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https://issues.apache.org/jira/browse/BEAM-5775?focusedWorklogId=226507&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-226507
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ASF GitHub Bot logged work on BEAM-5775:
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                Author: ASF GitHub Bot
            Created on: 12/Apr/19 07:19
            Start Date: 12/Apr/19 07:19
    Worklog Time Spent: 10m 
      Work Description: iemejia commented on issue #6714: [BEAM-5775] Spark: 
implement a custom class to lazily encode values for persistence.
URL: https://github.com/apache/beam/pull/6714#issuecomment-482466587
 
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 226507)
    Time Spent: 8h 20m  (was: 8h 10m)

> 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
>              Labels: triaged
>          Time Spent: 8h 20m
>  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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