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Posted to issues@beam.apache.org by "ASF GitHub Bot (Jira)" <ji...@apache.org> on 2019/10/19 09:09:00 UTC
[jira] [Work logged] (BEAM-5775) Make the spark runner not
serialize data unless spark is spilling to disk
[ https://issues.apache.org/jira/browse/BEAM-5775?focusedWorklogId=330906&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-330906 ]
ASF GitHub Bot logged work on BEAM-5775:
----------------------------------------
Author: ASF GitHub Bot
Created on: 19/Oct/19 09:08
Start Date: 19/Oct/19 09:08
Worklog Time Spent: 10m
Work Description: stale[bot] commented on issue #8371: [BEAM-5775] Move (most) of the batch spark pipelines' transformations to using lazy serialization.
URL: https://github.com/apache/beam/pull/8371#issuecomment-544118190
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Issue Time Tracking
-------------------
Worklog Id: (was: 330906)
Time Spent: 12h (was: 11h 50m)
> Make the spark runner not serialize data unless spark is spilling to disk
> -------------------------------------------------------------------------
>
> Key: BEAM-5775
> URL: https://issues.apache.org/jira/browse/BEAM-5775
> Project: Beam
> Issue Type: Improvement
> Components: runner-spark
> Reporter: Mike Kaplinskiy
> Assignee: Mike Kaplinskiy
> Priority: Minor
> Fix For: 2.17.0
>
> Time Spent: 12h
> Remaining Estimate: 0h
>
> Currently for storage level MEMORY_ONLY, Beam does not coder-ify the data. This lets Spark keep the data in memory avoiding the serialization round trip. Unfortunately the logic is fairly coarse - as soon as you switch to MEMORY_AND_DISK, Beam coder-ifys the data even though Spark might have chosen to keep the data in memory, incurring the serialization overhead.
>
> Ideally Beam would serialize the data lazily - as Spark chooses to spill to disk. This would be a change in behavior when using beam, but luckily Spark has a solution for folks that want data serialized in memory - MEMORY_AND_DISK_SER will keep the data serialized.
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