[SPARK-15748][SQL] Replace inefficient foldLeft() call with flatMap() in PartitionStatistics#13491
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JoshRosen wants to merge 1 commit intoapache:masterfrom
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[SPARK-15748][SQL] Replace inefficient foldLeft() call with flatMap() in PartitionStatistics#13491JoshRosen wants to merge 1 commit intoapache:masterfrom
JoshRosen wants to merge 1 commit intoapache:masterfrom
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@ericl also observed this same perf. bottleneck in his profiling. I'll update the benchmark numbers tomorrrow. |
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Test build #59921 has finished for PR 13491 at commit
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Jenkins, retest this please. |
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LGTM provided tests still pass |
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Test build #59942 has finished for PR 13491 at commit
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Hmm, weird; let me investigate what's going on with these tests... |
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Jenkins, retest this please. |
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Test build #59995 has finished for PR 13491 at commit
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Test build #3065 has finished for PR 13491 at commit
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Merging in master/2.0. |
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… in PartitionStatistics `PartitionStatistics` uses `foldLeft` and list concatenation (`++`) to flatten an iterator of lists, but this is extremely inefficient compared to simply doing `flatMap`/`flatten` because it performs many unnecessary object allocations. Simply replacing this `foldLeft` by a `flatMap` results in decent performance gains when constructing PartitionStatistics instances for tables with many columns. This patch fixes this and also makes two similar changes in MLlib and streaming to try to fix all known occurrences of this pattern. Author: Josh Rosen <joshrosen@databricks.com> Closes #13491 from JoshRosen/foldleft-to-flatmap. (cherry picked from commit 26c1089) Signed-off-by: Reynold Xin <rxin@databricks.com>
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PartitionStatisticsusesfoldLeftand list concatenation (++) to flatten an iterator of lists, but this is extremely inefficient compared to simply doingflatMap/flattenbecause it performs many unnecessary object allocations. Simply replacing thisfoldLeftby aflatMapresults in decent performance gains when constructing PartitionStatistics instances for tables with many columns.This patch fixes this and also makes two similar changes in MLlib and streaming to try to fix all known occurrences of this pattern.