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https://issues.apache.org/jira/browse/SPARK-26589?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17459455#comment-17459455
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Nicholas Chammas commented on SPARK-26589:
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It looks like making a distributed, memory-efficient implementation of median
is not possible using the design of Catalyst as it stands today. For more
details, please see [this thread on the dev
list|http://mail-archives.apache.org/mod_mbox/spark-dev/202112.mbox/%3cCAOhmDzev8d4H20XT1hUP9us=cpjeysgcf+xev7lg7dka1gj...@mail.gmail.com%3e].
It's possible to get an exact median today by using {{{}percentile(col,
0.5){}}}, which is [available via the SQL
API|https://spark.apache.org/docs/3.2.0/sql-ref-functions-builtin.html#aggregate-functions].
It's not memory-efficient, so it may not work well on large datasets.
The Python and Scala DataFrame APIs do not offer this exact percentile
function, so I've filed SPARK-37647 to track exposing this function in those
APIs.
> proper `median` method for spark dataframe
> ------------------------------------------
>
> Key: SPARK-26589
> URL: https://issues.apache.org/jira/browse/SPARK-26589
> Project: Spark
> Issue Type: New Feature
> Components: SQL
> Affects Versions: 3.1.0
> Reporter: Jan Gorecki
> Priority: Minor
>
> I found multiple tickets asking for median function to be implemented in
> Spark. Most of those tickets links to "SPARK-6761 Approximate quantile" as
> duplicate of it. The thing is that approximate quantile is a workaround for
> lack of median function. Thus I am filling this Feature Request for proper,
> exact, not approximation of, median function. I am aware about difficulties
> that are caused by distributed environment when trying to compute median,
> nevertheless I don't think those difficulties is reason good enough to drop
> out `median` function from scope of Spark. I am not asking about efficient
> median but exact median.
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