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  1. Hive
  2. HIVE-7292 Hive on Spark
  3. HIVE-9697

Hive on Spark is not as aggressive as MR on map join [Spark Branch]

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    • Type: Sub-task
    • Status: Resolved
    • Priority: Major
    • Resolution: Won't Fix
    • Affects Version/s: None
    • Fix Version/s: None
    • Component/s: Spark
    • Labels:

      Description

      We have a finding during running some Big-Bench cases:
      when the same small table size threshold is used, Map Join operator will not be generated in Stage Plans for Hive on Spark, while will be generated for Hive on MR.

      For example, When we run BigBench Q25, the meta info of one input ORC table is as below:
      totalSize=1748955 (about 1.5M)
      rawDataSize=123050375 (about 120M)
      If we use the following parameter settings,
      set hive.auto.convert.join=true;
      set hive.mapjoin.smalltable.filesize=25000000;
      set hive.auto.convert.join.noconditionaltask=true;
      set hive.auto.convert.join.noconditionaltask.size=100000000; (100M)
      Map Join will be enabled for Hive on MR mode, while will not be enabled for Hive on Spark.

      We found that for Hive on MR, the HDFS file size for the table (ContentSummary.getLength(), should approximate the value of ‘totalSize’) will be used to compare with the threshold 100M (smaller than 100M), while for Hive on Spark 'rawDataSize' will be used to compare with the threshold 100M (larger than 100M). That's why MapJoin is not enabled for Hive on Spark for this case. And as a result Hive on Spark will get much lower performance data than Hive on MR for this case.

      When we set hive.auto.convert.join.noconditionaltask.size=150000000; (150M), MapJoin will be enabled for Hive on Spark mode, and Hive on Spark will have similar performance data with Hive on MR by then.

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              • Assignee:
                Unassigned
                Reporter:
                xhao1 Xin Hao
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