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https://issues.apache.org/jira/browse/SPARK-30681?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Maciej Szymkiewicz updated SPARK-30681:
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Description:
As of 3.0.0 higher order functions are available in SQL and Scala, but not in
PySpark, forcing Python users to invoke these through {{expr}}, {{selectExpr}}
or {{sql}}.
This is error prone and not well documented. Spark should provide
{{pyspark.sql}} wrappers that accept plain Python functions (of course within
limits of {{(*Column) -> Column}}) as arguments.
{code:python}
df.select(transform("values", lambda c: trim(upper(c)))
def increment_values(k: Column, v: Column) -> Column:
return v + 1
df.select(transform_values("data"), increment_values)
{code}
was:
As of 3.0.0 higher order functions are available in SQL and Scala, but not in
PySpark, forcing Python users to invoke these through {{expr}}, {{selectExpr}}
or {{sql}}.
This is error prone and not well documented. Spark should provide
{{pyspark.sql}} wrappers that accept plain Python functions (of course within
limits of {{(*Column) -> Column}}) as arguments.
> Add higher order functions API to PySpark
> -----------------------------------------
>
> Key: SPARK-30681
> URL: https://issues.apache.org/jira/browse/SPARK-30681
> Project: Spark
> Issue Type: Improvement
> Components: PySpark, SQL
> Affects Versions: 3.0.0
> Reporter: Maciej Szymkiewicz
> Priority: Major
>
> As of 3.0.0 higher order functions are available in SQL and Scala, but not in
> PySpark, forcing Python users to invoke these through {{expr}},
> {{selectExpr}} or {{sql}}.
> This is error prone and not well documented. Spark should provide
> {{pyspark.sql}} wrappers that accept plain Python functions (of course within
> limits of {{(*Column) -> Column}}) as arguments.
> {code:python}
> df.select(transform("values", lambda c: trim(upper(c)))
> def increment_values(k: Column, v: Column) -> Column:
> return v + 1
> df.select(transform_values("data"), increment_values)
> {code}
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