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| <h1> |
| <a href="/2020/09/01/memory-management-improvements-for-flinks-jobmanager-in-apache-flink-1.11/">Memory Management improvements for Flink’s JobManager in Apache Flink 1.11</a> |
| </h1> |
| |
| |
| |
| September 1, 2020 - |
| |
| |
| |
| Andrey Zagrebin |
| |
| |
| |
| |
| <p><p>Apache Flink 1.11 comes with significant changes to the memory model of Flink’s JobManager and configuration options for your Flink clusters. |
| These recently-introduced changes make Flink adaptable to all kinds of deployment environments (e.g. Kubernetes, Yarn, Mesos), |
| providing better control over its memory consumption.</p> |
| <p>The <a href="https://flink.apache.org/news/2020/04/21/memory-management-improvements-flink-1.10.html">previous blog post</a> |
| focused on the memory model of the TaskManagers and how it was improved in Flink 1.10. This post addresses the same topic but for the JobManager instead. |
| Flink 1.11 unifies the memory model of Flink’s processes. The newly-introduced memory model of the JobManager follows a similar approach to that of the TaskManagers; |
| it is simpler and has fewer components and tuning knobs. This post might consequently seem very similar to our previous story on Flink’s memory |
| but aims at providing a complete overview of Flink’s JobManager memory model as of Flink 1.11. Read on for a full list of updates and changes below!</p> |
| <h2 id="introduction-to-flinks-process-memory-model"> |
| Introduction to Flink’s process memory model |
| <a class="anchor" href="#introduction-to-flinks-process-memory-model">#</a> |
| </h2> |
| <p>Having a clear understanding of Apache Flink’s process memory model allows you to manage resources for the various workloads more efficiently. |
| The following diagram illustrates the main memory components of a Flink process:</p> |
| <center> |
| <img src="/img/blog/2020-09-01-flink-1.11-memory-management-improvements/total-process-memory-flink-1.11.png" width="400px" alt="Backpressure sampling:high"/> |
| <br/> |
| <i><small>Flink: Total Process Memory</small></i> |
| </center> |
| <br/> |
| <p>The JobManager process is a JVM process. On a high level, its memory consists of the JVM Heap and Off-Heap memory. |
| These types of memory are consumed by Flink directly or by the JVM for its specific purposes (i.e. metaspace). |
| There are two major memory consumers within the JobManager process: the framework itself consuming memory for internal data structures, network communication, etc. |
| and the user code which runs within the JobManager process, e.g. in certain batch sources or during checkpoint completion callbacks.</p> |
| <div class="alert alert-info" markdown="1"> |
| <span class="label label-info" style="display: inline-block"><span class="glyphicon glyphicon-info-sign" aria-hidden="true"></span> Note</span> |
| Please note that the user code has direct access to all memory types: JVM Heap, Direct and Native memory. Therefore, Flink cannot really control its allocation and usage. |
| </div> |
| <h2 id="how-to-set-up-jobmanager-memory"> |
| How to set up JobManager memory |
| <a class="anchor" href="#how-to-set-up-jobmanager-memory">#</a> |
| </h2> |
| <p>With the release of Flink 1.11 and in order to provide better user experience, the Flink community introduced three alternatives to setting up memory in JobManagers.</p> |
| <p>The first two — and simplest — alternatives are configuring one of the two following options for total memory available for the JVM process of the JobManager:</p> |
| <ul> |
| <li><strong><em>Total Process Memory:</em></strong> total memory consumed by the Flink Java application (including user code) and by the JVM to run the whole process.</li> |
| <li><strong><em>Total Flink Memory:</em></strong> only the memory consumed by the Flink Java application, including user code but excluding any memory allocated by the JVM to run it.</li> |
| </ul> |
| <p>It is advisable to configure the <em>Total Flink Memory</em> for standalone deployments where explicitly declaring how much memory is given to Flink is a common practice, |
| while the outer JVM overhead is of little interest. For the cases of deploying Flink in containerized environments |
| (such as <a href="//nightlies.apache.org/flink/flink-docs-release-1.11/ops/deployment/kubernetes.html">Kubernetes</a>, |
| <a href="//nightlies.apache.org/flink/flink-docs-release-1.11/ops/deployment/yarn_setup.html">Yarn</a> or |
| <a href="//nightlies.apache.org/flink/flink-docs-release-1.11/ops/deployment/mesos.html">Mesos</a>), |
| the <em>Total Process Memory</em> option is recommended instead, because it becomes the size for the total memory of the requested container. |
| Containerized environments usually strictly enforce this memory limit.</p> |
| <p>If you want more fine-grained control over the size of the <em>JVM Heap</em>, there is also the third alternative of configuring it directly. |
| This alternative gives a clear separation between the heap memory and any other memory types.</p> |
| <p>The remaining memory components will be automatically adjusted either based on their default values or additionally-configured parameters. |
| Apache Flink also checks the overall consistency. You can find more information about the different memory components in the corresponding |
| <a href="//nightlies.apache.org/flink/flink-docs-release-1.11/ops/memory/mem_setup_jobmanager.html">documentation</a>. |
| You can try different configuration options with the <a href="https://docs.google.com/spreadsheets/d/1mJaMkMPfDJJ-w6nMXALYmTc4XxiV30P5U7DzgwLkSoE/edit#gid=605121894">configuration spreadsheet</a> |
| (you have to make a copy of the spreadsheet to edit it) of <a href="https://cwiki.apache.org/confluence/display/FLINK/FLIP-116%3A+Unified+Memory+Configuration+for+Job+Managers">FLIP-116</a> |
| and check the corresponding results for your individual case.</p> |
| <p>If you are migrating from a Flink version older than 1.11, we suggest following the steps in the |
| <a href="//nightlies.apache.org/flink/flink-docs-release-1.11/ops/memory/mem_migration.html#migrate-job-manager-memory-configuration">migration guide</a> of the Flink documentation.</p> |
| <p>Additionally, you can configure separately the <a href="//nightlies.apache.org/flink/flink-docs-release-1.11/ops/memory/mem_setup_jobmanager.html#configure-off-heap-memory">Off-heap memory</a> |
| (<em>JVM direct and non-direct memory</em>) as well as the <a href="//nightlies.apache.org/flink/flink-docs-release-1.11/ops/memory/mem_setup_jobmanager.html#detailed-configuration">JVM metaspace & overhead</a>. |
| The <em>JVM overhead</em> is a <a href="//nightlies.apache.org/flink/flink-docs-release-1.11/ops/memory/mem_setup.html#capped-fractionated-components">fraction</a> of the <em>Total Process Memory</em>. |
| The <em>JVM overhead</em> can be configured in a similar way as other fractions described in <a href="https://flink.apache.org/news/2020/04/21/memory-management-improvements-flink-1.10.html#fractions-of-the-total-flink-memory">our previous blog post</a> |
| about the TaskManager’s memory model.</p> |
| <h2 id="more-hints-to-control-the-container-memory-limit"> |
| More hints to control the container memory limit |
| <a class="anchor" href="#more-hints-to-control-the-container-memory-limit">#</a> |
| </h2> |
| <p>The heap and direct memory usage are managed by the JVM. There are also many other possible sources of native memory consumption in Apache Flink or its user applications |
| which are not managed directly by Flink or the JVM. Controlling their limits is often difficult which complicates debugging of potential memory leaks. |
| If Flink’s process allocates too much memory in an unmanaged way, it can often result in killing its containers for containerized environments. |
| In this case, understanding which type of memory consumption has exceeded its limit might be difficult to grasp and resolve. |
| Flink 1.11 introduces some specific tuning options to clearly represent such components for the JobManager’s process. |
| Although Flink cannot always enforce strict limits and borders among them, the idea here is to explicitly plan the memory usage. |
| Below we provide some examples of how memory setup can prevent containers from exceeding their memory limit:</p> |
| <ul> |
| <li> |
| <p><strong><a href="//nightlies.apache.org/flink/flink-docs-release-1.11/ops/memory/mem_setup_jobmanager.html#configure-off-heap-memory">User code or its dependencies consume significant off-heap memory</a>.</strong> |
| Tuning the <em>Off-heap</em> option can assign additional direct or native memory to the user code or any of its dependencies. |
| Flink cannot control native allocations but it sets the limit for <em>JVM Direct</em> memory allocations. The Direct memory limit is enforced by the JVM.</p> |
| </li> |
| <li> |
| <p><strong><a href="//nightlies.apache.org/flink/flink-docs-release-1.11/ops/memory/mem_setup_jobmanager.html#detailed-configuration">JVM metaspace requires additional memory</a>.</strong> |
| If you encounter <code>OutOfMemoryError: Metaspace</code>, Flink provides an option to increase its default limit and the JVM will ensure that it is not exceeded. |
| The metaspace size of a Flink JVM process is always explicitly set in contrast to the default JVM settings where it is not limited.</p> |
| </li> |
| <li> |
| <p><strong><a href="//nightlies.apache.org/flink/flink-docs-release-1.11/ops/memory/mem_setup_jobmanager.html#detailed-configuration">JVM requires more internal memory</a>.</strong> |
| There is no direct control over certain types of JVM process allocations but Flink provides <em>JVM Overhead</em> options. |
| The <em>JVM Overhead</em> options allow declaring an additional amount of memory, anticipated for those allocations and not covered by other options.</p> |
| </li> |
| </ul> |
| <h2 id="conclusion"> |
| Conclusion |
| <a class="anchor" href="#conclusion">#</a> |
| </h2> |
| <p>The latest Flink release (<a href="https://flink.apache.org/downloads.html#apache-flink-1111">Flink 1.11</a>) introduces some notable changes to the memory configuration of Flink’s JobManager, |
| making its memory management significantly easier than before. Stay tuned for more additions and features in upcoming releases. |
| If you have any suggestions or questions for the Flink community, we encourage you to sign up to the Apache Flink <a href="https://flink.apache.org/community.html#mailing-lists">mailing lists</a> |
| and become part of the discussion.</p> |
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