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From "Apache Spark (JIRA)" <j...@apache.org>
Subject [jira] [Assigned] (SPARK-17514) df.take(1) and df.limit(1).collect() perform differently in Python
Date Tue, 13 Sep 2016 00:56:20 GMT

     [ https://issues.apache.org/jira/browse/SPARK-17514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Apache Spark reassigned SPARK-17514:
------------------------------------

    Assignee: Josh Rosen  (was: Apache Spark)

> df.take(1) and df.limit(1).collect() perform differently in Python
> ------------------------------------------------------------------
>
>                 Key: SPARK-17514
>                 URL: https://issues.apache.org/jira/browse/SPARK-17514
>             Project: Spark
>          Issue Type: Bug
>          Components: PySpark, SQL
>            Reporter: Josh Rosen
>            Assignee: Josh Rosen
>
> In PySpark, {{df.take(1)}} ends up running a single-stage job which computes only one
partition of {{df}}, while {{df.limit(1).collect()}} ends up computing all partitions of {{df}}
and runs a two-stage job. This difference in performance is confusing, so I think that we
should generalize the fix from SPARK-10731 so that {{Dataset.collect()}} can be implemented
efficiently in Python.



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