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https://issues.apache.org/jira/browse/BEAM-9434?focusedWorklogId=417264&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-417264
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ASF GitHub Bot logged work on BEAM-9434:
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Author: ASF GitHub Bot
Created on: 06/Apr/20 22:56
Start Date: 06/Apr/20 22:56
Worklog Time Spent: 10m
Work Description: iemejia commented on issue #11037: [BEAM-9434]
performance improvements reading many Avro files in S3
URL: https://github.com/apache/beam/pull/11037#issuecomment-610078731
Run Java Spark PortableValidatesRunner Batch
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Issue Time Tracking
-------------------
Worklog Id: (was: 417264)
Time Spent: 6.5h (was: 6h 20m)
> Improve Spark runner reshuffle translation to maximize parallelism
> ------------------------------------------------------------------
>
> Key: BEAM-9434
> URL: https://issues.apache.org/jira/browse/BEAM-9434
> Project: Beam
> Issue Type: Improvement
> Components: runner-spark
> Affects Versions: 2.19.0
> Reporter: Emiliano Capoccia
> Assignee: Emiliano Capoccia
> Priority: Minor
> Fix For: 2.21.0
>
> Time Spent: 6.5h
> 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.
> It seems the Spark runner is using the parallelism of the input distributed
> collection (RDD) to calculate the number of partitions in Reshuffle. In the
> case of FileIO/AvroIO if the input pattern is a regex the size of the input
> is 1 which would be far from an optimal parallelism value. We may fix this by
> improving the translation of reshuffle to maximize parallelism.
>
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