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https://issues.apache.org/jira/browse/SPARK-11583?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14996206#comment-14996206
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Kent Yao commented on SPARK-11583:
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1. The _Compressed_ in MapStatus means *Array[Long]* -> *Array[Byte]* ->
*Long(1)* + BitSet(Array.length), which represents RealSize ->
CompressedMapStatus -> HiglyCompressedMapStatus, this is the in-memory
representation. Roaringbitmap is same as the BitSet now we use in
HiglyCompressedMapStatus, but take 20% memory usage more than BitSet. They both
don't be compressed in-memory. According to the annotations of the former
Roaring-HiglyCompressedMapStatus, it can be compressed during serialization not
in-memory.
2. I will try optimize my code. Maybe only need more constructors for
HiglyCompressedMapStatus but a new class.
3. The mapstatuses will be fetched by executors during next stage, if the size
> 0，it will fetch shuffle results produced by the former stage, if < 0, not. If
we can use much small mapstatus to realize this function, it can advoid
driver's oom and reduce nio consumption. Here we separate two
cases(sparse/dense), use little amount of Ints which means reduceIds where the
blocks belongs, as long as reduceIds[nonEmptyBlocks] or reduceId[emptyBlocks]
takes less bits than BitSet[TotalBlocks], we can use this optimization.
The thresholds here are 1/32 and 31/32, cases between 1/32~31/32 BitSet is
smaller. Here 32 represents sizeof(Int).
> 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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