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https://issues.apache.org/jira/browse/SPARK-17514?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15485821#comment-15485821
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Apache Spark commented on SPARK-17514:
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User 'JoshRosen' has created a pull request for this issue:
https://github.com/apache/spark/pull/15068
> 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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