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Posted to issues@beam.apache.org by "ASF GitHub Bot (JIRA)" <ji...@apache.org> on 2019/01/18 00:24:00 UTC

[jira] [Work logged] (BEAM-6243) TFX pipelines experience a huge blowup in intermediate data size

     [ https://issues.apache.org/jira/browse/BEAM-6243?focusedWorklogId=186635&page=com.atlassian.jira.plugin.system.issuetabpanels:worklog-tabpanel#worklog-186635 ]

ASF GitHub Bot logged work on BEAM-6243:
----------------------------------------

                Author: ASF GitHub Bot
            Created on: 18/Jan/19 00:23
            Start Date: 18/Jan/19 00:23
    Worklog Time Spent: 10m 
      Work Description: angoenka commented on issue #7297:  [BEAM-6243] Add an experiment to use Python's optimizer on Flink.
URL: https://github.com/apache/beam/pull/7297#issuecomment-455381754
 
 
   @robertwb I was trying to figure out which case is covered in Python optimizer as compared to the Java one. Can you please help me understand?
   This will make reviewing the PR easier.
 
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Issue Time Tracking
-------------------

    Worklog Id:     (was: 186635)
    Time Spent: 0.5h  (was: 20m)

> TFX pipelines experience a huge blowup in intermediate data size
> ----------------------------------------------------------------
>
>                 Key: BEAM-6243
>                 URL: https://issues.apache.org/jira/browse/BEAM-6243
>             Project: Beam
>          Issue Type: Bug
>          Components: runner-flink
>            Reporter: Robert Bradshaw
>            Priority: Major
>          Time Spent: 0.5h
>  Remaining Estimate: 0h
>
> The elements in TFX intermediate collections are dictionaries of (typically single-element) numpy arrays, which are (relatively) expensive to serialize (e.g. using pickle for the numpy wrapper of a primitive int/float, repeating the column names in every element).
> Though it'd be good to use a better intermediate representation, this is exacerbated because the fusion algorithm does not pack as much possible into executable stages. 



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