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https://issues.apache.org/jira/browse/SPARK-60990?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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ASF GitHub Bot updated SPARK-60990:
-----------------------------------
Labels: pull-request-available (was: )
> Sort nulls first in Window.orderBy of a column in Spark Connect Rust as
> PySpark does
> ------------------------------------------------------------------------------------
>
> Key: SPARK-60990
> URL: https://issues.apache.org/jira/browse/SPARK-60990
> Project: Spark
> Issue Type: Bug
> Components: Connect
> Affects Versions: connect-rust-4.3.0
> Reporter: Hyukjin Kwon
> Priority: Major
> Labels: pull-request-available
> Fix For: connect-rust-4.3.0
>
>
> In PySpark, {{Window.orderBy}} and {{WindowSpec.orderBy}} order by a column
> name or a Column that is not a sort order ascending with nulls first, as
> {{SortOrder(expr)}} defaults to and as Spark sorts ascending. The Rust-backed
> Python client orders such a column ascending with nulls last, so window
> functions over a column with nulls give other results. For example, over the
> values NULL, 1 and 2 of {{o}}:
> {code:python}
> df.select("o", F.row_number().over(Window.partitionBy("g").orderBy("o")))
> {code}
> gives the rows (NULL, 1), (1, 2), (2, 3) in PySpark 4.3.0, and (1, 1), (2,
> 2), (NULL, 3) in the client. {{rank}}, {{lag}}, running aggregates and the
> other functions over such a window differ the same way. Sort nulls first as
> PySpark does.
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