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Posted to issues@spark.apache.org by "Apache Spark (JIRA)" <ji...@apache.org> on 2015/04/27 20:24:40 UTC

[jira] [Assigned] (SPARK-2418) Custom checkpointing with an external function as parameter

     [ https://issues.apache.org/jira/browse/SPARK-2418?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Apache Spark reassigned SPARK-2418:
-----------------------------------

    Assignee: Apache Spark

> Custom checkpointing with an external function as parameter
> -----------------------------------------------------------
>
>                 Key: SPARK-2418
>                 URL: https://issues.apache.org/jira/browse/SPARK-2418
>             Project: Spark
>          Issue Type: Improvement
>          Components: Spark Core
>    Affects Versions: 1.0.0
>            Reporter: András Barják
>            Assignee: Apache Spark
>
> If a job consists of many shuffle heavy transformations the current resilience model might be unsatisfactory. In our current use-case we need a persistent checkpoint that we can use to save our RDDs on disk in a custom location and load it back even if the driver dies. (Possible other use cases: store the checkpointed data in various formats: SequenceFile, csv, Parquet file, MySQL etc.)
> After talking to [~pwendell] at the Spark Summit 2014 we concluded that a checkpoint where one can customize the saving and RDD reloading behavior can be a good solution. I am open to further suggestions if you have better ideas about how to make checkpointing more flexible.



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