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From "Reynold Xin (JIRA)" <j...@apache.org>
Subject [jira] [Updated] (SPARK-11879) Checkpoint support for DataFrame/Dataset
Date Wed, 02 Nov 2016 21:11:59 GMT

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

Reynold Xin updated SPARK-11879:
--------------------------------
    Fix Version/s: 2.1.0

> 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
>             Fix For: 2.1.0
>
>
> 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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