Spark JVMs and memory management

Spark jobs running on DataStax Enterprise are divided among several different JVM processes, each with different memory requirements.

DataStax Enterprise and Spark Master JVMs

The Spark Master runs in the same process as DataStax Enterprise, but its memory usage is negligible. The only way Spark could cause an OutOfMemoryError in DataStax Enterprise is indirectly by executing queries that fill the client request queue. For example, if it ran a query with a high limit and paging was disabled or it used a very large batch to update or insert data in a table. This is controlled by MAX_HEAP_SIZE in If you see an OutOfMemoryError in system.log, you should treat it as a standard OutOfMemoryError and follow the usual troubleshooting steps.

Spark executor JVMs

The Spark executor is where Spark performs transformations and actions on the RDDs and is usually where a Spark-related OutOfMemoryError would occur. An OutOfMemoryError in an executor will show up in the stderr log for the currently executing application (usually in /var/lib/spark). There are several configuration settings that control executor memory and they interact in complicated ways.

  • The memory_total option in the resource_manager_options.worker_options section of dse.yaml defines the maximum fraction of system memory to give all executors for all applications running on a particular node. It uses the following formula:

    memory_total * (total system memory - memory assigned to DataStax Enterprise)

  • spark.executor.memory is a system property that controls how much executor memory a specific application gets. It must be less than or equal to the calculated value of memory_total. It can be specified in the constructor for the SparkContext in the driver application, or via --conf spark.executor.memory or --executor-memory command line options when submitting the job using spark-submit.

The client driver JVM

The driver is the client program for the Spark job. Normally it shouldn’t need very large amounts of memory because most of the data should be processed within the executor. If it does need more than a few gigabytes, your application may be using an anti-pattern like pulling all of the data in an RDD into a local data structure by using collect or take. Generally you should never use collect in production code and if you use take, you should be only taking a few records. If the driver runs out of memory, you will see the OutOfMemoryError in the driver stderr or wherever it’s been configured to log. This is controlled one of two places:


  • spark.driver.memory system property which can be specified via --conf spark.driver.memory or --driver-memory command line options when submitting the job using spark-submit. This cannot be specified in the SparkContext constructor because by that point, the driver has already started.

Spark worker JVMs

The worker is a watchdog process that spawns the executor, and should never need its heap size increased. The worker’s heap size is controlled by SPARK_DAEMON_MEMORY in SPARK_DAEMON_MEMORY also affects the heap size of the Spark SQL thrift server.

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