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Version: 1.0

Memory

Executor memory​

On-heap​

  • Spark Memory (spark.memory.fraction)

    • Storage Memory (spark.memory.storageFraction) : used to store Spark cache data, such as RDD cache, Broadcast variable, Unroll data (process of deserializing a serialized data), and so on.
    • Execution Memory: used to store temporary data in the calculation process of Shuffle, Join, Sort, Aggregation, etc

  • User Memory: It's mainly used to store the data needed for RDD conversion operations, such as the information for RDD dependency

  • Reserved Memory (300MB): The memory is reserved for system and is used to store Spark's internal objects

    • Spark fail if we don't give executor memory at least 1.5 * Reveserved Memory = 450MB

    memory

Cache and Repartition​

  • Cache: use MEMORY_AND_DISK storage level.
  • Persist: the storage level can be changed.
  • Cache and persist are lazy operations (transformations)
  • Spark drops persisted data if not used or by using least-recently-used (LRU) algorithm.