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https://issues.apache.org/jira/browse/BEAM-9434?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17053885#comment-17053885
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Emiliano Capoccia commented on BEAM-9434:
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Both.
Reading the metadata wouldn't be a problem, it also happens on every node in
the proposed PR.
But the actual reading also happens on one node with unacceptably high reading
times.
What you say applies to the case of "bulky" files. For those, the shuffling
stage chunks the files and shuffle reading of each chunk.
However, my solution particularly applies to the case where there is a high
number of tiny files (I think I explained better in the Jira ticket).
In this latter case, the latency of reading each file from S3 dominates, but no
chunking / shuffling happens with the standard Beam.
> 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 20m
> 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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