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https://issues.apache.org/jira/browse/SPARK-41233?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17703455#comment-17703455
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Apache Spark commented on SPARK-41233:
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User 'ueshin' has created a pull request for this issue:
https://github.com/apache/spark/pull/40514
> High-order function: array_prepend
> ----------------------------------
>
> Key: SPARK-41233
> URL: https://issues.apache.org/jira/browse/SPARK-41233
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark, SQL
> Affects Versions: 3.4.0
> Reporter: Ruifeng Zheng
> Priority: Major
> Fix For: 3.5.0
>
>
> refer to
> https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/api/snowflake.snowpark.functions.array_prepend.html
> 1, about the data type validation:
> In Snowflake’s array_append, array_prepend and array_insert functions, the
> element data type does not need to match the data type of the existing
> elements in the array.
> While in Spark, we want to leverage the same data type validation as
> array_remove.
> 2, about the NULL handling
> Currently, SparkSQL, SnowSQL and PostgreSQL deal with NULL values in
> different ways.
> Existing functions array_contains, array_position and array_remove in
> SparkSQL handle NULL in this way, if the input array or/and element is NULL,
> returns NULL. However, this behavior should be broken.
> We should implement the NULL handling in array_prepend in this way:
> 2.1, if the array is NULL, returns NULL;
> 2.2 if the array is not NULL, the element is NULL, append the NULL value into
> the array
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