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https://issues.apache.org/jira/browse/BEAM-9346?focusedWorklogId=420376&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-420376
]
ASF GitHub Bot logged work on BEAM-9346:
----------------------------------------
Author: ASF GitHub Bot
Created on: 10/Apr/20 18:05
Start Date: 10/Apr/20 18:05
Worklog Time Spent: 10m
Work Description: lukecwik commented on pull request #11122: [BEAM-9346]
Improve the efficiency of TFRecordIO
URL: https://github.com/apache/beam/pull/11122#discussion_r406875232
##########
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:
You could always apply the trigger if it is guaranteed to produce output by
checking what the trigger definition is. I don't think you need a flag.
I'm not sure how much of a holistic picture of the whole WriteFiles
transform was done to see if there is a better way to structure the transforms.
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Issue Time Tracking
-------------------
Worklog Id: (was: 420376)
Time Spent: 4h 20m (was: 4h 10m)
> 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 20m
> 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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