Skip to content

Caching and Persistence

== RDD Caching and Persistence

Caching or persistence are optimisation techniques for (iterative and interactive) Spark computations. They help saving interim partial results so they can be reused in subsequent stages. These interim results as RDDs are thus kept in memory (default) or more solid storages like disk and/or replicated.

RDDs can be cached using <> operation. They can also be persisted using <> operation.

The difference between cache and persist operations is purely syntactic. cache is a synonym of persist or persist(MEMORY_ONLY), i.e. cache is merely persist with the default storage level MEMORY_ONLY.

NOTE: Due to the very small and purely syntactic difference between caching and persistence of RDDs the two terms are often used interchangeably and I will follow the "pattern" here.

RDDs can also be <> to remove RDD from a permanent storage like memory and/or disk.

=== [[cache]] Caching RDD -- cache Method

[source, scala]

cache(): this.type = persist()

cache is a synonym of <> with[MEMORY_ONLY storage level].

=== [[persist]] Persisting RDD -- persist Methods

[source, scala]

persist(): this.type persist(newLevel: StorageLevel): this.type

persist marks a RDD for persistence using newLevel[storage level].

You can only change the storage level once or persist reports an UnsupportedOperationException:

Cannot change storage level of an RDD after it was already assigned a level

NOTE: You can pretend to change the storage level of an RDD with already-assigned storage level only if the storage level is the same as it is currently assigned.

If the RDD is marked as persistent the first time, the RDD is[registered to ContextCleaner] (if available) and[SparkContext].

The internal storageLevel attribute is set to the input newLevel storage level.

=== [[unpersist]] Unpersisting RDDs (Clearing Blocks) -- unpersist Method

[source, scala]

unpersist(blocking: Boolean = true): this.type

When called, unpersist prints the following INFO message to the logs:

INFO [RddName]: Removing RDD [id] from persistence list

It then calls[SparkContext.unpersistRDD(id, blocking)] and sets[NONE storage level] as the current storage level.

Back to top