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https://issues.apache.org/jira/browse/SPARK-11879?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Reynold Xin updated SPARK-11879:
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Summary: Checkpoint support for DataFrame/Dataset (was: Checkpoint support
for DataFrame)
> 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
>
> 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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