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https://issues.apache.org/jira/browse/SPARK-2048?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Andrew Ash updated SPARK-2048:
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Fix Version/s: 1.1.0
> Optimizations to CPU usage of external spilling code
> ----------------------------------------------------
>
> Key: SPARK-2048
> URL: https://issues.apache.org/jira/browse/SPARK-2048
> Project: Spark
> Issue Type: Improvement
> Components: Spark Core
> Reporter: Matei Zaharia
> Fix For: 1.1.0
>
>
> In the external spilling code in ExternalAppendOnlyMap and CoGroupedRDD,
> there are a few opportunities for optimization:
> - There are lots of uses of pattern-matching on Tuple2 (e.g. val (k, v) =
> pair), which we found to be much slower than accessing fields directly
> - Hash codes for each element are computed many times in
> StreamBuffer.minKeyHash, which will be expensive for some data types
> - Uses of buffer.remove() may be expensive if there are lots of hash
> collisions (better to swap in the last element into that position)
> - More objects are allocated than is probably necessary, e.g. ArrayBuffers
> and pairs
> - Because ExternalAppendOnlyMap is only given one key-value pair at a time,
> it allocates a new update function on each one, unlike the way we pass a
> single update function to AppendOnlyMap in Aggregator
> These should help because situations where we're spilling are also ones where
> there is presumably a lot of GC pressure in the new generation.
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