Actions are spark-rdd-operations.md[RDD operations] that produce non-RDD values. They materialize a value in a Spark program. In other words, a RDD operation that returns a value of any type but
RDD[T] is an action.
action: RDD => a value
NOTE: Actions are synchronous. You can use <
They trigger execution of <
You can think of actions as a valve and until action is fired, the data to be processed is not even in the pipes, i.e. transformations. Only actions can materialize the entire processing pipeline with real data.
Actions are one of two ways to send data from executor:Executor.md[executors] to the spark-driver.md[driver] (the other being spark-accumulators.md[accumulators]).
- spark-io.md#saving-rdds-to-files[saveAs* actions], e.g.
Actions run spark-scheduler-ActiveJob.md[jobs] using ROOT:SparkContext.md#runJob[SparkContext.runJob] or directly scheduler:DAGScheduler.md#runJob[DAGScheduler.runJob].
scala> words.count // <1> res0: Long = 502
words is an RDD of
TIP: You should cache RDDs you work with when you want to execute two or more actions on it for a better performance. Refer to spark-rdd-caching.md[RDD Caching and Persistence].
Before calling an action, Spark does closure/function cleaning (using
SparkContext.clean) to make it ready for serialization and sending over the wire to executors. Cleaning can throw a
SparkException if the computation cannot be cleaned.
NOTE: Spark uses
ClosureCleaner to clean closures.
=== [[AsyncRDDActions]] AsyncRDDActions
AsyncRDDActions class offers asynchronous actions that you can use on RDDs (thanks to the implicit conversion
rddToAsyncRDDActions in RDD class). The methods return a <
The following asynchronous methods are available:
=== [[FutureAction]] FutureActions