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Posted to commits@beam.apache.org by "ASF GitHub Bot (JIRA)" <ji...@apache.org> on 2017/01/22 12:32:26 UTC

[jira] [Commented] (BEAM-648) Persist and restore Aggergator values in case of recovery from failure

    [ https://issues.apache.org/jira/browse/BEAM-648?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15833494#comment-15833494 ] 

ASF GitHub Bot commented on BEAM-648:
-------------------------------------

GitHub user aviemzur opened a pull request:

    https://github.com/apache/beam/pull/1815

    [BEAM-648] Persist and restore Aggergator values in case of recovery from failure

    Be sure to do all of the following to help us incorporate your contribution
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     - [ ] Make sure the PR title is formatted like:
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    ---


You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/aviemzur/incubator-beam fault-tolerant-accumulators

Alternatively you can review and apply these changes as the patch at:

    https://github.com/apache/beam/pull/1815.patch

To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:

    This closes #1815
    
----
commit 06c63fb88bbbebab210a1659fa71fdbe1a20b7fd
Author: Aviem Zur <av...@gmail.com>
Date:   2017-01-22T12:30:44Z

    [BEAM-648] Persist and restore Aggergator values in case of recovery from failure

----


> Persist and restore Aggergator values in case of recovery from failure
> ----------------------------------------------------------------------
>
>                 Key: BEAM-648
>                 URL: https://issues.apache.org/jira/browse/BEAM-648
>             Project: Beam
>          Issue Type: Bug
>          Components: runner-spark
>            Reporter: Amit Sela
>            Assignee: Aviem Zur
>
> While Aggregators are fault-tolerant (pending https://github.com/apache/incubator-beam/pull/909), they will recover into their initial value. 
> The SparkRunner should persist the values to durable storage and recover from the persisted state.
> One way to go would be to persist via Spark's listeners, for example "onBatchCompleted".
> For more details see SPARK-5206.



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