{"id":1045,"date":"2019-01-10T18:35:53","date_gmt":"2019-01-10T18:35:53","guid":{"rendered":"https:\/\/www.appservgrid.com\/paw93\/?p=1045"},"modified":"2019-01-12T10:21:27","modified_gmt":"2019-01-12T10:21:27","slug":"using-new-relic-splunk-appdynamics-and-netuitive-for-container-monitoring","status":"publish","type":"post","link":"https:\/\/www.appservgrid.com\/paw93\/index.php\/2019\/01\/10\/using-new-relic-splunk-appdynamics-and-netuitive-for-container-monitoring\/","title":{"rendered":"Using New Relic, Splunk, AppDynamics and Netuitive for Container Monitoring"},"content":{"rendered":"

\"Monitoring\nIcons\"<\/a><\/p>\n

If you use containers as part of your day-to-day operations, you need to
\nmonitor them — ideally, by using a monitoring solution that you
\nalready have in place, rather than implementing an entirely new tool.
\nContainers are often deployed quickly and at a high volume, and they
\nfrequently consume and release system resources at a rapid rate. You
\nneed to have some way of measuring container performance, and the impact
\nthat container deployment has on your system. In this article, we\u2019ll
\ntake a look at four widely used monitoring
\nplatforms\u2014Netuitive<\/a>, NewRelic<\/a>, Splunk<\/a>, and\u00a0AppDynamics<\/a>\u2014that support containers, and compare how they measure up when it comes to monitoring containers.
\nFirst, though, a question: When you monitor containers, what kind of
\nmetrics do you expect to see? The answer, as we\u2019ll see below, varies
\nwith the monitoring platform. But in general, container metrics fall
\ninto two categories\u2014those that measure overall container impact on the
\nsystem, and those that focus on the performance of individual
\ncontainers.<\/p>\n

Setting up the Monitoring System<\/h3>\n

The first step in any kind of software monitoring, of course, is to
\ninstall the monitoring service. For all of the platforms covered in this
\narticle, you can expect additional steps for setting up standard
\nmonitoring features. Here we cover only those directly related to
\ncontainer monitoring. Setup: New Relic With New Relic, you start by
\ninstalling New Relic Servers for Linux<\/a>,
\nwhich includes integrated Docker monitoring. It should be installed on
\nthe Docker server, rather than the Docker container. The Servers for
\nLinux package is available for most common Linux distributions; Docker
\nmonitoring, however, requires a 64-bit system. After you install New
\nRelic Servers for Linux, you will need to create a docker group (if it
\ndoesn\u2019t exist), then add the newrelic user to that group. You may need
\nto do some basic setup after that, including (depending on the Linux
\ndistribution) setting the location of the container data files and
\nenabling memory metrics. Setup: Netuitive Netuitive also requires
\nyou to install its Linux monitoring agent on the Docker server. You then
\nneed to enable Docker metrics collection in the Linux Agent config file,
\nand optionally limit the metrics and\/or containers by creating a
\nregex-based blacklist or whitelist. As with New Relic, you may wind up
\nsetting a few additional options. Netuitive, however, offers an
\nadditional installation method. If you are unable to install the Linux
\nAgent on the Docker server, you can install a Linux Agent Docker
\ncontainer, which will then do the job of monitoring the host and
\ncontainers. Setup: Splunk Splunk takes a very different approach to
\ncontainer monitoring. It uses a Docker API logging driver to send
\ncontainer log data directly to Splunk Enterprise and Splunk Cloud via
\nits HTTP Event Collector. You specify the splunk driver (and its
\nassociated options) on the Docker command line. Splunk\u2019s monitoring, in
\nother words, is integrated directly with Docker, rather than with the
\nhost system. The Splunk log driver requires an HTTP Event Collector
\ntoken, and the path (plus port) to the user\u2019s Splunk Cloud\/Splunk
\nEnterprise instance. It also takes several optional arguments. Setup:
\nAppDynamics AppDynamics uses a Docker Monitoring extension<\/a>
\nto send Docker Remote API metrics to its Machine Agent. In some ways,
\nthis places it in a middle ground between New Relic and Netuitive\u2019s
\nagent-based monitoring and Splunk\u2019s close interaction with Docker.
\nAppDynamics\u2019 extension installation, however, is much more hands-on.
\nThe instructions suggest that you can expect to come out of it with
\nengine grease up to your elbows, and perhaps a few scraped knuckles. You
\nmust first manually bind Docker to the TCP Socket or the Unix Socket.
\nAfter that, you need to install the Machine Agent, followed by the
\nextension. You then need to manually edit several sections of the config
\nfile, including the custom dashboard. You must also set executable
\npermissions, and AppDynamics asks you to review both the Docker Remote
\nAPI and the Docker Monitoring extension\u2019s socket command file. The
\nAppDynamics instructions also include troubleshooting instructions for
\nmost of the installation steps.<\/p>\n

What You Get<\/h3>\n

As you might expect, there are some significant differences in the
\nmetrics which each of these platforms monitors and displays. Output:
\nNew Relic New Relic displays Docker container metrics as part of its
\nApplication Performance Monitoring (APM) Overview page; containers are
\ngrouped host servers when Servers for Linux is installed, and are
\nindicated by a container symbol. The overview page includes drill-down
\nfor detailed performance features. The New Relic Servers monitor
\nincludes a Docker page, which shows the impact of Docker containers on
\nthe server\u2019s performance. It displays server-related metrics for
\nindividual Docker images, with drill-down to image details. It does not,
\nhowever, display data for individual containers. Output: Netuitive
\nNetuitive\u2019s Docker monitor collects a variety of metrics, including
\nseveral related to CPU and network performance, and almost two dozen
\ninvolving memory. It also collects computed CPU, memory, and throttling
\npercentages. With Netuitive, you build a dashboard by assembling
\nwidgets, so the actual data shown (and the manner in which it is
\ndisplayed) will depend on your choice of widgets and their
\nconfiguration. Output: Splunk Splunk is designed to use data from a
\nwide range of logs and related sources; for containers, it pulls data
\nfrom individual container logs, from Docker and cloud APIs, and from
\napplication logs. Since Splunk integrates such a large amount of data at
\nthe cloud and enterprise level, it is up to the user to configure
\nSplunk\u2019s analysis and monitoring tools to display the required data.
\nFor containers, Splunk recommends looking at CPU and memory use,
\ndowntime\/outage-related errors, and specific container and application
\nlogs to identify problem containers. Output: AppDynamics AppDynamics
\nreports basic container and system statistics (individual container
\nsize, container and image count, total memory, memory limit, and swap
\nlimit), along with various ongoing network, CPU, and memory statistics.
\nIt sends these to the dashboard, where they are displayed in a series of
\ncharts.<\/p>\n

Which Service Should You Use?<\/h3>\n

When it comes to the question of which monitoring service is right for
\nyour container deployment, there\u2019s no single answer. For most
\ncontainer-based operations, including Rancher-managed operations on a
\ntypical Linux distribution, either New Relic or Netuitive should do
\nquite well. With reasonably similar setup and monitoring features, the
\ntradeoff is between New Relic\u2019s preconfigured dashboard pages and the
\ndo-it-yourself customizability of Netuitive\u2019s dashboard system. For
\nenterprise-level operations concerned with integrated monitoring of
\nperformance at all scales, from system-level down to individual
\ncontainer and application logs, Splunk is the obvious choice. Since
\nSplunk works directly with the Docker API, it is also likely to be the
\nbest option for use with minimal-feature RancherOS deployments. If, on
\nthe other hand, you simply want to monitor container performance via the
\nDocker API in a no-frills, basic way, the AppDynamics approach might
\nwork best for you. So there it is: Look at what kind of container
\nmonitoring you need, and take your pick.<\/p>\n

Source<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

If you use containers as part of your day-to-day operations, you need to monitor them — ideally, by using a monitoring solution that you already have in place, rather than implementing an entirely new tool. Containers are often deployed quickly and at a high volume, and they frequently consume and release system resources at a … <\/p>\n

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