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https://issues.apache.org/jira/browse/BEAM-9434?focusedWorklogId=397365&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-397365
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ASF GitHub Bot logged work on BEAM-9434:
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Author: ASF GitHub Bot
Created on: 04/Mar/20 08:51
Start Date: 04/Mar/20 08:51
Worklog Time Spent: 10m
Work Description: andeb commented on issue #11037: [BEAM-9434]
performance improvements reading many Avro files in S3
URL: https://github.com/apache/beam/pull/11037#issuecomment-594398264
Awesome work, it looks good to me
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Issue Time Tracking
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Worklog Id: (was: 397365)
Time Spent: 0.5h (was: 20m)
> Performance improvements processiong 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: 0.5h
> Remaining Estimate: 0h
>
> There is a performance issue when processing in Spark on K8S a large number
> of small Avro files (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 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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