Set up JobManager Memory

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 migration guide because many changes were introduced with the 1.11 release.

Note This memory setup guide is relevant only for the JobManager! The JobManager memory components have a similar but simpler structure compared to the TaskManagers’ memory configuration.

Configure Total Memory

The simplest way to set up the memory configuration is to configure the total memory for the process. If you run the JobManager process using local execution mode you do not need to configure memory options, they will have no effect.

Detailed configuration

Flink's process memory model


The following table lists all memory components, depicted above, and references Flink configuration options which affect the size of the respective components:

  Component     Configuration options     Description  
JVM Heap jobmanager.memory.heap.size JVM Heap memory size for job manager.
Off-heap Memory jobmanager.memory.off-heap.size Off-heap memory size for job manager. This option covers all off-heap memory usage including direct and native memory allocation.
JVM metaspace jobmanager.memory.jvm-metaspace.size Metaspace size of the Flink JVM process
JVM Overhead jobmanager.memory.jvm-overhead.min
jobmanager.memory.jvm-overhead.max
jobmanager.memory.jvm-overhead.fraction
Native memory reserved for other JVM overhead: e.g. thread stacks, code cache, garbage collection space etc, it is a capped fractionated component of the total process memory


Configure JVM Heap

As mentioned before in the total memory description, another way to set up the memory for the JobManager is to specify explicitly the JVM Heap size (jobmanager.memory.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.

Note 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 JVM parameters.

Configure Off-heap Memory

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 jobmanager.memory.enable-jvm-direct-memory-limit 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 JVM parameters.

The size of this component can be configured by jobmanager.memory.off-heap.size option. This option can be tuned e.g. if the JobManager process throws ‘OutOfMemoryError: Direct buffer memory’, see the troubleshooting guide for more information.

There can be the following possible sources of Off-heap memory consumption:

  • Flink framework dependencies (e.g. Akka network communication)
  • User code executed during job submission (e.g. for certain batch sources) or in checkpoint completion callbacks

Note If you have configured the Total Flink Memory and the JVM Heap explicitly but you have not configured the Off-heap memory, the size of the Off-heap memory will be derived as the Total Flink Memory minus the JVM Heap. The default value of the Off-heap memory option will be ignored.

Local Execution

If you run Flink locally (e.g. from your IDE) without creating a cluster, then the JobManager memory configuration options are ignored.