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https://issues.apache.org/jira/browse/SPARK-11583?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14996266#comment-14996266
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Sean Owen commented on SPARK-11583:
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Yeah a lot of this depends on the savings in memory vs correctness and speed.
You'd probably want to keep this simple, since it needs good tests, so better
to reuse existing code and not make parallel implementations just to cover an
implementation detail.
> Make MapStatus use less memory uage
> -----------------------------------
>
> Key: SPARK-11583
> URL: https://issues.apache.org/jira/browse/SPARK-11583
> Project: Spark
> Issue Type: Improvement
> Reporter: Kent Yao
>
> In the resolved issue https://issues.apache.org/jira/browse/SPARK-11271, as I
> said, using BitSet can save ≈20% memory usage compared to RoaringBitMap.
> For a spark job contains quite a lot of tasks, 20% seems a drop in the ocean.
> Essentially, BitSet uses long[]. For example a BitSet[200k] = long[3125].
> So if we use a HashSet[Int] to store reduceId (when non-empty blocks are
> dense,use reduceId of empty blocks; when sparse, use non-empty ones).
> For dense cases: if HashSet[Int](numNonEmptyBlocks).size <
> BitSet[totalBlockNum], I use MapStatusTrackingNoEmptyBlocks
> For sparse cases: if HashSet[Int](numEmptyBlocks).size <
> BitSet[totalBlockNum], I use MapStatusTrackingEmptyBlocks
> sparse case, 299/300 are empty
> sc.makeRDD(1 to 30000, 3000).groupBy(x=>x).top(5)
> dense case, no block is empty
> sc.makeRDD(1 to 9000000, 3000).groupBy(x=>x).top(5)
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