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https://issues.apache.org/jira/browse/BEAM-9434?focusedWorklogId=410790&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-410790
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ASF GitHub Bot logged work on BEAM-9434:
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Author: ASF GitHub Bot
Created on: 27/Mar/20 02:40
Start Date: 27/Mar/20 02:40
Worklog Time Spent: 10m
Work Description: lukecwik commented on issue #11037: [BEAM-9434]
performance improvements reading many Avro files in S3
URL: https://github.com/apache/beam/pull/11037#issuecomment-604785112
Sorry about the long delay but **Reshuffle** should produce as many
partitions as the runner thinks is optimal. It is effectively a
**redistribute** operation.
It looks like the spark translation is copying the number of partitions from
the upstream transform for the reshuffle translation and in your case this is
likely 1.
Translation:
https://github.com/apache/beam/blob/f5a4a5afcd9425c0ddb9ec9c70067a5d5c0bc769/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/TransformTranslator.java#L681
Copying partitions:
https://github.com/apache/beam/blob/f5a4a5afcd9425c0ddb9ec9c70067a5d5c0bc769/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/GroupCombineFunctions.java#L191
@iemejia Shouldn't we be using a much larger value for partitions, e.g. the
number of nodes?
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Issue Time Tracking
-------------------
Worklog Id: (was: 410790)
Time Spent: 2h 50m (was: 2h 40m)
> Performance improvements processing a large number of Avro files in S3+Spark
> ----------------------------------------------------------------------------
>
> Key: BEAM-9434
> URL: https://issues.apache.org/jira/browse/BEAM-9434
> Project: Beam
> Issue Type: Improvement
> Components: io-java-aws, sdk-java-core
> Affects Versions: 2.19.0
> Reporter: Emiliano Capoccia
> Assignee: Emiliano Capoccia
> Priority: Minor
> Time Spent: 2h 50m
> Remaining Estimate: 0h
>
> There is a performance issue when processing a large number of small Avro
> files in Spark on K8S (tens of thousands or more).
> The recommended way of reading a pattern of Avro files in Beam is by means of:
>
> {code:java}
> PCollection<AvroGenClass> records = p.apply(AvroIO.read(AvroGenClass.class)
> .from("s3://my-bucket/path-to/*.avro").withHintMatchesManyFiles())
> {code}
> However, in the case of many small files, the above results in the entire
> reading taking place in a single task/node, which is considerably slow and
> has scalability issues.
> The option of omitting the hint is not viable, as it results in too many
> tasks being spawn, and the cluster being busy doing coordination of tiny
> tasks with high overhead.
> There are a few workarounds on the internet which mainly revolve around
> compacting the input files before processing, so that a reduced number of
> bulky files is processed in parallel.
>
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