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https://issues.apache.org/jira/browse/BEAM-9434?focusedWorklogId=399637&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-399637
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ASF GitHub Bot logged work on BEAM-9434:
----------------------------------------
Author: ASF GitHub Bot
Created on: 07/Mar/20 10:36
Start Date: 07/Mar/20 10:36
Worklog Time Spent: 10m
Work Description: ecapoccia commented on issue #11037: [BEAM-9434]
performance improvements reading many Avro files in S3
URL: https://github.com/apache/beam/pull/11037#issuecomment-596073078
This is the fundamental difference between the base and the current PR.
Notice, in the base case there's only 2 tasks (the entire job is a join of two
independent readings) whereas when using 10 partitions there are 20 tasks for
doing the same work (the image is a detail of one of the two independent
readings, showing its 10 parallel partitions).
This is reflected in the executors being used and in the time to complete
(16 minutes with 2 tasks, 2.3 minutes with 20).
See below the comparison of execution data.
<img width="1461" alt="Screenshot 2020-03-07 at 10 33 25"
src="https://user-images.githubusercontent.com/8372724/76141746-24931500-605f-11ea-8c98-7b99587ae2a4.png";>
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Issue Time Tracking
-------------------
Worklog Id: (was: 399637)
Time Spent: 1h 50m (was: 1h 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: 1h 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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