Project: Flink (App Definition and Development / Streaming & Messaging)
File: mem_setup_jobmanager.md

title: "Set up JobManager Memory" weight: 4 type: docs aliases: /deployment/memory/memsetupjobmanager.html /ops/memory/memsetupjobmanager.html <!-- Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. --> The JobManager is the controlling element of the Flink Cluster. It consists of three distinct components: Resource Manager, Dispatcher and one JobMaster per running Flink Job. This guide walks you through high level and fine-grained memory configurations for the JobManager. The further described memory configuration is applicable starting with the release version 1.11. If you upgrade Flink from earlier versions, check the because many changes were introduced with the 1.11 release. {{< hint info >}} This memory setup guide is relevant <strong>only for the JobManager</strong>! The JobManager memory components have a similar but simpler structure compared to the . {{< /hint >}} The simplest way to set up the memory configuration is to configure the for the process. If you run the JobManager process using local you do not need to configure memory options, they will have no effect. {{< img src="/fig/processmemmodel.svg" width="300px" alt="Flink's process memory model" usemap="#process-mem-model" >}} The following table lists all memory components, depicted above, and references Flink configuration options which affect the size of the respective components: | &nbsp;&nbsp;Component&nbsp;&nbsp; | &nbsp;&nbsp;Configuration options&nbsp;&nbsp; | &nbsp;&nbsp;Description&nbsp;&nbsp; | | :- | :-- | :-- | | | | JVM Heap memory size for job manager. | | | | Off-heap memory size for job manager. This option covers all off-heap memory usage including direct and native memory

| | | | Metaspace size of the Flink JVM process | | JVM Overhead | <br/> <br/> | Native memory reserved for other JVM overhead: e.g. thread stacks, code cache, garbage collection space etc, it is a of the | <br/> As mentioned before in the , another way to set up the memory for the JobManager is to specify explicitly the JVM Heap size (). It gives more control over the available JVM Heap which is used by: Flink framework User code executed during job submission (e.g. for certain batch sources) or in checkpoint completion callbacks The required size of JVM Heap is mostly driven by the number of running jobs, their structure, and requirements for the mentioned user code. <span class="label label-info">Note</span> If you have configured the JVM Heap explicitly, it is recommended to set neither total process memory nor total Flink memory. Otherwise, it may easily lead to memory configuration conflicts. The Flink scripts and CLI set the JVM Heap size via the JVM parameters -Xms and -Xmx when they start the JobManager process, see also . The Off-heap memory component accounts for any type of JVM direct memory and native memory usage. Therefore, you can also enable the JVM Direct Memory limit by setting the option. If this option is configured, Flink will set the limit to the Off-heap memory size via the corresponding JVM argument: -XX:MaxDirectMemorySize. See also . The size of this component can be configured by option. This option can be tuned e.g. if the JobManager process throws OutOfMemoryError: Direct buffer memory, see for more information. There can be the following possible sources of Off-heap memory consumption: Flink framework dependencies (e.g. Pekko network communication) User code executed during job submission (e.g. for certain batch sources) or in checkpoint completion callbacks <span class="label label-info">Note</span> If you have configured the and the explicitly but you have not configured the Off-heap memory, the size of the Off-heap memory will be derived as the minus the . The default value of the Off-heap memory option will be ignored. If you run Flink locally (e.g. from your IDE) without creating a cluster, then the JobManager memory configuration options are ignored.