Troubleshooting

IllegalConfigurationException

If you see an IllegalConfigurationException thrown from TaskExecutorProcessUtils or JobManagerProcessUtils, it usually indicates that there is either an invalid configuration value (e.g. negative memory size, fraction that is greater than 1, etc.) or configuration conflicts. Check the documentation chapters or configuration options related to the memory components mentioned in the exception message.

OutOfMemoryError: Java heap space

The exception usually indicates that the JVM Heap is too small. You can try to increase the JVM Heap size by increasing total memory. You can also directly increase task heap memory for TaskManagers or JVM Heap memory for JobManagers.

Note You can also increase the framework heap memory for TaskManagers, but you should only change this option if you are sure the Flink framework itself needs more memory.

OutOfMemoryError: Direct buffer memory

The exception usually indicates that the JVM direct memory limit is too small or that there is a direct memory leak. Check whether user code or other external dependencies use the JVM direct memory and that it is properly accounted for. You can try to increase its limit by adjusting direct off-heap memory. See also how to configure off-heap memory for TaskManagers, JobManagers and the JVM arguments which Flink sets.

OutOfMemoryError: Metaspace

The exception usually indicates that JVM metaspace limit is configured too small. You can try to increase the JVM metaspace option for TaskManagers or JobManagers.

IOException: Insufficient number of network buffers

This is only relevant for TaskManagers.

The exception usually indicates that the size of the configured network memory is not big enough. You can try to increase the network memory by adjusting the following options:

• taskmanager.memory.network.min
• taskmanager.memory.network.max
• taskmanager.memory.network.fraction

Container Memory Exceeded

If a Flink container tries to allocate memory beyond its requested size (Yarn, Mesos or Kubernetes), this usually indicates that Flink has not reserved enough native memory. You can observe this either by using an external monitoring system or from the error messages when a container gets killed by the deployment environment.

If you encounter this problem in the JobManager process, you can also enable the JVM Direct Memory limit by setting the jobmanager.memory.enable-jvm-direct-memory-limit option to exclude possible JVM Direct Memory leak.

If RocksDBStateBackend is used, and the memory controlling is disabled, you can try to increase the TaskManager’s managed memory.

Alternatively, you can increase the JVM Overhead.

See also how to configure memory for containers.