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Posted to issues@spark.apache.org by "Cristian Opris (JIRA)" <ji...@apache.org> on 2016/04/13 17:35:25 UTC
[jira] [Commented] (SPARK-11879) Checkpoint support for
DataFrame/Dataset
[ https://issues.apache.org/jira/browse/SPARK-11879?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15239438#comment-15239438 ]
Cristian Opris commented on SPARK-11879:
----------------------------------------
Any chance this will be supported given the new SQL on Streaming functionality in Spark 2.0 ?
> 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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