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https://issues.apache.org/jira/browse/SPARK-37185?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17437182#comment-17437182
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Hyukjin Kwon commented on SPARK-37185:
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can you show the perf diff between both codes?
> DataFrame.take() only uses one worker
> -------------------------------------
>
> Key: SPARK-37185
> URL: https://issues.apache.org/jira/browse/SPARK-37185
> Project: Spark
> Issue Type: Bug
> Components: SQL
> Affects Versions: 3.1.1, 3.2.0
> Environment: CentOS 7
> Reporter: mathieu longtin
> Priority: Major
>
> Say you have query:
> {code:java}
> >>> df = spark.sql("select * from mytable where x = 99"){code}
> Now, out of billions of row, there's only ten rows where x is 99.
> If I do:
> {code:java}
> >>> df.limit(10).collect()
> [Stage 1:> (0 + 1) / 1]{code}
> It only uses one worker. This takes a really long time since one CPU is
> reading the billions of row.
> However, if I do this:
> {code:java}
> >>> df.limit(10).rdd.collect()
> [Stage 1:> (0 + 10) / 22]{code}
> All the workers are running.
> I think there's some optimization issue DataFrame.take(...).
> This did not use to be an issue, but I'm not sure if it was working with 3.0
> or 2.4.
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