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https://issues.apache.org/jira/browse/BEAM-9434?focusedWorklogId=397160&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-397160
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ASF GitHub Bot logged work on BEAM-9434:
----------------------------------------
Author: ASF GitHub Bot
Created on: 04/Mar/20 01:13
Start Date: 04/Mar/20 01:13
Worklog Time Spent: 10m
Work Description: ecapoccia commented on pull request #11037: [BEAM-9434]
performance improvements reading many Avro files in S3
URL: https://github.com/apache/beam/pull/11037
The implemented solution consists in an extension of the FileSystem classes
for S3, that allows filtering of the matched objects. The filter consists in a
mod hash function of the filename.
This mechanism is exploited by a parallel partitioned read of the files in
the bucket, realised in the AvroIO and FileIO classes by means of the new hint
. withHintPartitionInto(int)
The net effect is that a controlled number of tasks is spawn to different
executors, each having a different partition number; each executor reads the
entire list of files in the bucket, but matches only those files whose modhash
matches the partition number.
In this way, all the executors in the cluster read in parallel.
------------------------
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Issue Time Tracking
-------------------
Worklog Id: (was: 397160)
Remaining Estimate: 0h
Time Spent: 10m
> 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: 10m
> 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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