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RDD Persistence

Spark provides a convenient way to work on the dataset by persisting it in memory across operations. While persisting an RDD, each node stores any partitions of it that it computes in memory. Now, we can also reuse them in other tasks on that dataset.

We can use either persist() or cache() method to mark an RDD to be persisted. Spark?s cache is fault-tolerant. In any case, if the partition of an RDD is lost, it will automatically be recomputed using the transformations that originally created it.

There is an availability of different storage levels which are used to store persisted RDDs. Use these levels by passing a StorageLevel object (Scala, Java, Python) to persist(). However, the cache() method is used for the default storage level, which is StorageLevel.MEMORY_ONLY.

The following are the set of storage levels:

Storage Level Description
MEMORY_ONLY It stores the RDD as deserialized Java objects in the JVM. This is the default level. If the RDD doesn't fit in memory, some partitions will not be cached and recomputed each time they're needed.
MEMORY_AND_DISK It stores the RDD as deserialized Java objects in the JVM. If the RDD doesn't fit in memory, store the partitions that don't fit on disk, and read them from there when they're needed.
(Java and Scala)
It stores RDD as serialized Java objects ( i.e. one-byte array per partition). This is generally more space-efficient than deserialized objects.
(Java and Scala)
It is similar to MEMORY_ONLY_SER, but spill partitions that don't fit in memory to disk instead of recomputing them.
DISK_ONLY It stores the RDD partitions only on disk.
MEMORY_ONLY_2, MEMORY_AND_DISK_2, etc. It is the same as the levels above, but replicate each partition on two cluster nodes.
OFF_HEAP (experimental) It is similar to MEMORY_ONLY_SER, but store the data in off-heap memory. The off-heap memory must be enabled.

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