Vladimir Pchelko created SPARK-18925: ----------------------------------------
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. -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org