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 <
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 <
=== [[cache]] Caching RDD -- cache
Method
[source, scala]¶
cache(): this.type = persist()¶
cache
is a synonym of <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:StorageLevel.md[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 core:ContextCleaner.md#registerRDDForCleanup[registered to ContextCleaner
] (if available) and SparkContext.md#persistRDD[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.md#unpersist[SparkContext.unpersistRDD(id, blocking)] and sets storage:StorageLevel.md[NONE
storage level] as the current storage level.