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Posted to issues@beam.apache.org by "ASF GitHub Bot (Jira)" <ji...@apache.org> on 2021/12/14 16:32:00 UTC
[jira] [Work logged] (BEAM-13459) Dataflow python runner should cache uploaded artifacts across job runs
[ https://issues.apache.org/jira/browse/BEAM-13459?focusedWorklogId=695926&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-695926 ]
ASF GitHub Bot logged work on BEAM-13459:
-----------------------------------------
Author: ASF GitHub Bot
Created on: 14/Dec/21 16:31
Start Date: 14/Dec/21 16:31
Worklog Time Spent: 10m
Work Description: steveniemitz opened a new pull request #16229:
URL: https://github.com/apache/beam/pull/16229
This adds support for caching artifacts across job runs to the python dataflow runner. The method it uses is slightly different than the JVM uses to work around some limitations to the python container boot code. Specifically:
- Certain artifacts must have a specific name (dataflow_python_sdk.tar, pipeline.pb, etc)
- All artifacts for a job must be in the same "directory" on GCS
To work around this, we stage artifacts in a two step process now (gated by a pipeline flag, default value TBD?):
- First upload the artifact to a common cache location (`artifact_cache`) directly under the staging directory. In this location the artifact is named using its sha256 hash. If the file already exists, this step is skipped (which is the slowest step)
- Once uploaded (either because it was a cache hit, or just uploaded), perform a GCS copy operation to "copy" the file from its cached location into where it should be in the job's unique staging directory. This operation is fast since its a GCS->GCS copy in the same bucket.
Ideally we wouldn't need these workarounds, but this works well for now. If the boot code is ever enhanced to fix the above two issues, it'd be easy to change this to use a new caching method.
Just choosing some reviewers based on who's last changed code here:
R: @ihji @tvalentyn
------------------------
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`ValidatesRunner` compliance status (on master branch)
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<th>Lang</th>
<th>ULR</th>
<th>Dataflow</th>
<th>Flink</th>
<th>Samza</th>
<th>Spark</th>
<th>Twister2</th>
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Examples testing status on various runners
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<th>Lang</th>
<th>ULR</th>
<th>Dataflow</th>
<th>Flink</th>
<th>Samza</th>
<th>Spark</th>
<th>Twister2</th>
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<td>Go</td>
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<td>---</td>
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<td>Java</td>
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<a href="https://ci-beam.apache.org/job/beam_PreCommit_Java_Examples_Dataflow_Cron/lastCompletedBuild/">
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</table>
Post-Commit SDK/Transform Integration Tests Status (on master branch)
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<th>Java</th>
<th>Python</th>
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<a href="https://ci-beam.apache.org/job/beam_PostCommit_Go/lastCompletedBuild/">
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<th>Website</th>
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</table>
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Issue Time Tracking
-------------------
Worklog Id: (was: 695926)
Remaining Estimate: 0h
Time Spent: 10m
> Dataflow python runner should cache uploaded artifacts across job runs
> ----------------------------------------------------------------------
>
> Key: BEAM-13459
> URL: https://issues.apache.org/jira/browse/BEAM-13459
> Project: Beam
> Issue Type: Improvement
> Components: runner-dataflow
> Reporter: Steve Niemitz
> Assignee: Steve Niemitz
> Priority: P1
> Time Spent: 10m
> Remaining Estimate: 0h
>
> Similar to how the jvm dataflow runner caches artifacts uploaded to GCS across job runs (based on their sha256), the python runner should do the same.
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