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https://issues.apache.org/jira/browse/BEAM-5775?focusedWorklogId=334507&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-334507
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ASF GitHub Bot logged work on BEAM-5775:
----------------------------------------
Author: ASF GitHub Bot
Created on: 26/Oct/19 09:38
Start Date: 26/Oct/19 09:38
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-546587395
This pull request has been closed due to lack of activity. If you think that
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Issue Time Tracking
-------------------
Worklog Id: (was: 334507)
Time Spent: 12h 10m (was: 12h)
> 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
> Time Spent: 12h 10m
> 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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