[ https://issues.apache.org/jira/browse/SPARK-2048?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Matei Zaharia updated SPARK-2048: --------------------------------- Description: 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. was: 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 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. > 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 > > 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. -- This message was sent by Atlassian JIRA (v6.2#6252)