[jira] [Resolved] (SPARK-22605) OutputMetrics empty for DataFrame writes

["Wenchen Fan (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/SPARK-22605?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Wenchen Fan resolved SPARK-22605.
---------------------------------
       Resolution: Fixed
    Fix Version/s: 2.3.0

Issue resolved by pull request 19833
[https://github.com/apache/spark/pull/19833]

> OutputMetrics empty for DataFrame writes
> ----------------------------------------
>
>                 Key: SPARK-22605
>                 URL: https://issues.apache.org/jira/browse/SPARK-22605
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 2.2.0
>            Reporter: Jason White
>            Priority: Minor
>             Fix For: 2.3.0
>
>
> I am trying to use the SparkListener interface to hook up some custom monitoring for some of our critical jobs. Among the first metrics I would like is an output row count & size metric. I'm using PySpark and the Py4J interface to implement the listener.
> I am able to see the recordsRead and bytesRead metrics via the taskEnd.taskMetrics().inputMetrics().recordsRead() and .bytesRead() methods. taskEnd.taskMetrics().outputMetrics().recordsWritten() and .bytesWritten() are always 0. I see similar output if I use the stageCompleted event instead.
> To trigger execution, I am using df.write.parquet(path). If I use df.rdd.saveAsTextFile(path) instead, the counts and bytes are correct.
> Another clue that this bug is deeper in Spark SQL is that the Spark Application Master doesn't show the Output Size / Records column with df.write.parquet or df.write.text, but does with df.rdd.saveAsTextFile. Since the Spark Application Master also gets its output via the Listener interface, this would seem related.
> There is a related PR: https://issues.apache.org/jira/browse/SPARK-21882, but I believe this to be a distinct issue.



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