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Posted to issues@spark.apache.org by "Cheng Lian (JIRA)" <ji...@apache.org> on 2016/11/02 21:11:58 UTC

[jira] [Resolved] (SPARK-11879) Checkpoint support for DataFrame/Dataset

     [ https://issues.apache.org/jira/browse/SPARK-11879?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Cheng Lian resolved SPARK-11879.
--------------------------------
    Resolution: Duplicate

> Checkpoint support for DataFrame/Dataset
> ----------------------------------------
>
>                 Key: SPARK-11879
>                 URL: https://issues.apache.org/jira/browse/SPARK-11879
>             Project: Spark
>          Issue Type: Improvement
>          Components: SQL
>            Reporter: Cristian Opris
>
> Explicit support for checkpointing DataFrames is need to be able to truncate lineages, prune the query plan (particularly the logical plan) and transparent failure recovery.
> While for recovery saving to a Parquet file may be sufficient, actually using that as a checkpoint (and truncating the lineage), requires reading the files back.
> This is required to be able to use DataFrames in iterative scenarios like Streaming and ML, as well as for avoiding expensive re-computations in case of executor failure when executing a complex chain of queries on very large datasets. 



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