gengliangwang opened a new pull request #25239: [SPARK-28495][SQL] AssignableCast: A new type coercion following store assignment rules of ANSI SQL URL: https://github.com/apache/spark/pull/25239 ## What changes were proposed in this pull request? In Spark version 2.4 and earlier, when inserting into a table, Spark will cast the data type of input query to the data type of target table by coercion. This can be super confusing, e.g. users make a mistake and write string values to an int column. In data source V2, by default, only upcasting is allowed when inserting data into a table. E.g. int -> long and int -> string are allowed, while decimal -> double or long -> int are not allowed. The rules of UpCast was originally created for Dataset type coercion. They are quite strict and different from the behavior of all existing popular DBMS. This is breaking change. It is possible that it would hurt some Spark users after 3.0 releases. This PR proposes that we can follow the rules of store assignment(section 9.2) in ANSI SQL. Two significant differences from Up-Cast: 1. Any numeric type can be assigned to another numeric type. 2. TimestampType can be assigned DateType The new behavior is consistent with PostgreSQL. It is more explainable and acceptable than using UpCast . ## How was this patch tested? Unit test
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