[jira] [Work logged] (BEAM-9434) Performance improvements processiong a large number of Avro files in S3+Spark
[
https://issues.apache.org/jira/browse/BEAM-9434?focusedWorklogId=397365&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-397365
]
ASF GitHub Bot logged work on BEAM-9434:
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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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 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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[jira] [Work logged] (BEAM-9434) Performance improvements processiong a large number of Avro files in S3+Spark
[
https://issues.apache.org/jira/browse/BEAM-9434?focusedWorklogId=397161&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-397161
]
ASF GitHub Bot logged work on BEAM-9434:
Author: ASF GitHub Bot
Created on: 04/Mar/20 01:15
Start Date: 04/Mar/20 01:15
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-594254609
@iemejia @andeb please review and let me have your comments. I have more
evidence of the tests that I've been carried out, and I'm happy with the
performance gains. However, I'm keen to understand if the approach is sound. I
look forward to hearing from you
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Issue Time Tracking
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Worklog Id: (was: 397161)
Time Spent: 20m (was: 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: 20m
> 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 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.
>
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
[jira] [Work logged] (BEAM-9434) Performance improvements processiong a large number of Avro files in S3+Spark
[ https://issues.apache.org/jira/browse/BEAM-9434?focusedWorklogId=397160&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-397160 ] 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. Thank you for your contribution! Follow this checklist to help us incorporate your contribution quickly and easily: - [ ] [**Choose reviewer(s)**](https://beam.apache.org/contribute/#make-your-change) and mention them in a comment (`R: @username`). - [ ] Format the pull request title like `[BEAM-XXX] Fixes bug in ApproximateQuantiles`, where you replace `BEAM-XXX` with the appropriate JIRA issue, if applicable. This will automatically link the pull request to the issue. - [ ] Update `CHANGES.md` with noteworthy changes. - [ ] If this contribution is large, please file an Apache [Individual Contributor License Agreement](https://www.apache.org/licenses/icla.pdf). See the [Contributor Guide](https://beam.apache.org/contribute) for more tips on [how to make review process smoother](https://beam.apache.org/contribute/#make-reviewers-job-easier). Post-Commit Tests Status (on master branch) Lang | SDK | Apex | Dataflow | Flink | Gearpump | Samza | Spark --- | --- | --- | --- | --- | --- | --- | --- Go | [](https://builds.apache.org/job/beam_PostCommit_Go/lastCompletedBuild/) | --- | --- | [](https://builds.apache.org/job/beam_PostCommit_Go_VR_Flink/lastCompletedBuild/) | --- | --- | [](https://builds.apache.org/job/beam_PostCommit_Go_VR_Spark/lastCompletedBuild/) Java | [](https://builds.apache.org/job/beam_PostCommit_Java/lastCompletedBuild/) | [](https://builds.apache.org/job/beam_PostCommit_Java_ValidatesRunner_Apex/lastCompletedBuild/) | [](https://builds.apache.org/job/beam_PostCommit_Java_ValidatesRunner_Dataflow/lastCompletedBuild/)[](https://builds.apache.org/job/beam_PostCommit_Java_ValidatesRunner_Dataflow_Java11/lastCompletedBuild/) | [](https://builds.apache.org/job/beam_PostCommit_Java_ValidatesRunner_Flink/lastCompletedBuild/)[](https://builds.apache.org/job/beam_PostCommit_Java_PVR_Flink_Batch/lastCompletedBuild/)[](https://builds.apache.org/job/beam_PostCommit_Java_PVR_Flink_Streaming/lastCompletedBuild/) | [](https://builds.apache.org/job/beam_PostCommit_Java_ValidatesRunner_Gearpump/lastCompletedBuild/) | [](https://builds.apache.org/job/beam_PostComm
