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https://issues.apache.org/jira/browse/SPARK-61028?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Hyukjin Kwon reassigned SPARK-61028:
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Assignee: Hyukjin Kwon
> Name the column of GroupedData.count, aggregate every numeric column by
> default, and take a dict in DataFrame.agg, in the Python client as PySpark
> does
> -------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: SPARK-61028
> URL: https://issues.apache.org/jira/browse/SPARK-61028
> Project: Spark
> Issue Type: Bug
> Components: Connect
> Affects Versions: connect-rust-4.3.0
> Reporter: Hyukjin Kwon
> Assignee: Hyukjin Kwon
> Priority: Major
> Labels: pull-request-available
> Fix For: connect-rust-4.3.0
>
>
> In PySpark, {{df.groupBy(...).count()}} names its column {{count}}, as it
> aggregates {{count(lit(1)).alias("count")}}. {{sum}}, {{avg}}, {{mean}},
> {{max}} and {{min}} of a GroupedData aggregate every numeric column of the
> DataFrame when no column is given, and raise {{NOT_NUMERIC_COLUMNS}} for a
> column that is not numeric. {{GroupedData.agg}} asserts that it is given
> expressions, which are Columns unless they are one dict. {{df.agg}} raises
> {{CANNOT_BE_EMPTY}} without expressions, and aggregates a dict as
> {{df.groupBy().agg}} does.
> The Rust-backed Python client names the column of {{count()}} {{count(1)}},
> so {{df.groupBy("k").count().orderBy("count")}} fails. {{sum()}}, {{avg()}},
> {{mean()}}, {{max()}} and {{min()}} without columns aggregate no column,
> {{df.groupBy("k").sum("k")}} fails on the server, and {{df.agg({"x":
> "max"})}} raises a TypeError. Do as PySpark does.
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