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  1. Spark
  2. SPARK-30602

SPIP: Support push-based shuffle to improve shuffle efficiency



    • Type: Improvement
    • Status: In Progress
    • Priority: Major
    • Resolution: Unresolved
    • Affects Version/s: 3.1.0
    • Fix Version/s: None
    • Component/s: Shuffle, Spark Core
    • Labels:


      In a large deployment of a Spark compute infrastructure, Spark shuffle is becoming a potential scaling bottleneck and a source of inefficiency in the cluster. When doing Spark on YARN for a large-scale deployment, people usually enable Spark external shuffle service and store the intermediate shuffle files on HDD. Because the number of blocks generated for a particular shuffle grows quadratically compared to the size of shuffled data (# mappers and reducers grows linearly with the size of shuffled data, but # blocks is # mappers * # reducers), one general trend we have observed is that the more data a Spark application processes, the smaller the block size becomes. In a few production clusters we have seen, the average shuffle block size is only 10s of KBs. Because of the inefficiency of performing random reads on HDD for small amount of data, the overall efficiency of the Spark external shuffle services serving the shuffle blocks degrades as we see an increasing # of Spark applications processing an increasing amount of data. In addition, because Spark external shuffle service is a shared service in a multi-tenancy cluster, the inefficiency with one Spark application could propagate to other applications as well.

      In this ticket, we propose a solution to improve Spark shuffle efficiency in above mentioned environments with push-based shuffle. With push-based shuffle, shuffle is performed at the end of mappers and blocks get pre-merged and move towards reducers. In our prototype implementation, we have seen significant efficiency improvements when performing large shuffles. We take a Spark-native approach to achieve this, i.e., extending Spark’s existing shuffle netty protocol, and the behaviors of Spark mappers, reducers and drivers. This way, we can bring the benefits of more efficient shuffle in Spark without incurring the dependency or overhead of either specialized storage layer or external infrastructure pieces.


      Link to dev mailing list discussion: http://apache-spark-developers-list.1001551.n3.nabble.com/Enabling-push-based-shuffle-in-Spark-td28732.html


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              • Assignee:
                mshen Min Shen
              • Votes:
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