[SPARK-11583][Scheduler][Core] Make MapStatus use less memory uage #9559
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Test build #2020 has finished for PR 9559 at commit
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retest plz |
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retest this please |
yaooqinn
changed the title
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Can you use the OpenHashSet in Spark? |
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retest this please |
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Test build #45450 has finished for PR 9559 at commit
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indent these by 2 more spaces
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@rxin OpenHashSet replaces HashSet |
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@andrewor14 Thanks for your advices |
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isSparse is the wrong name, I think -- both cases are sparse, its a question of whether or not you are storing the empty blocks.
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Sean has raised some important higher level questions on the jira -- I'd like us to resolve the discussion there before moving forward on this. |
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@yaooqinn Can you close this now that you have the new patch? |
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OK, close this pr and see #9661 |
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Disscuss about this pr is at https://issues.apache.org/jira/browse/SPARK-11583
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 HashSetInt.size < BitSet[totalBlockNum], I use MapStatusTrackingNoEmptyBlocks
For sparse cases: if HashSetInt.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)