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https://issues.apache.org/jira/browse/SPARK-41234?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Ruifeng Zheng resolved SPARK-41234.
-----------------------------------
Fix Version/s: 3.4.0
Resolution: Fixed
Issue resolved by pull request 38867
[https://github.com/apache/spark/pull/38867]
> High-order function: array_insert
> ---------------------------------
>
> Key: SPARK-41234
> URL: https://issues.apache.org/jira/browse/SPARK-41234
> Project: Spark
> Issue Type: Sub-task
> Components: PySpark, SQL
> Affects Versions: 3.4.0
> Reporter: Ruifeng Zheng
> Assignee: Daniel Davies
> Priority: Major
> Fix For: 3.4.0
>
>
> refer to
> https://docs.snowflake.com/en/developer-guide/snowpark/reference/python/api/snowflake.snowpark.functions.array_insert.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_insert 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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