Vladimir Pchelko created SPARK-18925:
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Summary: Reduce memory usage of mapWithState
Key: SPARK-18925
URL: https://issues.apache.org/jira/browse/SPARK-18925
Project: Spark
Issue Type: Improvement
Reporter: Vladimir Pchelko
Priority: Minor
With default settings mapWithState leads to storing up to 10 copies of
MapWithStateRDD in memory:
(DSream, InternalMapWithStateDStream, DEFAULT_CHECKPOINT_DURATION_MULTIPLIER,
rememberDuration, minRememberDuration)
In my project we quikly runs OutOfMemory, because we have to track many
millions of events * 2-3KB per event -> about 50 GB per MapWithStateRDD.
Using cluster with +500GB memory is unacceptable for our task.
Reducing CHECKPOINT_DURATION_MULTIPLIER is unacceptable, it slightly 'fixes'
memory issue, but lead to new one - we unable to process in real-time - because
the checkpointing duration is in several times longer that batchInterval.
So I inverstigated the mapWithState process and concluded that for proper
functioning of mapWithState, we need the current and the last checkpointed
MapWithStateRDD.
To fix memory issues in my project: I override clearMetadata for
InternalMapWithStateDStream and unpersist all oldRDDs:
val oldRDDs = generatedRDDs.filter(_._1 <= (time - slideDuration))
except the last checkpointed
val checkpointedKeys = oldRDDs.filter(_._2.isCheckpointed).keys
if (checkpointedKeys.nonEmpty) {
oldRDDs -= checkpointedKeys.max
}
... (C/P of DStream clearMetadata)
Please correct me.
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