[jira] [Updated] (SPARK-26052) Spark should output a _SUCCESS file for every partition correctly written

["ASF GitHub Bot (Jira)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-26052?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

ASF GitHub Bot updated SPARK-26052:
-----------------------------------
    Labels: bulk-closed pull-request-available  (was: bulk-closed)

> Spark should output a _SUCCESS file for every partition correctly written
> -------------------------------------------------------------------------
>
>                 Key: SPARK-26052
>                 URL: https://issues.apache.org/jira/browse/SPARK-26052
>             Project: Spark
>          Issue Type: Improvement
>          Components: Block Manager, Spark Core
>    Affects Versions: 2.3.0
>            Reporter: Matt Matolcsi
>            Priority: Minor
>              Labels: bulk-closed, pull-request-available
>
> When writing a set of partitioned Parquet files to HDFS using dataframe.write.parquet(), a _SUCCESS file is written to hdfs://path/to/table after successful completion, though the actual Parquet files will end up in hdfs://path/to/table/partition_key1=val1/partition_key2=val2/.... 
> If partitions are written out one at a time (e.g., an hourly ETL), the _SUCCESS file is overwritten by each subsequent run and information on what partitions were correctly written is lost.
> I would like to be able to keep track of what partitions were successfully written in HDFS. I think this could be done by writing the _SUCCESS files to the same partition directories where the Parquet files reside, i.e., hdfs://path/to/table/partition_key1=val1/partition_key2=val2/....
> Since https://issues.apache.org/jira/browse/SPARK-13207 has been resolved, I don't think this should break partition discovery.



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