithms When Using Histograms

- Frequency Histograms

- Height-Balanced Histograms (Legacy)

- Hybrid Histograms

11.1 Purpose of Histograms

By default the optimizer assumes a uniform distribution of rows across the distinct values in a column. For columns that contain data skew (a nonuniform distribution of data within the column), a histogram enables the optimizer to generate accurate cardinality estimates for filter and join predicates that involve these columns.

For example, a California-based book store ships 95% of the books to California, 4% to Oregon, and 1% to Nevada. The book orders table has 300,000 rows. A table column stores the state to which orders are shipped. A user queries the number of books shipped to Oregon. Without a histogram, the optimizer assumes an even distribution of 300000/3 (the NDV is 3), estimating cardinality at 100,000 rows. With this estimate, the optimizer chooses a full table scan. With a histogram, the optimizer calculates that 4% of the books are shipped to Oregon, and chooses an index scan.

See Also:
"Introduction to Access Paths"

11.2 When Oracle Database Creates Histograms

If DBMS_STATS gathers statistics for a table, and if queries have referenced the columns in this table, then Oracle Database creates histograms automatically as needed according to the previous query workload.

The basic process is as follows:

1. You run DBMS_STATS for a table with the METHOD_OPT parameter set to the default SIZE AUTO.

2. A user queries the table.

3. The database notes the predicates in the preceding query and updates the data dictionary table SYS.COL_USAGE$.

4. You run DBMS_STATS again, causing DBMS_STATS to query SYS.COL_USAGE$ to determine which columns require histograms based on the previous query workload.

Consequences of the AUTO feature include the following:

- As queries change over time, DBMS_STATS may change which statistics it gathers. For example, even if the data in a table does not change, queries and DBMS_STATS operations can cause the plans for queries that reference these tables to change.

- If you gather statistics for a table and do not query the table, then the database does not create histograms for columns in this table. For the database to create the histograms automatically, you must run one or more queries to populate the column usage information in SYS.COL_USAGE$.

Example 11-1 Automatic Histogram Creation

Assume that sh.sh_ext is an external table that contains the same rows as the sh.sales table. You create new table sales2 and perform a bulk load using sh_ext as a source, which automatically creates statistics for sales2 (see "Online Statistics Gathering for Bulk Loads"). You also create indexes as follows:

SQL> CREATE TABLE sales2 AS SELECT * FROM sh_ext;

SQL> CREATE INDEX sh_12c_idx1 ON sales2(prod_id);
SQL> CREATE INDEX sh_12c_idx2 ON sales2(cust_id,time_id);

You query the data dictionary to determine whether histograms exist for the sales2 columns. Because sales2 has not yet been queried, the database has not yet created histograms:

SQL> SELECT COLUMN_NAME, NOTES, HISTOGRAM
2  FROM   USER_TAB_COL_STATISTICS
3  WHERE  TABLE_NAME = 'SALES2';

COLUMN_NAME   NOTES          HISTOGRAM
------------- -------------- ---------
AMOUNT_SOLD   STATS_ON_LOAD  NONE
QUANTITY_SOLD STATS_ON_LOAD  NONE
PROMO_ID      STATS_ON_LOAD  NONE
CHANNEL_ID    STATS_ON_LOAD  NONE
TIME_ID       STATS_ON_LOAD  NONE
CUST_ID       STATS_ON_LOAD  NONE
PROD_ID       STATS_ON_LOAD  NONE

You query sales2 for the number of rows for product 42, and then gather table statistics using the GATHER AUTO option:

SQL> SELECT COUNT(*) FROM sales2 WHERE prod_id = 42;

COUNT(*)
----------
12116

SQL> EXEC DBMS_STATS.GATHER_TABLE_STATS(USER,'SALES2',OPTIONS=>'GATHER AUTO');

A query of the data dictionary now shows that the database created a histogram on the prod_id column based on the information gather during the preceding query:

SQL> SELECT COLUMN_NAME, NOTES, HISTOGRAM
2  FROM   USER_TAB_COL_STATISTICS
3  WHERE  TABLE_NAME = 'SALES2';

COLUMN_NAME   NOTES          HISTOGRAM
------------- -------------- ---------
AMOUNT_SOLD   STATS_ON_LOAD  NONE
QUANTITY_SOLD STATS_ON_LOAD  NONE
PROMO_ID      STATS_ON_LOAD  NONE
CHANNEL_ID    STATS_ON_LOAD  NONE
TIME_ID       STATS_ON_LOAD  NONE
CUST_ID       STATS_ON_LOAD  NONE
PROD_ID       HISTOGRAM_ONLY FREQUENCY

11.3 Cardinality Algorithms When Using Histograms

For histograms, the algorithm for cardinality depends on factors such as the endpoint numbers and values, and whether column values are popular or nonpopular.

This section contains the following topics:

- Endpoint Numbers and Values

- Popular and Nonpopular Values

- Bucket Compression

11.3.1 Endpoint Numbers and Values

An endpoint number is a number that uniquely identifies a bucket. In frequency and hybrid histograms, the endpoint number is the cumulative frequency of all values included in the current and previous buckets. For example, a bucket with endpoint number 100 means the total frequency of values in the current and all previous buckets is 100. In height-balanced histograms, the optimizer numbers buckets sequentially, starting at 0 or 1. In all cases, the endpoint number is the bucket number.

An endpoint value is the highest value in the range of values in a bucket. For example, if a bucket contains only the values 52794 and 52795, then the endpoint value is 52795.

11.3.2 Popular and Nonpopular Values

The popularity of a value in a histogram affects the cardinality estimate algorithm as follows:

- Popular values
A popular value occurs as an endpoint value of multiple buckets. The optimizer determines whether a value is popular by first checking whether it is the endpoint value for a bucket. If so, then for frequency histograms, the optimizer subtracts the endpoint number of the previous bucket from the endpoint number of the current bucket. Hybrid histograms already store this information for each endpoint individually. If this value is greater than 1, then the value is popular.

The optimizer calculates its cardinality estimate for popular values using the following formula:

cardinality of popular value =
(num of rows in table) *
(num of endpoints spanned by this value / total num of endpoints)

- Nonpopular values
Any value that is not popular is a nonpopular value. The optimizer calculates the cardinality estimates for nonpopular values using the following formula:

cardinality of nonpopular value =
(num of rows in table) * density

The optimizer calculates density using an internal algorithm based on factors such as the number of buckets and the NDV. Density is expressed as a decimal number between 0 and 1. Values close to 1 indicate that the optimizer expects many rows to be returned by a query referencing this column in its predicate list. Values close to 0 indicate that the optimizer expects few rows to be returned.

See Also:
Oracle Database Reference to learn about the DBA_TAB_COL_STATISTICS.DENSITY column

11.3.3 Bucket Compression

In some cases, to reduce the total number of buckets, the optimizer compresses multiple buckets into a single bucket. For example, the following frequency histogram indicates that the first bucket number is 1 and the last bucket number is 23:

ENDPOINT_NUMBER ENDPOINT_VALUE
--------------- --------------
1          52792
6          52793
8          52794
9          52795
10          52796
12          52797
14          52798
23          52799

Several buckets are "missing." Originally, buckets 2 through 6 each contained a single instance of value 52793. The optimizer compressed all of these buckets into the bucket with the highest endpoint number (bucket 6), which now contains 5 instances of value 52793. This value is popular because the difference between the endpoint number of the current bucket (6) and the previous bucket (1) is 5. Thus, before compression the value 52793 was the endpoint for 5 buckets.

The following annotations show which buckets are compressed, and which values are popular:

ENDPOINT_NUMBER ENDPOINT_VALUE
--------------- --------------
1          52792 -> nonpopular
6          52793 -> buckets 2-6 compressed into 6; popular
8          52794 -> buckets 7-8 compressed into 8; popular
9          52795 -> nonpopular
10          52796 -> nonpopular
12          52797 -> buckets 11-12 compressed into 12; popular
14          52798 -> buckets 13-14 compressed into 14; popular
23          52799 -> buckets 15-23 compressed into 23; popular

11.4 Frequency Histograms

In a frequency histogram, each distinct column value corresponds to a single bucket of the histogram. Because each value has its own dedicated bucket, some buckets may have many values, whereas others have few.

An analogy to a frequency histogram is sorting coins so that each individual coin initially gets its own bucket. For example, the first penny is in bucket 1, the second penny is in bucket 2, the first nickel is in bucket 3, and so on. You then consolidate all the pennies into a single penny bucket, all the nickels into a single nickel bucket, and so on with the remainder of the coins.

A top frequency histogram is a variation on a frequency histogram that ignores nonpopular values that are statistically insignificant. For example, if a pile of 1000 coins contains only a single penny, then you can ignore the penny when sorting the coins into buckets. A top frequency histogram can produce a better histogram for highly popular values.

This section contains the following topics:

- Criteria For Frequency Histograms

- Generating a Frequency Histogram

- Generating a Top Frequency Histogram

11.4.1 Criteria For Frequency Histograms

Frequency histograms depend on the number of requested histogram buckets, represented by the variable n. By default, n is 254 when the number of buckets is not specified using the method_opt parameter of the DBMS_STATS statistics gathering procedures.

The database creates a frequency histogram when the NDV is less than or equal to n. For example, the sh.countries.country_subregion_id column has 8 distinct values, ranging sequentially from 52792 to 52799. If n is the default of 254, then the optimizer creates a frequency histogram.

If a small number of values occupies most of the rows, then creating a frequency histogram on this small set of values is useful even when the NDV is greater than n. To create a better quality histogram for popular values, the optimizer ignores the nonpopular values. The database creates a top frequency histogram when all of the following conditions are met:

- The data set has more than n distinct values.

- The percentage of rows occupied by the top n frequent values is equal to or greater than threshold p, where p is (1-(1/n))*100.

- The estimate_percent parameter is set to AUTO_SAMPLE_SIZE in the DBMS_STATS statistics gathering procedure.

Starting in Oracle Database 12c, if the sampling size is the default of AUTO_SAMPLE_SIZE, then the database creates frequency histograms from a full table scan. For all other sampling percentage specifications, the database derives frequency histograms from a sample. In releases earlier than Oracle Database 12c, the database gathered histograms based on a small sample, which meant that low-frequency values often did not appear in the sample. Using density in this case sometimes led the optimizer to overestimate selectivity.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about AUTO_SAMPLE_SIZE

11.4.2 Generating a Frequency Histogram

This scenario shows how to generate a frequency histogram using the sample schemas.

Assumptions

This scenario assumes that you want to generate a frequency histogram on the sh.countries.country_subregion_id column. This table has 23 rows.

The following query shows that the country_subregion_id column contains 8 distinct values (sample output included) that are unevenly distributed:

SELECT country_subregion_id, count(*)
FROM   sh.countries
GROUP BY country_subregion_id
ORDER BY 1;

COUNTRY_SUBREGION_ID   COUNT(*)
-------------------- ----------
52792          1
52793          5
52794          2
52795          1
52796          1
52797          2
52798          2
52799          9

To generate a frequency histogram:

1. Gather statistics for sh.countries and the country_subregion_id column, letting the number of buckets default to 254.
For example, execute the following PL/SQL anonymous block:

BEGIN
DBMS_STATS.GATHER_TABLE_STATS (
ownname    => 'SH'
,   tabname    => 'COUNTRIES'
,   method_opt => 'FOR COLUMNS COUNTRY_SUBREGION_ID'
);
END;

2. Query the histogram information for the country_subregion_id column.
For example, use the following query (sample output included):

SELECT TABLE_NAME, COLUMN_NAME, NUM_DISTINCT, HISTOGRAM
FROM   USER_TAB_COL_STATISTICS
WHERE  TABLE_NAME='COUNTRIES'
AND    COLUMN_NAME='COUNTRY_SUBREGION_ID';

TABLE_NAME COLUMN_NAME          NUM_DISTINCT HISTOGRAM
---------- -------------------- ------------ ---------------
COUNTRIES  COUNTRY_SUBREGION_ID            8 FREQUENCY

The optimizer chooses a frequency histogram because n or fewer distinct values exist in the column, where n defaults to 254.

3. Query the endpoint number and endpoint value for the country_subregion_id column.
For example, use the following query (sample output included):

SELECT ENDPOINT_NUMBER, ENDPOINT_VALUE
FROM   USER_HISTOGRAMS
WHERE  TABLE_NAME='COUNTRIES'
AND    COLUMN_NAME='COUNTRY_SUBREGION_ID';

ENDPOINT_NUMBER ENDPOINT_VALUE
--------------- --------------
1          52792
6          52793
8          52794
9          52795
10          52796
12          52797
14          52798
23          52799

Figure 11-1 is a graphical illustration of the 8 buckets in the histogram. Each value is represented as a coin that is dropped into a bucket.

Figure 11-1 Frequency Histogram

As shown in Figure 11-1, each distinct value has its own bucket. Because this is a frequency histogram, the endpoint number is the cumulative frequency of endpoints. For 52793, the endpoint number 6 indicates that the value appears 5 times (6 - 1). For 52794, the endpoint number 8 indicates that the value appears 2 times (8 - 6).

Every bucket whose endpoint is at least 2 greater than the previous endpoint contains a popular value. Thus, buckets 6, 8, 12, 14, and 23 contain popular values. The optimizer calculates their cardinality based on endpoint numbers. For example, the optimizer calculates the cardinality (C) of value 52799 using the following formula, where the number of rows in the table is 23:

C = 23 * ( 9 / 23 )

Buckets 1, 9, and 10 contain nonpopular values. The optimizer estimates their cardinality based on density.

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure

- Oracle Database Reference to learn about the USER_TAB_COL_STATISTICS view

- Oracle Database Reference to learn about the USER_HISTOGRAMS view

11.4.3 Generating a Top Frequency Histogram

This scenario shows how to generate a top frequency histogram using the sample schemas.

Assumptions

This scenario assumes that you want to generate a top frequency histogram on the sh.countries.country_subregion_id column. This table has 23 rows.

The following query shows that the country_subregion_id column contains 8 distinct values (sample output included) that are unevenly distributed:

SELECT country_subregion_id, count(*)
FROM   sh.countries
GROUP BY country_subregion_id
ORDER BY 1;

COUNTRY_SUBREGION_ID   COUNT(*)
-------------------- ----------
52792          1
52793          5
52794          2
52795          1
52796          1
52797          2
52798          2
52799          9

To generate a top frequency histogram:

1. Gather statistics for sh.countries and the country_subregion_id column, specifying fewer buckets than distinct values.
For example, enter the following command to specify 7 buckets:

BEGIN
DBMS_STATS.GATHER_TABLE_STATS (
ownname    => 'SH'
,   tabname    => 'COUNTRIES'
,   method_opt => 'FOR COLUMNS COUNTRY_SUBREGION_ID SIZE 7'
);
END;

2. Query the histogram information for the country_subregion_id column.
For example, use the following query (sample output included):

SELECT TABLE_NAME, COLUMN_NAME, NUM_DISTINCT, HISTOGRAM
FROM   USER_TAB_COL_STATISTICS
WHERE  TABLE_NAME='COUNTRIES'
AND    COLUMN_NAME='COUNTRY_SUBREGION_ID';

TABLE_NAME COLUMN_NAME          NUM_DISTINCT HISTOGRAM
---------- -------------------- ------------ ---------------
COUNTRIES  COUNTRY_SUBREGION_ID            7 TOP-FREQUENCY

The sh.countries.country_subregion_id column contains 8 distinct values, but the histogram only contains 7 buckets, making n=7. In this case, the database can only create a top frequency or hybrid histogram. In the country_subregion_id column, the top 7 most frequent values occupy 95.6% of the rows, which exceeds the threshold of 85.7%, generating a top frequency histogram (see "Criteria For Frequency Histograms").

3. Query the endpoint number and endpoint value for the column.
For example, use the following query (sample output included):

SELECT ENDPOINT_NUMBER, ENDPOINT_VALUE
FROM   USER_HISTOGRAMS
WHERE  TABLE_NAME='COUNTRIES'
AND    COLUMN_NAME='COUNTRY_SUBREGION_ID';

ENDPOINT_NUMBER ENDPOINT_VALUE
--------------- --------------
1          52792
6          52793
8          52794
9          52796
11          52797
13          52798
22          52799

Figure 11-2 is a graphical illustration of the 7 buckets in the top frequency histogram. The values are represented in the diagram as coins.

Figure 11-2 Top Frequency Histogram

As shown in Figure 11-2, each distinct value has its own bucket except for 52795, which is excluded from the histogram because it is nonpopular and statistically insignificant. As in a standard frequency histogram, the endpoint number represents the cumulative frequency of values.

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure

- Oracle Database Reference to learn about the USER_TAB_COL_STATISTICS view

- Oracle Database Reference to learn about the USER_HISTOGRAMS view

11.5 Height-Balanced Histograms (Legacy)

In a legacy height-balanced histogram, column values are divided into buckets so that each bucket contains approximately the same number of rows. For example, if you have 99 coins to distribute among 4 buckets, each bucket contains about 25 coins. The histogram shows where the endpoints fall in the range of values.

This section contains the following topics:

- Criteria for Height-Balanced Histograms

- Generating a Height-Balanced Histogram

11.5.1 Criteria for Height-Balanced Histograms

Frequency histograms depend on the number of requested histogram buckets, which is represented in this section by the variable n. By default, n is 254 when the number of buckets is not specified through the method_opt parameter of the DBMS_STATS statistics gathering procedures. Before Oracle Database 12c, the database created a height-balanced histogram when the NDV was greater than n. This type of histogram was useful for range predicates, and equality predicates on values that appear as endpoints in at least two buckets.

Note:
If no sampling percentage is specified, then Oracle Database 12c no longer creates height-balanced histograms. If you upgrade the database from Oracle Database 11g to Oracle Database 12c, then any height-based histograms created before the upgrade remain in use. If Oracle Database 12c creates new histograms, and if the sampling percentage is AUTO_SAMPLE_SIZE, then the histograms are either top frequency or hybrid, but not height-balanced.

11.5.2 Generating a Height-Balanced Histogram

This scenario shows how to generate a height-balanced histogram using the sample schemas.

Assumptions

This scenario assumes that you want to generate a height-balanced histogram on the sh.countries.country_subregion_id column. This table has 23 rows.

The following query shows that the country_subregion_id column contains 8 distinct values (sample output included) that are unevenly distributed:

SELECT country_subregion_id, count(*)
FROM   sh.countries
GROUP BY country_subregion_id
ORDER BY 1;

COUNTRY_SUBREGION_ID   COUNT(*)
-------------------- ----------
52792          1
52793          5
52794          2
52795          1
52796          1
52797          2
52798          2
52799          9

To generate a height-balanced histogram:

1. Gather statistics for sh.countries and the country_subregion_id column, specifying fewer buckets than distinct values.

Note:
To simulate Oracle Database 11g behavior, which is necessary to create a height-based histogram, set estimate_percent to a nondefault value. If you specify a nondefault percentage, then the database creates frequency or height-balanced histograms.

For example, enter the following command:

BEGIN  DBMS_STATS.GATHER_TABLE_STATS (
ownname          => 'SH'
,   tabname          => 'COUNTRIES'
,   method_opt       => 'FOR COLUMNS COUNTRY_SUBREGION_ID SIZE 7'
,   estimate_percent => 100
);
END;

2. Query the histogram information for the country_subregion_id column.
For example, use the following query (sample output included):

SELECT TABLE_NAME, COLUMN_NAME, NUM_DISTINCT, HISTOGRAM
FROM   USER_TAB_COL_STATISTICS
WHERE  TABLE_NAME='COUNTRIES'
AND    COLUMN_NAME='COUNTRY_SUBREGION_ID';

TABLE_NAME COLUMN_NAME          NUM_DISTINCT HISTOGRAM
---------- -------------------- ------------ ---------------
COUNTRIES  COUNTRY_SUBREGION_ID            8 HEIGHT BALANCED

The optimizer chooses a height-balanced histogram because the number of distinct values (8) is greater than the number of buckets (7), and the estimate_percent value is nondefault.

3. Query the number of rows occupied by each distinct value.
For example, use the following query (sample output included):

SELECT COUNT(country_subregion_id) AS NUM_OF_ROWS, country_subregion_id
FROM   countries
GROUP BY country_subregion_id
ORDER BY 2;

NUM_OF_ROWS COUNTRY_SUBREGION_ID
----------- --------------------
1                52792
5                52793
2                52794
1                52795
1                52796
2                52797
2                52798
9                52799

4. Query the endpoint number and endpoint value for the country_subregion_id column.
For example, use the following query (sample output included):

SELECT ENDPOINT_NUMBER, ENDPOINT_VALUE
FROM   USER_HISTOGRAMS
WHERE  TABLE_NAME='COUNTRIES'
AND    COLUMN_NAME='COUNTRY_SUBREGION_ID';

ENDPOINT_NUMBER ENDPOINT_VALUE
--------------- --------------
0          52792
2          52793
3          52795
4          52798
7          52799

Figure 11-3 is a graphical illustration of the height-balanced histogram. The values are represented in the diagram as coins.

Figure 11-3 Height-Balanced Histogram

The bucket number is identical to the endpoint number. The optimizer records the value of the last row in each bucket as the endpoint value, and then checks to ensure that the minimum value is the endpoint value of the first bucket, and the maximum value is the endpoint value of the last bucket. In this example, the optimizer adds bucket 0 so that the minimum value 52792 is the endpoint of a bucket.

The optimizer must evenly distribute 23 rows into the 7 specified histogram buckets, so each bucket contains approximately 3 rows. However, the optimizer compresses buckets with the same endpoint. So, instead of bucket 1 containing 2 instances of value 52793, and bucket 2 containing 3 instances of value 52793, the optimizer puts all 5 instances of value 52793 into bucket 2. Similarly, instead of having buckets 5, 6, and 7 contain 3 values each, with the endpoint of each bucket as 52799, the optimizer puts all 9 instances of value 52799 into bucket 7.

In this example, buckets 3 and 4 contain nonpopular values because the difference between the current endpoint number and previous endpoint number is 1. The optimizer calculates cardinality for these values based on density. The remaining buckets contain popular values. The optimizer calculates cardinality for these values based on endpoint numbers.

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure

- Oracle Database Reference to learn about the USER_TAB_COL_STATISTICS view

- Oracle Database Reference to learn about the USER_HISTOGRAMS view

11.6 Hybrid Histograms

A hybrid histogram combines characteristics of both height-based histograms and frequency histograms. This "best of both worlds" approach enables the optimizer to obtain better selectivity estimates in some situations.

The height-based histogram sometimes produces inaccurate estimates for values that are almost popular. For example, a value that occurs as an endpoint value of only one bucket but almost occupies two buckets is not considered popular.

To solve this problem, a hybrid histogram distributes values so that no value occupies more than one bucket, and then stores the endpoint repeat count value, which is the number of times the endpoint value is repeated, for each endpoint (bucket) in the histogram. By using the repeat count, the optimizer can obtain accurate estimates for almost popular values.

This section contains the following topics:

- How Endpoint Repeat Counts Work

- Criteria for Hybrid Histograms

- Generating a Hybrid Histogram

11.6.1 How Endpoint Repeat Counts Work

To illustrate the utility of endpoint repeat counts, assume that a column coins contains the following values, sorted from low to high:

You gather statistics for this table, setting the method_opt argument of DBMS_STATS.GATHER_TABLE_STATS to FOR ALL COLUMNS SIZE 3. In this case, the optimizer initially groups the values in coins into three buckets, as follows:

If a bucket border splits a value so that some occurrences of the value are in one bucket and some in another, then the optimizer shifts the bucket border (and all other following bucket borders) forward to include all occurrences of the value. For example, the optimizer shifts value 5 so that it is now wholly in the first bucket, and the value 25 is now wholly in the second bucket:

The endpoint repeat count measures the number of times that the corresponding bucket endpoint, which is the value at the right bucket border, repeats itself. For example, in the first bucket, the value 5 is repeated 3 times, so the endpoint repeat count is 3:

Height-balanced histograms do not store as much information as hybrid histograms. By using repeat counts, the optimizer can determine exactly how many occurrences of an endpoint value exist. For example, the optimizer knows that the value 5 appears 3 times, the value 25 appears 4 times, and the value 100 appears 2 times. This frequency information helps the optimizer to generate better cardinality estimates.

11.6.2 Criteria for Hybrid Histograms

Starting in Oracle Database 12c, the database creates hybrid histograms when all of the following conditions are true:

- n is less than the NDV, where n is the user-specified number of buckets. If no number is specified, then n defaults to 254.

- The criteria for top frequency histograms do not apply. See "Criteria For Frequency Histograms."

- The sampling percentage is AUTO_SAMPLE_SIZE.
If users specify their own percentage, then the database creates frequency or height-balanced histograms.

See Also:
"Height-Balanced Histograms (Legacy)"

11.6.3 Generating a Hybrid Histogram

This scenario shows how to generate a hybrid histogram using the sample schemas.

Assumptions

This scenario assumes that you want to generate a hybrid histogram on the sh.products.prod_subcategory_id column. This table has 72 rows. The prod_subcategory_id column contains 22 distinct values.

To generate a hybrid histogram:

1. Gather statistics for sh.products and the prod_subcategory_id column, specifying 10 buckets.
For example, enter the following command:

BEGIN  DBMS_STATS.GATHER_TABLE_STATS (
ownname     => 'SH'
,   tabname     => 'PRODUCTS'
,   method_opt  => 'FOR COLUMNS PROD_SUBCATEGORY_ID SIZE 10'
);
END;

2. Query the number of rows occupied by each distinct value.
For example, use the following query (sample output included):

SELECT COUNT(prod_subcategory_id) AS NUM_OF_ROWS, prod_subcategory_id
FROM   products
GROUP BY prod_subcategory_id
ORDER BY 1 DESC;

NUM_OF_ROWS PROD_SUBCATEGORY_ID
----------- -------------------
8                2014
7                2055
6                2032
6                2054
5                2056
5                2031
5                2042
5                2051
4                2036
3                2043
2                2033
2                2034
2                2013
2                2012
2                2053
2                2035
1                2022
1                2041
1                2044
1                2011
1                2021
1                2052

22 rows selected.

The column contains 22 distinct values. Because the number of buckets (10) is less than 22, the optimizer cannot create a frequency histogram. The optimizer considers both hybrid and top frequency histograms. To qualify for a top frequency histogram, the percentage of rows occupied by the top 10 most frequent values must be equal to or greater than threshold p, where p is (1-(1/10))*100, or 90%. However, in this case the top 10 most frequent values occupy 54 rows out of 72, which is only 75% of the total. Therefore, the optimizer chooses a hybrid histogram because the criteria for a top frequency histogram do not apply.

3. Query the histogram information for the country_subregion_id column.
For example, use the following query (sample output included):

SELECT TABLE_NAME, COLUMN_NAME, NUM_DISTINCT, HISTOGRAM
FROM   USER_TAB_COL_STATISTICS
WHERE  TABLE_NAME='PRODUCTS'
AND    COLUMN_NAME='PROD_SUBCATEGORY_ID';

TABLE_NAME COLUMN_NAME         NUM_DISTINCT HISTOGRAM
---------- ------------------- ------------ ---------
PRODUCTS   PROD_SUBCATEGORY_ID 22           HYBRID

4. Query the endpoint number, endpoint value, and endpoint repeat count for the country_subregion_id column.
For example, use the following query (sample output included):

SELECT ENDPOINT_NUMBER, ENDPOINT_VALUE, ENDPOINT_REPEAT_COUNT
FROM   USER_HISTOGRAMS
WHERE  TABLE_NAME='PRODUCTS'
AND    COLUMN_NAME='PROD_SUBCATEGORY_ID'
ORDER BY 1;

ENDPOINT_NUMBER ENDPOINT_VALUE ENDPOINT_REPEAT_COUNT
--------------- -------------- ---------------------
1           2011                     1
13           2014                     8
26           2032                     6
36           2036                     4
45           2043                     3
51           2051                     5
52           2052                     1
54           2053                     2
60           2054                     6
72           2056                     5

10 rows selected.

In a height-based histogram, the optimizer would evenly distribute 72 rows into the 10 specified histogram buckets, so that each bucket contains approximately 7 rows. Because this is a hybrid histogram, the optimizer distributes the values so that no value occupies more than one bucket. For example, the optimizer does not put some instances of value 2036 into one bucket and some instances of this value into another bucket: all instances are in bucket 36.

The endpoint repeat count shows the number of times the highest value in the bucket is repeated. By using the endpoint number and repeat count for these values, the optimizer can estimate cardinality. For example, bucket 36 contains instances of values 2033, 2034, 2035, and 2036. The endpoint value 2036 has an endpoint repeat count of 4, so the optimizer knows that 4 instances of this value exist. For values such as 2033, which are not endpoints, the optimizer estimates cardinality using density.

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure

- Oracle Database Reference to learn about the USER_TAB_COL_STATISTICS view

- Oracle Database Reference to learn about the USER_HISTOGRAMS view

12 Managing Optimizer Statistics: Basic Topics

This chapter explains basic tasks relating to optimizer statistics management. "Managing Optimizer Statistics: Advanced Topics" covers advanced concepts and tasks.

This chapter contains the following topics:

- Controlling Automatic Optimizer Statistics Collection

- Setting Optimizer Statistics Preferences

- Gathering Optimizer Statistics Manually

- Gathering System Statistics Manually

See Also:

- "Optimizer Statistics Concepts"

- "Query Optimizer Concepts"

12.1 About Optimizer Statistics Collection

In Oracle Database, optimizer statistics collection is the gathering of optimizer statistics for database objects, including fixed objects. The database can collect optimizer statistics automatically. You can also collect them manually using the DBMS_STATS package (see "Gathering Optimizer Statistics Manually").

12.1.1 Purpose of Optimizer Statistics Collection

The contents of tables and associated indexes change frequently, which can lead the optimizer to choose suboptimal execution plan for queries. Thus, statistics must be kept current to avoid any potential performance issues because of suboptimal plans.

To minimize DBA involvement, Oracle Database automatically gathers optimizer statistics at various times. Some automatic options are configurable, such enabling AutoTask to run DBMS_STATS.

12.1.2 User Interfaces for Optimizer Statistics Management

You can manage optimizer statistics either through Oracle Enterprise Manager Cloud Control (Cloud Control) or using PL/SQL on the command line.

12.1.2.1 Graphical Interface for Optimizer Statistics Management

The Manage Optimizer Statistics page in Cloud Control is a GUI that enables you to manage optimizer statistics.

12.1.2.1.1 Accessing the Database Home Page in Cloud Control

Oracle Enterprise Manager Cloud Control enables you to manage multiple databases within a single GUI-based framework.

To access a database home page using Cloud Control:

1. Log in to Cloud Control with the appropriate credentials.

2. Under the Targets menu, select Databases.

3. In the list of database targets, select the target for the Oracle Database instance that you want to administer.

4. If prompted for database credentials, then enter the minimum credentials necessary for the tasks you intend to perform.

See Also:
Oracle Enterprise Manager Cloud Control Introduction for an overview of Cloud Control

12.1.2.1.2 Accessing the Manage Optimizer Statistics Page

You can perform most necessary tasks relating to optimizer statistics through pages linked to by the Manage Optimizer Statistics page.

To manage optimizer statistics using Cloud Control:

1. Access the Database Home page.

2. From the Performance menu, select SQL, then Optimizer Statistics.
The Manage Optimizer Statistics appears.

See Also:
Online Help for Oracle Enterprise Manager Cloud Control

12.1.2.2 Command-Line Interface for Optimizer Statistics Management

You can use the DBMS_STATS package to perform most optimizer statistics tasks. Use the DBMS_AUTO_TASK_ADMIN PL/SQL package to enable and disable automatic statistics gathering.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn how to use DBMS_STATS and DBMS_AUTO_TASK_ADMIN

12.2 Controlling Automatic Optimizer Statistics Collection

The automated maintenance tasks infrastructure (known as AutoTask) schedules tasks to run automatically in Oracle Scheduler windows known as maintenance windows. By default, one window is scheduled for each day of the week. Automatic optimizer statistics collection runs as part of AutoTask. By default, the collection runs in all predefined maintenance windows.

Note:
Data visibility and privilege requirements may differ when using automatic optimizer statistics collection with pluggable databases. See Oracle Database Administrator's Guide for a table that summarizes how manageability features work in a container database (CDB).

To collect the optimizer statistics, the database calls an internal procedure that operates similarly to the GATHER_DATABASE_STATS procedure with the GATHER AUTO option. Automatic statistics collection honors all preferences set in the database.

The principal difference between manual and automatic collection is that the latter prioritizes database objects that need statistics. Before the maintenance window closes, automatic collection assesses all objects and prioritizes objects that have no statistics or very old statistics.

Note:
When gathering statistics manually, you can reproduce the object prioritization of automatic collection by using the DBMS_AUTO_TASK_IMMEDIATE package. This package runs the same statistics gathering job that is executed during the automatic nightly statistics gathering job.

This section contains the following topics:

- Controlling Automatic Optimizer Statistics Collection Using Cloud Control

- Controlling Automatic Optimizer Statistics Collection from the Command Line

12.2.1 Controlling Automatic Optimizer Statistics Collection Using Cloud Control

You can enable and disable all automatic maintenance tasks, including automatic optimizer statistics collection, using Cloud Control.

The default window timing works well for most situations. However, you may have operations such as bulk loads that occur during the window. In such cases, to avoid potential conflicts that result from operations occurring at the same time as automatic statistics collection, Oracle recommends that you change the window accordingly.

To control automatic optimizer statistics collection using Cloud Control:

1. Access the Database Home page, as described in "Accessing the Database Home Page in Cloud Control."

2. From the Administration menu, select Oracle Scheduler, then Automated Maintenance Tasks.
The Automated Maintenance Tasks page appears.

This page shows the predefined tasks. To retrieve information about each task, click the corresponding link for the task.

3. Click Configure.
The Automated Maintenance Tasks Configuration page appears.

By default, automatic optimizer statistics collection executes in all predefined maintenance windows in MAINTENANCE_WINDOW_GROUP.

4. Perform the following steps:

1. In the Task Settings section for Optimizer Statistics Gathering, select either Enabled or Disabled to enable or disable an automated task.

Note:
Oracle strongly recommends that you not disable automatic statistics gathering because it is critical for the optimizer to generate optimal plans for queries against dictionary and user objects. If you disable automatic collection, ensure that you have a good manual statistics collection strategy for dictionary and user schemas.

2. To disable statistics gathering for specific days in the week, check the appropriate box next to the window name.

3. To change the characteristics of a window group, click Edit Window Group.

4. To change the times for a window, click the name of the window (for example, Monday Window), and then in the Schedule section, click Edit.
The Edit Window page appears.

In this page, you can change the parameters such as duration and start time for window execution.

5. Click Apply.

See Also:
Online Help for Oracle Enterprise Manager Cloud Control

12.2.2 Controlling Automatic Optimizer Statistics Collection from the Command Line

If you do not use Cloud Control to enable and disable automatic optimizer statistics collection, then you have the following options:

- Run the ENABLE or DISABLE procedure in the DBMS_AUTO_TASK_ADMIN PL/SQL package.
This package is the recommended command-line technique. For both the ENABLE or DISABLE procedures, you can specify a particular maintenance window with the window_name parameter. See Oracle Database PL/SQL Packages and Types Reference for complete reference information.

- Set the STATISTICS_LEVEL initialization level to BASIC to disable collection of all advisories and statistics, including Automatic SQL Tuning Advisor.

Note:
Because monitoring and many automatic features are disabled, Oracle strongly recommends that you do not set STATISTICS_LEVEL to BASIC.

To control automatic statistics collection using DBMS_AUTO_TASK_ADMIN:

1. Connect SQL*Plus to the database with administrator privileges, and then do one of the following:

- To enable the automated task, execute the following PL/SQL block:

BEGIN
DBMS_AUTO_TASK_ADMIN.ENABLE (
client_name  => 'auto optimizer stats collection'
,   operation    => NULL
,   window_name  => NULL
);
END;
/

- To disable the automated task, execute the following PL/SQL block:

BEGIN
DBMS_AUTO_TASK_ADMIN.DISABLE (
client_name  => 'auto optimizer stats collection'
,   operation    => NULL
,   window_name  => NULL
);
END;
/

2. Query the data dictionary to confirm the change.
For example, query DBA_AUTOTASK_CLIENT as follows:

COL CLIENT_NAME FORMAT a31

SELECT CLIENT_NAME, STATUS
FROM   DBA_AUTOTASK_CLIENT
WHERE  CLIENT_NAME = 'auto optimizer stats collection';

Sample output appears as follows:

CLIENT_NAME                     STATUS
------------------------------- --------
auto optimizer stats collection ENABLED

To change the window attributes for automatic statistics collection:

1. Connect SQL*Plus to the database with administrator privileges.

2. Change the attributes of the maintenance window as needed.
For example, to change the Monday maintenance window so that it starts at 5 a.m., execute the following PL/SQL program:

BEGIN
DBMS_SCHEDULER.SET_ATTRIBUTE (
'MONDAY_WINDOW'
,   'repeat_interval'
,   'freq=daily;byday=MON;byhour=05;byminute=0;bysecond=0'
);
END;
/

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_AUTO_TASK_ADMIN package

- Oracle Database Reference to learn about the STATISTICS_LEVEL initialization parameter

12.3 Setting Optimizer Statistics Preferences

This section contains the following topics:

- About Optimizer Statistics Preferences

- Setting Global Optimizer Statistics Preferences Using Cloud Control

- Setting Object-Level Optimizer Statistics Preferences Using Cloud Control

- Setting Optimizer Statistics Preferences from the Command Line

12.3.1 About Optimizer Statistics Preferences

The optimizer statistics preferences set the default values of the parameters used by automatic statistics collection and the DBMS_STATS statistics gathering procedures. You can set optimizer statistics preferences at the table, schema, database (all tables), and global (tables with no preferences and any tables created in the future) levels. In this way, you can automatically maintain optimizer statistics when some objects require settings that differ from the default.

For example, by default the DBMS_STATS preference INCREMENTAL is set to false. You can set INCREMENTAL to true for a range-partitioned table when the last few partitions are updated. Also, when performing a partition exchange operation on a nonpartitioned table, Oracle recommends that you set INCREMENTAL to true and INCREMENTAL_LEVEL to TABLE. With these settings, DBMS_STATS gathers table-level synopses on this table (see "Maintaining Incremental Statistics for Partition Maintenance Operations").

12.3.1.1 Procedures for Setting Statistics Gathering Preferences

Table 12-1 summarizes the DBMS_STATS procedures that change the defaults of parameters used by the DBMS_STATS.GATHER_*_STATS procedures. Parameter values set in DBMS_STAT.GATHER_*_STATS override other settings. If a parameter has not been set, then the database checks for a table-level preference. If no table preference exists, then the database uses the global preference. See Oracle Database PL/SQL Packages and Types Reference for descriptions of CASCADE, METHOD_OPT, and the other parameters.

Table 12-1 Setting Preferences for Gathering Statistics

DBMS_STATS Procedure

Scope

SET_TABLE_PREFS

Specified table only.

SET_SCHEMA_PREFS

All existing objects in the specified schema.

This procedure calls SET_TABLE_PREFS for each table in the specified schema. Calling SET_SCHEMA_PREFS does not affect any new objects created after it has been run. New objects use the GLOBAL_PREF values for all parameters.

SET_DATABASE_PREFS

All user-defined schemas in the database. You can include system-owned schemas such as SYS and SYSTEM by setting the ADD_SYS parameter to true.

This procedure calls SET_TABLE_PREFS for each table in the specified schema. Calling SET_DATABASE_PREFS does not affect any new objects created after it has been run. New objects use the GLOBAL_PREF values for all parameters.

SET_GLOBAL_PREFS

Any object in the database that does not have an existing table preference.

All parameters default to the global setting unless a table preference is set or the parameter is explicitly set in the DBMS_STATS.GATHER_*_STATS command. Changes made by this procedure affect any new objects created after it runs. New objects use the SET_GLOBAL_PREF values for all parameters.

With SET_GLOBAL_PREFS, you can set a default value for the parameter AUTOSTAT_TARGET. This additional parameter controls which objects the automatic statistic gathering job running in the nightly maintenance window looks after. Possible values for this parameter are ALL, ORACLE, and AUTO (default).

You can only set the CONCURRENT preference at the global level (see "About Concurrent Statistics Gathering"). You cannot set the preference INCREMENTAL_LEVEL using SET_GLOBAL_PREFS.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS procedures for setting optimizer statistics

12.3.1.2 Setting Statistics Preferences: Example

Table 12-2 illustrates the relationship between SET_TABLE_PREFS, SET_SCHEMA_STATS, and SET_DATABASE_PREFS.

Table 12-2 Changing Preferences for Statistics Gathering Procedures

Action

Description

SQL> SELECT DBMS_STATS.GET_PREFS ('incremental', 'sh',
'costs') FROM DUAL;

DBMS_STATS.GET_PREFS('INCREMENTAL','SH','COSTS')
------------------------------------------------
TRUE

You query the INCREMENTAL preference for costs and determine that it is set to true.

SQL> EXEC DBMS_STATS.SET_TABLE_PREFS ('sh', 'costs',
'incremental', 'false');

PL/SQL procedure successfully completed.

You use SET_TABLE_PREFS to set the INCREMENTAL preference to false for the costs table only.

SQL> SELECT DBMS_STATS.GET_PREFS ('incremental', 'sh',
'costs') FROM DUAL;

DBMS_STATS.GET_PREFS('INCREMENTAL','SH','COSTS')
------------------------------------------------
FALSE

You query the INCREMENTAL preference for costs and confirm that it is set to false.

SQL> EXEC DBMS_STATS.SET_SCHEMA_PREFS ('sh',
'incremental', 'true');

PL/SQL procedure successfully completed.

You use SET_SCHEMA_PREFS to set the INCREMENTAL preference to true for every table in the sh schema, including costs.

SQL> SELECT DBMS_STATS.GET_PREFS ('incremental', 'sh',
'costs') FROM DUAL;

DBMS_STATS.GET_PREFS('INCREMENTAL','SH','COSTS')
------------------------------------------------
TRUE

You query the INCREMENTAL preference for costs and confirm that it is set to true.

SQL> EXEC DBMS_STATS.SET_DATABASE_PREFS ('incremental',
'false');

PL/SQL procedure successfully completed.

You use SET_DATABASE_PREFS to set the INCREMENTAL preference for all tables in all user-defined schemas to false.

SQL> SELECT DBMS_STATS.GET_PREFS ('incremental', 'sh',
'costs') FROM DUAL;

DBMS_STATS.GET_PREFS('INCREMENTAL','SH','COSTS')
------------------------------------------------
FALSE

You query the INCREMENTAL preference for costs and confirm that it is set to false.

12.3.2 Setting Global Optimizer Statistics Preferences Using Cloud Control

A global preference applies to any object in the database that does not have an existing table preference. You can set optimizer statistics preferences at the global level using Cloud Control. See the Cloud Control Help for an explanation of the options on the preference page.

To set global optimizer statistics preferences using Cloud Control:

1. Go to the Manage Optimizer Statistics page, as explained in "Accessing the Manage Optimizer Statistics Page".

2. Click Global Statistics Gathering Options.
The Global Statistics Gathering Options page appears.

3. Make your desired changes, and click Apply.

See Also:
Online Help for Oracle Enterprise Manager Cloud Control

12.3.3 Setting Object-Level Optimizer Statistics Preferences Using Cloud Control

You can set optimizer statistics preferences at the database, schema, and table level using Cloud Control.

To set object-level optimizer statistics preferences using Cloud Control:

1. Go to the Manage Optimizer Statistics page, as explained in "Accessing the Manage Optimizer Statistics Page".

2. Click Object Level Statistics Gathering Preferences.
The Object Level Statistics Gathering Preferences page appears.

3. To modify table preferences for a table that has preferences set at the table level, do the following (otherwise, skip to the next step):

1. Enter values in Schema and Table. Leave Table blank to see all tables in the schema.
The page refreshes with the table names.

2. Select the desired tables and click Edit Preferences.
The General subpage of the Edit Preferences page appears.

3. Change preferences as needed and click Apply.

4. To set preferences for a table that does not have preferences set at the table level, do the following (otherwise, skip to the next step):

1. Click Add Table Preferences.
The General subpage of the Add Table Preferences page appears.

2. In Table Name, enter the schema and table name.

3. Change preferences as needed and click OK.

5. To set preferences for a schema, do the following:

1. Click Edit Schema Preferences.
The General subpage of the Edit Schema Preferences page appears.

2. In Schema, enter the schema name.

3. Change preferences as needed and click OK.

See Also:
Online Help for Oracle Enterprise Manager Cloud Control

12.3.4 Setting Optimizer Statistics Preferences from the Command Line

If you do not use Cloud Control to set optimizer statistics preferences, then you can invoke the DBMS_STATS procedures described in Table 12-1.

Prerequisites

This task has the following prerequisites:

- To set the global or database preferences, you must have SYSDBA privileges, or both ANALYZE ANY DICTIONARY and ANALYZE ANY system privileges.

- To set schema preferences, you must connect as owner, or have SYSDBA privileges, or have the ANALYZE ANY system privilege.

- To set table preferences, you must connect as owner of the table or have the ANALYZE ANY system privilege.

To set optimizer statistics preferences from the command line:

1. Connect SQL*Plus to the database with the necessary privileges.

2. Optionally, call the DBMS_STATS.GET_PREFS procedure to see preferences set at the object level, or at the global level if a specific table is not set.
For example, obtain the STALE_PERCENT parameter setting for the sh.sales table as follows:

SELECT DBMS_STATS.GET_PREFS('STALE_PERCENT', 'SH', 'SALES')
FROM   DUAL;

3. Execute the appropriate procedure from Table 12-1, specifying the following parameters:

- ownname - Set schema name (SET_TAB_PREFS and SET_SCHEMA_PREFS only)

- tabname - Set table name (SET_TAB_PREFS only)

- pname - Set parameter name

- pvalue - Set parameter value

- add_sys - Include system tables (optional, SET_DATABASE_PREFS only)

The following example specifies that 13% of rows in sh.sales must change before the statistics on that table are considered stale:

EXEC DBMS_STATS.SET_TABLE_PREFS('SH', 'SALES', 'STALE_PERCENT', '13');

4. Optionally, query the *_TAB_STAT_PREFS view to confirm the change.
For example, query DBA_TAB_STAT_PREFS as follows:

COL OWNER FORMAT a5
COL TABLE_NAME FORMAT a15
COL PREFERENCE_NAME FORMAT a20
COL PREFERENCE_VALUE FORMAT a30
SELECT * FROM DBA_TAB_STAT_PREFS;

Sample output appears as follows:

OWNER TABLE_NAME      PREFERENCE_NAME      PREFERENCE_VALUE
----- --------------- -------------------- ------------------------------
OE    CUSTOMERS       NO_INVALIDATE        DBMS_STATS.AUTO_INVALIDATE
SH    SALES           STALE_PERCENT        13

See Also:
Oracle Database PL/SQL Packages and Types Reference for descriptions of the parameter names and values for program units

12.4 Gathering Optimizer Statistics Manually

As an alternative or supplement to automatic statistics gathering, you can use the DBMS_STATS package to gather statistics manually.

This section contains the following topics:

- About Manual Statistics Collection with DBMS_STATS

- Guidelines for Gathering Optimizer Statistics Manually

- Determining When Optimizer Statistics Are Stale

- Gathering Schema and Table Statistics

- Gathering Statistics for Fixed Objects

- Gathering Statistics for Volatile Tables Using Dynamic Statistics

- Gathering Optimizer Statistics Concurrently

- Gathering Incremental Statistics on Partitioned Objects

See Also:

- "Controlling Automatic Optimizer Statistics Collection"

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS packages

12.4.1 About Manual Statistics Collection with DBMS_STATS

Use the DBMS_STATS package to manipulate optimizer statistics. You can gather statistics on objects and columns at various levels of granularity: object, schema, and database. You can also gather statistics for the physical system, as explained in "Gathering System Statistics Manually".

Table 12-3 summarizes the DBMS_STATS procedures for gathering optimizer statistics. This package does not gather statistics for table clusters. However, you can gather statistics on individual tables in a table cluster.

Table 12-3 DBMS_STATS Procedures for Gathering Optimizer Statistics

Procedure

Purpose

GATHER_INDEX_STATS

Collects index statistics

GATHER_TABLE_STATS

Collects table, column, and index statistics

GATHER_SCHEMA_STATS

Collects statistics for all objects in a schema

GATHER_DICTIONARY_STATS

Collects statistics for all system schemas, including SYS and SYSTEM, and other optional schemas, such as CTXSYS and DRSYS

GATHER_DATABASE_STATS

Collects statistics for all objects in a database

When the OPTIONS parameter is set to GATHER STALE or GATHER AUTO, the GATHER_SCHEMA_STATS and GATHER_DATABASE_STATS procedures gather statistics for any table that has stale statistics and any table that is missing statistics. If a monitored table has been modified more than 10%, then the database considers these statistics stale and gathers them again.

Note:
As explained in "Controlling Automatic Optimizer Statistics Collection", you can configure a nightly job to gather statistics automatically.

See Also:
Oracle Database PL/SQL Packages and Types Reference for complete syntax and semantics for the DBMS_STATS package

12.4.2 Guidelines for Gathering Optimizer Statistics Manually

In most cases, automatic statistics collection is sufficient for database objects modified at a moderate speed. However, automatic collection may sometimes be inadequate or unavailable, as in the following cases:

- You perform certain types of bulk load and cannot wait for the maintenance window to collect statistics because queries must be executed immediately. See "Online Statistics Gathering for Bulk Loads".

- During a nonrepresentative workload, automatic statistics collection gathers statistics for fixed tables. See "Gathering Statistics for Fixed Objects".

- Automatic statistics collection does not gather system statistics. See "Gathering System Statistics Manually".

- Volatile tables are being deleted or truncated, and then rebuilt during the day. See "Gathering Statistics for Volatile Tables Using Dynamic Statistics".

This section offers guidelines for typical situations in which you may choose to gather statistically manually:

- Guideline for Accurate Statistics

- Guideline for Gathering Statistics in Parallel

- Guideline for Partitioned Objects

- Guideline for Frequently Changing Objects

- Guideline for External Tables

12.4.2.1 Guideline for Accurate Statistics

In the context of optimizer statistics, sampling is the gathering of statistics from a random subset of table rows. By enabling the database to avoid full table scans and sorts of entire tables, sampling minimizes the resources necessary to gather statistics.

The database gathers the most accurate statistics when it processes all rows in the table, which is a 100% sample. However, the larger the sample size, the longer the statistics gathering operation. The problem is determining a sample size that provides accurate statistics in a reasonable time.

DBMS_STATS uses sampling when a user specifies the parameter ESTIMATE_PERCENT, which controls the percentage of the rows in the table to sample. To maximize performance gains while achieving necessary statistical accuracy, Oracle recommends that the ESTIMATE_PERCENT parameter be set to DBMS_STATS.AUTO_SAMPLE_SIZE (the default). With this setting, the database uses a hash-based algorithm that is much faster than sampling. This algorithm reads all rows and produces statistics that are nearly as accurate as statistics from a 100% sample. The statistics computed using this technique are deterministic.

12.4.2.2 Guideline for Gathering Statistics in Parallel

By default, the database gathers statistics with the parallelism degree specified at the table or index level. You can override this setting with the degree argument to the DBMS_STATS gathering procedures. Oracle recommends setting degree to DBMS_STATS.AUTO_DEGREE. This setting enables the database to choose an appropriate degree of parallelism based on the object size and the settings for the parallelism-related initialization parameters.

The database can gather most statistics serially or in parallel. However, the database does not gather some index statistics in parallel, including cluster indexes, domain indexes, and bitmap join indexes. The database can use sampling when gathering parallel statistics.

Note:
Do not confuse gathering statistics in parallel with gathering statistics concurrently. See "About Concurrent Statistics Gathering".

12.4.2.3 Guideline for Partitioned Objects

For partitioned tables and indexes, DBMS_STATS can gather separate statistics for each partition and global statistics for the entire table or index. Similarly, for composite partitioning, DBMS_STATS can gather separate statistics for subpartitions, partitions, and the entire table or index.

Use the granularity argument to the DBMS_STATS procedures to determine the type of partitioning statistics to be gathered. Oracle recommends setting granularity to the default value of AUTO to gather subpartition, partition, or global statistics, depending on partition type. The ALL setting gathers statistics for all types.

See Also:
"Gathering Incremental Statistics on Partitioned Objects"

12.4.2.4 Guideline for Frequently Changing Objects

When tables are frequently modified, gather statistics often enough so that they do not go stale, but not so often that collection overhead degrades performance. You may only need to gather new statistics every week or month. The best practice is to use a script or job scheduler to regularly run the DBMS_STATS.GATHER_SCHEMA_STATS and DBMS_STATS.GATHER_DATABASE_STATS procedures.

12.4.2.5 Guideline for External Tables

Because the database does not permit data manipulation against external tables, the database never marks statistics on external tables as stale. If new statistics are required for an external table, for example, because the underlying data files change, then regather the statistics. Gather statistics manually for external tables with the same procedures that you use for regular tables.

12.4.3 Determining When Optimizer Statistics Are Stale

Stale statistics on a table do not accurately reflect its data. The database provides a table monitoring facility to help determine when a database object needs new statistics. Monitoring tracks the approximate number of DML operations on a table and whether the table has been truncated since the most recent statistics collection.

Run DBMS_STATS.FLUSH_DATABASE_MONITORING_INFO to immediately reflect the outstanding monitored information stored in memory. After running this procedure, check whether statistics are stale by querying the STALE_STATS column in DBA_TAB_STATISTICS and DBA_IND_STATISTICS. This column is based on data in the DBA_TAB_MODIFICATIONS view and the STALE_PERCENT preference for DBMS_STATS. The STALE_STATS column has the following possible values:

- YES
The statistics are stale.

- NO
The statistics are not stale.

- null
The statistics are not collected.

Executing GATHER_SCHEMA_STATS or GATHER_DATABASE_STATS with the GATHER AUTO option collects statistics only for objects with no statistics or stale statistics.

Assumptions

This tutorial assumes the following:

- Table monitoring is enabled for sh.sales. It is enabled by default when the STATISTICS_LEVEL initialization parameter is set to TYPICAL or ALL.

- You have the ANALYZE_ANY system privilege so you can run the DBMS_STATS.FLUSH_DATABASE_MONITORING_INFO procedure.

To determine stale statistics:

1. Connect SQL*Plus to the database with the necessary privileges.

2. Optionally, write the database monitoring information from memory to disk.
For example, execute the following procedure:

BEGIN
DBMS_STATS.FLUSH_DATABASE_MONITORING_INFO;
END;
/

3. Query the data dictionary for stale statistics.
The following example queries stale statistics for the sh.sales table (partial output included):

COL PARTITION_NAME FORMAT a15

SELECT PARTITION_NAME, STALE_STATS
FROM   DBA_TAB_STATISTICS
WHERE  TABLE_NAME = 'SALES'
AND    OWNER = 'SH'
ORDER BY PARTITION_NAME;

PARTITION_NAME  STA
--------------- ---
SALES_1995      NO
SALES_1996      NO
SALES_H1_1997   NO
SALES_H2_1997   NO
SALES_Q1_1998   NO
SALES_Q1_1999   NO
.
.
.

See Also:

- Oracle Database Reference to learn about the DBA_TAB_MODIFICATIONS view

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.FLUSH_DATABASE_MONITORING_INFO procedure

12.4.4 Gathering Schema and Table Statistics

Use GATHER_TABLE_STATS to collect table statistics, and GATHER_SCHEMA_STATS to collect statistics for all objects in a schema.

To gather schema statistics using DBMS_STATS:

1. Start SQL*Plus, and connect to the database with the appropriate privileges for the procedure that you intend to run.

2. Run the GATHER_TABLE_STATS or GATHER_SCHEMA_STATS procedure, specifying the desired parameters.
Typical parameters include:

- Owner - ownname

- Object name - tabname, indname, partname

- Degree of parallelism - degree

Example 12-1 Gathering Statistics for a Table

This example uses the DBMS_STATS package to gather statistics on the sh.customers table with a parallelism setting of 2.

BEGIN
DBMS_STATS.GATHER_TABLE_STATS (
ownname => 'sh'
,   tabname => 'customers'
,   degree  => 2
);
END;
/

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the GATHER_TABLE_STATS procedure

12.4.5 Gathering Statistics for Fixed Objects

Fixed objects are dynamic performance tables and their indexes. These objects record current database activity.

Unlike other database tables, the database does not automatically use dynamic statistics for SQL statement referencing X$ tables when optimizer statistics are missing. Instead, the optimizer uses predefined default values. These defaults may not be representative and could potentially lead to a suboptimal execution plan. Thus, it is important to keep fixed object statistics current.

Oracle Database automatically gathers fixed object statistics as part of automated statistics gathering if they have not been previously collected (see "Controlling Automatic Optimizer Statistics Collection"). You can also manually collect statistics on fixed objects by calling DBMS_STATS.GATHER_FIXED_OBJECTS_STATS. Oracle recommends that you gather statistics when the database has representative activity.

Prerequisites

You must have the SYSDBA or ANALYZE ANY DICTIONARY system privilege to execute this procedure.

To gather schema statistics using GATHER_FIXED_OBJECTS_STATS:

1. Start SQL*Plus, and connect to the database with the appropriate privileges for the procedure that you intend to run.

2. Run the DBMS_STATS.GATHER_FIXED_OBJECTS_STATS procedure, specifying the desired parameters.
Typical parameters include:

- Table identifier describing where to save the current statistics - stattab

- Identifier to associate with these statistics within stattab (optional) - statid

- Schema containing stattab (if different from current schema) - statown

Example 12-2 Gathering Statistics for a Table

This example uses the DBMS_STATS package to gather fixed object statistics.

BEGIN
DBMS_STATS.GATHER_FIXED_OBJECTS_STATS;
END;
/

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the GATHER_TABLE_STATS procedure

12.4.6 Gathering Statistics for Volatile Tables Using Dynamic Statistics

Statistics for volatile tables, which are tables modified significantly during the day, go stale quickly. For example, a table may be deleted or truncated, and then rebuilt.

When you set the statistics of a volatile object to null, Oracle Database dynamically gathers the necessary statistics during optimization using dynamic statistics. The OPTIMIZER_DYNAMIC_SAMPLING initialization parameter controls this feature.

Note:
As described in "Locking and Unlocking Optimizer Statistics," gathering representative statistics and them locking them is an alternative technique for preventing statistics for volatile tables from going stale.

Assumptions

This tutorial assumes the following:

- The oe.orders table is extremely volatile.

- You want to delete and then lock the statistics on the orders table to prevent the database from gathering statistics on the table. In this way, the database can dynamically gather necessary statistics as part of query optimization.

- The oe user has the necessary privileges to query DBMS_XPLAN.DISPLAY_CURSOR.

To delete and the lock optimizer statistics:

1. Connect to the database as user oe, and then delete the statistics for the oe table.
For example, execute the following procedure:

BEGIN
DBMS_STATS.DELETE_TABLE_STATS('OE','ORDERS');
END;
/

2. Lock the statistics for the oe table.
For example, execute the following procedure:

BEGIN
DBMS_STATS.LOCK_TABLE_STATS('OE','ORDERS');
END;
/

3. You query the orders table.
For example, use the following statement:

SELECT COUNT(order_id) FROM orders;

4. You query the plan in the cursor.
You run the following commands (partial output included):

SET LINESIZE 150
SET PAGESIZE 0

SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR);

SQL_ID  aut9632fr3358, child number 0
-------------------------------------
SELECT COUNT(order_id) FROM orders

Plan hash value: 425895392

---------------------------------------------------------------------
| Id  | Operation          | Name   | Rows  | Cost (%CPU)| Time     |
---------------------------------------------------------------------
|   0 | SELECT STATEMENT   |        |       |     2 (100)|          |
|   1 |  SORT AGGREGATE    |        |     1 |            |          |
|   2 |   TABLE ACCESS FULL| ORDERS |   105 |     2   (0)| 00:00:01 |
---------------------------------------------------------------------

Note
-----
- dynamic statistics used for this statement (level=2)

The Note in the preceding execution plan shows that the database used dynamic statistics for the SELECT statement.

See Also:
"Controlling Dynamic Statistics"

12.4.7 Gathering Optimizer Statistics Concurrently

Oracle Database enables you to gather statistics on multiple tables or partitions concurrently. This section contains the following topics:

- About Concurrent Statistics Gathering

- Enabling Concurrent Statistics Gathering

- Configuring the System for Parallel Execution and Concurrent Statistics Gathering

- Monitoring Statistics Gathering Operations

12.4.7.1 About Concurrent Statistics Gathering

When concurrent statistics gathering mode is enabled, the database can simultaneously gather optimizer statistics for the following:

- Multiple tables in a schema

- Multiple partitions or subpartitions in a table

Concurrency can reduce the overall time required to gather statistics by enabling the database to fully use multiple CPUs.

Note:
Concurrent statistics gathering mode does not rely on parallel query processing, but is usable with it.

12.4.7.1.1 How DBMS_STATS Gathers Statistics Concurrently

Oracle Database employs the following tools and technologies to create and manage multiple statistics gathering jobs concurrently:

- Oracle Scheduler

- Oracle Database Advanced Queuing (AQ)

- Oracle Database Resource Manager (the Resource Manager)

Enable concurrent statistics gathering by setting the CONCURRENT preference with DBMS_STATS.SET_GLOBAL_PREF (see "Enabling Concurrent Statistics Gathering").

The database runs as many concurrent jobs as possible. The Job Scheduler decides how many jobs to execute concurrently and how many to queue. As running jobs complete, the scheduler dequeues and runs more jobs until the database has gathered statistics on all tables, partitions, and subpartitions. The maximum number of jobs is bounded by the JOB_QUEUE_PROCESSES initialization parameter and available system resources.

In most cases, the DBMS_STATS procedures create a separate job for each table partition or subpartition. However, if the partition or subpartition is very small or empty, the database may automatically batch the object with other small objects into a single job to reduce the overhead of job maintenance.

The following figure illustrates the creation of jobs at different levels, where Table 3 is a partitioned table, and the other tables are nonpartitioned. Job 3 acts as a coordinator job for Table 3, and creates a job for each partition in that table, and a separate job for the global statistics of Table 3. This example assumes that incremental statistics gathering is disabled; if enabled, then the database derives global statistics from partition-level statistics after jobs for partitions complete.

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package

- Oracle Database Reference to learn about the JOB_QUEUE_PROCESSES initialization parameter

12.4.7.1.2 Concurrent Statistics Gathering and Resource Management

The DBMS_STATS package does not explicitly manage resources used by concurrent statistics gathering jobs that are part of a user-initiated statistics gathering call. Thus, the database may use system resources fully during concurrent statistics gathering. To address this situation, use the Resource Manager to cap resources consumed by concurrent statistics gathering jobs. The Resource Manager must be enabled to gather statistics concurrently.

The system-supplied consumer group ORA$AUTOTASK registers all statistics gathering jobs. You can create a resource plan with proper resource allocations for ORA$AUTOTASK to prevent concurrent statistics gathering from consuming all available resources. If you lack your own resource plan, and if choose not to create one, then consider activating the Resource Manager with the system-supplied DEFAULT_PLAN.

Note:
The ORA$AUTOTASK consumer group is shared with the maintenance tasks that automatically run during the maintenance windows. Thus, when concurrency is activated for automatic statistics gathering, the database automatically manages resources, with no extra steps required.

See Also:
Oracle Database Administrator's Guide to learn about the Resource Manager

12.4.7.2 Enabling Concurrent Statistics Gathering

To enable concurrent statistics gathering, use the DBMS_STATS.SET_GLOBAL_PREFS procedure to set the CONCURRENT preference. Possible values are as follows:

- MANUAL
Concurrency is enabled only for manual statistics gathering.

- AUTOMATIC
Concurrency is enabled only for automatic statistics gathering.

- ALL
Concurrency is enabled for both manual and automatic statistics gathering.

- OFF
Concurrency is disabled for both manual and automatic statistics gathering. This is the default value.

This tutorial in this section explains how to enable concurrent statistics gathering.

Prerequisites

This tutorial has the following prerequisites:

- In addition to the standard privileges for gathering statistics, you must have the following privileges:

- CREATE JOB

- MANAGE SCHEDULER

- MANAGE ANY QUEUE

- The SYSAUX tablespace must be online because the scheduler stores its internal tables and views in this tablespace.

- The JOB_QUEUE_PROCESSES initialization parameter must be set to at least 4.

- The Resource Manager must be enabled.
By default, the Resource Manager is disabled. If you do not have a resource plan, then consider enabling the Resource Manager with the system-supplied DEFAULT_PLAN (see Oracle Database Administrator's Guide).

Assumptions

This tutorial assumes that you want to do the following:

- Enable concurrent statistics gathering

- Gather statistics for the sh schema

- Monitor the gathering of the sh statistics

To enable concurrent statistics gathering:

1. Connect SQL*Plus to the database with the appropriate privileges, and then enable the Resource Manager.
The following example uses the default plan for the Resource Manager:

ALTER SYSTEM SET RESOURCE_MANAGER_PLAN = 'DEFAULT_PLAN';

2. Set the JOB_QUEUE_PROCESSES initialization parameter to at least twice the number of CPU cores.
In Oracle Real Application Clusters, the JOB_QUEUE_PROCESSES setting applies to each node.

Assume that the system has 4 CPU cores. The following example sets the parameter to 8 (twice the number of cores):

ALTER SYSTEM SET JOB_QUEUE_PROCESSES=8;

3. Confirm that the parameter change took effect.
For example, enter the following command in SQL*Plus (sample output included):

SHOW PARAMETER PROCESSES;

NAME                                 TYPE        VALUE
------------------------------------ ----------- ------------------------------
_high_priority_processes             string      VKTM
aq_tm_processes                      integer     1
db_writer_processes                  integer     1
gcs_server_processes                 integer     0
global_txn_processes                 integer     1
job_queue_processes                  integer     8
log_archive_max_processes            integer     4
processes                            integer     100

4. Enable concurrent statistics.
For example, execute the following PL/SQL anonymous block:

BEGIN
DBMS_STATS.SET_GLOBAL_PREFS('CONCURRENT','ALL');
END;
/

5. Confirm that the statistics were enabled.
For example, execute the following query (sample output included):

SELECT DBMS_STATS.GET_PREFS('CONCURRENT') FROM DUAL;

DBMS_STATS.GET_PREFS('CONCURRENT')
-------------------------------------------------------------------------------
ALL

6. Gather the statistics for the SH schema.
For example, execute the following procedure:

EXEC DBMS_STATS.GATHER_SCHEMA_STATS('SH');

7. In a separate session, monitor the job progress by querying DBA_OPTSTAT_OPERATION_TASKS.
For example, execute the following query (sample output included):

SET LINESIZE 1000

COLUMN TARGET FORMAT a17
COLUMN TARGET_TYPE FORMAT a25
COLUMN JOB_NAME FORMAT a14
COLUMN START_TIME FORMAT a40

SELECT TARGET, TARGET_TYPE, JOB_NAME,
TO_CHAR(START_TIME, 'dd-mon-yyyy hh24:mi:ss')
FROM   DBA_OPTSTAT_OPERATION_TASKS
WHERE  STATUS = 'IN PROGRESS'
AND    OPID = (SELECT MAX(ID)
FROM   DBA_OPTSTAT_OPERATIONS
WHERE  OPERATION = 'gather_schema_stats');

TARGET            TARGET_TYPE               JOB_NAME       TO_CHAR(START_TIME,'
----------------- ------------------------- -------------- --------------------
SH.SALES          TABLE (GLOBAL STATS ONLY) ST$T292_1_B29  30-nov-2012 14:22:47
SH.SALES          TABLE (COORDINATOR JOB)   ST$SD290_1_B10 30-nov-2012 14:22:08

8. In the original session, disable concurrent statistics gathering.
For example, execute the following query:

EXEC DBMS_STATS.SET_GLOBAL_PREFS('CONCURRENT','OFF');

See Also:

- "Monitoring Statistics Gathering Operations"

- Oracle Database PL/SQL Packages and Types Reference to learn how to use the DBMS_STATS.SET_GLOBAL_PREFS procedure

12.4.7.3 Configuring the System for Parallel Execution and Concurrent Statistics Gathering

When CONCURRENT statistics gathering is enabled, you can execute each statistics gathering job in parallel. This combination is useful when you are analyzing large tables, partitions, or subpartitions.

The following procedure describes the recommended configuration.

To configure the system for parallel execution and concurrent statistics gathering:

1. Connect SQL*Plus to the database with the administrator privileges.

2. Disable the parallel adaptive multiuser initialization parameter.
For example, use the following SQL statement:

ALTER SYSTEM SET PARALLEL_ADAPTIVE_MULTI_USER=false;

3. Enable parallel statement queuing.
Perform the following steps:

1. If Oracle Database Resource Manager (the Resource Manager) is not activated, then activate it. By default, the Resource Manager is activated only during the maintenance windows.

2. Create a temporary resource plan in which the consumer group OTHER_GROUPS has queuing enabled.

The following sample script illustrates one way to create a temporary resource plan (pqq_test), and enable the Resource Manager with this plan:

-- connect as a user with dba privileges
BEGIN
DBMS_RESOURCE_MANAGER.CREATE_PENDING_AREA();
DBMS_RESOURCE_MANAGER.CREATE_PLAN('pqq_test', 'pqq_test');
DBMS_RESOURCE_MANAGER.CREATE_PLAN_DIRECTIVE(
'pqq_test'
,   'OTHER_GROUPS'
,   'OTHER_GROUPS directive for pqq'
,   parallel_target_percentage       => 90
,   max_utilization_limit            => 90
);
DBMS_RESOURCE_MANAGER.SUBMIT_PENDING_AREA();
END;
/
ALTER SYSTEM SET RESOURCE_MANAGER_PLAN = 'pqq_test' SID='*';

See Also:

- Oracle Database Reference to learn about the PARALLEL_ADAPTIVE_MULTI_USER initialization parameter

- Oracle Database Administrator's Guide to learn how to manage resources with the Resource Manager

12.4.7.4 Monitoring Statistics Gathering Operations

To monitor statistics gathering jobs, use the following views:

- DBA_OPTSTAT_OPERATION_TASKS
This view contains the history of tasks that are performed or currently in progress as part of statistics gathering operations (recorded in DBA_OPTSTAT_OPERATIONS). Each task represents a target object to be processed in the corresponding parent operation.

- DBA_OPTSTAT_OPERATIONS
This view contains a history of statistics operations performed or currently in progress at the table, schema, and database level using the DBMS_STATS package.

The TARGET column in the preceding views shows the target object for that statistics gathering job in the following form:

OWNER.TABLE_NAME.PARTITION_OR_SUBPARTITION_NAME

All statistics gathering job names start with the string ST$.

To display currently running statistics tasks and jobs:

- To list statistics gathering currently running tasks from all user sessions, use the following SQL statement (sample output included):

SELECT OPID, TARGET, JOB_NAnME,
(SYSTIMESTAMP - START_TIME) AS elapsed_time
FROM   DBA_OPTSTAT_OPERATION_TASKS
WHERE  STATUS = 'IN PROGRESS';

OPID TARGET                    JOB_NAME      ELAPSED_TIME
---- ------------------------- ------------- --------------------------
981 SH.SALES.SALES_Q4_1998    ST$T82_1_B29  +000000000 00:00:00.596321
981 SH.SALES                  ST$SD80_1_B10 +000000000 00:00:27.972033

To display completed statistics tasks and jobs:

- To list only completed tasks and jobs from a particular operation, first identify the operation ID from the DBA_OPTSTAT_OPERATIONS view based on the statistics gathering operation name, target, and start time. After you identify the operation ID, you can query the DBA_OPTSTAT_OPERATION_TASKS view to find the corresponding tasks in that operation
For example, to list operations with the ID 981, use the following commands in SQL*Plus (sample output included):

VARIABLE id NUMBER
EXEC :id := 985

SELECT TARGET, JOB_NAME, (END_TIME - START_TIME) AS ELAPSED_TIME
FROM   DBA_OPTSTAT_OPERATION_TASKS
WHERE  STATUS <> 'IN PROGRESS'
AND    OPID = :id;

TARGET                    JOB_NAME      ELAPSED_TIME
------------------------- ------------- --------------------------
SH.SALES_TRANSACTIONS_EXT               +000000000 00:00:45.479233
SH.CAL_MONTH_SALES_MV     ST$SD88_1_B10 +000000000 00:00:45.382764
SH.CHANNELS               ST$SD88_1_B10 +000000000 00:00:45.307397

To display statistics gathering tasks and jobs that have failed:

- Use the following SQL statement (partial sample output included):

SET LONG 10000

SELECT TARGET, JOB_NAME,
(END_TIME - START_TIME) AS ELAPSED_TIME, NOTES
FROM   DBA_OPTSTAT_OPERATION_TASKS
WHERE  STATUS = 'FAILED';

TARGET             JOB_NAME ELAPSED_TIME               NOTES
------------------ -------- -------------------------- ----------------------
SYS.OPATCH_XML_INV          +000000007 02:36:31.130314 <error>ORA-20011:
Approximate NDV failed:
ORA-29913: error in
.
.
.

See Also:
Oracle Database Reference to learn about the DBA_SCHEDULER_JOBS view

12.4.8 Gathering Incremental Statistics on Partitioned Objects

Incremental statistics scan only changed partitions. Starting in Oracle Database 11g, incremental statistics maintenance improves the performance of gathering statistics on large partitioned table by deriving global statistics from partition-level statistics.

This section contains the following topics:

- Purpose of Incremental Statistics

- How Incremental Statistics Maintenance Derives Global Statistics

- How to Enable Incremental Statistics Maintenance

- Maintaining Incremental Statistics for Partition Maintenance Operations

- Maintaining Incremental Statistics for Tables with Stale or Locked Partition Statistics

12.4.8.1 Purpose of Incremental Statistics

In a typical case, an application loads data into a new partition of a range-partitioned table. As applications add new partitions and load data, the database must gather statistics on the new partition and keep global statistics up to date.

Without incremental statistics, statistics collection typically uses a two-pass approach:

1. The database scans the table to gather the global statistics.

2. The database scans the changed partitions to gather their partition-level statistics.

The full scan of the table for global statistics collection can be very expensive, depending on the size of the table. As the table adds partitions, the longer the execution time for GATHER_TABLE_STATS because of the full table scan required for the global statistics. The database must perform the scan of the entire table even if only a small subset of partitions change. In contrast, incremental statistics enable the database to avoid these full table scans.

12.4.8.2 How Incremental Statistics Maintenance Derives Global Statistics

Starting in Oracle Database 11g, the database avoids a full table scan when computing global statistics by deriving global statistics from the partition statistics. The database can accurately derive some statistics from partition statistics. For example, the number of rows at the global level is the sum of number of rows of partitions. Even global histograms can be derived from partition histograms.

However, the database cannot derive all statistics from partition-level statistics. For example, the database cannot derive the NDV of a column from partition-level NDVs. So, the database maintains a structure called a synopsis for each column at the partition level. A synopsis can be viewed as a sample of distinct values. The database can accurately derive the NDV for each column by merging partition-level synopses.

When incremental statistics maintenance is enabled, the database does the following:

- Gathers statistics and creates synopses for changed partitions only

- Merges partition-level synopses into a global synopsis automatically

- Derives global statistics automatically from the partition-level statistics and global synopses

Example 12-3 Deriving Global Statistics

The following graphic shows how the database gathers statistics for the initial six partitions of the sales table, and then creates synopses for each partition (S1, S2, and so on). The database creates global statistics by aggregating the partition-level statistics and synopses.

The following graphic shows a new partition, containing data for May 24, being added to the sales table. The database gathers statistics for the newly added partition, retrieves synopses for the other partitions, and then aggregates the synopses to create global statistics.

12.4.8.3 How to Enable Incremental Statistics Maintenance

Use DBMS_STATS.SET_TABLE_PREFS to set the INCREMENTAL value, and in this way control incremental statistics maintenance. When INCREMENTAL is set to false (default), the database always uses a full table scan to maintain global statistics. When the following criteria are met, the database updates global statistics incrementally by scanning only the partitions that have changed:

- The INCREMENTAL value for the partitioned table is true.

- The PUBLISH value for the partitioned table is true.

- The user specifies AUTO_SAMPLE_SIZE for ESTIMATE_PERCENT and AUTO for GRANULARITY when gathering statistics on the table.

Enabling incremental statistics maintenance has the following consequences:

- The SYSAUX tablespace consumes additional space to maintain global statistics for partitioned tables.

- If a table uses composite partitioning, then the database only gathers statistics for modified subpartitions. The database does not gather statistics at the subpartition level for unmodified subpartitions. In this way, the database reduces work by skipping unmodified partitions.

- If a table uses incremental statistics, and if this table has a locally partitioned index, then the database gathers index statistics at the global level and for modified (not unmodified) index partitions. The database does not generate global index statistics from the partition-level index statistics. Rather, the database gathers global index statistics by performing a full index scan.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS

12.4.8.4 Maintaining Incremental Statistics for Partition Maintenance Operations

A partition maintenance operation is a partition-related operation such as adding, exchanging, merging, or splitting table partitions. Oracle Database 12c introduces the following enhancements for maintaining incremental statistics:

- If a partition maintenance operation triggers statistics gathering, then the database can reuse synopses that would previously have been dropped with the old segments.

- DBMS_STATS can create a synopsis on a nonpartitioned table. The synopsis enables the database to maintain incremental statistics as part of a partition exchange operation without having to explicitly gather statistics on the partition after the exchange.

When the DBMS_STATS preference INCREMENTAL is set to true on a table, the INCREMENTAL_LEVEL preference controls which synopses are collected and when. This preference takes the following values:

- TABLE
DBMS_STATS gathers table-level synopses on this table. You can only set INCREMENTAL_LEVEL to TABLE at the table level, not at the schema, database, or global level.

- PARTITION (default)
DBMS_STATS only gathers synopsis at the partition level of partitioned tables.

When performing a partition exchange, to have synopses after the exchange for the partition being exchanged, set INCREMENTAL to true and INCREMENTAL_LEVEL to TABLE on the table to be exchanged with the partition.

Assumptions

This tutorial assumes the following:

- You want to load empty partition p_sales_01_2010 in a sales table.

- You create a staging table t_sales_01_2010, and then populate the table.

- You want the database to maintain incremental statistics as part of the partition exchange operation without having to explicitly gather statistics on the partition after the exchange.

To maintain incremental statistics as part of a partition exchange operation:

1. Set incremental statistics preferences for staging table t_sales_01_2010.
For example, run the following statement:

BEGIN
DBMS_STATS.SET_TABLE_PREFS (
'sh'
,   't_sales_01_2010'
,   'INCREMENTAL'
,   'true'
);
DBMS_STATS.SET_TABLE_PREFS (
'sh'
,   't_sales_01_2010'
,   'INCREMENTAL_LEVEL'
,   'table'
);
END;

2. Gather statistics on staging table t_sales_01_2010.
For example, run the following PL/SQL code:

BEGIN
DBMS_STATS.GATHER_TABLE_STATS (
ownname    => 'SH'
,   tabname    => 'T_SALES_01_2010'
);
END;
/

DBMS_STATS gathers table-level synopses on t_sales_01_2010.

3. Ensure that the INCREMENTAL preference is true on the sh.sales table.
For example, run the following PL/SQL code:

BEGIN
DBMS_STATS.SET_TABLE_PREFS (
'sh'
,   'sales'
,   'INCREMENTAL'
,   'true'
);
END;
/

4. If you have never gathered statistics on sh.sales before with INCREMENTAL set to true, then gather statistics on the partition to be exchanged.
For example, run the following PL/SQL code:

BEGIN
DBMS_STATS.GATHER_TABLE_STATS (
'sh'
,   'sales'
,   'p_sales_01_2010'
,   granularity=>'partition'
);
END;
/

5. Perform the partition exchange.
For example, use the following SQL statement:

ALTER TABLE sales EXCHANGE PARTITION p_sales_01_2010 WITH TABLE t_sales_01_2010

After the exchange, the partitioned table has both statistics and a synopsis for partition p_sales_01_2010.

In releases before Oracle Database 12c, the preceding statement swapped the segment data and statistics of p_sales_01_2010 with t_sales_01_2010. The database did not maintain synopses for nonpartitioned tables such as t_sales_01_2010. To gather global statistics on the partitioned table, you needed to rescan the p_sales_01_2010 partition to obtain its synopses.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS.SET_TABLE_PREFS

12.4.8.5 Maintaining Incremental Statistics for Tables with Stale or Locked Partition Statistics

Starting in Oracle Database 12c, incremental statistics can automatically calculate global statistics for a partitioned table even if the partition or subpartition statistics are stale and locked.

When incremental statistics are enabled in releases before Oracle Database 12c, if any DML occurs on a partition, then the optimizer considers statistics on this partition to be stale. Thus, DBMS_STATS must gather the statistics again to accurately aggregate the global statistics. Furthermore, if DML occurs on a partition whose statistics are locked, then DBMS_STATS cannot regather the statistics on the partition, so a full table scan is the only means of gathering global statistics. The necessity to regather statistics creates performance overhead.

In Oracle Database 12c, the statistics preference INCREMENTAL_STALENESS controls how the database determines whether the statistics on a partition or subpartition are stale. This preference takes the following values:

- USE_STALE_PERCENT
A partition or subpartition is not considered stale if DML changes are less than the STALE_PERCENT preference specified for the table. The default value of STALE_PERCENT is 10, which means that if DML causes more than 10% of row changes, then the table is considered stale.

- USE_LOCKED_STATS
Locked partition or subpartition statistics are not considered stale, regardless of DML changes.

- NULL (default)
A partition or subpartition is considered stale if it has any DML changes. This behavior is identical to the Oracle Database 11g behavior. When the default value is used, statistics gathered in incremental mode are guaranteed to be the same as statistics gathered in nonincremental mode. When a nondefault value is used, statistics gathered in incremental mode might be less accurate than those gathered in nonincremental mode.

You can specify USE_STALE_PERCENT and USE_LOCKED_STATS together. For example, you can write the following anonymous block:

BEGIN
DBMS_STATS.SET_TABLE_PREFS (
null
,   't'
,   'incremental_staleness'
,   'use_stale_percent, use_locked_stats'
);
END;

Assumptions

This tutorial assumes the following:

- The STALE_PERCENT for a partitioned table is set to 10.

- The INCREMENTAL value is set to true.

- The table has had statistics gathered in INCREMENTAL mode before.

- You want to discover how statistics gathering changes depending on the setting for INCREMENTAL_STALENESS, whether the statistics are locked, and the percentage of DML changes.

To test for tables with stale or locked partition statistics:

1. Set INCREMENTAL_STALENESS to NULL.
Afterward, 5% of the rows in one partition change because of DML activity.

2. Use DBMS_STATS to gather statistics on the table.
DBMS_STATS regathers statistics for the partition that had the 5% DML activity, and incrementally maintains the global statistics.

3. Set INCREMENTAL_STALENESS to USE_STALE_PERCENT.
Afterward, 5% of the rows in one partition change because of DML activity.

4. Use DBMS_STATS to gather statistics on the table.
DBMS_STATS does not regather statistics for the partition that had DML activity (because the changes are under the staleness threshold of 10%), and incrementally maintains the global statistics.

5. Lock the partition statistics.
Afterward, 20% of the rows in one partition change because of DML activity.

6. Use DBMS_STATS to gather statistics on the table.
DBMS_STATS does not regather statistics for the partition because the statistics are locked. The database gathers the global statistics with a full table scan.

Afterward, 5% of the rows in one partition change because of DML activity.

7. Use DBMS_STATS to gather statistics on the table.
When you gather statistics on this table, DBMS_STATS does not regather statistics for the partition because they are not considered stale. The database maintains global statistics incrementally using the existing statistics for this partition.

8. Set INCREMENTAL_STALENESS to USE_LOCKED_STATS and USE_STALE_PERCENT.
Afterward, 20% of the rows in one partition change because of DML activity.

9. Use DBMS_STATS to gather statistics on the table.
Because USE_LOCKED_STATS is set, DBMS_STATS ignores the fact that the statistics are stale and uses the locked statistics. The database maintains global statistics incrementally using the existing statistics for this partition.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS.SET_TABLE_PREFS

12.5 Gathering System Statistics Manually

System statistics describe the system's hardware characteristics, such as I/O and CPU performance and utilization, to the optimizer. System statistics enable the optimizer to choose a more efficient execution plan. Oracle recommends that you gather system statistics when a physical change occurs in the environment, for example, the server has faster CPUs, more memory, or different disk storage.

12.5.1 About Gathering System Statistics with DBMS_STATS

To gather system statistics, use DBMS_STATS.GATHER_SYSTEM_STATS. When the database gathers system statistics, it analyzes activity in a specified time period (workload statistics) or simulates a workload (noworkload statistics). The input arguments to DBMS_STATS.GATHER_SYSTEM_STATS are:

- NOWORKLOAD
The optimizer gathers statistics based on system characteristics only, without regard to the workload.

- INTERVAL
After the specified number of minutes has passed, the optimizer updates system statistics either in the data dictionary, or in an alternative table (specified by stattab). Statistics are based on system activity during the specified interval.

- START and STOP
START initiates gathering statistics. STOP calculates statistics for the elapsed period (since START) and refreshes the data dictionary or an alternative table (specified by stattab). The optimizer ignores INTERVAL.

- EXADATA
The system statistics consider the unique capabilities provided by using Exadata, such as large I/O size and high I/O throughput. The optimizer sets the multiblock read count and I/O throughput statistics along with CPU speed.

Table 12-4 lists the optimizer system statistics gathered by DBMS_STATS and the options for gathering or manually setting specific system statistics.

Table 12-4 Optimizer System Statistics in the DBMS_STAT Package

Parameter Name

Description

Initialization

Options for Gathering or Setting Statistics

Unit

cpuspeedNW

Represents noworkload CPU speed. CPU speed is the average number of CPU cycles in each second.

At system startup

Set gathering_mode = NOWORKLOAD or set statistics manually.

Millions/sec.

ioseektim

Represents the time it takes to position the disk head to read data. I/O seek time equals seek time + latency time + operating system overhead time.

At system startup

10 (default)

Set gathering_mode = NOWORKLOAD or set statistics manually.

ms

iotfrspeed

Represents the rate at which an Oracle database can read data in the single read request.

At system startup

4096 (default)

Set gathering_mode = NOWORKLOAD or set statistics manually.

Bytes/ms

cpuspeed

Represents workload CPU speed. CPU speed is the average number of CPU cycles in each second.

None

Set gathering_mode = NOWORKLOAD, INTERVAL, or START|STOP, or set statistics manually.

Millions/sec.

maxthr

Maximum I/O throughput is the maximum throughput that the I/O subsystem can deliver.

None

Set gathering_mode = NOWORKLOAD, INTERVAL, or START|STOP, or set statistics manually.

Bytes/sec.

slavethr

Slave I/O throughput is the average parallel execution server I/O throughput.

None

Set gathering_mode = INTERVAL or START|STOP, or set statistics manually.

Bytes/sec.

sreadtim

Single-block read time is the average time to read a single block randomly.

None

Set gathering_mode = INTERVAL or START|STOP, or set statistics manually.

ms

mreadtim

Multiblock read is the average time to read a multiblock sequentially.

None

Set gathering_mode = INTERVAL or START|STOP, or set statistics manually.

ms

mbrc

Multiblock count is the average multiblock read count sequentially.

None

Set gathering_mode = INTERVAL or START|STOP, or set statistics manually.

blocks

See Also:
Oracle Database PL/SQL Packages and Types Reference for detailed information on the procedures in the DBMS_STATS package for implementing system statistics

12.5.2 Guidelines for Gathering System Statistics

The database automatically gathers essential parts of system statistics at startup. CPU and I/O characteristics tend to remain constant over time. Typically, these characteristics only change when some aspect of the configuration is upgraded. For this reason, Oracle recommends that you gather system statistics only when a physical change occurs in your environment, for example, the server gets faster CPUs, more memory, or different disk storage.

Note the following guidelines:

- Oracle Database initializes noworkload statistics to default values at the first instance startup. Oracle recommends that you gather noworkload statistics after you create new tablespaces on storage that is not used by any other tablespace.

- The best practice is to capture statistics in the interval of time when the system has the most common workload. Gathering workload statistics does not generate additional overhead.

12.5.3 Gathering Workload Statistics

Use DBMS_STATS.GATHER_SYSTEM_STATS to capture statistics when the database has the most typical workload. For example, database applications can process OLTP transactions during the day and generate OLAP reports at night.

12.5.3.1 About Workload Statistics

Workload statistics include the following statistics listed in Table 12-4:

- Single block (sreadtim) and multiblock (mreadtim) read times

- Multiblock count (mbrc)

- CPU speed (cpuspeed)

- Maximum system throughput (maxthr)

- Average parallel execution throughput (slavethr)

The database computes sreadtim, mreadtim, and mbrc by comparing the number of physical sequential and random reads between two points in time from the beginning to the end of a workload. The database implements these values through counters that change when the buffer cache completes synchronous read requests.

Because the counters are in the buffer cache, they include not only I/O delays, but also waits related to latch contention and task switching. Thus, workload statistics depend on system activity during the workload window. If system is I/O bound (both latch contention and I/O throughput), then the statistics promote a less I/O-intensive plan after the database uses the statistics.

As shown in Figure 12-1, if you gather workload statistics, then the optimizer uses the mbrc value gathered for workload statistics to estimate the cost of a full table scan.

Figure 12-1 Workload Statistics Counters

When gathering workload statistics, the database may not gather the mbrc and mreadtim values if no table scans occur during serial workloads, as is typical of OLTP systems. However, full table scans occur frequently on DSS systems. These scans may run parallel and bypass the buffer cache. In such cases, the database still gathers the sreadtim because index lookups use the buffer cache.

If the database cannot gather or validate gathered mbrc or mreadtim values, but has gathered sreadtim and cpuspeed, then the database uses only sreadtim and cpuspeed for costing. In this case, the optimizer uses the value of the initialization parameter DB_FILE_MULTIBLOCK_READ_COUNT to cost a full table scan. However, if DB_FILE_MULTIBLOCK_READ_COUNT is 0 or is not set, then the optimizer uses a value of 8 for calculating cost.

Use the DBMS_STATS.GATHER_SYSTEM_STATS procedure to gather workload statistics. The GATHER_SYSTEM_STATS procedure refreshes the data dictionary or a staging table with statistics for the elapsed period. To set the duration of the collection, use either of the following techniques:

- Specify START the beginning of the workload window, and then STOP at the end of the workload window.

- Specify INTERVAL and the number of minutes before statistics gathering automatically stops. If needed, you can use GATHER_SYSTEM_STATS (gathering_mode=>'STOP') to end gathering earlier than scheduled.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the initialization parameter DB_FILE_MULTIBLOCK_READ_COUNT

12.5.3.2 Using GATHER_SYSTEM_STATS with START and STOP

This tutorial explains how to set the workload interval with the START and STOP parameters of GATHER_SYSTEM_STATS.

Assumptions

This tutorial assumes the following:

- The hour between 10 a.m. and 11 a.m. is representative of the daily workload.

- You intend to collect system statistics directly in the data dictionary.

To gather workload statistics using START and STOP:

1. Start SQL*Plus and connect to the database with administrator privileges.

2. Start statistics collection.
For example, at 10 a.m., execute the following procedure to start collection:

EXECUTE DBMS_STATS.GATHER_SYSTEM_STATS( gathering_mode => 'START' );

3. Generate the workload.

4. End statistics collection.
For example, at 11 a.m., execute the following procedure to end collection:

EXECUTE DBMS_STATS.GATHER_SYSTEM_STATS( gathering_mode => 'STOP' );

The optimizer can now use the workload statistics to generate execution plans that are effective during the normal daily workload.

5. Optionally, query the system statistics.
For example, run the following query:

COL PNAME FORMAT a15
SELECT PNAME, PVAL1
FROM   SYS.AUX_STATS$
WHERE  SNAME = 'SYSSTATS_MAIN';

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_SYSTEM_STATS procedure

12.5.3.3 Using GATHER_SYSTEM_STATS with INTERVAL

This tutorial explains how to set the workload interval with the INTERVAL parameter of GATHER_SYSTEM_STATS.

Assumptions

This tutorial assumes the following:

- The database application processes OLTP transactions during the day and runs OLAP reports at night. To gather representative statistics, you collect them during the day for two hours and then at night for two hours.

- You want to store statistics in a table named workload_stats.

- You intend to switch between the statistics gathered.

To gather workload statistics using INTERVAL:

1. Start SQL*Plus and connect to the production database as administrator dba1.

2. Create a table to hold the production statistics.
For example, execute the following PL/SQL program to create user statistics table workload_stats:

BEGIN
DBMS_STATS.CREATE_STAT_TABLE (
ownname => 'dba1'
,   stattab => 'workload_stats'
);
END;
/

3. Ensure that JOB_QUEUE_PROCESSES is not 0 so that DBMS_JOB jobs and Oracle Scheduler jobs run.

ALTER SYSTEM SET JOB_QUEUE_PROCESSES = 1;

4. Gather statistics during the day.
For example, gather statistics for two hours with the following program:

BEGIN
DBMS_STATS.GATHER_SYSTEM_STATS (
interval => 120
,   stattab  => 'workload_stats'
,   statid   => 'OLTP'
);
END;
/

5. Gather statistics during the evening.
For example, gather statistics for two hours with the following program:

BEGIN
DBMS_STATS.GATHER_SYSTEM_STATS (
interval => 120
,   stattab  => 'workload_stats'
,   statid   => 'OLAP'
);
END;
/

6. In the day or evening, import the appropriate statistics into the data dictionary.
For example, during the day you can import the OLTP statistics from the staging table into the dictionary with the following program:

BEGIN
EXECUTE DBMS_STATS.IMPORT_SYSTEM_STATS (
stattab => 'workload_stats'
,   statid  => 'OLTP'
);
END;
/

For example, during the night you can import the OLAP statistics from the staging table into the dictionary with the following program:

BEGIN
EXECUTE DBMS_STATS.IMPORT_SYSTEM_STATS (
stattab => 'workload_stats'
,   statid  => 'OLAP'
);
END;
/

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_SYSTEM_STATS procedure

12.5.4 Gathering Noworkload Statistics

Noworkload statistics capture characteristics of the I/O system. By default, Oracle Database uses noworkload statistics and the CPU cost model. The values of noworkload statistics are initialized to defaults at the first instance startup. You can also use the DBMS_STATS.GATHER_SYSTEM_STATS procedure to gather noworkload statistics manually.

Noworkload statistics include the following system statistics listed in Table 12-4:

- I/O transfer speed (iotfrspeed)

- I/O seek time (ioseektim)

- CPU speed (cpuspeednw)

The major difference between workload statistics and noworkload statistics is in the gathering method. Noworkload statistics gather data by submitting random reads against all data files, whereas workload statistics uses counters updated when database activity occurs. If you gather workload statistics, then Oracle Database uses them instead of noworkload statistics.

To gather noworkload statistics, run DBMS_STATS.GATHER_SYSTEM_STATS with no arguments or with the gathering mode set to noworkload. There is an overhead on the I/O system during the gathering process of noworkload statistics. The gathering process may take from a few seconds to several minutes, depending on I/O performance and database size.

When you gather noworkload statistics, the database analyzes the information and verifies it for consistency. In some cases, the values of noworkload statistics may retain their default values. You can either gather the statistics again, or use SET_SYSTEM_STATS to set the values manually to the I/O system specifications.

Assumptions

This tutorial assumes that you want to gather noworkload statistics manually.

To gather noworkload statistics manually:

1. Start SQL*Plus and connect to the database with administrator privileges.

2. Gather the noworkload statistics.
For example, run the following statement:

BEGIN
DBMS_STATS.GATHER_SYSTEM_STATS (
gathering_mode => 'NOWORKLOAD'
);
END;

3. Optionally, query the system statistics.
For example, run the following query:

COL PNAME FORMAT a15

SELECT PNAME, PVAL1
FROM   SYS.AUX_STATS$
WHERE  SNAME = 'SYSSTATS_MAIN';

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_SYSTEM_STATS procedure

12.5.5 Deleting System Statistics

Use the DBMS_STATS.DELETE_SYSTEM_STATS function to delete system statistics. This procedure deletes workload statistics collected using the INTERVAL or START and STOP options, and then resets the default to noworkload statistics. However, if the stattab parameter specifies a table for storing statistics, then the subprogram deletes all system statistics with the associated statid from the statistics table.

Assumptions

This tutorial assumes the following:

- You gathered statistics for a specific intensive workload, but no longer want the optimizer to use these statistics.

- You stored workload statistics in the default location, not in a user-specified table.

To delete system statistics:

1. Start SQL*Plus and connect to the database as a user with administrative privileges.

2. Delete the system statistics.
For example, run the following statement:

EXEC DBMS_STATS.DELETE_SYSTEM_STATS;

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.DELETE_SYSTEM_STATS procedure

13 Managing Optimizer Statistics: Advanced Topics

This chapter explains advanced concepts and tasks relating to optimizer statistics management, including extended statistics.

This chapter contains the following topics:

- Controlling Dynamic Statistics

- Publishing Pending Optimizer Statistics

- Managing Extended Statistics

- Locking and Unlocking Optimizer Statistics

- Restoring Optimizer Statistics

- Managing Optimizer Statistics Retention

- Importing and Exporting Optimizer Statistics

- Running Statistics Gathering Functions in Reporting Mode

- Reporting on Past Statistics Gathering Operations

- Managing SQL Plan Directives

See Also:
Chapter 12, "Managing Optimizer Statistics: Basic Topics"

13.1 Controlling Dynamic Statistics

By default, when optimizer statistics are missing, stale, or insufficient, dynamic statistics automatically run recursive SQL during parsing to scan a small random sample of table blocks.

This section contains the following topics:

- About Dynamic Statistics Levels

- Setting Dynamic Statistics Levels Manually

- Disabling Dynamic Statistics

See Also:
"Dynamic Statistics"

13.1.1 About Dynamic Statistics Levels

The dynamic statistics level controls both when the database gathers dynamic statistics, and the size of the sample that the optimizer uses to gather the statistics. Set the dynamic statistics level using either the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter (dynamic statistics were called dynamic sampling in releases earlier than Oracle Database 12c) or a statement hint.

Dynamic statistics are enabled in the database by default. Table 13-1 describes the levels. The default level is 2.

Table 13-1 Dynamic Statistics Levels

Level

When the Optimizer Uses Dynamic Statistics

Sample Size (Blocks)

0

Do not use dynamic statistics

n/a

1

Use dynamic statistics for all tables that do not have statistics, but only if the following criteria are met:

- There is at least 1 nonpartitioned table in the query that does not have statistics.

- This table has no indexes.

- This table has more blocks than the number of blocks that would be used for dynamic statistics of this table.

32

2

Use dynamic statistics if at least one table in the statement has no statistics. This is the default setting.

64

3

Use dynamic statistics if any of the following conditions is true:

- The statement meets level 2 criteria.

- The statement has one or more expressions used in the WHERE clause predicates, for example, WHERE SUBSTR(CUSTLASTNAME,1,3).

64

4

Use dynamic statistics if any of the following conditions is true:

- The statement meets level 3 criteria.

- The statement uses complex predicates (an OR or AND operator between multiple predicates on the same table).

64

5

Use dynamic statistics if the statement meets level 4 criteria.

128

6

Use dynamic statistics if the statement meets level 4 criteria.

256

7

Use dynamic statistics if the statement meets level 4 criteria.

512

8

Use dynamic statistics if the statement meets level 4 criteria.

1024

9

Use dynamic statistics if the statement meets level 4 criteria.

4086

10

Use dynamic statistics if the statement meets level 4 criteria.

All blocks

11

Use dynamic statistics automatically when the optimizer deems it necessary. The resulting statistics are persistent in the statistics repository, making them available to other queries.

Automatically determined

See Also:
Oracle Database Reference to learn about the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter

13.1.2 Setting Dynamic Statistics Levels Manually

When setting the level for dynamic statistics, the best practice is to use ALTER SESSION to set the value for the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter. Determining a systemwide setting that would be beneficial to all SQL statements can be difficult.

Assumptions

This tutorial assumes the following:

- You want correct selectivity estimates for the following query, which has WHERE clause predicates on two correlated columns:

SELECT *
FROM   sh.customers
WHERE  cust_city='Los Angeles'
AND    cust_state_province='CA';

- The preceding query uses serial processing.

- The sh.customers table contains 932 rows that meet the conditions in the query.

- You have gathered statistics on the sh.customers table.

- You created an index on the cust_city and cust_state_province columns.

- The OPTIMIZER_DYNAMIC_SAMPLING initialization parameter is set to the default level of 2.

To set the dynamic statistics level manually:

1. Connect SQL*Plus to the database with the appropriate privileges, and then explain the execution plan as follows:

EXPLAIN PLAN FOR
SELECT *
FROM   sh.customers
WHERE  cust_city='Los Angeles'
AND    cust_state_province='CA';

2. Query the plan as follows:

SET LINESIZE 130
SET PAGESIZE 0
SELECT *
FROM   TABLE(DBMS_XPLAN.DISPLAY);

The output appears below (the example has been reformatted to fit on the page):

-------------------------------------------------------------------------------
|Id| Operation                   | Name             |Rows|Bytes|Cost | Time   |
-------------------------------------------------------------------------------
| 0| SELECT STATEMENT            |                   | 53| 9593|53(0)|00:00:01|
| 1|  TABLE ACCESS BY INDEX ROWID|CUSTOMERS          | 53| 9593|53(0)|00:00:01|
|*2|   INDEX RANGE SCAN          |CUST_CITY_STATE_IND| 53| 9593| 3(0)|00:00:01|
-------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

2 - access("CUST_CITY"='Los Angeles' AND "CUST_STATE_PROVINCE"='CA')

The columns in the WHERE clause have a real-world correlation, but the optimizer is not aware that Los Angeles is in California and assumes both predicates reduce the number of rows returned. Thus, the table contains 932 rows that meet the conditions, but the optimizer estimates 53, as shown in bold.

If the database had used dynamic statistics for this plan, then the Note section of the plan output would have indicated this fact. The optimizer did not use dynamic statistics because the statement executed serially, standard statistics exist, and the parameter OPTIMIZER_DYNAMIC_SAMPLING is set to the default of 2.

3. Set the dynamic statistics level to 4 in the session using the following statement:

ALTER SESSION SET OPTIMIZER_DYNAMIC_SAMPLING=4;

4. Explain the plan again:

EXPLAIN PLAN FOR
SELECT *
FROM   sh.customers
WHERE  cust_city='Los Angeles'
AND    cust_state_province='CA';

The new plan shows a more accurate estimate of the number of rows, as shown by the value 932 in bold:

PLAN_TABLE_OUTPUT
-------------------------------------------------------------------------------
Plan hash value: 2008213504

-------------------------------------------------------------------------------
| Id  | Operation         | Name      | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------
|   0 | SELECT STATEMENT  |           |   932 |   271K|   406   (1)| 00:00:05 |
|*  1 |  TABLE ACCESS FULL| CUSTOMERS |   932 |   271K|   406   (1)| 00:00:05 |
-------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - filter("CUST_CITY"='Los Angeles' AND "CUST_STATE_PROVINCE"='CA')

Note
-----
- dynamic statistics used for this statement (level=4)

The note at the bottom of the plan indicates that the sampling level is 4. The additional dynamic statistics made the optimizer aware of the real-world relationship between the cust_city and cust_state_province columns, thereby enabling it to produce a more accurate estimate for the number of rows: 932 rather than 53.

See Also:

- Oracle Database SQL Language Reference to learn about setting sampling levels with the DYNAMIC_SAMPLING hint

- Oracle Database PL/SQL Packages and Types Reference to learn about the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter

13.1.3 Disabling Dynamic Statistics

In general, the best practice is not to incur the cost of dynamic statistics for queries whose compile times must be as fast as possible, for example, unrepeated OLTP queries. You can disable the feature by setting the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter.

To disable dynamic statistics at the session level:

1. Connect SQL*Plus to the database with the appropriate privileges.

2. Set the dynamic statistics level to 0.
For example, run the following statement:

ALTER SESSION SET OPTIMIZER_DYNAMIC_SAMPLING=0;

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter

13.2 Publishing Pending Optimizer Statistics

By default, the database automatically publishes statistics when the statistics collection ends. Alternatively, you can use pending statistics to save the statistics and not publish them immediately after the collection. This technique is useful for testing queries in a session with pending statistics. When the test results are satisfactory, you can publish the statistics to make them available for the entire database.

The database stores pending statistics in the data dictionary just as for published statistics. By default, the optimizer uses published statistics. You can change the default behavior by setting the OPTIMIZER_USE_PENDING_STATISTICS initialization parameter to true (the default is false).

The top part of Figure 13-1 shows the optimizer gathering statistics for the sh.customers table and storing them in the data dictionary with pending status. The bottom part of the diagram shows the optimizer using only published statistics to process a query of sh.customers.

Figure 13-1 Published and Pending Statistics

In some cases, the optimizer can use a combination of published and pending statistics. For example, the database stores both published and pending statistics for the customers table. For the orders table, the database stores only published statistics. If OPTIMIZER_USE_PENDING_STATS = true, then the optimizer uses pending statistics for customers and published statistics for orders. If OPTIMIZER_USE_PENDING_STATS = false, then the optimizer uses published statistics for customers and orders.

See Also:
Oracle Database Reference to learn about the OPTIMIZER_USE_PENDING_STATISTICS initialization parameter

13.2.1 User Interfaces for Publishing Optimizer Statistics

You can use the DBMS_STATS package to perform operations relating to publishing statistics. Table 13-2 lists the relevant program units.

Table 13-2 DBMS_STATS Program Units Relevant for Publishing Optimizer Statistics

Program Unit

Description

GET_PREFS

Check whether the statistics are automatically published as soon as DBMS_STATS gathers them. For the parameter PUBLISH, true indicates that the statistics must be published when the database gathers them, whereas false indicates that the database must keep the statistics pending.

SET_TABLE_PREFS

Set the PUBLISH setting to true or false at the table level.

SET_SCHEMA_PREFS

Set the PUBLISH setting to true or false at the schema level.

PUBLISH_PENDING_STATS

Publish valid pending statistics for all objects or only specified objects.

DELETE_PENDING_STATS

Delete pending statistics.

EXPORT_PENDING_STATS

Export pending statistics.

The initialization parameter OPTIMIZER_USE_PENDING_STATISTICS determines whether the database uses pending statistics when they are available. The default value is false, which means that the optimizer uses only published statistics. Set to true to specify that the optimizer uses any existing pending statistics instead. The best practice is to set this parameter at the session level rather than at the database level.

You can use access information about published statistics from data dictionary views. Table 13-3 lists relevant views.

Table 13-3 Views Relevant for Publishing Optimizer Statistics

View

Description

USER_TAB_STATISTICS

Displays optimizer statistics for the tables accessible to the current user.

USER_TAB_COL_STATISTICS

Displays column statistics and histogram information extracted from ALL_TAB_COLUMNS.

USER_PART_COL_STATISTICS

Displays column statistics and histogram information for the table partitions owned by the current user.

USER_SUBPART_COL_STATISTICS

Describes column statistics and histogram information for subpartitions of partitioned objects owned by the current user.

USER_IND_STATISTICS

Displays optimizer statistics for the indexes accessible to the current user.

USER_TAB_PENDING_STATS

Describes pending statistics for tables, partitions, and subpartitions accessible to the current user.

USER_COL_PENDING_STATS

Describes the pending statistics of the columns accessible to the current user.

USER_IND_PENDING_STATS

Describes the pending statistics for tables, partitions, and subpartitions accessible to the current user collected using the DBMS_STATS package.

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package

- Oracle Database Reference to learn about USER_TAB_PENDING_STATS and related views

13.2.2 Managing Published and Pending Statistics

This section explains how to use DBMS_STATS program units to change the publishing behavior of optimizer statistics, and also to export and delete these statistics.

Assumptions

This tutorial assumes the following:

- You want to change the preferences for the sh.customers and sh.sales tables so that newly collected statistics have pending status.

- You want the current session to use pending statistics.

- You want to gather and publish pending statistics on the sh.customers table.

- You gather the pending statistics on the sh.sales table, but decide to delete them without publishing them.

- You want to change the preferences for the sh.customers and sh.sales tables so that newly collected statistics are published.

To manage published and pending statistics:

1. Start SQL*Plus and connect to the database as user sh.

2. Query the global optimizer statistics publishing setting.
Run the following query (sample output included):

sh@PROD> SELECT DBMS_STATS.GET_PREFS('PUBLISH') PUBLISH FROM DUAL;

PUBLISH
-------
TRUE

The value true indicates that the database publishes statistics as it gathers them. Every table uses this value unless a specific table preference has been set.

When using GET_PREFS, you can also specify a schema and table name. The function returns a table preference if it is set. Otherwise, the function returns the global preference.

3. Query the pending statistics.
For example, run the following query (sample output included):

sh@PROD> SELECT * FROM USER_TAB_PENDING_STATS;

no rows selected

This example shows that the database currently stores no pending statistics for the sh schema.

4. Change the publishing preferences for the sh.customers table.
For example, execute the following procedure so that statistics are marked as pending:

BEGIN
DBMS_STATS.SET_TABLE_PREFS('sh', 'customers', 'publish', 'false');
END;
/

Subsequently, when you gather statistics on the customers table, the database does not automatically publish statistics when the gather job completes. Instead, the database stores the newly gathered statistics in the USER_TAB_PENDING_STATS table.

5. Gather statistics for sh.customers.
For example, run the following program:

BEGIN
DBMS_STATS.GATHER_TABLE_STATS('sh','customers');
END;
/

6. Query the pending statistics.
For example, run the following query (sample output included):

sh@PROD> SELECT TABLE_NAME, NUM_ROWS FROM USER_TAB_PENDING_STATS;

TABLE_NAME                       NUM_ROWS
------------------------------ ----------
CUSTOMERS                           55500

This example shows that the database now stores pending statistics for the sh.customers table.

7. Instruct the optimizer to use the pending statistics in this session.
Set the initialization parameter OPTIMIZER_USE_PENDING_STATISTICS to true as shown:

ALTER SESSION SET OPTIMIZER_USE_PENDING_STATISTICS = true;

8. Run a workload.
The following example changes the email addresses of all customers named Bruce Chalmers:

UPDATE  sh.customers
SET   cust_email='ChalmersB@company.com'
WHERE cust_first_name = 'Bruce'
AND   cust_last_name = 'Chalmers';
COMMIT;

The optimizer uses the pending statistics instead of the published statistics when compiling all SQL statements in this session.

9. Publish the pending statistics for sh.customers.
For example, execute the following program:

BEGIN
DBMS_STATS.PUBLISH_PENDING_STATS('SH','CUSTOMERS');
END;
/

10. Change the publishing preferences for the sh.sales table.
For example, execute the following program:

BEGIN
DBMS_STATS.SET_TABLE_PREFS('sh', 'sales', 'publish', 'false');
END;
/

Subsequently, when you gather statistics on the sh.sales table, the database does not automatically publish statistics when the gather job completes. Instead, the database stores the statistics in the USER_TAB_PENDING_STATS table.

11. Gather statistics for sh.sales.
For example, run the following program:

BEGIN
DBMS_STATS.GATHER_TABLE_STATS('sh','sales');
END;
/

12. Delete the pending statistics for sh.sales.
Assume you change your mind and now want to delete pending statistics for sh.sales. Run the following program:

BEGIN
DBMS_STATS.DELETE_PENDING_STATS('sh','sales');
END;
/

13. Change the publishing preferences for the sh.customers and sh.sales tables back to their default setting.
For example, execute the following program:

BEGIN
DBMS_STATS.SET_TABLE_PREFS('sh', 'customers', 'publish', null);
DBMS_STATS.SET_TABLE_PREFS('sh', 'sales', 'publish', null);
END;
/

13.3 Managing Extended Statistics

DBMS_STATS enables you to collect extended statistics, which are statistics that can improve cardinality estimates when multiple predicates exist on different columns of a table, or when predicates use expressions. An extension is either a column group or an expression.

Oracle Database supports the following types of extended statistics:

- Column group statistics
This type of extended statistics can improve cardinality estimates when multiple columns from the same table occur together in a SQL statement. See "Managing Column Group Statistics".

- Expression statistics
This type of extended statistics improves optimizer estimates when predicates use expressions, for example, built-in or user-defined functions. See "Managing Expression Statistics".

Note:
You cannot create extended statistics on virtual columns. See Oracle Database SQL Language Reference for a list of restrictions on virtual columns.

13.3.1 Managing Column Group Statistics

A column group is a set of columns that is treated as a unit. Essentially, a column group is a virtual column. By gathering statistics on a column group, the optimizer can more accurately determine the cardinality estimate when a query groups these columns together.

As explained in "SQL Plan Directives", the optimizer can use SQL plan directives to generate a more optimal plan. When applicable, a SQL plan directive can automatically trigger the creation of column group statistics.

The following sections provide an overview of column group statistics, and explain how to manage them manually:

- About Statistics on Column Groups

- Detecting Useful Column Groups for a Specific Workload

- Creating Column Groups Detected During Workload Monitoring

- Creating and Gathering Statistics on Column Groups Manually

- Displaying Column Group Information

- Dropping a Column Group

See Also:

- "SQL Plan Directives"

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package

13.3.1.1 About Statistics on Column Groups

Individual column statistics are useful for determining the selectivity of a single predicate in a WHERE clause. However, when the WHERE clause includes multiple predicates on different columns from the same table, individual column statistics do not show the relationship between the columns. The optimizer assumes no relationship exists between the columns, so it calculates the selectivity of the predicates independently, and then combines them. However, if a correlation between the individual columns exists, then the optimizer cannot take it into account when determining a cardinality estimate, which it creates by multiplying the selectivity of each table predicate by the number of rows.

Figure 13-2 contrasts two ways of gathering statistics on the cust_state_province and country_id columns of the sh.customers table. The diagram shows DBMS_STATS collecting statistics on each column individually and on the group. The column group has a system-generated name.

Figure 13-2 Column Group Statistics

Use DBMS_STATS to detect and create column groups as follows:

- Detect column groups, as explained in "Detecting Useful Column Groups for a Specific Workload"

- Create previously detected column groups, as explained in "Creating Column Groups Detected During Workload Monitoring"

- Create column groups manually and gather column group statistics, as explained in "Creating and Gathering Statistics on Column Groups Manually"

Note:
The optimizer uses column group statistics for equality predicates, inlist predicates, and for estimating the GROUP BY cardinality.

13.3.1.1.1 Why Column Group Statistics Are Needed: Example

The following query of the DBA_TAB_COL_STATISTICS table shows information about statistics that have been gathered on the columns cust_state_province and country_id from the sh.customers table:

COL COLUMN_NAME FORMAT a20
COL NDV FORMAT 999

SELECT COLUMN_NAME, NUM_DISTINCT AS "NDV", HISTOGRAM
FROM   DBA_TAB_COL_STATISTICS
WHERE  OWNER = 'SH'
AND    TABLE_NAME = 'CUSTOMERS'
AND    COLUMN_NAME IN ('CUST_STATE_PROVINCE', 'COUNTRY_ID');

Sample output is as follows:

COLUMN_NAME                 NDV HISTOGRAM
-------------------- ---------- ---------------
CUST_STATE_PROVINCE         145 FREQUENCY
COUNTRY_ID                   19 FREQUENCY

As shown in the following query, 3341 customers reside in California:

SELECT COUNT(*)
FROM   sh.customers
WHERE  cust_state_province = 'CA';

COUNT(*)
----------
3341

Consider an explain plan for a query of customers in the state CA and in the country with ID 52790 (USA):

EXPLAIN PLAN FOR
SELECT *
FROM   sh.customers
WHERE  cust_state_province = 'CA'
AND    country_id=52790;

Explained.

sys@PROD> SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY);

PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------
Plan hash value: 1683234692

-------------------------------------------------------------------------------
| Id  | Operation         | Name      | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------
|   0 | SELECT STATEMENT  |           |   128 | 24192 |   442   (7)| 00:00:06 |
|*  1 |  TABLE ACCESS FULL| CUSTOMERS |   128 | 24192 |   442   (7)| 00:00:06 |
-------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------

1 - filter("CUST_STATE_PROVINCE"='CA' AND "COUNTRY_ID"=52790)

13 rows selected.

Based on the single-column statistics for the country_id and cust_state_province columns, the optimizer estimates that the query of California customers in the USA will return 128 rows. In fact, 3341 customers reside in California, but the optimizer does not know that California is in the USA, and so greatly underestimates cardinality by assuming that both predicates reduce the number of returned rows.

You can make the optimizer aware of the real-world relationship between values in country_id and cust_state_province by gathering column group statistics. These statistics enable the optimizer to give a more accurate cardinality estimate.

See Also:

- "Detecting Useful Column Groups for a Specific Workload"

- "Creating Column Groups Detected During Workload Monitoring"

- "Creating and Gathering Statistics on Column Groups Manually"

13.3.1.1.2 User Interface for Column Group Statistics

Table 13-4 lists the DBMS_STATS program units that are relevant for detecting and creating column groups.

Table 13-4 DBMS_STATS Column Group Program Units

Program Unit

Description

SEED_COL_USAGE

Iterates over the SQL statements in the specified workload, compiles them, and then seeds column usage information for the columns that appear in these statements.

To determine the appropriate column groups, the database must observe a representative workload. You do not need to run the queries themselves during the monitoring period. Instead, you can run EXPLAIN PLAN for some longer-running queries in your workload to ensure that the database is recording column group information for these queries.

REPORT_COL_USAGE

Generates a report that lists the columns that were seen in filter predicates, join predicates, and GROUP BY clauses in the workload.

You can use this function to review column usage information recorded for a specific table.

CREATE_EXTENDED_STATS

Creates extensions, which are either column groups or expressions. The database gathers statistics for the extension when either a user-generated or automatic statistics gathering job gathers statistics for the table.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package

13.3.1.2 Detecting Useful Column Groups for a Specific Workload

You can use DBMS_STATS.SEED_COL_USAGE and REPORT_COL_USAGE to determine which column groups are required for a table based on a specified workload. This technique is useful when you do not know which extended statistics to create. This technique does not work for expression statistics.

Note:
You can seed column usage from a SQL tuning set (see Chapter 19, "Managing SQL Tuning Sets").

Assumptions

This tutorial assumes the following:

- Cardinality estimates have been incorrect for queries of the sh.customers_test table (created from the customers table) that use predicates referencing the columns country_id and cust_state_province.

- You want the database to monitor your workload for 5 minutes (300 seconds).

- You want the database to determine which column groups are needed automatically.

To detect column groups:

1. Connect SQL*Plus to the database as user sh, and then create the customers_test table and gather statistics for it:

CONNECT SH/SH
DROP TABLE customers_test;
CREATE TABLE customers_test AS SELECT * FROM customer;
EXEC DBMS_STATS.GATHER_TABLE_STATS(user, 'customers_test');

2. Enable workload monitoring.
In a different SQL*Plus session, connect as SYS and run the following PL/SQL program to enable monitoring for 300 seconds:

BEGIN
DBMS_STATS.SEED_COL_USAGE(null,null,300);
END;
/

3. As user sh, run explain plans for two queries in the workload.

The following examples show the explain plans for two queries on the customers_test table:

EXPLAIN PLAN FOR
SELECT *
FROM   customers_test
WHERE  cust_city = 'Los Angeles'
AND    cust_state_province = 'CA'
AND    country_id = 52790;

SELECT PLAN_TABLE_OUTPUT
FROM   TABLE(DBMS_XPLAN.DISPLAY('plan_table', null,'basic rows'));

EXPLAIN PLAN FOR
SELECT   country_id, cust_state_province, count(cust_city)
FROM     customers_test
GROUP BY country_id, cust_state_province;

SELECT PLAN_TABLE_OUTPUT
FROM   TABLE(DBMS_XPLAN.DISPLAY('plan_table', null,'basic rows'));

Sample output appears below:

PLAN_TABLE_OUTPUT
-------------------------------------------------------------------------------
Plan hash value: 4115398853

----------------------------------------------------
| Id  | Operation         | Name           | Rows  |
----------------------------------------------------
|   0 | SELECT STATEMENT  |                |     1 |
|   1 |  TABLE ACCESS FULL| CUSTOMERS_TEST |     1 |
----------------------------------------------------

8 rows selected.

PLAN_TABLE_OUTPUT
-------------------------------------------------------------------------------
Plan hash value: 3050654408

-----------------------------------------------------
| Id  | Operation          | Name           | Rows  |
-----------------------------------------------------
|   0 | SELECT STATEMENT   |                |  1949 |
|   1 |  HASH GROUP BY     |                |  1949 |
|   2 |   TABLE ACCESS FULL| CUSTOMERS_TEST | 55500 |
-----------------------------------------------------

9 rows selected.

The first plan shows a cardinality of 1 row for a query that returns 932 rows. The second plan shows a cardinality of 1949 rows for a query that returns 145 rows.

4. Optionally, review the column usage information recorded for the table.
Call the DBMS_STATS.REPORT_COL_USAGE function to generate a report:

SET LONG 100000
SET LINES 120
SET PAGES 0
SELECT DBMS_STATS.REPORT_COL_USAGE(user, 'customers_test')
FROM   DUAL;

The report appears below:

LEGEND:
.......

EQ         : Used in single table EQuality predicate
RANGE      : Used in single table RANGE predicate
LIKE       : Used in single table LIKE predicate
NULL       : Used in single table is (not) NULL predicate
EQ_JOIN    : Used in EQuality JOIN predicate
NONEQ_JOIN : Used in NON EQuality JOIN predicate
FILTER     : Used in single table FILTER predicate
JOIN       : Used in JOIN predicate
GROUP_BY   : Used in GROUP BY expression
...............................................................................

###############################################################################

COLUMN USAGE REPORT FOR SH.CUSTOMERS_TEST
.........................................

1. COUNTRY_ID                          : EQ
2. CUST_CITY                           : EQ
3. CUST_STATE_PROVINCE                 : EQ
4. (CUST_CITY, CUST_STATE_PROVINCE,
COUNTRY_ID)                        : FILTER
5. (CUST_STATE_PROVINCE, COUNTRY_ID)   : GROUP_BY
###############################################################################

In the preceding report, the first three columns were used in equality predicates in the first monitored query:

...
WHERE  cust_city = 'Los Angeles'
AND    cust_state_province = 'CA'
AND    country_id = 52790;

All three columns appeared in the same WHERE clause, so the report shows them as a group filter. In the second query, two columns appeared in the GROUP BY clause, so the report labels them as GROUP_BY. The sets of columns in the FILTER and GROUP_BY report are candidates for column groups.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package

13.3.1.3 Creating Column Groups Detected During Workload Monitoring

As explained in Table 13-4, you can use the DBMS_STATS.CREATE_EXTENDED_STATS function to create column groups that were detected previously by executing DBMS_STATS.SEED_COL_USAGE.

Assumptions

This tutorial assumes that you have performed the steps in "Detecting Useful Column Groups for a Specific Workload".

To create column groups:

1. Create column groups for the customers_test table based on the usage information captured during the monitoring window.
For example, run the following query:

SELECT DBMS_STATS.CREATE_EXTENDED_STATS(user, 'customers_test') FROM DUAL;

Sample output appears below:

###############################################################################
EXTENSIONS FOR SH.CUSTOMERS_TEST
................................
1. (CUST_CITY, CUST_STATE_PROVINCE,
COUNTRY_ID)                        : SYS_STUMZ$C3AIHLPBROI#SKA58H_N created
2. (CUST_STATE_PROVINCE, COUNTRY_ID)   : SYS_STU#S#WF25Z#QAHIHE#MOFFMM_ created
###############################################################################

The database created two column groups for customers_test: one column group for the filter predicate and one group for the GROUP BY operation.

2. Regather table statistics.
Run GATHER_TABLE_STATS to regather the statistics for customers_test:

EXEC DBMS_STATS.GATHER_TABLE_STATS(user,'customers_test');

3. As user sh, run explain plans for two queries in the workload.
Check the USER_TAB_COL_STATISTICS view to determine which additional statistics were created by the database:

SELECT COLUMN_NAME, NUM_DISTINCT, HISTOGRAM
FROM   USER_TAB_COL_STATISTICS
WHERE  TABLE_NAME = 'CUSTOMERS_TEST'
ORDER BY 1;

Partial sample output appears below:

CUST_CITY                               620 HEIGHT BALANCED
...
SYS_STU#S#WF25Z#QAHIHE#MOFFMM_          145 NONE
SYS_STUMZ$C3AIHLPBROI#SKA58H_N          620 HEIGHT BALANCED

This example shows the two column group names returned from the DBMS_STATS.CREATE_EXTENDED_STATS function. The column group created on CUST_CITY, CUST_STATE_PROVINCE, and COUNTRY_ID has a height-balanced histogram.

4. Explain the plans again.
The following examples show the explain plans for two queries on the customers_test table:

EXPLAIN PLAN FOR
SELECT *
FROM   customers_test
WHERE  cust_city = 'Los Angeles'
AND    cust_state_province = 'CA'
AND    country_id = 52790;

SELECT PLAN_TABLE_OUTPUT
FROM   TABLE(DBMS_XPLAN.DISPLAY('plan_table', null,'basic rows'));

EXPLAIN PLAN FOR
SELECT   country_id, cust_state_province, count(cust_city)
FROM     customers_test
GROUP BY country_id, cust_state_province;

SELECT PLAN_TABLE_OUTPUT
FROM   TABLE(DBMS_XPLAN.DISPLAY('plan_table', null,'basic rows'));

The new plans show more accurate cardinality estimates:

----------------------------------------------------
| Id  | Operation         | Name           | Rows  |
----------------------------------------------------
|   0 | SELECT STATEMENT  |                |  1093 |
|   1 |  TABLE ACCESS FULL| CUSTOMERS_TEST |  1093 |
----------------------------------------------------

8 rows selected.

Plan hash value: 3050654408

-----------------------------------------------------
| Id  | Operation          | Name           | Rows  |
-----------------------------------------------------
|   0 | SELECT STATEMENT   |                |   145 |
|   1 |  HASH GROUP BY     |                |   145 |
|   2 |   TABLE ACCESS FULL| CUSTOMERS_TEST | 55500 |
-----------------------------------------------------
9 rows selected.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package

13.3.1.4 Creating and Gathering Statistics on Column Groups Manually

In some cases, you may know the column group that you want to create. The METHOD_OPT argument of the DBMS_STATS.GATHER_TABLE_STATS function can create and gather statistics on a column group automatically. You can create a new column group by specifying the group of columns using FOR COLUMNS.

Assumptions

This tutorial assumes the following:

- You want to create a column group for the cust_state_province and country_id columns in the customers table in sh schema.

- You want to gather statistics (including histograms) on the entire table and the new column group.

To create a column group and gather statistics for this group:

1. Start SQL*Plus and connect to the database as the sh user.

2. Create the column group and gather statistics.
For example, execute the following PL/SQL program:

BEGIN
DBMS_STATS.GATHER_TABLE_STATS( 'sh','customers',
METHOD_OPT => 'FOR ALL COLUMNS SIZE SKEWONLY ' ||
'FOR COLUMNS SIZE SKEWONLY (cust_state_province,country_id)' );
END;
/

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure

13.3.1.5 Displaying Column Group Information

To obtain the name of a column group, use the DBMS_STATS.SHOW_EXTENDED_STATS_NAME function or a database view. You can also use views to obtain information such as the number of distinct values, and whether the column group has a histogram.

Assumptions

This tutorial assumes the following:

- You created a column group for the cust_state_province and country_id columns in the customers table in sh schema.

- You want to determine the column group name, the number of distinct values, and whether a histogram has been created for a column group.

To monitor a column group:

1. Start SQL*Plus and connect to the database as the sh user.

2. To determine the column group name, do one of the following.

- Execute the SHOW_EXTENDED_STATS_NAME function.
For example, run the following PL/SQL program:

SELECT SYS.DBMS_STATS.SHOW_EXTENDED_STATS_NAME( 'sh','customers',
'(cust_state_province,country_id)' ) col_group_name
FROM   DUAL;

The output is similar to the following:

COL_GROUP_NAME
----------------
SYS_STU#S#WF25Z#QAHIHE#MOFFMM_

- Query the USER_STAT_EXTENSIONS view.
For example, run the following query:

SELECT EXTENSION_NAME, EXTENSION
FROM   USER_STAT_EXTENSIONS
WHERE  TABLE_NAME='CUSTOMERS';

EXTENSION_NAME                     EXTENSION
-------------------------------------------------------------------------
SYS_STU#S#WF25Z#QAHIHE#MOFFMM_     ("CUST_STATE_PROVINCE","COUNTRY_ID")

3. Query the number of distinct values and find whether a histogram has been created for a column group.
For example, run the following query:

SELECT e.EXTENSION col_group, t.NUM_DISTINCT, t.HISTOGRAM
FROM   USER_STAT_EXTENSIONS e, USER_TAB_COL_STATISTICS t
WHERE  e.EXTENSION_NAME=t.COLUMN_NAME
AND    e.TABLE_NAME=t.TABLE_NAME
AND    t.TABLE_NAME='CUSTOMERS';

COL_GROUP                             NUM_DISTINCT        HISTOGRAM
-------------------------------------------------------------------------------
("COUNTRY_ID","CUST_STATE_PROVINCE")  145                 FREQUENCY

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.SHOW_EXTENDED_STATS_NAME function

13.3.1.6 Dropping a Column Group

Use the DBMS_STATS.DROP_EXTENDED_STATS function to delete a column group from a table.

Assumptions

This tutorial assumes the following:

- You created a column group for the cust_state_province and country_id columns in the customers table in sh schema.

- You want to drop the column group.

To drop a column group:

1. Start SQL*Plus and connect to the database as the sh user.

2. Drop the column group.
For example, the following PL/SQL program deletes a column group from the customers table:

BEGIN
DBMS_STATS.DROP_EXTENDED_STATS( 'sh', 'customers',
'(cust_state_province, country_id)' );
END;
/

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.DROP_EXTENDED_STATS function

13.3.2 Managing Expression Statistics

The type of extended statistics known as expression statistics improve optimizer estimates when a WHERE clause has predicates that use expressions.

This section contains the following topics:

- About Expression Statistics

- Creating Expression Statistics

- Displaying Expression Statistics

- Dropping Expression Statistics

13.3.2.1 About Expression Statistics

When an expression is applied to a column in the WHERE clause in the form(function(col)=constant), the optimizer has no way of knowing how this SQL function affects the cardinality of the predicate unless a function-based index had been created. Starting in Oracle Database 11g, you can gather expression statistics on the expression(function(col) itself.

Figure 13-3 shows the optimizer using statistics to generate a plan for a query that uses a function. The top shows the optimizer checking statistics for the column. The bottom shows the optimizer checking statistics corresponding to the expression used in the query. The expression statistics yield more accurate estimates.

Figure 13-3 Expression Statistics

As shown in Figure 13-3, when expression statistics are not available, the optimizer can produce suboptimal plans.

See Also:
Oracle Database SQL Language Reference to learn about SQL functions

13.3.2.1.1 When Expression Statistics Are Useful: Example

The following query of the sh.customers table shows that 3341 customers are in the state of California:

sys@PROD> SELECT COUNT(*) FROM sh.customers WHERE cust_state_province='CA';

COUNT(*)
----------
3341

Consider the plan for the same query with the LOWER() function applied:

sys@PROD> EXPLAIN PLAN FOR
2  SELECT * FROM sh.customers WHERE LOWER(cust_state_province)='ca';
Explained.

sys@PROD> select * from table(dbms_xplan.display);

PLAN_TABLE_OUTPUT
-------------------------------------------------------------------------------
Plan hash value: 2008213504

-------------------------------------------------------------------------------
| Id  | Operation         | Name      | Rows  | Bytes | Cost (%CPU)| Time     |
-------------------------------------------------------------------------------
|   0 | SELECT STATEMENT  |           |   555 |   108K|   406   (1)| 00:00:05 |
|*  1 |  TABLE ACCESS FULL| CUSTOMERS |   555 |   108K|   406   (1)| 00:00:05 |
-------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

1 - filter(LOWER("CUST_STATE_PROVINCE")='ca')

Because no expression statistics exist for LOWER(cust_state_province)='ca', the optimizer estimate is significantly off. You can use DBMS_STATS procedures to correct these estimates.

13.3.2.2 Creating Expression Statistics

You can use DBMS_STATS to create statistics for a user-specified expression. You have the option of using either of the following program units:

- GATHER_TABLE_STATS procedure

- CREATE_EXTENDED_STATISTICS function followed by the GATHER_TABLE_STATS procedure

Assumptions

This tutorial assumes the following:

- Selectivity estimates are inaccurate for queries of sh.customers that use the UPPER(cust_state_province) function.

- You want to gather statistics on the UPPER(cust_state_province) expression.

To create expression statistics:

1. Start SQL*Plus and connect to the database as the sh user.

2. Gather table statistics.
For example, run the following command, specifying the function in the method_opt argument:

BEGIN
DBMS_STATS.GATHER_TABLE_STATS(
'sh'
,   'customers'
,   method_opt => 'FOR ALL COLUMNS SIZE SKEWONLY FOR COLUMNS
(LOWER(cust_state_province)) SIZE SKEWONLY'
);
END;

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GATHER_TABLE_STATS procedure

13.3.2.3 Displaying Expression Statistics

You can use the database view DBA_STAT_EXTENSIONS and the DBMS_STATS.SHOW_EXTENDED_STATS_NAME function to obtain information about expression statistics. You can also use views to obtain information such as the number of distinct values, and whether the column group has a histogram.

Assumptions

This tutorial assumes the following:

- You created extended statistics for the LOWER(cust_state_province) expression.

- You want to determine the column group name, the number of distinct values, and whether a histogram has been created for a column group.

To monitor expression statistics:

1. Start SQL*Plus and connect to the database as the sh user.

2. Query the name and definition of the statistics extension.
For example, run the following query:

COL EXTENSION_NAME FORMAT a30
COL EXTENSION FORMAT a35

SELECT EXTENSION_NAME, EXTENSION
FROM   USER_STAT_EXTENSIONS
WHERE  TABLE_NAME='CUSTOMERS';

Sample output appears as follows:

EXTENSION_NAME                 EXTENSION
------------------------------ -----------------------------------
SYS_STUBPHJSBRKOIK9O2YV3W8HOUE (LOWER("CUST_STATE_PROVINCE"))

3. Query the number of distinct values and find whether a histogram has been created for the expression.
For example, run the following query:

SELECT e.EXTENSION expression, t.NUM_DISTINCT, t.HISTOGRAM
FROM   USER_STAT_EXTENSIONS e, USER_TAB_COL_STATISTICS t
WHERE  e.EXTENSION_NAME=t.COLUMN_NAME
AND    e.TABLE_NAME=t.TABLE_NAME
AND    t.TABLE_NAME='CUSTOMERS';

EXPRESSION                            NUM_DISTINCT        HISTOGRAM
-------------------------------------------------------------------------------
(LOWER("CUST_STATE_PROVINCE"))        145                 FREQUENCY

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.SHOW_EXTENDED_STATS_NAME procedure

- Oracle Database Reference to learn about the DBA_STAT_EXTENSIONS view

13.3.2.4 Dropping Expression Statistics

Use the DBMS_STATS.DROP_EXTENDED_STATS function to delete a column group from a table.

Assumptions

This tutorial assumes the following:

- You created extended statistics for the LOWER(cust_state_province) expression.

- You want to drop the expression statistics.

To drop expression statistics:

1. Start SQL*Plus and connect to the database as the sh user.

2. Drop the column group.
For example, the following PL/SQL program deletes a column group from the customers table:

BEGIN
DBMS_STATS.DROP_EXTENDED_STATS(
'sh'
,   'customers'
,   '(LOWER(cust_state_province))'
);
END;
/

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.DROP_EXTENDED_STATS procedure

13.4 Locking and Unlocking Optimizer Statistics

You can lock statistics to prevent them from changing. After statistics are locked, you cannot make modifications to the statistics until the statistics have been unlocked.

Locking procedures are useful in a static environment when you want to guarantee that the statistics and resulting plan never change. For example, you may want to prevent new statistics from being gathered on a table or schema by the DBMS_STATS_JOB process, such as highly volatile tables.

When you lock statistics on a table, all dependent statistics are locked. The locked statistics include table statistics, column statistics, histograms, and dependent index statistics. To overwrite statistics even when they are locked, you can set the value of the FORCE argument in various DBMS_STATS procedures, for example, DELETE_*_STATS and RESTORE_*_STATS, to true.

13.4.1 Locking Statistics

The DBMS_STATS package provides two procedures for locking statistics: LOCK_SCHEMA_STATS and LOCK_TABLE_STATS.

Assumptions

This tutorial assumes the following:

- You gathered statistics on the oe.orders table and on the hr schema.

- You want to prevent the oe.orders table statistics and hr schema statistics from changing.

To lock statistics:

1. Start SQL*Plus and connect to the database as the oe user.

2. Lock the statistics on oe.orders.
For example, execute the following PL/SQL program:

BEGIN
DBMS_STATS.LOCK_TABLE_STATS('OE','ORDERS');
END;
/

3. Connect to the database as the hr user.

4. Lock the statistics in the hr schema.
For example, execute the following PL/SQL program:

BEGIN
DBMS_STATS.LOCK_SCHEMA_STATS('HR');
END;
/

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.LOCK_TABLE_STATS procedure

13.4.2 Unlocking Statistics

The DBMS_STATS package provides two procedures for unlocking statistics: UNLOCK_SCHEMA_STATS and UNLOCK_TABLE_STATS.

Assumptions

This tutorial assumes the following:

- You locked statistics on the oe.orders table and on the hr schema.

- You want to unlock these statistics.

To unlock statistics:

1. Start SQL*Plus and connect to the database as the oe user.

2. Unlock the statistics on oe.orders.
For example, execute the following PL/SQL program:

BEGIN
DBMS_STATS.UNLOCK_TABLE_STATS('OE','ORDERS');
END;
/

3. Connect to the database as the hr user.

4. Unlock the statistics in the hr schema.
For example, execute the following PL/SQL program:

BEGIN
DBMS_STATS.UNLOCK_SCHEMA_STATS('HR');
END;
/

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.UNLOCK_TABLE_STATS procedure

13.5 Restoring Optimizer Statistics

Whenever statistics in the data dictionary are modified, the database automatically saves old versions of statistics. If newly collected statistics lead to suboptimal execution plans, then you may want to revert to the previous statistics. In this way, restoring optimizer statistics can aid in troubleshooting suboptimal plans.

Figure 13-4 illustrates a timeline for restoring statistics. In the graphic, statistics collection occurs on August 10 and August 20. On August 24, the DBA determines that the current statistics may be causing the optimizer to generate suboptimal plans. On August 25, the administrator restores the statistics collected on August 10.

Figure 13-4 Restoring Optimizer Statistics

13.5.1 Guidelines for Restoring Optimizer Statistics

Restoring statistics is similar to importing and exporting statistics. In general, restore statistics instead of exporting them in the following situations:

- You want to recover older versions of the statistics. For example, you want to restore the optimizer behavior to an earlier date.

- You want the database to manage the retention and purging of statistics histories.

Export statistics rather than restoring them in the following situations:

- You want to experiment with multiple sets of statistics and change the values back and forth.

- You want to move the statistics from one database to another database. For example, moving statistics from a production system to a test system.

- You want to preserve a known set of statistics for a longer period than the desired retention date for restoring statistics.

See Also:
Oracle Database PL/SQL Packages and Types Reference for an overview of the procedures for restoring and importing statistics

13.5.2 Restrictions for Restoring Optimizer Statistics

When restoring previous versions of statistics, the following limitations apply:

- DBMS_STATS.RESTORE_*_STATS procedures cannot restore user-defined statistics.

- Old versions of statistics are not stored when the ANALYZE command has been used for collecting statistics.

- When you drop a table, workload information used by the auto-histogram gathering feature and saved statistics history used by the RESTORE_*_STATS procedures is lost. Without this data, these features do not function properly. To remove all rows from a table, and to restore these statistics with DBMS_STATS, use TRUNCATE instead of dropping and re-creating the same table.

13.5.3 Restoring Optimizer Statistics

You can restore statistics using the DBMS_STATS.RESTORE_*_STATS procedures. The procedures listed in Table 13-5 accept a timestamp as an argument and restore statistics as of the specified time (as_of_timestamp).

Table 13-5 DBMS_STATS Restore Procedures

Procedure

Description

RESTORE_DICTIONARY_STATS

Restores statistics of all dictionary tables (tables of SYS, SYSTEM, and RDBMS component schemas) as of a specified timestamp.

RESTORE_FIXED_OBJECTS_STATS

Restores statistics of all fixed tables as of a specified timestamp.

RESTORE_SCHEMA_STATS

Restores statistics of all tables of a schema as of a specified timestamp.

RESTORE_SYSTEM_STATS

Restores system statistics as of a specified timestamp.

RESTORE_TABLE_STATS

Restores statistics of a table as of a specified timestamp. The procedure also restores statistics of associated indexes and columns. If the table statistics were locked at the specified timestamp, then the procedure locks the statistics.

Dictionary views display the time of statistics modifications. You can use the following views to determine the time stamp to be use for the restore operation:

- The DBA_OPTSTAT_OPERATIONS view contain history of statistics operations performed at schema and database level using DBMS_STATS.

- The DBA_TAB_STATS_HISTORY views contains a history of table statistics modifications.

Assumptions

This tutorial assumes the following:

- After the most recent statistics collection for the oe.orders table, the optimizer began choosing suboptimal plans for queries of this table.

- You want to restore the statistics from before the most recent statistics collection to see if the plans improve.

To restore optimizer statistics:

1. Start SQL*Plus and connect to the database with administrator privileges.

2. Query the statistics history for oe.orders.
For example, run the following query:

COL TABLE_NAME FORMAT a10
SELECT TABLE_NAME,
TO_CHAR(STATS_UPDATE_TIME,'YYYY-MM-DD:HH24:MI:SS') AS STATS_MOD_TIME
FROM   DBA_TAB_STATS_HISTORY
WHERE  TABLE_NAME='ORDERS'
AND    OWNER='OE'
ORDER BY STATS_UPDATE_TIME DESC;

Sample output is as follows:

TABLE_NAME STATS_MOD_TIME
---------- -------------------
ORDERS     2012-08-20:11:36:38
ORDERS     2012-08-10:11:06:20

3. Restore the optimizer statistics to the previous modification time.
For example, restore the oe.orders table statistics to August 10, 2012:

BEGIN
DBMS_STATS.RESTORE_TABLE_STATS( 'OE','ORDERS',
TO_TIMESTAMP('2012-08-10:11:06:20','YYYY-MM-DD:HH24:MI:SS') );
END;
/

You can specify any date between 8/10 and 8/20 because DBMS_STATS restores statistics as of the specified time.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn more about the DBMS_STATS.RESTORE_TABLE_STATS procedure

13.6 Managing Optimizer Statistics Retention

By default, the database retains optimizer statistics for 31 days, after which time the statistics are scheduled for purging. You can use the DBMS_STATS package to determine the retention period, change the period, and manually purge old statistics.

This section contains the following topics:

- Obtaining Optimizer Statistics History

- Changing the Optimizer Statistics Retention Period

- Purging Optimizer Statistics

13.6.1 Obtaining Optimizer Statistics History

You can use DBMS_STATS procedures to obtain historical information for optimizer statistics. This information is useful when you want to determine how long the database retains optimizer statistics, and how far back these statistics can be restored.

You can use the following procedure to obtain information about the optimizer statistics history:

- GET_STATS_HISTORY_RETENTION
This function can retrieve the current statistics history retention value.

- GET_STATS_HISTORY_AVAILABILITY
This function retrieves the oldest time stamp when statistics history is available. Users cannot restore statistics to a time stamp older than the oldest time stamp.

To obtain optimizer statistics history information:

1. Start SQL*Plus and connect to the database with the necessary privileges.

2. Execute the following PL/SQL program:

DECLARE
v_stats_retn  NUMBER;
v_stats_date  DATE;
BEGIN
v_stats_retn := DBMS_STATS.GET_STATS_HISTORY_RETENTION;
DBMS_OUTPUT.PUT_LINE('The retention setting is ' || v_stats_retn || '.');
v_stats_date := DBMS_STATS.GET_STATS_HISTORY_AVAILABILITY;
DBMS_OUTPUT.PUT_LINE('The earliest restore date is ' || v_stats_date || '.');
END;
/

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.GET_STATS_HISTORY_RETENTION procedure

13.6.2 Changing the Optimizer Statistics Retention Period

By default, the database retains optimizer statistics for 31 days. You can configure the retention period using the DBMS_STATS.ALTER_STATS_HISTORY_RETENTION procedure.

Prerequisites

To run this procedure, you must have either the SYSDBA privilege, or both the ANALYZE ANY DICTIONARY and ANALYZE ANY system privileges.

Assumptions

This tutorial assumes the following:

- The current retention period for optimizer statistics is 31 days.

- You run queries annually as part of an annual report. To keep the statistics history for more than 365 days so that you have access to last year's plan (in case a suboptimal plan occurs now), you set the retention period to 366 days.

- You want to create a PL/SQL procedure set_opt_stats_retention that you can use to change the optimizer statistics retention period.

To change the optimizer statistics retention period:

1. Start SQL*Plus and connect to the database with the necessary privileges.

2. Create a procedure that changes the retention period.
For example, create the following procedure:

CREATE OR REPLACE PROCEDURE set_opt_stats_retention
( p_stats_retn   IN NUMBER )
IS
v_stats_retn NUMBER;
BEGIN
v_stats_retn := DBMS_STATS.GET_STATS_HISTORY_RETENTION;
DBMS_OUTPUT.PUT_LINE('The old retention setting is ' || v_stats_retn || '.');
DBMS_STATS.ALTER_STATS_HISTORY_RETENTION(p_stats_retn);
v_stats_retn := DBMS_STATS.GET_STATS_HISTORY_RETENTION;
DBMS_OUTPUT.PUT_LINE('The new retention setting is ' || v_stats_retn || '.');
END;
/

3. Change the retention period to 366 days.
For example, execute the procedure that you created in the previous step (sample output included):

SQL> EXECUTE set_opt_stats_retention(366)

The old retention setting is 31.
The new retention setting is 366.

PL/SQL procedure successfully completed.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.ALTER_STATS_HISTORY_RETENTION procedure

13.6.3 Purging Optimizer Statistics

Automatic purging is enabled when the STATISTICS_LEVEL initialization parameter is set to TYPICAL or ALL. The database purges all history older than the older of (current time - the ALTER_STATS_HISTORY_RETENTION setting) and (time of the most recent statistics gathering - 1).

You can purge old statistics manually using the PURGE_STATS procedure. If you do not specify an argument, then this procedure uses the automatic purging policy. If you specify the before_timestamp parameter, then the database purges statistics saved before the specified timestamp.

Prerequisites

To run this procedure, you must have either the SYSDBA privilege, or both the ANALYZE ANY DICTIONARY and ANALYZE ANY system privileges.

Assumptions

This tutorial assumes that you want to purge statistics more than one week old.

To purge optimizer statistics:

1. Start SQL*Plus and connect to the database with the necessary privileges.

2. Execute the DBMS_STATS.PURGE_STATS procedure.
For example, execute the procedure as follows:

EXEC DBMS_STATS.PURGE_STATS( SYSDATE-7 );

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.PURGE_STATS procedure

13.7 Importing and Exporting Optimizer Statistics

You can export and import optimizer statistics from the data dictionary to user-defined statistics tables. You can also copy statistics from one database to another database.

Importing and exporting are especially useful for testing an application using production statistics. You use DBMS_STATS to export schema statistics from a production database to a test database so that developers can tune execution plans in a realistic environment before deploying applications.

13.7.1 About Transporting Optimizer Statistics

When you transport optimizer statistics between databases, you must use DBMS_STATS to copy the statistics to and from a staging table, and tools to make the table contents accessible to the destination database. Figure 13-5 illustrates the process using Oracle Data Pump and ftp.

Figure 13-5 Transporting Optimizer Statistics

As shown in Figure 13-5, the basic steps are as follows:

1. In the production database, copy the statistics from the data dictionary to a staging table using DBMS_STATS.EXPORT_SCHEMA_STATS.

2. Export the statistics from the staging table to a .dmp file using Oracle Data Pump.

3. Transfer the .dmp file from the production host to the test host using a transfer tool such as ftp.

4. In the test database, import the statistics from the .dmp file to a staging table using Oracle Data Pump.

5. Copy the statistics from the staging table to the data dictionary using DBMS_STATS.IMPORT_SCHEMA_STATS.

13.7.2 Transporting Optimizer Statistics to a Test Database

This section explains how to transport schema statistics from a production database to a test database.

Prerequisites and Restrictions

When preparing to export optimizer statistics, note the following:

- Before exporting statistics, you must create a table to hold the statistics. The procedure DBMS_STATS.CREATE_STAT_TABLE creates the statistics table.

- The optimizer does not use statistics stored in a user-owned table. The only statistics used by the optimizer are the statistics stored in the data dictionary. To make the optimizer use statistics in user-defined tables, import these statistics into the data dictionary using the DBMS_STATS import procedure.

- The Data Pump Export and Import utilities export and import optimizer statistics from the database along with the table. When a column has system-generated names, Original Export (exp) does not export statistics with the data, but this restriction does not apply to Data Pump Export.

Note:
Exporting and importing statistics using DBMS_STATS is a distinct operation from using Data Pump Export and Import.

Assumptions

This tutorial assumes the following:

- You want to generate representative sh schema statistics on a production database and use DBMS_STATS to import them into a test database.

- Administrative user dba1 exists on both production and test databases.

- You intend to create table opt_stats to store the schema statistics.

- You intend to use Oracle Data Pump to export and import table opt_stats.

To generate schema statistics and import them into a separate database:

1. On the production host, start SQL*Plus and connect to the production database as administrator dba1.

2. Create a table to hold the production statistics.
For example, execute the following PL/SQL program to create user statistics table opt_stats:

BEGIN
DBMS_STATS.CREATE_STAT_TABLE (
ownname => 'dba1'
,   stattab => 'opt_stats'
);
END;
/

3. Gather schema statistics.
For example, manually gather schema statistics as follows:

-- generate representative workload
EXEC DBMS_STATS.GATHER_SCHEMA_STATS('SH');

4. Use DBMS_STATS to export the statistics.
For example, retrieve schema statistics and store them in the opt_stats table created previously:

BEGIN
DBMS_STATS.EXPORT_SCHEMA_STATS (
ownname => 'dba1'
,   stattab => 'opt_stats'
);
END;
/

5. Use Oracle Data Pump to export the contents of the statistics table.
For example, run the expdp command at the operating schema prompt:

expdp dba1 DIRECTORY=dpump_dir1 DUMPFILE=stat.dmp TABLES=opt_stats

6. Transfer the dump file to the test database host.

7. Log in to the test host, and then use Oracle Data Pump to import the contents of the statistics table.
For example, run the impdp command at the operating schema prompt:

impdp dba1 DIRECTORY=dpump_dir1 DUMPFILE=stat.dmp TABLES=opt_stats

8. On the test host, start SQL*Plus and connect to the test database as administrator dba1.

9. Use DBMS_STATS to import statistics from the user statistics table and store them in the data dictionary.
The following PL/SQL program imports schema statistics from table opt_stats into the data dictionary:

BEGIN
DBMS_STATS.IMPORT_SCHEMA_STATS(
ownname => 'dba1'
,   stattab => 'opt_stats'
);
END;
/

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS.CREATE_STAT_TABLE function

- Oracle Database PL/SQL Packages and Types Reference for an overview of the statistics transfer functions

- Oracle Database Utilities to learn about Oracle Data Pump

13.8 Running Statistics Gathering Functions in Reporting Mode

You can run the DBMS_STATS statistics gathering procedures in reporting mode. In this case, the optimizer does not actually gather statistics, but reports objects that would be processed if you were to use a specified statistics gathering function.

Table 13-6 lists the DBMS_STATS.REPORT_GATHER_*_STATS functions. For all functions, the input parameters are the same as for the corresponding GATHER_*_STATS procedure, with the following additional parameters: detail_level and format. Supported formats are XML, HTML, and TEXT. See Oracle Database PL/SQL Packages and Types Reference for complete syntax and semantics for the reporting mode functions.

Table 13-6 DBMS_STATS Reporting Mode Functions

Function

Description

REPORT_GATHER_TABLE_STATS

Runs GATHER_TABLE_STATS in reporting mode. The procedure does not collect statistics, but reports all objects that would be affected by invoking GATHER_TABLE_STATS.

REPORT_GATHER_SCHEMA_STATS

Runs GATHER_SCHEMA_STATS in reporting mode. The procedure does not actually collect statistics, but reports all objects that would be affected by invoking GATHER_SCHEMA_STATS.

REPORT_GATHER_DICTIONARY_STATS

Runs GATHER_DICTIONARY_STATS in reporting mode. The procedure does not actually collect statistics, but reports all objects that would be affected by invoking GATHER_DICTIONARY_STATS.

REPORT_GATHER_DATABASE_STATS

Runs GATHER_DATABASE_STATS in reporting mode. The procedure does not actually collect statistics, but reports all objects that would be affected by invoking GATHER_DATABASE_STATS.

REPORT_GATHER_FIXED_OBJ_STATS

Runs GATHER_FIXED_OBJ_STATS in reporting mode. The procedure does not actually collect statistics, but reports all objects that would be affected by invoking GATHER_FIXED_OBJ_STATS.

REPORT_GATHER_AUTO_STATS

Runs the automatic statistics gather job in reporting mode. The procedure does not actually collect statistics, but reports all objects that would be affected by running the job.

Assumptions

This tutorial assumes that you want to generate an HTML report of the objects that would be affected by running GATHER_SCHEMA_STATS on the oe schema.

To report on objects affected by running GATHER_SCHEMA_STATS:

1. Start SQL*Plus and connect to the database with administrator privileges.

2. Run the DBMS_STATS.REPORT_GATHER_SCHEMA_STATS function.
For example, run the following commands in SQL*Plus:

SET LINES 200 PAGES 0
SET LONG 100000
COLUMN REPORT FORMAT A200

VARIABLE my_report CLOB;
BEGIN
:my_report :=DBMS_STATS.REPORT_GATHER_SCHEMA_STATS(
ownname      => 'OE'       ,
detail_level => 'TYPICAL'  ,
format       => 'HTML'     );
END;
/

The following graphic shows a partial example report:

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS

13.9 Reporting on Past Statistics Gathering Operations

You can use DBMS_STATS functions to report on a specific statistics gathering operation or on operations that occurred during a specified time. This section shows the command-line interface. To learn about the Cloud Control interface, see "Graphical Interface for Optimizer Statistics Management".

Table 13-7 lists the functions. See Oracle Database PL/SQL Packages and Types Reference for complete syntax and semantics for the functions that report on statistics operations.

Table 13-7 DBMS_STATS Reporting Functions

Function

Description

REPORT_STATS_OPERATIONS

Generates a report of all statistics operations that occurred between two points in time. You can narrow the scope of the report to include only automatic statistics gathering runs. You can also provide a set of pluggable database (PDB) IDs so that the database reports only statistics operations from the specified PDBs.

REPORT_SINGLE_STATS_OPERATION

Generates a report of the specified operation. Optionally, you can specify a particular PDB ID in a container database (CDB).

Assumptions

This tutorial assumes that you want to generate HTML reports of the following:

- All statistics gathering operations within the last day

- The most recent statistics gathering operation

To report on all operations in the past day:

1. Start SQL*Plus and connect to the database with administrator privileges.

2. Run the DBMS_STATS.REPORT_STATS_OPERATIONS function.
For example, run the following commands:

SET LINES 200 PAGES 0
SET LONG 100000
COLUMN REPORT FORMAT A200

VARIABLE my_report CLOB;
BEGIN
:my_report := DBMS_STATS.REPORT_STATS_OPERATIONS (
since        => SYSDATE-1
,    until        => SYSDATE
,    detail_level => 'TYPICAL'
,    format       => 'HTML'
);
END;
/

The following graphic shows a sample report:

3. Run the DBMS_STATS.REPORT_SINGLE_STATS_OPERATION function for an individual operation.
For example, run the following program to generate a report of operation 848:

BEGIN
:my_report :=DBMS_STATS.REPORT_SINGLE_STATS_OPERATION (
OPID    => 848
,    FORMAT  => 'HTML'
);
END;

The following graphic shows a sample report:

See Also:

- "Graphical Interface for Optimizer Statistics Management" to learn about the Cloud Control GUI for statistics management

- Oracle Database PL/SQL Packages and Types Reference to learn more about DBMS_STATS

13.10 Managing SQL Plan Directives

As explained in "SQL Plan Directives", the database automatically manages SQL plan directives. If the directives are not used in 53 weeks, then the database automatically purges them.

You can use DBMS_SPD procedures and functions to manage directives manually.

Table 13-8 lists some of the more commonly used procedures and functions. See Oracle Database PL/SQL Packages and Types Reference for complete syntax and semantics for the DBMS_SPD package.

Table 13-8 DBMS_SPD Procedures

Procedure

Description

FLUSH_SQL_PLAN_DIRECTIVE

Forces the database to write directives from memory to persistent storage in the SYSAUX tablespace.

DROP_SQL_PLAN_DIRECTIVE

Drops a SQL plan directive.

Prerequisites

You must have the Administer SQL Management Object privilege to execute the DBMS_SPD APIs.

Assumptions

This tutorial assumes that you want to do the following:

- Write all directives for the sh schema to persistent storage.

- Delete all directives for the sh schema.

To write and then delete all sh schema plan directives:

1. Start SQL*Plus and connect to the database with administrator privileges.

2. Force the database to write the SQL plan directives to disk.
For example, execute the following DBMS_SPD program:

BEGIN
DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE;
END;
/

3. Query the data dictionary for information about existing directives in the sh schema.
Example 13-1 queries the data dictionary for information about the directive.

Example 13-1 Display Directives for sh Schema

SELECT TO_CHAR(d.DIRECTIVE_ID) dir_id, o.OWNER, o.OBJECT_NAME,
o.SUBOBJECT_NAME col_name, o.OBJECT_TYPE, d.TYPE, d.STATE, d.REASON
FROM   DBA_SQL_PLAN_DIRECTIVES d, DBA_SQL_PLAN_DIR_OBJECTS o
WHERE  d.DIRECTIVE_ID=o.DIRECTIVE_ID
AND    o.OWNER IN ('SH')
ORDER BY 1,2,3,4,5;

DIR_ID                 OWN OBJECT_NAME   COL_NAME   OBJECT  TYPE             STATE     REASON
-------------------    --- -----------   ---------- ------- ---------------- --------- ------------------------
1484026771529551585     SH   CUSTOMERS   COUNTRY_ID  COLUMN DYNAMIC_SAMPLING SUPERSEDED SINGLE TABLE CARDINALITY
MISESTIMATE
1484026771529551585     SH   CUSTOMERS   CUST_STATE_ COLUMN DYNAMIC_SAMPLING SUPERSEDED SINGLE TABLE CARDINALITY
PROVINCE                                       MISESTIMATE
1484026771529551585     SH   CUSTOMERS                TABLE DYNAMIC_SAMPLING SUPERSEDED SINGLE TABLE CARDINALITY
MISESTIMATE

4. Delete the existing SQL plan directive for the sh schema.
The following PL/SQL program unit deletes the SQL plan directive with the ID 1484026771529551585:

BEGIN
DBMS_SPD.DROP_SQL_PLAN_DIRECTIVE ( directive_id => 1484026771529551585 );
END;
/

See Also:

- Oracle Database Reference to learn about DBA_SQL_PLAN_DIRECTIVES

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPD.FLUSH_SQL_PLAN_DIRECTIVE procedure

Part VI

Optimizer Controls

This part contains the following chapters:

- Chapter 14, "Influencing the Optimizer"

- Chapter 15, "Controlling Cursor Sharing"

14 Influencing the Optimizer

This chapter contains the following topics:

- About Influencing the Optimizer

- Influencing the Optimizer with Initialization Parameters

- Influencing the Optimizer with Hints

14.1 About Influencing the Optimizer

In general, optimizer defaults are adequate for most operations. However, in some cases you may have information unknown to the optimizer, or need to tune the optimizer for a specific type of statement or workload. In such cases, influencing the optimizer may provide better performance.

You can influence the optimizer using several techniques, including SQL profiles, SQL Plan Management, initialization parameters, and hints. Figure 14-1 shows the principal techniques for influencing the optimizer.

Figure 14-1 Techniques for Influencing the Optimizer

The overlapping squares in Figure 14-1 show that SQL plan management uses both initialization parameters and hints. SQL profiles also technically include hints.

You can use the following techniques to influence the optimizer:

- Initialization parameters
Parameters influence many types of optimizer behavior at the database instance and session level. The most important parameters are covered in "Influencing the Optimizer with Initialization Parameters".

- Hints
A hint is a commented instruction in a SQL statement. Hints control a wide range of behavior. See "Influencing the Optimizer with Hints".

- DBMS_STATS
This package updates and manages optimizer statistics. The more accurate the statistics, the better the optimizer estimates.

This chapter does not cover DBMS_STATS. See Chapter 12, "Managing Optimizer Statistics: Basic Topics."

- SQL profiles
A SQL profile is a database object that contains auxiliary statistics specific to a SQL statement. Conceptually, a SQL profile is to a SQL statement what a set of object-level statistics is to a table or index. A SQL profile can correct suboptimal optimizer estimates discovered during SQL tuning.

This chapter does not cover SQL profiles. See Chapter 22, "Managing SQL Profiles."

- SQL plan management and stored outlines
SQL plan management is a preventative mechanism that enables the optimizer to automatically manage execution plans, ensuring that the database uses only known or verified plans.

This chapter does not cover SQL plan management. See Chapter 23, "Managing SQL Plan Baselines."

Note:
A stored outline is a legacy technique that serve a similar purpose to SQL plan baselines. See Chapter 24, "Migrating Stored Outlines to SQL Plan Baselines" to learn how to migrate stored outlines to SQL plan baselines.

In some cases, multiple techniques optimize the same behavior. For example, you can set optimizer goals using both initialization parameters and hints.

14.2 Influencing the Optimizer with Initialization Parameters

This section contains the following topics:

- About Optimizer Initialization Parameters

- Enabling Optimizer Features

- Choosing an Optimizer Goal

- Controlling Adaptive Optimization

14.2.1 About Optimizer Initialization Parameters

Oracle Database includes several initialization parameters that can influence optimizer behavior. Table 14-1 lists some of the most important.

Table 14-1 Initialization Parameters That Control Optimizer Behavior

Initialization Parameter

Description

CURSOR_SHARING

Converts literal values in SQL statements to bind variables. Converting the values improves cursor sharing and can affect the execution plans of SQL statements. The optimizer generates the execution plan based on the presence of the bind variables and not the actual literal values.

Set to FORCE to enable the creation of a new cursor when sharing an existing cursor, or when the cursor plan is not optimal. Set to EXACT to allow only statements with identical text to share the same cursor.

DB_FILE_MULTIBLOCK_READ_COUNT

Specifies the number of blocks that are read in a single I/O during a full table scan or index fast full scan. The optimizer uses the value of this parameter to calculate the cost of full table scans and index fast full scans. Larger values result in a lower cost for full table scans, which may result in the optimizer choosing a full table scan over an index scan.

The default value of this parameter corresponds to the maximum I/O size that the database can perform efficiently. This value is platform-dependent and is 1MB for most platforms. Because the parameter is expressed in blocks, it is set to a value equal to the maximum I/O size that can be performed efficiently divided by the standard block size. If the number of sessions is extremely large, then the multiblock read count value decreases to avoid the buffer cache getting flooded with too many table scan buffers.

OPTIMIZER_ADAPTIVE_REPORTING_ONLY

Controls the reporting mode for automatic reoptimization and adaptive plans (see "Adaptive Plans"). By default, reporting mode is off (false), which means that adaptive optimizations are enabled.

If set to true, then adaptive optimizations run in reporting-only mode. In this case, the database gathers information required for an adaptive optimization, but takes no action to change the plan. For example, an adaptive plan always choose the default plan, but the database collects information about which plan the database would use if the parameter were set to false. You can view the report by using DBMS_XPLAN.DISPLAY_CURSOR.

OPTIMIZER_MODE

Sets the optimizer mode at database instance startup. Possible values are ALL_ROWS, FIRST_ROWS_n, and FIRST_ROWS.

OPTIMIZER_INDEX_CACHING

Controls the cost analysis of an index probe with a nested loop. The range of values 0 to 100 indicates percentage of index blocks in the buffer cache, which modifies optimizer assumptions about index caching for nested loops and IN-list iterators. A value of 100 infers that 100% of the index blocks are likely to be found in the buffer cache, so the optimizer adjusts the cost of an index probe or nested loop accordingly. Use caution when setting this parameter because execution plans can change in favor of index caching.

OPTIMIZER_INDEX_COST_ADJ

Adjusts the cost of index probes. The range of values is 1 to 10000. The default value is 100, which means that the optimizer evaluates indexes as an access path based on the normal cost model. A value of 10 means that the cost of an index access path is one-tenth the normal cost of an index access path.

OPTIMIZER_INMEMORY_AWARE

This parameter enables (TRUE) or disables (FALSE) all of the in-memory optimizer features, including the cost model for in-memory, table expansion, bloom filters, and so on. Setting the parameter to FALSE causes the optimizer to ignore the in-memory property of tables during the optimization of SQL statements.

OPTIMIZER_USE_INVISIBLE_INDEXES

Enables or disables the use of invisible indexes.

RESULT_CACHE_MODE

Controls whether the database uses the SQL query result cache for all queries, or only for the queries that are annotated with the result cache hint. When set to MANUAL (the default), you must use the RESULT_CACHE hint to specify that a specific result is to be stored in the cache. When set to FORCE, the database stores all results in the cache.

When setting this parameter, consider how the result cache handles PL/SQL functions. The database invalidates query results in the result cache using the same mechanism that tracks data dependencies for PL/SQL functions, but otherwise permits caching of queries that contain PL/SQL functions. Because PL/SQL function result cache invalidation does not track all kinds of dependencies (such as on sequences, SYSDATE, SYS_CONTEXT, and package variables), indiscriminate use of the query result cache on queries calling such functions can result in changes to results, that is, incorrect results. Thus, consider correctness and performance when choosing to enable the result cache, especially when setting RESULT_CACHE_MODE to FORCE.

RESULT_CACHE_MAX_SIZE

Changes the memory allocated to the result cache. If you set this parameter to 0, then the result cache is disable. The value of this parameter is rounded to the largest multiple of 32 KB that is not greater than the specified value. If the rounded value is 0, then the feature is disabled.

RESULT_CACHE_MAX_RESULT

Specifies the maximum amount of cache memory that any single result can use. The default value is 5%, but you can specify any percentage value between 1 and 100.

RESULT_CACHE_REMOTE_EXPIRATION

Specifies the number of minutes for which a result that depends on remote database objects remains valid. The default is 0, which implies that the database should not cache results using remote objects. Setting this parameter to a nonzero value can produce stale answers, such as if a remote database modifies a table that is referenced in a result.

STAR_TRANSFORMATION_ENABLED

Enables the optimizer to cost a star transformation for star queries (if true). The star transformation combines the bitmap indexes on the various fact table columns. See Oracle Database Data Warehousing Guide.

See Also:

- Oracle Database Reference for complete information about the preceding initialization parameters

- Oracle Database Performance Tuning Guide to learn how to tune the query result cache

14.2.2 Enabling Optimizer Features

The OPTIMIZER_FEATURES_ENABLE initialization parameter controls a set of optimizer-related features, depending on the release. The parameter accepts one of a list of valid string values corresponding to the release numbers, such as 10.2.0.1 or 11.2.0.1.

You can use this parameter to preserve the old behavior of the optimizer after a database upgrade. For example, if you upgrade Oracle Database 11g Release 1 (11.1.0.7) to Oracle Database 11g Release 2 (11.2.0.2), then the default value of the OPTIMIZER_FEATURES_ENABLE parameter changes from 11.1.0.7 to 11.2.0.2. This upgrade results in the optimizer enabling optimization features based on Oracle Database 11g Release 2 (11.2.0.2).

For backward compatibility, you may not want the execution plans to change because of new optimizer features in a new release. In such cases, you can set OPTIMIZER_FEATURES_ENABLE to an earlier version. If you upgrade to a new release, and if you want to enable the features in the new release, then you do not need to explicitly set the OPTIMIZER_FEATURES_ENABLE initialization parameter.

Caution:
Oracle does not recommend explicitly setting the OPTIMIZER_FEATURES_ENABLE initialization parameter to an earlier release. To avoid SQL performance regression that may result from execution plan changes, consider using SQL plan management instead. See Chapter 23, "Managing SQL Plan Baselines."

Assumptions

This tutorial assumes the following:

- You recently upgraded the database from Oracle Database 10g Release 2 (10.2.0.5) to Oracle Database 11g Release 2 (11.2.0.2).

- You want to preserve the optimizer behavior from the earlier release.

To enable query optimizer features for a specific release:

1. Connect SQL*Plus to the database with the appropriate privileges, and then query the current optimizer features settings.
For example, run the following SQL*Plus command:

SQL> SHOW PARAMETER optimizer_features_enable

NAME                                 TYPE        VALUE
------------------------------------ ----------- ------------------------------
optimizer_features_enable            string      11.2.0.2

2. Set the optimizer features setting at the instance or session level.
For example, run the following SQL statement to set the optimizer version to 10.2.0.5:

SQL> ALTER SYSTEM SET OPTIMIZER_FEATURES_ENABLE='10.2.0.5';

The preceding statement restores the optimizer functionality that existed in Oracle Database 10g Release 2 (10.2.0.5).

See Also:
Oracle Database Reference to learn about optimizer features enabled when you set OPTIMIZER_FEATURES_ENABLE to different release values

14.2.3 Choosing an Optimizer Goal

The optimizer goal is the prioritization of resource usage by the optimizer. Using the OPTIMIZER_MODE initialization parameter, you can set the following optimizer goals:

- Best throughput (default)
When you set the OPTIMIZER_MODE value to ALL_ROWS, the database uses the least amount of resources necessary to process all rows that the statement accessed.

For batch applications such as Oracle Reports, optimize for best throughput. Usually, throughput is more important in batch applications because the user is only concerned with the time necessary for the application to complete. Response time is less important because the user does not examine the results of individual statements while the application is running.

- Best response time
When you set the OPTIMIZER_MODE value to FIRST_ROWS_n, the database optimizes with a goal of best response time to return the first n rows, where n equals 1, 10, 100, or 1000.

For interactive applications in Oracle Forms or SQL*Plus, optimize for response time. Usually, response time is important because the interactive user is waiting to see the first row or rows that the statement accessed.

Assumptions

This tutorial assumes the following:

- The primary application is interactive, so you want to set the optimizer goal for the database instance to minimize response time.

- For the current session only, you want to run a report and optimize for throughput.

To enable query optimizer features for a specific release:

1. Connect SQL*Plus to the database with the appropriate privileges, and then query the current optimizer mode.
For example, run the following SQL*Plus command:

dba1@PROD> SHOW PARAMETER OPTIMIZER_MODE

NAME                                 TYPE        VALUE
------------------------------------ ----------- ------------------------------
optimizer_mode                       string      ALL_ROWS

2. At the instance level, optimize for response time.
For example, run the following SQL statement to configure the system to retrieve the first 10 rows as quickly as possible:

SQL> ALTER SYSTEM SET OPTIMIZER_MODE='FIRST_ROWS_10';

3. At the session level only, optimize for throughput before running a report.
For example, run the following SQL statement to configure only this session to optimize for throughput:

SQL> ALTER SESSION SET OPTIMIZER_MODE='ALL_ROWS';

See Also:
Oracle Database Reference to learn about the OPTIMIZER_MODE initialization parameter

14.2.4 Controlling Adaptive Optimization

In Oracle Database, adaptive query optimization is the process by which the optimizer adapts an execution plan based on statistics collected at run time (see "About Adaptive Query Optimization"). Adaptive optimization is enabled under the following conditions:

- The OPTIMIZER_FEATURES_ENABLE initialization parameter is set to 12.1.0.1 or later.

- The OPTIMIZER_ADAPTIVE_REPORTING_ONLY initialization parameter is set to false (default).

If OPTIMIZER_ADAPTIVE_REPORTING_ONLY is set to true, then adaptive optimization runs in reporting-only mode. In this case, the database gathers information required for adaptive optimization, but does not change the plans. An adaptive plan always chooses the default plan, but the database collects information about the execution as if the parameter were set to false.

Assumptions

This tutorial assumes the following:

- The OPTIMIZER_FEATURES_ENABLE initialization parameter is set to 12.1.0.1 or later.

- The OPTIMIZER_ADAPTIVE_REPORTING_ONLY initialization parameter is set to false (default).

- You want to disable adaptive optimization for testing purposes so that the database generates only reports.

To disable adaptive optimization and view reports:

1. Connect SQL*Plus to the database as SYSTEM, and then query the current settings.
For example, run the following SQL*Plus command:

SHOW PARAMETER OPTIMIZER_ADAPTIVE_REPORTING_ONLY

NAME                                 TYPE        VALUE
------------------------------------ ----------- ------------------------------
optimizer_adaptive_reporting_only    boolean     FALSE

2. At the session level, set the OPTIMIZER_ADAPTIVE_REPORTING_ONLY initialization parameter to true.
For example, in SQL*Plus run the following SQL statement:

ALTER SESSION SET OPTIMIZER_ADAPTIVE_REPORTING_ONLY=true;

3. Run a query.

4. Run DBMS_XPLAN.DISPLAY_CURSOR to view the report.

Note:
The format argument that you pass to DBMS_XPLAN.DISPLAY_CURSOR must include the +REPORT parameter. When this parameter is set, the report shows the plan the optimizer would have picked if automatic reoptimization had been enabled.

See Also:

- Oracle Database Reference to learn about the OPTIMIZER_ADAPTIVE_REPORTING_ONLY initialization parameter

- Oracle Database PL/SQL Packages and Types Reference to learn about the +REPORT parameter of the DBMS_XPLAN.DISPLAY_CURSOR function

14.3 Influencing the Optimizer with Hints

Optimizer hints are special comments in a SQL statement that pass instructions to the optimizer. The optimizer uses hints to choose an execution plan for the statement unless prevented by some condition.

This section contains the following topics:

- About Optimizer Hints

- Guidelines for Join Order Hints

Note:
Oracle Database SQL Language Reference contains a complete reference for all SQL hints

14.3.1 About Optimizer Hints

Use hints to influence the optimizer mode, query transformation, access path, join order, and join methods. For example, Figure 14-2 shows how you can use a hint to tell the optimizer to use a specific index for a specific statement. Oracle Database SQL Language Reference lists the most common hints by functional category.

Figure 14-2 Optimizer Hint

The advantage of hints is that they enable you to make decisions normally made by the optimizer. In a test environment, hints are useful for testing the performance of a specific access path. For example, you may know that an index is more selective for certain queries, as in Figure 14-2. In this case, the hint may cause the optimizer to generate a better plan.

The disadvantage of hints is the extra code that you must manage, check, and control. Hints were introduced in Oracle7, when users had little recourse if the optimizer generated suboptimal plans. Because changes in the database and host environment can make hints obsolete or have negative consequences, a good practice is to test using hints, but use other techniques to manage execution plans.

Oracle provides several tools, including SQL Tuning Advisor, SQL plan management, and SQL Performance Analyzer, to address performance problems not solved by the optimizer. Oracle strongly recommends that you use these tools instead of hints because they provide fresh solutions as the data and database environment change.

14.3.1.1 Types of Hints

Hints fall into the following types:

- Single-table
Single-table hints are specified on one table or view. INDEX and USE_NL are examples of single-table hints. The following statement uses a single-table hint:

SELECT /*+ INDEX (employees emp_department_ix)*/ employee_id, department_id
FROM   employees
WHERE  department_id > 50;

- Multi-table
Multi-table hints are like single-table hints except that the hint can specify multiple tables or views. LEADING is an example of a multi-table hint. The following statement uses a multi-table hint:

SELECT /*+ LEADING(e j) */ *
FROM   employees e, departments d, job_history j
WHERE  e.department_id = d.department_id
AND    e.hire_date = j.start_date;

Note:
USE_NL(table1 table2) is not considered a multi-table hint because it is a shortcut for USE_NL(table1) and USE_NL(table2).

- Query block
Query block hints operate on single query blocks. STAR_TRANSFORMATION and UNNEST are examples of query block hints. The following statement uses a query block hint:

SELECT /*+ STAR_TRANSFORMATION */ s.time_id, s.prod_id, s.channel_id
FROM   sales s, times t, products p, channels c
WHERE s.time_id = t.time_id AND s.prod_id = p.prod_id
AND s.channel_id = c.channel_id AND c.channel_desc = 'Tele Sales';

- Statement
Statement hints apply to the entire SQL statement. ALL_ROWS is an example of a statement hint. The following statement uses a statement hint:

SELECT /*+ ALL_ROWS */ * FROM sales;

14.3.1.2 Scope of Hints

When you specify a hint, it optimizes only the statement block in which it appears, overriding any instance-level or session-level parameters. A statement block is one of the following:

- A simple MERGE, SELECT, INSERT, UPDATE, or DELETE statement

- A parent statement or a subquery of a complex statement

- A part of a query using set operators (UNION, MINUS, INTERSECT)

Example 14-1 shows a query consisting of two component queries and the UNION operator. This statement has two blocks, one for each component query. Hints in the first component query apply only to its optimization, not to the optimization of the second component query. For example, in the first week of 2011 you query current year and last year sales. You apply FIRST_ROWS(10) to the query of last year's (2010) sales and the ALL_ROWS hint to the query of this year's (2011) sales.

Example 14-1 Query Using a Set Operator

SELECT /*+ FIRST_ROWS(10) */ prod_id, time_id FROM 2010_sales
UNION ALL
SELECT /*+ ALL_ROWS */ prod_id, time_id FROM current_year_sales;

See Also:
Oracle Database SQL Language Reference for an overview of hints

14.3.1.3 Considerations for Hints

You must enclose hints within a SQL comment. The hint comment must immediately follow the first keyword of a SQL statement block. You can use either style of comment: a slash-star (/*) or pair of dashes (--). The plus-sign (+) hint delimiter must come immediately after the comment delimiter, as in the following fragment:

SELECT /*+ hint_text */ ...

The database ignores incorrectly specified hints. The database also ignores combinations of conflicting hints, even if these hints are correctly specified. If one hint is incorrectly specified, but a hint in the same comment is correctly specified, then the database considers the correct hint.

Caution:
The database does not issue error messages for hints that it ignores.

A statement block can have only one comment containing hints, but it can contain many space-separated hints. For example, a complex query may include multiple table joins. If you specify only the INDEX hint for a specified table, then the optimizer must determine the remaining access paths and corresponding join methods. The optimizer may not use the INDEX hint because the join methods and access paths prevent it. Example 14-2 uses multiple hints to specify the exact join order.

Example 14-2 Multiple Hints

SELECT   /*+ LEADING(e2 e1) USE_NL(e1) INDEX(e1 emp_emp_id_pk)
USE_MERGE(j) FULL(j) */
e1.first_name, e1.last_name, j.job_id, sum(e2.salary) total_sal
FROM     employees e1, employees e2, job_history j
WHERE    e1.employee_id = e2.manager_id
AND      e1.employee_id = j.employee_id
AND      e1.hire_date = j.start_date
GROUP BY e1.first_name, e1.last_name, j.job_id
ORDER BY total_sal;

See Also:
Oracle Database SQL Language Reference to learn about the syntax rules for comments and hints

14.3.2 Guidelines for Join Order Hints

The join order can have a significant effect on the performance of a SQL statement. In some cases, you can specify join order hints in a SQL statement so that it does not access rows that have no effect on the result.

The driving table in a join is the table to which other tables are joined. In general, the driving table contains the filter condition that eliminates the highest percentage of rows in the table.

Consider the following guidelines:

- Avoid a full table scan when an index retrieves the requested rows more efficiently.

- Avoid using an index that fetches many rows from the driving table when you can use a different index that fetches a small number of rows.

- Choose the join order so that you join fewer rows to tables later in the join order.

The following example shows how to tune join order effectively:

SELECT *
FROM   taba a, tabb b, tabc c
WHERE  a.acol BETWEEN   100 AND   200

AND    b.bcol BETWEEN 10000 AND 20000
AND    c.ccol BETWEEN 10000 AND 20000
AND    a.key1 = b.key1
AND    a.key2 = c.key2;

1. Choose the driving table and the driving index (if any).
Each of the first three conditions in the previous example is a filter condition that applies to a single table. The last two conditions are join conditions.

Filter conditions dominate the choice of driving table and index. In general, the driving table contains the filter condition that eliminates the highest percentage of rows. Thus, because the range of 100 to 200 is narrow compared with the range of acol, but the ranges of 10000 and 20000 are relatively large, taba is the driving table, all else being equal.

With nested loops joins, the joins occur through the join indexes, which are the indexes on the primary or foreign keys used to connect that table to an earlier table in the join tree. Rarely do you use the indexes on the non-join conditions, except for the driving table. Thus, after taba is chosen as the driving table, use the indexes on b.key1 and c.key2 to drive into tabb and tabc, respectively.

2. Choose the best join order, driving to the best unused filters earliest.
You can reduce the work of the following join by first joining to the table with the best still-unused filter. Thus, if bcol BETWEEN ... is more restrictive (rejects a higher percentage of the rows) than ccol BETWEEN ..., then the last join becomes easier (with fewer rows) if tabb is joined before tabc.

3. You can use the ORDERED or STAR hint to force the join order.

See Also:
Oracle Database Reference to learn about OPTIMIZER_MODE

15 Controlling Cursor Sharing

This chapter contains the following topics:

- About Bind Variables and Cursors

- Designing Applications for Cursor Sharing

- Sharing Cursors for Existing Applications

15.1 About Bind Variables and Cursors

A bind variable is a placeholder in a SQL statement that must be replaced with a valid value or value address for the statement to execute successfully. By using bind variables, you can write a SQL statement that accepts inputs or parameters at run time. The following query uses v_empid as a bind variable:

SELECT * FROM employees WHERE employee_id = :v_empid;

This section contains the following topics:

- Bind Variable Peeking

- SQL Sharing Criteria

- Adaptive Cursor Sharing

15.1.1 Bind Variable Peeking

In bind variable peeking (also known as bind peeking), the optimizer looks at the value in a bind variable when the database performs a hard parse of a statement.

When a query uses literals, the optimizer can use the literal values to find the best plan. However, when a query uses bind variables, the optimizer must select the best plan without the presence of literals in the SQL text. This task can be extremely difficult. By peeking at bind values, the optimizer can determine the selectivity of a WHERE clause condition as if literals had been used, thereby improving the plan.

Example 15-1 Bind Peeking

The following 100,000 row emp table exists in the database. The table has the following definition:

SQL> DESCRIBE emp

Name                   Null?    Type
---------------------- -------- ----------------------------------
ENAME                           VARCHAR2(20)
EMPNO                           NUMBER
PHONE                           VARCHAR2(20)
DEPTNO                          NUMBER

The data is significantly skewed in the deptno column. The value 10 is found in 99.9% of the rows. Each of the other deptno values (0 through 9) is found in 1% of the rows. You have gathered statistics for the table, resulting in a histogram on the deptno column. You define a bind variable and query emp using the bind value 9 as follows:

VARIABLE deptno NUMBER
EXEC :deptno := 9

SELECT /*ACS_1*/ COUNT(*), MAX(empno)
FROM   emp
WHERE  deptno = :deptno;

The query returns 10 rows:

COUNT(*) MAX(EMPNO)
---------- ----------
10         99

To generate the execution plan for the query, the database peeked at the value 9 during the hard parse. The optimizer generated selectivity estimates as if the user had executed the following query:

SELECT /*ACS_1*/ COUNT(*), MAX(empno)
FROM   emp
WHERE  deptno = 9;

When choosing a plan, the optimizer only peeks at the bind value during the hard parse. This plan may not be optimal for all possible values.

15.1.2 SQL Sharing Criteria

Oracle Database automatically determines whether the SQL statement or PL/SQL block being issued is identical to another statement currently in the shared pool.

Oracle Database performs the following steps to compare the text of the SQL statement to existing SQL statements in the shared pool:

1. The text of the statement is hashed.

2. If no matching hash value exists, then the SQL statement does not currently exist in the shared pool, so the database performs a hard parse.

3. The database looks for a matching hash value for an existing SQL statement in the shared pool. The following options are possible:

- No matching hash value exists.
In this case, the SQL statement does not currently exist in the shared pool, so the database performs a hard parse. This ends the shared pool check.

- A matching has value exists.
In this case, the database compares the text of the matched statement to the text of the hashed statement to see if they are identical. The text of the SQL statements or PL/SQL blocks must be identical, character for character, including spaces, case, and comments. For example, the following statements cannot use the same shared SQL area:

SELECT * FROM employees;
SELECT * FROM Employees;
SELECT *  FROM employees;

Usually, SQL statements that differ only in literals cannot use the same shared SQL area. For example, the following statements do not resolve to the same SQL area:

SELECT count(1) FROM employees WHERE manager_id = 121;
SELECT count(1) FROM employees WHERE manager_id = 247;

The only exception to this rule is when the parameter CURSOR_SHARING has been set to FORCE, in which case similar statements can share SQL areas. The costs and benefits involved in using CURSOR_SHARING are explained in "When to Set CURSOR_SHARING to FORCE".

See Also:
Oracle Database Reference for more information about the CURSOR_SHARING initialization parameter

4. The database compares objects referenced in the issued statement to the referenced objects of all existing statements in the pool to ensure that they are identical.
References to schema objects in the SQL statements or PL/SQL blocks must resolve to the same object in the same schema. For example, if two users issue the following SQL statement, and if each user has its own employees table, then the following statement is not identical because the statement references different employees tables for each user:

SELECT * FROM employees;

5. The database determines whether bind variables in the SQL statements match in name, data type, and length.
For example, the following statements cannot use the same shared SQL area because the bind variable names differ:

SELECT * FROM employees WHERE department_id = :department_id;
SELECT * FROM employees WHERE department_id = :dept_id;

Many Oracle products, such as Oracle Forms and the precompilers, convert the SQL before passing statements to the database. The conversion uniformly changes characters to uppercase, compresses white space, and renames bind variables so that a consistent set of SQL statements is produced.

6. The database determines whether the session environment is identical.
For example, SQL statements must be optimized using the same optimizer goal (see "Choosing an Optimizer Goal").

15.1.3 Adaptive Cursor Sharing

The adaptive cursor sharing feature enables a single statement that contains bind variables to use multiple execution plans. Cursor sharing is "adaptive" because the cursor adapts its behavior so that the database does not always use the same plan for each execution or bind variable value.

For appropriate queries, the database monitors data accessed over time for different bind values, ensuring the optimal choice of cursor for a specific bind value. For example, the optimizer might choose one plan for bind value 9 and a different plan for bind value 10. Cursor sharing is "adaptive" because the cursor adapts its behavior so that the same plan is not always used for each execution or bind variable value.

Adaptive cursor sharing is enabled for the database by default and cannot be disabled. Adaptive cursor sharing does not apply to SQL statements containing more than 14 bind variables.

Note:
Adaptive cursor sharing is independent of the CURSOR_SHARING initialization parameter (see "Sharing Cursors for Existing Applications"). Adaptive cursor sharing is equally applicable to statements that contain user-defined and system-generated bind variables.

15.1.3.1 Bind-Sensitive Cursors

A bind-sensitive cursor is a cursor whose optimal plan may depend on the value of a bind variable. The database monitors the behavior of a bind-sensitive cursor that uses different bind values to determine whether a different plan is beneficial.

The criteria used by the optimizer to decide whether a cursor is bind-sensitive include the following:

- The optimizer has peeked at the bind values to generate selectivity estimates.

- A histogram exists on the column containing the bind value (see Chapter 11, "Histograms").

- The bind is used in a range predicate.

Example 15-2 Bind-Sensitive Cursors

Example 15-1 queried the emp table using the bind value 9 for deptno. In this example, you run the DBMS_XPLAN.DISPLAY_CURSOR function to show the execution plan:

SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY_CURSOR);

The output is as follows:

----------------------------------------------------------------------------------
| Id   Operation                    | Name   | Rows | Bytes |Cost (%CPU)|  Time  |
----------------------------------------------------------------------------------
|  0 | SELECT STATEMENT             |        |      |       |   2 (100)|         |
|  1 |  SORT AGGREGATE              |        |    1 |    16 |          |         |
|  2 |   TABLE ACCESS BY INDEX ROWID| EMP    |    1 |    16 |   2   (0)| 00:00:01|
|* 3 |    INDEX RANGE SCAN          | EMP_I1 |    1 |       |   1   (0)| 00:00:01|
----------------------------------------------------------------------------------

The plan indicates that the optimizer chose an index range scan, which is expected because of the low cardinality of the value 9. Query V$SQL to view statistics about the cursor:

COL BIND_SENSI FORMAT a10
COL BIND_AWARE FORMAT a10
COL BIND_SHARE FORMAT a10
SELECT CHILD_NUMBER, EXECUTIONS, BUFFER_GETS, IS_BIND_SENSITIVE AS "BIND_SENSI",
IS_BIND_AWARE AS "BIND_AWARE", IS_SHAREABLE AS "BIND_SHARE"
FROM   V$SQL
WHERE  SQL_TEXT LIKE 'select /*ACS_1%';

As shown in the following output, one child cursor exists for this statement and has been executed once. A small number of buffer gets are associated with the child cursor. Because the deptno data is skewed, the database created a histogram. This histogram led the database to mark the cursor as bind-sensitive (IS_BIND_SENSITIVE is Y).

CHILD_NUMBER EXECUTIONS BUFFER_GETS BIND_SENSI BIND_AWARE BIND_SHARE
------------ ---------- ----------- ---------- ---------- ----------
0          1          56 Y          N          Y

For each execution of the query with a new bind value, the database records the execution statistics for the new value and compares them to the execution statistics for the previous value. If execution statistics vary greatly, then the database marks the cursor bind-aware.

See Also:
Oracle Database Reference to learn about V$SQL

15.1.3.2 Bind-Aware Cursors

A bind-aware cursor is a bind-sensitive cursor that is eligible to use different plans for different bind values. After a cursor has been made bind-aware, the optimizer chooses plans for future executions based on the bind value and its selectivity estimate.

When a statement with a bind-sensitive cursor executes, the database decides whether to mark the cursor bind-aware. The decision depends on whether the cursor produces significantly different data access patterns for different bind values. If the database marks the cursor bind-aware, then the next time that the cursor executes the database does the following:

- Generates a new plan based on the new bind value.

- Marks the original cursor generated for the statement as not sharable (V$SQL.IS_SHAREABLE is N). This cursor is no longer usable. The database marks the cursor as able to age out of the shared SQL area quickly.

Example 15-3 Bind-Aware Cursors

In Example 15-1 you queried emp using the bind value 9. Now you query emp using the bind value 10. The query returns 99,900 rows that contain the value 10:

COUNT(*)   MAX(EMPNO)
---------- ----------
99900      100000

Because the cursor for this statement is bind-sensitive, the optimizer assumes that the cursor can be shared. Consequently, the optimizer uses the same index range scan for the value 10 as for the value 9.

The V$SQL output shows that the same bind-sensitive cursor was executed a second time (the query using 10) and required many more buffer gets than the first execution:

SELECT CHILD_NUMBER, EXECUTIONS, BUFFER_GETS, IS_BIND_SENSITIVE AS "BIND_SENSI",
IS_BIND_AWARE AS "BIND_AWARE", IS_SHAREABLE AS "BIND_SHARE"
FROM   V$SQL
WHERE  SQL_TEXT LIKE 'select /*ACS_1%';

CHILD_NUMBER EXECUTIONS BUFFER_GETS BIND_SENSI BIND_AWARE BIND_SHARE
------------ ---------- ----------- ---------- ---------- ----------
0          2        1010 Y          N          Y

Now you execute the query using the value 10 a second time. The database compares statistics for previous executions and marks the cursor as bind-aware. In this case, the optimizer decides that a new plan is warranted, so it performs a hard parse of the statement and generates a new plan. The new plan uses a full table scan instead of an index range scan:

---------------------------------------------------------------------------
| Id  | Operation          | Name | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |      |       |       |   208 (100)|          |
|   1 |  SORT AGGREGATE    |      |     1 |    16 |            |          |
|*  2 |   TABLE ACCESS FULL| EMP  | 95000 |  1484K|   208   (1)| 00:00:03 |
---------------------------------------------------------------------------

A query of V$SQL shows that the database created an additional child cursor (child number 1) that represents the plan containing the full table scan. This new cursor shows a lower number of buffer gets and is marked bind-aware:

SELECT CHILD_NUMBER, EXECUTIONS, BUFFER_GETS, IS_BIND_SENSITIVE AS "BIND_SENSI",
IS_BIND_AWARE AS "BIND_AWARE", IS_SHAREABLE AS "BIND_SHARE"
FROM   V$SQL
WHERE  SQL_TEXT LIKE 'select /*ACS_1%';

CHILD_NUMBER EXECUTIONS BUFFER_GETS BIND_SENSI BIND_AWARE BIND_SHARE
------------ ---------- ----------- ---------- ---------- ----------
0          2        1010 Y          N          Y
1          2        1522 Y          Y          Y

After you execute the query twice with value 10, you execute it again using the more selective value 9. Because of adaptive cursor sharing, the optimizer "adapts" the cursor and chooses an index range scan rather than a full table scan for this value (see "Introduction to Access Paths").

A query of V$SQL indicates that the database created a new child cursor (child number 2) for the execution of the query:

CHILD_NUMBER EXECUTIONS BUFFER_GETS BIND_SENSI BIND_AWARE BIND_SHARE
------------ ---------- ----------- ---------- ---------- ----------
0          2        1010 Y          N          N
1          1        1522 Y          Y          Y
2          1           7 Y          Y          Y

Because the database is now using adaptive cursor sharing, the database no longer uses the original cursor (child 0), which is not bind-aware. The shared SQL area can now age out the defunct cursor.

15.1.3.3 Cursor Merging

If the optimizer creates a plan for a bind-aware cursor, and if this plan is the same as an existing cursor, then the optimizer can perform cursor merging. In this case, the database merges cursors to save space in the shared SQL area. The database increases the selectivity range for the cursor to include the selectivity of the new bind.

Suppose you execute a query with a bind value that does not fall within the selectivity ranges of the existing cursors. The database performs a hard parse and generates a new plan and new cursor. If this new plan is the same plan used by an existing cursor, then the database merges these two cursors and deletes one of the old cursors.

15.1.4 Bind-Related Performance Views

You can use the V$ views for adaptive cursor sharing to see selectivity ranges, cursor information (such as whether a cursor is bind-aware or bind-sensitive), and execution statistics:

- V$SQL shows whether a cursor is bind-sensitive or bind-aware

- V$SQL_CS_HISTOGRAM shows the distribution of the execution count across a three-bucket execution history histogram

- V$SQL_CS_SELECTIVITY shows the selectivity ranges stored for every predicate containing a bind variable if the selectivity was used to check cursor sharing

- V$SQL_CS_STATISTICS summarizes the information that the optimizer uses to determine whether to mark a cursor bind-aware.

See Also:
Oracle Database Reference to learn about V$SQL and its related views

15.2 Designing Applications for Cursor Sharing

Reuse of shared SQL for multiple users running the same application, avoids hard parsing. Soft parses provide a significant reduction in the use of resources such as the shared pool and library cache latches.

To share cursors:

- Use bind variables rather than literals in SQL statements whenever possible.
For example, the following statements cannot use the same shared area because they do not match character for character:

SELECT employee_id FROM employees WHERE department_id = 10;
SELECT employee_id FROM employees WHERE department_id = 20;

By replacing the literals with a bind variable, only one SQL statement would result, which could be executed twice:

SELECT employee_id FROM employees WHERE department_id = :dept_id;

Note:
For existing applications where rewriting the code to use bind variables is impractical, you can use the CURSOR_SHARING initialization parameter to avoid some hard parse overhead. See "Sharing Cursors for Existing Applications".

- Avoid application designs that result in large numbers of users issuing dynamic, unshared SQL statements.
Typically, the majority of data required by most users can be satisfied using preset queries. Use dynamic SQL where such functionality is required.

- Ensure that users of the application do not change the optimization approach and goal for their individual sessions.

- Establish the following policies for application developers:

- Standardize naming conventions for bind variables and spacing conventions for SQL statements and PL/SQL blocks.

- Consider using stored procedures whenever possible.
Multiple users issuing the same stored procedure use the same shared PL/SQL area automatically. Because stored procedures are stored in a parsed form, their use reduces run-time parsing.

- For SQL statements that are identical but are not being shared, query V$SQL_SHARED_CURSOR to determine why the cursors are not shared. This would include optimizer settings and bind variable mismatches.

See Also:
Oracle Database Reference to learn about V$SQL_SHARED_CURSOR

15.3 Sharing Cursors for Existing Applications

In SQL parsing, an identical statement is a statement whose text is identical to another, character for character, including spaces, case, and comments. A similar statement is identical except for the values of some literals.

The parse phase compares the statement text with statements in the shared pool to determine whether the statement can be shared. If the initialization parameter CURSOR_SHARING=EXACT (default), and if a statement in the pool is not identical, then the database does not share the SQL area. Each statement has its own parent cursor and its own execution plan based on the literal in the statement.

15.3.1 How Similar Statements Can Share SQL Areas

When SQL statements use literals rather than bind variables, a nondefault setting for the CURSOR_SHARING initialization parameter enables the database to replace literals with system-generated bind variables. Using this technique, the database can sometimes reduce the number of parent cursors in the shared SQL area.

When CURSOR_SHARING is set to a nondefault value, the database performs the following steps during the parse:

1. Searches for an identical statement in the shared pool
If an identical statement is found, then the database skips to Step 3. Otherwise, the database proceeds to the next step.

2. Searches for a similar statement in the shared pool
If a similar statement is not found, then the database performs a hard parse. If a similar statement is found, then the database proceeds to the next step.

3. Proceeds through the remaining steps of the parse phase to ensure that the execution plan of the existing statement is applicable to the new statement
If the plan is not applicable, then the database performs a hard parse. If the plan is applicable, then the database proceeds to the next step.

4. Shares the SQL area of the statement

Note:
The database does not perform literal replacement on the ORDER BY clause because it is not semantically correct to consider the constant column number as a literal. The column number in the ORDER BY clause affects the query plan and execution, so the database cannot share two cursors having different column numbers.

See Also:

- "SQL Sharing Criteria" for more details on the various checks performed

- Oracle Database Reference to learn about the CURSOR_SHARING initialization parameter

15.3.2 When to Set CURSOR_SHARING to FORCE

The best practice is to write sharable SQL and use the default of EXACT for CURSOR_SHARING. However, for applications with many similar statements, setting CURSOR_SHARING to FORCE can significantly improve cursor sharing, resulting in reduced memory usage, faster parses, and reduced latch contention. Consider this approach when statements in the shared pool differ only in the values of literals, and when response time is poor because of a very high number of library cache misses.

Note:
Staring in Oracle Database 12c, the SIMILAR value for CURSOR_SHARING is deprecated. Use FORCE instead.

When CURSOR_SHARING is set to FORCE, the database uses one parent cursor and one child cursor for each distinct SQL statement. The database uses the same plan for each execution of the same statement. For example, consider the following statement:

SELECT * FROM hr.employees WHERE employee_id = 101

If you use FORCE, then the database optimizes this statement as if it contained a bind variable and uses bind peeking to estimate cardinality. Statements that differ only in the bind variable share the same execution plan.

Setting CURSOR_SHARING to FORCE has the following drawbacks:

- The database must perform extra work during the soft parse to find a similar statement in the shared pool.

- There is an increase in the maximum lengths (as returned by DESCRIBE) of any selected expressions that contain literals in a SELECT statement. However, the actual length of the data returned does not change.

- Star transformation is not supported.

See Also:

- "Adaptive Cursor Sharing"

- Oracle Database Reference to learn about the CURSOR_SHARING initialization parameter

Part VII

Monitoring and Tracing SQL

This part contains the following chapters:

- Chapter 16, "Monitoring Database Operations"

- Chapter 17, "Gathering Diagnostic Data with SQL Test Case Builder"

- Chapter 18, "Performing Application Tracing"

16 Monitoring Database Operations

This chapter describes how to monitor database operations.

This chapter contains the following topics:

- About Monitoring Database Operations

- Enabling and Disabling Monitoring of Database Operations

- Creating a Database Operation

- Reporting on Database Operations Using SQL Monitor

16.1 About Monitoring Database Operations

A database operation is a set of database tasks defined by end users or application code, for example, a batch job or Extraction, Transformation, and Loading (ETL) processing. You can define, monitor, and report on database operations.

Database operations are either simple or composite. A simple database operation is a single SQL statement or PL/SQL procedure or function. A composite database operation is activity between two points in time in a database session, with each session defining its own beginning and end points. A session can participate in at most one composite database operation at a time.

Real-Time SQL Monitoring, which was introduced in Oracle Database 11g, enables you to monitor a single SQL statement or PL/SQL procedure. Starting in Oracle Database 12c, Real-Time Database Operations provides the ability to monitor composite operations automatically. The database automatically monitors parallel queries, DML, and DDL statements as soon as execution begins. By default, Real-Time SQL Monitoring automatically starts when a SQL statement runs in parallel, or when it has consumed at least 5 seconds of CPU or I/O time in a single execution.

This section contains the following topics:

- Purpose of Monitoring Database Operations

- Database Operation Monitoring Concepts

- User Interfaces for Database Operations Monitoring

- Basic Tasks in Database Operations Monitoring

See Also:
Oracle Database Concepts for a brief conceptual overview of database operations

16.1.1 Purpose of Monitoring Database Operations

In general, monitoring database operations is useful for the following users:

- DBAs whose responsibilities include identifying expensive (high response time) SQL statements and PL/SQL functions

- DBAs who manage batch jobs in a data warehouse or OLTP system

- Application or database developers who need to monitor the activities related to particular operations, for example, Data Pump operations

Monitoring database operations is useful for performing the following tasks:

- Tracking and reporting
Tracking requires first defining a database operation, for example, though PL/SQL, OCI, or JDBC APIs. After the operation is defined, the database infrastructure determines what to track on behalf of this operation. You can then generate reports on the operation. For example, your tuning task may involve determining which SQL statements run on behalf of a specific batch job, what their execution statistics were, what was occurring in the database when the operation was executing, and so on.

- Monitoring execution progress
This task involves monitoring a currently executing database operation. The information is particularly useful when you are investigating why an operation is taking a long time to complete.

- Monitoring resource usage
You may want to detect when a SQL execution uses excessive CPU, issues an excessive amount of I/O, or takes a long time to complete. With Oracle Database Resource Manager (the Resource Manager), you can configure thresholds for each consumer group that specify the maximum resource usage for all SQL executions in the group. When a SQL operation reaches a specified threshold, Resource Manager can switch the operation into a lower-priority consumer group, terminate the session or call, or log the event (see Oracle Database Administrator's Guide). You can then monitor these SQL operations (see "Reporting on Database Operations Using SQL Monitor").

- Tuning for response time
When tuning a database operation, you typically aim to improve the response time. Often the database operation performance issues are mainly SQL performance issues.

The following graphic illustrates the different tasks involved in monitoring database operations:

16.1.1.1 Simple Database Operation Use Cases

For simple operations, Real-Time SQL Monitoring helps determine where a currently executing SQL statement is in its execution plan and where the statement is spending its time. You can also see the breakdown of time and resource usage for recently completed statements. In this way, you can better determine why a particular operation is expensive.

Typical use cases for Real-Time SQL Monitoring include the following:

- A frequently executed SQL statement is executing more slowly than normal. You must identify the root cause of this problem.

- A database session is experiencing slow performance.

- A parallel SQL statement is taking a long time. You want to determine how the server processes are dividing the work.

16.1.1.2 Composite Database Operation Use Cases

In OLTP and data warehouse environments, a job often logically groups related SQL statements. The job can involve multiple sessions. Database operation monitoring is useful for digging through a suboptimally performing job to determine where resources are being consumed. Thus, database operations enable you to track related information and improve performance tuning time.

Typical use cases for monitoring composite operations include the following:

- A periodic batch job containing many SQL statements must complete in a certain number of hours, but took twice as long as expected.

- After a database upgrade, the execution time of an important batch job doubled. To resolve this problem, you must collect enough relevant statistical data from the batch job before and after the upgrade, compare the two sets of data, and then identify the changes.

- Packing a SQL tuning set (STS) took far longer than anticipated (see "About SQL Tuning Sets"). To diagnose the problem, you need to know what was being executed over time. Because this issue cannot be easily reproduced, you need to monitor the process while it is running.

16.1.2 Database Operation Monitoring Concepts

This section describes the most important concepts for database monitoring:

- About the Architecture of Database Operations

- Composite Database Operations

- Attributes of Database Operations

16.1.2.1 About the Architecture of Database Operations

Setting the CONTROL_MANAGEMENT_PACK_ACCESS initialization parameter to DIAGNOSTIC+TUNING (default) enables monitoring of database operations. Real-Time SQL Monitoring is a feature of the Oracle Database Tuning Pack.

Figure 16-1 gives an overview of the architecture for database operations.

Figure 16-1 Architecture for Database Operations

As shown in Figure 16-1, you can use the DBMS_SQL_MONITOR package to define database operations. After monitoring is initiated, the database stores metadata about the database operations in AWR (see "Reporting on Database Operations Using SQL Monitor"). The database refreshes monitoring statistics in close to real time as each monitored statement executes, typically once every second. The database periodically saves the data to disk.

Every monitored database operation has an entry in the V$SQL_MONITOR view. This entry tracks key performance metrics collected for the execution, including the elapsed time, CPU time, number of reads and writes, I/O wait time, and various other wait times. The V$SQL_PLAN_MONITOR view includes monitoring statistics for each operation in the execution plan of the SQL statement being monitored. You can access reports by using DBMS_SQL_MONITOR.REPORT_SQL_MONITOR, which has an Oracle Enterprise Manager Cloud Control (Cloud Control) interface.

See Also:

- Oracle Database Reference to learn about the initialization parameters and views related to database monitoring

- Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_SQLTUNE and DBMS_SQL_MONITOR packages

16.1.2.2 Composite Database Operations

A composite database operation consists of the activity of one session between two points in time. Exactly one session exists for the duration of the database operation.

SQL statements or PL/SQL procedures running in this session are part of the composite operation. Composite database operations can also be defined in the database kernel. Typical composite operations include SQL*Plus scripts, batch jobs, and ETL processing.

16.1.2.3 Attributes of Database Operations

A database operation is uniquely identified by the following information:

- Database operation name
This is a user-created name such as daily_sales_report. The name is the same for a job even if it is executed concurrently by different sessions or on different databases. Database operation names do not reside in different namespaces.

- Database operation execution ID
Two or more occurrences of the same DB operation can run at the same time, with the same name but different execution IDs. This numeric ID uniquely identifies different executions of the same database operation.

The database automatically creates an execution ID when you begin a database operation. You can also specify a user-created execution ID.

The database uses the following triplet of values to identify each SQL and PL/SQL statement monitored in the V$SQL_MONITOR view, regardless of whether the statement is included in a database operation:

- SQL identifier to identify the SQL statement (SQL_ID)

- Start execution timestamp (SQL_EXEC_START)

- An internally generated identifier to ensure that this primary key is truly unique (SQL_EXEC_ID)

You can use zero or more additional attributes to describe and identify the characteristics of a DB operation. Each attribute has a name and value. For example, for operation daily_sales_report, you might define the attribute db_name and assign it the value prod.

See Also:

- Oracle Database Reference to learn about the V$SQL_MONITOR view

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SQL_MONITOR.BEGIN_OPERATION function

16.1.3 User Interfaces for Database Operations Monitoring

This section contains the following topics:

- Monitored SQL Executions Page in Cloud Control

- DBMS_SQL_MONITOR Package

- Views for Database Operations Monitoring

16.1.3.1 Monitored SQL Executions Page in Cloud Control

The Monitored SQL Executions page in Cloud Control is the recommended interface for reporting on database operations.

16.1.3.1.1 Accessing the Monitored SQL Executions Page

To access the Monitored SQL Executions page:

1. Access the Database Home page, as described in "Accessing the Database Home Page in Cloud Control."

2. From the Performance menu, select SQL Monitoring.
The Monitored SQL Executions page appears.

16.1.3.2 DBMS_SQL_MONITOR Package

You can use the DBMS_SQL_MONITOR package to define the beginning and ending of a database operation, and generate a report of the database operations.

Table 16-1 DBMS_SQL_MONITOR

Program Unit

Description

REPORT_SQL_MONITOR

This function accepts several input parameters to specify the execution, the level of detail in the report, and the report type. If no parameters are specified, then the function generates a text report for the last execution that was monitored.

BEGIN_OPERATION

This function associates a session with a database operation.

END_OPERATION

This function disassociates a session from the specified database operation execution.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SQL_MONITOR package

16.1.3.3 Views for Database Operations Monitoring

You can monitor the statistics for SQL statement execution using the V$SQL_MONITOR, V$SQL_PLAN_MONITOR, and V$SQL_MONITOR_SESSTAT views. Table 16-2 summarizes these views.

Table 16-2 Views for Database Operations Monitoring

View

Description

V$SQL_MONITOR

This view contains global, high-level information about the top SQL statements in a database operation.

Each monitored SQL statement has an entry in this view. Each row contains a SQL statement whose statistics are accumulated from multiple sessions and all of its executions in the operation. The primary key is the combination of the columns DBOP_NAME, DBOP_EXEC_ID, and SQL_ID.

The V$SQL_MONITOR view contains a subset of the statistics available in V$SQL. However, unlike V$SQL, monitoring statistics are not cumulative over several executions. Instead, one entry in V$SQL_MONITOR is dedicated to a single execution of a SQL statement. If the database monitors two executions of the same SQL statement, then each execution has a separate entry in V$SQL_MONITOR.

V$SQL_MONITOR has one entry for the parallel execution coordinator process, and one entry for each parallel execution server process. Each entry has corresponding entries in V$SQL_PLAN_MONITOR. Because the processes allocated for the parallel execution of a SQL statement are cooperating for the same execution, these entries share the same execution key (the composite SQL_ID, SQL_EXEC_START and SQL_EXEC_ID). You can aggregate the execution key to determine the overall statistics for a parallel execution.

V$SQL_MONITOR_SESSTAT

This view contains the statistics for all sessions involved in the database operation.

Most of the statistics are cumulative. The database stores the statistics in XML format instead of using each column for each statistic. This view is primarily intended for the report generator. Oracle recommends that you use V$SESSTAT instead of V$SQL_MONITOR_SESSTAT.

V$SQL_PLAN_MONITOR

This view contains monitoring statistics for each step in the execution plan of the monitored SQL statement.

The database updates statistics in V$SQL_PLAN_MONITOR every second while the SQL statement is executing. Multiple entries exist in V$SQL_PLAN_MONITOR for every monitored SQL statement. Each entry corresponds to a step in the execution plan of the statement.

You can use the preceding views with the following views to get additional information about the monitored execution:

- V$ACTIVE_SESSION_HISTORY

- V$SESSION

- V$SESSION_LONGOPS

- V$SQL

- V$SQL_PLAN

See Also:
Oracle Database Reference to learn about the V$ views for database operations monitoring

16.1.4 Basic Tasks in Database Operations Monitoring

This section explains the basic tasks in database operations monitoring. Basic tasks are as follows:

- "Enabling and Disabling Monitoring of Database Operations"
This task explains how you can enable automatic monitoring of database operations at the system and statement level.

- "Creating a Database Operation"
This section explains how you can define the beginning and end of a database operation using PL/SQL.

- "Reporting on Database Operations Using SQL Monitor"
This section explains how you can generate and interpret reports on a database operation.

16.2 Enabling and Disabling Monitoring of Database Operations

This section contains the following topics:

- Enabling Monitoring of Database Operations at the System Level

- Enabling and Disabling Monitoring of Database Operations at the Statement Level

16.2.1 Enabling Monitoring of Database Operations at the System Level

The SQL monitoring feature is enabled by default when the STATISTICS_LEVEL initialization parameter is either set to TYPICAL (the default value) or ALL. SQL monitoring starts automatically for all long-running queries.

Prerequisites

Because SQL monitoring is a feature of the Oracle Database Tuning Pack, the CONTROL_MANAGEMENT_PACK_ACCESS initialization parameter must be set to DIAGNOSTIC+TUNING (the default value).

Assumptions

This tutorial assumes the following:

- The STATISTICS_LEVEL initialization parameter is set to BASIC.

- You want to enable automatic monitoring of database operations.

To enable monitoring of database operations:

1. Connect SQL*Plus to the database with the appropriate privileges, and then query the current database operations settings.
For example, run the following SQL*Plus command:

SQL> SHOW PARAMETER statistics_level

NAME                                 TYPE        VALUE
------------------------------------ ----------- ------------------------------
statistics_level                    string      BASIC

2. Set the statistics level to TYPICAL.
For example, run the following SQL statement:

SQL> ALTER SYSTEM SET STATISTICS_LEVEL='TYPICAL';

See Also:
Oracle Database Reference to learn about the STATISTICS_LEVEL and CONTROL_MANAGEMENT_PACK_ACCESS initialization parameter

16.2.2 Enabling and Disabling Monitoring of Database Operations at the Statement Level

When the CONTROL_MANAGEMENT_PACK_ACCESS initialization parameter is set to DIAGNOSTIC+TUNING, you can use hints to enable or disable monitoring of specific SQL statements. The MONITOR hint enables monitoring, whereas the NO_MONITOR hint disables monitoring.

Two statement-level hints are available to force or prevent the database from monitoring a SQL statement. To force SQL monitoring, use the MONITOR hint:

SELECT /*+ MONITOR */ SYSDATE FROM DUAL;

This hint is effective only when the CONTROL_MANAGEMENT_PACK_ACCESS parameter is set to DIAGNOSTIC+TUNING. To prevent the hinted SQL statement from being monitored, use the NO_MONITOR reverse hint.

Assumptions

This tutorial assumes the following:

- Database monitoring is currently enabled at the system level.

- You want to disable automatic monitoring for the statement SELECT * FROM sales ORDER BY time_id.

To disable monitoring of database operations for a SQL statement:

1. Execute the query with the NO_MONITOR hint.
For example, run the following statement:

SQL> SELECT * /*+NO_MONITOR*/ FROM sales ORDER BY time_id;

See Also:
Oracle Database SQL Language Reference for information about using the MONITOR and NO_MONITOR hints

16.3 Creating a Database Operation

Creating a database operation involves explicitly defining its beginning and end points. You can start a database operation by using the DBMS_SQL_MONITOR.BEGIN_OPERATION function and end the operation by using the DBMS_SQL_MONITOR.END_OPERATION procedure.

Assumptions

This tutorial assumes the following:

- You are an administrator and want to query the number of items in the sh.sales table and the number of customers in the sh.customers table.

- You want these two queries to be monitored as a database operation named sh_count.

To create a database operation:

1. Start SQL*Plus and connect as a user with the appropriate privileges.

2. Define a variable to hold the execution ID.
For example, run the following SQL*Plus command:

VAR eid NUMBER

3. Begin the database operation.
For example, execute the BEGIN_OPERATION function as follows:

EXEC :eid := DBMS_SQL_MONITOR.BEGIN_OPERATION('sh_count');

4. Run the queries in the operation.
For example, run the following statements:

SQL> SELECT count(*) FROM sh.sales;

COUNT(*)
----------
918843

SQL> SELECT COUNT(*) FROM sh.customers;

COUNT(*)
----------
55500

5. End the database operation.
For example, execute the END_OPERATION procedure as follows:

EXEC DBMS_SQL_MONITOR.END_OPERATION('sh_count', :eid);

6. Confirm that the database operation completed.
For example, run the following query (sample output included):

SELECT SUBSTR(DBOP_NAME, 1, 10), DBOP_EXEC_ID,
SUBSTR(STATUS, 1, 10)
FROM  V$SQL_MONITOR
WHERE DBOP_NAME IS NOT NULL
ORDER BY EXEC_ID;

DBOP_NAME     EXEC_ID STATUS
---------- ---------- ----------
sh_count            1 DONE

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SQL_MONITOR package

16.4 Reporting on Database Operations Using SQL Monitor

By default, AWR automatically captures SQL monitoring reports in XML format. The reports capture only SQL statements that are not executing or queued and have finished execution since the last capture cycle. AWR captures reports only for the most expensive statements according to elapsed execution time.

The Monitored SQL Executions page in Cloud Control summarizes the activity for monitored statements. You can use this page to drill down and obtain additional details about particular statements. The Monitored SQL Executions Details page uses data from several views, including the following:

- GV$SQL_MONITOR

- GV$SQL_PLAN_MONITOR

- GV$SQL_MONITOR_SESSTAT

- GV$SQL

- GV$SQL_PLAN

- GV$ACTIVE_SESSION_HISTORY

- GV$SESSION_LONGOPS

- DBA_HIST_REPORTS

- DBA_HIST_REPORTS_DETAILS

Assumptions

This tutorial assumes the following:

- The user sh is executing the following long-running parallel query of the sales made to each customer:

SELECT c.cust_id, c.cust_last_name, c.cust_first_name,
s.prod_id, p.prod_name, s.time_id
FROM   sales s, customers c, products p
WHERE  s.cust_id = c.cust_id
AND    s.prod_id = p.prod_id
ORDER BY c.cust_id, s.time_id;

- You want to ensure that the preceding query does not consume excessive resources. While the statement executes, you want to determine basic statistics about the database operation, such as the level of parallelism, the total database time, and number of I/O requests.

- You use Cloud Control to monitor statement execution.

Note:
To generate the SQL monitor report from the command line, run the REPORT_SQL_MONITOR function in the DBMS_SQLTUNE package, as in the following sample SQL*Plus script:

VARIABLE my_rept CLOB
BEGIN
:my_rept :=DBMS_SQLTUNE.REPORT_SQL_MONITOR();
END;
/
PRINT :my_rept

To monitor SQL executions:

1. Access the Monitored SQL Executions page, as described in "Monitored SQL Executions Page in Cloud Control".
In the following graphic, the top row shows the parallel query.

In this example, the query has been executing for 1.4 minutes.

2. Click the value in the SQL ID column to see details about the statement.
The Monitored SQL Details page appears.

The preceding report shows the execution plan and statistics relating to statement execution. For example, the Timeline column shows when each step of the execution plan was active. Times are shown relative to the beginning and end of the statement execution. The Executions column shows how many times an operation was executed.

3. In the Overview section, click the link next to the SQL text.
A message shows the full text of the SQL statement.

4. In the Time & Wait Statistics section, next to Database Time, move the cursor over the largest portion on the bar graph.
A message shows that user I/O is consuming over half of database time.

Database Time measures the amount of time the database has spent working on this SQL statement. This value includes CPU and wait times, such as I/O time. The bar graph is divided into several color-coded portions to highlight CPU resources, user I/O resources, and other resources. You can move the cursor over any portion to view the percentage value of the total.

5. In the Details section, in the IO Requests column, move the cursor over the I/O requests bar to view the percentage value of the total.
A message appears.

In the preceding graphic, the IO Requests message shows the total number of read requests issued by the monitored SQL. The message shows that read requests form 80% of the total I/O requests.

See Also:

- Cloud Control Online Help for descriptions of the elements on the Monitored SQL Executions Details page, and for complete descriptions of all statistics in the report.

- Oracle Database Reference to learn about the V$ and data dictionary views for database operations monitoring

17 Gathering Diagnostic Data with SQL Test Case Builder

A SQL test case is a set of information that enables a developer to reproduce the execution plan for a specific SQL statement that has encountered a performance problem. SQL Test Case Builder is a tool that automatically gathers information needed to reproduce the problem in a different database instance.

This chapter contains the following topics:

- Purpose of SQL Test Case Builder

- Concepts for SQL Test Case Builder

- User Interfaces for SQL Test Case Builder

- Running SQL Test Case Builder

17.1 Purpose of SQL Test Case Builder

In many cases, a reproducible test case makes it easier to resolve SQL-related problems. SQL Test Case Builder automates the sometimes difficult and time-consuming process of gathering and reproducing as much information as possible about a problem and the environment in which it occurred.

The output of SQL Test Case Builder is a set of scripts in a predefined directory. These scripts contain the commands required to re-create all the necessary objects and the environment. After the test case is ready, you can create a zip file of the directory and move it to another database, or upload the file to Oracle Support.

17.2 Concepts for SQL Test Case Builder

This section contains the following topics:

- SQL Incidents

- What SQL Test Case Builder Captures

- Output of SQL Test Case Builder

17.2.1 SQL Incidents

In the fault diagnosability infrastructure of Oracle Database, an incident is a single occurrence of a problem. A SQL incident is a SQL-related problem. When a problem (critical error) occurs multiple times, the database creates an incident for each occurrence. Incidents are timestamped and tracked in the Automatic Diagnostic Repository (ADR). Each incident is identified by a numeric incident ID, which is unique within the ADR.

SQL Test Case Builder is accessible any time on the command line. In Oracle Enterprise Manager Cloud Control (Cloud Control), the SQL Test Case pages are only available after a SQL incident is found.

See Also:

- Oracle Database Concepts for a conceptual overview of ADR

- Oracle Database Administrator's Guide to learn how to investigate, report, and resolve a problem

17.2.2 What SQL Test Case Builder Captures

SQL Test Case Builder captures permanent information such as the query being executed, table and index definitions (but not the actual data), PL/SQL packages and program units, optimizer statistics, SQL plan baselines, and initialization parameter settings. Starting in Oracle Database 12c, SQL Test Case Builder also captures and replays transient information, including information only available as part of statement execution.

SQL Test Case Builder supports the following:

- Adaptive plans
SQL Test Case Builder captures inputs to the decisions made regarding adaptive plans, and replays them at each decision point (see "Adaptive Plans"). For adaptive plans, the final statistics value at each buffering statistics collector is sufficient to decide on the final plan.

- Automatic memory management
The database automatically handles the memory requested for each SQL operation. Actions such as sorting can affect performance significantly. SQL Test Case Builder keeps track of the memory activities, for example, where the database allocated memory and how much it allocated.

- Dynamic statistics
Regathering dynamic statistics on a different database does not always generate the same results, for example, when data is missing (see "Dynamic Statistics"). To reproduce the problem, SQL Test Case Builder exports the dynamic statistics result from the source database. In the testing database, SQL Test Case Builder reuses the same values captured from the source database instead of regathering dynamic statistics.

- Multiple execution support
SQL Test Case Builder can capture dynamic information accumulated during multiple executions of the query. This capability is important for automatic reoptimization (see "Automatic Reoptimization").

- Compilation environment and bind values replay
The compilation environment setting is an important part of the query optimization context. SQL Test Case Builder captures nondefault settings altered by the user when running the problem query in the source database. If any nondefault parameter values are used, SQL Test Case Builder re-establishes the same values before running the query.

- Object statistics history
The statistics history for objects is helpful to determine whether a plan change was caused by a change in statistics values. DBMS_STATS stores the history in the data dictionary. SQL Test Case Builder stores this statistics data into a staging table during export. During import, SQL Test Case Builder automatically reloads the statistics history data into the target database from the staging table.

- Statement history
The statement history is important for diagnosing problems related to adaptive cursor sharing, statistics feedback, and cursor sharing bugs. The history includes execution plans and compilation and execution statistics.

See Also:

- Chapter 23, "Managing SQL Plan Baselines"

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_STATS package

17.2.3 Output of SQL Test Case Builder

The output of the SQL Test Case Builder is a set of files that contains the commands required to re-create all the necessary objects and the environment. By default, SQL Test Case Builder stores the files in the following location, where incnum refers to the incident number and runnum refers to the run number:

$ADR_HOME/incident/incdir_incnum/SQLTCB_runnum

For example, a valid output file name could be as follows:

$ORACLE_HOME/log/diag/rdbms/dbsa/dbsa/incident/incdir_2657/SQLTCB_1

You can specify a nondefault location by creating an Oracle directory and invoking DBMS_SQLDIAG.EXPORT_SQL_TESTCASE, as in the following example:

CREATE OR REPLACE DIRECTORY my_tcb_dir_exp '/tmp';

BEGIN
DBMS_SQLDIAG.EXPORT_SQL_TESTCASE (
directory => 'my_tcb_dir_exp'
,   sql_text  => 'SELECT COUNT(*) FROM sales'
,   testcase  => tco
);
END;

See Also:
Oracle Database Administrator's Guide to learn about the structure of the ADR repository

17.3 User Interfaces for SQL Test Case Builder

You can access SQL Test Case Builder either through Cloud Control or using PL/SQL on the command line.

17.3.1 Graphical Interface for SQL Test Case Builder

Within Cloud Control, you can access SQL Test Case Builder from the Incident Manager page or the Support Workbench page.

17.3.1.1 Accessing the Incident Manager

This task explains how to navigate to the Incident Manager from the Incidents and Problems section on the Database Home page.

To access the Incident Manager:

1. Access the Database Home page, as described in "Accessing the Database Home Page in Cloud Control."

2. In the Incidents and Problems section, locate the SQL incident to be investigated.
In the following example, the ORA 600 error is a SQL incident.

3. Click the summary of the incident.
The Problem Details page of the Incident Manager appears.

The Support Workbench page appears, with the incidents listed in a table.

See Also:

- Oracle Database Administrator's Guide to learn how to view problems with the Cloud Control Support Workbench

- Online help for Cloud Control

17.3.1.2 Accessing the Support Workbench

This task explains how to navigate to the Incident Manager from the Oracle Database menu.

To access the Support Workbench:

1. Access the Database Home page, as described in "Accessing the Database Home Page in Cloud Control."

2. From the Oracle Database menu, select Diagnostics, then Support Workbench.
The Support Workbench page appears, with the incidents listed in a table.

See Also:
Online help for Cloud Control

17.3.2 Command-Line Interface for SQL Test Case Builder

You can use the DBMS_SQLDIAG package to perform tasks relating to SQL Test Case Builder. This package consists of various subprograms for the SQL Test Case Builder, some of which are listed in Table 17-1.

Table 17-1 SQL Test Case Functions in DBMS_SQLDIAG

Procedure

Description

EXPORT_SQL_TESTCASE

Exports a SQL test case to a user-specified directory

EXPORT_SQL_TESTCASE_DIR_BY_INC

Exports a SQL test case corresponding to the incident ID passed as an argument

EXPORT_SQL_TESTCASE_DIR_BY_TXT

Exports a SQL test case corresponding to the SQL text passed as an argument

IMPORT_SQL_TESTCASE

Imports a SQL test case into a schema

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn more about the DBMS_SQLDIAG package

17.4 Running SQL Test Case Builder

This tutorial explains how to run SQL Test Case Builder using Cloud Control.

Assumptions

This tutorial assumes the following:

- You ran the following EXPLAIN PLAN statement as user sh, which causes an internal error:

EXPLAIN PLAN FOR
SELECT unit_cost, sold
FROM   costs c,
( SELECT /*+ merge */ p.prod_id, SUM(quantity_sold) AS sold
FROM   products p, sales s
WHERE  p.prod_id = s.prod_id
GROUP BY p.prod_id ) v
WHERE  c.prod_id = v.prod_id;

- In the Incidents and Problems section on the Database Home page, a SQL incident generated by the internal error appears.

To run SQL Test Case Builder:

1. Access the Incident Details page, as explained in "Accessing the Incident Manager".

2. Click the Incidents tab.
The Problem Details page appears.

3. Click the summary for the incident.
The Incident Details page appears.

4. In Guided Resolution, click View Diagnostic Data.
The Incident Details: incident_number page appears.

5. In the Application Information section, click Additional Diagnostics.
The Additional Diagnostics subpage appears.

6. Select SQL Test Case Builder, and then click Run.
The Run User Action page appears.

7. Select a sampling percentage (optional), and then click Submit.
After processing completes, the Confirmation page appears.

8. Access the SQL Test Case files in the location described in "Output of SQL Test Case Builder".

See Also:
Online help for Cloud Control

18 Performing Application Tracing

This chapter contains the following sections:

- Overview of End-to-End Application Tracing

- Enabling Statistics Gathering for End-to-End Tracing

- Enabling End-to-End Application Tracing

- Generating Output Files Using SQL Trace and TKPROF

- Guidelines for Interpreting TKPROF Output

See Also:
SQL*Plus User's Guide and Reference for information about the use of Autotrace to trace and tune SQL*Plus statements

18.1 Overview of End-to-End Application Tracing

End-to-End application tracing can identify the source of an excessive workload, such as a high load SQL statement, by client identifier, service, module, action, session, instance, or an entire database. This isolates the problem to a specific user, service, session, or application component.

In multitier environments, the middle tier routes a request from an end client to different database sessions, making it difficult to track a client across database sessions. End-to-End application tracing is an infrastructure that uses a client ID to uniquely trace a specific end-client through all tiers to the database.

18.1.1 Purpose of End-to-End Application Tracing

End-to-End application tracing simplifies diagnosing performance problems in multitier environments. For example, you can identify the source of an excessive workload, such as a high-load SQL statement, and contact the user responsible. Also, a user having problems can contact you. You can then identify what this user's session is doing at the database level.

End-to-End application tracing also simplifies management of application workloads by tracking specific modules and actions in a service. The module and action names are set by the application developer. For example, you would use the SET_MODULE and SET_ACTION procedures in the DBMS_APPLICATION_INFO package to set these values in a PL/SQL program.

End-to-End application tracing can identify workload problems for:

- Client identifier - specifies an end user based on the logon ID, such as HR.HR

- Service - specifies a group of applications with common attributes, service level thresholds, and priorities; or a single application, such as ACCTG for an accounting application

- Module - specifies a functional block, such as Accounts Receivable or General Ledger, of an application

- Action - specifies an action, such as an INSERT or UPDATE operation, in a module

- Session - specifies a session based on a given database session identifier (SID), on the local instance

- Instance - specifies a given instance based on the instance name

18.1.2 User Interfaces for End-to-End Application Tracing

The TRCSESS command-line utility consolidates tracing information based on specific criteria. The SQL Trace facility and TKPROF are two basic performance diagnostic tools that can help you accurately assess the efficiency of the SQL statements an application runs. For best results, use these tools with EXPLAIN PLAN rather than using EXPLAIN PLAN alone. After tracing information is written to files, you can consolidate this data with the TRCSESS utility, and then diagnose it with TKPROF or SQL Trace.

The recommended interface for end-to-end application tracing is Oracle Enterprise Manager Cloud Control (Cloud Control). Using Cloud Control, you can view the top consumers for each consumer type, and enable or disable statistics gathering and SQL tracing for specific consumers. If Cloud Control is unavailable, then you can manage this feature using the DBMS_MONITOR APIs.

See Also:
Oracle Database PL/SQL Packages and Types Reference for information about the DBMS_MONITOR, DBMS_SESSION, DBMS_SERVICE, and DBMS_APPLICATION_INFO packages

18.1.2.1 Overview of the SQL Trace Facility

The SQL Trace facility provides performance information on individual SQL statements. It generates the following statistics for each statement:

- Parse, execute, and fetch counts

- CPU and elapsed times

- Physical reads and logical reads

- Number of rows processed

- Misses on the library cache

- User name under which each parse occurred

- Each commit and rollback

- Wait event data for each SQL statement, and a summary for each trace file

If the cursor for the SQL statement is closed, then SQL Trace also provides row source information that includes:

- Row operations showing the actual execution plan of each SQL statement

- Number of rows, number of consistent reads, number of physical reads, number of physical writes, and time elapsed for each operation on a row

Although you can enable the SQL Trace facility for a session or an instance, Oracle recommends that you use the DBMS_SESSION or DBMS_MONITOR packages instead. When the SQL Trace facility is enabled for a session or for an instance, performance statistics for all SQL statements executed in a user session or in the instance are placed into trace files. Using the SQL Trace facility can affect performance and may result in increased system overhead, excessive CPU usage, and inadequate disk space.

The TRCSESS command-line utility consolidates tracing information from several trace files based on specific criteria, such as session or client ID. See "TRCSESS".

See Also:
"Enabling End-to-End Application Tracing" to learn how to use the DBMS_SESSION or DBMS_MONITOR packages to enable SQL tracing for a session or an instance

18.1.2.2 Overview of TKPROF

You can run the TKPROF program to format the contents of the trace file and place the output into a readable output file. TKPROF can also:

- Create a SQL script that stores the statistics in the database

- Determine the execution plans of SQL statements

Note:
If the cursor for a SQL statement is not closed, then TKPROF output does not automatically include the actual execution plan of the SQL statement. In this situation, use the EXPLAIN option with TKPROF to generate an execution plan.

TKPROF reports each statement executed with the resources it has consumed, the number of times it was called, and the number of rows which it processed. This information enables you to locate those statements that are using the greatest resource. With baselines available, you can assess whether the resources used are reasonable given the work done.

18.2 Enabling Statistics Gathering for End-to-End Tracing

To gather the appropriate statistics using PL/SQL, you must enable statistics gathering for client identifier, service, module, or action using procedures in DBMS_MONITOR. The default level is the session-level statistics gathering. Statistics gathering is global for the database and continues after a database instance is restarted.

You can gather statistics by the following criteria:

- Enabling Statistics Gathering for a Client ID

- Enabling Statistics Gathering for a Service, Module, and Action

18.2.1 Enabling Statistics Gathering for a Client ID

The procedure CLIENT_ID_STAT_ENABLE enables statistics gathering for a given client ID, whereas the procedure CLIENT_ID_STAT_DISABLE disables it. You can view client identifiers in the CLIENT_IDENTIFIER column in V$SESSION.

Assumptions

This tutorial assumes that you want to enable and then disable statistics gathering for the client with the ID oe.oe.

To enable and disable statistics gathering for a client identifier:

1. Start SQL*Plus, and then connect to the database with the appropriate privileges.

2. Enable statistics gathering for oe.oe.
For example, run the following command:

EXECUTE DBMS_MONITOR.CLIENT_ID_STAT_ENABLE(client_id => 'OE.OE');

3. Disable statistics gathering for oe.oe.
For example, run the following command:

EXECUTE DBMS_MONITOR.CLIENT_ID_STAT_DISABLE(client_id => 'OE.OE');

18.2.2 Enabling Statistics Gathering for a Service, Module, and Action

The procedure SERV_MOD_ACT_STAT_ENABLE enables statistic gathering for a combination of service, module, and action, whereas the procedure SERV_MOD_ACT_STAT_DISABLE disables statistic gathering for a combination of service, module, and action.

When you change the module or action using the preceding DBMS_MONITOR procedures, the change takes effect when the next user call is executed in the session. For example, if a module is set to module1 in a session, and if the module is reset to module2 in a user call in the session, then the module remains module1 during this user call. The module is changed to module2 in the next user call in the session.

Assumptions

This tutorial assumes that you want to gather statistics as follows:

- For the ACCTG service

- For all actions in the PAYROLL module

- For the INSERT ITEM action within the GLEDGER module

To enable and disable statistics gathering for a service, module, and action:

1. Start SQL*Plus, and then connect to the database with the appropriate privileges.

2. Enable statistics gathering for the desired service, module, and action.
For example, run the following commands:

BEGIN
DBMS_MONITOR.SERV_MOD_ACT_STAT_ENABLE(
service_name => 'ACCTG'   ,
module_name  => 'PAYROLL' );
END;

BEGIN
DBMS_MONITOR.SERV_MOD_ACT_STAT_ENABLE(
service_name => 'ACCTG'       ,
module_name  => 'GLEDGER'     ,
action_name  => 'INSERT ITEM' );
END;

3. Disable statistic gathering for the previously specified combination of service, module, and action.
For example, run the following command:

BEGIN
DBMS_MONITOR.SERV_MOD_ACT_STAT_DISABLE(
service_name => 'ACCTG'       ,
module_name  => 'GLEDGER'     ,
action_name  => 'INSERT ITEM' );
END;

18.3 Enabling End-to-End Application Tracing

To enable tracing for client identifier, service, module, action, session, instance or database, execute the appropriate procedures in the DBMS_MONITOR package. You can enable tracing for specific diagnosis and workload management by the following criteria:

- Enabling Tracing for a Client Identifier

- Enabling Tracing for a Service, Module, and Action

- Enabling Tracing for a Session

- Enabling Tracing for the Instance or Database

With the criteria that you provide, specific trace information is captured in a set of trace files and combined into a single output trace file.

18.3.1 Enabling Tracing for a Client Identifier

To enable tracing globally for the database for a specified client identifier, use the CLIENT_ID_TRACE_ENABLE procedure. The CLIENT_ID_TRACE_DISABLE procedure disables tracing globally for the database for a given client identifier.

Assumptions

This tutorial assumes the following:

- OE.OE is the client identifier for which SQL tracing is to be enabled.

- You want to include wait information in the trace.

- You want to exclude bind information from the trace.

To enable and disable tracing for a client identifier:

1. Start SQL*Plus, and then connect to the database with the appropriate privileges.

2. Enable tracing for the client.
For example, execute the following program:

BEGIN
DBMS_MONITOR.CLIENT_ID_TRACE_ENABLE(
client_id => 'OE.OE' ,
waits     => true    ,
binds     => false   );
END;

3. Disable tracing for the client.
For example, execute the following command:

EXECUTE DBMS_MONITOR.CLIENT_ID_TRACE_DISABLE(client_id => 'OE.OE');

18.3.2 Enabling Tracing for a Service, Module, and Action

The SERV_MOD_ACT_TRACE_ENABLE procedure enables SQL tracing for a specified combination of service name, module, and action globally for a database, unless the procedure specifies a database instance name. The SERV_MOD_ACT_TRACE_DISABLE procedure disables the trace at all enabled instances for a given combination of service name, module, and action name globally.

Assumptions

This tutorial assumes the following:

- You want to enable tracing for the service ACCTG.

- You want to enable tracing for all actions for the combination of the ACCTG service and the PAYROLL module.

- You want to include wait information in the trace.

- You want to exclude bind information from the trace.

- You want to enable tracing only for the inst1 instance.

To enable and disable tracing for a service, module, and action:

1. Start SQL*Plus, and then connect to the database with the appropriate privileges.

2. Enable tracing for the service, module, and actions.
For example, execute the following command:

BEGIN
DBMS_MONITOR.SERV_MOD_ACT_TRACE_ENABLE(
service_name  => 'ACCTG'   ,
module_name   => 'PAYROLL' ,
waits         =>  true     ,
binds         =>  false    ,
instance_name => 'inst1'   );
END;

3. Disable tracing for the service, module, and actions.
For example, execute the following command:

BEGIN
DBMS_MONITOR.SERV_MOD_ACT_TRACE_DISABLE(
service_name  => 'ACCTG'   ,
module_name   => 'PAYROLL' ,
instance_name => 'inst1'   );
END;

18.3.3 Enabling Tracing for a Session

The SESSION_TRACE_ENABLE procedure enables the trace for a given database session identifier (SID), on the local instance. Whereas the DBMS_MONITOR package can only be invoked by a user with the DBA role, any user can also enable SQL tracing for their own session by using the DBMS_SESSION package. A user can invoke the SESSION_TRACE_ENABLE procedure to enable session-level SQL trace for the user's session. For example:

EXECUTE DBMS_SESSION.SESSION_TRACE_ENABLE(waits => true, binds => false);

Assumptions

This tutorial assumes the following:

- You want to log in to the database with administrator privileges.

- User OE has one active session.

- You want to temporarily enable tracing for the OE session.

- You want to include wait information in the trace.

- You want to exclude bind information from the trace.

To enable and disable tracing for a session:

1. Start SQL*Plus, and then connect to the database with the administrator privileges.

2. Determine the session ID and serial number values for the session to trace.
For example, query V$SESSION as follows:

SELECT SID, SERIAL#, USERNAME
FROM   V$SESSION
WHERE  USERNAME = 'OE';

SID    SERIAL# USERNAME
---------- ---------- ------------------------------
27         60 OE

3. Use the values from the preceding step to enable tracing for a specific session.
For example, execute the following program to enable tracing for the OE session, where the true argument includes wait information in the trace and the false argument excludes bind information from the trace:

BEGIN
DBMS_MONITOR.SESSION_TRACE_ENABLE(
session_id => 27 ,
serial_num => 60 ,
waits => true    ,
binds => false   );
END;

4. Disable tracing for the session.
The SESSION_TRACE_DISABLE procedure disables the trace for a given database session identifier (SID) and serial number. For example:

EXECUTE DBMS_MONITOR.SESSION_TRACE_DISABLE(session_id => 27, serial_num => 60);

18.3.4 Enabling Tracing for the Instance or Database

The DBMS_MONITOR.DATABASE_TRACE_ENABLE procedure overrides all other session-level traces, but is complementary to the client identifier, service, module, and action traces. Tracing is enabled for all current and future sessions.

All new sessions inherit the wait and bind information specified by this procedure until you call the DATABASE_TRACE_DISABLE procedure. When you invoke this procedure with the instance_name parameter, the procedure resets the session-level SQL trace for the named instance. If you invoke this procedure without the instance_name parameter, then the procedure resets the session-level SQL trace for the entire database.

Prerequisites

You must be logged in as an administrator execute the DATABASE_TRACE_ENABLE procedure.

Assumptions

This tutorial assumes the following:

- You want to enable tracing for all SQL the inst1 instance.

- You want wait information to be in the trace.

- You do not want bind information in the trace.

To enable and disable tracing for a session:

1. Start SQL*Plus, and then connect to the database with the administrator privileges.

2. Call the DATABASE_TRACE_ENABLE procedure to enable SQL tracing for a given instance or an entire database.
For example, execute the following program, where the true argument specifies that wait information is in the trace, and the false argument specifies that no bind information is in the trace:

BEGIN
DBMS_MONITOR.DATABASE_TRACE_ENABLE(
waits         => true    ,
binds         => false   ,     instance_name => 'inst1' );
END;

3. Disable tracing for the session.
The SESSION_TRACE_DISABLE procedure disables the trace. For example, the following program disables tracing for inst1:

EXECUTE DBMS_MONITOR.DATABASE_TRACE_DISABLE(instance_name => 'inst1');

To disable the session-level SQL tracing for an entire database, invoke the DATABASE_TRACE_DISABLE procedure without specifying the instance_name parameter:

EXECUTE DBMS_MONITOR.DATABASE_TRACE_DISABLE();

18.4 Generating Output Files Using SQL Trace and TKPROF

This section explains the basic procedure for using SQL Trace and TKPROF.

To use the SQL Trace facility and TKPROF:

1. Set initialization parameters for trace file management.
See "Step 1: Setting Initialization Parameters for Trace File Management".

2. Enable the SQL Trace facility for the desired session, and run the application. This step produces a trace file containing statistics for the SQL statements issued by the application.
See "Step 2: Enabling the SQL Trace Facility".

3. Run TKPROF to translate the trace file created in Step 2 into a readable output file. This step can optionally create a SQL script that you can use to store the statistics in a database.
See "Step 3: Generating Output Files with TKPROF".

4. Optionally, run the SQL script produced in Step 3 to store the statistics in the database.
See "Step 4: Storing SQL Trace Facility Statistics".

18.4.1 Step 1: Setting Initialization Parameters for Trace File Management

When the SQL Trace facility is enabled for a session, Oracle Database generates a trace file containing statistics for traced SQL statements for that session. When the SQL Trace facility is enabled for an instance, Oracle Database creates a separate trace file for each process.

To set initialization parameters for trace file management:

1. Check the settings of the TIMED_STATISTICS, MAX_DUMP_FILE_SIZE, and DIAGNOSTIC_DEST initialization parameters, as indicated in Table 18-1.

Table 18-1 Initialization Parameters to Check Before Enabling SQL Trace

Parameter

Description

DIAGNOSTIC_DEST

Specifies the location of the Automatic Diagnostic Repository (ADR) Home. The diagnostic files for each database instance are located in this dedicated directory. Oracle Database Reference for information about the DIAGNOSTIC_DEST initialization parameter.

MAX_DUMP_FILE_SIZE

When the SQL Trace facility is enabled at the database instance level, every call to the database writes a text line in a file in the operating system's file format. The maximum size of these files in operating system blocks is limited by this initialization parameter. The default is UNLIMITED. See Oracle Database Administrator's Guide to learn how to control the trace file size.

TIMED_STATISTICS

Enables and disables the collection of timed statistics, such as CPU and elapsed times, by the SQL Trace facility, and the collection of various statistics in the V$ views.

If STATISTICS_LEVEL is set to TYPICAL or ALL, then the default value of TIMED_STATISTICS is true. If STATISTICS_LEVEL is set to BASIC, then the default value of TIMED_STATISTICS is false. See Oracle Database Reference for information about the STATISTICS_LEVEL initialization parameter.

Enabling timing causes extra timing calls for low-level operations. This is a dynamic parameter. It is also a session parameter. See Oracle Database Reference for information about the TIMED_STATISTICS initialization parameter.

2. Devise a way of recognizing the resulting trace file.
Be sure you know how to distinguish the trace files by name. You can tag trace files by including in your programs a statement such as SELECT 'program_name' FROM DUAL. You can then trace each file back to the process that created it.

You can also set the TRACEFILE_IDENTIFIER initialization parameter to specify a custom identifier that becomes part of the trace file name (see Oracle Database Reference for information about the TRACEFILE_IDENTIFIER initialization parameter). For example, you can add my_trace_id to subsequent trace file names for easy identification with the following:

ALTER SESSION SET TRACEFILE_IDENTIFIER = 'my_trace_id';

3. If the operating system retains multiple versions of files, then ensure that the version limit is high enough to accommodate the number of trace files you expect the SQL Trace facility to generate.

4. If the generated trace files can be owned by an operating system user other than yourself, then ensure that you have the necessary permissions to use TKPROF to format them.

18.4.2 Step 2: Enabling the SQL Trace Facility

Enable the SQL Trace facility at either of the following levels:

- Database instance
Use DBMS_MONITOR.DATABASE_TRACE_ENABLE procedure to enable tracing, and DBMS_MONITOR.DATABASE_TRACE_DISABLE procedure to disable tracing.

- Database session
Use DBMS_SESSION.SET_SQL_TRACE procedure to enable tracing (true) or disable tracing (false).

Caution:
Because running the SQL Trace facility increases system overhead, enable it only when tuning SQL statements, and disable it when you are finished.

To enable and disable tracing at the database instance level:

1. Start SQL*Plus, and connect to the database with administrator privileges.

2. Enable tracing at the database instance level.
The following example enables tracing for the orcl instance:

EXEC DBMS_MONITOR.DATABASE_TRACE_ENABLE(INSTANCE_NAME => 'orcl');

3. Execute the statements to be traced.

4. Disable tracing for the database instance.
The following example disables tracing for the orcl instance:

EXEC DBMS_MONITOR.DATABASE_TRACE_DISABLE(INSTANCE_NAME => 'orcl');

To enable and disable tracing at the session level:

1. Start SQL*Plus, and connect to the database with the desired credentials.

2. Enable tracing for the current session.
The following example enables tracing for the current session:

EXEC DBMS_SESSION.SET_SQL_TRACE(sql_trace => true);

3. Execute the statements to be traced.

4. Disable tracing for the current session.
The following example disables tracing for the current session:

EXEC DBMS_SESSION.SET_SQL_TRACE(sql_trace => false);

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_MONITOR.DATABASE_TRACE_ENABLE

18.4.3 Step 3: Generating Output Files with TKPROF

TKPROF accepts as input a trace file produced by the SQL Trace facility, and it produces a formatted output file. TKPROF can also generate execution plans.

After the SQL Trace facility has generated trace files, you can:

- Run TKPROF on each individual trace file, producing several formatted output files, one for each session.

- Concatenate the trace files, and then run TKPROF on the result to produce a formatted output file for the entire instance.

- Run the TRCSESS command-line utility to consolidate tracing information from several trace files, then run TKPROF on the result.

TKPROF does not report COMMIT and ROLLBACK statements recorded in the trace file.

Note:
The following SQL statements are truncated to 25 characters in the SQL Trace file:

SET ROLE
GRANT
ALTER USER
ALTER ROLE
CREATE USER
CREATE ROLE

Example 18-1 TKPROF Output

SELECT * FROM emp, dept
WHERE emp.deptno = dept.deptno;

call   count      cpu    elapsed     disk    query current    rows
---- -------  -------  --------- -------- -------- -------  ------
Parse      1     0.16      0.29         3       13       0       0
Execute    1     0.00      0.00         0        0       0       0
Fetch      1     0.03      0.26         2        2       4      14

Misses in library cache during parse: 1
Parsing user id: (8) SCOTT

Rows     Execution Plan
-------  ---------------------------------------------------

14  MERGE JOIN
4   SORT JOIN
4     TABLE ACCESS (FULL) OF 'DEPT'
14    SORT JOIN
14      TABLE ACCESS (FULL) OF 'EMP'

For this statement, TKPROF output includes the following information:

- The text of the SQL statement

- The SQL Trace statistics in tabular form

- The number of library cache misses for the parsing and execution of the statement.

- The user initially parsing the statement.

- The execution plan generated by EXPLAIN PLAN.

TKPROF also provides a summary of user level statements and recursive SQL calls for the trace file.

18.4.4 Step 4: Storing SQL Trace Facility Statistics

You might want to keep a history of the statistics generated by the SQL Trace facility for an application, and compare them over time. TKPROF can generate a SQL script that creates a table and inserts rows of statistics into it. This script contains:

- A CREATE TABLE statement that creates an output table named TKPROF_TABLE.

- INSERT statements that add rows of statistics, one for each traced SQL statement, to TKPROF_TABLE.

After running TKPROF, run this script to store the statistics in the database.

18.4.4.1 Generating the TKPROF Output SQL Script

When you run TKPROF, use the INSERT parameter to specify the name of the generated SQL script. If you omit this parameter, then TKPROF does not generate a script.

18.4.4.2 Editing the TKPROF Output SQL Script

After TKPROF has created the SQL script, you might want to edit the script before running it. If you have created an output table for previously collected statistics, and if you want to add new statistics to this table, then remove the CREATE TABLE statement from the script. The script then inserts the new rows into the existing table.

If you have created multiple output tables, perhaps to store statistics from different databases in different tables, then edit the CREATE TABLE and INSERT statements to change the name of the output table.

18.4.4.3 Querying the Output Table

The following CREATE TABLE statement creates the TKPROF_TABLE:

CREATE TABLE TKPROF_TABLE (

DATE_OF_INSERT    DATE,
CURSOR_NUM        NUMBER,
DEPTH             NUMBER,
USER_ID           NUMBER,
PARSE_CNT         NUMBER,
PARSE_CPU         NUMBER,
PARSE_ELAP        NUMBER,
PARSE_DISK        NUMBER,
PARSE_QUERY       NUMBER,
PARSE_CURRENT     NUMBER,
PARSE_MISS        NUMBER,
EXE_COUNT         NUMBER,
EXE_CPU           NUMBER,
EXE_ELAP          NUMBER,
EXE_DISK          NUMBER,
EXE_QUERY         NUMBER,
EXE_CURRENT       NUMBER,
EXE_MISS          NUMBER,
EXE_ROWS          NUMBER,
FETCH_COUNT       NUMBER,
FETCH_CPU         NUMBER,
FETCH_ELAP        NUMBER,
FETCH_DISK        NUMBER,
FETCH_QUERY       NUMBER,
FETCH_CURRENT     NUMBER,
FETCH_ROWS        NUMBER,
CLOCK_TICKS       NUMBER,
SQL_STATEMENT     LONG);

Most output table columns correspond directly to the statistics that appear in the formatted output file. For example, the PARSE_CNT column value corresponds to the count statistic for the parse step in the output file.

The columns in Table 18-2 help you identify a row of statistics.

Table 18-2 TKPROF_TABLE Columns for Identifying a Row of Statistics

Column

Description

SQL_STATEMENT

This is the SQL statement for which the SQL Trace facility collected the row of statistics. Because this column has data type LONG, you cannot use it in expressions or WHERE clause conditions.

DATE_OF_INSERT

This is the date and time when the row was inserted into the table. This value is different from the time when the SQL Trace facility collected the statistics.

DEPTH

This indicates the level of recursion at which the SQL statement was issued. For example, a value of 0 indicates that a user issued the statement. A value of 1 indicates that Oracle Database generated the statement as a recursive call to process a statement with a value of 0 (a statement issued by a user). A value of n indicates that Oracle Database generated the statement as a recursive call to process a statement with a value of n-1.

USER_ID

This identifies the user issuing the statement. This value also appears in the formatted output file.

CURSOR_NUM

Oracle Database uses this column value to keep track of the cursor to which each SQL statement was assigned.

The output table does not store the statement's execution plan. The following query returns the statistics from the output table. These statistics correspond to the formatted output shown in "TKPROF Body".

SELECT * FROM TKPROF_TABLE;

Sample output appears as follows:

DATE_OF_INSERT CURSOR_NUM DEPTH USER_ID PARSE_CNT PARSE_CPU PARSE_ELAP
-------------- ---------- ----- ------- --------- --------- ----------
21-DEC-2012          1      0     8         1        16         22

PARSE_DISK PARSE_QUERY PARSE_CURRENT PARSE_MISS EXE_COUNT EXE_CPU
---------- ----------- ------------- ---------- --------- -------
3          11           0            1           1         0

EXE_ELAP EXE_DISK EXE_QUERY EXE_CURRENT EXE_MISS EXE_ROWS FETCH_COUNT
-------- -------- --------- ----------- -------- -------- -----------
0        0        0          0          0        0         1

FETCH_CPU FETCH_ELAP FETCH_DISK FETCH_QUERY FETCH_CURRENT FETCH_ROWS
--------- ---------- ---------- ----------- ------------- ----------
2        20          2          2            4           10

SQL_STATEMENT
---------------------------------------------------------------------
SELECT * FROM EMP, DEPT WHERE EMP.DEPTNO = DEPT.DEPTNO

18.5 Guidelines for Interpreting TKPROF Output

This section provides guidelines for interpreting TKPROF output.

- Guideline for Interpreting the Resolution of Statistics

- Guideline for Recursive SQL Statements

- Guideline for Deciding Which Statements to Tune

- Guidelines for Avoiding Traps in TKPROF Interpretation

While TKPROF provides a useful analysis, the most accurate measure of efficiency is the performance of the application. At the end of the TKPROF output is a summary of the work that the process performed during the period that the trace was running.

18.5.1 Guideline for Interpreting the Resolution of Statistics

Timing statistics have a resolution of one hundredth of a second. Therefore, any operation on a cursor that takes a hundredth of a second or less might not be timed accurately. Keep this limitation in mind when interpreting statistics. In particular, be careful when interpreting the results from simple queries that execute very quickly.

18.5.2 Guideline for Recursive SQL Statements

Sometimes, to execute a SQL statement issued by a user, Oracle Database must issue additional SQL statements. Such statements are called recursive calls or recursive SQL. For example, if a session inserts a row into a table that has insufficient space to hold that row, then the database makes recursive calls to allocate the space dynamically. The database also generates recursive calls when data dictionary information is not available in memory and so must be retrieved from disk.

If recursive calls occur while the SQL Trace facility is enabled, then TKPROF produces statistics for the recursive SQL statements and marks them clearly as recursive SQL statements in the output file. You can suppress the listing of Oracle Database internal recursive calls (for example, space management) in the output file by setting the SYS command-line parameter to NO. The statistics for a recursive SQL statement are included in the listing for that statement, not in the listing for the SQL statement that caused the recursive call. So, when you are calculating the total resources required to process a SQL statement, consider the statistics for that statement and those for recursive calls caused by that statement.

Note:
Recursive SQL statistics are not included for SQL-level operations.

18.5.3 Guideline for Deciding Which Statements to Tune

You must determine which SQL statements use the most CPU or disk resource. If the TIMED_STATISTICS parameter is enabled, then you can find high CPU activity in the CPU column. If TIMED_STATISTICS is not enabled, then check the QUERY and CURRENT columns.

With the exception of locking problems and inefficient PL/SQL loops, neither the CPU time nor the elapsed time is necessary to find problem statements. The key is the number of block visits, both query (that is, subject to read consistency) and current (that is, not subject to read consistency). Segment headers and blocks that are going to be updated are acquired in current mode, but all query and subquery processing requests the data in query mode. These are precisely the same measures as the instance statistics CONSISTENT GETS and DB BLOCK GETS. You can find high disk activity in the disk column.

The following listing shows TKPROF output for one SQL statement as it appears in the output file:

SELECT *
FROM emp, dept
WHERE emp.deptno = dept.deptno;

call   count      cpu    elapsed     disk    query current    rows
---- -------  -------  --------- -------- -------- -------  ------
Parse     11     0.08      0.18        0       0       0         0
Execute   11     0.23      0.66        0       3       6         0
Fetch     35     6.70      6.83      100   12326       2       824
------------------------------------------------------------------
total     57     7.01      7.67      100   12329       8       826

Misses in library cache during parse: 0

If it is acceptable to have 7.01 CPU seconds and to retrieve 824 rows, then you need not look any further at this trace output. In fact, a major use of TKPROF reports in a tuning exercise is to eliminate processes from the detailed tuning phase.

The output indicates that 10 unnecessary parse call were made (because 11 parse calls exist for this single statement) and that array fetch operations were performed. More rows were fetched than there were fetches performed. A large gap between CPU and elapsed timings indicates Physical I/Os.

See Also:
Example 18-4, "Printing the Most Resource-Intensive Statements"

18.5.4 Guidelines for Avoiding Traps in TKPROF Interpretation

This section describes some fine points of TKPROF interpretation:

- Guideline for Avoiding the Argument Trap

- Guideline for Avoiding the Read Consistency Trap

- Guideline for Avoiding the Schema Trap

- Guideline for Avoiding the Time Trap

18.5.4.1 Guideline for Avoiding the Argument Trap

If you are not aware of the values being bound at run time, then it is possible to fall into the argument trap. EXPLAIN PLAN cannot determine the type of a bind variable from the text of SQL statements, and it always assumes that the type is VARCHAR. If the bind variable is actually a number or a date, then TKPROF can cause implicit data conversions, which can cause inefficient plans to be executed. To avoid this situation, experiment with different data types in the query, and perform the conversion yourself.

18.5.4.2 Guideline for Avoiding the Read Consistency Trap

The next example illustrates the read consistency trap. Without knowing that an uncommitted transaction had made a series of updates to the NAME column, it is very difficult to see why so many block visits would be incurred.

Cases like this are not normally repeatable: if the process were run again, it is unlikely that another transaction would interact with it in the same way.

SELECT name_id
FROM cq_names
WHERE name = 'FLOOR';

call     count     cpu     elapsed     disk     query current     rows
----     -----     ---     -------     ----     ----- -------     ----
Parse        1    0.10        0.18        0         0       0        0
Execute      1    0.00        0.00        0         0       0        0
Fetch        1    0.11        0.21        2       101       0        1

Misses in library cache during parse: 1
Parsing user id: 01 (USER1)

Rows     Execution Plan
e----     --------- ----
0     SELECT STATEMENT
1       TABLE ACCESS (BY ROWID) OF 'CQ_NAMES'
2         INDEX (RANGE SCAN) OF 'CQ_NAMES_NAME' (NON_UNIQUE)

18.5.4.3 Guideline for Avoiding the Schema Trap

This example shows an extreme (and thus easily detected) example of the schema trap. At first, it is difficult to see why such an apparently straightforward indexed query must look at so many database blocks, or why it should access any blocks at all in current mode.

SELECT name_id
FROM cq_names
WHERE name = 'FLOOR';

call        count        cpu      elapsed     disk  query current rows
--------  -------   --------    ---------  ------- ------ ------- ----
Parse           1       0.06         0.10        0      0       0    0
Execute         1       0.02         0.02        0      0       0    0
Fetch           1       0.23         0.30       31     31       3    1

Misses in library cache during parse: 0
Parsing user id: 02  (USER2)

Rows     Execution Plan
-------  ---------------------------------------------------
0  SELECT STATEMENT
2340    TABLE ACCESS (BY ROWID) OF 'CQ_NAMES'
0      INDEX (RANGE SCAN) OF 'CQ_NAMES_NAME' (NON-UNIQUE)

Two statistics suggest that the query might have been executed with a full table scan. These statistics are the current mode block visits, plus the number of rows originating from the Table Access row source in the execution plan. The explanation is that the required index was built after the trace file had been produced, but before TKPROF had been run.

Generating a new trace file gives the following data:

SELECT name_id
FROM cq_names
WHERE name = 'FLOOR';

call    count    cpu   elapsed  disk  query current     rows
-----  ------ ------  -------- ----- ------ -------    -----
Parse       1   0.01      0.02     0      0       0        0
Execute     1   0.00      0.00     0      0       0        0
Fetch       1   0.00      0.00     0      2       0        1

Misses in library cache during parse: 0
Parsing user id: 02  (USER2)

Rows     Execution Plan
-------  ---------------------------------------------------
0  SELECT STATEMENT
1    TABLE ACCESS (BY ROWID) OF 'CQ_NAMES'
2      INDEX (RANGE SCAN) OF 'CQ_NAMES_NAME' (NON-UNIQUE)

One of the marked features of this correct version is that the parse call took 10 milliseconds of CPU time and 20 milliseconds of elapsed time, but the query apparently took no time at all to execute and perform the fetch. These anomalies arise because the clock tick of 10 milliseconds is too long relative to the time taken to execute and fetch the data. In such cases, it is important to get lots of executions of the statements, so that you have statistically valid numbers.

18.5.4.4 Guideline for Avoiding the Time Trap

Sometimes, as in the following example, you might wonder why a particular query has taken so long.

UPDATE cq_names SET ATTRIBUTES = lower(ATTRIBUTES)
WHERE ATTRIBUTES = :att

call       count       cpu    elapsed     disk    query current        rows
-------- -------  --------  --------- -------- -------- -------  ----------
Parse          1      0.06       0.24        0        0       0           0
Execute        1      0.62      19.62       22      526      12           7
Fetch          0      0.00       0.00        0        0       0           0

Misses in library cache during parse: 1
Parsing user id: 02  (USER2)

Rows     Execution Plan
-------  ---------------------------------------------------
0  UPDATE STATEMENT
2519  TABLE ACCESS (FULL) OF 'CQ_NAMES'

Again, the answer is interference from another transaction. In this case, another transaction held a shared lock on the table cq_names for several seconds before and after the update was issued. It takes a fair amount of experience to diagnose that interference effects are occurring. On the one hand, comparative data is essential when the interference is contributing only a short delay (or a small increase in block visits in the previous example). However, if the interference contributes only modest overhead, and if the statement is essentially efficient, then its statistics may not require analysis.

Application Tracing Utilities

This section describes the syntax and semantics for the following utilities:

- TRCSESS

- TKPROF

TRCSESS

The TRCSESS utility consolidates trace output from selected trace files based on user-specified criteria. After TRCSESS merges the trace information into a single output file, TKPROF can process the output file.

Purpose

TRCSESS is useful for consolidating the tracing of a particular session for performance or debugging purposes.

Tracing a specific session is usually not a problem in the dedicated server model because one process serves a session during its lifetime. You can see the trace information for the session from the trace file belonging to the server process. However, in a shared server configuration, a user session is serviced by different processes over time. The trace for the user session is scattered across different trace files belonging to different processes, which makes it difficult to get a complete picture of the life cycle of a session.

Guidelines

You must specify one of the session, clientid, service, action, or module options. If you specify multiple options, then TRCSESS consolidates all trace files that satisfy the specified criteria into the output file.

Syntax

trcsess  [output=output_file_name]
[session=session_id]
[clientid=client_id]
[service=service_name]
[action=action_name]
[module=module_name]
[trace_files]

Options

Argument

Description

output

Specifies the file where the output is generated. If this option is not specified, then the utility writes to standard output.

session

Consolidates the trace information for the session specified. The session identifier is a combination of session index and session serial number, such as 21.2371. You can locate these values in the V$SESSION view.

clientid

Consolidates the trace information for the specified client ID.

service

Consolidates the trace information for the specified service name.

action

Consolidates the trace information for the specified action name.

module

Consolidates the trace information for the specified module name.

trace_files

Lists the trace file names, separated by spaces, in which TRCSESS should look for trace information. You can use the wildcard character (*) to specify the trace file names. If you do not specify trace files, then TRCSESS uses all files in the current directory as input.

Examples

Example 18-2 Tracing a Single Session

This sample output of TRCSESS shows the container of traces for a particular session. In this example, the session index and serial number equals 21.2371. All files in current directory are taken as input.

trcsess session=21.2371

Example 18-3 Specifying Multiple Trace Files

The following example specifies two trace files:

trcsess session=21.2371 main_12359.trc main_12995.trc

The sample output is similar to the following:

[PROCESS ID = 12359]
*** 2014-04-02 09:48:28.376
PARSING IN CURSOR #1 len=17 dep=0 uid=27 oct=3 lid=27 tim=868373970961 hv=887450622 ad='22683fb4'
select * from cat
END OF STMT
PARSE #1:c=0,e=339,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=4,tim=868373970944
EXEC #1:c=0,e=221,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=4,tim=868373971411
FETCH #1:c=0,e=791,p=0,cr=7,cu=0,mis=0,r=1,dep=0,og=4,tim=868373972435
FETCH #1:c=0,e=1486,p=0,cr=20,cu=0,mis=0,r=6,dep=0,og=4,tim=868373986238
*** 2014-04-02 10:03:58.058
XCTEND rlbk=0, rd_only=1
STAT #1 id=1 cnt=7 pid=0 pos=1 obj=0 op='FILTER  '
STAT #1 id=2 cnt=7 pid=1 pos=1 obj=18 op='TABLE ACCESS BY INDEX ROWID OBJ$ '
STAT #1 id=3 cnt=7 pid=2 pos=1 obj=37 op='INDEX RANGE SCAN I_OBJ2 '
STAT #1 id=4 cnt=0 pid=1 pos=2 obj=4 op='TABLE ACCESS CLUSTER TAB$J2 '
STAT #1 id=5 cnt=6 pid=4 pos=1 obj=3 op='INDEX UNIQUE SCAN I_OBJ# '
[PROCESS ID=12995]
*** 2014-04-02 10:04:32.738
Archiving is disabled

TKPROF

The TKPROF program formats the contents of the trace file and places the output into a readable output file. TKPROF can also:

- Create a SQL script that stores the statistics in the database

- Determine the execution plans of SQL statements

Note:
If the cursor for a SQL statement is not closed, then TKPROF output does not automatically include the actual execution plan of the SQL statement. In this situation, use the EXPLAIN option with TKPROF to generate an execution plan.

TKPROF reports each statement executed with the resources it has consumed, the number of times it was called, and the number of rows which it processed.

Purpose

TKPROF can locate statements using the greatest resource. With baselines available, you can assess whether the resources used are reasonable given the work done.

Guidelines

The input and output files are the only required arguments. If you invoke TKPROF without arguments, then the tool displays online help.

Syntax

tkprof input_file output_file
[ waits=yes|no ]
[ sort=option ]
[ print=n ]
[ aggregate=yes|no ]
[ insert=filename3 ]
[ sys=yes|no ]
[ table=schema.table ]
[ explain=user/password ]
[ record=filename4 ]
[ width=n ]

Options

Table 18-3 TKPROF Arguments

Argument

Description

input_file

Specifies the input file, a trace file containing statistics produced by the SQL Trace facility. This file can be either a trace file produced for a single session, or a file produced by concatenating individual trace files from multiple sessions.

output_file

Specifies the file to which TKPROF writes its formatted output.

WAITS

Specifies whether to record summary for any wait events found in the trace file. Valid values are YES (default) and NO.

SORT

Sorts traced SQL statements in descending order of specified sort option before listing them in the output file. If multiple options are specified, then the output is sorted in descending order by the sum of the values specified in the sort options. If you omit this parameter, then TKPROF lists statements into the output file in order of first use. Sort options are listed as follows:

- PRSCNT - Number of times parsed

- PRSCPU - CPU time spent parsing

- PRSELA - Elapsed time spent parsing

- PRSDSK - Number of physical reads from disk during parse

- PRSQRY - Number of consistent mode block reads during parse

- PRSCU - Number of current mode block reads during parse

- PRSMIS - Number of library cache misses during parse

- EXECNT - Number of executions

- EXECPU - CPU time spent executing

- EXEELA - Elapsed time spent executing

- EXEDSK - Number of physical reads from disk during execute

- EXEQRY - Number of consistent mode block reads during execute

- EXECU - Number of current mode block reads during execute

- EXEROW - Number of rows processed during execute

- EXEMIS - Number of library cache misses during execute

- FCHCNT - Number of fetches

- FCHCPU - CPU time spent fetching

- FCHELA - Elapsed time spent fetching

- FCHDSK - Number of physical reads from disk during fetch

- FCHQRY - Number of consistent mode block reads during fetch

- FCHCU - Number of current mode block reads during fetch

- FCHROW - Number of rows fetched

- USERID - Userid of user that parsed the cursor

PRINT

Lists only the first integer sorted SQL statements from the output file. If you omit this parameter, then TKPROF lists all traced SQL statements. This parameter does not affect the optional SQL script. The SQL script always generates insert data for all traced SQL statements.

AGGREGATE

If you specify AGGREGATE = NO, then TKPROF does not aggregate multiple users of the same SQL text.

INSERT

Creates a SQL script that stores the trace file statistics in the database. TKPROF creates this script with the name filename3. This script creates a table and inserts a row of statistics for each traced SQL statement into the table.

SYS

Enables and disables the listing of SQL statements issued by the user SYS, or recursive SQL statements, into the output file. The default value of YES causes TKPROF to list these statements. The value of NO causes TKPROF to omit them. This parameter does not affect the optional SQL script. The SQL script always inserts statistics for all traced SQL statements, including recursive SQL statements.

TABLE

Specifies the schema and name of the table into which TKPROF temporarily places execution plans before writing them to the output file. If the specified table exists, then TKPROF deletes all rows in the table, uses it for the EXPLAIN PLAN statement (which writes more rows into the table), and then deletes those rows. If this table does not exist, then TKPROF creates it, uses it, and then drops it.

The specified user must be able to issue INSERT, SELECT, and DELETE statements against the table. If the table does not exist, then the user must also be able to issue CREATE TABLE and DROP TABLE statements. For the privileges to issue these statements, see Oracle Database SQL Language Reference.

This option enables multiple individuals to run TKPROF concurrently with the same user in the EXPLAIN value. These individuals can specify different TABLE values and avoid destructively interfering with each other's processing on the temporary plan table.

TKPROF supports the following combinations:

- The EXPLAIN parameter without the TABLE parameter
TKPROF uses the table PROF$PLAN_TABLE in the schema of the user specified by the EXPLAIN parameter

- The TABLE parameter without the EXPLAIN parameter
TKPROF ignores the TABLE parameter.

If no plan table exists, then TKPROF creates the table PROF$PLAN_TABLE and then drops it at the end.

EXPLAIN

Determines the execution plan for each SQL statement in the trace file and writes these execution plans to the output file. TKPROF also displays the number of rows processed by each step of the execution plan.

TKPROF determines execution plans by issuing the EXPLAIN PLAN statement after connecting to Oracle Database with the user and password specified in this parameter. The specified user must have CREATE SESSION system privileges. TKPROF takes longer to process a large trace file if the EXPLAIN option is used.

Note: Trace files generated immediately after instance startup contain data that reflects the activity of the startup process. In particular, they reflect a disproportionate amount of I/O activity as caches in the system global area (SGA) are filled. For the purposes of tuning, ignore such trace files.

RECORD

Creates a SQL script with the specified filename with all of the nonrecursive SQL in the trace file. You can use this script to replay the user events from the trace file.

WIDTH

An integer that controls the output line width of some TKPROF output, such as the explain plan. This parameter is useful for post-processing of TKPROF output.

Output

This section explains the TKPROF output.

Identification of User Issuing the SQL Statement in TKPROF

TKPROF lists the user ID of the user issuing each SQL statement. If the SQL Trace input file contained statistics from multiple users, and if the statement was issued by multiple users, then TKPROF lists the ID of the last user to parse the statement. The user ID of all database users appears in the data dictionary in the column ALL_USERS.USER_ID.

Tabular Statistics in TKPROF

TKPROF lists the statistics for a SQL statement returned by the SQL Trace facility in rows and columns. Each row corresponds to one of three steps of SQL statement processing. Statistics are identified by the value of the CALL column. See Table 18-4.

Table 18-4 CALL Column Values

CALL Value

Meaning

PARSE

Translates the SQL statement into an execution plan, including checks for proper security authorization and checks for the existence of tables, columns, and other referenced objects.

EXECUTE

Actual execution of the statement by Oracle Database. For INSERT, UPDATE, and DELETE statements, this modifies the data. For SELECT statements, this identifies the selected rows.

FETCH

Retrieves rows returned by a query. Fetches are only performed for SELECT statements.

The other columns of the SQL Trace facility output are combined statistics for all parses, executions, and fetches of a statement. The sum of query and current is the total number of buffers accessed, also called Logical I/Os (LIOs). See Table 18-5.

Table 18-5 SQL Trace Statistics for Parses, Executes, and Fetches.

SQL Trace Statistic

Meaning

COUNT

Number of times a statement was parsed, executed, or fetched.

CPU

Total CPU time in seconds for all parse, execute, or fetch calls for the statement. This value is zero (0) if TIMED_STATISTICS is not enabled.

ELAPSED

Total elapsed time in seconds for all parse, execute, or fetch calls for the statement. This value is zero (0) if TIMED_STATISTICS is not enabled.

DISK

Total number of data blocks physically read from the data files on disk for all parse, execute, or fetch calls.

QUERY

Total number of buffers retrieved in consistent mode for all parse, execute, or fetch calls. Usually, buffers are retrieved in consistent mode for queries.

CURRENT

Total number of buffers retrieved in current mode. Buffers are retrieved in current mode for statements such as INSERT, UPDATE, and DELETE.

Statistics about the processed rows appear in the ROWS column. The column shows the number of rows processed by the SQL statement. This total does not include rows processed by subqueries of the SQL statement. For SELECT statements, the number of rows returned appears for the fetch step. For UPDATE, DELETE, and INSERT statements, the number of rows processed appears for the execute step.

Note:
The row source counts are displayed when a cursor is closed. In SQL*Plus, there is only one user cursor, so each statement executed causes the previous cursor to be closed; therefore, the row source counts are displayed. PL/SQL has its own cursor handling and does not close child cursors when the parent cursor is closed. Exiting (or reconnecting) causes the counts to be displayed.

Library Cache Misses in TKPROF

TKPROF also lists the number of library cache misses resulting from parse and execute steps for each SQL statement. These statistics appear on separate lines following the tabular statistics. If the statement resulted in no library cache misses, then TKPROF does not list the statistic. In "Examples", the statement resulted in one library cache miss for the parse step and no misses for the execute step.

Row Source Operations in TKPROF

In the TKPROF output, row source operations show the number of rows processed for each operation executed on the rows, and additional row source information, such as physical reads and writes.

Table 18-6 Row Source Operations

Row Source Operation

Meaning

cr

Consistent reads performed by the row source.

r

Physical reads performed by the row source

w

Physical writes performed by the row source

time

Time in microseconds

In the following sample TKPROF output, note the cr, r, w, and time values under the Row Source Operation column:

Rows     Row Source Operation
-------  ---------------------------------------------------
0  DELETE  (cr=43141 r=266947 w=25854 time=60235565 us)
28144   HASH JOIN ANTI (cr=43057 r=262332 w=25854 time=48830056 us)
51427    TABLE ACCESS FULL STATS$SQLTEXT (cr=3465 r=3463 w=0 time=865083 us)
647529    INDEX FAST FULL SCAN STATS$SQL_SUMMARY_PK
(cr=39592 r=39325 w=0 time=10522877 us) (object id 7409)

Wait Event Information in TKPROF

If wait event information exists, then the TKPROF output includes a section similar to the following:

Elapsed times include waiting on following events:
Event waited on                             Times   Max. Wait  Total Waited
----------------------------------------   Waited  ----------  ------------
db file sequential read                      8084        0.12          5.34
direct path write                             834        0.00          0.00
direct path write temp                        834        0.00          0.05
db file parallel read                           8        1.53          5.51
db file scattered read                       4180        0.07          1.45
direct path read                             7082        0.00          0.05
direct path read temp                        7082        0.00          0.44
rdbms ipc reply                                20        0.00          0.01
SQL*Net message to client                       1        0.00          0.00
SQL*Net message from client                     1        0.00          0.00

In addition, wait events are summed for the entire trace file at the end of the file.

To ensure that wait events information is written to the trace file for the session, run the following SQL statement:

ALTER SESSION SET EVENTS '10046 trace name context forever, level 8';

Examples

Example 18-4 Printing the Most Resource-Intensive Statements

If you are processing a large trace file using a combination of SORT parameters and the PRINT parameter, then you can produce a TKPROF output file containing only the highest resource-intensive statements. The following statement prints the 10 statements in the trace file that have generated the most physical I/O:

TKPROF ora53269.trc ora53269.prf SORT = (PRSDSK, EXEDSK, FCHDSK) PRINT = 10

Example 18-5 Generating a SQL Script

This example runs TKPROF, accepts a trace file named examp12_jane_fg_sqlplus_007.trc, and writes a formatted output file named outputa.prf:

TKPROF examp12_jane_fg_sqlplus_007.trc OUTPUTA.PRF EXPLAIN=hr TABLE=hr.temp_plan_table_a INSERT=STOREA.SQL SYS=NO
SORT=(EXECPU,FCHCPU)

This example is likely to be longer than a single line on the screen, and you might need to use continuation characters, depending on the operating system.

Note the other parameters in this example:

- The EXPLAIN value causes TKPROF to connect as the user hr and use the EXPLAIN PLAN statement to generate the execution plan for each traced SQL statement. You can use this to get access paths and row source counts.

Note:
If the cursor for a SQL statement is not closed, then TKPROF output does not automatically include the actual execution plan of the SQL statement. In this situation, you can use the EXPLAIN option with TKPROF to generate an execution plan.

- The TABLE value causes TKPROF to use the table temp_plan_table_a in the schema scott as a temporary plan table.

- The INSERT value causes TKPROF to generate a SQL script named STOREA.SQL that stores statistics for all traced SQL statements in the database.

- The SYS parameter with the value of NO causes TKPROF to omit recursive SQL statements from the output file. In this way, you can ignore internal Oracle Database statements such as temporary table operations.

- The SORT value causes TKPROF to sort the SQL statements in order of the sum of the CPU time spent executing and the CPU time spent fetching rows before writing them to the output file. For greatest efficiency, always use SORT parameters.

Example 18-6 TKPROF Header

This example shows a sample header for the TKPROF report.

TKPROF: Release 12.1.0.0.2

Copyright (c) 1982, 2012, Oracle and/or its affiliates.  All rights reserved.

Trace file: /disk1/oracle/log/diag/rdbms/orcla/orcla/trace/orcla_ora_917.trc
Sort options: default

********************************************************************************
count    = number of times OCI procedure was executed
cpu      = cpu time in seconds executing
elapsed  = elapsed time in seconds executing
disk     = number of physical reads of buffers from disk
query    = number of buffers gotten for consistent read
current  = number of buffers gotten in current mode (usually for update)
rows     = number of rows processed by the fetch or execute call
********************************************************************************

Example 18-7 TKPROF Body

This example shows a sample body for a TKPROF report.

call     count       cpu    elapsed       disk      query    current        rows
------- ------  -------- ---------- ---------- ---------- ----------  ----------
Parse        1      0.01       0.00          0          0          0           0
Execute      1      0.00       0.00          0          0          0           0
Fetch        0      0.00       0.00          0          0          0           0
------- ------  -------- ---------- ---------- ---------- ----------  ----------
total        2      0.01       0.00          0          0          0           0

Misses in library cache during parse: 1
Optimizer mode: FIRST_ROWS
Parsing user id: 44

Elapsed times include waiting on following events:
Event waited on                             Times   Max. Wait  Total Waited
----------------------------------------   Waited  ----------  ------------
SQL*Net message to client                       1        0.00          0.00
SQL*Net message from client                     1       28.59         28.59
********************************************************************************

select condition
from
cdef$ where rowid=:1

call     count       cpu    elapsed       disk      query    current        rows
------- ------  -------- ---------- ---------- ---------- ----------  ----------
Parse        1      0.00       0.00          0          0          0           0
Execute      1      0.00       0.00          0          0          0           0
Fetch        1      0.00       0.00          0          2          0           1
------- ------  -------- ---------- ---------- ---------- ----------  ----------
total        3      0.00       0.00          0          2          0           1

Misses in library cache during parse: 1
Optimizer mode: CHOOSE
Parsing user id: SYS   (recursive depth: 1)

Rows     Row Source Operation
-------  ---------------------------------------------------
1  TABLE ACCESS BY USER ROWID OBJ#(31) (cr=1 r=0 w=0 time=151 us)

********************************************************************************

SELECT last_name, job_id, salary
FROM employees
WHERE salary =
(SELECT max(salary) FROM employees)

call     count       cpu    elapsed       disk      query    current        rows
------- ------  -------- ---------- ---------- ---------- ----------  ----------
Parse        1      0.02       0.01          0          0          0           0
Execute      1      0.00       0.00          0          0          0           0
Fetch        2      0.00       0.00          0         15          0           1
------- ------  -------- ---------- ---------- ---------- ----------  ----------
total        4      0.02       0.01          0         15          0           1

Misses in library cache during parse: 1
Optimizer mode: FIRST_ROWS
Parsing user id: 44

Rows     Row Source Operation
-------  ---------------------------------------------------
1  TABLE ACCESS FULL EMPLOYEES (cr=15 r=0 w=0 time=1743 us)
1   SORT AGGREGATE (cr=7 r=0 w=0 time=777 us)
107    TABLE ACCESS FULL EMPLOYEES (cr=7 r=0 w=0 time=655 us)

Elapsed times include waiting on following events:
Event waited on                             Times   Max. Wait  Total Waited
----------------------------------------   Waited  ----------  ------------
SQL*Net message to client                       2        0.00          0.00
SQL*Net message from client                     2        9.62          9.62
********************************************************************************

********************************************************************************
delete
from stats$sqltext st
where (hash_value, text_subset) not in
(select --+ hash_aj
hash_value, text_subset
from stats$sql_summary ss
where (   (   snap_id     < :lo_snap
or snap_id     > :hi_snap
)
and dbid            = :dbid
and instance_number = :inst_num
)
or (   dbid            != :dbid
or instance_number != :inst_num)
)

call     count       cpu    elapsed       disk      query    current rows
------- ------  -------- ---------- ---------- ---------- ---------- ----------
Parse        1      0.00       0.00          0          0          0          0
Execute      1     29.60      60.68     266984      43776     131172      28144
Fetch        0      0.00       0.00          0          0          0          0
------- ------  -------- ---------- ---------- ---------- ---------- ----------
total        2     29.60      60.68     266984      43776     131172      28144

Misses in library cache during parse: 1
Misses in library cache during execute: 1
Optimizer mode: CHOOSE
Parsing user id: 22

Rows     Row Source Operation
-------  ---------------------------------------------------
0  DELETE  (cr=43141 r=266947 w=25854 time=60235565 us)
28144   HASH JOIN ANTI (cr=43057 r=262332 w=25854 time=48830056 us)
51427    TABLE ACCESS FULL STATS$SQLTEXT (cr=3465 r=3463 w=0 time=865083 us)
647529    INDEX FAST FULL SCAN STATS$SQL_SUMMARY_PK
(cr=39592 r=39325 w=0 time=10522877 us) (object id 7409)

Elapsed times include waiting on following events:
Event waited on                             Times   Max. Wait  Total Waited
----------------------------------------   Waited  ----------  ------------
db file sequential read                      8084        0.12          5.34
direct path write                             834        0.00          0.00
direct path write temp                        834        0.00          0.05
db file parallel read                           8        1.53          5.51
db file scattered read                       4180        0.07          1.45
direct path read                             7082        0.00          0.05
direct path read temp                        7082        0.00          0.44
rdbms ipc reply                                20        0.00          0.01
SQL*Net message to client                       1        0.00          0.00
SQL*Net message from client                     1        0.00          0.00
********************************************************************************

Example 18-8 TKPROF Summary

This example that shows a summary for the TKPROF report.

OVERALL TOTALS FOR ALL NON-RECURSIVE STATEMENTS

call     count       cpu    elapsed       disk      query    current        rows
------- ------  -------- ---------- ---------- ---------- ----------  ----------
Parse        4      0.04       0.01          0          0          0           0
Execute      5      0.00       0.04          0          0          0           0
Fetch        2      0.00       0.00          0         15          0           1
------- ------  -------- ---------- ---------- ---------- ----------  ----------
total       11      0.04       0.06          0         15          0           1

Misses in library cache during parse: 4
Misses in library cache during execute: 1
Elapsed times include waiting on following events:
Event waited on                             Times   Max. Wait  Total Waited
----------------------------------------   Waited  ----------  ------------
SQL*Net message to client                       6        0.00          0.00
SQL*Net message from client                     5       77.77        128.88

OVERALL TOTALS FOR ALL RECURSIVE STATEMENTS

call     count       cpu    elapsed       disk      query    current        rows
------- ------  -------- ---------- ---------- ---------- ----------  ----------
Parse        1      0.00       0.00          0          0          0           0
Execute      1      0.00       0.00          0          0          0           0
Fetch        1      0.00       0.00          0          2          0           1
------- ------  -------- ---------- ---------- ---------- ----------  ----------
total        3      0.00       0.00          0          2          0           1

Misses in library cache during parse: 1
5  user  SQL statements in session.
1  internal SQL statements in session.
6  SQL statements in session.
********************************************************************************
Trace file: main_ora_27621.trc
Trace file compatibility: 9.00.01
Sort options: default
1  session in tracefile.
5  user  SQL statements in trace file.
1  internal SQL statements in trace file.
6  SQL statements in trace file.
6  unique SQL statements in trace file.
76  lines in trace file.
128  elapsed seconds in trace file.

Views for Application Tracing

This section includes the following topics:

- Views Relevant for Trace Statistics

- Views Related to Enabling Tracing

Views Relevant for Trace Statistics

You can display the statistics that have been gathered with the following V$ views:

- The DBA_ENABLED_AGGREGATIONS view displays the accumulated global statistics for the currently enabled statistics.

- The V$CLIENT_STATS view displays the accumulated statistics for a specified client identifier.

- The V$SERVICE_STATS view displays accumulated statistics for a specified service.

- The V$SERV_MOD_ACT_STATS view displays accumulated statistics for a combination of specified service, module, and action.

- The V$SERVICEMETRIC view displays accumulated statistics for elapsed time of database calls and for CPU use.

Views Related to Enabling Tracing

A Cloud Control report or the DBA_ENABLED_TRACES view can display outstanding traces. In the DBA_ENABLED_TRACES view, you can determine detailed information about how a trace was enabled, including the trace type. The trace type specifies whether the trace is enabled for client identifier, session, service, database, or a combination of service, module, and action.

Part VIII

Automatic SQL Tuning

This part contains the following chapters:

- Chapter 19, "Managing SQL Tuning Sets"

- Chapter 20, "Analyzing SQL with SQL Tuning Advisor"

- Chapter 21, "Optimizing Access Paths with SQL Access Advisor"

19 Managing SQL Tuning Sets

This chapter contains the following topics:

- About SQL Tuning Sets

- Creating a SQL Tuning Set

- Loading a SQL Tuning Set

- Displaying the Contents of a SQL Tuning Set

- Modifying a SQL Tuning Set

- Transporting a SQL Tuning Set

- Dropping a SQL Tuning Set

19.1 About SQL Tuning Sets

A SQL tuning set (STS) is a database object that includes:

- A set of SQL statements

- Associated execution context, such as user schema, application module name and action, list of bind values, and the environment for SQL compilation of the cursor

- Associated basic execution statistics, such as elapsed time, CPU time, buffer gets, disk reads, rows processed, cursor fetches, the number of executions, the number of complete executions, optimizer cost, and the command type

- Associated execution plans and row source statistics for each SQL statement (optional)

The database stores SQL tuning sets in a database-provided schema.

This section contains the following topics:

- Purpose of SQL Tuning Sets

- Concepts for SQL Tuning Sets

- User Interfaces for SQL Tuning Sets

- Basic Tasks for SQL Tuning Sets

Note:
Data visibility and privilege requirements may differ when using an STS with pluggable databases. See Oracle Database Administrator's Guide for a table that summarizes how manageability features work in a container database (CDB).

19.1.1 Purpose of SQL Tuning Sets

An STS enables you to group SQL statements and related metadata in a single database object, which you can use to meet your tuning goals. Specifically, SQL tuning sets achieve the following goals:

- Providing input to the performance tuning advisors
You can use an STS as input to multiple database advisors, including SQL Tuning Advisor, SQL Access Advisor, and SQL Performance Analyzer.

- Transporting SQL between databases
You can export SQL tuning sets from one database to another, enabling transfer of SQL workloads between databases for remote performance diagnostics and tuning. When suboptimally performing SQL statements occur on a production database, developers may not want to investigate and tune directly on the production database. The DBA can transport the problematic SQL statements to a test database where the developers can safely analyze and tune them.

19.1.2 Concepts for SQL Tuning Sets

To create an STS, you must load SQL statements into an STS from a source. As shown in Figure 19-1, the source can be Automatic Workload Repository (AWR), the shared SQL area, customized SQL provided by the user, trace files, or another STS.

Figure 19-1 SQL Tuning Sets

SQL tuning sets can do the following:

- Filter SQL statements using the application module name and action, or any execution statistics

- Rank SQL statements based on any combination of execution statistics

- Serve as input to the advisors or transport it to a different database

See Also:
Oracle Database Performance Tuning Guide to learn about AWR

19.1.3 User Interfaces for SQL Tuning Sets

You can use either Oracle Enterprise Manager Cloud Control (Cloud Control) or the DBMS_SQLTUNE package to manage SQL tuning sets. Oracle recommends that you use Cloud Control.

19.1.3.1 Graphical User Interface to SQL Tuning Sets

The SQL Tuning Sets page in Cloud Control is the starting page from which you can perform most operations relating to SQL tuning sets.

To access the SQL Tuning Sets page:

1. Access the Database Home page, as described in "Accessing the Database Home Page in Cloud Control."

2. From the Performance menu, select SQL, then SQL Tuning Sets.
The SQL Tuning Sets page appears, as shown in Figure 19-2.

Figure 19-2 SQL Tuning Sets

See Also:
Oracle Database 2 Day + Performance Tuning Guide

19.1.3.2 Command-Line Interface to SQL Tuning Sets

On the command line, you can use the DBMS_SQLTUNE package to manage SQL tuning sets. You must have the ADMINISTER SQL TUNING SET system privilege to manage SQL tuning sets that you own, or the ADMINISTER ANY SQL TUNING SET system privilege to manage any SQL tuning sets.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_SQLTUNE

19.1.4 Basic Tasks for SQL Tuning Sets

This section explains the basic tasks involved in managing SQL tuning sets. Figure 19-3 shows the basic workflow for creating, using, and deleting an STS.

Figure 19-3 SQL Tuning Sets APIs

Typically, you perform STS operations in the following sequence:

1. Create a new STS.
"Creating a SQL Tuning Set" describes this task.

2. Load the STS with SQL statements and associated metadata.
"Loading a SQL Tuning Set" describes this task.

3. Optionally, display the contents of the STS.
"Displaying the Contents of a SQL Tuning Set" describes this task.

4. Optionally, update or delete the contents of the STS.
"Modifying a SQL Tuning Set" describes this task.

5. Create a tuning task with the STS as input.

6. Optionally, transport the STS to another database.
"Transporting a SQL Tuning Set" describes this task.

7. Drop the STS when finished.
"Dropping a SQL Tuning Set" describes this task.

19.2 Creating a SQL Tuning Set

Execute the DBMS_SQLTUNE.CREATE_SQLSET procedure to create an empty STS in the database. Using the function instead of the procedure causes the database to generate a name for the STS.

Table 19-1 describes some procedure parameters. See Oracle Database PL/SQL Packages and Types Reference for complete reference information.

Table 19-1 DBMS_SQLTUNE.CREATE_SQLSET Parameters

Parameter

Description

sqlset_name

Name of the STS

description

Optional description of the STS

Assumptions

This tutorial assumes that you want to create an STS named SQLT_WKLD_STS.

To create an STS:

1. Connect SQL*Plus to the database with the appropriate privileges, and then run the DBMS_SQLTUNE.CREATE_SQLSET procedure.
For example, execute the following PL/SQL program:

BEGIN
DBMS_SQLTUNE.CREATE_SQLSET (
sqlset_name  => 'SQLT_WKLD_STS'
,   description  => 'SQL tuning set to store SQL from the private SQL area'
);
END;

2. Optionally, confirm that the STS was created.
The following example queries the status of all SQL tuning sets owned by the current user:

COLUMN NAME FORMAT a20
COLUMN COUNT FORMAT 99999
COLUMN DESCRIPTION FORMAT a30

SELECT NAME, STATEMENT_COUNT AS "SQLCNT", DESCRIPTION
FROM   USER_SQLSET;

Sample output appears below:

NAME                 SQLCNT DESCRIPTION
-------------------- ------ ------------------------------
SQLT_WKLD_STS             2 SQL Cache

19.3 Loading a SQL Tuning Set

To load an STS with SQL statements, execute the DBMS_SQLTUNE.LOAD_SQLSET procedure. The standard sources for populating an STS are AWR, another STS, or the shared SQL area. For both the workload repository and SQL tuning sets, predefined table functions can select columns from the source to populate a new STS.

Table 19-2 describes some DBMS_SQLTUNE.LOAD_SQLSET procedure parameters. See Oracle Database PL/SQL Packages and Types Reference for complete reference information.

Table 19-2 DBMS_SQLTUNE.LOAD_SQLSET Parameters

Parameter

Description

populate_cursor

Specifies the cursor reference from which to populate the STS.

load_option

Specifies how the statements are loaded into the STS. The possible values are INSERT (default), UPDATE, and MERGE.

The DBMS_SQLTUNE.SELECT_CURSOR_CACHE function collects SQL statements from the shared SQL area according to the specified filter. This function returns one SQLSET_ROW per SQL ID or PLAN_HASH_VALUE pair found in each data source.

Use the CAPTURE_CURSOR_CACHE_SQLSET function to repeatedly poll the shared SQL area over a specified interval. This function is more efficient than repeatedly calling the SELECT_CURSOR_CACHE and LOAD_SQLSET procedures. This function effectively captures the entire workload, as opposed to the AWR, which only captures the workload of high-load SQL statements, or the LOAD_SQLSET procedure, which accesses the data source only once.

Prerequisites

This tutorial has the following prerequisites:

- Filters provided to the SELECT_CURSOR_CACHE function are evaluated as part of SQL statements run by the current user. As such, they are executed with that user's security privileges and can contain any constructs and subqueries that user can access, but no more.

- The current user must have privileges on the shared SQL area views.

Assumptions

This tutorial assumes that you want to load the SQL tuning set named SQLT_WKLD_STS with statements from the shared SQL area.

To load an STS:

1. Connect SQL*Plus to the database as a user with the appropriate privileges.

2. Run the DBMS_SQLTUNE.LOAD_SQLSET procedure.
For example, execute the following PL/SQL program to populate a SQL tuning set with all cursor cache statements that belong to the sh schema:

DECLARE
c_sqlarea_cursor DBMS_SQLTUNE.SQLSET_CURSOR;
BEGIN
OPEN c_sqlarea_cursor FOR
SELECT VALUE(p)
FROM   TABLE(
DBMS_SQLTUNE.SELECT_CURSOR_CACHE(
' module = ''SQLT_WKLD'' AND parsing_schema_name = ''SH'' ')
) p;
-- load the tuning set
DBMS_SQLTUNE.LOAD_SQLSET (
sqlset_name     => 'SQLT_WKLD_STS'
,   populate_cursor =>  c_sqlarea_cursor
);
END;
/

19.4 Displaying the Contents of a SQL Tuning Set

After an STS has been created and populated, execute the DBMS_SQLTUNE.SELECT_SQLSET function to read the contents of the STS, optionally using filtering criteria.

You select the output of SELECT_SQLSET using a PL/SQL pipelined table function, which accepts a collection of rows as input. You invoke the table function as the operand of the table operator in the FROM list of a SELECT statement.

Table 19-3 describes some SELECT_SQLSET function parameters. See Oracle Database PL/SQL Packages and Types Reference for complete reference information.

Table 19-3 DBMS_SQLTUNE.SELECT_SQLSET Parameters

Parameter

Description

basic_filter

The SQL predicate to filter the SQL from the STS defined on attributes of the SQLSET_ROW

object_filter

Specifies the objects that exist in the object list of selected SQL from the shared SQL area

Table 19-4 describes some attributes of the SQLSET_ROW object. These attributes appears as columns when you query TABLE(DBMS_SQLTUNE.SELECT_SQLSET()).

Table 19-4 SQLSET_ROW Attributes

Parameter

Description

parsing_schema_name

Schema in which the SQL is parsed

elapsed_time

Sum of the total number of seconds elapsed for this SQL statement

buffer_gets

Total number of buffer gets (number of times the database accessed a block) for this SQL statement

Assumptions

This tutorial assumes that you want to display the contents of an STS named SQLT_WKLD_STS.

To display the contents of an STS:

1. Connect SQL*Plus to the database with the appropriate privileges, and then query the STS contents using the TABLE function.
For example, execute the following query:

COLUMN SQL_TEXT FORMAT a30
COLUMN SCH FORMAT a3
COLUMN ELAPSED FORMAT 999999999

SELECT SQL_ID, PARSING_SCHEMA_NAME AS "SCH", SQL_TEXT,
ELAPSED_TIME AS "ELAPSED", BUFFER_GETS
FROM   TABLE( DBMS_SQLTUNE.SELECT_SQLSET( 'SQLT_WKLD_STS' ) );

Sample output appears below:

SQL_ID        SCH SQL_TEXT                          ELAPSED BUFFER_GETS
------------- --- ------------------------------ ---------- -----------
79f8shn041a1f SH  select * from sales where quan    8373148       24016
tity_sold < 5 union select * f
rom sales where quantity_sold
> 500

2cqsw036j5u7r SH  select promo_name, count(*) c     3557373         309
from promotions p, sales s whe
re s.promo_id = p.promo_id and
p.promo_category = 'internet'
group by p.promo_name order b
y c desc

fudq5z56g642p SH  select sum(quantity_sold) from    4787891       12118
sales s, products p where s.p
rod_id = p.prod_id and s.amoun
t_sold > 20000 and p.prod_name
= 'Linen Big Shirt'

bzmnj0nbvmz8t SH  select * from sales where amou     442355       15281
nt_sold = 4

2. Optionally, filter the results based on user-specific criteria.
The following example displays statements with a disk reads to buffer gets ratio greater than or equal to 50%:

COLUMN SQL_TEXT FORMAT a30
COLUMN SCH FORMAT a3
COLUMN BUF_GETS FORMAT 99999999
COLUMN DISK_READS FORMAT 99999999
COLUMN %_DISK FORMAT 9999.99
SELECT sql_id, parsing_schema_name as "SCH", sql_text,
buffer_gets as "BUF_GETS",
disk_reads, ROUND(disk_reads/buffer_gets*100,2) "%_DISK"
FROM TABLE( DBMS_SQLTUNE.SELECT_SQLSET(
'SQLT_WKLD_STS',
'(disk_reads/buffer_gets) >= 0.50' ) );

Sample output appears below:

SQL_ID        SCH SQL_TEXT                        BUF_GETS DISK_READS   %_DISK
------------- --- ------------------------------ --------- ---------- --------
79f8shn041a1f SH  select * from sales where quan     24016      17287    71.98
tity_sold < 5 union select * f
rom sales where quantity_sold
> 500

fudq5z56g642p SH  select sum(quantity_sold) from     12118       6355    52.44
sales s, products p where s.p
rod_id = p.prod_id and s.amoun
t_sold > 20000 and p.prod_name
= 'Linen Big Shirt'

19.5 Modifying a SQL Tuning Set

Use the DBMS_SQLTUNE.DELETE_SQLSET procedure to delete SQL statements from an STS. You can use the UPDATE_SQLSET procedure to update the attributes of SQL statements (such as PRIORITY or OTHER) in an existing STS identified by STS name and SQL ID. See Oracle Database PL/SQL Packages and Types Reference for more information.

Assumptions

This tutorial assumes that you want to modify SQLT_WKLD_STS as follows:

- You want to delete all SQL statements with fetch counts over 100.

- You want to change the priority of the SQL statement with ID fudq5z56g642p to 1. You can use priority as a ranking criteria when running SQL Tuning Advisor.

To modify the contents of an STS:

1. Connect SQL*Plus to the database with the appropriate privileges, and then optionally query the STS contents using the TABLE function.
For example, execute the following query:

SELECT SQL_ID, ELAPSED_TIME, FETCHES, EXECUTIONS
FROM   TABLE(DBMS_SQLTUNE.SELECT_SQLSET('SQLT_WKLD_STS'));

Sample output appears below:

SQL_ID        ELAPSED_TIME    FETCHES EXECUTIONS
------------- ------------ ---------- ----------
2cqsw036j5u7r      3407459          2          1
79f8shn041a1f      9453965      61258          1
bzmnj0nbvmz8t       401869          1          1
fudq5z56g642p      5300264          1          1

2. Delete SQL statements based on user-specified criteria.
Use the basic_filter predicate to filter the SQL from the STS defined on attributes of the SQLSET_ROW. The following example deletes all statements in the STS with fetch counts over 100:

BEGIN
DBMS_SQLTUNE.DELETE_SQLSET (
sqlset_name  => 'SQLT_WKLD_STS'
,     basic_filter => 'fetches > 100'
);
END;
/

3. Set attribute values for SQL statements.
The following example sets the priority of statement 2cqsw036j5u7r to 1:

BEGIN
DBMS_SQLTUNE.UPDATE_SQLSET (
sqlset_name     => 'SQLT_WKLD_STS'
,     sql_id          => '2cqsw036j5u7r'
,     attribute_name  => 'PRIORITY'
,     attribute_value =>  1
);
END;
/

4. Optionally, query the STS to confirm that the intended modifications were made.
For example, execute the following query:

SELECT SQL_ID, ELAPSED_TIME, FETCHES, EXECUTIONS, PRIORITY
FROM   TABLE(DBMS_SQLTUNE.SELECT_SQLSET('SQLT_WKLD_STS'));

Sample output appears below:

SQL_ID        ELAPSED_TIME    FETCHES EXECUTIONS   PRIORITY
------------- ------------ ---------- ---------- ----------
2cqsw036j5u7r      3407459          2          1          1
bzmnj0nbvmz8t       401869          1          1
fudq5z56g642p      5300264          1          1

19.6 Transporting a SQL Tuning Set

You can transport an STS to any database created in Oracle Database 10g Release 2 (10.2) or later. This technique is useful when using SQL Performance Analyzer to tune regressions on a test database.

19.6.1 About Transporting SQL Tuning Sets

When you transport SQL tuning sets between databases, use DBMS_SQLTUNE to copy the SQL tuning sets to and from a staging table, and use other tools (such as Oracle Data Pump or a database link) to move the staging table to the destination database.

19.6.1.1 Basic Steps for Transporting SQL Tuning Sets

Figure 19-4 shows the process using Oracle Data Pump and ftp.

Figure 19-4 Transporting SQL Tuning Sets

As shown in Figure 19-4, the steps are as follows:

1. In the production database, pack the STS into a staging table using DBMS_SQLTUNE.PACK_STGTAB_SQLSET.

2. Export the STS from the staging table to a .dmp file using Oracle Data Pump.

3. Transfer the .dmp file from the production host to the test host using a transfer tool such as ftp.

4. In the test database, import the STS from the .dmp file to a staging table using Oracle Data Pump.

5. Unpack the STS from the staging table using DBMS_SQLTUNE.UNPACK_STGTAB_SQLSET.

19.6.1.2 Basic Steps for Transporting SQL Tuning Sets from a Non-CDB to a CDB

When you transport an STS from a non-CDB to a CDB database, you must remap the con_dbid of each SQL statement in the STS to a con_dbid within the destination CDB. The basic steps are as follows:

1. Pack the STS into a staging table using DBMS_SQLTUNE.PACK_STGTAB_SQLSET.

2. Remap each con_dbid in the staging table using DBMS_SQLTUNE.REMAP_STGTAB_SQLSET.

3. Export the STS.

4. Unpack the STS in the destination CDB.

The following sample PL/SQL program remaps con_dbid 1234 to 5678:

BEGIN
DBMS_SQLTUNE.REMAP_STGTAB_SQLSET (
staging_table_name   => 'non_cdb_sts1'
,   staging_schema_owner => 'dba1'
,   old_con_dbid         => 1234
,   new_con_dbid         => 5678
);
END;

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about REMAP_STGTAB_SQLSET

19.6.2 Transporting SQL Tuning Sets with DBMS_SQLTUNE

Table 19-5 describes the DBMS_SQLTUNE procedures relevant for transporting SQL tuning sets. See Oracle Database PL/SQL Packages and Types Reference for complete reference information.

Table 19-5 DBMS_SQLTUNE Procedures for Transporting SQL Tuning Sets

Procedure

Description

CREATE_STGTAB_SQLSET

Create a staging table to hold the exported SQL tuning sets

PACK_STGTAB_SQLSET

Populate a staging table with SQL tuning sets

UNPACK_STGTAB_SQLSET

Copy the SQL tuning sets from the staging table into a database

Assumptions

This tutorial assumes the following:

- An STS with regressed SQL resides in a production database created in the current release.

- You run SQL Performance Analyzer trials on a remote test database created in Oracle Database 11g Release 2 (11.2).

- You want to copy the STS from the production database to the test database and tune the regressions from the SQL Performance Analyzer trials.

- You want to use Oracle Database Pump to transfer the SQL tuning sets between database hosts.

To transport an STS:

1. Connect SQL*Plus to the production database with administrator privileges.

2. Use the CREATE_STGTAB_SQLSET procedure to create a staging table to hold the exported SQL tuning sets.
The following example creates my_11g_staging_table in the dba1 schema and specifies the format of the staging table as 11.2:

BEGIN
DBMS_SQLTUNE.CREATE_STGTAB_SQLSET (
table_name  => 'my_10g_staging_table'
,   schema_name => 'dba1'
,   db_version  => DBMS_SQLTUNE.STS_STGTAB_11_2_VERSION
);
END;
/

3. Use the PACK_STGTAB_SQLSET procedure to populate the staging table with SQL tuning sets.
The following example populates dba1.my_11g_staging_table with the STS my_sts owned by hr:

BEGIN
DBMS_SQLTUNE.PACK_STGTAB_SQLSET (
sqlset_name         => 'sqlt_wkld_sts'
,   sqlset_owner        => 'sh'
,   staging_table_name  => 'my_11g_staging_table'
,   staging_table_owner => 'dba1'
,   db_version          => DBMS_SQLTUNE.STS_STGTAB_11_2_VERSION
);
END;
/

4. Use Oracle Data Pump to export the contents of the statistics table.
For example, run the expdp command at the operating system prompt:

expdp dba1 DIRECTORY=dpump_dir1 DUMPFILE=sts.dmp TABLES=my_11g_staging_table

5. Transfer the dump file to the test database host.

6. Log in to the test host as an administrator, and then use Oracle Data Pump to import the contents of the statistics table.
For example, run the impdp command at the operating system prompt:

impdp dba1 DIRECTORY=dpump_dir1 DUMPFILE=sts.dmp TABLES=my_11g_staging_table

7. On the test database, execute the UNPACK_STGTAB_SQLSET procedure to copy the SQL tuning sets from the staging table into the database.
The following example shows how to unpack the SQL tuning sets:

BEGIN
DBMS_SQLTUNE.UNPACK_STGTAB_SQLSET (
sqlset_name        => '%'
,   replace            => true
,   staging_table_name => 'my_11g_staging_table');
END;
/

19.7 Dropping a SQL Tuning Set

Execute the DBMS_SQLTUNE.DROP_SQLSET procedure to drop an STS from the database.

Prerequisites

Ensure that no tuning task is currently using the STS to be dropped. If a tuning task exists that is using this STS, then drop the task before dropping the STS. Otherwise, the database issues an ORA-13757 error.

Assumptions

This tutorial assumes that you want to drop an STS named SQLT_WKLD_STS.

To drop an STS:

1. Connect SQL*Plus to the database with the appropriate privileges, and then run the DBMS_SQLTUNE.DROP_SQLSET procedure.
For example, execute the following PL/SQL program:

BEGIN
DBMS_SQLTUNE.DROP_SQLSET( sqlset_name => 'SQLT_WKLD_STS' );
END;
/

2. Optionally, confirm that the STS was deleted.
The following example counts the number of SQL tuning sets named SQLT_WKLD_STS owned by the current user (sample output included):

SELECT COUNT(*)
FROM   USER_SQLSET
WHERE  NAME = 'SQLT_WKLD_STS';

COUNT(*)
----------
0

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the STS procedures in DBMS_SQLTUNE

20 Analyzing SQL with SQL Tuning Advisor

This chapter explains the concepts and tasks relating to SQL Tuning Advisor.

This chapter contains the following topics:

- About SQL Tuning Advisor

- Managing the Automatic SQL Tuning Task

- Running SQL Tuning Advisor On Demand

20.1 About SQL Tuning Advisor

SQL Tuning Advisor is SQL diagnostic software in the Oracle Database Tuning Pack. You can submit one or more SQL statements as input to the advisor and receive advice or recommendations for how to tune the statements, along with a rationale and expected benefit.

This section contains the following topics:

- Purpose of SQL Tuning Advisor

- SQL Tuning Advisor Architecture

- Automatic Tuning Optimizer Concepts

20.1.1 Purpose of SQL Tuning Advisor

SQL Tuning Advisor is a mechanism for resolving problems related to suboptimally performing SQL statements. Use SQL Tuning Advisor to obtain recommendations for improving performance of high-load SQL statements, and prevent regressions by only executing optimal plans.

Tuning recommendations include:

- Collection of object statistics

- Creation of indexes

- Rewriting SQL statements

- Creation of SQL profiles

- Creation of SQL plan baselines

The recommendations generated by SQL Tuning Advisor help you achieve the following specific goals:

- Avoid labor-intensive manual tuning
Identifying and tuning high-load SQL statements is challenging even for an expert. SQL Tuning Advisor uses the optimizer to tune SQL for you.

- Generate recommendations and implement SQL profiles automatically
You can configure an Automatic SQL Tuning task to run nightly in maintenance windows. When invoked in this way, the advisor can generate recommendations and also implement SQL profiles automatically.

- Analyze database-generated statistics to achieve optimal plans
The database contains a vast amount of statistics about its own operations. SQL Tuning Advisor can perform deep mining and analysis of internal information to improve execution plans.

- Enable developers to tune SQL on a test system instead of the production system
When suboptimally performing SQL statements occur on a production database, developers may not want to investigate and tune directly on the production database. The DBA can transport the problematic SQL statements to a test database where the developers can safely analyze and tune them.

When tuning multiple statements, SQL Tuning Advisor does not recognize interdependencies between the statements. Instead, SQL Tuning Advisor offers a convenient way to get tuning recommendations for many statements.

Note:
Data visibility and privilege requirements may differ when using SQL Tuning Advisor with pluggable databases. The advisor can tune a query in the current pluggable database (PDB), and in other PDBs in which this query has been executed. In this way, a container database (CDB) administrator can tune the same query in many PDBs at the same time, whereas a PDB administrator can only tune a single PDB. See Oracle Database Administrator's Guide for a table that summarizes how manageability features work in a CDB.

See Also:
Chapter 23, "Managing SQL Plan Baselines" to learn about SQL plan management

20.1.2 SQL Tuning Advisor Architecture

Automatic Tuning Optimizer is the central tool used by SQL Tuning Advisor. The advisor can receive SQL statements as input from the sources shown in Figure 20-1, analyze these statements using the optimizer, and then make recommendations.

Invoking Automatic Tuning Optimizer for every hard parse consumes significant time and resources (see "SQL Parsing"). Tuning mode is meant for complex and high-load SQL statements that significantly affect database performance.

Figure 20-1 shows the basic architecture of SQL Tuning Advisor.

Figure 20-1 SQL Tuning Advisor Architecture

20.1.2.1 Invocation of SQL Tuning Advisor

SQL Tuning Advisor is invoked in either of the following ways:

- Automatically
You can configure SQL Tuning Advisor to run during nightly system maintenance windows. When run by AUTOTASK, the advisor is known as Automatic SQL Tuning Advisor and performs automatic SQL tuning. See "Managing the Automatic SQL Tuning Task".

- On-Demand
In on-demand SQL tuning, you manually invoke SQL Tuning Advisor to diagnose and fix SQL-related performance problems after they have been discovered. Oracle Enterprise Manager Cloud Control (Cloud Control) is the preferred interface for tuning SQL on demand, but you can also use the DBMS_SQLTUNE PL/SQL package. See "Running SQL Tuning Advisor On Demand".

SQL Tuning Advisor uses Automatic Tuning Optimizer to perform its analysis. This optimization is "automatic" because the optimizer analyzes the SQL instead of the user. Do not confuse Automatic Tuning Optimizer with automatic SQL tuning, which in this document refers only to the work performed by the Automatic SQL Tuning task.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_SQLTUNE

20.1.2.2 Input to SQL Tuning Advisor

Input for SQL Tuning Advisor can come from several sources, including the following:

- Automatic Database Diagnostic Monitor (ADDM)
The primary input source is ADDM (pronounced Adam). By default, ADDM runs proactively once every hour and analyzes key statistics gathered by Automatic Workload Repository (AWR) over the last hour to identify any performance problems including high-load SQL statements. If a high-load SQL is identified, then ADDM recommends running SQL Tuning Advisor on the SQL. See Oracle Database Performance Tuning Guide to learn about ADDM.

- AWR
AWR takes regular snapshots of system activity, including high-load SQL statements ranked by relevant statistics, such as CPU consumption and wait time.

You can view the AWR and manually identify high-load SQL statements. You can run SQL Tuning Advisor on these statements, although Oracle Database automatically performs this work as part of automatic SQL tuning. By default, AWR retains data for the last eight days. You can locate and tune any high-load SQL that ran within the retention period of AWR using this technique. See Oracle Database Performance Tuning Guide to learn about AWR.

- Shared SQL area
The database uses the shared SQL area to tune recent SQL statements that have yet to be captured in AWR. The shared SQL area and AWR provide the capability to identify and tune high-load SQL statements from the current time going as far back as the AWR retention allows, which by default is at least 8 days. See Oracle Database Concepts to learn about the shared SQL area.

- SQL tuning set
A SQL tuning set (STS) is a database object that stores SQL statements along with their execution context. An STS can include SQL statements that are yet to be deployed, with the goal of measuring their individual performance, or identifying the ones whose performance falls short of expectation. When a set of SQL statements serve as input, the database must first construct and use an STS. See "About SQL Tuning Sets".

20.1.2.3 Output of SQL Tuning Advisor

After analyzing the SQL statements, SQL Tuning Advisor produces the following types of output:

- Advice on optimizing the execution plan

- Rationale for the proposed optimization

- Estimated performance benefit

- SQL statement to implement the advice

The benefit percentage shown for each recommendation is calculated using the following formula:

abnf% = (time_old - time_new)/(time_old)

For example, assume that before tuning the execution time was 100 seconds, and after implementing the recommendation the new execution time is expected to be 33 seconds. This benefit calculation for this performance improvement is as follows:

67% = (100 - 33)/(100)

You choose whether to accept the recommendations to optimize the SQL statements. Depending on how it is configured, Automatic SQL Tuning Advisor can implement the SQL profile recommendations to tune the statement without user intervention. When invoked on demand, SQL Tuning Advisor can recommend that the user implement a SQL profile, but can never implement it automatically.

20.1.3 Automatic Tuning Optimizer Concepts

In tuning mode, the optimizer has more time to consider options and gather statistics. For example, Automatic Tuning Optimizer can use dynamic statistics and partial statement execution. The following graphic depicts the different types of analysis that Automatic Tuning Optimizer performs.

This section contains the following topics:

- Statistical Analysis

- SQL Profiling

- Access Path Analysis

- SQL Structural Analysis

- Alternative Plan Analysis

See Also:
Chapter 4, "Query Optimizer Concepts"

20.1.3.1 Statistical Analysis

The optimizer relies on object statistics to generate execution plans. If these statistics are stale or missing, then the optimizer can generate suboptimal plans. Automatic Tuning Optimizer checks each query object for missing or stale statistics, and recommends gathering fresh statistics if needed. Figure 20-2 depicts the process of statistical analysis.

Figure 20-2 Statistical Analysis by Automatic Tuning Optimizer

20.1.3.2 SQL Profiling

SQL profiling is the verification by the Automatic Tuning Optimizer of its own estimates. By reviewing execution history and testing the SQL, the optimizer can ensure that it has the most accurate information available to generate execution plans. SQL profiling is related to but distinct from the steps of generating SQL Tuning Advisor recommendations and implementing these recommendations.

The following graphic shows SQL Tuning Advisor recommending a SQL profile and automatically implementing it. After the profile is created, the optimizer can use the profile as additional input when generating execution plans.

See Also:
"About SQL Profiles"

20.1.3.2.1 How SQL Profiling Works

The database can profile the following types of statement:

- DML statements (SELECT, INSERT with a SELECT clause, UPDATE, DELETE, and the update or insert operations of MERGE)

- CREATE TABLE statements (only with the AS SELECT clause)

After SQL Tuning Advisor performs its analysis, it either recommends or does not recommend implementing a SQL profile.

The following graphic shows the SQL profiling process.

During SQL profiling, the optimizer verifies cost, selectivity, and cardinality for a statement. The optimizer uses either of the following methods:

- Samples the data and applies appropriate predicates to the sample
The optimizer compares the new estimate to the regular estimate and, if the difference is great enough, applies a correction factor.

- Executes a fragment of the SQL statement
This method is more efficient than the sampling method when the predicates provide efficient access paths.

The optimizer uses the past statement execution history to determine correct settings. For example, if the history indicates that a SQL statement is usually executed only partially, then the optimizer uses FIRST_ROWS instead of ALL_ROWS optimization (see "Choosing an Optimizer Goal").

20.1.3.2.2 SQL Profile Implementation

If the optimizer generates auxiliary information during statistical analysis or SQL profiling, then the optimizer recommends implementing a SQL profile. As shown in Figure 20-3, the following options are possible:

- When SQL Tuning Advisor is run on demand, the user must choose whether to implement the SQL profile.

- When the Automatic SQL Tuning task is configured to implement SQL profiles automatically, advisor behavior depends on the setting of the ACCEPT_SQL_PROFILE tuning task parameter (see "Configuring the Automatic SQL Tuning Task Using the Command Line"):

- If set to true, then the advisor implements SQL profiles automatically.

- If set to false, then user intervention is required.

- If set to AUTO (default), then the setting is true when at least one SQL statement exists with a SQL profile, and false when this condition is not satisfied.

Note:
The Automatic SQL Tuning task cannot automatically create SQL plan baselines or add plans to them (see "Plan Evolution").

Figure 20-3 Implementing SQL Profiles

At any time during or after automatic SQL tuning, you can view a report. This report describes in detail the SQL statements that were analyzed, the recommendations generated, and any SQL profiles that were automatically implemented.

See Also:
"About SQL Profiles"

20.1.3.3 Access Path Analysis

An access path is the means by which the database retrieves data. For example, a query using an index and a query using a full table scan use different access paths. In some cases, indexes can greatly enhance the performance of a SQL statement by eliminating full table scans.

The following graphic illustrates access path analysis.

Automatic Tuning Optimizer explores whether a new index can significantly enhance query performance and recommends either of the following:

- Creating an index
Index recommendations are specific to the SQL statement processed by SQL Tuning Advisor. Sometimes a new index provides a quick solution to the performance problem associated with a single SQL statement.

- Running SQL Access Advisor
Because the Automatic Tuning Optimizer does not analyze how its index recommendation can affect the entire SQL workload, it also recommends running SQL Access Advisor on the SQL statement along with a representative SQL workload. SQL Access Advisor examines the effect of creating an index on the SQL workload before making recommendations.

20.1.3.4 SQL Structural Analysis

During structural analysis, Automatic Tuning Optimizer tries to identify syntactic, semantic, or design problems that can lead to suboptimal performance. The goal is to identify poorly written SQL statements and to advise you how to restructure them.

Figure 20-4 illustrates structural analysis.

Figure 20-4 Structural Analysis

Some syntax variations negatively affect performance. In structural analysis, the automatic tuning optimizer evaluates statements against a set of rules, identifies inefficient coding techniques, and recommends an alternative statement if possible.

As shown in Figure 20-4, Automatic Tuning Optimizer identifies the following categories of structural problems:

- Inefficient use of SQL constructors
A suboptimally performing statement may be using NOT IN instead of NOT EXISTS, or UNION instead of UNION ALL. The UNION operator, as opposed to the UNION ALL operator, uses a unique sort to ensure that no duplicate rows are in the result set. If you know that two queries do not return duplicates, then use UNION ALL.

- Data type mismatches
If the indexed column and the compared value have a data type mismatch, then the database does not use the index because of the implicit data type conversion. For example, if the indexed cust_id column has a VARCHAR2 data type, then the predicate WHERE cust_id=7777 does not use the index.

- Design mistakes
A classic example of a design mistake is a missing join condition that leads to a Cartesian product.

In each case, Automatic Tuning Optimizer makes relevant suggestions to restructure the statements. The suggested alternative statement is similar, but not equivalent, to the original statement. For example, the suggested statement may use UNION ALL instead of UNION. You can then determine if the advice is sound.

20.1.3.5 Alternative Plan Analysis

While tuning a SQL statement, SQL Tuning Advisor searches real-time and historical performance data for alternative execution plans for the statement. If plans other than the original plan exist, then SQL Tuning Advisor reports an alternative plan finding. The follow graphic shows SQL Tuning Advisor finding two alternative plans and generating an alternative plan finding.

SQL Tuning Advisor validates the alternative execution plans and notes any plans that are not reproducible. When reproducible alternative plans are found, you can create a SQL plan baseline to instruct the optimizer to choose these plans in the future.

Example 20-1 shows an alternative plan finding for a SELECT statement.

Example 20-1 Alternative Plan Finding

2- Alternative Plan Finding
---------------------------
Some alternative execution plans for this statement were found by searching
the system's real-time and historical performance data.

The following table lists these plans ranked by their average elapsed time.
See section "ALTERNATIVE PLANS SECTION" for detailed information on each
plan.

id plan hash  last seen            elapsed (s)  origin          note
-- ---------- -------------------- ------------ --------------- ----------------
1 1378942017  2009-02-05/23:12:08        0.000 Cursor Cache    original plan
2 2842999589  2009-02-05/23:12:08        0.002 STS

Information
-----------
- The Original Plan appears to have the best performance, based on the
elapsed time per execution.  However, if you know that one alternative
plan is better than the Original Plan, you can create a SQL plan baseline
for it. This will instruct the Oracle optimizer to pick it over any other
choices in the future.
execute dbms_sqltune.create_sql_plan_baseline(task_name => 'TASK_XXXXX',
object_id => 2, task_owner => 'SYS', plan_hash => xxxxxxxx);

Example 20-1 shows that SQL Tuning Advisor found two plans, one in the shared SQL area and one in a SQL tuning set. The plan in the shared SQL area is the same as the original plan.

SQL Tuning Advisor only recommends an alternative plan if the elapsed time of the original plan is worse than alternative plans. In this case, SQL Tuning Advisor recommends that users create a SQL plan baseline on the plan with the best performance. In Example 20-1, the alternative plan did not perform as well as the original plan, so SQL Tuning Advisor did not recommend using the alternative plan.

In Example 20-2, the alternative plans section of the SQL Tuning Advisor output includes both the original and alternative plans and summarizes their performance. The most important statistic is elapsed time. The original plan used an index, whereas the alternative plan used a full table scan, increasing elapsed time by .002 seconds.

Example 20-2 Alternative Plans Section

Plan 1
------

Plan Origin                 :Cursor Cache
Plan Hash Value             :1378942017
Executions                  :50
Elapsed Time                :0.000 sec
CPU Time                    :0.000 sec
Buffer Gets                 :0
Disk Reads                  :0
Disk Writes                 :0

Notes:
1. Statistics shown are averaged over multiple executions.
2. The plan matches the original plan.

--------------------------------------------
| Id  | Operation            | Name        |
--------------------------------------------
|   0 | SELECT STATEMENT     |             |
|   1 |  SORT AGGREGATE      |             |
|   2 |   MERGE JOIN         |             |
|   3 |    INDEX FULL SCAN   | TEST1_INDEX |
|   4 |    SORT JOIN         |             |
|   5 |     TABLE ACCESS FULL| TEST        |
--------------------------------------------

Plan 2
------

Plan Origin                 :STS
Plan Hash Value             :2842999589
Executions                  :10
Elapsed Time                :0.002 sec
CPU Time                    :0.002 sec
Buffer Gets                 :3
Disk Reads                  :0
Disk Writes                 :0

Notes:
1. Statistics shown are averaged over multiple executions.

-------------------------------------
| Id  | Operation           | Name  |
-------------------------------------
|   0 | SELECT STATEMENT    |       |
|   1 |  SORT AGGREGATE     |       |
|   2 |   HASH JOIN         |       |
|   3 |    TABLE ACCESS FULL| TEST  |
|   4 |    TABLE ACCESS FULL| TEST1 |
-------------------------------------

To adopt an alternative plan regardless of whether SQL Tuning Advisor recommends it, call DBMS_SQLTUNE.CREATE_SQL_PLAN_BASELINE. You can use this procedure to create a SQL plan baseline on any existing reproducible plan.

See Also:
"Differences Between SQL Plan Baselines and SQL Profiles"

20.2 Managing the Automatic SQL Tuning Task

When your goal is to identify SQL performance problems proactively, configuring SQL Tuning Advisor as an automated task is a simple solution. The task processes selected high-load SQL statements from AWR that qualify as tuning candidates.

This section explains how to manage the Automatic SQL Tuning task. This section contains the following topics:

- About the Automatic SQL Tuning Task

- Enabling and Disabling the Automatic SQL Tuning Task

- Configuring the Automatic SQL Tuning Task

- Viewing Automatic SQL Tuning Reports

See Also:
Oracle Database Administrator's Guide to learn more about automated maintenance tasks

20.2.1 About the Automatic SQL Tuning Task

This section contains the following topics:

- Purpose of Automatic SQL Tuning

- Automatic SQL Tuning Concepts

- Command-Line Interface to SQL Tuning Advisor

- Basic Tasks for Automatic SQL Tuning

20.2.1.1 Purpose of Automatic SQL Tuning

Many DBAs do not have the time needed for the intensive analysis required for SQL tuning. Even when they do, SQL tuning involves several manual steps. Because several different SQL statements may be high load on any given day, DBAs may have to expend considerable effort to monitor and tune them. Configuring automatic SQL tuning instead of tuning manually decreases cost and increases manageability.

The automated task does not process the following types of SQL:

- Ad hoc SQL statements or SQL statements that do not repeat within a week

- Parallel queries

- Queries that take too long to run after being SQL profiled, so that it is not practical for SQL Tuning Advisor to test-execute them

- Recursive SQL

You can run SQL Tuning Advisor on demand to tune the preceding types of SQL statements.

20.2.1.2 Automatic SQL Tuning Concepts

Oracle Scheduler uses the automated maintenance tasks infrastructure (known as AutoTask) to schedules tasks to run automatically. By default, the Automatic SQL Tuning task runs for at most one hour in a nightly maintenance window. You can customize attributes of the maintenance windows, including start and end time, frequency, and days of the week.

See Also:

- Oracle Database Administrator's Guide to learn about Oracle Scheduler

- Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_AUTO_TASK_ADMIN

20.2.1.3 Command-Line Interface to SQL Tuning Advisor

On the command line, you can use PL/SQL packages to perform SQL tuning tasks. Table 20-1 describes the most relevant packages.

Table 20-1 SQL Tuning Advisor Packages

Package

Description

DBMS_AUTO_SQLTUNE

Enables you run SQL Tuning Advisor, manage SQL profiles, manage SQL tuning sets, and perform real-time SQL performance monitoring. To use this API, you must have the ADVISOR privilege.

DBMS_AUTO_TASK_ADMIN

Provides an interface to AUTOTASK. You can use this interface to enable and disable the Automatic SQL Tuning task.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_SQLTUNE ad DBMS_AUTO_TASK_ADMIN

20.2.1.4 Basic Tasks for Automatic SQL Tuning

This section explains the basic tasks in running SQL Tuning Advisor as an automatic task. Figure 20-5 shows the basic workflow.

Figure 20-5 Automatic SQL Tuning APIs

As shown in Figure 20-6, the basic procedure is as follows:

1. Enable the Automatic SQL Tuning task.
See "Enabling and Disabling the Automatic SQL Tuning Task".

2. Optionally, configure the Automatic SQL Tuning task.
See "Configuring the Automatic SQL Tuning Task".

3. Display the results of the Automatic SQL Tuning task.
"Viewing Automatic SQL Tuning Reports".

4. Disable the Automatic SQL Tuning task.
See "Enabling and Disabling the Automatic SQL Tuning Task".

20.2.2 Enabling and Disabling the Automatic SQL Tuning Task

This section explains how to enable and disable the Automatic SQL Tuning task using Cloud Control (preferred) or a command-line interface.

20.2.2.1 Enabling and Disabling the Automatic SQL Tuning Task Using Cloud Control

You can enable and disable all automatic maintenance tasks, including the Automatic SQL Tuning task, using Cloud Control.

To enable or disable the Automatic SQL Tuning task using Cloud Control:

1. Access the Database Home page, as described in "Accessing the Database Home Page in Cloud Control."

2. From the Administration menu, select Oracle Scheduler, then Automated Maintenance Tasks.
The Automated Maintenance Tasks page appears.

This page shows the predefined tasks. You access each task by clicking the corresponding link to get more information about the task.

3. Click Automatic SQL Tuning.
The Automatic SQL Tuning Result Summary page appears.

The Task Status section shows whether the Automatic SQL Tuning Task is enabled or disabled. In the following graphic, the task is disabled:

4. In Automatic SQL Tuning, click Configure.
The Automated Maintenance Tasks Configuration page appears.

By default, Automatic SQL Tuning executes in all predefined maintenance windows in MAINTENANCE_WINDOW_GROUP.

5. Perform the following steps:

1. In the Task Settings for Automatic SQL Tuning, select either Enabled or Disabled to enable or disable the automated task.

2. To disable Automatic SQL Tuning for specific days in the week, check the appropriate box next to the window name.

3. To change the characteristics of a window, click Edit Window Group.

4. Click Apply.

20.2.2.2 Enabling and Disabling the Automatic SQL Tuning Task from the Command Line

If you do not use Cloud Control to enable and disable the Automatic SQL Tuning task, then you have the following options:

- Run the ENABLE or DISABLE procedure in the DBMS_AUTO_TASK_ADMIN PL/SQL package.
This package is the recommended command-line technique. For both the ENABLE or DISABLE procedures, you can specify a particular maintenance window with the window_name parameter. See Oracle Database PL/SQL Packages and Types Reference for complete reference information.

- Set the STATISTICS_LEVEL initialization parameter to BASIC to disable collection of all advisories and statistics, including Automatic SQL Tuning Advisor.
Because monitoring and many automatic features are disabled, Oracle strongly recommends that you do not set STATISTICS_LEVEL to BASIC. See Oracle Database Reference for complete reference information.

To enable or disable Automatic SQL Tuning using DBMS_AUTO_TASK_ADMIN:

1. Connect SQL*Plus to the database with administrator privileges, and then do one of the following:

- To enable the automated task, execute the following PL/SQL block:

BEGIN
DBMS_AUTO_TASK_ADMIN.ENABLE (
client_name => 'sql tuning advisor'
,   operation   => NULL
,   window_name => NULL
);
END;
/

- To disable the automated task, execute the following PL/SQL block:

BEGIN
DBMS_AUTO_TASK_ADMIN.DISABLE (
client_name => 'sql tuning advisor'
,   operation   => NULL
,   window_name => NULL
);
END;
/

2. Query the data dictionary to confirm the change.
For example, query DBA_AUTOTASK_CLIENT as follows (sample output included):

COL CLIENT_NAME FORMAT a20

SELECT CLIENT_NAME, STATUS
FROM   DBA_AUTOTASK_CLIENT
WHERE  CLIENT_NAME = 'sql tuning advisor';

CLIENT_NAME          STATUS
-------------------- --------
sql tuning advisor   ENABLED

To disable collection of all advisories and statistics:

1. Connect SQL*Plus to the database with administrator privileges, and then query the current statistics level setting.
The following SQL*Plus command shows that STATISTICS_LEVEL is set to ALL:

sys@PROD> SHOW PARAMETER statistics_level

NAME                                 TYPE        VALUE
------------------------------------ ----------- ------------------------------
statistics_level                     string      ALL

2. Set STATISTICS_LEVEL to BASIC as follows:

sys@PROD> ALTER SYSTEM SET STATISTICS_LEVEL ='BASIC';

System altered.

20.2.3 Configuring the Automatic SQL Tuning Task

This section explains how to configure settings for the Automatic SQL Tuning task.

20.2.3.1 Configuring the Automatic SQL Tuning Task Using Cloud Control

You can enable and disable all automatic maintenance tasks, including the Automatic SQL Tuning task, using Cloud Control. You must perform the operation as SYS or have the EXECUTE privilege on the PL/SQL package DBMS_AUTO_SQLTUNE.

To configure the Automatic SQL Tuning task using Cloud Control:

1. Access the Database Home page, as described in "Accessing the Database Home Page in Cloud Control."

2. From the Administration menu, click Oracle Scheduler, then Automated Maintenance Tasks.
The Automated Maintenance Tasks page appears.

This page shows the predefined tasks. You access each task by clicking the corresponding link to get more information about the task itself.

3. Click Automatic SQL Tuning.
The Automatic SQL Tuning Result Summary page appears.

4. Under Task Settings, click Configure next to Automatic SQL Tuning (SYS_AUTO_SQL_TUNING_TASK).
The Automated Maintenance Tasks Configuration page appears.

5. Under Task Settings, click Configure next to Automatic SQL Tuning.
The Automatic SQL Tuning Settings page appears.

6. Make the desired changes and click Apply.

20.2.3.2 Configuring the Automatic SQL Tuning Task Using the Command Line

The DBMS_AUTO_SQLTUNE package enables you to configure automatic SQL tuning by specifying the task parameters using the SET_AUTO_TUNING_TASK_PARAMETER procedure. Because the task is owned by SYS, only SYS can set task parameters.

The ACCEPT_SQL_PROFILE tuning task parameter specifies whether to implement SQL profiles automatically (true) or require user intervention (false). The default is AUTO, which means true if at least one SQL statement exists with a SQL profile and false if this condition is not satisfied.

Note:
When automatic implementation is enabled, the advisor only implements recommendations to create SQL profiles. Recommendations such as creating new indexes, gathering optimizer statistics, and creating SQL plan baselines are not automatically implemented.

Assumptions

This tutorial assumes the following:

- You want the database to implement SQL profiles automatically, but to implement no more than 50 SQL profiles per execution, and no more than 50 profiles total on the database.

- You want the task to time out after 1200 seconds per execution.

To set Automatic SQL Tuning task parameters:

1. Connect SQL*Plus to the database with the appropriate privileges, and then optionally query the current task settings.
For example, connect SQL*Plus to the database with administrator privileges and execute the following query:

COL PARAMETER_NAME FORMAT a25
COL VALUE FORMAT a10

SELECT PARAMETER_NAME, PARAMETER_VALUE AS "VALUE"
FROM   DBA_ADVISOR_PARAMETERS
WHERE  ( (TASK_NAME = 'SYS_AUTO_SQL_TUNING_TASK') AND
( (PARAMETER_NAME LIKE '%PROFILE%') OR
(PARAMETER_NAME = 'LOCAL_TIME_LIMIT') OR
(PARAMETER_NAME = 'EXECUTION_DAYS_TO_EXPIRE') ) );

Sample output appears as follows:

PARAMETER_NAME            VALUE
------------------------- ----------
EXECUTION_DAYS_TO_EXPIRE  30
LOCAL_TIME_LIMIT          1000
ACCEPT_SQL_PROFILES       FALSE
MAX_SQL_PROFILES_PER_EXEC 20
MAX_AUTO_SQL_PROFILES     10000

2. Set parameters using PL/SQL code of the following form:

BEGIN
DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER (
task_name => 'SYS_AUTO_SQL_TUNING_TASK'
,   parameter => parameter_name
,   value     => value
);
END;
/

Example 20-3 Setting SQL Tuning Task Parameters

The following PL/SQL block sets a time limit to 20 minutes, and also automatically implements SQL profiles and sets limits for these profiles:

BEGIN
DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER('SYS_AUTO_SQL_TUNING_TASK',
'LOCAL_TIME_LIMIT', 1200);
DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER('SYS_AUTO_SQL_TUNING_TASK',
'ACCEPT_SQL_PROFILES', 'true');
DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER('SYS_AUTO_SQL_TUNING_TASK',
'MAX_SQL_PROFILES_PER_EXEC', 50);
DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER('SYS_AUTO_SQL_TUNING_TASK',
'MAX_AUTO_SQL_PROFILES', 10002);
END;
/

See Also:
Oracle Database PL/SQL Packages and Types Reference for complete reference information for DBMS_AUTO_SQLTUNE

20.2.4 Viewing Automatic SQL Tuning Reports

At any time during or after the running of the Automatic SQL Tuning task, you can view a tuning report. This report contains information about all executions of the automatic SQL tuning task.

Depending on the sections that were included in the report, you can view information in the following sections:

- General information
This section has a high-level description of the automatic SQL tuning task, including information about the inputs given for the report, the number of SQL statements tuned during the maintenance, and the number of SQL profiles created.

- Summary
This section lists the SQL statements (by their SQL identifiers) that were tuned during the maintenance window and the estimated benefit of each SQL profile, or the execution statistics after performing a test execution of the SQL statement with the SQL profile.

- Tuning findings
This section contains the following information about each SQL statement analyzed by SQL Tuning Advisor:

- All findings associated with each SQL statement

- Whether the profile was implemented on the database, and why

- Whether the SQL profile is currently enabled on the database

- Detailed execution statistics captured when testing the SQL profile

- Explain plans
This section shows the old and new explain plans used by each SQL statement analyzed by SQL Tuning Advisor.

- Errors
This section lists all errors encountered by the automatic SQL tuning task.

20.2.4.1 Viewing Automatic SQL Tuning Reports Using the Command Line

To generate a SQL tuning report as a CLOB, execute the DBMS_SQLTUNE.REPORT_AUTO_TUNING_TASK function. You can store the CLOB in a variable and then print the variable to view the report. See Oracle Database PL/SQL Packages and Types Reference for complete reference information.

Assumptions

This section assumes that you want to show all SQL statements that were analyzed in the most recent execution, including recommendations that were not implemented.

To create and access an Automatic SQL Tuning Advisor report:

1. Connect SQL*Plus to the database with administrator privileges, and then execute the DBMS_SQLTUNE.REPORT_AUTO_TUNING_TASK function.
The following example generates a text report to show all SQL statements that were analyzed in the most recent execution, including recommendations that were not implemented:

VARIABLE my_rept CLOB;
BEGIN
:my_rept :=DBMS_SQLTUNE.REPORT_AUTO_TUNING_TASK (
begin_exec   => NULL
,   end_exec     => NULL
,   type         => 'TEXT'
,   level        => 'TYPICAL'
,   section      => 'ALL'
,   object_id    => NULL
,   result_limit => NULL
);
END;
/

PRINT :my_rept

2. Read the general information section for an overview of the tuning execution.
The following sample shows the Automatic SQL Tuning task analyzed 17 SQL statements in just over 7 minutes:

MY_REPT
-------------------------------------------------------------------------------
GENERAL INFORMATION SECTION
-------------------------------------------------------------------------------
Tuning Task Name                        : SYS_AUTO_SQL_TUNING_TASK
Tuning Task Owner                       : SYS
Workload Type                           : Automatic High-Load SQL Workload
Execution Count                         : 6
Current Execution                       : EXEC_170
Execution Type                          : TUNE SQL
Scope                                   : COMPREHENSIVE
Global Time Limit(seconds)              : 3600
Per-SQL Time Limit(seconds)             : 1200
Completion Status                       : COMPLETED
Started at                              : 04/16/2012 10:00:00
Completed at                            : 04/16/2012 10:07:11
Number of Candidate SQLs                : 17
Cumulative Elapsed Time of SQL (s)      : 8

3. Look for findings and recommendations.
If SQL Tuning Advisor makes a recommendation, then weigh the pros and cons of accepting it.

The following example shows that SQL Tuning Advisor found a plan for a statement that is potentially better than the existing plan. The advisor recommends implementing a SQL profile.

-------------------------------------------------------------------------------
SQLs with Findings Ordered by Maximum (Profile/Index) Benefit, Object ID
-------------------------------------------------------------------------------
object ID  SQL ID        statistics profile(benefit) index(benefit) restructure
---------- ------------- ---------- ---------------- -------------- -----------
82 dqjcc345dd4ak                      58.03%
72 51bbkcd9zwsjw                                                      2
81 03rxjf8gb18jg

-------------------------------------------------------------------------------
DETAILS SECTION
-------------------------------------------------------------------------------
Statements with Results Ordered by Maximum (Profile/Index) Benefit, Object ID
-------------------------------------------------------------------------------
Object ID  : 82
Schema Name: DBA1
SQL ID     : dqjcc345dd4ak
SQL Text   : SELECT status FROM dba_autotask_client WHERE client_name=:1

-------------------------------------------------------------------------------
FINDINGS SECTION (1 finding)
-------------------------------------------------------------------------------

1- SQL Profile Finding (see explain plans section below)
--------------------------------------------------------
A potentially better execution plan was found for this statement.
The SQL profile was not automatically created because the verified benefit
was too low.

Recommendation (estimated benefit: 58.03%)
------------------------------------------
- Consider accepting the recommended SQL profile.
execute dbms_sqltune.accept_sql_profile(task_name =>
'SYS_AUTO_SQL_TUNING_TASK', object_id => 82, replace => TRUE);

Validation results
------------------
The SQL profile was tested by executing both its plan and the original plan
and measuring their respective execution statistics. A plan may have been
only partially executed if the other could be run to completion in less time.

Original Plan  With SQL Profile  % Improved
-------------  ----------------  ----------
Completion Status:            COMPLETE          COMPLETE
Elapsed Time(us):               26963              8829      67.25 %
CPU Time(us):                   27000              9000      66.66 %
User I/O Time(us):                 25                14         44 %
Buffer Gets:                      905               380      58.01 %
Physical Read Requests:             0                 0
Physical Write Requests:            0                 0
Physical Read Bytes:                0                 0
Physical Write Bytes:            7372              7372          0 %
Rows Processed:                     1                 1
Fetches:                            1                 1
Executions:                         1                 1

Notes
-----
1. The original plan was first executed to warm the buffer cache.
2. Statistics for original plan were averaged over next 9 executions.
3. The SQL profile plan was first executed to warm the buffer cache.
4. Statistics for the SQL profile plan were averaged over next 9 executions.

20.3 Running SQL Tuning Advisor On Demand

This section contains the following topics:

- About On-Demand SQL Tuning

- Creating a SQL Tuning Task

- Configuring a SQL Tuning Task

- Executing a SQL Tuning Task

- Monitoring a SQL Tuning Task

- Displaying the Results of a SQL Tuning Task

20.3.1 About On-Demand SQL Tuning

In this context, on-demand SQL tuning is defined as any invocation of SQL Tuning Advisor that does not result from the Automatic SQL Tuning task.

20.3.1.1 Purpose of On-Demand SQL Tuning

Typically, you invoke SQL Tuning Advisor on demand in the following situations:

- You proactively run ADDM, which reports that some SQL statements do not meet your performance requirements.

- You reactively tune SQL statement because users complain about suboptimal SQL performance.

In both situations, running SQL Tuning Advisor is usually the quickest way to fix unexpected SQL performance problems.

20.3.1.2 User Interfaces for On-Demand SQL Tuning

The recommended user interface for running SQL Tuning Advisor manually is Cloud Control.

20.3.1.2.1 Graphic Interface to On-Demand SQL Tuning

Automatic Database Diagnostic Monitor (ADDM) automatically identifies high-load SQL statements. If ADDM identifies such statements, then click Schedule/Run SQL Tuning Advisor on the Recommendation Detail page to run SQL Tuning Advisor.

To tune SQL statements manually using SQL Tuning Advisor:

1. Access the Database Home page, as described in "Accessing the Database Home Page in Cloud Control."

2. From the Performance menu, click SQL, then SQL Tuning Advisor.
The Schedule SQL Tuning Advisor page appears.

3. See Oracle Database 2 Day + Performance Tuning Guide to learn how to configure and run SQL Tuning Advisor using Cloud Control.

20.3.1.2.2 Command-Line Interface to On-Demand SQL Tuning

If Cloud Control is unavailable, then you can run SQL Tuning Advisor using procedures in the DBMS_SQLTUNE package. To use the APIs, the user must have the ADVISOR privilege.

See Also:
Oracle Database PL/SQL Packages and Types Reference for complete reference information

20.3.1.3 Basic Tasks in On-Demand SQL Tuning

This section explains the basic tasks in running SQL Tuning Advisor using the DBMS_SQLTUNE package. Oracle Database 2 Day + Performance Tuning Guide explains how to tune SQL using Cloud Control.

Figure 20-6 shows the basic workflow when using the PL/SQL APIs.

Figure 20-6 SQL Tuning Advisor APIs

As shown in Figure 20-6, the basic procedure is as follows:

1. Prepare or create the input to SQL Tuning Advisor. The input can be either:

- The text of a single SQL statement

- A SQL tuning set that contains one or more statements

2. Create a SQL tuning task.
See "Creating a SQL Tuning Task".

3. Optionally, configure the SQL tuning task that you created.
See "Configuring a SQL Tuning Task".

4. Execute a SQL tuning task.
See "Executing a SQL Tuning Task".

5. Optionally, check the status or progress of a SQL tuning task.
"Monitoring a SQL Tuning Task".

6. Display the results of a SQL tuning task.
"Displaying the Results of a SQL Tuning Task".

7. Implement recommendations as appropriate.

20.3.2 Creating a SQL Tuning Task

To create a SQL tuning task execute the DBMS_SQLTUNE.CREATE_TUNING_TASK function. You can create tuning tasks from any of the following:

- The text of a single SQL statement

- A SQL tuning set containing multiple statements

- A SQL statement selected by SQL identifier from the shared SQL area

- A SQL statement selected by SQL identifier from AWR

The scope parameter is one of the most important for this function. You can set this parameter to the following values:

- LIMITED
SQL Tuning Advisor produces recommendations based on statistical checks, access path analysis, and SQL structure analysis. SQL profile recommendations are not generated.

- COMPREHENSIVE
SQL Tuning Advisor carries out all the analysis it performs under limited scope plus SQL profiling.

Assumptions

This tutorial assumes the following:

- You want to tune as user hr, who has the ADVISOR privilege.

- You want to tune the following query:

SELECT /*+ ORDERED */ *
FROM   employees e, locations l, departments d
WHERE  e.department_id = d.department_id
AND    l.location_id = d.location_id
AND    e.employee_id < :bnd;

- You want to pass the bind variable 100 to the preceding query.

- You want SQL Tuning Advisor to perform SQL profiling.

- You want the task to run no longer than 60 seconds.

To create a SQL tuning task:

1. Connect SQL*Plus to the database with the appropriate privileges, and then run the DBMS_SQLTUNE.CREATE_TUNING_TASK function.
For example, execute the following PL/SQL program:

DECLARE
my_task_name VARCHAR2(30);
my_sqltext   CLOB;
BEGIN
my_sqltext := 'SELECT /*+ ORDERED */ * '                      ||
'FROM employees e, locations l, departments d ' ||
'WHERE e.department_id = d.department_id AND '  ||
'l.location_id = d.location_id AND '      ||
'e.employee_id < :bnd';

my_task_name := DBMS_SQLTUNE.CREATE_TUNING_TASK (
sql_text    => my_sqltext
,         bind_list   => sql_binds(anydata.ConvertNumber(100))
,         user_name   => 'HR'
,         scope       => 'COMPREHENSIVE'
,         time_limit  => 60
,         task_name   => 'STA_SPECIFIC_EMP_TASK'
,         description => 'Task to tune a query on a specified employee'
);
END;
/

2. Optionally, query the status of the task.
The following example queries the status of all tasks owned by the current user, which in this example is hr:

COL TASK_ID FORMAT 999999
COL TASK_NAME FORMAT a25
COL STATUS_MESSAGE FORMAT a33

SELECT TASK_ID, TASK_NAME, STATUS, STATUS_MESSAGE
FROM   USER_ADVISOR_LOG;

Sample output appears below:

TASK_ID TASK_NAME                 STATUS      STATUS_MESSAGE
------- ------------------------- ----------- ---------------------------------
884 STA_SPECIFIC_EMP_TASK     INITIAL

In the preceding output, the INITIAL status indicates that the task has not yet started execution.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SQLTUNE.CREATE_TUNING_TASK function

20.3.3 Configuring a SQL Tuning Task

To change the parameters of a tuning task after it has been created, execute the DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER function. See Oracle Database PL/SQL Packages and Types Reference for complete reference information.

Assumptions

This tutorial assumes the following:

- You want to tune as user hr, who has the ADVISOR privilege.

- You want to tune the STA_SPECIFIC_EMP_TASK created in "Creating a SQL Tuning Task".

- You want to change the maximum time that the SQL tuning task can run to 300 seconds.

To configure a SQL tuning task:

1. Connect SQL*Plus to the database with the appropriate privileges, and then run the DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER function.
For example, execute the following PL/SQL program to change the time limit of the tuning task to 300 seconds:

BEGIN
DBMS_SQLTUNE.SET_TUNING_TASK_PARAMETER (
task_name => 'STA_SPECIFIC_EMP_TASK'
,   parameter => 'TIME_LIMIT'
,   value     => 300
);
END;
/

2. Optionally, verify that the task parameter was changed.
The following example queries the values of all used parameters in task STA_SPECIFIC_EMP_TASK:

COL PARAMETER_NAME FORMAT a25
COL VALUE FORMAT a15

SELECT PARAMETER_NAME, PARAMETER_VALUE AS "VALUE"
FROM   USER_ADVISOR_PARAMETERS
WHERE  TASK_NAME = 'STA_SPECIFIC_EMP_TASK'
AND    PARAMETER_VALUE != 'UNUSED'
ORDER BY PARAMETER_NAME;

Sample output appears below:

PARAMETER_NAME            VALUE
------------------------- ---------------
DAYS_TO_EXPIRE            30
DEFAULT_EXECUTION_TYPE    TUNE SQL
EXECUTION_DAYS_TO_EXPIRE  UNLIMITED
JOURNALING                INFORMATION
MODE                      COMPREHENSIVE
SQL_LIMIT                 -1
SQL_PERCENTAGE            1
TARGET_OBJECTS            1
TEST_EXECUTE              AUTO
TIME_LIMIT                300

20.3.4 Executing a SQL Tuning Task

To execute a SQL tuning task, use the DBMS_SQLTUNE.EXECUTE_TUNING_TASK function. The most important parameter is task_name.

Note:
You can also execute the automatic tuning task SYS_AUTO_SQL_TUNING_TASK using the EXECUTE_TUNING_TASK API. SQL Tuning Advisor performs the same analysis and actions as it would when run automatically.

Assumptions

This tutorial assumes the following:

- You want to tune as user hr, who has the ADVISOR privilege.

- You want to execute the STA_SPECIFIC_EMP_TASK created in "Creating a SQL Tuning Task".

To execute a SQL tuning task:

1. Connect SQL*Plus to the database with the appropriate privileges, and then run the DBMS_SQLTUNE.EXECUTE_TUNING_TASK function.
For example, execute the following PL/SQL program:

BEGIN
DBMS_SQLTUNE.EXECUTE_TUNING_TASK(task_name=>'STA_SPECIFIC_EMP_TASK');
END;
/

2. Optionally, query the status of the task.
The following example queries the status of all tasks owned by the current user, which in this example is hr:

COL TASK_ID FORMAT 999999
COL TASK_NAME FORMAT a25
COL STATUS_MESSAGE FORMAT a33

SELECT TASK_ID, TASK_NAME, STATUS, STATUS_MESSAGE
FROM   USER_ADVISOR_LOG;

Sample output appears below:

TASK_ID TASK_NAME                 STATUS      STATUS_MESSAGE
------- ------------------------- ----------- ---------------------------------
884 STA_SPECIFIC_EMP_TASK     COMPLETED

See Also:
Oracle Database PL/SQL Packages and Types Reference for complete reference information about the DBMS_SQLTUNE.EXECUTE_TUNING_TASK function

20.3.5 Monitoring a SQL Tuning Task

When you create a SQL tuning task in Cloud Control, no separate monitoring step is necessary. Cloud Control displays the status page automatically.

If you do not use Cloud Control, then you can monitor currently executing SQL tuning tasks by querying the data dictionary and dynamic performance views. Table 20-2 describes the relevant views.

Table 20-2 DBMS_SQLTUNE.EXECUTE_TUNING_TASK Parameters

View

Description

USER_ADVISOR_TASKS

Displays information about tasks owned by the current user. The view contains one row for each task. Each task has a name that is unique to the owner. Task names are just informational and no uniqueness is enforced within any other namespace.

V$ADVISOR_PROGRESS

Displays information about the progress of advisor execution.

Assumptions

This tutorial assumes the following:

- You tune as user hr, who has the ADVISOR privilege.

- You monitor the STA_SPECIFIC_EMP_TASK that you executed in "Executing a SQL Tuning Task".

To monitor a SQL tuning task:

1. Connect SQL*Plus to the database with the appropriate privileges, and then determine whether the task is executing or completed.
For example, query the status of STA_SPECIFIC_EMP_TASK as follows:

SELECT STATUS
FROM   USER_ADVISOR_TASKS
WHERE  TASK_NAME = 'STA_SPECIFIC_EMP_TASK';

The following output shows that the task has completed:

STATUS
-----------
EXECUTING

2. Determine the progress of an executing task.
The following example queries the status of the task with task ID 884:

VARIABLE my_tid NUMBER;
EXEC :my_tid := 884
COL ADVISOR_NAME FORMAT a20
COL SOFAR FORMAT 999
COL TOTALWORK FORMAT 999

SELECT TASK_ID, ADVISOR_NAME, SOFAR, TOTALWORK,
ROUND(SOFAR/TOTALWORK*100,2) "%_COMPLETE"
FROM   V$ADVISOR_PROGRESS
WHERE  TASK_ID = :my_tid;

Sample output appears below:

TASK_ID ADVISOR_NAME         SOFAR TOTALWORK %_COMPLETE
---------- -------------------- ----- --------- ----------
884 SQL Tuning Advisor       1         2         50

See Also:
Oracle Database Reference to learn about the V$ADVISOR_PROGRESS view

20.3.6 Displaying the Results of a SQL Tuning Task

To report the results of a tuning task, use the DBMS_SQLTUNE.REPORT_TUNING_TASK function. The report contains all the findings and recommendations of SQL Tuning Advisor. For each proposed recommendation, the report provides the rationale and benefit along with the SQL statements needed to implement the recommendation.

Assumptions

This tutorial assumes the following:

- You want to tune as user hr, who has the ADVISOR privilege.

- You want to access the report for the STA_SPECIFIC_EMP_TASK executed in "Executing a SQL Tuning Task".

To view the report for a SQL tuning task:

1. Connect SQL*Plus to the database with the appropriate privileges, and then run the DBMS_SQLTUNE.REPORT_TUNING_TASK function.
For example, you run the following statements:

SET LONG 1000
SET LONGCHUNKSIZE 1000
SET LINESIZE 100
SELECT DBMS_SQLTUNE.REPORT_TUNING_TASK( 'STA_SPECIFIC_EMP_TASK' )
FROM   DUAL;

Truncated sample output appears below:

DBMS_SQLTUNE.REPORT_TUNING_TASK('STA_SPECIFIC_EMP_TASK')
-------------------------------------------------------------------------------
GENERAL INFORMATION SECTION
-------------------------------------------------------------------------------
Tuning Task Name   : STA_SPECIFIC_EMP_TASK
Tuning Task Owner  : HR
Workload Type      : Single SQL Statement
Execution Count    : 11
Current Execution  : EXEC_1057
Execution Type     : TUNE SQL
Scope              : COMPREHENSIVE
Time Limit(seconds): 300
Completion Status  : COMPLETED
Started at         : 04/22/2012 07:35:49
Completed at       : 04/22/2012 07:35:50

-------------------------------------------------------------------------------
Schema Name: HR
SQL ID     : dg7nfaj0bdcvk
SQL Text   : SELECT /*+ ORDERED */ * FROM employees e, locations l,
departments d WHERE e.department_id = d.department_id AND
l.location_id = d.location_id AND e.employee_id < :bnd
Bind Variables :
1 -  (NUMBER):100

-------------------------------------------------------------------------------
FINDINGS SECTION (4 findings)
-----------------------------------------------

2. Interpret the results, as described in "Viewing Automatic SQL Tuning Reports Using the Command Line".

See Also:
Oracle Database PL/SQL Packages and Types Reference for complete reference information

21 Optimizing Access Paths with SQL Access Advisor

This chapter contains the following topics:

- About SQL Access Advisor

- Using SQL Access Advisor: Basic Tasks

- Performing a SQL Access Advisor Quick Tune

- Using SQL Access Advisor: Advanced Tasks

- SQL Access Advisor Examples

- SQL Access Advisor Reference

21.1 About SQL Access Advisor

SQL Access Advisor is diagnostic software that identifies and helps resolve SQL performance problems by recommending indexes, materialized views, materialized view logs, or partitions to create, drop, or retain.

This section contains the following topics:

- Purpose of SQL Access Advisor

- SQL Access Advisor Architecture

- User Interfaces for SQL Access Advisor

Note:
Data visibility and privilege requirements may differ when using SQL Access Advisor with pluggable databases. See Oracle Database Administrator's Guide for a table that summarizes how manageability features work in a container database (CDB).

21.1.1 Purpose of SQL Access Advisor

SQL Access Advisor helps you achieve your performance goals by recommending the proper set of materialized views, materialized view logs, partitions, and indexes for a given workload. Materialized views, partitions, and indexes are essential when tuning a database to achieve optimum performance for complex, data-intensive queries.

SQL Access Advisor takes an actual workload as input, or derives a hypothetical workload from a schema. The advisor then recommends access structures for faster execution path. The advisor provides the following advantages:

- Does not require you to have expert knowledge

- Makes decisions based on rules that reside in the optimizer

- Covers all aspects of SQL access in a single advisor

- Provides simple, user-friendly GUI wizards in Cloud Control

- Generates scripts for implementation of recommendations

See Also:
Oracle Database 2 Day + Performance Tuning Guide to learn how to use SQL Access Advisor with Cloud Control

21.1.2 SQL Access Advisor Architecture

Automatic Tuning Optimizer is the central tool used by SQL Access Advisor. The advisor can receive SQL statements as input from the sources shown in Figure 21-1, analyze these statements using the optimizer, and then make recommendations.

Figure 21-1 shows the basic architecture of SQL Access Advisor.

Figure 21-1 SQL Access Advisor Architecture

See Also:
"About Automatic Tuning Optimizer"

21.1.2.1 Input to SQL Access Advisor

SQL Access Advisor requires a workload, which consists of one or more SQL statements, plus statistics and attributes that fully describe each statement. A full workload contains all SQL statements from a target business application. A partial workload contains a subset of SQL statements.

As shown in Figure 21-1, SQL Access Advisor input can come from the following sources:

- Shared SQL area
The database uses the shared SQL area to analyze recent SQL statements that are currently in V$SQL.

- SQL tuning set
A SQL tuning set (STS) is a database object that stores SQL statements along with their execution context. When a set of SQL statements serve as input, the database must first construct and use an STS.

Note:
For best results, provide a workload as a SQL tuning set. The DBMS_SQLTUNE package provides helper functions that can create SQL tuning sets from common workload sources, such as the SQL cache, a user-defined workload stored in a table, and a hypothetical workload.

- Hypothetical workload
You can create a hypothetical workload from a schema by analyzing dimensions and constraints. This option is useful when you are initially designing your application.

See Also:

- Oracle Database Concepts to learn about the shared SQL area

- "About SQL Tuning Sets"

21.1.2.2 Filter Options for SQL Access Advisor

As shown in Figure 21-1, you can apply a filter to a workload to restrict what is analyzed. For example, specify that the advisor look at only the 30 most resource-intensive statements in the workload, based on optimizer cost. This restriction can generate different sets of recommendations based on different workload scenarios.

SQL Access Advisor parameters control the recommendation process and customization of the workload. These parameters control various aspects of the process, such as the type of recommendation required and the naming conventions for what it recommends.

To set these parameters, use the DBMS_ADVISOR.SET_TASK_PARAMETER procedure. Parameters are persistent in that they remain set for the life span of the task. When a parameter value is set using DBMS_ADVISOR.SET_TASK_PARAMETER, the value does not change until you make another call to this procedure.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_ADVISOR.SET_TASK_PARAMETER procedure

21.1.2.3 SQL Access Advisor Recommendations

A task recommendation can range from a simple to a complex solution. The advisor can recommend that you create database objects such as the following:

- Indexes
SQL Access Advisor index recommendations include bitmap, function-based, and B-tree indexes. A bitmap index offers a reduced response time for many types of ad hoc queries and reduced storage requirements compared to other indexing techniques. B-tree indexes are most commonly used in a data warehouse to index unique or near-unique keys. SQL Access Advisor materialized view recommendations include fast refreshable and full refreshable MVs, for either general rewrite or exact text match rewrite.

- Materialized views
SQL Access Advisor, using the TUNE_MVIEW procedure, also recommends how to optimize materialized views so that they can be fast refreshable and take advantage of general query rewrite.

- Materialized view logs
A materialized view log is a table at the materialized view's master site or master materialized view site that records all DML changes to the master table or master materialized view. A fast refresh of a materialized view is possible only if the materialized view's master has a materialized view log.

- Partitions
SQL Access Advisor can recommend partitioning on an existing unpartitioned base table to improve performance. Furthermore, it may recommend new indexes and materialized views that are themselves partitioned.

While creating new partitioned indexes and materialized view is no different from the unpartitioned case, partition existing base tables with care. This is especially true when indexes, views, constraints, or triggers are defined on the table.

To make recommendations, SQL Access Advisor relies on structural statistics about table and index cardinalities of dimension level columns, JOIN KEY columns, and fact table key columns. You can gather exact or estimated statistics with the DBMS_STATS package (see "About Manual Statistics Collection with DBMS_STATS").

Because gathering statistics is time-consuming and full statistical accuracy is not required, it is usually preferable to estimate statistics. Without gathering statistics on a specified table, queries referencing this table are marked as invalid in the workload, resulting in no recommendations for these queries. It is also recommended that all existing indexes and materialized views have been analyzed.

See Also:

- Oracle Database Data Warehousing Guide to learn more about materialized views

- Oracle Database VLDB and Partitioning Guide to learn more about partitions

21.1.2.4 SQL Access Advisor Actions

In general, each recommendation provides a benefit for one query or a set of queries. All individual actions in a recommendation must be implemented together to achieve the full benefit. Recommendations can share actions.

For example, a CREATE INDEX statement could provide a benefit for several queries, but some queries might benefit from an additional CREATE MATERIALIZED VIEW statement. In that case, the advisor would generate two recommendations: one for the set of queries that require only the index, and another one for the set of queries that require both the index and the materialized view.

21.1.2.4.1 Types of Actions

SQL Access Advisor recommendations include the following types of actions:

- PARTITION BASE TABLE
This action partitions an existing unpartitioned base table.

- CREATE|DROP|RETAIN {MATERIALIZED VIEW|MATERIALIZED VIEW LOG|INDEX}
The CREATE actions corresponds to new access structures. RETAIN recommends keeping existing access structures. SQL Access Advisor only recommends DROP when the WORKLOAD_SCOPE parameter is set to FULL.

- GATHER STATS
This action generates a call to a DBMS_STATS procedure to gather statistics on a newly generated access structure (see "About Manual Statistics Collection with DBMS_STATS").

Multiple recommendations may refer to the same action. However, when generating a script for the recommendation, you only see each action once.

See Also:
"Viewing SQL Access Advisor Task Results" to learn how to view actions and recommendations

21.1.2.4.2 Special Considerations for Partitioning Recommendations

The partition recommendation is a special type of recommendation. When SQL Access Advisor determines that partitioning a specified base table would improve workload performance, the advisor adds a partition action to every recommendation containing a query referencing the base table. This technique ensures that index and materialized view recommendations are implemented on the correctly partitioned tables.

SQL Access Advisor may recommend partitioning an existing unpartitioned base table to improve query performance. When the advisor implementation script contains partition recommendations, note the following issues:

- Partitioning an existing table is a complex and extensive operation, which may take considerably longer than implementing a new index or materialized view. Sufficient time should be reserved for implementing this recommendation.

- While index and materialized view recommendations are easy to reverse by deleting the index or view, a table, after being partitioned, cannot easily be restored to its original state. Therefore, ensure that you back up the database before executing a script containing partition recommendations.

- While repartitioning a base table, SQL Access Advisor scripts make a temporary copy of the original table, which occupies the same amount of space as the original table. Therefore, the repartitioning process requires sufficient free disk space for another copy of the largest table to be repartitioned. Ensure that such space is available before running the implementation script.
The partition implementation script attempts to migrate dependent objects such as indexes, materialized views, and constraints. However, some object cannot be automatically migrated. For example, PL/SQL stored procedures defined against a repartitioned base table typically become invalid and must be recompiled.

- If you decide not to implement a partition recommendation, then all other recommendations on the same table in the same script (such as CREATE INDEX and CREATE MATERIALIZED VIEW recommendations) depend on the partitioning recommendation. To obtain accurate recommendations, do not simply remove the partition recommendation from the script. Rather, rerun the advisor with partitioning disabled, for example, by setting parameter ANALYSIS_SCOPE to a value that does not include the keyword TABLE.

See Also:
Oracle Database SQL Language Reference for CREATE DIRECTORY syntax, and Oracle Database PL/SQL Packages and Types Reference for detailed information about the GET_TASK_SCRIPT procedure.

21.1.2.5 SQL Access Advisor Repository

All information required and generated by SQL Access Advisor resides in the Advisor repository, which is in the data dictionary. The repository has the following benefits:

- Collects a complete workload for SQL Access Advisor

- Supports historical data

- Is managed by the database

21.1.3 User Interfaces for SQL Access Advisor

Oracle recommends that you use SQL Access Advisor through its GUI wizard, which is available in Cloud Control. Oracle Database 2 Day + Performance Tuning Guide explains how to use the SQL Access Advisor wizard.

You can also invoke SQL Access Advisor through the DBMS_ADVISOR package. This chapter explains how to use the API. See Oracle Database PL/SQL Packages and Types Reference for complete semantics and syntax.

21.1.3.1 Graphical Interface to SQL Access Advisor

The SQL Access Advisor: Initial Options page in Cloud Control is the starting page for a wizard that guides you through the process of obtaining recommendations.

To access the SQL Access Advisor: Initial Options page:

1. Access the Database Home page, as described in "Accessing the Database Home Page in Cloud Control."

2. From the Performance menu, select SQL, then SQL Access Advisor.
The SQL Access Advisor: Initial Options page appears., shown in Figure 21-2.

Figure 21-2 SQL Access Advisor: Initial Options

You can perform most SQL plan management tasks in this page or in pages accessed through this page.

See Also:

- Cloud Control context-sensitive online help to learn about the options on the SQL Access Advisor: Initial Options page

- Oracle Database 2 Day + Performance Tuning Guide

21.1.3.2 Command-Line Interface to SQL Tuning Sets

On the command line, you can use the DBMS_ADVISOR package to manage SQL tuning sets. The DBMS_ADVISOR package consists of a collection of analysis and advisory functions and procedures callable from any PL/SQL program. You must have the ADVISOR privilege to use DBMS_ADVISOR.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about DBMS_ADVISOR

21.2 Using SQL Access Advisor: Basic Tasks

Figure 21-3 shows the basic workflow for SQL Access Advisor.

Figure 21-3 Using SQL Access Advisor

Typically, you use SQL Access Advisor by performing the following steps:

1. Create a SQL tuning set
The input workload source for SQL Access Advisor is a SQL tuning set (STS). Use DBMS_SQLTUNE.CREATE_SQLSET to create a SQL tuning set.

"Creating a SQL Tuning Set as Input for SQL Access Advisor" describes this task.

2. Load the SQL tuning set
SQL Access Advisor performs best when a workload based on actual usage is available. Use DBMS_SQLTUNE.LOAD_SQLSET to populate the SQL tuning set with your workload.

"Populating a SQL Tuning Set with a User-Defined Workload" describes this task.

3. Create and configure a task
In the task, you define what SQL Access Advisor must analyze and the location of the analysis results. Create a task using the DBMS_ADVISOR.CREATE_TASK procedure. You can then define parameters for the task using the SET_TASK_PARAMETER procedure, and then link the task to an STS by using the DBMS_ADVISOR.ADD_STS_REF procedure.

"Creating and Configuring a SQL Access Advisor Task" describes this task.

4. Execute the task
Run the DBMS_ADVISOR.EXECUTE_TASK procedure to generate recommendations. Each recommendation specifies one or more actions. For example, a recommendation could be to create several materialized view logs, create a materialized view, and then analyze it to gather statistics.

"Executing a SQL Access Advisor Task" describes this task.

5. View the recommendations
You can view the recommendations by querying data dictionary views.

"Viewing SQL Access Advisor Task Results" describes this task.

6. Optionally, generate and execute a SQL script that implements the recommendations.
"Generating and Executing a Task Script" that describes this task.

21.2.1 Creating a SQL Tuning Set as Input for SQL Access Advisor

The input workload source for SQL Access Advisor is an STS. Because an STS is stored as a separate entity, multiple advisor tasks can share it. Create an STS with the DBMS_SQLTUNE.CREATE_SQLSET statement.

After an advisor task has referenced an STS, you cannot delete or modify the STS until all advisor tasks have removed their dependency on it. A workload reference is removed when a parent advisor task is deleted, or when you manually remove the workload reference from the advisor task.

Prerequisites

The user creating the STS must have been granted the ADMINISTER SQL TUNING SET privilege. To run SQL Access Advisor on SQL tuning sets owned by other users, the user must have the ADMINISTER ANY SQL TUNING SET privilege.

Assumptions

This tutorial assumes the following:

- You want to create an STS named MY_STS_WORKLOAD.

- You want to use this STS as input for a workload derived from the sh schema.

To create an STS :

1. Connect SQL*Plus to the database as user sh, and then set SQL*Plus variables.
For example, enter the following commands:

CONNECT SH
Password: ********
SET SERVEROUTPUT ON;
VARIABLE task_id NUMBER;
VARIABLE task_name VARCHAR2(255);
VARIABLE workload_name VARCHAR2(255);

2. Create the SQL tuning set.
For example, assign a value to the workload_name variable and create the STS as follows:

EXECUTE :workload_name := 'MY_STS_WORKLOAD';
EXECUTE DBMS_SQLTUNE.CREATE_SQLSET(:workload_name, 'test purpose');

See Also:

- "About SQL Tuning Sets"

- Oracle Database PL/SQL Packages and Types Reference to learn about CREATE_SQLSET

21.2.2 Populating a SQL Tuning Set with a User-Defined Workload

A workload consists of one or more SQL statements, plus statistics and attributes that fully describe each statement. A full workload contains all SQL statements from a target business application. A partial workload contains a subset of SQL statements. The difference is that for full workloads SQL Access Advisor may recommend dropping unused materialized views and indexes.

You cannot use SQL Access Advisor without a workload. SQL Access Advisor ranks the entries according to a specific statistic, business importance, or combination of the two, which enables the advisor to process the most important SQL statements first.

SQL Access Advisor performs best with a workload based on actual usage. You can store multiple workloads in the form of SQL tuning sets, so that you can view the different uses of a real-world data warehousing or OLTP environment over a long period and across the life cycle of database instance startup and shutdown.

Table 21-1 describes procedures that you can use to populate an STS with a user-defined workload.

Table 21-1 Procedures for Loading an STS

Procedure

Description

DBMS_SQLTUNE.LOAD_SQLSET

Populates the SQL tuning set with a set of selected SQL. You can call the procedure multiple times to add new SQL statements or replace attributes of existing statements. See Oracle Database PL/SQL Packages and Types Reference.

DBMS_ADVISOR.COPY_SQLWKLD_TO_STS

Copies SQL workload data to a user-designated SQL tuning set. The user must have the required SQL tuning set privileges and the required ADVISOR privilege. See Oracle Database PL/SQL Packages and Types Reference.

Assumptions

This tutorial assumes that you want to do the following:

- Create a table named sh.user_workload to store information about SQL statements

- Load the sh.user_workload table with information about three queries of tables in the sh schema

- Populate the STS created in "Creating a SQL Tuning Set as Input for SQL Access Advisor" with the workload contained in sh.user_workload

To populate an STS with a user-defined workload:

1. Connect SQL*Plus to the database as user sh, and then create the user_workload table.
For example, enter the following commands:

DROP TABLE user_workload;
CREATE TABLE user_workload
(
username              varchar2(128),      /* User who executes statement */
module                varchar2(64),           /* Application module name */
action                varchar2(64),           /* Application action name */
elapsed_time          number,                  /* Elapsed time for query */
cpu_time              number,                      /* CPU time for query */
buffer_gets           number,           /* Buffer gets consumed by query */
disk_reads            number,            /* Disk reads consumed by query */
rows_processed        number,       /* Number of rows processed by query */
executions            number,          /* Number of times query executed */
optimizer_cost        number,                /* Optimizer cost for query */
priority              number,                /* User-priority (1,2 or 3) */
last_execution_date   date,                  /* Last time query executed */
stat_period           number,        /* Window execution time in seconds */
sql_text              clob                              /* Full SQL Text */
);

2. Load the user_workload table with information about queries.
For example, execute the following statements:

-- aggregation with selection
INSERT INTO user_workload (username, module, action, priority, sql_text)
VALUES ('SH', 'Example1', 'Action', 2,
'SELECT   t.week_ending_day, p.prod_subcategory,
SUM(s.amount_sold) AS dollars, s.channel_id, s.promo_id
FROM     sales s, times t, products p
WHERE    s.time_id = t.time_id
AND      s.prod_id = p.prod_id
AND      s.prod_id > 10
AND      s.prod_id < 50
GROUP BY t.week_ending_day, p.prod_subcategory, s.channel_id, s.promo_id')
/

-- aggregation with selection
INSERT INTO user_workload (username, module, action, priority, sql_text)
VALUES ('SH', 'Example1', 'Action', 2,
'SELECT   t.calendar_month_desc, SUM(s.amount_sold) AS dollars
FROM     sales s , times t
WHERE    s.time_id = t.time_id
AND      s.time_id BETWEEN TO_DATE(''01-JAN-2000'', ''DD-MON-YYYY'')
AND      TO_DATE(''01-JUL-2000'', ''DD-MON-YYYY'')
GROUP BY t.calendar_month_desc')
/

-- order by
INSERT INTO user_workload (username, module, action, priority, sql_text)
VALUES ('SH', 'Example1', 'Action', 2,
'SELECT   c.country_id, c.cust_city, c.cust_last_name
FROM     customers c
WHERE    c.country_id IN (52790, 52789)
ORDER BY c.country_id, c.cust_city, c.cust_last_name')
/
COMMIT;

3. Execute a PL/SQL program that fills a cursor with rows from the user_workload table, and then loads the contents of this cursor into the STS named MY_STS_WORKLOAD.
For example, execute the following PL/SQL program:

DECLARE
sqlset_cur DBMS_SQLTUNE.SQLSET_CURSOR;
BEGIN
OPEN sqlset_cur FOR
SELECT SQLSET_ROW(null,null, SQL_TEXT, null, null, 'SH', module,
'Action', 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, null, 2, 3,
sysdate, 0, 0, null, 0, null, null)
FROM USER_WORKLOAD;
DBMS_SQLTUNE.LOAD_SQLSET('MY_STS_WORKLOAD', sqlset_cur);
END;
/

21.2.3 Creating and Configuring a SQL Access Advisor Task

Use the DBMS_ADVISOR.CREATE_TASK procedure to create a SQL Access Advisor task. In the SQL Access Advisor task, you define what the advisor must analyze and the location of the results. You can create multiple tasks, each with its own specialization. All are based on the same Advisor task model and share the same repository.

Configuring the task involves the following steps:

- Defining task parameters
At the time the recommendations are generated, you can apply a filter to the workload to restrict what is analyzed. This restriction provides the ability to generate different sets of recommendations based on different workload scenarios.

SQL Access Advisor parameters control the recommendation process and customization of the workload. These parameters control various aspects of the process, such as the type of recommendation required and the naming conventions for what it recommends. See "Categories for SQL Access Advisor Task Parameters".

If parameters are not defined, then the database uses the defaults. You can set task parameters by using the DBMS_ADVISOR.SET_TASK_PARAMETER procedure. Parameters are persistent in that they remain set for the life span of the task. When a parameter value is set using SET_TASK_PARAMETER, it does not change until you make another call to this procedure.

- Linking the task to the workload
Because the workload is independent, you must link it to a task using the DBMS_ADVISOR.ADD_STS_REF procedure. After this link has been established, you cannot delete or modify the workload until all advisor tasks have removed their dependency on the workload. A workload reference is removed when a user deletes a parent advisor task or manually removes the workload reference from the task by using the DBMS_ADVISOR.DELETE_STS_REF procedure (see "Deleting SQL Access Advisor Tasks").

Prerequisites and Restrictions

The user creating the task must have been granted the ADVISOR privilege.

Assumptions

This tutorial assumes the following:

- You want to create a task named MYTASK.

- You want to use this task to analyze the workload that you defined in "Populating a SQL Tuning Set with a User-Defined Workload".

- You want to terminate the task if it takes longer than 30 minutes to execute.

- You want to SQL Access Advisor to only consider indexes.

To create and configure a SQL Access Advisor task:

1. Connect SQL*Plus to the database as user sh, and then create the task.
For example, enter the following commands:

EXECUTE :task_name := 'MYTASK';
EXECUTE DBMS_ADVISOR.CREATE_TASK('SQL Access Advisor', :task_id, :task_name);

2. Set task parameters.
For example, execute the following statements:

EXECUTE DBMS_ADVISOR.SET_TASK_PARAMETER(:task_name, 'TIME_LIMIT', 30);
EXECUTE DBMS_ADVISOR.SET_TASK_PARAMETER(:task_name, 'ANALYSIS_SCOPE', 'ALL');

3. Link the task to the workload.
For example, execute the following statement:

EXECUTE DBMS_ADVISOR.ADD_STS_REF(:task_name, 'SH', :workload_name);

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the CREATE_TASK procedure and its parameters

- Oracle Database PL/SQL Packages and Types Reference to learn about the SET_TASK_PARAMETER procedure and its parameters

- Oracle Database PL/SQL Packages and Types Reference to learn about the ADD_STS_REF procedure and its parameters

21.2.4 Executing a SQL Access Advisor Task

The DBMS_ADVISOR.EXECUTE_TASK procedure performs SQL Access Advisor analysis or evaluation for the specified task. Task execution is a synchronous operation, so the database does not return control to the user until the operation has completed, or the database detects a user interrupt. After the return or execution of the task, you can check the DBA_ADVISOR_LOG table for the execution status.

Running EXECUTE_TASK generates recommendations. A recommendation includes one or more actions, such as creating a materialized view log or a materialized view.

Prerequisites and Restrictions

When processing a workload, SQL Access Advisor attempts to validate each statement to identify table and column references. The database achieves validation by processing each statement as if it were being executed by the statement's original user.

If the user does not have SELECT privileges to a particular table, then SQL Access Advisor bypasses the statement referencing the table. This behavior can cause many statements to be excluded from analysis. If SQL Access Advisor excludes all statements in a workload, then the workload is invalid. SQL Access Advisor returns the following message:

QSM-00774, there are no SQL statements to process for task TASK_NAME

To avoid missing critical workload queries, the current database user must have SELECT privileges on the tables targeted for materialized view analysis. For these tables, these SELECT privileges cannot be obtained through a role.

Assumptions

This tutorial assumes that you want to execute the task you configured in "Creating and Configuring a SQL Access Advisor Task".

To create and configure a SQL Access Advisor task:

1. Connect SQL*Plus to the database as user sh, and then execute the task.
For example, execute the following statement:

EXECUTE DBMS_ADVISOR.EXECUTE_TASK(:task_name);

2. Optionally, query USER_ADVISOR_LOG to check the status of the task.
For example, execute the following statements (sample output included):

COL TASK_ID FORMAT 999
COL TASK_NAME FORMAT a25
COL STATUS_MESSAGE FORMAT a25

SELECT TASK_ID, TASK_NAME, STATUS, STATUS_MESSAGE
FROM   USER_ADVISOR_LOG;

TASK_ID TASK_NAME                 STATUS      STATUS_MESSAGE
------- ------------------------- ----------- -------------------------
103 MYTASK                    COMPLETED   Access advisor execution
completed

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn more about the EXECUTE_TASK procedure and its parameters

21.2.5 Viewing SQL Access Advisor Task Results

You can view each recommendation generated by SQL Access Advisor using several data dictionary views, which are summarized in Table 21-2. However, it is easier to use the DBMS_ADVISOR.GET_TASK_SCRIPT procedure or Cloud Control, which graphically displays the recommendations and provides hyperlinks to quickly see which SQL statements benefit from a recommendation.

Each recommendation produced by SQL Access Advisor is linked to the SQL statement it benefits. Each recommendation corresponds to one or more actions. EAch action has one or more attributes.

Each action has attributes pertaining to the access structure properties. The name and tablespace for each applicable access structure are in the ATTR1 and ATTR2 columns of USER_ADVISOR_ATTRIBUTES (see "Action Attributes in the DBA_ADVISOR_ACTIONS View"). The space occupied by each new access structure is in the NUM_ATTR1 column. Other attributes are different for each action.

Table 21-2 Views Showing Task Results

Data Dictionary View (DBA, USER)

Description

DBA_ADVISOR_TASKS

Displays information about advisor tasks. To see SQL Access Advisor tasks, select where ADVISOR_NAME = 'SQL Access Advisor'.

DBA_ADVISOR_RECOMMENDATIONS

Displays the results of an analysis of all recommendations in the database. A recommendation can have multiple actions associated with it. The DBA_ADVISOR_ACTIONS view describe the actions. A recommendation also points to a set of rationales that present a justification/reasoning for that recommendation. The DBA_ADVISOR_RATIONALE view describes the rationales.

DBA_ADVISOR_ACTIONS

Displays information about the actions associated with all recommendations in the database. Each action is specified by the COMMAND and ATTR1 through ATTR6 columns. Each command defines how to use the attribute columns.

DBA_ADVISOR_RATIONALE

Displays information about the rationales for all recommendations in the database.

DBA_ADVISOR_SQLA_WK_STMTS

Displays information about all workload objects in the database after a SQL Access Advisor analysis. The precost and postcost numbers are in terms of the estimated optimizer cost (shown in EXPLAIN PLAN) without and with the recommended access structure.

Assumptions

This tutorial assumes that you want to view results of the task you executed in "Executing a SQL Access Advisor Task".

To view the results of a SQL Access Advisor task:

1. Connect SQL*Plus to the database with the appropriate privileges, and then query the advisor recommendations.
For example, execute the following statements (sample output included):

VARIABLE workload_name VARCHAR2(255);
VARIABLE task_name VARCHAR2(255);
EXECUTE :task_name := 'MYTASK';
EXECUTE :workload_name := 'MY_STS_WORKLOAD';

SELECT REC_ID, RANK, BENEFIT
FROM   USER_ADVISOR_RECOMMENDATIONS
WHERE  TASK_NAME = :task_name
ORDER BY RANK;

REC_ID       RANK    BENEFIT
---------- ---------- ----------
1          1        236
2          2        356

The preceding output shows the recommendations (rec_id) produced by an SQL Access Advisor run, with their rank and total benefit. The rank is a measure of the importance of the queries that the recommendation helps. The benefit is the total improvement in execution cost (in terms of optimizer cost) of all queries using the recommendation.

2. Identify which query benefits from which recommendation.
For example, execute the following query of USER_ADVISOR_SQLA_WK_STMTS (sample output included):

SELECT SQL_ID, REC_ID, PRECOST, POSTCOST,
(PRECOST-POSTCOST)*100/PRECOST AS PERCENT_BENEFIT
FROM   USER_ADVISOR_SQLA_WK_STMTS
WHERE  TASK_NAME = :task_name
AND    WORKLOAD_NAME = :workload_name
ORDER BY percent_benefit DESC;

SQL_ID            REC_ID    PRECOST   POSTCOST PERCENT_BENEFIT
------------- ---------- ---------- ---------- ---------------
fn4bsxdm98w3u          2        578        222      61.5916955
29bbju72rv3t2          1       5750       5514      4.10434783
133ym38r6gbar          0        772        772               0

The precost and postcost numbers are in terms of the estimated optimizer cost (shown in EXPLAIN PLAN) both without and with the recommended access structure changes.

3. Display the number of distinct actions for this set of recommendations.
For example, use the following query (sample output included):

SELECT 'Action Count', COUNT(DISTINCT action_id) cnt
FROM   USER_ADVISOR_ACTIONS
WHERE  TASK_NAME = :task_name;

'ACTIONCOUNT        CNT
------------ ----------
Action Count          4

4. Display the actions for this set of recommendations.
For example, use the following query (sample output included):

SELECT REC_ID, ACTION_ID, SUBSTR(COMMAND,1,30) AS command
FROM   USER_ADVISOR_ACTIONS
WHERE  TASK_NAME = :task_name
ORDER BY rec_id, action_id;

REC_ID  ACTION_ID COMMAND
---------- ---------- ------------------------------
1          1 PARTITION TABLE
1          2 RETAIN INDEX
2          1 PARTITION TABLE
2          3 RETAIN INDEX
2          4 RETAIN INDEX

5. Display attributes of the recommendations.
For example, create the following PL/SQL procedure show_recm, and then execute it to see attributes of the actions:

CREATE OR REPLACE PROCEDURE show_recm (in_task_name IN VARCHAR2) IS
CURSOR curs IS
SELECT DISTINCT action_id, command, attr1, attr2, attr3, attr4
FROM user_advisor_actions
WHERE task_name = in_task_name
ORDER BY action_id;
v_action        number;
v_command     VARCHAR2(32);
v_attr1       VARCHAR2(4000);
v_attr2       VARCHAR2(4000);
v_attr3       VARCHAR2(4000);
v_attr4       VARCHAR2(4000);
v_attr5       VARCHAR2(4000);
BEGIN
OPEN curs;
DBMS_OUTPUT.PUT_LINE('=========================================');
DBMS_OUTPUT.PUT_LINE('Task_name = ' || in_task_name);
LOOP
FETCH curs INTO
v_action, v_command, v_attr1, v_attr2, v_attr3, v_attr4 ;
EXIT when curs%NOTFOUND;
DBMS_OUTPUT.PUT_LINE('Action ID: ' || v_action);
DBMS_OUTPUT.PUT_LINE('Command : ' || v_command);
DBMS_OUTPUT.PUT_LINE('Attr1 (name)      : ' || SUBSTR(v_attr1,1,30));
DBMS_OUTPUT.PUT_LINE('Attr2 (tablespace): ' || SUBSTR(v_attr2,1,30));
DBMS_OUTPUT.PUT_LINE('Attr3             : ' || SUBSTR(v_attr3,1,30));
DBMS_OUTPUT.PUT_LINE('Attr4             : ' || v_attr4);
DBMS_OUTPUT.PUT_LINE('Attr5             : ' || v_attr5);
DBMS_OUTPUT.PUT_LINE('----------------------------------------');
END LOOP;
CLOSE curs;
DBMS_OUTPUT.PUT_LINE('=========END RECOMMENDATIONS============');
END show_recm;
/

SET SERVEROUTPUT ON SIZE 99999
EXECUTE show_recm(:task_name);

The following output shows attributes of actions in the recommendations:

=========================================
Task_name = MYTASK
Action ID: 1
Command : PARTITION TABLE
Attr1 (name)      : "SH"."SALES"
Attr2 (tablespace):
Attr3             : ("TIME_ID")
Attr4             : INTERVAL
Attr5             :
----------------------------------------
Action ID: 2
Command : RETAIN INDEX
Attr1 (name)      : "SH"."PRODUCTS_PK"
Attr2 (tablespace):
Attr3             : "SH"."PRODUCTS"
Attr4             : BTREE
Attr5             :
----------------------------------------
Action ID: 3
Command : RETAIN INDEX
Attr1 (name)      : "SH"."TIMES_PK"
Attr2 (tablespace):
Attr3             : "SH"."TIMES"
Attr4             : BTREE
Attr5             :
----------------------------------------
Action ID: 4
Command : RETAIN INDEX
Attr1 (name)      : "SH"."SALES_TIME_BIX"
Attr2 (tablespace):
Attr3             : "SH"."SALES"
Attr4             : BITMAP
Attr5             :
----------------------------------------
=========END RECOMMENDATIONS============

See Also:
Oracle Database PL/SQL Packages and Types Reference for details regarding Attr5 and Attr6

21.2.6 Generating and Executing a Task Script

You can use the procedure DBMS_ADVISOR.GET_TASK_SCRIPT to create a script of the SQL statements for the SQL Access Advisor recommendations. The script is an executable SQL file that can contain DROP, CREATE, and ALTER statements. For new objects, the names of the materialized views, materialized view logs, and indexes are automatically generated by using the user-specified name template. Review the generated SQL script before attempting to execute it.

Assumptions

This tutorial assumes that you want to save and execute a script that contains the recommendations generated in "Executing a SQL Access Advisor Task".

To save and execute a SQL script:

1. Connect SQL*Plus to the database as an administrator.

2. Create a directory object and grant permissions to read and write to it.
For example, use the following statements:

CREATE DIRECTORY ADVISOR_RESULTS AS '/tmp';
GRANT READ ON DIRECTORY ADVISOR_RESULTS TO PUBLIC;
GRANT WRITE ON DIRECTORY ADVISOR_RESULTS TO PUBLIC;

3. Connect to the database as sh, and then save the script to a file.
For example, use the following statement:

EXECUTE DBMS_ADVISOR.CREATE_FILE(DBMS_ADVISOR.GET_TASK_SCRIPT('MYTASK'),
'ADVISOR_RESULTS', 'advscript.sql');

4. Use a text editor to view the contents of the script.
The following is a fragment of a script generated by this procedure:

Rem  Username:        SH
Rem  Task:            MYTASK
Rem  Execution date:
Rem

Rem
Rem  Repartitioning table "SH"."SALES"
Rem

SET SERVEROUTPUT ON
SET ECHO ON

Rem
Rem Creating new partitioned table
Rem
CREATE TABLE "SH"."SALES1"
(    "PROD_ID" NUMBER,
"CUST_ID" NUMBER,
"TIME_ID" DATE,
"CHANNEL_ID" NUMBER,
"PROMO_ID" NUMBER,
"QUANTITY_SOLD" NUMBER(10,2),
"AMOUNT_SOLD" NUMBER(10,2)
) PCTFREE 5 PCTUSED 40 INITRANS 1 MAXTRANS 255
NOCOMPRESS  NOLOGGING
TABLESPACE "EXAMPLE"
PARTITION BY RANGE ("TIME_ID") INTERVAL( NUMTOYMINTERVAL( 1, 'MONTH')) (
PARTITION VALUES LESS THAN (TO_DATE(' 1998-02-01 00:00:00',
'SYYYY-MM-DD HH24:MI:SS', 'NLS_CALENDAR=GREGORIAN')) );
.
.
.

5. Optionally, in SQL*Plus, run the SQL script.
For example, enter the following command:

@/tmp/advscript.sql

See Also:
Oracle Database SQL Language Reference for CREATE DIRECTORY syntax, and Oracle Database PL/SQL Packages and Types Reference to learn about the GET_TASK_SCRIPT procedure

21.3 Performing a SQL Access Advisor Quick Tune

To tune a single SQL statement, the DBMS_ADVISOR.QUICK_TUNE procedure accepts as its input a task_name and a single SQL statement. The procedure creates a task and workload and executes this task. EXECUTE_TASK and QUICK_TUNE produce the same results. However, QUICK_TUNE is easier when tuning a single SQL statement.

Assumptions

This tutorial assumes the following:

- You want to tune a single SQL statement.

- You want to name the task MY_QUICKTUNE_TASK.

To create a template and base a task on this template:

1. Connect SQL*Plus to the database as user sh, and then initialize SQL*Plus variables for the SQL statement and task name.
For example, enter the following commands:

VARIABLE t_name VARCHAR2(255);
VARIABLE sq VARCHAR2(4000);
EXEC :sq := 'SELECT COUNT(*) FROM customers WHERE cust_state_province =''CA''';
EXECUTE :t_name := 'MY_QUICKTUNE_TASK';

2. Perform the quick tune.
For example, the following statement executes MY_QUICKTUNE_TASK:

EXEC DBMS_ADVISOR.QUICK_TUNE(DBMS_ADVISOR.SQLACCESS_ADVISOR,:t_name,:sq);

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn more about the QUICK_TUNE procedure and its parameters

21.4 Using SQL Access Advisor: Advanced Tasks

This section contains the following topics:

- Evaluating Existing Access Structures

- Updating SQL Access Advisor Task Attributes

- Creating and Using SQL Access Advisor Task Templates

- Terminating SQL Access Advisor Task Execution

- Deleting SQL Access Advisor Tasks

- Marking SQL Access Advisor Recommendations

- Modifying SQL Access Advisor Recommendations

21.4.1 Evaluating Existing Access Structures

SQL Access Advisor operates in two modes: problem-solving and evaluation. By default, SQL Access Advisor attempts to solve access method problems by looking for enhancements to index structures, partitions, materialized views, and materialized view logs. For example, a problem-solving run may recommend creating a new index, adding a new column to a materialized view log, and so on.

When you set the ANALYSIS_SCOPE parameter to EVALUATION, SQL Access Advisor comments only on which access structures the supplied workload uses. An evaluation-only run may only produce recommendations such as retaining an index, retaining a materialized view, and so on. The evaluation mode can be useful to see exactly which indexes and materialized views a workload is using. SQL Access Advisor does not evaluate the performance impact of existing base table partitioning.

To create a task and set it to evaluation mode:

1. Connect SQL*Plus to the database with the appropriate privileges, and then create a task.
For example, enter the following statement, where t_name is a SQL*Plus variable set to the name of the task:

EXECUTE DBMS_ADVISOR.EXECUTE_TASK(:t_name);

2. Perform the quick tune.
For example, the following statement sets the previous task to evaluation mode:

EXECUTE DBMS_ADVISOR.SET_TASK_PARAMETER(:t_name,'ANALYSIS_SCOPE','EVALUATION');

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the SET_TASK_PARAMETER procedure and its parameters

21.4.2 Updating SQL Access Advisor Task Attributes

You can use the DBMS_ADVISOR.UPDATE_TASK_ATTRIBUTES procedure to do the following:

- Change the name of a task.

- Give a task a description.

- Set the task to be read-only so it cannot be changed.

- Make the task a template upon which you can define other tasks (see "Creating and Using SQL Access Advisor Task Templates").

- Changes various attributes of a task or a task template.

Assumptions

This tutorial assumes the following:

- You want to change the name of existing task MYTASK to TUNING1.

- You want to make the task TUNING1 read-only.

To update task attributes:

1. Connect SQL*Plus to the database as user sh, and then change the name of the task.
For example, use the following statement:

EXECUTE DBMS_ADVISOR.UPDATE_TASK_ATTRIBUTES('MYTASK', 'TUNING1');

2. Set the task to read-only.
For example, use the following statement:

EXECUTE DBMS_ADVISOR.UPDATE_TASK_ATTRIBUTES('TUNING1', read_only => 'true');

See Also:
Oracle Database PL/SQL Packages and Types Reference for more information regarding the UPDATE_TASK_ATTRIBUTES procedure and its parameters

21.4.3 Creating and Using SQL Access Advisor Task Templates

A task template is a saved configuration on which to base future tasks and workloads. A template enables you to set up any number of tasks or workloads that can serve as starting points or templates for future task creation. By setting up a template, you can save time when performing tuning analysis. This approach also enables you to custom fit a tuning analysis to the business operation.

Physically, there is no difference between a task and a template. However, a template cannot be executed. To create a task from a template, you specify the template to be used when a new task is created. At that time, SQL Access Advisor copies the data and parameter settings from the template into the newly created task. You can also set an existing task to be a template by setting the template attribute when creating the task or later using the UPDATE_TASK_ATTRIBUTE procedure.

Table 21-3 describes procedures that you can use to manage task templates.

Table 21-3 DBMS_ADVISOR Procedures for Task Templates

Procedure

Description

CREATE_TASK

The template parameter is an optional task name of an existing task or task template. To specify built-in SQL Access Advisor templates, use the template name as described in Table 21-6. is_template is an optional parameter that enables you to set the newly created task as a template. Valid values are true and false.

SET_TASK_PARAMETER

The INDEX_NAME_TEMPLATE parameter specifies the method by which new index names are formed. The MVIEW_NAME_TEMPLATE parameter specifies the method by which new materialized view names are formed. The PARTITION_NAME_TEMPLATE parameter specifies the method by which new partition names are formed. See Oracle Database PL/SQL Packages and Types Reference to for task parameter descriptions.

UPDATE_TASK_ATTRIBUTES

is_template marks the task as a template. Physically, there is no difference between a task and a template; however, a template cannot be executed. Possible values are: true and false. If the value is NULL or contains the value ADVISOR_UNUSED, then the setting is not changed.

Assumptions

This tutorial assumes the following:

- You want to create a template named MY_TEMPLATE.

- You want to set naming conventions for indexes and materialized views that are recommended by tasks based on MY_TEMPLATE.

- You want to create task NEWTASK based on MY_TEMPLATE.

To create a template and base a task on this template:

1. Connect SQL*Plus to the database as user sh, and then create a task as a template.
For example, create a template named MY_TEMPLATE as follows:

VARIABLE template_id NUMBER;
VARIABLE template_name VARCHAR2(255);
EXECUTE :template_name := 'MY_TEMPLATE';
BEGIN
DBMS_ADVISOR.CREATE_TASK (
'SQL Access Advisor'
,   :template_id
,   :template_name
,   is_template => 'true'
);
END;

2. Set template parameters.
For example, the following statements set the naming conventions for recommended indexes and materialized views:

-- set naming conventions for recommended indexes/mvs
BEGIN
DBMS_ADVISOR.SET_TASK_PARAMETER (
:template_name
,   'INDEX_NAME_TEMPLATE'
,   'SH_IDX$$_<SEQ>'
);
END;

BEGIN
DBMS_ADVISOR.SET_TASK_PARAMETER (
:template_name
,   'MVIEW_NAME_TEMPLATE'
,   'SH_MV$$_<SEQ>'
);
END;

3. Create a task based on a pre-existing template.
For example, enter the following commands to create NEWTASK based on MY_TEMPLATE:

VARIABLE task_id NUMBER;
VARIABLE task_name VARCHAR2(255);
EXECUTE :task_name := 'NEWTASK';
BEGIN
DBMS_ADVISOR.CREATE_TASK (
'SQL Access Advisor'
,   :task_id
,   :task_name
,   template=>'MY_TEMPLATE'
);
END;

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the CREATE_TASK procedure and its parameters

- Oracle Database PL/SQL Packages and Types Reference to learn about the SET_TASK_PARAMETER procedure and its parameters

21.4.4 Terminating SQL Access Advisor Task Execution

SQL Access Advisor enables you to interrupt the recommendation process or allow it to complete. An interruption signals SQL Access Advisor to stop processing and marks the task as INTERRUPTED. At that point, you may update recommendation attributes and generate scripts.

Intermediate results represent recommendations for the workload contents up to that point in time. If recommendations must be sensitive to the entire workload, then Oracle recommends that you let the task complete. Additionally, recommendations made by the advisor early in the recommendation process do not contain base table partitioning recommendations. The partitioning analysis requires a large part of the workload to be processed before it can determine whether partitioning would be beneficial. Therefore, if SQL Access Advisor detects a benefit, then only later intermediate results contain base table partitioning recommendations.

This section describes two ways to terminate SQL Access Advisor task execution:

- Interrupting SQL Access Advisor Tasks

- Canceling SQL Access Advisor Tasks

21.4.4.1 Interrupting SQL Access Advisor Tasks

The DBMS_ADVISOR.INTERRUPT_TASK procedure causes a SQL Access Advisor task execution to terminate as if it had reached its normal end. Thus, you can see any recommendations that have been formed up to the point of the interruption. An interrupted task cannot be restarted. The syntax is as follows:

DBMS_ADVISOR.INTERRUPT_TASK (task_name IN VARCHAR2);

Assumptions

This tutorial assumes the following:

- Long-running task MYTASK is currently executing.

- You want to interrupt this task, and then view the recommendations.

To interrupt a currently executing task:

1. Connect SQL*Plus to the database as sh, and then interrupt the task.
For example, create a template named MY_TEMPLATE as follows:

EXECUTE DBMS_ADVISOR.INTERRUPT_TASK ('MYTASK');

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the INTERRUPT_TASK procedure

21.4.4.2 Canceling SQL Access Advisor Tasks

You can stop task execution by calling the DBMS_ADVISOR.CANCEL_TASK procedure and passing in the task name for this recommendation process. SQL Access Advisor may take a few seconds to respond to this request. Because all advisor task procedures are synchronous, to cancel an operation, you must use a separate database session. If you use CANCEL_TASK, then SQL Access Advisor makes no recommendations.

A cancel command effective restores the task to its condition before the start of the canceled operation. Therefore, a canceled task or data object cannot be restarted. However, you can reset the task using DBMS_ADVISOR.RESET_TASK, and then execute it again. The CANCEL_TASK syntax is as follows:

DBMS_ADVISOR.CANCEL_TASK (task_name   IN  VARCHAR2);

The RESET_TASK procedure resets a task to its initial starting point, which has the effect of removing all recommendations and intermediate data from the task. The task status is set to INITIAL. The syntax is as follows:

DBMS_ADVISOR.RESET_TASK (task_name     IN VARCHAR2);

Assumptions

This tutorial assumes the following:

- Long-running task MYTASK is currently executing. This task is set to make partitioning recommendations.

- You want to cancel this task, and then reset it so that the task makes only index recommendations.

To cancel a currently executing task:

1. Connect SQL*Plus to the database as user sh, and then cancel the task.
For example, create a template named MY_TEMPLATE as follows:

EXECUTE DBMS_ADVISOR.CANCEL_TASK ('MYTASK');

2. Reset the task.
For example, execute the RESET_TASK procedure as follows:

EXECUTE DBMS_ADVISOR.RESET_TASK('MYTASK');

3. Set task parameters.
For example, change the analysis scope to INDEX as follows:

EXECUTE DBMS_ADVISOR.SET_TASK_PARAMETER(:task_name, 'ANALYSIS_SCOPE', 'INDEX');

4. Execute the task.
For example, execute MYTASK as follows:

EXECUTE DBMS_ADVISOR.EXECUTE_TASK ('MYTASK');

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn more about the RESET_TASK procedure and its parameters

- Oracle Database PL/SQL Packages and Types Reference to learn more about the CANCEL_TASK procedure and its parameters

21.4.5 Deleting SQL Access Advisor Tasks

The DBMS_ADVISOR.DELETE_TASK procedure deletes existing SQL Access Advisor tasks from the repository. The syntax is as follows:

DBMS_ADVISOR.DELETE_TASK (task_name  IN VARCHAR2);

If a task is linked to an STS workload, and if you want to delete the task or workload, then you must remove the link between the task and the workload using the DELETE_STS_REF procedure. The following example deletes the link between task MYTASK and the current user's SQL tuning set MY_STS_WORKLOAD:

EXECUTE DBMS_ADVISOR.DELETE_STS_REF('MYTASK', null, 'MY_STS_WORKLOAD');

Assumptions

This tutorial assumes the following:

- User sh currently owns multiple SQL Access Advisor tasks.

- You want to delete MYTASK.

- The task MYTASK is currently linked to workload MY_STS_WORKLOAD.

To delete a SQL Access Advisor task:

1. Connect SQL*Plus to the database as user sh, and then query existing SQL Access Advisor tasks.
For example, query the data dictionary as follows (sample output included):

SELECT TASK_NAME
FROM   USER_ADVISOR_TASKS
WHERE  ADVISOR_NAME = 'SQL Access Advisor';

TASK_NAME
-------------------------
MYTASK
NEWTASK

2. Delete the link between MYTASK and MY_STS_WORKLOAD.
For example, delete the reference as follows:

EXECUTE DBMS_ADVISOR.DELETE_STS_REF('MYTASK', null, 'MY_STS_WORKLOAD');

3. Delete the desired task.
For example, delete MYTASK as follows:

EXECUTE DBMS_ADVISOR.DELETE_TASK('MYTASK');

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn more about the DELETE_TASK procedure and its parameters

21.4.6 Marking SQL Access Advisor Recommendations

By default, all SQL Access Advisor recommendations are ready to be implemented. However, you can choose to skip or exclude selected recommendations by using the DBMS_ADVISOR.MARK_RECOMMENDATION procedure. MARK_RECOMMENDATION enables you to annotate a recommendation with a REJECT or IGNORE setting, which causes the GET_TASK_SCRIPT to skip it when producing the implementation procedure.

If SQL Access Advisor makes a recommendation to partition one or multiple previously unpartitioned base tables, then consider carefully before skipping this recommendation. Changing a table's partitioning scheme affects the cost of all queries, indexes, and materialized views defined on the table. Therefore, if you skip the partitioning recommendation, then the advisor's remaining recommendations on this table are no longer optimal. To see recommendations on your workload that do not contain partitioning, reset the advisor task and rerun it with the ANALYSIS_SCOPE parameter changed to exclude partitioning recommendations.

The syntax is as follows:

DBMS_ADVISOR.MARK_RECOMMENDATION (
task_name          IN VARCHAR2
id                 IN NUMBER,
action             IN VARCHAR2);

Assumptions

This tutorial assumes the following:

- You are reviewing the recommendations as described in tutorial "Viewing SQL Access Advisor Task Results".

- You want to reject the first recommendation, which partitions a table.

To mark a recommendation:

1. Connect SQL*Plus to the database as user sh, and then mark the recommendation.
For example, reject recommendation 1 as follows:

EXECUTE DBMS_ADVISOR.MARK_RECOMMENDATION('MYTASK', 1, 'REJECT');

This recommendation and any dependent recommendations do not appear in the script.

2. Generate the script as explained in "Generating and Executing a Task Script".

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn more about the MARK_RECOMMENDATIONS procedure and its parameters

21.4.7 Modifying SQL Access Advisor Recommendations

Using the UPDATE_REC_ATTRIBUTES procedure, SQL Access Advisor names and assigns ownership to new objects such as indexes and materialized views during analysis. However, it does not necessarily choose appropriate names, so you may manually set the owner, name, and tablespace values for new objects. For recommendations referencing existing database objects, owner and name values cannot be changed. The syntax is as follows:

DBMS_ADVISOR.UPDATE_REC_ATTRIBUTES (
task_name            IN VARCHAR2
rec_id               IN NUMBER,
action_id            IN NUMBER,
attribute_name       IN VARCHAR2,
value                IN VARCHAR2);

The attribute_name parameter can take the following values:

- OWNER
Specifies the owner name of the recommended object.

- NAME
Specifies the name of the recommended object.

- TABLESPACE
Specifies the tablespace of the recommended object.

Assumptions

This tutorial assumes the following:

- You are reviewing the recommendations as described in tutorial "Viewing SQL Access Advisor Task Results".

- You want to change the tablespace for recommendation 1, action 1 to SH_MVIEWS.

To mark a recommendation:

1. Connect SQL*Plus to the database as user sh, and then update the recommendation attribute.
For example, change the tablespace name to SH_MVIEWS as follows:

BEGIN
DBMS_ADVISOR.UPDATE_REC_ATTRIBUTES (
'MYTASK'
,   1
,   1
,   'TABLESPACE'
,   'SH_MVIEWS'
);
END;

2. Generate the script as explained in "Generating and Executing a Task Script".

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn more about the UPDATE_REC_ATTRIBUTES procedure and its parameters

21.5 SQL Access Advisor Examples

Oracle Database provides a script that contains several SQL Access Advisor examples that you can run on a test database. The script is named ORACLE_HOME/rdbms/demo/aadvdemo.sql.

21.6 SQL Access Advisor Reference

This section contains the following topics:

- Action Attributes in the DBA_ADVISOR_ACTIONS View

- Categories for SQL Access Advisor Task Parameters

- SQL Access Advisor Constants

21.6.1 Action Attributes in the DBA_ADVISOR_ACTIONS View

Table 21-4 maps SQL Access Advisor actions to attribute columns in the DBA_ADVISOR_ACTIONS view. In the table, MV refers to a materialized view.

Table 21-4 SQL Access Advisor Action Attributes

Action

ATTR1 Column

ATTR2 Column

ATTR3 Column

ATTR4 Column

ATTR5 Column

ATTR6 Column

NUM_ATTR1 Column

CREATE INDEX

Index name

Index tablespace

Target table

BITMAP or BTREE

Index column list / expression

Unused

Storage size in bytes for the index

CREATE MATERIALIZED VIEW

MV name

MV tablespace

REFRESH COMPLETE REFRESH FAST, REFRESH FORCE, NEVER REFRESH

ENABLE QUERY REWRITE, DISABLE QUERY REWRITE

SQL SELECT statement

Unused

Storage size in bytes for the MV

CREATE MATERIALIZED VIEW LOG

Target table name

MV log tablespace

ROWID PRIMARY KEY, SEQUENCE OBJECT ID

INCLUDING NEW VALUES, EXCLUDING NEW VALUES

Table column list

Partitioning subclauses

Unused

CREATE REWRITE EQUIVALENCE

Name of equivalence

Checksum value

Unused

Unused

Source SQL statement

Equivalent SQL statement

Unused

DROP INDEX

Index name

Unused

Unused

Unused

Index columns

Unused

Storage size in bytes for the index

DROP MATERIALIZED VIEW

MV name

Unused

Unused

Unused

Unused

Unused

Storage size in bytes for the MV

DROP MATERIALIZED VIEW LOG

Target table name

Unused

Unused

Unused

Unused

Unused

Unused

PARTITION TABLE

Table name

RANGE, INTERVAL, LIST, HASH, RANGE-HASH, RANGE-LIST

Partition key for partitioning (column name or list of column names)

Partition key for subpartitioning (column name or list of column names)

SQL PARTITION clause

SQL SUBPARTITION clause

Unused

PARTITION INDEX

Index name

LOCAL, RANGE, HASH

Partition key for partitioning (list of column names)

Unused

SQL PARTITION clause

Unused

Unused

PARTITION ON MATERIALIZED VIEW

MV name

RANGE, INTERVAL, LIST, HASH, RANGE-HASH, RANGE-LIST

Partition key for partitioning (column name or list of column names)

Partition key for subpartitioning (column name or list of column names)

SQL SUBPARTITION clause

SQL SUBPARTITION clause

Unused

RETAIN INDEX

Index name

Unused

Target table

BITMAP or BTREE

Index columns

Unused

Storage size in bytes for the index

RETAIN MATERIALIZED VIEW

MV name

Unused

REFRESH COMPLETE or REFRESH FAST

Unused

SQL SELECT statement

Unused

Storage size in bytes for the MV

RETAIN MATERIALIZED VIEW LOG

Target table name

Unused

Unused

Unused

Unused

Unused

Unused

21.6.2 Categories for SQL Access Advisor Task Parameters

Table 21-5 groups the most relevant SQL Access Advisor task parameters into categories. All task parameters for workload filtering are deprecated.

Table 21-5 Types of Advisor Task Parameters And Their Uses

Workload Filtering

Task Configuration

Schema Attributes

Recommendation Options

END_TIME

DAYS_TO_EXPIRE

DEF_INDEX_OWNER

ANALYSIS_SCOPE

INVALID_ACTION_LIST

JOURNALING

DEF_INDEX_TABLESPACE

COMPATIBILITY

INVALID_MODULE_LIST

REPORT_DATE_FORMAT

DEF_MVIEW_OWNER

CREATION_COST

INVALID_SQLSTRING_LIMIT

DEF_MVIEW_TABLESPACE

DML_VOLATILITY

INVALID_TABLE_LIST

DEF_MVLOG_TABLESPACE

LIMIT_PARTITION_SCHEMES

INVALID_USERNAME_LIST

DEF_PARTITION_TABLESPACE

MODE

RANKING_MEASURE

INDEX_NAME_TEMPLATE

PARTITIONING_TYPES

SQL_LIMIT

MVIEW_NAME_TEMPLATE

REFRESH_MODE

START_TIME

STORAGE_CHANGE

TIME_LIMIT

USE_SEPARATE_TABLESPACES

VALID_ACTION_LIST

WORKLOAD_SCOPE

VALID_MODULE_LIST

VALID_SQLSTRING_LIST

VALID_TABLE_LIST

VALID_USERNAME_LIST

21.6.3 SQL Access Advisor Constants

You can use the constants shown in Table 21-6 with SQL Access Advisor.

Table 21-6 SQL Access Advisor Constants

Constant

Description

ADVISOR_ALL

A value that indicates all possible values. For string parameters, this value is equivalent to the wildcard % character.

ADVISOR_CURRENT

Indicates the current time or active set of elements. Typically, this is used in time parameters.

ADVISOR_DEFAULT

Indicates the default value. Typically used when setting task or workload parameters.

ADVISOR_UNLIMITED

A value that represents an unlimited numeric value.

ADVISOR_UNUSED

A value that represents an unused entity. When a parameter is set to ADVISOR_UNUSED, it has no effect on the current operation. A typical use for this constant is to set a parameter as unused for its dependent operations.

SQLACCESS_GENERAL

Specifies the name of a default SQL Access general-purpose task template. This template sets the DML_VOLATILITY task parameter to true and ANALYSIS_SCOPE to INDEX, MVIEW.

SQLACCESS_OLTP

Specifies the name of a default SQL Access OLTP task template. This template sets the DML_VOLATILITY task parameter to true and ANALYSIS_SCOPE to INDEX.

SQLACCESS_WAREHOUSE

Specifies the name of a default SQL Access warehouse task template. This template sets the DML_VOLATILITY task parameter to false and EXECUTION_TYPE to INDEX, MVIEW.

SQLACCESS_ADVISOR

Contains the formal name of SQL Access Advisor. You can specify this name when procedures require the Advisor name as an argument.

Part IX

SQL Controls

This part contains the following chapters:

- Chapter 22, "Managing SQL Profiles"

- Chapter 23, "Managing SQL Plan Baselines"

- Chapter 24, "Migrating Stored Outlines to SQL Plan Baselines"

22 Managing SQL Profiles

This chapter contains the following topics:

- About SQL Profiles

- Implementing a SQL Profile

- Listing SQL Profiles

- Altering a SQL Profile

- Dropping a SQL Profile

- Transporting a SQL Profile

22.1 About SQL Profiles

A SQL profile is a database object that contains auxiliary statistics specific to a SQL statement. Conceptually, a SQL profile is to a SQL statement what object-level statistics are to a table or index. SQL profiles are created when a DBA invokes SQL Tuning Advisor (see "About SQL Tuning Advisor").

This section contains the following topics:

- Purpose of SQL Profiles

- Concepts for SQL Profiles

- User Interfaces for SQL Profiles

- Basic Tasks for SQL Profiles

22.1.1 Purpose of SQL Profiles

When profiling a SQL statement, SQL Tuning Advisor uses a specific set of bind values as input, and then compares the optimizer estimate with values obtained by executing fragments of the statement on a data sample. When significant variances are found, SQL Tuning Advisor bundles corrective actions together in a SQL profile, and then recommends its acceptance.

The corrected statistics in a SQL profile can improve optimizer cardinality estimates, which in turn leads the optimizer to select better plans. SQL profiles provide the following benefits over other techniques for improving plans:

- Unlike hints and stored outlines, SQL profiles do not tie the optimizer to a specific plan or subplan. SQL profiles fix incorrect estimates while giving the optimizer the flexibility to pick the best plan in different situations.

- Unlike hints, no changes to application source code are necessary when using SQL profiles. The use of SQL profiles by the database is transparent to the user.

See Also:
"Influencing the Optimizer with Hints"

22.1.2 Concepts for SQL Profiles

A SQL profile is a collection of auxiliary statistics on a query, including all tables and columns referenced in the query. The profile stores this information in the data dictionary. The optimizer uses this information at optimization time to determine the correct plan.

Note:
The SQL profile contains supplemental statistics for the entire statement, not individual plans. The profile does not itself determine a specific plan.

A SQL profile contains, among other statistics, a set of cardinality adjustments. The cardinality measure is based on sampling the WHERE clause rather than on statistical projection. A profile uses parts of the query to determine whether the estimated cardinalities are close to the actual cardinalities and, if a mismatch exists, uses the corrected cardinalities. For example, if a SQL profile exists for SELECT * FROM t WHERE x=5 AND y=10, then the profile stores the actual number of rows returned.

When choosing plans, the optimizer has the following sources of information:

- The environment, which contains the database configuration, bind variable values, optimizer statistics, data set, and so on

- The supplemental statistics in the SQL profile

Figure 22-1 shows the relationship between a SQL statement and the SQL profile for this statement. The optimizer uses the SQL profile and the environment to generate an execution plan. In this example, the plan is in the SQL plan baseline for the statement.

Figure 22-1 SQL Profile

If either the optimizer environment or SQL profile change, then the optimizer can create a new plan. As tables grow, or as indexes are created or dropped, the plan for a SQL profile can change. The profile continues to be relevant even if the data distribution or access path of the corresponding statement changes. In general, you do not need to refresh SQL profiles.

Over time, profile content can become outdated. In this case, performance of the SQL statement may degrade. The statement may appear as high-load or top SQL. In this case, the Automatic SQL Tuning task again captures the statement as high-load SQL. You can implement a new SQL profile for the statement.

Internally, a SQL profile is implemented using hints that address different types of problems. These hints do not specify any particular plan. Rather, the hints correct errors in the optimizer estimation algorithm that lead to suboptimal plans. For example, a profile may use the TABLE_STATS hint to set object statistics for tables when the statistics are missing or stale.

See Also:

- "Differences Between SQL Plan Baselines and SQL Profiles"

- "Introduction to Optimizer Statistics"

22.1.2.1 SQL Profile Recommendations

As explained in "SQL Profiling", SQL Tuning Advisor invokes Automatic Tuning Optimizer to generate SQL profile recommendations. Recommendations to implement SQL profiles occur in a finding, which appears in a separate section of the SQL Tuning Advisor report.

When you implement (or accept) a SQL profile, the database creates the profile and stores it persistently in the data dictionary. However, the SQL profile information is not exposed through regular dictionary views.

Example 22-1 SQL Profile Recommendation

In this example, the database found a better plan for a SELECT statement that uses several expensive joins. The database recommends running DBMS_SQLTUNE.ACCEPT_SQL_PROFILE to implement the profile, which enables the statement to run 98.53% faster.

-------------------------------------------------------------------------------
FINDINGS SECTION (2 findings)
-------------------------------------------------------------------------------

1- SQL Profile Finding (see explain plans section below)
--------------------------------------------------------
A potentially better execution plan was found for this statement. Choose
one of the following SQL profiles to implement.

Recommendation (estimated benefit: 99.45%)
------------------------------------------
- Consider accepting the recommended SQL profile.
execute dbms_sqltune.accept_sql_profile(task_name => 'my_task',
object_id => 3, task_owner => 'SH', replace => TRUE);

Validation results
------------------
The SQL profile was tested by executing both its plan and the original plan
and measuring their respective execution statistics. A plan may have been
only partially executed if the other could be run to completion in less time.

Original Plan  With SQL Profile  % Improved
-------------  ----------------  ----------
Completion Status:             PARTIAL          COMPLETE
Elapsed Time(us):            15467783            226902      98.53 %
CPU Time(us):                15336668            226965      98.52 %
User I/O Time(us):                  0                 0
Buffer Gets:                  3375243             18227      99.45 %
Disk Reads:                         0                 0
Direct Writes:                      0                 0
Rows Processed:                     0               109
Fetches:                            0               109
Executions:                         0                 1

Notes
-----
1. The SQL profile plan was first executed to warm the buffer cache.
2. Statistics for the SQL profile plan were averaged over next 3 executions.

Sometimes SQL Tuning Advisor may recommend implementing a profile that uses the Automatic Degree of Parallelism (Auto DOP) feature. A parallel query profile is only recommended when the original plan is serial and when parallel execution can significantly reduce the elapsed time for a long-running query.

When it recommends a profile that uses Auto DOP, SQL Tuning Advisor gives details about the performance overhead of using parallel execution for the SQL statement in the report. For parallel execution recommendations, SQL Tuning Advisor may provide two SQL profile recommendations, one using serial execution and one using parallel.

The following example shows a parallel query recommendation. In this example, a degree of parallelism of 7 improves response time significantly at the cost of increasing resource consumption by almost 25%. You must decide whether the reduction in database throughput is worth the increase in response time.

Recommendation (estimated benefit: 99.99%)
------------------------------------------
- Consider accepting the recommended SQL profile to use parallel execution
for this statement.
execute dbms_sqltune.accept_sql_profile(task_name => 'gfk_task',
object_id => 3, task_owner => 'SH', replace => TRUE,
profile_type => DBMS_SQLTUNE.PX_PROFILE);

Executing this query parallel with DOP 7 will improve its response time
82.22% over the SQL profile plan. However, there is some cost in enabling
parallel execution. It will increase the statement's resource consumption by
an estimated 24.43% which may result in a reduction of system throughput.
Also, because these resources are consumed over a much smaller duration, the
response time of concurrent statements might be negatively impacted if
sufficient hardware capacity is not available.

The following data shows some sampled statistics for this SQL from the past
week and projected weekly values when parallel execution is enabled.

Past week sampled statistics for this SQL
-----------------------------------------
Number of executions                                                   0
Percent of total activity                                            .29
Percent of samples with #Active Sessions > 2*CPU                       0
Weekly DB time (in sec)                                            76.51

Projected statistics with Parallel Execution
--------------------------------------------
Weekly DB time (in sec)                                            95.21

See Also:

- Oracle Database VLDB and Partitioning Guide to learn more about Auto DOP

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SQLTUNE.ACCEPT_SQL_PROFILE procedure

22.1.2.2 SQL Profiles and SQL Plan Baselines

You can use SQL profiles with or without SQL plan management. No strict relationship exists between the SQL profile and the plan baseline. If a statement has multiple plans in a SQL plan baseline, then a SQL profile is useful because it enables the optimizer to choose the lowest-cost plan in the baseline.

See Also:
Chapter 23, "Managing SQL Plan Baselines"

22.1.3 User Interfaces for SQL Profiles

Oracle Enterprise Manager Cloud Control (Cloud Control) usually handles SQL profiles as part of automatic SQL tuning.

On the command line, you can manage SQL profiles with the DBMS_SQLTUNE package. To use the APIs, you must have the ADMINISTER SQL MANAGEMENT OBJECT privilege.

See Also:

- Oracle Database PL/SQL Packages and Types Reference for information about the DBMS_SQLTUNE package

- Oracle Database 2 Day + Performance Tuning Guide to learn how to manage SQL profiles with Cloud Control

22.1.4 Basic Tasks for SQL Profiles

This section explains the basic tasks involved in managing SQL profiles. Figure 22-2 shows the basic workflow for implementing, altering, and dropping SQL profiles.

Figure 22-2 Managing SQL Profiles

Typically, you manage SQL profiles in the following sequence:

1. Implement a recommended SQL profile.
"Implementing a SQL Profile" describes this task.

2. Obtain information about SQL profiles stored in the database.
"Listing SQL Profiles" describes this task.

3. Optionally, modify the implemented SQL profile.
"Altering a SQL Profile" describes this task.

4. Drop the implemented SQL profile when it is no longer needed.
"Dropping a SQL Profile" describes this task.

To tune SQL statements on another database, you can transport both a SQL tuning set and a SQL profile to a separate database. "Transporting a SQL Profile" describes this task.

See Also:
Oracle Database PL/SQL Packages and Types Reference for information about the DBMS_SQLTUNE package

22.2 Implementing a SQL Profile

Implementing (also known as accepting) a SQL profile means storing it persistently in the database. A profile must be implemented before the optimizer can use it as input when generating plans.

22.2.1 About SQL Profile Implementation

As a rule of thumb, implement a SQL profile recommended by SQL Tuning Advisor. If the database recommends both an index and a SQL profile, then either use both or use the SQL profile only. If you create an index, then the optimizer may need the profile to pick the new index.

In some situations, SQL Tuning Advisor may find an improved serial plan in addition to an even better parallel plan. In this case, the advisor recommends both a standard and a parallel SQL profile, enabling you to choose between the best serial and best parallel plan for the statement. Implement a parallel plan only if the increase in response time is worth the decrease in throughput.

To implement a SQL profile, execute the DBMS_SQLTUNE.ACCEPT_SQL_PROFILE procedure. Some important parameters are as follows:

- profile_type
Set this parameter to REGULAR_PROFILE for a SQL profile without a change to parallel execution, or PX_PROFLE for a SQL profile with a change to parallel execution.

- force_match
This parameter controls statement matching. Typically, an accepted SQL profile is associated with the SQL statement through a SQL signature that is generated using a hash function. This hash function changes the SQL statement to upper case and removes all extra whites spaces before generating the signature. Thus, the same SQL profile works for all SQL statements in which the only difference is case and white spaces.

By setting force_match to true, the SQL profile additionally targets all SQL statements that have the same text after the literal values in the WHERE clause have been replaced by bind variables. This setting may be useful for applications that use only literal values because it enables SQL with text differing only in its literal values to share a SQL profile. If both literal values and bind variables are in the SQL text, or if force_match is set to false (default), then the literal values in the WHERE clause are not replaced by bind variables.

See Also:
Oracle Database PL/SQL Packages and Types Reference for information about the ACCEPT_SQL_PROFILE procedure

22.2.2 Implementing a SQL Profile

This section shows how to use the ACCEPT_SQL_PROFILE procedure to implement a SQL profile.

Assumptions

This tutorial assumes the following:

- The SQL Tuning Advisor task STA_SPECIFIC_EMP_TASK includes a recommendation to create a SQL profile.

- The name of the SQL profile is my_sql_profile.

- The PL/SQL block accepts a profile that uses parallel execution (profile_type).

- The profile uses force matching.

To implement a SQL profile:

- Connect SQL*Plus to the database with the appropriate privileges, and then execute the ACCEPT_SQL_PROFILE function.
For example, execute the following PL/SQL:

DECLARE
my_sqlprofile_name VARCHAR2(30);
BEGIN
my_sqlprofile_name := DBMS_SQLTUNE.ACCEPT_SQL_PROFILE (
task_name    => 'STA_SPECIFIC_EMP_TASK'
,   name         => 'my_sql_profile'
,   profile_type => DBMS_SQLTUNE.PX_PROFILE
,   force_match  => true
);
END;
/

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SQLTUNE.ACCEPT_SQL_PROFILE procedure

22.3 Listing SQL Profiles

The data dictionary view DBA_SQL_PROFILES stores SQL profiles persistently in the database. The statistics are in an Oracle internal format, so you cannot query profiles directly. However, you can list profiles.

To list SQL profiles:

- Connect SQL*Plus to the database with the appropriate privileges, and then query the DBA_SQL_PROFILES view.
For example, execute the following query:

COLUMN category FORMAT a10
COLUMN sql_text FORMAT a20

SELECT NAME, SQL_TEXT, CATEGORY, STATUS
FROM   DBA_SQL_PROFILES;

Sample output appears below:

NAME                           SQL_TEXT             CATEGORY   STATUS
------------------------------ -------------------- ---------- --------
SYS_SQLPROF_01285f6d18eb0000   select promo_name, c DEFAULT    ENABLED
ount(*) c from promo
tions p, sales s whe
re s.promo_id = p.pr
omo_id and p.promo_c
ategory = 'internet'
group by p.promo_na
me order by c desc

See Also:
Oracle Database Reference to learn about the DBA_SQL_PROFILES view

22.4 Altering a SQL Profile

You can alter attributes of an existing SQL profile using the attribute_name parameter of the ALTER_SQL_PROFILE procedure.

The CATEGORY attribute determines which sessions can apply a profile. View the CATEGORY attribute by querying DBA_SQL_PROFILES.CATEGORY. By default, all profiles are in the DEFAULT category, which means that all sessions in which the SQLTUNE_CATEGORY initialization parameter is set to DEFAULT can use the profile.

By altering the category of a SQL profile, you determine which sessions are affected by profile creation. For example, by setting the category to DEV, only sessions in which the SQLTUNE_CATEGORY initialization parameter is set to DEV can use the profile. Other sessions do not have access to the SQL profile and execution plans for SQL statements are not impacted by the SQL profile. This technique enables you to test a profile in a restricted environment before making it available to other sessions.

The example in this section assumes that you want to change the category of the SQL profile so it is used only by sessions with the SQL profile category set to TEST, run the SQL statement, and then change the profile category back to DEFAULT.

To alter a SQL profile:

1. Connect SQL*Plus to the database with the appropriate privileges, and then use the ALTER_SQL_PROFILE procedure to set the attribute_name.
For example, execute the following code to set the attribute CATEGORY to TEST:

VARIABLE pname my_sql_profile
BEGIN DBMS_SQLTUNE.ALTER_SQL_PROFILE (
name            =>  :pname
,  attribute_name  =>  'CATEGORY'
,  value           =>  'TEST'
);
END;

2. Change the initialization parameter setting in the current database session.
For example, execute the following SQL:

ALTER SESSION SET SQLTUNE_CATEGORY = 'TEST';

3. Test the profiled SQL statement.

4. Use the ALTER_SQL_PROFILE procedure to set the attribute_name.
For example, execute the following code to set the attribute CATEGORY to DEFAULT:

VARIABLE pname my_sql_profile
BEGIN
DBMS_SQLTUNE.ALTER_SQL_PROFILE (
name            =>  :pname
,    attribute_name  =>  'CATEGORY'
,    value           =>  'DEFAULT'
);
END;

See Also:

- Oracle Database Reference to learn about the SQLTUNE_CATEGORY initialization parameter

- Oracle Database PL/SQL Packages and Types Reference to learn about the ALTER_SQL_PROFILE procedure

22.5 Dropping a SQL Profile

You can drop a SQL profile with the DROP_SQL_PROFILE procedure.

Assumptions

This section assumes the following:

- You want to drop my_sql_profile.

- You want to ignore errors raised if the name does not exist.

To drop a SQL profile:

- Connect SQL*Plus to the database with the appropriate privileges, call the DBMS_SQLTUNE.DROP_SQL_PROFILE procedure.
The following example drops the profile named my_sql_profile:

BEGIN
DBMS_SQLTUNE.DROP_SQL_PROFILE (
name => 'my_sql_profile'
);
END;
/

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the DROP_SQL_PROFILE procedure

- Oracle Database Reference to learn about the SQLTUNE_CATEGORY initialization parameter

22.6 Transporting a SQL Profile

You can transport SQL profiles. This operation involves exporting the SQL profile from the SYS schema in one database to a staging table, and then importing the SQL profile from the staging table into another database. You can transport a SQL profile to any Oracle database created in the same release or later.

Table 22-1 shows the main procedures and functions for managing SQL profiles.

Table 22-1 APIs for Transporting SQL Profiles

Procedure or Function

Description

CREATE_STGTAB_SQLPROF

Creates the staging table used for copying SQL profiles from one system to another.

PACK_STGTAB_SQLPROF

Moves profile data out of the SYS schema into the staging table.

UNPACK_STGTAB_SQLPROF

Uses the profile data stored in the staging table to create profiles on this system.

The following graphic shows the basic workflow of transporting SQL profiles:

Assumptions

This tutorial assumes the following:

- You want to transport my_profile from a production database to a test database.

- You want to create the staging table in the dba1 schema.

To transport a SQL profile:

1. Connect SQL*Plus to the database with the appropriate privileges, and then use the CREATE_STGTAB_SQLPROF procedure to create a staging table to hold the SQL profiles.
The following example creates my_staging_table in the dba1 schema:

BEGIN
DBMS_SQLTUNE.CREATE_STGTAB_SQLPROF (
table_name  => 'my_staging_table'
,   schema_name => 'dba1'
);
END;
/

2. Use the PACK_STGTAB_SQLPROF procedure to export SQL profiles into the staging table.
The following example populates dba1.my_staging_table with the SQL profile my_profile:

BEGIN
DBMS_SQLTUNE.PACK_STGTAB_SQLPROF (
profile_name         => 'my_profile'
,   staging_table_name   => 'my_staging_table'
,   staging_schema_owner => 'dba1'
);
END;
/

3. Move the staging table to the database where you plan to unpack the SQL profiles.
Move the table using your utility of choice. For example, use Oracle Data Pump or a database link.

4. On the database where you plan to import the SQL profiles, use UNPACK_STGTAB_SQLPROF to unpack SQL profiles from the staging table.
The following example shows how to unpack SQL profiles in the staging table:

BEGIN
DBMS_SQLTUNE.UNPACK_STGTAB_SQLPROF(
replace            => true
,    staging_table_name => 'my_staging_table'
);
END;
/

See Also:

- Oracle Database PL/SQL Packages and Types Reference for complete reference information about DBMS_SQLTUNE

- Oracle Database Utilities to learn how to use Oracle Data Pump

23 Managing SQL Plan Baselines

This chapter explains the concepts and tasks relating to SQL plan management using the DBMS_SPM package.

This chapter contains the following topics:

- About SQL Plan Management

- Configuring SQL Plan Management

- Displaying Plans in a SQL Plan Baseline

- Loading SQL Plan Baselines

- Evolving SQL Plan Baselines Manually

- Dropping SQL Plan Baselines

- Managing the SQL Management Base

See Also:
Chapter 24, "Migrating Stored Outlines to SQL Plan Baselines"

23.1 About SQL Plan Management

SQL plan management is a preventative mechanism that enables the optimizer to automatically manage execution plans, ensuring that the database uses only known or verified plans. In this context, a plan includes all plan-related information (for example, SQL plan identifier, set of hints, bind values, and optimizer environment) that the optimizer needs to reproduce an execution plan.

SQL plan management uses a mechanism called a SQL plan baseline. A plan baseline is a set of accepted plans that the optimizer is allowed to use for a SQL statement. In the typical use case, the database accepts a plan into the plan baseline only after verifying that the plan performs well.

The main components of SQL plan management are as follows:

- Plan capture
This component stores relevant information about plans for a set of SQL statements. See "Plan Capture".

- Plan selection
This component is the detection by the optimizer of plan changes based on stored plan history, and the use of SQL plan baselines to select appropriate plans to avoid potential performance regressions. See "Plan Selection".

- Plan evolution
This component is the process of adding new plans to existing SQL plan baselines, either manually or automatically. See "Plan Evolution".

This section contains the following topics:

- Purpose of SQL Plan Management

- Plan Capture

- Plan Selection

- Plan Evolution

- Storage Architecture for SQL Plan Management

- User Interfaces for SQL Plan Management

- Basic Tasks in SQL Plan Management

23.1.1 Purpose of SQL Plan Management

The primary goal of SQL plan management is to prevent performance regressions caused by plan changes. A secondary goal is to gracefully adapt to changes such as new optimizer statistics or indexes by verifying and accepting only plan changes that improve performance.

Note:
SQL plan baselines cannot help when an event has caused irreversible execution plan changes, such as dropping an index.

23.1.1.1 Benefits of SQL Plan Management

Typical scenarios in which SQL plan management can improve or preserve SQL performance include:

- A database upgrade that installs a new optimizer version usually results in plan changes for a small percentage of SQL statements.
Most plan changes result in either improvement or no performance change. However, some plan changes may cause performance regressions. SQL plan baselines significantly minimize potential regressions resulting from an upgrade.

When you upgrade, the database only uses plans from the plan baseline. The database puts new plans that are not in the current baseline into a holding area, and later evaluates them to determine whether they use fewer resources than the current plan in the baseline. If the plans perform better, then the database promotes them into the baseline; otherwise, the database does not promote them.

- Ongoing system and data changes can affect plans for some SQL statements, potentially causing performance regressions.
SQL plan baselines help minimize performance regressions and stabilize SQL performance.

- Deployment of new application modules introduces new SQL statements into the database.
The application software may use appropriate SQL execution plans developed in a standard test configuration for the new statements. If the system configuration is significantly different from the test configuration, then the database can evolve SQL plan baselines over time to produce better performance.

See Also:
Oracle Database Upgrade Guide to learn how to upgrade an Oracle database

23.1.1.2 Differences Between SQL Plan Baselines and SQL Profiles

Both SQL profiles and SQL plan baselines help improve the performance of SQL statements by ensuring that the optimizer uses only optimal plans. Both profiles and baselines are internally implemented using hints (see "About Optimizer Hints"). However, these mechanisms have the following significant differences:

- In general, SQL plan baselines are proactive, whereas SQL profiles are reactive.
Typically, you create SQL plan baselines before significant performance problems occur. SQL plan baselines prevent the optimizer from using suboptimal plans in the future.

The database creates SQL profiles when you invoke SQL Tuning Advisor, which you do typically only after a SQL statement has shown high-load symptoms. SQL profiles are primarily useful by providing the ongoing resolution of optimizer mistakes that have led to suboptimal plans. Because the SQL profile mechanism is reactive, it cannot guarantee stable performance as drastic database changes occur.

The following graphic illustrates the difference:

- SQL plan baselines reproduce a specific plan, whereas SQL profiles correct optimizer cost estimates.
A SQL plan baseline is a set of accepted plans. Each plan is implemented using a set of outline hints that fully specify a particular plan. SQL profiles are also implemented using hints, but these hints do not specify any specific plan. Rather, the hints correct miscalculations in the optimizer estimates that lead to suboptimal plans. For example, a hint may correct the cardinality estimate of a table.

Because a profile does not constrain the optimizer to any one plan, a SQL profile is more flexible than a SQL plan baseline. For example, changes in initialization parameters and optimizer statistics allow the optimizer to choose a better plan.

Oracle recommends that you use SQL Tuning Advisor. In this way, you follow the recommendations made by the advisor for SQL profiles and plan baselines rather than trying to determine which mechanism is best for each SQL statement.

See Also:

- Chapter 22, "Managing SQL Profiles"

- Chapter 20, "Analyzing SQL with SQL Tuning Advisor"

23.1.2 Plan Capture

SQL plan capture refers to techniques for capturing and storing relevant information about plans in the SQL Management Base for a set of SQL statements. Capturing a plan means making SQL plan management aware of this plan.

You can configure initial plan capture to occur automatically by setting an initialization parameter, or you can capture plans manually by using the DBMS_SPM package.

23.1.2.1 Automatic Initial Plan Capture

You enable automatic initial plan capture by setting the initialization parameter OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES to true (the default is false). When enabled, the database automatically creates a SQL plan baseline for any repeatable SQL statement executed on the database.

If automatic initial plan capture is enabled, and if the database executes a repeatable SQL statement, then the capture algorithm is as follows:

- If a SQL plan baseline does not exist, then the optimizer creates a plan history and SQL plan baseline for the statement, marking the initial plan for the statement as accepted and adding it to the SQL plan baseline.

- If a SQL plan baseline exists, then the optimizer behavior depends on the cost-based plan derived at parse time:

- If this plan does not match a plan in the SQL plan baseline, then the optimizer marks the new plan as unaccepted and adds it to the SQL plan baseline.

- If this plan does match a plan in the SQL plan baseline, then nothing is added to the SQL plan baseline.

The following graphic shows the decision tree for automatic initial plan capture when OPTIMIZER_USE_SQL_PLAN_BASELINES is set to true (see "Plan Selection" for more information):

Note:
The settings of OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES and OPTIMIZER_USE_SQL_PLAN_BASELINES are independent. For example, if OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES is true, then the database creates initial plan baselines regardless of whether OPTIMIZER_USE_SQL_PLAN_BASELINES is true or false.

See Also:
Oracle Database Reference to learn about the OPTIMIZER_USE_SQL_PLAN_BASELINES initialization parameter

23.1.2.2 Manual Plan Capture

In SQL plan management, manual plan capture refers to the user-initiated bulk load of existing plans into a SQL plan baseline. Use Cloud Control or PL/SQL to load the execution plans for SQL statements from a SQL tuning set (STS), the shared SQL area, a staging table, or a stored outline.

The following graphic illustrates loading plans into a SQL plan baseline.

The loading behavior varies depending on whether a SQL plan baseline exists for each statement represented in the bulk load:

- If a baseline for the statement does not exist, then the database does the following:

1. Creates a plan history and plan baseline for the statement

2. Marks the initial plan for the statement as accepted

3. Adds the plan to the new baseline

- If a baseline for the statement exists, then the database does the following:

1. Marks the loaded plan as accepted

2. Adds the plan to the plan baseline for the statement without verifying the plan's performance

Manually loaded plans are always marked accepted because the optimizer assumes that any plan loaded manually by the administrator has acceptable performance.

23.1.3 Plan Selection

SQL plan selection is the optimizer ability to detect plan changes based on stored plan history, and the use of SQL plan baselines to select plans to avoid potential performance regressions.

When the database performs a hard parse of a SQL statement, the optimizer generates a best-cost plan. By default, the optimizer then attempts to find a matching plan in the SQL plan baseline for the statement. If no plan baseline exists, then the database runs the statement with the best-cost plan.

If a plan baseline exists, then the optimizer behavior depends on whether the newly generated plan is in the plan baseline:

- If the new plan is in the baseline, then the database executes the statement using the found plan.

- If the new plan is not in the baseline, then the optimizer marks the newly generated plan as unaccepted and adds it to the plan history. Optimizer behavior depends on the contents of the plan baseline:

- If fixed plans exist in the plan baseline, then the optimizer uses the fixed plan (see "Fixed Plans") with the lowest cost.

- If no fixed plans exist in the plan baseline, then the optimizer uses the baseline plan with the lowest cost.

- If no reproducible plans exist in the plan baseline, which could happen if every plan in the baseline referred to a dropped index, then the optimizer uses the newly generated cost-based plan.

The following graphic shows the decision tree for SQL plan selection.

23.1.4 Plan Evolution

In general, SQL plan evolution is the process by which the optimizer verifies new plans and adds them to an existing SQL plan baseline. Specifically, plan evolution consists of the following distinct steps:

1. Verifying that unaccepted plans perform at least as well as accepted plans in a SQL plan baseline (known as plan verification)

2. Adding unaccepted plans to the plan baseline as accepted plans after the database has proved that they perform as well as accepted plans

In the standard case of plan evolution, the optimizer performs the preceding steps sequentially, so that a new plan is not usable by SQL plan management until the optimizer verifies plan performance relative to the SQL plan baseline. However, you can configure SQL plan management to perform one step without performing the other. The following graphic shows the possible paths for plan evolution:

23.1.4.1 Purpose of Plan Evolution

Typically, a SQL plan baseline for a SQL statement starts with a single accepted plan. However, some SQL statements perform well when executed with different plans under different conditions. For example, a SQL statement with bind variables whose values result in different selectivities may have several optimal plans. Creating a materialized view or an index or repartitioning a table may make current plans more expensive than other plans.

If new plans were never added to SQL plan baselines, then the performance of some SQL statements might degrade. Thus, it is sometimes necessary to evolve newly accepted plans into SQL plan baselines. Plan evolution prevents performance regressions by verifying the performance of a new plan before including it in a SQL plan baseline.

23.1.4.2 PL/SQL Procedures for Plan Evolution

The DBMS_SPM package provides procedures and functions for plan evolution. These procedures use the task infrastructure. For example, CREATE_EVOLVE_TASK creates an evolution task, whereas EXECUTE_EVOLVE_TASK executes it. All task evolution procedures have the string EVOLVE_TASK in the name.

Use the evolve procedures on demand, or configure the procedures to run automatically. The automatic maintenance task SYS_AUTO_SPM_EVOLVE_TASK executes daily in the scheduled maintenance window. The task perform the following actions automatically:

1. Selects and ranks unaccepted plans for verification

2. Accepts each plan if it satisfies the performance threshold

See Also:

- "Managing the SPM Evolve Advisor Task"

- "Evolving SQL Plan Baselines Manually"

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM package

23.1.5 Storage Architecture for SQL Plan Management

This section describes the SQL plan management storage architecture:

- SQL Management Base

- SQL Statement Log

- SQL Plan History

23.1.5.1 SQL Management Base

The SQL management base (SMB) is a logical repository in the data dictionary that contains the following:

- SQL statement log, which contains only SQL IDs

- SQL plan history, which includes the SQL plan baselines

- SQL profiles

- SQL patches

The SMB stores information that the optimizer can use to maintain or improve SQL performance.

The SMB resides in the SYSAUX tablespace and uses automatic segment-space management. Because the SMB is located entirely within the SYSAUX tablespace, the database does not use SQL plan management and SQL tuning features when this tablespace is unavailable.

The following graphic illustrates the SMB architecture.

Note:
Data visibility and privilege requirements may differ when using the SMB with pluggable databases. See Oracle Database Administrator's Guide for a table that summarizes how manageability features work in a container database (CDB).

See Also:
Oracle Database Administrator's Guide to learn about the SYSAUX tablespace

23.1.5.2 SQL Statement Log

When automatic SQL plan capture is enabled, the SQL statement log contains the SQL ID of SQL statements that the optimizer has evaluated over time. The database tracks a statement when its SQL ID exists in the SQL statement log. When the database parses or executes a statement that is tracked, the database recognizes it as a repeatable SQL statement.

Example 23-1 Logging SQL Statements

This example illustrates how the database tracks statements in the statement log and creates baselines automatically for repeatable statements. An initial query of the statement log shows no tracked SQL statements. After a query of hr.jobs for AD_PRES, the log shows one tracked statement.

SQL> ALTER SYSTEM SET OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES=true;

System altered.

SQL> SELECT * FROM SQLLOG$;

no rows selected

SQL> SELECT job_title FROM hr.jobs WHERE job_id = 'AD_PRES';

JOB_TITLE
-----------------------------------
President

SQL> SELECT * FROM SQLLOG$;

SIGNATURE     BATCH#
---------- ----------
1.8096E+19          1

Now the session executes a different jobs query. The log shows two tracked statements:

SQL> SELECT job_title FROM hr.jobs WHERE job_id='PR_REP';

JOB_TITLE
-----------------------------------
Public Relations Representative

SQL> SELECT * FROM SQLLOG$;

SIGNATURE     BATCH#
---------- ----------
1.7971E+19          1
1.8096E+19          1

A query of DBA_SQL_PLAN_BASELINES shows that no baseline for either statement exists because neither statement is repeatable:

SQL> SELECT SQL_HANDLE, SQL_TEXT
2  FROM DBA_SQL_PLAN_BASELINES
3  WHERE SQL_TEXT LIKE 'SELECT job_title%';

no rows selected

The session executes the query for job_id='PR_REP' a second time. Because this statement is now repeatable, and because automatic SQL plan capture is enabled, the database creates a plan baseline for this statement. The query for job_id='AD_PRES' has only been executed once, so no plan baseline exists for it.

SQL> SELECT job_title FROM hr.jobs WHERE job_id='PR_REP';

JOB_TITLE
-----------------------------------
Public Relations Representative

SQL> SELECT SQL_HANDLE, SQL_TEXT
2  FROM DBA_SQL_PLAN_BASELINES
3  WHERE SQL_TEXT LIKE 'SELECT job_title%';

SQL_HANDLE           SQL_TEXT
-------------------- --------------------
SQL_f9676a330f972dd5 SELECT job_title FRO
M hr.jobs WHERE job_
id='PR_REP'

See Also:

- "Automatic Initial Plan Capture"

- Oracle Database Reference to learn about DBA_SQL_PLAN_BASELINES

23.1.5.3 SQL Plan History

The SQL plan history is the set of plans generated for a repeatable SQL statement over time. The history contains both SQL plan baselines and unaccepted plans.

In SQL plan management, the database detects plan changes and records the new plan in the history so that the DBA can manually evolve (verify) it. Because ad hoc SQL statements do not repeat and so do not have performance degradation, the database maintains plan history only for repeatable SQL statements.

Starting in Oracle Database 12c, the SMB stores the rows for new plans added to the plan history of a SQL statement. The DBMS_XPLAN.DISPLAY_SQL_PLAN_BASELINE function fetches and displays the plan from the SMB. For plans created before Oracle Database 12c, the function must compile the SQL statement and generate the plan because the SMB does not store the rows.

See Also:

- "Displaying Plans in a SQL Plan Baseline"

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_XPLAN.DISPLAY_SQL_PLAN_BASELINE function

23.1.5.3.1 Enabled Plans

An enabled plan is eligible for use by the optimizer. The database automatically marks all plans in the plan history as enabled even if they are still unaccepted. You can manually change an enabled plan to a disabled plan, which means the optimizer can no longer use the plan even if it is accepted.

23.1.5.3.2 Accepted Plans

A plan is accepted if and only if it is in the plan baseline. The plan history for a statement contains all plans, both accepted and unaccepted. After the optimizer generates the first accepted plan in a plan baseline, every subsequent unaccepted plan is added to the plan history, awaiting verification, but is not in the SQL plan baseline.

Figure 23-1 shows plan histories for three different SQL statements. The SQL plan baseline for one statement contains two accepted plans. The plan history for this statement includes two unaccepted plans. A DBA has marked one unaccepted plan as disabled so that the optimizer cannot use it.

Figure 23-1 SQL Plan Management Architecture

23.1.5.3.3 Fixed Plans

A fixed plan is an accepted plan that is marked as preferred, so that the optimizer considers only the fixed plans in the baseline. Fixed plans influence the plan selection process of the optimizer.

Assume that three plans exist in the SQL plan baseline for a statement. You want the optimizer to give preferential treatment to only two of the plans. As shown in Figure 23-2, you mark these two plans as fixed so that the optimizer uses only the best plan from these two, ignoring the other plans.

Figure 23-2 Fixed Plans

If new plans are added to a baseline that contains at least one enabled fixed plan, then the optimizer cannot use the new plans until you manually declare them as fixed.

23.1.6 User Interfaces for SQL Plan Management

Access the DBMS_SPM package through Cloud Control or through the command line.

23.1.6.1 SQL Plan Baseline Page in Cloud Control

The SQL Plan Control page in Cloud Control is a GUI that shows information about SQL profiles, SQL patches, and SQL plan baselines.

To access the SQL Plan Baseline page:

1. Access the Database Home page, as described in "Accessing the Database Home Page in Cloud Control."

2. From the Performance menu, select SQL, then SQL Plan Control.
The SQL Plan Control page appears.

3. Click Files to view the SQL Plan Baseline subpage, shown in Figure 23-3.

Figure 23-3 SQL Plan Baseline Subpage

You can perform most SQL plan management tasks in this page or in pages accessed through this page.

See Also:

- Cloud Control context-sensitive online help to learn about the options on the SQL Plan Baseline subpage

- "Managing the SPM Evolve Advisor Task"

23.1.6.2 DBMS_SPM Package

On the command line, use the DBMS_SPM and DBMS_XPLAN PL/SQL packages to perform most SQL plan management tasks. Table 23-1 describes the most relevant DBMS_SPM procedures and functions for creating, dropping, and loading SQL plan baselines.

Table 23-1 DBMS_SPM Procedures and Functions

Package

Procedure or Function

Description

DBMS_SPM

CONFIGURE

This procedure changes configuration options for the SMB in name/value format.

DBMS_SPM

CREATE_STGTAB_BASELINE

This procedure creates a staging table that enables you to transport SQL plan baselines from one database to another.

DBMS_SPM

DROP_SQL_PLAN_BASELINE

This function drops some or all plans in a plan baseline.

DBMS_SPM

LOAD_PLANS_FROM_CURSOR_CACHE

This function loads plans in the shared SQL area (also called the cursor cache) into SQL plan baselines.

DBMS_SPM

LOAD_PLANS_FROM_SQLSET

This function loads plans in an STS into SQL plan baselines.

DBMS_SPM

PACK_STGTAB_BASELINE

This function packs SQL plan baselines, which means that it copies them from the SMB into a staging table.

DBMS_SPM

UNPACK_STGTAB_BASELINE

This function unpacks SQL plan baselines, which means that it copies SQL plan baselines from a staging table into the SMB.

DBMS_XPLAN

DISPLAY_SQL_PLAN_BASELINE

This function displays one or more execution plans for the SQL statement identified by SQL handle.

"About the DBMS_SPM Evolve Functions" describes the functions related to SQL plan evolution.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM package

23.1.7 Basic Tasks in SQL Plan Management

This section explains the basic tasks in using SQL plan management to prevent plan regressions and permit the optimizer to consider new plans. The tasks are as follows:

- Set initialization parameters to control whether the database captures and uses SQL plan baselines, and whether it evolves new plans.
See "Configuring SQL Plan Management".

- Display plans in a SQL plan baseline.
See "Displaying Plans in a SQL Plan Baseline".

- Manually load plans into SQL plan baselines.
Load plans from SQL tuning sets, the shared SQL area, a staging table, or stored outlines.

See "Loading SQL Plan Baselines".

- Manually evolve plans into SQL plan baselines.
Use PL/SQL to verify the performance of specified plans and add them to plan baselines.

See "Evolving SQL Plan Baselines Manually".

- Drop all or some plans in SQL plan baselines.
See "Dropping SQL Plan Baselines".

- Manage the SMB.
Alter disk space limits and change the length of the plan retention policy.

See "Managing the SQL Management Base".

- Migrate stored outlines to SQL plan baselines.
See "Migrating Stored Outlines to SQL Plan Baselines".

23.2 Configuring SQL Plan Management

This section contains the following topics:

- Configuring the Capture and Use of SQL Plan Baselines

- Managing the SPM Evolve Advisor Task

23.2.1 Configuring the Capture and Use of SQL Plan Baselines

You control SQL plan management with initialization parameters. The default values are as follows:

- OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES=false
For any repeatable SQL statement that does not already exist in the plan history, the database does not automatically create an initial SQL plan baseline for the statement. See "Automatic Initial Plan Capture".

- OPTIMIZER_USE_SQL_PLAN_BASELINES=true
For any SQL statement that has an existing SQL plan baseline, the database automatically adds new plans to the SQL plan baseline as nonaccepted plans. See "Plan Selection".

Note:
The settings of the preceding parameters are independent of each other. For example, if OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES is true, then the database creates initial plan baselines for new statements even if OPTIMIZER_USE_SQL_PLAN_BASELINES is false.

If the default behavior is what you intend, then skip this section.

The following sections explain how to change the default parameter settings from the command line. If you use Cloud Control, then set these parameters in the SQL Plan Baseline subpage (shown in Figure 23-3).

23.2.1.1 Enabling Automatic Initial Plan Capture for SQL Plan Management

Setting the OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES initialization parameter to true is all that is necessary for the database to automatically create an initial SQL plan baseline for any SQL statement not already in the plan history. This parameter does not control the automatic addition of newly discovered plans to a previously created SQL plan baseline.

Caution:
When automatic baseline capture is enabled, the database creates a SQL plan baseline for every repeatable statement, including all recursive SQL and monitoring SQL. Thus, automatic capture may result in the creation of an extremely large number of plan baselines.

To enable automatic initial plan capture for SQL plan management:

1. Connect SQL*Plus to the database with the appropriate privileges, and then show the current settings for SQL plan management.
For example, connect SQL*Plus to the database with administrator privileges and execute the following command (sample output included):

SQL> SHOW PARAMETER SQL_PLAN

NAME                                 TYPE        VALUE
------------------------------------ ----------- ------------------------------
optimizer_capture_sql_plan_baselines boolean     FALSE
optimizer_use_sql_plan_baselines     boolean     TRUE

If the parameters are set as you intend, then skip the remaining steps.

2. To enable the automatic recognition of repeatable SQL statements and the generation of SQL plan baselines for these statements, enter the following statement:

SQL> ALTER SYSTEM SET OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES=true;

23.2.1.2 Disabling All SQL Plan Baselines

When you set the OPTIMIZER_USE_SQL_PLAN_BASELINES initialization parameter to false, the database does not use any plan baselines in the database. Typically, you might want to disable one or two plan baselines, but not all of them. A possible use case might be testing the benefits of SQL plan management.

To disable all SQL plan baselines in the database:

1. Connect SQL*Plus to the database with the appropriate privileges, and then show the current settings for SQL plan management.
For example, connect SQL*Plus to the database with administrator privileges and execute the following command (sample output included):

SQL> SHOW PARAMETER SQL_PLAN

NAME                                 TYPE        VALUE
------------------------------------ ----------- ------------------------------
optimizer_capture_sql_plan_baselines boolean     FALSE
optimizer_use_sql_plan_baselines     boolean     TRUE

If the parameters are set as you intend, then skip the remaining steps.

2. To ignore all existing plan baselines enter the following statement:

SQL> ALTER SYSTEM SET OPTIMIZER_USE_SQL_PLAN_BASELINES=false;

See Also:
Oracle Database Reference to learn about the SQL plan baseline initialization parameters

23.2.2 Managing the SPM Evolve Advisor Task

SPM Evolve Advisor is a SQL advisor that evolves plans that have recently been added to the SQL plan baseline. The advisor simplifies plan evolution by eliminating the requirement to do it manually.

By default, SYS_AUTO_SPM_EVOLVE_TASK runs daily in the scheduled maintenance window. The SPM Evolve Advisor task ranks all unaccepted plans, and then performs test executions of as many plans as possible during the window. The evolve task selects the lowest-cost plan to compare against each unaccepted plan. If a plan performs sufficiently better than the existing accepted plan, then the database automatically accepts it. The task can accept more than one plan.

23.2.2.1 Enabling and Disabling the SPM Evolve Advisor Task

No separate scheduler client exists for the Automatic SPM Evolve Advisor task. One client controls both Automatic SQL Tuning Advisor and Automatic SPM Evolve Advisor. Thus, the same task enables or disables both. See "Enabling and Disabling the Automatic SQL Tuning Task" to learn how to enable and disable Automatic SPM Evolve Advisor.

23.2.2.2 Configuring the Automatic SPM Evolve Advisor Task

The DBMS_SPM package enables you to configure automatic plan evolution by specifying the task parameters using the SET_EVOLVE_TASK_PARAMETER procedure. Because the task is owned by SYS, only SYS can set task parameters.

The ACCEPT_PLANS tuning task parameter specifies whether to accept recommended plans automatically. When ACCEPT_PLANS is true (default), SQL plan management automatically accepts all plans recommended by the task. When set to false, the task verifies the plans and generates a report if its findings, but does not evolve the plans.

Assumptions

The tutorial in this section assumes the following:

- You do not want the database to evolve plans automatically.

- You want the task to time out after 1200 seconds per execution.

To set automatic evolution task parameters:

1. Connect SQL*Plus to the database with the appropriate privileges, and then optionally query the current task settings.
For example, connect SQL*Plus to the database with administrator privileges and execute the following query:

COL PARAMETER_NAME FORMAT a25
COL VALUE FORMAT a10
SELECT PARAMETER_NAME, PARAMETER_VALUE AS "VALUE"
FROM   DBA_ADVISOR_PARAMETERS
WHERE  ( (TASK_NAME = 'SYS_AUTO_SPM_EVOLVE_TASK') AND
( (PARAMETER_NAME = 'ACCEPT_PLANS') OR
(PARAMETER_NAME = 'TIME_LIMIT') ) );

Sample output appears as follows:

PARAMETER_NAME            VALUE
------------------------- ----------
ACCEPT_PLANS              TRUE
TIME_LIMIT                3600

2. Set parameters using PL/SQL code of the following form:

BEGIN
DBMS_SPM.SET_EVOLVE_TASK_PARAMETER(
task_name => 'SYS_AUTO_SPM_EVOLVE_TASK'
,   parameter => parameter_name
,   value     => value
);
END;
/

For example, the following PL/SQL block sets a time limit to 20 minutes, and also automatically accepts plans:

BEGIN
DBMS_SPM.SET_EVOLVE_TASK_PARAMETER(
task_name => 'SYS_AUTO_SPM_EVOLVE_TASK'
,   parameter => 'LOCAL_TIME_LIMIT'
,   value     => 1200
);
DBMS_SPM.SET_EVOLVE_TASK_PARAMETER(
task_name => 'SYS_AUTO_SPM_EVOLVE_TASK'
,   parameter => 'ACCEPT_PLANS'
,   value     => 'true'
);
END;
/

See Also:
Oracle Database PL/SQL Packages and Types Reference for complete reference information for DBMS_SPM

23.3 Displaying Plans in a SQL Plan Baseline

To view the plans stored in the SQL plan baseline for a specific statement, use the DBMS_XPLAN.DISPLAY_SQL_PLAN_BASELINE function. This function uses plan information stored in the plan history to display the plans. Table 23-2 describes some function parameters.

Table 23-2 DBMS_XPLAN.DISPLAY_SQL_PLAN_BASELINE Parameters

Function Parameter

Description

sql_handle

SQL handle of the statement. Retrieve the SQL handle by joining the V$SQL.SQL_PLAN_BASELINE and DBA_SQL_PLAN_BASELINES views on the PLAN_NAME columns.

plan_name

Name of the plan for the statement.

This section explains how to show plans in a baseline from the command line. If you use Cloud Control, then display plan baselines from the SQL Plan Baseline subpage shown in Figure 23-3.

To display SQL plans:

1. Connect SQL*Plus to the database with the appropriate privileges, and then obtain the SQL ID of the query whose plan you want to display.
For example, assume that a SQL plan baseline exists for a SELECT statement with the SQL ID 31d96zzzpcys9.

2. Query the plan by SQL ID.
The following query displays execution plans for the statement with the SQL ID 31d96zzzpcys9:

SELECT PLAN_TABLE_OUTPUT
FROM   V$SQL s, DBA_SQL_PLAN_BASELINES b,
TABLE(
DBMS_XPLAN.DISPLAY_SQL_PLAN_BASELINE(b.sql_handle,b.plan_name,'basic')
) t
WHERE  s.EXACT_MATCHING_SIGNATURE=b.SIGNATURE
AND    b.PLAN_NAME=s.SQL_PLAN_BASELINE
AND    s.SQL_ID='31d96zzzpcys9';

The sample query results are as follows:

PLAN_TABLE_OUTPUT
-------------------------------------------------------------------------------

-------------------------------------------------------------------------------
SQL handle: SQL_513f7f8a91177b1a
SQL text: select * from hr.employees where employee_id=100
-------------------------------------------------------------------------------

-------------------------------------------------------------------------------
Plan name: SQL_PLAN_52gvzja8jfysuc0e983c6         Plan id: 3236529094
Enabled: YES     Fixed: NO      Accepted: YES     Origin: AUTO-CAPTURE
-------------------------------------------------------------------------------

Plan hash value: 3236529094

-----------------------------------------------------
| Id  | Operation                   | Name          |
-----------------------------------------------------
|   0 | SELECT STATEMENT            |               |
|   1 |  TABLE ACCESS BY INDEX ROWID| EMPLOYEES     |
|   2 |   INDEX UNIQUE SCAN         | EMP_EMP_ID_PK |
-----------------------------------------------------

The results show that the plan for SQL ID 31d96zzzpcys is named SQL_PLAN_52gvzja8jfysuc0e983c6 and was captured automatically.

See Also:

- "SQL Management Base"

- Oracle Database PL/SQL Packages and Types Reference to learn about additional parameters used by the DISPLAY_SQL_PLAN_BASELINE function

23.4 Loading SQL Plan Baselines

You can initiate the user-initiated bulk load of a set of existing plans into a SQL plan baseline. The goal of this task is to load plans from the following sources:

- SQL tuning set (STS)
Capture the plans for a SQL workload into an STS, and then load the plans into the SQL plan baselines. The optimizer uses the plans the next time that the database executes the SQL statements. Bulk loading execution plans from an STS is an effective way to prevent plan regressions after a database upgrade.

Note:
You can load plans from Automatic Workload Repository snapshots into an STS, and then load plans from the STS into the SQL plan baseline.

- Shared SQL area
Load plans for statements directly from the shared SQL area, which is in the shared pool of the SGA. By applying a filter on the module name, the schema, or the SQL ID you identify the SQL statement or set of SQL statements to capture. The optimizer uses the plans the next time that the database executes the SQL statements.

Loading plans directly from the shared SQL area is useful when application SQL has been hand-tuned using hints. Because you probably cannot change the SQL to include the hint, populating the SQL plan baseline ensures that the application SQL uses optimal plans.

- Staging table
Use the DBMS_SPM package to define a staging table, DBMS_SPM.PACK_STGTAB_BASELINE to copy the baselines into a staging table, and Oracle Data Pump to transfer the table to another database. On the destination database, use DBMS_SPM.UNPACK_STGTAB_BASELINE to unpack the plans from the staging table and put the baselines into the SMB.

A use case is the introduction of new SQL statements into the database from a new application module. A vendor can ship application software with SQL plan baselines for the new SQL. In this way, the new SQL uses plans that are known to give optimal performance under a standard test configuration. Alternatively, if you develop or test an application in-house, export the correct plans from the test database and import them into the production database.

- Stored outline
Migrate stored outlines to SQL plan baselines. After the migration, you maintain the same plan stability that you had using stored outlines while being able to use the more advanced features provided by SQL Plan Management, such as plan evolution. See "Migrating Stored Outlines to SQL Plan Baselines".

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM.PACK_STGTAB_BASELINE Function

23.4.1 Loading Plans from a SQL Tuning Set

A SQL tuning set is a database object that includes one or more SQL statements, execution statistics, and execution context. This section explains how to load plans from an STS.

Load plans with the DBMS_SPM.LOAD_PLANS_FROM_SQLSET function or using Cloud Control. Table 23-3 describes some function parameters.

Table 23-3 LOAD_PLANS_FROM_SQLSET Parameters

Function Parameter

Description

sqlset_name

Name of the STS from which the plans are loaded into SQL plan baselines.

basic_filter

A filter applied to the STS to select only qualifying plans to be loaded. The filter can take the form of any WHERE clause predicate that can be specified against the view DBA_SQLSET_STATEMENTS.

fixed

Default NO means the loaded plans are used as nonfixed plans. YES means the loaded plans are fixed plans. "Plan Selection" explains that the optimizer chooses a fixed plan in the plan baseline over a nonfixed plan.

This section explains how to load plans from the command line. In Cloud Control, go to the SQL Plan Baseline subpage (shown in Figure 23-3) and click Load to load plan baselines from SQL tuning sets.

Assumptions

This tutorial assumes the following:

- You want the loaded plans to be nonfixed.

- You have executed the following query:

SELECT /*LOAD_STS*/ *
FROM   sh.sales
WHERE  quantity_sold > 40
ORDER BY prod_id;

- You have loaded the plan from the shared SQL area into the SQL tuning set named SPM_STS, which is owned by user SPM.

To load plans from a SQL tuning set:

1. Connect SQL*Plus to the database with the appropriate privileges, and then verify which plans are in the SQL tuning set.
For example, query DBA_SQLSET_STATEMENTS for the STS name (sample output included):

SELECT SQL_TEXT
FROM   DBA_SQLSET_STATEMENTS
WHERE  SQLSET_NAME = 'SPM_STS';

SQL_TEXT
--------------------
SELECT /*LOAD_STS*/
*
FROM sh.sales
WHERE quantity_sold
> 40
ORDER BY prod_id

The output shows that the plan for the select /*LOAD_STS*/ statement is in the STS.

2. Load the plan from the STS into the SQL plan baseline.
For example, in SQL*Plus execute the function as follows:

VARIABLE cnt NUMBER
EXECUTE :cnt := DBMS_SPM.LOAD_PLANS_FROM_SQLSET( -
sqlset_name  => 'SPM_STS', -
basic_filter => 'sql_text like ''SELECT /*LOAD_STS*/%''' );

The basic_filter parameter specifies a WHERE clause that loads only the plans for the queries of interest. The variable cnt stores the number of plans loaded from the STS.

3. Query the data dictionary to ensure that the plan was loaded into the baseline for the statement.
Example 23-2 executes the following query (sample output included).

Example 23-2 DBA_SQL_PLAN_BASELINES

SQL> SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME,
2         ORIGIN, ENABLED, ACCEPTED
3  FROM DBA_SQL_PLAN_BASELINES;

SQL_HANDLE               SQL_TEXT             PLAN_NAME                      ORIGIN         ENA ACC
------------------------ -------------------- ------------------------------ -------------- --- ---
SQL_a8632bd857a4a25e     SELECT /*LOAD_STS*/  SQL_PLAN_ahstbv1bu98ky1694fc6b MANUAL-LOAD    YES YES
*
FROM sh.sales
WHERE quantity_sold
> 40
ORDER BY prod_id

The output shows that the plan is accepted, which means that it is in the plan baseline. Also, the origin is MANUAL-LOAD, which means that the plan was loaded by an end user rather than automatically captured.

4. Optionally, drop the STS.
For example, execute DBMS_SQLTUNE.DROP_SQLSET to drop the SPM_STS tuning set as follows:

EXEC SYS.DBMS_SQLTUNE.DROP_SQLSET( sqlset_name  => 'SPM_STS', -
sqlset_owner => 'SPM' );

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM.LOAD_PLANS_FROM_SQLSET function

23.4.2 Loading Plans from the Shared SQL Area

This section explains how to load plans from the shared SQL area using PL/SQL.

Load plans with the LOAD_PLANS_FROM_CURSOR_CACHE function of the DBMS_SPM package. Table 23-4 describes some function parameters.

Table 23-4 LOAD_PLANS_FROM_CURSOR_CACHE Parameters

Function Parameter

Description

sql_id

SQL statement identifier. Identifies a SQL statement in the shared SQL area.

fixed

Default NO means the loaded plans are used as nonfixed plans. YES means the loaded plans are fixed plans (see "Fixed Plans"). "Plan Selection" explains that the optimizer chooses a fixed plan in the plan baseline over a nonfixed plan.

This section explains how to load plans using the command line. In Cloud Control, go to the SQL Plan Baseline subpage (shown in Figure 23-3) and click Load to load plan baselines from the shared SQL area.

Assumptions

This tutorial assumes the following:

- You have executed the following query:

SELECT /*LOAD_CC*/ *
FROM   sh.sales
WHERE  quantity_sold > 40
ORDER BY prod_id;

- You want the loaded plans to be nonfixed.

To load plans from the shared SQL area:

1. Connect SQL*Plus to the database with the appropriate privileges, and then determine the SQL IDs of the relevant statements in the shared SQL area.
For example, query V$SQL for the SQL ID of the sh.sales query (sample output included):

SELECT   SQL_ID, CHILD_NUMBER AS "Child Num",
PLAN_HASH_VALUE AS "Plan Hash",
OPTIMIZER_ENV_HASH_VALUE AS "Opt Env Hash"
FROM     V$SQL
WHERE    SQL_TEXT LIKE 'SELECT /*LOAD_CC*/%';

SQL_ID         Child Num  Plan Hash Opt Env Hash
------------- ---------- ---------- ------------
27m0sdw9snw59          0 1421641795   3160571937

The preceding output shows that the SQL ID of the statement is 27m0sdw9snw59.

2. Load the plans for the specified statements into the SQL plan baseline.
For example, execute the LOAD_PLANS_FROM_CURSOR_CACHE function in SQL*Plus to load the plan for the statement with the SQL ID 27m0sdw9snw59:

VARIABLE cnt NUMBER
EXECUTE :cnt := DBMS_SPM.LOAD_PLANS_FROM_CURSOR_CACHE( -
sql_id => '27m0sdw9snw59');

In the preceding example, the variable cnt contains the number of plans that were loaded.

3. Query the data dictionary to ensure that the plans were loaded into the baseline for the statement.
Example 23-3 queries DBA_SQL_PLAN_BASELINES (sample output included).

Example 23-3 DBA_SQL_PLAN_BASELINES

SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME,
ORIGIN, ENABLED, ACCEPTED
FROM   DBA_SQL_PLAN_BASELINES;

SQL_HANDLE               SQL_TEXT             PLAN_NAME                      ORIGIN         ENA ACC
------------------------ -------------------- ------------------------------ -------------- --- ---
SQL_a8632bd857a4a25e     SELECT /*LOAD_CC*/   SQL_PLAN_gdkvzfhrgkda71694fc6b MANUAL-LOAD    YES YES
*
FROM sh.sales
WHERE quantity_sold
> 40
ORDER BY prod_id

The output shows that the plan is accepted, which means that it is in the plan baseline for the statement. Also, the origin is MANUAL-LOAD, which means that the statement was loaded by an end user rather than automatically captured.

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn how to use the DBMS_SPM.LOAD_PLANS_FROM_CURSOR_CACHE function

23.4.3 Loading Plans from a Staging Table

You may want to transfer optimal plans from a source database to a different destination database. For example, you may have investigated a set of plans on a test database and confirmed that they have performed well. You may then want to load these plans into a production database.

A staging table is a table that, for the duration of its existence, stores plans so that the plans do not disappear from the table while you are unpacking them. Use the DBMS.CREATE_STGTAB_BASELINE procedure to create a staging table. To pack (insert row into) and unpack (extract rows from) the staging table, use the PACK_STGTAB_BASELINE and UNPACK_STGTAB_BASELINE functions of the DBMS_SPM package. Oracle Data Pump Import and Export enable you to copy the staging table to a different database.

The following graphic depicts the basic steps.

Assumptions

This tutorial assumes the following:

- You want to create a staging table named stage1 in the source database.

- You want to load all plans owned by user spm into the staging table.

- You want to transfer the staging table to a destination database.

- You want to load the plans in stage1 as fixed plans.

To transfer a set of SQL plan baselines from one database to another:

1. Connect SQL*Plus to the source database with the appropriate privileges, and then create a staging table using the CREATE_STGTAB_BASELINE procedure.
The following example creates a staging table named stage1:

BEGIN
DBMS_SPM.CREATE_STGTAB_BASELINE (
table_name => 'stage1');
END;
/

2. On the source database, pack the SQL plan baselines you want to export from the SQL management base into the staging table.
The following example packs enabled plan baselines created by user spm into staging table stage1. Select SQL plan baselines using the plan name (plan_name), SQL handle (sql_handle), or any other plan criteria. The table_name parameter is mandatory.

DECLARE
my_plans number;
BEGIN
my_plans := DBMS_SPM.PACK_STGTAB_BASELINE (
table_name => 'stage1'
,   enabled    => 'yes'
,   creator    => 'spm'
);
END;
/

3. Export the staging table stage1 into a dump file using Oracle Data Pump Export.

4. Transfer the dump file to the host of the destination database.

5. On the destination database, import the staging table stage1 from the dump file using the Oracle Data Pump Import utility.

6. On the destination database, unpack the SQL plan baselines from the staging table into the SQL management base.
The following example unpacks all fixed plan baselines stored in the staging table stage1:

DECLARE
my_plans NUMBER;
BEGIN
my_plans := DBMS_SPM.UNPACK_STGTAB_BASELINE (
table_name => 'stage1'
,   fixed      => 'yes'
);
END;
/

See Also:

- Oracle Database PL/SQL Packages and Types Reference for more information about using the DBMS_SPM package

- Oracle Database Utilities for detailed information about using the Data Pump Export and Import utilities

23.5 Evolving SQL Plan Baselines Manually

Oracle recommends that you configure the SQL Plan Management Evolve task to run automatically, as explained in "Managing the SPM Evolve Advisor Task". You can also use PL/SQL or Cloud Control to manually evolve an unaccepted plan to determine whether it performs better than any plan currently in the plan baseline.

This section contains the following topics:

- About the DBMS_SPM Evolve Functions

- Managing an Evolve Task

23.5.1 About the DBMS_SPM Evolve Functions

Table 23-5 describes the most relevant DBMS_SPM procedures and functions for managing plan evolution. Execute evolution tasks manually or schedule them to run automatically.

Table 23-5 DBMS_SPM Functions and Procedures for Managing Plan Evolution Tasks

Package

Procedure or Function

Description

DBMS_SPM

ACCEPT_SQL_PLAN_BASELINE

This function accepts one recommendation to evolve a single plan into a SQL plan baseline.

DBMS_SPM

CREATE_EVOLVE_TASK

This function creates an advisor task to prepare the plan evolution of one or more plans for a specified SQL statement. The input parameters can be a SQL handle, plan name or a list of plan names, time limit, task name, and description.

DBMS_SPM

EXECUTE_EVOLVE_TASK

This function executes an evolution task. The input parameters can be the task name, execution name, and execution description. If not specified, the advisor generates the name, which is returned by the function.

DBMS_SPM

IMPLEMENT_EVOLVE_TASK

This function implements all recommendations for an evolve task. Essentially, this function is equivalent to using ACCEPT_SQL_PLAN_BASELINE for all recommended plans. Input parameters include task name, plan name, owner name, and execution name.

DBMS_SPM

REPORT_EVOLVE_TASK

This function displays the results of an evolve task as a CLOB. Input parameters include the task name and section of the report to include.

DBMS_SPM

SET_EVOLVE_TASK_PARAMETER

This function updates the value of an evolve task parameter. In this release, the only valid parameter is TIME_LIMIT.

Oracle recommends that you configure SPM Evolve Advisor to run automatically (see "Configuring the Automatic SPM Evolve Advisor Task"). You can also evolve SQL plan baselines manually. Figure 23-4 shows the basic workflow for managing SQL plan management tasks.

Figure 23-4 Evolving SQL Plan Baselines

Typically, you manage SQL plan evolution tasks in the following sequence:

1. Create an evolve task

2. Optionally, set evolve task parameters

3. Execute the evolve task

4. Implement the recommendations in the task

5. Report on the task outcome

See Also:
Oracle Database PL/SQL Packages and Types Reference for information about the DBMS_SPM package

23.5.2 Managing an Evolve Task

This section describes a typical use case in which you create and execute a task, and then implements its recommendations. Table 23-6 describes some parameters of the CREATE_EVOLVE_TASK function.

Table 23-6 DBMS_SPM.CREATE_EVOLVE_TASK Parameters

Function Parameter

Description

sql_handle

SQL handle of the statement. The default NULL considers all SQL statements with unaccepted plans.

plan_name

Plan identifier. The default NULL means consider all unaccepted plans of the specified SQL handle or all SQL statements if the SQL handle is NULL.

time_limit

Time limit in number of minutes. The time limit for first unaccepted plan equals the input value. The time limit for the second unaccepted plan equals the input value minus the time spent in first plan verification, and so on. The default DBMS_SPM.AUTO_LIMIT means let the system choose an appropriate time limit based on the number of plan verifications required to be done.

task_name

User-specified name of the evolution task.

This section explains how to evolve plan baselines from the command line. In Cloud Control, from the SQL Plan Baseline subpage (shown in Figure 23-3), select a plan, and then click Evolve.

Assumptions

This tutorial assumes the following:

- You do not have the automatic evolve task enabled (see "Managing the SPM Evolve Advisor Task").

- You want to create a SQL plan baseline for the following query:

SELECT /* q1_group_by */ prod_name, sum(quantity_sold)
FROM   products p, sales s
WHERE  p.prod_id = s.prod_id
AND    p.prod_category_id =203
GROUP BY prod_name;

- You want to create two indexes to improve the query performance, and then evolve the plan that uses these indexes if it performs better than the plan currently in the plan baseline.

To evolve a specified plan:

1. Perform the initial setup as follows:

1. Connect SQL*Plus to the database with administrator privileges, and then prepare for the tutorial by flushing the shared pool and the buffer cache:

ALTER SYSTEM FLUSH SHARED_POOL;
ALTER SYSTEM FLUSH BUFFER_CACHE;

2. Enable the automatic capture of SQL plan baselines.
For example, enter the following statement:

ALTER SYSTEM SET OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES=true;

3. Connect to the database as user sh, and then set SQL*Plus display parameters:

CONNECT sh
-- enter password
SET PAGES 10000 LINES 140
SET SERVEROUTPUT ON
COL SQL_TEXT FORMAT A20
COL SQL_HANDLE FORMAT A20
COL PLAN_NAME FORMAT A30
COL ORIGIN FORMAT A12
SET LONGC 60535
SET LONG 60535
SET ECHO ON

2. Execute the SELECT statements so that SQL plan management captures them:

1. Execute the SELECT /* q1_group_by */ statement for the first time.
Because the database only captures plans for repeatable statements, the plan baseline for this statement is empty.

2. Query the data dictionary to confirm that no plans exist in the plan baseline.
For example, execute the following query (sample output included):

SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME, ORIGIN, ENABLED,
ACCEPTED, FIXED, AUTOPURGE
FROM   DBA_SQL_PLAN_BASELINES
WHERE  SQL_TEXT LIKE '%q1_group%';

no rows selected

SQL plan management only captures repeatable statements, so this result is expected.

3. Execute the SELECT /* q1_group_by */ statement for the second time.

3. Query the data dictionary to ensure that the plans were loaded into the plan baseline for the statement.
Example 23-4 executes the following query (sample output included).

Example 23-4 DBA_SQL_PLAN_BASELINES

SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME,
ORIGIN, ENABLED, ACCEPTED, FIXED
FROM   DBA_SQL_PLAN_BASELINES
WHERE  SQL_TEXT LIKE '%q1_group%';

SQL_HANDLE           SQL_TEXT             PLAN_NAME                      ORIGIN       ENA ACC FIX
-------------------- -------------------- ------------------------------ ------------ --- --- ---
SQL_07f16c76ff893342 SELECT /* q1_group_b SQL_PLAN_0gwbcfvzskcu242949306 AUTO-CAPTURE YES YES NO
y */ prod_name, sum(
quantity_sold)
FROM   products p, s
ales s
WHERE  p.prod_id = s
.prod_id
AND    p.prod_catego
ry_id =203
GROUP BY prod_name

The output shows that the plan is accepted, which means that it is in the plan baseline for the statement. Also, the origin is AUTO-CAPTURE, which means that the statement was automatically captured and not manually loaded.

4. Explain the plan for the statement and verify that the optimizer is using this plan.

For example, explain the plan as follows, and then display it:

EXPLAIN PLAN FOR
SELECT /* q1_group_by */ prod_name, sum(quantity_sold)
FROM   products p, sales s
WHERE  p.prod_id = s.prod_id
AND    p.prod_category_id =203
GROUP BY prod_name;

SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY(null, null, 'basic +note'));

Sample output appears below:

Plan hash value: 1117033222

------------------------------------------
| Id  | Operation             | Name     |
------------------------------------------
|   0 | SELECT STATEMENT      |          |
|   1 |  HASH GROUP BY        |          |
|   2 |   HASH JOIN           |          |
|   3 |    TABLE ACCESS FULL  | PRODUCTS |
|   4 |    PARTITION RANGE ALL|          |
|   5 |     TABLE ACCESS FULL | SALES    |
------------------------------------------

Note
-----
- SQL plan baseline "SQL_PLAN_0gwbcfvzskcu242949306" used for this statement

The note indicates that the optimizer is using the plan shown with the plan name listed in Example 23-4.

5. Create two indexes to improve the performance of the SELECT /* q1_group_by */ statement.
For example, use the following statements:

CREATE INDEX ind_prod_cat_name
ON products(prod_category_id, prod_name, prod_id);
CREATE INDEX ind_sales_prod_qty_sold
ON sales(prod_id, quantity_sold);

6. Execute the select /* q1_group_by */ statement again.
Because automatic capture is enabled, the plan baseline is populated with the new plan for this statement.

7. Query the data dictionary to ensure that the plan was loaded into the SQL plan baseline for the statement.
Example 23-5 executes the following query (sample output included).

Example 23-5 DBA_SQL_PLAN_BASELINES

SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME, ORIGIN, ENABLED, ACCEPTED
FROM   DBA_SQL_PLAN_BASELINES
WHERE  SQL_HANDLE IN ('SQL_07f16c76ff893342')
ORDER BY SQL_HANDLE, ACCEPTED;

SQL_HANDLE           SQL_TEXT             PLAN_NAME                      ORIGIN       ENA ACC
-------------------- -------------------- ------------------------------ ------------ --- ---
SQL_07f16c76ff893342 SELECT /* q1_group_b SQL_PLAN_0gwbcfvzskcu20135fd6c AUTO-CAPTURE YES NO
y */ prod_name, sum(
quantity_sold)
FROM   products p, s
ales s
WHERE  p.prod_id = s
.prod_id
AND    p.prod_catego
ry_id =203
GROUP BY prod_name

SQL_07f16c76ff893342 SELECT /* q1_group_b SQL_PLAN_0gwbcfvzskcu242949306 AUTO-CAPTURE YES YES
y */ prod_name, sum(
quantity_sold)
FROM   products p, s
ales s
WHERE  p.prod_id = s
.prod_id
AND    p.prod_catego
ry_id =203
GROUP BY prod_name

The output shows that the new plan is unaccepted, which means that it is in the statement history but not the SQL plan baseline.

8. Explain the plan for the statement and verify that the optimizer is using the original nonindexed plan.
For example, explain the plan as follows, and then display it:

EXPLAIN PLAN FOR
SELECT /* q1_group_by */ prod_name, sum(quantity_sold)
FROM   products p, sales s
WHERE  p.prod_id = s.prod_id
AND    p.prod_category_id =203
GROUP BY prod_name;
SELECT * FROM TABLE(DBMS_XPLAN.DISPLAY(null, null, 'basic +note'));

Sample output appears below:

Plan hash value: 1117033222

------------------------------------------
| Id  | Operation             | Name     |
------------------------------------------
|   0 | SELECT STATEMENT      |          |
|   1 |  HASH GROUP BY        |          |
|   2 |   HASH JOIN           |          |
|   3 |    TABLE ACCESS FULL  | PRODUCTS |
|   4 |    PARTITION RANGE ALL|          |
|   5 |     TABLE ACCESS FULL | SALES    |
------------------------------------------

Note
-----
- SQL plan baseline "SQL_PLAN_0gwbcfvzskcu242949306" used for this statement

The note indicates that the optimizer is using the plan shown with the plan name listed in Example 23-4.

9. Connect as an administrator, and then create an evolve task that considers all SQL statements with unaccepted plans.
For example, execute the DBMS_SPM.CREATE_EVOLVE_TASK function and then obtain the name of the task:

CONNECT / AS SYSDBA
VARIABLE cnt NUMBER
VARIABLE tk_name VARCHAR2(50)
VARIABLE exe_name VARCHAR2(50)
VARIABLE evol_out CLOB

EXECUTE :tk_name := DBMS_SPM.CREATE_EVOLVE_TASK(
sql_handle => 'SQL_07f16c76ff893342',
plan_name  => 'SQL_PLAN_0gwbcfvzskcu20135fd6c');

SELECT :tk_name FROM DUAL;

The following sample output shows the name of the task:

:EVOL_OUT
------------------------------------------------------------------------------
TASK_11

Now that the task has been created and has a unique name, execute the task.

10. Execute the task.
For example, execute the DBMS_SPM.EXECUTE_EVOLVE_TASK function (sample output included):

EXECUTE :exe_name :=DBMS_SPM.EXECUTE_EVOLVE_TASK(task_name=>:tk_name);
SELECT :exe_name FROM DUAL;

:EXE_NAME
-------------------------------------------------------------------------------
EXEC_1

11. View the report.
For example, execute the DBMS_SPM.REPORT_EVOLVE_TASK function (sample output included):

EXECUTE :evol_out := DBMS_SPM.REPORT_EVOLVE_TASK( task_name=>:tk_name, execution_name=>:exe_name );
SELECT :evol_out FROM DUAL;

GENERAL INFORMATION SECTION
------------------------------------------------------------------------------

Task Information:
---------------------------------------------
Task Name            : TASK_11
Task Owner           : SYS
Execution Name       : EXEC_1
Execution Type       : SPM EVOLVE
Scope                : COMPREHENSIVE
Status               : COMPLETED
Started              : 01/09/2012 12:21:27
Finished             : 01/09/2012 12:21:29
Last Updated         : 01/09/2012 12:21:29
Global Time Limit    : 2147483646
Per-Plan Time Limit  : UNUSED
Number of Errors     : 0
-------------------------------------------------------------------------------

SUMMARY SECTION
-------------------------------------------------------------------------------
Number of plans processed  : 1
Number of findings         : 1
Number of recommendations  : 1
Number of errors           : 0
-------------------------------------------------------------------------------

DETAILS SECTION
-------------------------------------------------------------------------------
Object ID          : 2
Test Plan Name     : SQL_PLAN_0gwbcfvzskcu20135fd6c
Base Plan Name     : SQL_PLAN_0gwbcfvzskcu242949306
SQL Handle         : SQL_07f16c76ff893342
Parsing Schema     : SH
Test Plan Creator  : SH
SQL Text           : SELECT /* q1_group_by */ prod_name, sum(quantity_sold)
FROM products p, sales s WHERE p.prod_id = s.prod_id AND
p.prod_category_id =203 GROUP BY prod_name

Execution Statistics:
-----------------------------
Base Plan                     Test Plan
----------------------------  ----------------------------
Elapsed Time (s):  .044336                       .012649
CPU Time (s):      .044003                       .012445
Buffer Gets:       360                           99
Optimizer Cost:    924                           891
Disk Reads:        341                           82
Direct Writes:     0                             0
Rows Processed:    4                             2
Executions:        5                             9

FINDINGS SECTION
-------------------------------------------------------------------------------

Findings (1):
-----------------------------
1. The plan was verified in 2.18 seconds. It passed the benefit criterion
because its verified performance was 2.01 times better than that of the
baseline plan.

Recommendation:
-----------------------------
Consider accepting the plan. Execute
dbms_spm.accept_sql_plan_baseline(task_name => 'TASK_11', object_id => 2,
task_owner => 'SYS');

EXPLAIN PLANS SECTION
-------------------------------------------------------------------------------

Baseline Plan
-----------------------------
Plan Id          : 1
Plan Hash Value  : 1117033222

-------------------------------------------------------------------------------
| Id| Operation               | Name     | Rows   | Bytes    | Cost | Time    |
-------------------------------------------------------------------------------
| 0 | SELECT STATEMENT        |          |     21 |      861 |  924 | 00:00:12|
| 1 |   HASH GROUP BY         |          |     21 |      861 |  924 | 00:00:12|
| *2|    HASH JOIN            |          | 267996 | 10987836 |  742 | 00:00:09|
| *3|     TABLE ACCESS FULL   | PRODUCTS |     21 |      714 |    2 | 00:00:01|
| 4 |     PARTITION RANGE ALL |          | 918843 |  6431901 |  662 | 00:00:08|
| 5 |      TABLE ACCESS FULL  | SALES    | 918843 |  6431901 |  662 | 00:00:08|
------------------------------------------------------------------------------

Predicate Information (identified by operation id):
------------------------------------------
* 2 - access("P"."PROD_ID"="S"."PROD_ID")
* 3 - filter("P"."PROD_CATEGORY_ID"=203)

Test Plan
-----------------------------
Plan Id          : 2
Plan Hash Value  : 20315500

-------------------------------------------------------------------------------
|Id| Operation            | Name                  | Rows | Bytes  | Cost| Time|
-------------------------------------------------------------------------------
| 0|SELECT STATEMENT      |                       |    21|     861|891|00:00:11
| 1|  SORT GROUP BY NOSORT|                       |    21|     861|891|00:00:11
| 2|   NESTED LOOPS       |                       |267996|10987836|891|00:00:11
|*3|    INDEX RANGE SCAN  |IND_PROD_CAT_NAME      |    21|     714|  1|00:00:01
|*4|    INDEX RANGE SCAN  |IND_SALES_PROD_QTY_SOLD| 12762|   89334| 42|00:00:01
-------------------------------------------------------------------------------

Predicate Information (identified by operation id):
------------------------------------------
* 3 - access("P"."PROD_CATEGORY_ID"=203)
* 4 - access("P"."PROD_ID"="S"."PROD_ID")

This report indicates that the new execution plan, which uses the two new indexes, performs better than the original plan.

12. Implement the recommendations of the evolve task.
For example, execute the IMPLEMENT_EVOLVE_TASK function:

EXECUTE :cnt := DBMS_SPM.IMPLEMENT_EVOLVE_TASK( task_name=>:tk_name, execution_name=>:exe_name );

13. Query the data dictionary to ensure that the new plan is accepted.
Example 23-5 executes the following query (sample output included).

Example 23-6 DBA_SQL_PLAN_BASELINES

SQL_HANDLE           SQL_TEXT             PLAN_NAME                      ORIGIN       ENA ACC
-------------------- -------------------- ------------------------------ ------------ --- ---
SQL_07f16c76ff893342 SELECT /* q1_group_b SQL_PLAN_0gwbcfvzskcu20135fd6c AUTO-CAPTURE YES YES
y */ prod_name, sum(
quantity_sold)
FROM   products p, s
ales s
WHERE  p.prod_id = s
.prod_id
AND    p.prod_catego
ry_id =203
GROUP BY prod_name

SQL_07f16c76ff893342 SELECT /* q1_group_b SQL_PLAN_0gwbcfvzskcu242949306 AUTO-CAPTURE YES YES
y */ prod_name, sum(
quantity_sold)
FROM   products p, s
ales s
WHERE  p.prod_id = s
.prod_id
AND    p.prod_catego
ry_id =203
GROUP BY prod_name

The output shows that the new plan is accepted.

14. Clean up after the example.
For example, enter the following statements:

EXEC :cnt := DBMS_SPM.DROP_SQL_PLAN_BASELINE('SQL_07f16c76ff893342');
EXEC :cnt := DBMS_SPM.DROP_SQL_PLAN_BASELINE('SQL_9049245213a986b3');
EXEC :cnt := DBMS_SPM.DROP_SQL_PLAN_BASELINE('SQL_bb77077f5f90a36b');
EXEC :cnt := DBMS_SPM.DROP_SQL_PLAN_BASELINE('SQL_02a86218930bbb20');
DELETE FROM SQLLOG$;
CONNECT sh
-- enter password
DROP INDEX IND_SALES_PROD_QTY_SOLD;
DROP INDEX IND_PROD_CAT_NAME;

See Also:
Oracle Database PL/SQL Packages and Types Reference for information about using the DBMS_SPM evolve functions

23.6 Dropping SQL Plan Baselines

You can remove some or all plans from a SQL plan baseline. This technique is sometimes useful when testing SQL plan management.

Drop plans with the DBMS_SPM.DROP_SQL_PLAN_BASELINE function. This function returns the number of dropped plans. Table 23-8 describes input parameters.

Table 23-7 DROP_SQL_PLAN_BASELINE Parameters

Function Parameter

Description

sql_handle

SQL statement identifier.

plan_name

Name of a specific plan. Default NULL drops all plans associated with the SQL statement identified by sql_handle.

This section explains how to drop baselines from the command line. In Cloud Control, from the SQL Plan Baseline subpage (shown in Figure 23-3), select a plan, and then click Drop.

Assumptions

This tutorial assumes that you want to drop all plans for the following SQL statement, effectively dropping the SQL plan baseline:

SELECT /* repeatable_sql */ COUNT(*) FROM hr.jobs;

To drop a SQL plan baseline:

1. Connect SQL*Plus to the database with the appropriate privileges, and then query the data dictionary for the plan baseline.
Example 23-7 executes the following query (sample output included).

Example 23-7 DBA_SQL_PLAN_BASELINES

SQL> SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME, ORIGIN,
2         ENABLED, ACCEPTED
3  FROM   DBA_SQL_PLAN_BASELINES
4  WHERE  SQL_TEXT LIKE 'SELECT /* repeatable_sql%';

SQL_HANDLE           SQL_TEXT             PLAN_NAME                      ORIGIN         ENA ACC
-------------------- -------------------- ------------------------------ -------------- --- ---
SQL_b6b0d1c71cd1807b SELECT /* repeatable SQL_PLAN_bdc6jswfd303v2f1e9c20 AUTO-CAPTURE   YES YES
_sql */ count(*) fro
m hr.jobs

2. Drop the SQL plan baseline for the statement.
The following example drops the plan baseline with the SQL handle SQL_b6b0d1c71cd1807b, and returns the number of dropped plans. Specify plan baselines using the plan name (plan_name), SQL handle (sql_handle), or any other plan criteria. The table_name parameter is mandatory.

DECLARE
v_dropped_plans number;
BEGIN
v_dropped_plans := DBMS_SPM.DROP_SQL_PLAN_BASELINE (
sql_handle => 'SQL_b6b0d1c71cd1807b'
);
DBMS_OUTPUT.PUT_LINE('dropped ' || v_dropped_plans || ' plans');
END;
/

3. Confirm that the plans were dropped.
For example, execute the following query:

SELECT SQL_HANDLE, SQL_TEXT, PLAN_NAME, ORIGIN,
ENABLED, ACCEPTED
FROM   DBA_SQL_PLAN_BASELINES
WHERE  SQL_TEXT LIKE 'SELECT /* repeatable_sql%';

no rows selected

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DROP_SQL_PLAN_BASELINE function

23.7 Managing the SQL Management Base

The SQL management base is a part of the data dictionary that resides in the SYSAUX tablespace. It stores statement logs, plan histories, SQL plan baselines, and SQL profiles. This section explains how to change the disk space usage parameters for the SMB, and change the retention time for plans in the SMB.

The DBA_SQL_MANAGEMENT_CONFIG view shows the current configuration settings for the SMB. Table 23-8 describes the parameters in the PARAMETER_NAME column.

Table 23-8 Parameters in DBA_SQL_MANAGEMENT_CONFIG.PARAMETER_NAME

Parameter

Description

SPACE_BUDGET_PERCENT

Maximum percent of SYSAUX space that the SQL management base can use. The default is 10. The allowable range for this limit is between 1% and 50%.

PLAN_RETENTION_WEEKS

Number of weeks to retain unused plans before they are purged. The default is 53.

23.7.1 Changing the Disk Space Limit for the SMB

A weekly background process measures the total space occupied by the SMB. When the defined limit is exceeded, the process writes a warning to the alert log. The database generates alerts weekly until either the SMB space limit is increased, the size of the SYSAUX tablespace is increased, or the disk space used by the SMB is decreased by purging SQL management objects (SQL plan baselines or SQL profiles). This task explains how to change the limit with the DBMS_SPM.CONFIGURE procedure.

Assumptions

This tutorial assumes the following:

- The current SMB space limit is the default of 10%.

- You want to change the percentage limit to 30%

To change the percentage limit of the SMB:

1. Connect SQL*Plus to the database with the appropriate privileges, and then query the data dictionary to see the current space budget percent.
For example, execute the following query (sample output included):

SELECT PARAMETER_NAME, PARAMETER_VALUE AS "%_LIMIT",
( SELECT sum(bytes/1024/1024) FROM DBA_DATA_FILES
WHERE TABLESPACE_NAME = 'SYSAUX' ) AS SYSAUX_SIZE_IN_MB,
PARAMETER_VALUE/100 *
( SELECT sum(bytes/1024/1024) FROM DBA_DATA_FILES
WHERE TABLESPACE_NAME = 'SYSAUX' ) AS "CURRENT_LIMIT_IN_MB"
FROM DBA_SQL_MANAGEMENT_CONFIG
WHERE PARAMETER_NAME = 'SPACE_BUDGET_PERCENT';

PARAMETER_NAME                    %_LIMIT SYSAUX_SIZE_IN_MB CURRENT_LIMIT_IN_MB
------------------------------ ---------- ----------------- -------------------
SPACE_BUDGET_PERCENT                   10          211.4375            21.14375

2. Change the percentage setting.
For example, execute the following command to change the setting to 30%:

EXECUTE DBMS_SPM.CONFIGURE('space_budget_percent',30);

3. Query the data dictionary to confirm the change.
For example, execute the following join (sample output included):

SELECT PARAMETER_NAME, PARAMETER_VALUE AS "%_LIMIT",
( SELECT sum(bytes/1024/1024) FROM DBA_DATA_FILES
WHERE TABLESPACE_NAME = 'SYSAUX' ) AS SYSAUX_SIZE_IN_MB,
PARAMETER_VALUE/100 *
( SELECT sum(bytes/1024/1024) FROM DBA_DATA_FILES
WHERE TABLESPACE_NAME = 'SYSAUX' ) AS "CURRENT_LIMIT_IN_MB"
FROM   DBA_SQL_MANAGEMENT_CONFIG
WHERE  PARAMETER_NAME = 'SPACE_BUDGET_PERCENT';

PARAMETER_NAME                    %_LIMIT SYSAUX_SIZE_IN_MB CURRENT_LIMIT_IN_MB
------------------------------ ---------- ----------------- -------------------
SPACE_BUDGET_PERCENT                   30          211.4375            63.43125

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the CONFIGURE function

23.7.2 Changing the Plan Retention Policy in the SMB

A weekly scheduled purging task manages disk space used by SQL plan management. The task runs as an automated task in the maintenance window. The database purges plans that have not been used for longer than the plan retention period, as identified by the LAST_EXECUTED timestamp stored in the SMB for that plan. The default retention period is 53 weeks. The period can range between 5 and 523 weeks.

This task explains how to change the plan retention period with the DBMS_SPM.CONFIGURE procedure. In Cloud Control, set the plan retention policy in the SQL Plan Baseline subpage (shown in Figure 23-3).

To change the plan retention period for the SMB:

1. Connect SQL*Plus to the database with the appropriate privileges, and then query the data dictionary to see the current plan retention period.
For example, execute the following query (sample output included):

SQL> SELECT PARAMETER_NAME, PARAMETER_VALUE
2  FROM   DBA_SQL_MANAGEMENT_CONFIG
3  WHERE  PARAMETER_NAME = 'PLAN_RETENTION_WEEKS';

PARAMETER_NAME                 PARAMETER_VALUE
------------------------------ ---------------
PLAN_RETENTION_WEEKS                        53

2. Change the retention period.
For example, execute the CONFIGURE procedure to change the period to 105 weeks:

EXECUTE DBMS_SPM.CONFIGURE('plan_retention_weeks',105);

3. Query the data dictionary to confirm the change.
For example, execute the following query:

SQL> SELECT PARAMETER_NAME, PARAMETER_VALUE
2  FROM   DBA_SQL_MANAGEMENT_CONFIG
3  WHERE  PARAMETER_NAME = 'PLAN_RETENTION_WEEKS';

PARAMETER_NAME                 PARAMETER_VALUE
------------------------------ ---------------
PLAN_RETENTION_WEEKS                       105

See Also:
Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM.CONFIGURE procedure

24 Migrating Stored Outlines to SQL Plan Baselines

This chapter explains the concepts and tasks relating to stored outline migration. This chapter contains the following topics:

- About Stored Outline Migration

- Preparing for Stored Outline Migration

- Migrating Outlines to Utilize SQL Plan Management Features

- Migrating Outlines to Preserve Stored Outline Behavior

- Performing Follow-Up Tasks After Stored Outline Migration

Note:
Starting in Oracle Database 12c, stored outlines are deprecated. See Chapter 24, "Migrating Stored Outlines to SQL Plan Baselines" for an alternative.

24.1 About Stored Outline Migration

A stored outline is a set of hints for a SQL statement. The hints direct the optimizer to choose a specific plan for the statement. A stored outline is a legacy technique for providing plan stability.

Stored outline migration is the user-initiated process of converting stored outlines to SQL plan baselines. A SQL plan baseline is a set of plans proven to provide optimal performance.

This section contains the following topics:

- Purpose of Stored Outline Migration

- How Stored Outline Migration Works

- User Interface for Stored Outline Migration

- Basic Steps in Stored Outline Migration

24.1.1 Purpose of Stored Outline Migration

This section assumes that you rely on stored outlines to maintain plan stability and prevent performance regressions. The goal of this section is to provide a convenient method to safely migrate from stored outlines to SQL plan baselines. After the migration, you can maintain the same plan stability that you had using stored outlines while being able to use the more advanced features provided by the SQL Plan Management framework.

Specifically, the section explains how to address the following problems:

- Stored outlines cannot automatically evolve over time. Consequently, a stored outline may be optimal when you create it, but become a suboptimal plan after a database change, leading to performance degradation.

- Hints in a stored outline can become invalid, as with an index hint on a dropped index. In such cases, the database still uses the outlines but excludes the invalid hints, producing a plan that is often worse than the original plan or the current best-cost plan generated by the optimizer.

- For a SQL statement, the optimizer can only choose the plan defined in the stored outline in the currently specified category. The optimizer cannot choose from other stored outlines in different categories or the current cost-based plan even if they improve performance.

- Stored outlines are a reactive tuning technique, which means that you only use a stored outline to address a performance problem after it has occurred. For example, you may implement a stored outline to correct the plan of a SQL statement that became high-load. In this case, you used stored outlines instead of proactively tuning the statement before it became high-load.

The stored outline migration PL/SQL API helps solve the preceding problems in the following ways:

- SQL plan baselines enable the optimizer to use the same optimal plan and allow this plan to evolve over time.
For a specified SQL statement, you can add new plans as SQL plan baselines after they are verified not to cause performance regressions.

- SQL plan baselines prevent plans from becoming unreproducible because of invalid hints.
If hints stored in a plan baseline become invalid, then the plan may not be reproducible by the optimizer. In this case, the optimizer selects an alternative reproducible plan baseline or the current best-cost plan generated by optimizer.

- For a specific SQL statement, the database can maintain multiple plan baselines.
The optimizer can choose from a set of optimal plans for a specific SQL statement instead of being restricted to a single plan per category, as required by stored outlines.

24.1.2 How Stored Outline Migration Works

This section explains how the database migrates stored outlines to SQL plan baselines. This information is important for performing the task of migrating stored outlines.

24.1.2.1 Stages of Stored Outline Migration

The following graphic shows the main stages in stored outline migration:

The migration process has the following stages:

1. The user invokes a function that specifies which outlines to migrate.

2. The database processes the outlines as follows:

1. The database copies information in the outline needed by the plan baseline.
The database copies it directly or calculates it based on information in the outline. For example, the text of the SQL statement exists in both schemas, so the database can copy the text from outline to baseline.

2. The database reparses the hints to obtain information not in the outline.
The plan hash value and plan cost cannot be derived from the existing information in the outline, which necessitates reparsing the hints.

3. The database creates the baselines.

3. The database obtains missing information when it chooses the SQL plan baseline for the first time to execute the SQL statement.
The compilation environment and execution statistics are only available during execution when the plan baseline is parsed and compiled.

The migration is complete only after the preceding phases complete.

24.1.2.2 Outline Categories and Baseline Modules

An outline is a set of hints, whereas a SQL plan baseline is a set of plans. Because they are different technologies, some functionality of outlines does not map exactly to functionality of baselines. For example, a single SQL statement can have multiple outlines, each of which is in a different outline category, but the only category that currently exists for baselines is DEFAULT.

The equivalent of a category for an outline is a module for a SQL plan baseline. Table 24-1 explains how outline categories map to modules.

Table 24-1 Outline Categories

Concept

Description

Default Value

Outline Category

Specifies a user-defined grouping for a set of stored outlines.

You can use categories to maintain different stored outlines for a SQL statement. For example, a single statement can have an outline in the OLTP category and the DW category.

Each SQL statement can have one or more stored outlines. Each stored outline is in one and only one outline category. A statement can have multiple stored outlines in different categories, but only one stored outline exists for each category of each statement.

During migration, the database maps each outline category to a SQL plan baseline module.

DEFAULT

Baseline Module

Specifies a high-level function being performed.

A SQL plan baseline can belong to one and only one module.

After an outline is migrated to a SQL plan baseline, the module name defaults to outline category name.

Baseline Category

Only one SQL plan baseline category exists. This category is named DEFAULT. During stored outline migration, the module name of the SQL plan baseline is set to the category name of the stored outline.

A statement can have multiple SQL plan baselines in the DEFAULT category.

DEFAULT

When migrating stored outlines to SQL plan baselines, Oracle Database maps every outline category to a SQL plan baseline module with the same name. As shown in the following diagram, the outline category OLTP is mapped to the baseline module OLTP. After migration, DEFAULT is a super-category that contains all SQL plan baselines.

24.1.3 User Interface for Stored Outline Migration

You can use the DBMS_SPM package to perform the stored outline migration. Table 24-2 describes the relevant functions in this package.

Table 24-2 DBMS_SPM Functions Relating to Stored Outline Migration

DBMS_SPM Function

Description

MIGRATE_STORED_OUTLINE

Migrates existing stored outlines to plan baselines.

Use either of the following formats:

- Specify outline name, SQL text, outline category, or all stored outlines.

- Specify a list of outline names.

ALTER_SQL_PLAN_BASELINE

Changes an attribute of a single plan or all plans associated with a SQL statement.

DROP_MIGRATED_STORED_OUTLINE

Drops stored outlines that have been migrated to SQL plan baselines.

The function finds stored outlines marked as MIGRATED in the DBA_OUTLINES view, and then drops these outlines from the database.

You can control stored outline and plan baseline behavior with initialization and session parameters. Table 24-3 describes the relevant parameters. See Table 24-5 and Table 24-6 for an explanation of how these parameter settings interact.

Table 24-3 Parameters Relating to Stored Outline Migration

Initialization or Session Parameter

Description

Parameter Type

CREATE_STORED_OUTLINES

Determines whether Oracle Database automatically creates and stores an outline for each query submitted during the session.

Initialization parameter

OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES

Enables or disables the automatic recognition of repeatable SQL statement and the generation of SQL plan baselines for these statements.

Initialization parameter

USE_STORED_OUTLINES

Determines whether the optimizer uses stored outlines to generate execution plans.

Note: This is a session parameter, not an initialization parameter.

Session

OPTIMIZER_USE_SQL_PLAN_BASELINES

Enables or disables the use of SQL plan baselines stored in SQL Management Base.

Initialization parameter

You can use database views to access information relating to stored outline migration. Table 24-4 describes the following main views.

Table 24-4 Views Relating to Stored Outline Migration

View

Description

DBA_OUTLINES

Describes all stored outlines in the database.

The MIGRATED column is important for outline migration and shows one of the following values: NOT-MIGRATED and MIGRATED. When MIGRATED, the stored outline has been migrated to a plan baseline and is not usable.

DBA_SQL_PLAN_BASELINES

Displays information about the SQL plan baselines currently created for specific SQL statements.

The ORIGIN column indicates how the plan baseline was created. The value STORED-OUTLINE indicates the baseline was created by migrating an outline.

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM package

- Oracle Database Reference to learn about database initialization parameters and database fixed views

24.1.4 Basic Steps in Stored Outline Migration

This section explains the basic steps in using the PL/SQL API to perform stored outline migration. The basic steps are as follows:

1. Prepare for stored outline migration.
Review the migration prerequisites and determine how you want the migrated plan baselines to behave.

See "Preparing for Stored Outline Migration".

2. Do one of the following:

- Migrate to baselines to use SQL Plan Management features.
See "Migrating Outlines to Utilize SQL Plan Management Features".

- Migrate to baselines while exactly preserving the behavior of the stored outlines.
See "Migrating Outlines to Preserve Stored Outline Behavior".

3. Perform post-migration confirmation and cleanup.
See "Performing Follow-Up Tasks After Stored Outline Migration".

24.2 Preparing for Stored Outline Migration

This section explains how to prepare for stored outline migration.

To prepare for stored outline migration:

1. Connect SQL*Plus to the database with SYSDBA privileges or the EXECUTE privilege on the DBMS_SPM package.
For example, do the following to use operating system authentication to log on to a database as SYS:

% sqlplus /nolog
SQL> CONNECT / AS SYSDBA

2. Query the stored outlines in the database.
The following example queries all stored outlines that have not been migrated to SQL plan baselines:

SELECT NAME, CATEGORY, SQL_TEXT
FROM   DBA_OUTLINES
WHERE  MIGRATED = 'NOT-MIGRATED';

3. Determine which stored outlines meet the following prerequisites for migration eligibility:

- The statement must not be a run-time INSERT AS SELECT statement.

- The statement must not reference a remote object.

- This statement must not be a private stored outline.

4. Decide whether to migrate all outlines, specified stored outlines, or outlines belonging to a specified outline category.
If you do not decide to migrate all outlines, then identify the outlines or categories that you intend to migrate.

5. Decide whether the stored outlines migrated to SQL plan baselines use fixed plans or nonfixed plans:

- Fixed plans
A fixed plan is frozen. If a fixed plan is reproducible using the hints stored in plan baseline, then the optimizer always chooses the lowest-cost fixed plan baseline over plan baselines that are not fixed. Essentially, a fixed plan baseline acts as a stored outline with valid hints.

A fixed plan is reproducible when the database can parse the statement based on the hints stored in the plan baseline and create a plan with the same plan hash value as the one in the plan baseline. If one of more of the hints become invalid, then the database may not be able to create a plan with the same plan hash value. In this case, the plan is nonreproducible.

If a fixed plan cannot be reproduced when parsed using its hints, then the optimizer chooses a different plan, which can be either of the following:

- Another plan for the SQL plan baseline

- The current cost-based plan created by the optimizer

In some cases, a performance regression occurs because of the different plan, requiring SQL tuning.

- Nonfixed plans
If a plan baseline does not contain fixed plans, then SQL Plan Management considers the plans equally when picking a plan for a SQL statement.

6. Before beginning the actual migration, ensure that the Oracle database meets the following prerequisites:

- The database must be Enterprise Edition.

- The database must be open and must not be in a suspended state.

- The database must not be in restricted access (DBA), read-only, or migrate mode.

- Oracle Call Interface (OCI) must be available.

See Also:

- Oracle Database Administrator's Guide to learn about administrator privileges

- Oracle Database Reference to learn about the DBA_OUTLINES views

24.3 Migrating Outlines to Utilize SQL Plan Management Features

The goals of this task are as follows:

- To allow SQL Plan Management to select from all plans in a plan baseline for a SQL statement instead of applying the same fixed plan after migration

- To allow the SQL plan baseline to evolve in the face of database changes by adding new plans to the baseline

Assumptions

This tutorial assumes the following:

- You migrate all outlines.
To migrate specific outlines, see Oracle Database PL/SQL Packages and Types Reference for details about the DBMS_SPM.MIGRATE_STORED_OUTLINE function.

- You want the module names of the baselines to be identical to the category names of the migrated outlines.

- You do not want the SQL plans to be fixed.
By default, generated plans are not fixed and SQL Plan Management considers all plans equally when picking a plan for a SQL statement. This situation permits the advanced feature of plan evolution to capture new plans for a SQL statement, verify their performance, and accept these new plans into the plan baseline.

To migrate stored outlines to SQL plan baselines:

1. Connect SQL*Plus to the database with the appropriate privileges.

2. Call PL/SQL function MIGRATE_STORED_OUTLINE.
The following sample PL/SQL block migrates all stored outlines to fixed baselines:

DECLARE
my_report CLOB;
BEGIN
my_outlines := DBMS_SPM.MIGRATE_STORED_OUTLINE( attribute_name => 'all' );
END;
/

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM package

- Oracle Database SQL Language Reference to learn about the ALTER SYSTEM statement

24.4 Migrating Outlines to Preserve Stored Outline Behavior

The goal of this task is to migrate stored outlines to SQL plan baselines and preserve the original behavior of the stored outlines by creating fixed plan baselines. A fixed plan has higher priority over other plans for the same SQL statement. If a plan is fixed, then the plan baseline cannot be evolved. The database does not add new plans to a plan baseline that contains a fixed plan.

Assumptions

This tutorial assumes the following:

- You want to migrate only the stored outlines in the category named firstrow.
See Oracle Database PL/SQL Packages and Types Reference for syntax and semantics of the DBMS_SPM.MIGRATE_STORED_OUTLINE function.

- You want the module names of the baselines to be identical to the category names of the migrated outlines.

To migrate stored outlines to plan baselines:

1. Connect SQL*Plus to the database with the appropriate privileges.

2. Call PL/SQL function MIGRATE_STORED_OUTLINE.
The following sample PL/SQL block migrates stored outlines in the category firstrow to fixed baselines:

DECLARE
my_report CLOB;
BEGIN
my_outlines := DBMS_SPM.MIGRATE_STORED_OUTLINE(
attribute_name => 'category',
attribute_value => 'firstrow',
fixed => 'YES' );
END;
/

After migration, the SQL plan baselines is in module firstrow and category DEFAULT.

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM package

- Oracle Database SQL Language Reference to learn about the ALTER SYSTEM statement

24.5 Performing Follow-Up Tasks After Stored Outline Migration

The goals of this task are as follows:

- To configure the database to use plan baselines instead of stored outlines for stored outlines that have been migrated to SQL plan baselines

- To create SQL plan baselines instead of stored outlines for future SQL statements

- To drop the stored outlines that have been migrated to SQL plan baselines

This section explains how to set initialization parameters relating to stored outlines and plan baselines. The OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES and CREATE_STORED_OUTLINES initialization parameters determine how and when the database creates stored outlines and SQL plan baselines. Table 24-5 explains the interaction between these parameters.

Table 24-5 Creation of Outlines and Baselines

CREATE_STORED_OUTLINES Initialization Parameter

OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES Initialization Parameter

Database Behavior

false

false

When executing a SQL statement, the database does not create stored outlines or SQL plan baselines.

false

true

The automatic recognition of repeatable SQL statements and the generation of SQL plan baselines for these statements is enabled. When executing a SQL statement, the database creates only new SQL plan baselines (if they do not exist) with the category name DEFAULT for the statement.

true

false

Oracle Database automatically creates and stores an outline for each query submitted during the session. When executing a SQL statement, the database creates only new stored outlines (if they do not exist) with the category name DEFAULT for the statement.

category

false

When executing a SQL statement, the database creates only new stored outlines (if they do not exist) with the specified category name for the statement.

true

true

Oracle Database automatically creates and stores an outline for each query submitted during the session. The automatic recognition of repeatable SQL statements and the generation of SQL plan baselines for these statements is also enabled.

When executing a SQL statement, the database creates both stored outlines and SQL plan baselines with the category name DEFAULT.

category

true

Oracle Database automatically creates and stores an outline for each query submitted during the session. The automatic recognition of repeatable SQL statements and the generation of SQL plan baselines for these statements is also enabled.

When executing a SQL statement, the database creates stored outlines with the specified category name and SQL plan baselines with the category name DEFAULT.

The USE_STORED_OUTLINES session parameter (it is not an initialization parameter) and OPTIMIZER_USE_SQL_PLAN_BASELINES initialization parameter determine how the database uses stored outlines and plan baselines. Table 24-6 explains how these parameters interact.

Table 24-6 Use of Stored Outlines and SQL Plan Baselines

USE_STORED_OUTLINES Session Parameter

OPTIMIZER_USE_SQL_PLAN_BASELINES Initialization Parameter

Database Behavior

false

false

When choosing a plan for a SQL statement, the database does not use stored outlines or plan baselines.

false

true

When choosing a plan for a SQL statement, the database uses only SQL plan baselines.

true

false

When choosing a plan for a SQL statement, the database uses stored outlines with the category name DEFAULT.

category

false

When choosing a plan for a SQL statement, the database uses stored outlines with the specified category name.

If a stored outline with the specified category name does not exist, then the database uses a stored outline in the DEFAULT category if it exists.

true

true

When choosing a plan for a SQL statement, stored outlines take priority over plan baselines.

If a stored outline with the category name DEFAULT exists for the statement and is applicable, then the database applies the stored outline. Otherwise, the database uses SQL plan baselines. However, if the stored outline has the property MIGRATED, then the database does not use the outline and uses the corresponding SQL plan baseline instead (if it exists).

category

true

When choosing a plan for a SQL statement, stored outlines take priority over plan baselines.

If a stored outline with the specified category name or the DEFAULT category exists for the statement and is applicable, then the database applies the stored outline. Otherwise, the database uses SQL plan baselines. However, if the stored outline has the property MIGRATED, then the database does not use the outline and uses the corresponding SQL plan baseline instead (if it exists).

Assumptions

This tutorial assumes the following:

- You have completed the basic steps in the stored outline migration (see "Basic Steps in Stored Outline Migration").

- Some stored outlines may have been created before Oracle Database 10g.
Hints in releases before Oracle Database 10g use a local hint format. After migration, hints stored in a plan baseline use the global hints format introduced in Oracle Database 10g.

To place the database in the proper state after the migration:

1. Connect SQL*Plus to the database with the appropriate privileges, and then check that SQL plan baselines have been created as the result of migration.
Ensure that the plans are enabled and accepted. For example, enter the following query (partial sample output included):

SELECT SQL_HANDLE, PLAN_NAME, ORIGIN, ENABLED, ACCEPTED, FIXED, MODULE
FROM   DBA_SQL_PLAN_BASELINES;

SQL_HANDLE                     PLAN_NAME  ORIGIN         ENA ACC FIX MODULE
------------------------------ ---------- -------------- --- --- --- ------
SYS_SQL_f44779f7089c8fab       STMT01     STORED-OUTLINE YES YES NO  DEFAULT
.
.
.

2. Optionally, change the attributes of the SQL plan baselines.
For example, the following statement changes the status of the baseline for the specified SQL statement to fixed:

DECLARE
v_cnt PLS_INTEGER;
BEGIN
v_cnt := DBMS_SPM.ALTER_SQL_PLAN_BASELINE(
sql_handle=>'SYS_SQL_f44779f7089c8fab',
attribute_name=>'FIXED',
attribute_value=>'NO');
DBMS_OUTPUT.PUT_LINE('Plans altered: ' || v_cnt);
END;
/

3. Check the status of the original stored outlines.
For example, enter the following query (partial sample output included):

SELECT NAME, OWNER, CATEGORY, USED, MIGRATED
FROM   DBA_OUTLINES
ORDER BY NAME;

NAME       OWNER      CATEGORY   USED   MIGRATED
---------- ---------- ---------- ------ ------------
STMT01     SYS        DEFAULT    USED   MIGRATED
STMT02     SYS        DEFAULT    USED   MIGRATED
.
.
.

4. Drop all stored outlines that have been migrated to SQL plan baselines.
For example, the following statements drops all stored outlines with status MIGRATED in DBA_OUTLINES:

DECLARE
v_cnt PLS_INTEGER;
BEGIN
v_cnt := DBMS_SPM.DROP_MIGRATED_STORED_OUTLINE();
DBMS_OUTPUT.PUT_LINE('Migrated stored outlines dropped: ' || v_cnt);
END;
/

5. Set initialization parameters so that:

- When executing a SQL statement, the database creates plan baselines but does not create stored outlines.

- The database only uses stored outlines when the equivalent SQL plan baselines do not exist.

For example, the following SQL statements instruct the database to create SQL plan baselines instead of stored outlines when a SQL statement is executed. The example also instructs the database to apply a stored outline in category allrows or DEFAULT only if it exists and has not been migrated to a SQL plan baseline. In other cases, the database applies SQL plan baselines instead.

ALTER SYSTEM
SET CREATE_STORED_OUTLINE = false SCOPE = BOTH;

ALTER SYSTEM
SET OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES = true SCOPE = BOTH;

ALTER SYSTEM
SET OPTIMIZER_USE_SQL_PLAN_BASELINES = true SCOPE = BOTH;

ALTER SESSION
SET USE_STORED_OUTLINES = allrows SCOPE = BOTH;

See Also:

- Oracle Database PL/SQL Packages and Types Reference to learn about the DBMS_SPM package

- Oracle Database Reference to learn about database fixed views

A Guidelines for Indexes and Table Clusters

This appendix provides an overview of data access methods using indexes and clusters that can enhance or degrade performance.

The appendix contains the following topics:

- Guidelines for Tuning Index Performance

- Guidelines for Using Function-Based Indexes for Performance

- Guidelines for Using Partitioned Indexes for Performance

- Guidelines for Using Index-Organized Tables for Performance

- Guidelines for Using Bitmap Indexes for Performance

- Guidelines for Using Bitmap Join Indexes for Performance

- Guidelines for Using Domain Indexes for Performance

- Guidelines for Using Table Clusters

- Guidelines for Using Hash Clusters for Performance

A.1 Guidelines for Tuning Index Performance

This section describes the following:

- Guidelines for Tuning the Logical Structure

- Guidelines for Using SQL Access Advisor

- Guidelines for Choosing Columns and Expressions to Index

- Guidelines for Choosing Composite Indexes

- Guidelines for Writing SQL Statements That Use Indexes

- Guidelines for Writing SQL Statements That Avoid Using Indexes

- Guidelines for Re-Creating Indexes

- Guidelines for Compacting Indexes

- Guidelines for Using Nonunique Indexes to Enforce Uniqueness

A.1.1 Guidelines for Tuning the Logical Structure

Although query optimization helps avoid the use of nonselective indexes within query execution, the SQL engine must continue to maintain all indexes defined against a table, regardless of whether queries use them. Index maintenance can present a significant CPU and I/O resource demand in any write-intensive application. In other words, do not build indexes unless necessary.

To maintain optimal performance, drop indexes that an application is not using. You can find indexes that are not being used by using the ALTER INDEX MONITORING USAGE functionality over a period that is representative of your workload. This monitoring feature records whether an index has been used. If you find that an index has not been used, then drop it. Make sure you are monitoring a representative workload to avoid dropping an index which is used, but not by the workload you sampled.

Also, indexes within an application sometimes have uses that are not immediately apparent from a survey of statement execution plans. An example of this is a foreign key index on a parent table, which prevents share locks from being taken out on a child table.

If you are deciding whether to create new indexes to tune statements, then you can also use the EXPLAIN PLAN statement to determine whether the optimizer chooses to use these indexes when the application is run. If you create new indexes to tune a statement that is currently parsed, then Oracle Database invalidates the statement.

When the statement is next parsed, the optimizer automatically chooses a new execution plan that could potentially use the new index. If you create new indexes on a remote database to tune a distributed statement, then the optimizer considers these indexes when the statement is next parsed.

Creating an index to tune one statement can affect the optimizer's choice of execution plans for other statements. For example, if you create an index to be used by one statement, then the optimizer can choose to use that index for other statements in the application as well. For this reason, reexamine the application's performance and execution plans, and rerun the SQL trace facility after you have tuned those statements that you initially identified for tuning.

See Also:

- Oracle Database SQL Language Reference for syntax and semantics of the ALTER INDEX MONITORING USAGE statement

- Oracle Database Development Guide to learn about foreign keys

A.1.2 Guidelines for Using SQL Access Advisor

SQL Access Advisor is an alternative to manually determining which indexes are required. This advisor recommends a set of indexes when invoked from Oracle Enterprise Manager Cloud Control (Cloud Control) or run through the DBMS_ADVISOR package APIs. SQL Access Advisor either recommends using a workload or it generates a hypothetical workload for a specified schema.

Various workload sources are available, such as the current contents of the SQL cache, a user-defined set of SQL statements, or a SQL tuning set. Given a workload, SQL Access Advisor generates a set of recommendations from which you can select the indexes to be implemented. SQL Access Advisor provides an implementation script that can be executed manually or automatically through Cloud Control.

See Also:
"About SQL Access Advisor"

A.1.3 Guidelines for Choosing Columns and Expressions to Index

A key is a column or expression on which you can build an index. Follow these guidelines for choosing keys to index:

- Consider indexing keys that appear frequently in WHERE clauses.

- Consider indexing keys that frequently join tables in SQL statements.

- Choose index keys that are highly selective. The selectivity of an index is the percentage of rows in a table having the same value for the indexed key. An index selectivity is optimal if few rows have the same value.

Note:
Oracle Database automatically creates indexes, or uses existing indexes, on the keys and expressions of unique and primary keys that you define with integrity constraints.

Indexing low selectivity columns can be helpful when the data distribution is skewed so that one or two values occur much less often than other values.

- Do not use standard B-tree indexes on keys or expressions with few distinct values. Such keys or expressions are usually unselective and therefore do not optimize performance unless the frequently selected key values appear less frequently than the other key values. You can use bitmap indexes effectively in such cases, unless the index is modified frequently, as in a high concurrency OLTP application.

- Do not index frequently modified columns. UPDATE statements that modify indexed columns and INSERT and DELETE statements that modify indexed tables take longer than if there were no index. Such SQL statements must modify data in indexes and data in tables. They also create additional undo and redo.

- Do not index keys that appear only in WHERE clauses with functions or operators. A WHERE clause that uses a function, other than MIN or MAX, or an operator with an indexed key does not make available the access path that uses the index except with function-based indexes.

- Consider indexing foreign keys of referential integrity constraints in cases in which many concurrent INSERT, UPDATE, and DELETE statements access the parent and child tables. Such an index allows UPDATEs and DELETEs on the parent table without share locking the child table.

- When choosing to index a key, consider whether the performance gain for queries is worth the performance loss for INSERTs, UPDATEs, and DELETEs and the use of the space required to store the index. You might want to experiment by comparing the processing times of the SQL statements with and without indexes. You can measure processing time with the SQL trace facility.

See Also:
Oracle Database Development Guide for more information about the effects of foreign keys on locking

A.1.4 Guidelines for Choosing Composite Indexes

A composite index contains multiple key columns. Composite indexes can provide additional advantages over single-column indexes:

- Improved selectivity
Sometimes you can combine two or more columns or expressions, each with low selectivity, to form a composite index with higher selectivity.

- Reduced I/O
If all columns selected by a query are in a composite index, then Oracle Database can return these values from the index without accessing the table.

A SQL statement can use an access path involving a composite index when the statement contains constructs that use a leading portion of the index.

Note:
This is no longer the case with index skip scans. See "Index Skip Scans".

A leading portion of an index is a set of one or more columns that were specified first and consecutively in the list of columns in the CREATE INDEX statement that created the index. Consider this CREATE INDEX statement:

CREATE INDEX comp_ind
ON table1(x, y, z);

- x, xy, and xyz combinations of columns are leading portions of the index

- yz, y, and z combinations of columns are not leading portions of the index

A.1.4.1 Guidelines for Choosing Keys for Composite Indexes

Follow these guidelines for choosing keys for composite indexes:

- Consider creating a composite index on keys that appear together frequently in WHERE clause conditions combined with AND operators, especially if their combined selectivity is better than the selectivity of either key individually.

- If several queries select the same set of keys based on one or more key values, then consider creating a composite index containing all of these keys.

Of course, consider the guidelines associated with the general performance advantages and trade-offs of indexes described in the previous sections.

A.1.4.2 Guidelines for Ordering Keys for Composite Indexes

Follow these guidelines for ordering keys in composite indexes:

- Create the index so the keys used in WHERE clauses comprise a leading portion.

- If some keys appear in WHERE clauses more frequently, then create the index so that the more frequently selected keys comprise a leading portion to allow the statements that use only these keys to use the index.

- If all keys appear in WHERE clauses equally often but the data is physically ordered on one of the keys, then place this key first in the composite index.

A.1.5 Guidelines for Writing SQL Statements That Use Indexes

Even after you create an index, the optimizer cannot use an access path that uses the index simply because the index exists. The optimizer can choose such an access path for a SQL statement only if it contains a construct that makes the access path available. To allow the query optimizer the option of using an index access path, ensure that the statement contains a construct that makes such an access path available.

A.1.6 Guidelines for Writing SQL Statements That Avoid Using Indexes

In some cases, you might want to prevent a SQL statement from using an access path that uses an existing index. You may want to take this approach if you know that the index is not very selective and a full table scan would be more efficient. If the statement contains a construct that makes such an index access path available, then you can force the optimizer to use a full table scan through one of the following methods:

- Use the NO_INDEX hint to give the query optimizer maximum flexibility while disallowing the use of a certain index.

- Use the FULL hint to instruct the optimizer to choose a full table scan instead of an index scan.

- Use the INDEX or INDEX_COMBINE hints to instruct the optimizer to use one index or a set of listed indexes instead of another.

Parallel execution uses indexes effectively. It does not perform parallel index range scans, but it does perform parallel index lookups for parallel nested loops join execution. If an index is very selective (few rows correspond to each index entry), then a sequential index lookup might be better than a parallel table scan.

See Also:
Chapter 14, "Influencing the Optimizer" for more information about the NO_INDEX, FULL, INDEX, and INDEX_COMBINE and hints

A.1.7 Guidelines for Re-Creating Indexes

You might want to re-create an index to compact it and minimize fragmented space, or to change the index's storage characteristics. When creating a new index that is a subset of an existing index, or when rebuilding an existing index with new storage characteristics, Oracle Database might use the existing index instead of the base table to improve the performance of the index build.

However, in some cases using the base table instead of the existing index is beneficial. Consider an index on a table on which a lot of DML has been performed. Because of the DML, the size of the index can increase to the point where each block is only 50% full, or even less. If the index refers to most of the columns in the table, then the index could actually be larger than the table. In this case, it is faster to use the base table rather than the index to re-create the index.

To reorganize or compact an existing index or to change its storage characteristics, use the ALTER INDEX . . . REBUILD statement. The REBUILD statement uses the existing index as the basis for the new one. All index storage statements are supported, such as STORAGE (for extent allocation), TABLESPACE (to move the index to a new tablespace), and INITRANS (to change the initial number of entries).

Usually, ALTER INDEX . . . REBUILD is faster than dropping and re-creating an index, because this statement uses the fast full scan feature. It reads all the index blocks using multiblock I/O, then discards the branch blocks. A further advantage of this approach is that the old index is still available for queries while the rebuild is in progress.

See Also:
Oracle Database SQL Language Reference for more information about the CREATE INDEX and ALTER INDEX statements and restrictions on rebuilding indexes

A.1.8 Guidelines for Compacting Indexes

You can coalesce leaf blocks of an index by using the ALTER INDEX statement with the COALESCE option. This option enables you to combine leaf levels of an index to free blocks for reuse. You can also rebuild the index online.

See Also:
Oracle Database SQL Language Reference and Oracle Database Administrator's Guide for more information about the syntax for this statement

A.1.9 Guidelines for Using Nonunique Indexes to Enforce Uniqueness

You can use an existing nonunique index on a table to enforce uniqueness, either for UNIQUE constraints or the unique aspect of a PRIMARY KEY constraint. The advantage of this approach is that the index remains available and valid when the constraint is disabled. Therefore, enabling a disabled UNIQUE or PRIMARY KEY constraint does not require rebuilding the unique index associated with the constraint. This can yield significant time savings on enable operations for large tables.

Using a nonunique index to enforce uniqueness also enables you to eliminate redundant indexes. You do not need a unique index on a primary key column if that column is included as the prefix of a composite index. You can use the existing index to enable and enforce the constraint. You also save significant space by not duplicating the index. However, if the existing index is partitioned, then the partitioning key of the index must also be a subset of the UNIQUE key; otherwise, Oracle Database creates an additional unique index to enforce the constraint.

A.1.10 Guidelines for Using Enabled Novalidated Constraints

An enabled novalidated constraint behaves similarly to an enabled validated constraint for new data. Placing a constraint in the enabled novalidated state signifies that any new data entered into the table must conform to the constraint. Existing data is not checked. By placing a constraint in the enabled novalidated state, you enable the constraint without locking the table.

If you change a constraint from disabled to enabled, then the table must be locked. No new DML, queries, or DDL can occur, because no mechanism can ensure that operations on the table conform to the constraint during the enable operation. The enabled novalidated state prevents users from performing operations on the table that violate the constraint.

The database can validate an enabled novalidated constraint with a parallel, consistent-read query of the table to determine whether any data violates the constraint. The database performs no locking, so the enable operation does not block readers or writers. In addition, the database can validate enabled novalidated constraints in parallel. The database can validate multiple constraints at the same time and check the validity of each constraint using parallel query.

To create tables with constraints and indexes:

1. Create the tables with the constraints.
NOT NULL constraints can be unnamed and should be created enabled and validated. Name all other constraints (CHECK, UNIQUE, PRIMARY KEY, and FOREIGN KEY) and create them disabled.

Note:
By default, constraints are created in the ENABLED state.

2. Load old data into the tables.

3. Create all indexes, including indexes needed for constraints.

4. Enable novalidate all constraints. Do this to primary keys before foreign keys.

5. Allow users to query and modify data.

6. With a separate ALTER TABLE statement for each constraint, validate all constraints. Do this to primary keys before foreign keys. For example,

CREATE TABLE t (a NUMBER CONSTRAINT apk PRIMARY KEY DISABLE,
b NUMBER NOT NULL);
CREATE TABLE x (c NUMBER CONSTRAINT afk REFERENCES t DISABLE);

Now load data into table t.

CREATE UNIQUE INDEX tai ON t (a);
CREATE INDEX tci ON x (c);
ALTER TABLE t MODIFY CONSTRAINT apk ENABLE NOVALIDATE;
ALTER TABLE x MODIFY CONSTRAINT afk ENABLE NOVALIDATE;

At this point, users can start performing INSERT, UPDATE, DELETE, and SELECT operations on table t.

ALTER TABLE t ENABLE CONSTRAINT apk;
ALTER TABLE x ENABLE CONSTRAINT afk;

Now the constraints are enabled and validated.

See Also:
Oracle Database Concepts for an overview of integrity constraints

A.2 Guidelines for Using Function-Based Indexes for Performance

A function-based index includes columns that are either transformed by a function, such as the UPPER function, or included in an expression, such as col1 + col2. With a function-based index, you can store computation-intensive expressions in the index.

Defining a function-based index on the transformed column or expression allows that data to be returned using the index when that function or expression is used in a WHERE clause or an ORDER BY clause. In this way, the database can avoid computing the value of the expression when processing SELECT and DELETE statements. Thus, a function-based index is useful when frequently executed SQL statements include transformed columns, or columns in expressions, in a WHERE or ORDER BY clause.

Oracle Database treats descending indexes as function-based indexes. The columns marked DESC are sorted in descending order.

For example, function-based indexes defined with the UPPER(column_name) or LOWER(column_name) keywords allow case-insensitive searches. Assume that you create an index on the following statement:

CREATE INDEX uppercase_idx ON employees (UPPER(last_name));

The preceding index facilitates processing queries such as:

SELECT *
FROM   employees
WHERE  UPPER(last_name) = 'MARKSON';

See Also:

- Oracle Database Development Guide and Oracle Database Administrator's Guide for more information about using function-based indexes

- Oracle Database SQL Language Reference for more information about the CREATE INDEX statement

A.3 Guidelines for Using Partitioned Indexes for Performance

Similar to partitioned tables, partitioned indexes improve manageability, availability, performance, and scalability. They can either be partitioned independently (global indexes) or automatically linked to a table's partitioning method (local indexes).

Oracle Database supports both range and hash partitioned global indexes. In a range partitioned global index, each index partition contains values defined by a partition bound. In a hash partitioned global index, each partition contains values determined by the Oracle Database hash function.

The hash method can improve performance of indexes where a small number leaf blocks in the index have high contention in multiuser OLTP environment. In some OLTP applications, index insertions happen only at the right edge of the index. This situation could occur when the index is defined on monotonically increasing columns. In such situations, the right edge of the index becomes a hot spot because of contention for index pages, buffers, latches for update, and additional index maintenance activity, which results in performance degradation.

With hash partitioned global indexes index entries are hashed to different partitions based on partitioning key and the number of partitions. This spreads out contention over number of defined partitions, resulting in increased throughput. Hash-partitioned global indexes would benefit TPC-H refresh functions that are executed as massive PDMLs into huge fact tables because contention for buffer latches would be spread out over multiple partitions.

With hash partitioning, an index entry is mapped to a particular index partition based on the hash value generated by Oracle Database. The syntax to create hash-partitioned global index is very similar to hash-partitioned table. Queries involving equality and IN predicates on index partitioning key can efficiently use global hash partitioned index to answer queries quickly.

See Also:
Oracle Database Concepts and Oracle Database Administrator's Guide for more information about global indexes tables

A.4 Guidelines for Using Index-Organized Tables for Performance

An index-organized table differs from an ordinary table in that the data for the table is held in its associated index. Changes to the table data, such as adding new rows, updating rows, or deleting rows, result only in updating the index. Because data rows are stored in the index, index-organized tables provide faster key-based access to table data for queries that involve exact match or range search or both.

A parent/child relationship is an example of a situation that may warrant an index-organized table. For example, a members table has a child table containing phone numbers. Phone numbers for a member are changed and added over time. In a heap-organized table, rows are inserted in data blocks where they fit. However, when you query the members table, you always retrieve the phone numbers from the child table. To make the retrieval more efficient, you can store the phone numbers in an index-organized table so that phone records for a given member are inserted near each other in the data blocks.

In some circumstances, an index-organized table may provide a performance advantage over a heap-organized table. For example, if a query requires fewer blocks in the cache, then the database uses the buffer cache more efficiently. If fewer distinct blocks are needed for a query, then a single physical I/O may retrieve all necessary data, requiring a smaller amount of I/O for each query.

Global hash-partitioned indexes are supported for index-organized tables and can provide performance benefits in a multiuser OLTP environment. Index-organized tables are useful when you must store related pieces of data together or physically store data in a specific order.

See Also:
Oracle Database Concepts and Oracle Database Administrator's Guide for more information about index-organized tables

A.5 Guidelines for Using Bitmap Indexes for Performance

Bitmap indexes can substantially improve performance of queries that have all of the following characteristics:

- The WHERE clause contains multiple predicates on low- or medium-cardinality columns.

- The individual predicates on these low- or medium-cardinality columns select many rows.

- The bitmap indexes used in the queries have been created on some or all of these low- or medium-cardinality columns.

- The tables in the queries contain many rows.

You can use multiple bitmap indexes to evaluate the conditions on a single table. Bitmap indexes are thus highly advantageous for complex ad hoc queries that contain lengthy WHERE clauses. Bitmap indexes can also provide optimal performance for aggregate queries and for optimizing joins in star schemas.

See Also:
Oracle Database Concepts and Oracle Database Data Warehousing Guide for more information about bitmap indexing

A.6 Guidelines for Using Bitmap Join Indexes for Performance

In addition to a bitmap index on a single table, you can create a bitmap join index, which is a bitmap index for the join of two or more tables. A bitmap join index is a space-saving way to reduce the volume of data that must be joined by performing restrictions in advance. For each value in a column of a table, a bitmap join index stores the rowids of corresponding rows in another table. In a data warehousing environment, the join condition is an equi-inner join between the primary key column(s) of the dimension tables and the foreign key column(s) in the fact table.

Bitmap join indexes are much more efficient in storage than materialized join views, an alternative for materializing joins in advance. Materialized join views do not compress the rowids of the fact tables.

See Also:
Oracle Database Data Warehousing Guide for examples and restrictions of bitmap join indexes

A.7 Guidelines for Using Domain Indexes for Performance

Domain indexes are built using the indexing logic supplied by a user-defined indextype. An indextype provides an efficient mechanism to access data that satisfy certain operator predicates. Typically, the user-defined indextype is part of an Oracle Database option, like the Spatial option. For example, the SpatialIndextype allows efficient search and retrieval of spatial data that overlap a given bounding box.

The cartridge determines the parameters you can specify in creating and maintaining the domain index. Similarly, the performance and storage characteristics of the domain index are presented in the specific cartridge documentation.

Refer to the appropriate cartridge documentation for information such as the following:

- What data types can be indexed?

- What indextypes are provided?

- What operators does the indextype support?

- How can the domain index be created and maintained?

- How do we efficiently use the operator in queries?

- What are the performance characteristics?

Note:
You can also create index types with the CREATE INDEXTYPE statement.

See Also:
Oracle Spatial and Graph Developer's Guide for information about the SpatialIndextype

A.8 Guidelines for Using Table Clusters

A table cluster is a group of one or more tables that are physically stored together because they share common columns and usually appear together in SQL statements. Because the database physically stores related rows together, disk access time improves. To create a cluster, use the CREATE CLUSTER statement.

Consider clustering tables in the following circumstances:

- The application frequently accesses the tables in join statements.

- In master-detail tables, the application often selects a master record and then the corresponding detail records.
Detail records are stored in the same data blocks as the master record, so they are likely still to be in memory when you select them, requiring Oracle Database to perform less I/O.

- The application often selects many detail records of the same master.
In this case, consider storing a detail table alone in a cluster. This measure improves the performance of queries that select detail records of the same master, but does not decrease the performance of a full table scan on the master table. An alternative is to use an index organized table.

Avoid clustering tables in the following circumstances:

- The application joins the tables only occasionally or modifies their common column values frequently.
Modifying a row's cluster key value takes longer than modifying the value in an nonclustered table, because Oracle Database might need to migrate the modified row to another block to maintain the cluster.

- The application often performs full table scans of only one of the tables.
A full table scan of a clustered table can take longer than a full table scan of an nonclustered table. Oracle Database is likely to read more blocks because the tables are stored together.

- The data from all tables with the same cluster key value exceeds more than one or two data blocks.
To access a row in a clustered table, Oracle Database reads all blocks containing rows with that value. If these rows take up multiple blocks, then accessing a single row could require more reads than accessing the same row in a nonclustered table.

- The number of rows for each cluster key value varies significantly.
This causes waste of space for the low cardinality key value. It causes collisions for the high cardinality key values. Collisions degrade performance.

Consider the benefits and drawbacks of clusters for the application. For example, you might decide that the performance gain for join statements outweighs the performance loss for statements that modify cluster key values. You might want to experiment and compare processing times with the tables both clustered and stored separately.

See Also:

- Oracle Database Concepts for more information about clusters

- Oracle Database Administrator's Guide for more information about creating clusters

A.9 Guidelines for Using Hash Clusters for Performance

Hash clusters group table data by applying a hash function to each row's cluster key value. All rows with the same cluster key value are stored together on disk. Consider the benefits and drawbacks of hash clusters for the application. You might want to experiment and compare processing times with a particular table in a hash cluster and alone with an index.

Follow these guidelines for choosing when to use hash clusters:

- Use hash clusters to store tables accessed frequently by SQL statements with WHERE clauses, if the WHERE clauses contain equality conditions that use the same column or combination of columns. Designate this column or combination of columns as the cluster key.

- Store a table in a hash cluster if you can determine how much space is required to hold all rows with a given cluster key value, including rows to be inserted immediately and rows to be inserted in the future.

- Use sorted hash clusters, where rows corresponding to each value of the hash function are sorted on a specific columns in ascending order, when the database can improve response time on operations with this sorted clustered data.

- Do not store a table in a hash cluster in the following cases:

- The application often performs full table scans.

- You must allocate a great deal of space to the hash cluster in anticipation of the table growing.

Full table scans must read all blocks allocated to the hash cluster, even though some blocks might contain few rows. Storing the table alone reduces the number of blocks read by full table scans.

- Do not store a table in a hash cluster if the application frequently modifies the cluster key values. Modifying a row's cluster key value can take longer than modifying the value in an nonclustered table, because Oracle Database might need to migrate the modified row to another block to maintain the cluster.

If hashing is appropriate for the table based on the considerations in this list, then storing a single table in a hash cluster can be useful. This is true regardless of whether the table is joined frequently with other tables.

See Also:

- Oracle Database Administrator's Guide to learn how to manage hash clusters

- Oracle Database SQL Language Reference to learn about the CREATE CLUSTER statement

Glossary

accepted plan

In the context of SQL plan management, a plan that is in a SQL plan baseline for a SQL statement and thus available for use by the optimizer. An accepted plan contains a set of hints, a plan hash value, and other plan-related information.

access path

The means by which the database retrieves data from a database. For example, a query using an index and a query using a full table scan use different access paths.

adaptive cursor sharing

A feature that enables a single statement that contains bind variables to use multiple execution plans. Cursor sharing is "adaptive" because the cursor adapts its behavior so that the database does not always use the same plan for each execution or bind variable value.

adaptive optimizer

A feature of the optimizer that enables it to adapt plans based on run-time statistics.

adaptive plan

An execution plan that changes after optimization because run-time conditions indicate that optimizer estimates are inaccurate. An adaptive plan has different built-in plan options. During the first execution, before a specific subplan becomes active, the optimizer makes a a final decision about which option to use. The optimizer bases its choice on observations made during the execution up to this point. Thus, an adaptive plan enables the final plan for a statement to differ from the default plan.

adaptive query optimization

A set of capabilities that enables the adaptive optimizer to make run-time adjustments to execution plans and discover additional information that can lead to better statistics. Adaptive optimization is helpful when existing statistics are not sufficient to generate an optimal plan.

ADDM

See Automatic Database Diagnostic Monitor (ADDM).

antijoin

A join that returns rows that fail to match the subquery on the right side. For example, an antijoin can list departments with no employees. Antijoins use the NOT EXISTS or NOT IN constructs.

Automatic Database Diagnostic Monitor (ADDM)

ADDM is self-diagnostic software built into Oracle Database. ADDM examines and analyzes data captured in Automatic Workload Repository (AWR) to determine possible database performance problems.

automatic optimizer statistics collection

The automatic running of the DBMS_STATS package to collect optimizer statistics for all schema objects for which statistics are missing or stale.

automatic initial plan capture

The mechanism by which the database automatically creates a SQL plan baseline for any repeatable SQL statement executed on the database. Enable automatic initial plan capture by setting the OPTIMIZER_CAPTURE_SQL_PLAN_BASELINES initialization parameter to true (the default is false).

See repeatable SQL statement.

automatic reoptimization

The ability of the optimizer to automatically change a plan on subsequent executions of a SQL statement. Automatic reoptimization can fix any suboptimal plan chosen due to incorrect optimizer estimates, from a suboptimal distribution method to an incorrect choice of degree of parallelism.

automatic SQL tuning

The work performed by Automatic SQL Tuning Advisor it runs as an automated task within system maintenance windows.

Automatic SQL Tuning Advisor

SQL Tuning Advisor when run as an automated maintenance task. Automatic SQL Tuning runs during system maintenance windows as an automated maintenance task, searching for ways to improve the execution plans of high-load SQL statements.

See SQL Tuning Advisor.

Automatic Tuning Optimizer

The optimizer when invoked by SQL Tuning Advisor. In SQL tuning mode, the optimizer performs additional analysis to check whether it can further improve the plan produced in normal mode. The optimizer output is not an execution plan, but a series of actions, along with their rationale and expected benefit for producing a significantly better plan.

Automatic Workload Repository (AWR)

The infrastructure that provides services to Oracle Database components to collect, maintain, and use statistics for problem detection and self-tuning.

AWR

See Automatic Workload Repository (AWR).

AWR snapshot

A set of data for a specific time that is used for performance comparisons. The delta values captured by the snapshot represent the changes for each statistic over the time period. Statistics gathered by are queried from memory. You can display the gathered data in both reports and views.

baseline

In the context of AWR, the interval between two AWR snapshots that represent the database operating at an optimal level.

bind-aware cursor

A bind-sensitive cursor that is eligible to use different plans for different bind values. After a cursor has been made bind-aware, the optimizer chooses plans for future executions based on the bind value and its cardinality estimate.

bind-sensitive cursor

A cursor whose optimal plan may depend on the value of a bind variable. The database monitors the behavior of a bind-sensitive cursor that uses different bind values to determine whether a different plan is beneficial.

bind variable

A placeholder in a SQL statement that must be replaced with a valid value or value address for the statement to execute successfully. By using bind variables, you can write a SQL statement that accepts inputs or parameters at run time. The following query uses v_empid as a bind variable:

SELECT * FROM employees WHERE employee_id = :v_empid;

bind variable peeking

The ability of the optimizer to look at the value in a bind variable during a hard parse. By peeking at bind values, the optimizer can determine the selectivity of a WHERE clause condition as if literals had been used, thereby improving the plan.

bitmap join index

A bitmap index for the join of two or more tables.

bitmap piece

A subcomponent of a single bitmap index entry. Each indexed column value may have one or more bitmap pieces. The database uses bitmap pieces to break up an index entry that is large in relation to the size of a block.

B-tree index

An index organized like an upside-down tree. A B-tree index has two types of blocks: branch blocks for searching and leaf blocks that store values. The leaf blocks contain every indexed data value and a corresponding rowid used to locate the actual row. The "B" stands for "balanced" because all leaf blocks automatically stay at the same depth.

bulk load

A CREATE TABLE AS SELECT or INSERT INTO ... SELECT operation.

cardinality

The number of rows that is expected to be or actually is returned by an operation in an execution plan. Data has low cardinality when the number of distinct values in a column is low in relation to the total number of rows.

Cartesian join

A join in which one or more of the tables does not have any join conditions to any other tables in the statement. The optimizer joins every row from one data source with every row from the other data source, creating the Cartesian product of the two sets.

child cursor

The cursor containing the plan, compilation environment, and other information for a statement whose text is stored in a parent cursor. The parent cursor is number 0, the first child is number 1, and so on. Child cursors reference the same SQL text as the parent cursor, but are different. For example, two queries with the text SELECT * FROM t use different cursors when they reference two different tables named t.

cluster scan

An access path for a table cluster. In an indexed table cluster, Oracle Database first obtains the rowid of one of the selected rows by scanning the cluster index. Oracle Database then locates the rows based on this rowid.

column group

A set of columns that is treated as a unit.

column group statistics

Extended statistics gathered on a group of columns treated as a unit.

column statistics

Statistics about columns that the optimizer uses to determine optimal execution plans. Column statistics include the number of distinct column values, low value, high value, and number of nulls.

complex view merging

The merging of views containing the GROUP BY or DISTINCT keywords.

composite database operation

In a database operation, the activity between two points in time in a database session, with each session defining its own beginning and end points. A session can participate in at most one composite database operation at a time.

concurrency

Simultaneous access of the same data by many users. A multiuser database management system must provide adequate concurrency controls so that data cannot be updated or changed improperly, compromising data integrity.

concurrent statistics gathering mode

A mode that enables the database to simultaneously gather optimizer statistics for multiple tables in a schema, or multiple partitions or subpartitions in a table. Concurrency can reduce the overall time required to gather statistics by enabling the database to fully use multiple CPUs.

condition

A combination of one or more expressions and logical operators that returns a value of TRUE, FALSE, or UNKNOWN.

cost

A numeric internal measure that represents the estimated resource usage for an execution plan. The lower the cost, the more efficient the plan.

cost-based optimizer (CBO)

The legacy name for the optimizer. In earlier releases, the cost-based optimizer was an alternative to the rule-based optimizer (RBO).

cost model

The internal optimizer model that accounts for the cost of the I/O, CPU, and network resources that a query is predicted to use.

cumulative statistics

A count such as the number of block reads. Oracle Database generates many types of cumulative statistics for the system, sessions, and individual SQL statements.

cursor

A handle or name for a private SQL area in the PGA. Because cursors are closely associated with private SQL areas, the terms are sometimes used interchangeably.

cursor cache

See shared SQL area.

cursor merging

Combining cursors to save space in the shared SQL area. If the optimizer creates a plan for a bind-aware cursor, and if this plan is the same as an existing cursor, then the optimizer can merge the cursors.

data flow operator (DFO)

The unit of work between data redistribution stages in a parallel query.

data skew

Large variations in the number of duplicate values in a column.

database operation

A set of database tasks defined by end users or application code, for example, a batch job or ETL processing.

default plan

For an adaptive plan, the execution plan initially chosen by the optimizer using the statistics from the data dictionary. The default plan can differ from the final plan.

disabled plan

A plan that a database administrator has manually marked as ineligible for use by the optimizer.

degree of parallelism (DOP)

The number of parallel execution servers associated with a single operation. Parallel execution is designed to effectively use multiple CPUs. Oracle Database parallel execution framework enables you to either explicitly choose a specific degree of parallelism or to rely on Oracle Database to automatically control it.

dense key

A numeric key that is stored as a native integer and has a range of values.

dense grouping key

A key that represents all grouping keys whose grouping columns come from a particular fact table or dimension.

dense join key

A key that represents all join keys whose join columns come from a particular fact table or dimension.

density

A decimal number between 0 and 1 that measures the selectivity of a column. Values close to 1 indicate that the column is unselective, whereas values close to 0 indicate that this column is more selective.

direct path read

A single or multiblock read into the PGA, bypassing the SGA.

driving table

The table to which other tables are joined. An analogy from programming is a for loop that contains another for loop. The outer for loop is the analog of a driving table, which is also called an outer table.

dynamic performance view

A view created on dynamic performance tables, which are virtual tables that record current database activity. The dynamic performance views are called fixed views because they cannot be altered or removed by the database administrator. They are also called V$ views because they begin with the string V$ (GV$ in Oracle RAC).

dynamic statistics

An optimization technique in which the database executes a recursive SQL statement to scan a small random sample of a table's blocks to estimate predicate selectivities.

dynamic statistics level

The level that controls both when the database gathers dynamic statistics, and the size of the sample that the optimizer uses to gather the statistics. Set the dynamic statistics level using either the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter or a statement hint.

enabled plan

In SQL plan management, a plan that is eligible for use by the optimizer.

endpoint number

A number that uniquely identifies a bucket in a histogram. In frequency and hybrid histograms, the endpoint number is the cumulative frequency of endpoints. In height-balanced histograms, the endpoint number is the bucket number.

endpoint repeat count

In a hybrid histogram, the number of times the endpoint value is repeated, for each endpoint (bucket) in the histogram. By using the repeat count, the optimizer can obtain accurate estimates for almost popular values.

endpoint value

An endpoint value is the highest value in the range of values in a histogram bucket.

equijoin

A join whose join condition contains an equality operator.

estimator

The component of the optimizer that determines the overall cost of a given execution plan.

execution plan

The combination of steps used by the database to execute a SQL statement. Each step either retrieves rows of data physically from the database or prepares them for the user issuing the statement. You can override execution plans by using hints.

execution tree

A tree diagram that shows the flow of row sources from one step to another in an execution plan.

expression

A combination of one or more values, operators, and SQL functions that evaluates to a value. For example, the expression 2*2 evaluates to 4. In general, expressions assume the data type of their components.

expression statistics

A type of extended statistics that improves optimizer estimates when a WHERE clause has predicates that use expressions.

extended statistics

A type of optimizer statistics that improves estimates for cardinality when multiple predicates exist or when predicates contain expressions.

extensible optimizer

An optimizer capability that enables authors of user-defined functions and indexes to create statistics collection, selectivity, and cost functions that the optimizer uses when choosing an execution plan. The optimizer cost model is extended to integrate information supplied by the user to assess CPU and I/O cost.

extension

A column group or an expression. The statistics collected for column groups and expressions are called extended statistics.

external table

A read-only table whose metadata is stored in the database but whose data in stored in files outside the database. The database uses the metadata describing external tables to expose their data as if they were relational tables.

filter condition

A WHERE clause component that eliminates rows from a single object referenced in a SQL statement.

final plan

In an adaptive plan, the plan that executes to completion. The default plan can differ from the final plan.

fixed object

A dynamic performance table or its index. The fixed objects are owned by SYS. Fixed object tables have names beginning with X$ and are the base tables for the V$ views.

fixed plan

An accepted plan that is marked as preferred, so that the optimizer considers only the fixed plans in the SQL plan baseline. You can use fixed plans to influence the plan selection process of the optimizer.

frequency histogram

A type of histogram in which each distinct column value corresponds to a single bucket. An analogy is sorting coins: all pennies go in bucket 1, all nickels go in bucket 2, and so on.

full outer join

A combination of a left and right outer join. In addition to the inner join, the database uses nulls to preserve rows from both tables that have not been returned in the result of the inner join. In other words, full outer joins join tables together, yet show rows with no corresponding rows in the joined tables.

full table scan

A scan of table data in which the database sequentially reads all rows from a table and filters out those that do not meet the selection criteria. All data blocks under the high water mark are scanned.

global temporary table

A special temporary table that stores intermediate session-private data for a specific duration.

hard parse

The steps performed by the database to build a new executable version of application code. The database must perform a hard parse instead of a soft parse if the parsed representation of a submitted statement does not exist in the shared SQL area.

hash cluster

A type of table cluster that is similar to an indexed cluster, except the index key is replaced with a hash function. No separate cluster index exists. In a hash cluster, the data is the index.

hash collision

Hashing multiple input values to the same output value.

hash function

A function that operates on an arbitrary-length input value and returns a fixed-length hash value.

hash join

A method for joining large data sets. The database uses the smaller of two data sets to build a hash table on the join key in memory. It then scans the larger data set, probing the hash table to find the joined rows.

hash scan

An access path for a table cluster. The database uses a hash scan to locate rows in a hash cluster based on a hash value. In a hash cluster, all rows with the same hash value are stored in the same data block. To perform a hash scan, Oracle Database first obtains the hash value by applying a hash function to a cluster key value specified by the statement, and then scans the data blocks containing rows with that hash value.

hash table

An in-memory data structure that associates join keys with rows in a hash join. For example, in a join of the employees and departments tables, the join key might be the department ID. A hash function uses the join key to generate a hash value. This hash value is an index in an array, which is the hash table.

hash value

In a hash cluster, a unique numeric ID that identifies a bucket. Oracle Database uses a hash function that accepts an infinite number of hash key values as input and sorts them into a finite number of buckets. Each hash value maps to the database block address for the block that stores the rows corresponding to the hash key value (department 10, 20, 30, and so on).

hashing

A mathematical technique in which an infinite set of input values is mapped to a finite set of output values, called hash values. Hashing is useful for rapid lookups of data in a hash table.

heap-organized table

A table in which the data rows are stored in no particular order on disk. By default, CREATE TABLE creates a heap-organized table.

height-balanced histogram

A histogram in which column values are divided into buckets so that each bucket contains approximately the same number of rows.

hint

An instruction passed to the optimizer through comments in a SQL statement. The optimizer uses hints to choose an execution plan for the statement.

histogram

A special type of column statistic that provides more detailed information about the data distribution in a table column.

hybrid hash distribution technique

An adaptive parallel data distribution that does not decide the final data distribution method until execution time.

hybrid histogram

An enhanced height-based histogram that stores the exact frequency of each endpoint in the sample, and ensures that a value is never stored in multiple buckets.

implicit query

A component of a DML statement that retrieves data without a subquery. An UPDATE, DELETE, or MERGE statement that does not explicitly include a SELECT statement uses an implicit query to retrieve the rows to be modified.

in-memory scan

A table scan that retrieves rows from the In-Memory Column Store.

incremental statistics maintenance

The ability of the database to generate global statistics for a partitioned table by aggregating partition-level statistics.

index

Optional schema object associated with a nonclustered table, table partition, or table cluster. In some cases indexes speed data access.

index cluster

An table cluster that uses an index to locate data. The cluster index is a B-tree index on the cluster key.

index clustering factor

A measure of row order in relation to an indexed value such as employee last name. The more scattered the rows among the data blocks, the lower the clustering factor.

index fast full scan

A scan of the index blocks in unsorted order, as they exist on disk. This scan reads the index instead of the table.

index full scan

The scan of an entire index in key order.

index-organized table

A table whose storage organization is a variant of a primary B-tree index. Unlike a heap-organized table, data is stored in primary key order.

index range scan

An index range scan is an ordered scan of an index that has the following characteristics:

- One or more leading columns of an index are specified in conditions.

- 0, 1, or more values are possible for an index key.

index range scan descending

An index range scan in which the database returns rows in descending order.

index skip scan

An index scan occurs in which the initial column of a composite index is "skipped" or not specified in the query. For example, if the composite index key is (cust_gender,cust_email), then the query predicate does not reference the cust_gender column.

index statistics

Statistics about indexes that the optimizer uses to determine whether to perform a full table scan or an index scan. Index statistics include B-tree levels, leaf block counts, the index clustering factor, distinct keys, and number of rows in the index.

index unique scan

A scan of an index that returns either 0 or 1 rowid.

inner join

A join of two or more tables that returns only those rows that satisfy the join condition.

inner table

In a nested loops join, the table that is not the outer table (driving table). For every row in the outer table, the database accesses all rows in the inner table. The outer loop is for every row in the outer table and the inner loop is for every row in the inner table.

join

A statement that retrieves data from multiple tables specified in the FROM clause of a SQL statement. Join types include inner joins, outer joins, and Cartesian joins.

join condition

A condition that compares two row sources using an expression. The database combines pairs of rows, each containing one row from each row source, for which the join condition evaluates to true.

join elimination

The removal of redundant tables from a query. A table is redundant when its columns are only referenced in join predicates, and those joins are guaranteed to neither filter nor expand the resulting rows.

join factorization

A cost-based transformation that can factorize common computations from branches of a UNION ALL query. Without join factorization, the optimizer evaluates each branch of a UNION ALL query independently, which leads to repetitive processing, including data access and joins. Avoiding an extra scan of a large base table can lead to a huge performance improvement.

join method

A method of joining a pair of row sources. The possible join methods are nested loop, sort merge, and hash joins. A Cartesian join requires one of the preceding join methods

join order

The order in which multiple tables are joined together. For example, for each row in the employees table, the database can read each row in the departments table. In an alternative join order, for each row in the departments table, the database reads each row in the employees table.

To execute a statement that joins more than two tables, Oracle Database joins two of the tables and then joins the resulting row source to the next table. This process continues until all tables are joined into the result.

join predicate

A predicate in a WHERE or JOIN clause that combines the columns of two tables in a join.

key vector

A data structure that maps between dense join keys and dense grouping keys.

latch

A low-level serialization control mechanism used to protect shared data structures in the SGA from simultaneous access.

left join tree

A join tree in which the left input of every join is the result of a previous join.

left table

In an outer join, the table specified on the left side of the OUTER JOIN keywords (in ANSI SQL syntax).

library cache

An area of memory in the shared pool. This cache includes the shared SQL areas, private SQL areas (in a shared server configuration), PL/SQL procedures and packages, and control structures such as locks and library cache handles.

library cache hit

The reuse of SQL statement code found in the library cache.

library cache miss

During SQL processing, the act of searching for a usable plan in the library cache and not finding it.

maintenance window

A contiguous time interval during which automated maintenance tasks run. The maintenance windows are Oracle Scheduler windows that belong to the window group named MAINTENANCE_WINDOW_GROUP.

manual plan capture

The user-initiated bulk load of existing plans into a SQL plan baseline.

materialized view

A schema object that stores a query result. All materialized views are either read-only or updatable.

multiblock read

An I/O call that reads multiple database blocks. Multiblock reads can significantly speed up full table scans.

NDV

Number of distinct values. The NDV is important in generating selectivity estimates.

nested loops join

A type of join method. A nested loops join determines the outer table that drives the join, and for every row in the outer table, probes each row in the inner table. The outer loop is for each row in the outer table and the inner loop is for each row in the inner table. An analogy from programming is a for loop inside of another for loop.

nonequijoin

A join whose join condition does not contain an equality operator.

nonjoin column

A predicate in a WHERE clause that references only one table.

nonpopular value

In a histogram, any value that does not span two or more endpoints. Any value that is not nonpopular is a popular value.

noworkload statistics

Optimizer system statistics gathered when the database simulates a workload.

on-demand SQL tuning

The manual invocation of SQL Tuning Advisor. Any invocation of SQL Tuning Advisor that is not the result of an Automatic SQL Tuning task is on-demand tuning.

optimization

The overall process of choosing the most efficient means of executing a SQL statement.

optimizer

Built-in database software that determines the most efficient way to execute a SQL statement by considering factors related to the objects referenced and the conditions specified in the statement.

optimizer cost model

The model that the optimizer uses to select an execution plan. The optimizer selects the execution plan with the lowest cost, where cost represents the estimated resource usage for that plan. The optimizer cost model accounts for the I/O, CPU, and network resources that the query will use.

optimizer environment

The totality of session settings that can affect execution plan generation, such as the work area size or optimizer settings (for example, the optimizer mode).

optimizer goal

The prioritization of resource usage by the optimizer. Using the OPTIMIZER_MODE initialization parameter, you can set the optimizer goal best throughput or best response time.

optimizer statistics

Details about the database its object used by the optimizer to select the best execution plan for each SQL statement. Categories include table statistics such as numbers of rows, index statistics such as B-tree levels, system statistics such as CPU and I/O performance, and column statistics such as number of nulls.

optimizer statistics collection

The gathering of optimizer statistics for database objects. The database can collect these statistics automatically, or you can collect them manually by using the system-supplied DBMS_STATS package.

optimizer statistics collector

A row source inserted into an execution plan at key points to collect run-time statistics for use in adaptive plans.

optimizer statistics preferences

The default values of the parameters used by automatic statistics collection and the DBMS_STATS statistics gathering procedures.

outer join

A join condition using the outer join operator (+) with one or more columns of one of the tables. The database returns all rows that meet the join condition. The database also returns all rows from the table without the outer join operator for which there are no matching rows in the table with the outer join operator.

outer table

See driving table

parallel execution

The application of multiple CPU and I/O resources to the execution of a single database operation.

parallel query

A query in which multiple processes work together simultaneously to run a single SQL query. By dividing the work among multiple processes, Oracle Database can run the statement more quickly. For example, four processes retrieve rows for four different quarters in a year instead of one process handling all four quarters by itself.

parent cursor

The cursor that stores the SQL text and other minimal information for a SQL statement. The child cursor contains the plan, compilation environment, and other information. When a statement first executes, the database creates both a parent and child cursor in the shared pool.

parse call

A call to Oracle to prepare a SQL statement for execution. The call includes syntactically checking the SQL statement, optimizing it, and then building or locating an executable form of that statement.

parsing

The stage of SQL processing that involves separating the pieces of a SQL statement into a data structure that can be processed by other routines.

A hard parse occurs when the SQL statement to be executed is either not in the shared pool, or it is in the shared pool but it cannot be shared. A soft parse occurs when a session attempts to execute a SQL statement, and the statement is already in the shared pool, and it can be used.

partition maintenance operation

A partition-related operation such as adding, exchanging, merging, or splitting table partitions.

partition-wise join

A join optimization that divides a large join of two tables, one of which must be partitioned on the join key, into several smaller joins.

pending statistics

Unpublished optimizer statistics. By default, the optimizer uses published statistics but does not use pending statistics.

performance feedback

This form of automatic reoptimization helps improve the degree of parallelism automatically chosen for repeated SQL statements when PARALLEL_DEGREE_POLICY is set to ADAPTIVE.

pipelined table function

A PL/SQL function that accepts a collection of rows as input. You invoke the table function as the operand of the table operator in the FROM list of a SELECT statement.

plan evolution

The manual change of an unaccepted plan in the SQL plan history into an accepted plan in the SQL plan baseline.

plan generator

The part of the optimizer that tries different access paths, join methods, and join orders for a given query block to find the plan with the lowest cost.

plan selection

The optimizer's attempt to find a matching plan in the SQL plan baseline for a statement after performing a hard parse.

plan verification

Comparing the performance of an unaccepted plan to a plan in a SQL plan baseline and ensuring that it performs better.

popular value

In a histogram, any value that spans two or more endpoints. Any value that is not popular is an nonpopular value.

predicate pushing

A transformation technique in which the optimizer "pushes" the relevant predicates from the containing query block into the view query block. For views that are not merged, this technique improves the subplan of the unmerged view because the database can use the pushed-in predicates to access indexes or to use as filters.

private SQL area

An area in memory that holds a parsed statement and other information for processing. The private SQL area contains data such as bind variable values, query execution state information, and query execution work areas.

proactive SQL tuning

Using SQL tuning tools to identify SQL statements that are candidates for tuning before users have complained about a performance problem.

See reactive SQL tuning, SQL tuning.

projection view

An optimizer-generated view that appear in queries in which a DISTINCT view has been merged, or a GROUP BY view is merged into an outer query block that also contains GROUP BY, HAVING, or aggregates.

See simple view merging, complex view merging.

query

An operation that retrieves data from tables or views. For example, SELECT * FROM employees is a query.

query block

A top-level SELECT statement, subquery, or unmerged view

query optimizer

See optimizer.

reactive SQL tuning

Diagnosing and fixing SQL-related performance problems after users have complained about them.

See proactive SQL tuning, SQL tuning.

recursive SQL

Additional SQL statements that the database must issue to execute a SQL statement issued by a user. The generation of recursive SQL is known as a recursive call. For example, the database generates recursive calls when data dictionary information is not available in memory and so must be retrieved from disk.

reoptimization

See automatic reoptimization.

repeatable SQL statement

A statement that the database parses or executes after recognizing that it is tracked in the SQL statement log.

response time

The time required to complete a unit of work.

See throughput.

result set

In a query, the set of rows generated by the execution of a cursor.

right join tree

A join tree in which the right input of every join is the result of a previous join.

right table

In an outer join, the table specified on the right side of the OUTER JOIN keywords (in ANSI SQL syntax).

rowid

A globally unique address for a row in a table.

row set

A set of rows returned by a step in an execution plan.

row source

An iterative control structure that processes a set of rows in an iterated manner and produces a row set.

row source generator

Software that receives the optimal plan from the optimizer and outputs the execution plan for the SQL statement.

row source tree

A collection of row sources produced by the row source generator. The row source tree for a SQL statement shows information such as table order, access methods, join methods, and data operations such as filters and sorts.

sample table scan

A scan that retrieves a random sample of data from a simple table or a complex SELECT statement, such as a statement involving joins and views.

sampling

Gathering statistics from a random subset of rows in a table.

selectivity

A value indicating the proportion of a row set retrieved by a predicate or combination of predicates, for example, WHERE last_name = 'Smith'. A selectivity of 0 means that no rows pass the predicate test, whereas a value of 1 means that all rows pass the test.

The adjective selective means roughly "choosy." Thus, a highly selective query returns a low proportion of rows (selectivity close to 0), whereas an unselective query returns a high proportion of rows (selectivity close to 1).

semijoin

A join that returns rows from the first table when at least one match exists in the second table. For example, you list departments with at least one employee. The difference between a semijoin and a conventional join is that rows in the first table are returned at most once. Semijoins use the EXISTS or IN constructs.

shared pool

Portion of the SGA that contains shared memory constructs such as shared SQL areas.

shared SQL area

An area in the shared pool that contains the parse tree and execution plan for a SQL statement. Only one shared SQL area exists for a unique statement. The shared SQL area is sometimes referred to as the cursor cache.

simple database operation

A database operation consisting of a single SQL statement or PL/SQL procedure or function.

simple view merging

The merging of select-project-join views. For example, a query joins the employees table to a subquery that joins the departments and locations tables.

SMB

See SQL management base (SMB).

soft parse

Any parse that is not a hard parse. If a submitted SQL statement is the same as a reusable SQL statement in the shared pool, then Oracle Database reuses the existing code. This reuse of code is also called a library cache hit.

sort merge join

A type of join method. The join consists of a sort join, in which both inputs are sorted on the join key, followed by a merge join, in which the sorted lists are merged.

SQL Access Advisor

SQL Access Advisor is internal diagnostic software that recommends which materialized views, indexes, and materialized view logs to create, drop, or retain.

SQL compilation

In the context of Oracle SQL processing, this term refers collectively to the phases of parsing, optimization, and plan generation.

SQL plan directive

Additional information and instructions that the optimizer can use to generate a more optimal plan. For example, a SQL plan directive might instruct the optimizer to collect missing statistics or gather dynamic statistics.

SQL handle

A string value derived from the numeric SQL signature. Like the signature, the handle uniquely identifies a SQL statement. It serves as a SQL search key in user APIs.

SQL ID

For a specific SQL statement, the unique identifier of the parent cursor in the library cache. A hash function applied to the text of the SQL statement generates the SQL ID. The V$SQL.SQL_ID column displays the SQL ID.

SQL incident

In the fault diagnosability infrastructure of Oracle Database, a single occurrence of a SQL-related problem. When a problem (critical error) occurs multiple times, the database creates an incident for each occurrence. Incidents are timestamped and tracked in the Automatic Diagnostic Repository (ADR).

SQL management base (SMB)

A logical repository that stores statement logs, plan histories, SQL plan baselines, and SQL profiles. The SMB is part of the data dictionary and resides in the SYSAUX tablespace.

SQL plan baseline

A set of one or more accepted plans for a repeatable SQL statement. Each accepted plan contains a set of hints, a plan hash value, and other plan-related information. SQL plan management uses SQL plan baselines to record and evaluate the execution plans of SQL statements over time.

SQL plan capture

Techniques for capturing and storing relevant information about plans in the SQL management base (SMB) for a set of SQL statements. Capturing a plan means making SQL plan management aware of this plan.

SQL plan directive

Additional information that the optimizer uses to generate a more optimal plan. The optimizer collects SQL plan directives on query expressions rather than at the statement level. In this way, the directives are usable for multiple SQL statements.

SQL plan history

The set of plans generated for a repeatable SQL statement over time. The history contains both SQL plan baselines and unaccepted plans.

SQL plan management

SQL plan management is a preventative mechanism that records and evaluates the execution plans of SQL statements over time. SQL plan management can prevent SQL plan regressions caused by environmental changes such as a new optimizer version, changes to optimizer statistics, system settings, and so on.

SQL processing

The stages of parsing, optimization, row source generation, and execution of a SQL statement.

SQL profile

A set of auxiliary information built during automatic tuning of a SQL statement. A SQL profile is to a SQL statement what statistics are to a table. The optimizer can use SQL profiles to improve cardinality and selectivity estimates, which in turn leads the optimizer to select better plans.

SQL profiling

The verification and validation by the Automatic Tuning Advisor of its own estimates.

SQL signature

A numeric hash value computed using a SQL statement text that has been normalized for case insensitivity and white space. It uniquely identifies a SQL statement. The database uses this signature as a key to maintain SQL management objects such as SQL profiles, SQL plan baselines, and SQL patches.

SQL statement log

When automatic SQL plan capture is enabled, a log that contains the SQL ID of SQL statements that the optimizer has evaluated over time. A statement is tracked when it exists in the log.

SQL test case

A problematic SQL statement and related information needed to reproduce the execution plan in a different environment. A SQL test case is stored in an Oracle Data Pump file.

SQL test case builder

A database feature that gathers information related to a SQL statement and packages it so that a user can reproduce the problem on a different database. The DBMS_SQLDIAG package is the interface for SQL test case builder.

SQL trace file

A server-generated file that provides performance information on individual SQL statements. For example, the trace file contains parse, execute, and fetch counts, CPU and elapsed times, physical reads and logical reads, and misses in the library cache.

SQL tuning

The process of improving SQL statement efficiency to meet measurable goals.

SQL Tuning Advisor

Built-in database diagnostic software that optimizes high-load SQL statements.

See Automatic SQL Tuning Advisor.

SQL tuning set (STS)

A database object that includes one or more SQL statements along with their execution statistics and execution context.

star schema

A relational schema whose design represents a dimensional data model. The star schema consists of one or more fact tables and one or more dimension tables that are related through foreign keys.

statistics feedback

A form of automatic reoptimization that automatically improves plans for repeated queries that have cardinality misestimates. The optimizer may estimate cardinalities incorrectly for many reasons, such as missing statistics, inaccurate statistics, or complex predicates.

stored outline

A set of hints for a SQL statement. The hints in stored outlines direct the optimizer to choose a specific plan for the statement.

subplan

A portion of an adaptive plan that the optimizer can switch to as an alternative at run time. A subplan can consist of multiple operations in the plan. For example, the optimizer can treat a join method and the corresponding access path as one unit when determining whether to change the plan at run time.

subquery

A query nested within another SQL statement. Unlike implicit queries, subqueries use a SELECT statement to retrieve data.

subquery unnesting

A transformation technique in which the optimizer transforms a nested query into an equivalent join statement, and then optimizes the join.

synopsis

A set of auxiliary statistics gathered on a partitioned table when the INCREMENTAL value is set to true.

system statistics

Statistics that enable the optimizer to use CPU and I/O characteristics. Index statistics include B-tree levels, leaf block counts, clustering factor, distinct keys, and number of rows in the index.

table cluster

A schema object that contains data from one or more tables, all of which have one or more columns in common. In table clusters, the database stores together all the rows from all tables that share the same cluster key.

table expansion

A transformation technique that enables the optimizer to generate a plan that uses indexes on the read-mostly portion of a partitioned table, but not on the active portion of the table.

table statistics

Statistics about tables that the optimizer uses to determine table access cost, join cardinality, join order, and so on. Table statistics include row counts, block counts, empty blocks, average free space per block, number of chained rows, average row length, and staleness of the statistics on the table.

throughput

The amount of work completed in a unit of time.

See response time.

top frequency histogram

A variation of a frequency histogram that ignores nonpopular values that are statistically insignificant, thus producing a better histogram for highly popular values.

tuning mode

One of the two optimizer modes. When running in tuning mode, the optimizer is known as the Automatic Tuning Optimizer. In tuning mode, the optimizer determines whether it can further improve the plan produced in normal mode. The optimizer output is not an execution plan, but a series of actions, along with their rationale and expected benefit for producing a significantly better plan.

unaccepted plan

A plan for a statement that is in the SQL plan history but has not been added to the SQL plan management.

unselective

A relatively large fraction of rows from a row set. A query becomes more unselective as the selectivity approaches 1. For example, a query that returns 999,999 rows from a table with one million rows is unselective. A query of the same table that returns one row is selective.

user response time

The time between when a user submits a command and receives a response.

See throughput.

V$ view

See dynamic performance view.

vector I/O

A type of I/O in which the database obtains a set of rowids, sends them batched in an array to the operating system, which performs the read.

view merging

The merging of a query block representing a view into the query block that contains it. View merging can improve plans by enabling the optimizer to consider additional join orders, access methods, and other transformations.

workload statistics

Optimizer statistics for system activity in a specified time period.

Index
A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W

A

access paths, 3.1.4

execution plans, 6.1

full table scan, 10.2.3.1

full table scans, 11.1

adaptive plans, 4.4.1, 7.2.1, 7.2.1, 7.3.1, 14.2.1, 17.2.2

cardinality misestimates, 4.4.1

join methods, 4.4.1.2

optimizer statistics collector, 4.4.1.1

parallel distribution methods, 4.4.1.3

reporting mode, 14.2.1

subplans, 4.4.1.1

adaptive query optimization, 4.4

adaptive plans, 4.4.1, 7.2.1, 7.3.1, 14.2.1, 17.2.2

controlling, 14.2.4

dynamic statistics, 10.3.2

adaptive statistics, 4.4.2

automatic reoptimization, 4.4.2.2

dynamic statistics, 4.4.2.1

SQL plan directives, 4.4.2.3, 13.3.1

ADDM, 1.4.2.1.1 ALTER INDEX statement, A.1.7 ALTER SESSION statement

examples, 18.4.2

antijoins, 9.1.3 applications

implementing, 2.2

automatic reoptimization, 4.4.2.2, 7.2.1, 10.4.1.2

cardinality misestimates, 4.4.2.2.1

performance feedback, 4.4.2.2.2

statistics feedback, 4.4.2.2.1

automatic statistics collection, 12.2 Automatic Tuning Optimizer, 1.4.2.1.2 Automatic Workload Repository (AWR), 1.4.2.1.1

B

big bang rollout strategy, 2.2.2

bind variables, 15.1.2

bitmap indexes

inlist iterator, 7.2.5.5.3

on joins, A.6

when to use, A.5

BOOLEAN data type, 8.3.3.1 broadcast

distribution value, 7.2.6, 7.3.2

BYTES column

PLAN_TABLE table, 7.2.6, 7.3.2

C

cardinality, 1.4.2.1.2, 4.2.2, 4.4.1, 4.4.1.2, 4.4.2.2.1, 4.4.2.3, 6.2.1, 8.4.1.1, 10.2.1, 10.4.1.1, 11.1, 11.1, 11.3

CARDINALITY column

PLAN_TABLE table, 7.2.6, 7.3.2

cartesian joins, 9.2.4 clusters, A.8, A.8

sorted hash, A.9

column group statistics, 10.4.1.1 column groups, 13.3.1, 13.3.1.2 columns

cardinality, 4.2.2

to index, A.1.3

compilation, SQL, 10.4, 10.4.1.1, 10.4.3 composite indexes, A.1.4 composite partitioning

examples of, 7.2.5.2

concurrent statistics gathering, 12.4.7.1, 12.4.7.3, Glossary consistent mode

TKPROF, 18.5.4.4

constraints, A.1.10 COST column

PLAN_TABLE table, 7.2.6, 7.3.2

create_extended_statistics, 13.3.2.2, 13.3.2.2 current mode

TKPROF, 18.5.4.4

CURSOR_NUM column

TKPROF_TABLE table, 18.4.4.3

CURSOR_SHARING initialization parameter, 15.2 cursors, SQL, 3.1.1

D

data

modeling, 2.1.1

data blocks, 3.2.1 data dictionary cache, 3.1.1.3 data flow operator (DFO), 5.7.2 Data Pump

Export utility

statistics on system-generated columns names, 13.7.2

Import utility

copying statistics, 13.7.2

data skew, 11.1 data types

BOOLEAN, 8.3.3.1

database operations

composite, 1.4.2.2.2, 16.1

definition, 1.4.2.2.2, 16.1

simple, 1.4.2.2.2, 16.1

database operations, monitoring, 1.4.2.2.2, 16.1

composite, 16.1.2.2

composite operations, 16.1

creating database operations, 16.3

enabling with hints, 16.2.2

enabling with initialization parameters, 16.2.1

Enterprise Manager interface, 16.1.3.1

generating a report, 16.4

PL/SQL interface, 16.1.3.2

purpose, 16.1.1

real-time SQL, 16.1

simple operations, 16.1

DATE_OF_INSERT column

TKPROF_TABLE table, 18.4.4.3

DB_FILE_MULTIBLOCK_READ_COUNT initialization parameter, 8.2.2.2, 8.2.2.2 DBMS_ADVISOR package, 21.1.1 DBMS_MONITOR package

end-to-end application tracing, 18.1.2

DBMS_SQLTUNE package

SQL Tuning Advisor, 19.1.3.1, 20.3.1.2.1

dbms_stats functions

create_extended_statistics, 13.3.2.2, 13.3.2.2

drop_extended_stats, 12.5.5, 13.3.1.6, 13.3.2.4

gather_table_stats, 13.3.2.2, 13.3.2.2

show_extended_stats_name, 13.3.1.5

DBMS_STATS package, 21.1.2.3 DBMS_XPLAN package

displaying plan table output, 6.4

DDL (data definition language)

processing of, 3.3

deadlocks, 3.1.1 debugging designs, 2.2.1 dedicated server, 3.1.1.3 dense keys, 5.7.2.1

dense grouping keys, 5.7.2.1

dense join keys, 5.7.2.1

density, histogram, 11.3.2 DEPTH column

TKPROF_TABLE table, 18.4.4.3

designs

debugging, 2.2.1

testing, 2.2.1

validating, 2.2.1

development environments, 2.2 DIAGNOSTIC_DEST initialization parameter, 18.4.1 disabled constraints, A.1.10 DISTRIBUTION column

PLAN_TABLE table, 7.2.6, 7.3.2

domain indexes

and EXPLAIN PLAN, 7.2.5.6

using, A.7

drop_extended_stats, 12.5.5, 13.3.1.6, 13.3.2.4 dynamic statistics, 4.4.2.1, 10.3.2, 10.4, 10.4.2, 12.4.6, 17.2.2

controlling, 13.1

process, 13.1.1

sampling levels, 13.1.1

when to use, 13.1.3

E

enabled constraints, A.1.10

endpoint repeat counts, in histograms, 11.6

end-to-end application tracing, 1.4.2.2.3

action and module names, 18.1.2

creating a service, 18.1.2

DBMS_APPLICATION_INFO package, 18.1.2

DBMS_MONITOR package, 18.1.2

enforced constraints, A.1.10 examples

ALTER SESSION statement, 18.4.2

EXPLAIN PLAN output, 18.5.3

SQL trace facility output, 18.5.3

EXECUTE_TASK procedure, 21.2.4 execution plans, 3.1.1.3

adaptive, 4.4.1, 7.2.1, 7.2.1, 7.3.1

examples, 18.4.3

overview of, 6.1

TKPROF, 18.4.3, 18.5.4.4

V$ views, 7.3.1

viewing with the utlxpls.sql script, 6.3

execution trees, 3.1.4 EXPLAIN PLAN statement

access paths, 8.2.4.2, 8.2.5.3

and domain indexes, 7.2.5.6

and full partition-wise joins, 7.2.5.4

and partial partition-wise joins, 7.2.5.3

and partitioned objects, 7.2.5

basic steps, 6.3

examples of output, 18.5.3

execution order of steps in output, 6.3

invoking with the TKPROF program, 18.5.4.4

PLAN_TABLE table, 6.2.6

restrictions, 6.2.5

scripts for viewing output, 6.3

viewing the output, 6.3

extended statistics, 10.2.2 extensions, 10.4.1

F

fixed objects

gathering statistics for, 12.1, 12.4.5

frequency histograms, 11.4 FULL hint, A.1.6 full outer joins, 9.3.2.4 full partition-wise joins, 7.2.5.4 full table scans, 10.2.3.1, 11.1 function-based indexes, A.2

G

gather_table_stats, 13.3.2.2, 13.3.2.2

global temporary tables, 10.2.4

H

hard parsing, 2.1.2, 3.1.1.3

hash

distribution value, 7.2.6, 7.3.2

hash clusters

sorted, A.9

hash joins, 9.2.2

cost-based optimization, 9.1.3

hash partitions, 7.2.5

examples of, 7.2.5.1

hashing, A.9 height-balanced histograms, 11.5 high-load SQL

tuning, 12.1.2.1.2, 20.3.1.2.1

hints, optimizer, 1.4.2.2.4

FULL, A.1.6

NO_INDEX, A.1.6

NO_MONITOR, 16.2.2

histograms, 11

cardinality algorithms, 11.3

data skew, 11.1

definition, 11

density, 11.3.2

endpoint numbers, 11.3.1

endpoint repeat counts, 11.6

endpoint values, 11.3.1

frequency, 11.4

height-balanced, 11.5

hybrid, 11.6

NDV, 11

nonpopular values, 11.3.2

popular values, 11.3.2

purpose, 11.1

top frequency, 11.4

hybrid histograms, 11.6

I

ID column

PLAN_TABLE table, 7.2.6, 7.3.2

incremental statistics, 12.4.8.4, 12.4.8.5 index clustering factor, 10.2.3.1 INDEX hint, A.1.6 index statistics, 10.2.3

index clustering factor, 10.2.3.1

INDEX_COMBINE hint, A.1.6 indexes

avoiding the use of, A.1.6

bitmap, A.5

choosing columns for, A.1.3

composite, A.1.4

domain, A.7

dropping, A.1.1

enforcing uniqueness, A.1.9

ensuring the use of, A.1.5

function-based, A.2

improving selectivity, A.1.4

low selectivity, A.1.6

modifying values of, A.1.3

non-unique, A.1.9

rebuilding, A.1.7

re-creating, A.1.7

scans, 8.3.3

selectivity of, A.1.3

initialization parameters

DIAGNOSTIC_DEST, 18.4.1

INLIST ITERATOR operation, 7.2.5.5 inlists, 7.2.5.5 in-memory aggregation, 5.7

controls, 5.7.3

how it works, 5.7.2

purpose, 5.7.1

in-memory table scans, 8.2.5

controls, 8.2.5.2

example, 8.2.5.3

when chosen, 8.2.5.1

I/O

reducing, A.1.4

J

joins

antijoins, 9.1.3

cartesian, 9.2.4

full outer, 9.3.2.4

hash, 9.2.2

nested loops, 3.1.4, 9.2.1.1

nested loops and cost-based optimization, 9.1.3

order, 14.3.2

outer, 9.3.2

partition-wise

examples of full, 7.2.5.4

examples of partial, 7.2.5.3

full, 7.2.5.4

semijoins, 9.1.3 sort-merge and cost-based optimization, 9.1.3, 9.1.3

K

key vectors, 5.7.2.1

L

latches

parsing and, 3.1.1.3

library cache, 3.1.1.3 library cache miss, 3.1.1.3 locks

deadlocks, 3.1.1

M

manual plan capture, 23.1.2.2

MAX_DUMP_FILE_SIZE initialization parameter

SQL Trace, 18.4.1

modeling

data, 2.1.1

multiversion read consistency, 3.2.1

N

NDV, 11

nested loops joins, 9.2.1.1

cost-based optimization, 9.1.3

NO_INDEX hint, A.1.6 nonpopular values, in histograms, 11.3.2 NOT IN subquery, 9.1.3

O

OBJECT_INSTANCE column

PLAN_TABLE table, 7.2.6, 7.3.2

OBJECT_NAME column

PLAN_TABLE table, 7.2.6, 7.3.2

OBJECT_NODE column

PLAN_TABLE table, 7.2.6, 7.3.2

OBJECT_OWNER column

PLAN_TABLE table, 7.2.6, 7.3.2

OBJECT_TYPE column

PLAN_TABLE table, 7.2.6, 7.3.2

OPERATION column

PLAN_TABLE table, 7.2.6, 7.2.6, 7.3.2, 7.3.2

optimization, SQL, 4.1.2 optimizer

adaptive, 7.2.1

definition, 4.1

environment, 3.1.1.3

estimator, 4.2.2

execution, 3.1.4

goals, 14.2.3

purpose of, 4.1.1

row sources, 3.1.3, 3.1.3

statistics, 14.3

throughput, 14.2.3

OPTIMIZER column

PLAN_TABLE, 7.2.6, 7.3.2

optimizer environment, 3.1.1.3 optimizer hints, 1.4.2.2.4

FULL, A.1.6

MONITOR, 16.2.2

NO_INDEX, A.1.6

optimizer statistics

adaptive statistics, 4.4.2

automatic collection, 12.2

bulk loads, 10.3.3

cardinality, 11.1

collection, 12.1

column group, 10.4.1.1

column groups, 13.3.1

dynamic, 10.3.2, 10.4, 12.4.6, 13.1, 17.2.2

extended, 10.2.2

gathering concurrently, 12.4.7, 12.4.7.1, Glossary

gathering in parallel, 12.4.7.3

histograms, 11

incremental, 12.4.8.4, 12.4.8.5

index, 10.2.3

pluggable databases and, 12.2

preferences, 12.3.1

SQL plan directives, 10.4.1, 13.3.1

system, 12.5.1

temporary, 10.2.4

optimizer statistics collection, 12.1 optimizer statistics collectors, 4.4.1.1 OPTIONS column

PLAN_TABLE table, 7.2.6, 7.3.2

OTHER column

PLAN_TABLE table, 7.2.6, 7.3.2

OTHER_TAG column

PLAN_TABLE table, 7.2.6, 7.3.2

outer joins, 9.3.2

P

packages

DBMS_ADVISOR, 21.1.1

DBMS_STATS, 21.1.2.3

parallel execution

gathering statistics, 12.4.7.3

PARENT_ID column

PLAN_TABLE table, 7.2.6, 7.3.2

parse calls, 3.1.1 parsing, SQL, 3.1.1

hard, 2.1.2

hard parse, 3.1.1.3

parse trees, 3.1.4

soft, 2.1.2

soft parse, 3.1.1.3

partition maintenance operations, 12.4.8.4 PARTITION_ID column

PLAN_TABLE table, 7.2.6, 7.3.2

PARTITION_START column

PLAN_TABLE table, 7.2.6, 7.3.2

PARTITION_STOP column

PLAN_TABLE table, 7.2.6, 7.3.2

partitioned objects

and EXPLAIN PLAN statement, 7.2.5

partitioning

distribution value, 7.2.6, 7.3.2

examples of, 7.2.5.1

examples of composite, 7.2.5.2

hash, 7.2.5

range, 7.2.5

start and stop columns, 7.2.5.1

partition-wise joins

full, 7.2.5.4

full, and EXPLAIN PLAN output, 7.2.5.4

partial, and EXPLAIN PLAN output, 7.2.5.3

performance

viewing execution plans, 6.3

PLAN_TABLE table

BYTES column, 7.2.6, 7.3.2

CARDINALITY column, 7.2.6, 7.3.2

COST column, 7.2.6, 7.3.2

creating, 6.2.6, 6.2.6

displaying, 6.4

DISTRIBUTION column, 7.2.6, 7.3.2

ID column, 7.2.6, 7.3.2

OBJECT_INSTANCE column, 7.2.6, 7.3.2

OBJECT_NAME column, 7.2.6, 7.3.2

OBJECT_NODE column, 7.2.6, 7.3.2

OBJECT_OWNER column, 7.2.6, 7.3.2

OBJECT_TYPE column, 7.2.6, 7.3.2

OPERATION column, 7.2.6, 7.3.2

OPTIMIZER column, 7.2.6, 7.3.2

OPTIONS column, 7.2.6, 7.3.2

OTHER column, 7.2.6, 7.3.2

OTHER_TAG column, 7.2.6, 7.3.2

PARENT_ID column, 7.2.6, 7.3.2

PARTITION_ID column, 7.2.6, 7.3.2

PARTITION_START column, 7.2.6, 7.3.2

PARTITION_STOP column, 7.2.6, 7.3.2

POSITION column, 7.2.6, 7.3.2

REMARKS column, 7.2.6, 7.3.2

SEARCH_COLUMNS column, 7.2.6, 7.3.2

STATEMENT_ID column, 7.2.6, 7.3.2

TIMESTAMP column, 7.2.6, 7.3.2

pluggable databases

automatic optimizer statistics collection, 12.2

manageability features, 12.2

SQL management base, 23.1.5.1

SQL Tuning Advisor, 20.1.1, 21.1

SQL tuning sets, 19.1

popular values, in histograms, 11.3.2 POSITION column

PLAN_TABLE table, 7.2.6, 7.3.2

PRIMARY KEY constraint, A.1.9 private SQL areas

parsing and, 3.1.1

processes

dedicated server, 3.1.1.3

programming languages, 2.2

Q

queries

avoiding the use of indexes, A.1.6

ensuring the use of indexes, A.1.5

R

range

distribution value, 7.2.6, 7.3.2

examples of partitions, 7.2.5.1

partitions, 7.2.5

Real-Time Database Operations, 1.4.2.2.2 Real-Time SQL Monitoring, 1.4.2.2.2, 16.1 REBUILD clause, A.1.7 recursive calls, 18.5.2 recursive SQL, 3.3, 10.3.2, 10.4.3 REMARKS column

PLAN_TABLE table, 7.2.6, 7.3.2

reoptimization, automatic, 4.4.2.2, 7.2.1, 10.4.1.2

cardinality misestimates, 4.4.2.2.1

performance feedback, 4.4.2.2.2

statistics feedback, 4.4.2.2.1

result sets, SQL, 3.1.3, 3.2 rollout strategies

big bang approach, 2.2.2

trickle approach, 2.2.2

round-robin

distribution value, 7.2.6, 7.3.2

row source generation, 3.1.3 rowids

table access by, 8.2.3

rows

row set, 3.1.3

row source, 3.1.3

rowids used to locate, 8.2.3

S

SAMPLE BLOCK clause, 8.2.4

SAMPLE clause, 8.2.4

sample table scans, 8.2.4

scans

in-memory, 8.2.5

sample table, 8.2.4

SEARCH_COLUMNS column

PLAN_TABLE table, 7.2.6, 7.3.2

SELECT statement

SAMPLE clause, 8.2.4

selectivity

creating indexes, A.1.3

improving for an index, A.1.4

indexes, A.1.6

semijoins, 9.1.3 shared pool, 10.4.1.1

parsing check, 3.1.1.3

shared SQL areas, 3.1.1.3 show_extended_stats_name, 13.3.1.5 soft parsing, 2.1.2, 3.1.1.3 sort merge joins

cost-based optimization, 9.1.3

SQL

execution, 3.1.4

optimization, 4.1.2

parsing, 3.1.1

recursive, 3.3

result sets, 3.1.3, 3.2

stages of processing, 8.2, 8.3

SQL Access Advisor, 1.4.2.1.3, 21.1.1, 21.1.1

constants, 21.6.3

EXECUTE_TASK procedure, 21.2.4

SQL compilation, 10.4, 10.4.1.1, 10.4.3 SQL management base

pluggable databases and, 23.1.5.1

SQL parsing

parse calls, 3.1.1

SQL Performance Analyzer, 1.4.2.1.5 SQL plan baselines, 1.4.2.1.4, 23.1

displaying, 23.3

SQL plan capture, 23.1.2 SQL plan directives, 4.4.2.3, 10.4.1, 13.3.1

cardinality misestimates, 10.4.1.1

managing, 13.10

SQL plan management, 1.4.2.1.4

automatic plan capture, 23.1.2.1

introduction, 23.1

manual plan capture, 23.1.2.2

plan capture, 23.1

plan evolution, 23.1, 23.1.4

plan selection, 23.1, 23.1.3

plan verification, 23.1.4

purpose, 23.1.1

SQL plan baselines, 23.1

SQL plan capture, 23.1.2

SQL processing

semantic check, 3.1.1.2

shared pool check, 3.1.1.3

stages, 3.1

syntax check, 3.1.1.1

SQL profiles, 1.4.2.1.2

and SQL plan baselines, 23.1.1.2

SQL statements

avoiding the use of indexes, A.1.6

ensuring the use of indexes, A.1.5

execution plans of, 6.1

modifying indexed data, A.1.3

SQL Test Case Builder, 17.1 SQL test cases, 17 SQL trace facility, 1.4.2.2.3, 18.1.2.1, 18.4.3

example of output, 18.5.3

output, 18.5.4.4

statement truncation, 18.4.3

trace files, 18.4.1

SQL trace files, 1.4.2.2.3 SQL tuning

definition, 1.1

introduction, 1

tools overview, 1.4.2

SQL Tuning Advisor, 1.4.2.1.2

administering with APIs, 19.1.3.1, 20.3.1.2.1

input sources, 20.1.2.2, 21.1.2.1

pluggable databases and, 20.1.1, 21.1

using, 12.1.2.1.2, 20.3.1.2.1

SQL tuning sets

pluggable databases and, 19.1

SQL Tuning Sets

managing with APIs, 19.1

SQL, recursive, 10.4.3, 13.1 SQL_STATEMENT column

TKPROF_TABLE, 18.4.4.3

SQLTUNE_CATEGORY initialization parameter

determining the SQL Profile category, 22.4

start columns

in partitioning and EXPLAIN PLAN statement, 7.2.5.1

STATEMENT_ID column

PLAN_TABLE table, 7.2.6, 7.3.2

statistics, optimizer, 4.1.2, 14.3

adaptive statistics, 4.4.2

automatic collection, 12.2

bulk loads, 10.3.3

cardinality, 11.1

collection, 12.1

column group, 10.4.1.1

column groups, 13.3.1, 13.3.1.2

dynamic, 10.3.2, 10.4, 12.4.6, 13.1, 17.2.2

dynamic statistics, 10.4.2

exporting and importing, 13.7

extended, 10.2.2

gathering concurrently, 12.4.7

incremental, 12.4.8.4, 12.4.8.5

index, 10.2.3

limitations on restoring previous versions, 13.5.2

preferences, 12.3.1

system, 10.2.5, 12.5, 12.5.1

user-defined, 10.2.6

stop columns

in partitioning and EXPLAIN PLAN statement, 7.2.5.1

subqueries

NOT IN, 9.1.3

system statistics, 12.5.1

T

table statistics, 10.2.1

temporary tables, global, 10.2.4

testing designs, 2.2.1

throughput

optimizer goal, 14.2.3

TIMED_STATISTICS initialization parameter

SQL Trace, 18.4.1

TIMESTAMP column

PLAN_TABLE table, 7.2.6, 7.3.2

TKPROF program, 18.1.2.2, 18.4.3, 18.5.4.4

editing the output SQL script, 18.4.4.2

example of output, 18.5.3

generating the output SQL script, 18.4.4.1

row source operations, 18.5.4.4

using the EXPLAIN PLAN statement, 18.5.4.4

wait event information, 18.5.4.4

TKPROF_TABLE, 18.4.4.3, 18.4.4.3 top frequency histograms, 11.4 TRACEFILE_IDENTIFIER initialization parameter

identifying trace files, 18.4.1

tracing

consolidating with trcsess, 18.5.4.4

identifying files, 18.4.1

trcsess utility, 18.5.4.4 trickle rollout strategy, 2.2.2 tuning

logical structure, A.1.1

U

UNIQUE constraint, A.1.9

uniqueness, A.1.9

USER_DUMP_DEST initialization parameter

SQL Trace, 18.4.1

USER_ID column, TKPROF_TABLE, 18.4.4.3 user_stat_extensions, 13.3.1.5 UTLXPLP.SQL script

displaying plan table output, 6.4

for viewing EXPLAIN PLANs, 6.3

UTLXPLS.SQL script

displaying plan table output, 6.4

for viewing EXPLAIN PLANs, 6.3

used for displaying EXPLAIN PLANs, 6.3

V

V$SQL_PLAN view

using to display execution plan, 6.2.4.1

V$SQL_PLAN_STATISTICS view

using to display execution plan statistics, 6.2.4.1

V$SQL_PLAN_STATISTICS_ALL view

using to display execution plan information, 6.2.4.1

validating designs, 2.2.1

W

workloads, 2.2.1

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