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https://issues.apache.org/jira/browse/BEAM-9346?focusedWorklogId=407048&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-407048
]
ASF GitHub Bot logged work on BEAM-9346:
----------------------------------------
Author: ASF GitHub Bot
Created on: 20/Mar/20 14:06
Start Date: 20/Mar/20 14:06
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_r395658938
##########
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. Agree with Luke. Combine globally has shuffle (GBK) inside hence
breaks above statements. This could be a regression at least for Dataflow when
there are no outputs. We should try following cases.
(1) Dataflow (and possibly other runners may have similar regressions ?)
with an empty output.
(2) Writing using a non-global window while WriteFiles.withWindowedWrites()
not set.
Luke, does that make sense ? Anything else to try out to make sure there's
no regression here ?
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Issue Time Tracking
-------------------
Worklog Id: (was: 407048)
Time Spent: 3.5h (was: 3h 20m)
> 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: 3.5h
> 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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