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https://issues.apache.org/jira/browse/BEAM-9346?focusedWorklogId=420384&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-420384
]
ASF GitHub Bot logged work on BEAM-9346:
----------------------------------------
Author: ASF GitHub Bot
Created on: 10/Apr/20 18:11
Start Date: 10/Apr/20 18:11
Worklog Time Spent: 10m
Work Description: chamikaramj commented on pull request #11122:
[BEAM-9346] Improve the efficiency of TFRecordIO
URL: https://github.com/apache/beam/pull/11122#discussion_r406877630
##########
File path: sdks/java/core/src/main/java/org/apache/beam/sdk/io/WriteFiles.java
##########
@@ -410,13 +412,44 @@ private GatherResults(Coder<ResultT> resultCoder) {
} else {
// Pass results via a side input rather than reshuffle, because we
need to get an empty
// iterable to finalize if there are no results.
- return input
- .getPipeline()
- .apply(Reify.viewInGlobalWindow(input.apply(View.asList()),
ListCoder.of(resultCoder)));
+ return input.apply("ToList", Combine.globally(new
ToListCombineFn<>()));
Review comment:
Thanks for reverting.
My suggestion was to fork the transform expansion to use GlobalCombine when
specifically requested through a transform parameter (for example see how we
fork TextIO.Read expansion based on withHintMatchesManyFiles()).
https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/io/TextIO.java#L371
https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/io/TextIO.java#L401
I agree that the more flags we add the more confusing it becomes for users.
So we should make sure that there are significant advantages in adding this
option through performance benchmarking.
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Issue Time Tracking
-------------------
Worklog Id: (was: 420384)
Time Spent: 4h 40m (was: 4.5h)
> TFRecordIO inefficient read from sideinput causing pipeline to be slow
> ----------------------------------------------------------------------
>
> Key: BEAM-9346
> URL: https://issues.apache.org/jira/browse/BEAM-9346
> Project: Beam
> Issue Type: Improvement
> Components: sdk-java-core
> Reporter: Ban Piao
> Assignee: Piotr Szuberski
> Priority: Major
> Labels: dataflow, easyfix, performance
> Fix For: Not applicable
>
> Time Spent: 4h 40m
> Remaining Estimate: 0h
>
> In TFRecordIO, Reify.viewInGlobalWindow(input.apply(View.asList()),
> ListCoder.of(resultCoder)) is an inefficient way of reading large set of side
> input.
> Pipeline can be sped up significantly by combinging the PCollection<ResultT>
> to a single element PCollection<List<ResultT>>.
> Sample code:
>
> https://github.com/apache/beam/blob/master/sdks/java/core/src/main/java/org/apache/beam/sdk/io/WriteFiles.java#L412
> from
> ```
> return input
> .getPipeline()
> .apply(Reify.viewInGlobalWindow(input.apply(View.asList()),
> ListCoder.of(resultCoder)));
> ```
> to
> ```
> return input.apply("ToList", Combine.globally(new ToListCombineFn<>()));
> ```
> where ToListCombineFn is defined as
> ```
> public static class ToListCombineFn<ResultT> extends CombineFn<ResultT,
> List<ResultT>, List<ResultT>> {
> @Override
> public List<ResultT> createAccumulator() {
> return new ArrayList<>();
> }
> @Override
> public List<ResultT> addInput(List<ResultT> mutableAccumulator, ResultT
> input) {
> mutableAccumulator.add(input);
> return mutableAccumulator;
> }
> @Override
> public List<ResultT> mergeAccumulators(Iterable<List<ResultT>>
> accumulators) {
> Iterator<List<ResultT>> iter = accumulators.iterator();
> if (!iter.hasNext()) {
> return new ArrayList<>();
> }
> List<ResultT> merged = iter.next();
> while (iter.hasNext()) {
> merged.addAll(iter.next());
> }
> return merged;
> }
> @Override
> public List<ResultT> extractOutput(List<ResultT> accumulator) {
> return accumulator;
> }
> }
> ```
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