spark on yarn driver memory

The driver memory in Spark on YARN refers to the amount of memory allocated to the driver program that coordinates the execution of Spark tasks across the cluster.

The driver program is responsible for maintaining the SparkContext, which represents the entry point to the Spark cluster, and for managing the execution of Spark jobs. The driver memory is used to store the metadata required by the SparkContext and to buffer the results of Spark tasks, such as intermediate results of map and reduce operations.

The amount of driver memory required depends on the size of the Spark job and the resources available on the YARN cluster. Typically, the driver memory should be set to a value that is large enough to accommodate the metadata and intermediate results of the Spark job, but not so large that it causes YARN to run out of memory.

The driver memory can be configured in the Spark configuration file using the spark.driver.memory property, which specifies the amount of memory in MB or GB. For example, to set the driver memory to 4 GB, you can add the following line to the spark-defaults.conf file:

spark.driver.memory 4g