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https://issues.apache.org/jira/browse/SPARK-28730?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Gengliang Wang updated SPARK-28730:
-----------------------------------
Parent: SPARK-28589
Issue Type: Sub-task (was: Improvement)
> Configurable type coercion policy for table insertion
> -----------------------------------------------------
>
> Key: SPARK-28730
> URL: https://issues.apache.org/jira/browse/SPARK-28730
> Project: Spark
> Issue Type: Sub-task
> Components: SQL
> Affects Versions: 3.0.0
> Reporter: Gengliang Wang
> Priority: Major
>
> After all the discussions in the dev list:
> http://apache-spark-developers-list.1001551.n3.nabble.com/Discuss-Follow-ANSI-SQL-on-table-insertion-td27531.html#a27562.
>
> Here I propose that we can make the store assignment rules in the analyzer
> configurable, and the behavior of V1 and V2 should be consistent.
> When inserting a value into a column with a different data type, Spark will
> perform type coercion. After this PR, we support 2 policies for the type
> coercion rules:
> legacy and strict.
> 1. With legacy policy, Spark allows casting any value to any data type and
> null result is returned when the conversion is invalid. The legacy policy is
> the only behavior in Spark 2.x and it is compatible with Hive.
> 2. With strict policy, Spark doesn't allow any possible precision loss or
> data truncation in type coercion, e.g. `int` and `long`, `float` -> `double`
> are not allowed.
> To ensure backward compatibility with existing queries, the default store
> assignment policy is "legacy".
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