[jira] [Commented] (BEAM-2669) Kryo serialization exception when DStreams containing non-Kryo-serializable data are cached

["ASF GitHub Bot (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/BEAM-2669?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16138063#comment-16138063 ] 

ASF GitHub Bot commented on BEAM-2669:
--------------------------------------

GitHub user kobisalant opened a pull request:

    https://github.com/apache/beam/pull/3748

    BEAM-2669 Kryo serialization exception when DStreams containing non-Kryo-serializable data are cached

    remove kryo Serializer hard coded settings and add Serializable implementation to window/state/timer classes
    
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You can merge this pull request into a Git repository by running:

    $ git pull https://github.com/kobisalant/incubator-beam BEAM-2669-Kryo-serialization-exception-when-dstream-cached

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

    https://github.com/apache/beam/pull/3748.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 #3748
    
----
commit f3cbbdf9c43caab3b9c497a49b6aa69f564d1462
Author: ksalant <ko...@gmail.com>
Date:   2017-08-23T08:23:11Z

    remove kryo Serializer hard coded settings and add Serializable implementation to window/state/timer classes

----


> Kryo serialization exception when DStreams containing non-Kryo-serializable data are cached
> -------------------------------------------------------------------------------------------
>
>                 Key: BEAM-2669
>                 URL: https://issues.apache.org/jira/browse/BEAM-2669
>             Project: Beam
>          Issue Type: Bug
>          Components: runner-spark
>    Affects Versions: 0.4.0, 0.5.0, 0.6.0, 2.0.0
>            Reporter: Aviem Zur
>            Assignee: Kobi Salant
>
> Today, when we detect re-use of a dataset in a pipeline in Spark runner we eagerly cache it to avoid calculating the same data multiple times.
> ([EvaluationContext.java|https://github.com/apache/beam/blob/v2.0.0/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/EvaluationContext.java#L148])
> When the dataset is bounded, which in Spark is represented by an {{RDD}}, we call {{RDD#persist}} and use storage level provided by the user via {{SparkPipelineOptions}}. ([BoundedDataset.java|https://github.com/apache/beam/blob/v2.0.0/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/BoundedDataset.java#L103-L103])
> When the dataset is unbounded, which in Spark is represented by a {{DStream}} we call {{DStream.cache()}} which defaults to persist the {{DStream}} using storage level {{MEMORY_ONLY_SER}} ([UnboundedDataset.java|https://github.com/apache/beam/blob/v2.0.0/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/streaming/UnboundedDataset.java#L61])
>  ([DStream.scala|https://github.com/apache/spark/blob/v1.6.3/streaming/src/main/scala/org/apache/spark/streaming/dstream/DStream.scala#L169])
> Storage level {{MEMORY_ONLY_SER}} means Spark will serialize the data using its configured serializer. Since we configure this to be Kryo in a hard coded fashion, this means the data will be serialized using Kryo. ([SparkContextFactory.java|https://github.com/apache/beam/blob/v2.0.0/runners/spark/src/main/java/org/apache/beam/runners/spark/translation/SparkContextFactory.java#L99-L99])
> Due to this, if your {{DStream}} contains non-Kryo-serializable data you will encounter Kryo serialization exceptions and your task will fail.
> Possible actions we should consider:
> # Remove the hard coded Spark serializer configuration, this should be taken from the user's configuration of Spark, no real reason for us to interfere with this.
> # Use the user's configured storage level configuration from {{SparkPipelineOptions}} when caching unbounded datasets ({{DStream}}s), same as we do for bounded datasets.
> # Make caching of re-used datasets configurable in {{SparkPipelineOptions}} (enable/disable). Although overloading our configuration with more options is always something not to be taken lightly.



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