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 existing queries are broken after 3.0 releases.
Following ANSI SQL standard makes Spark consistent with the table insertion behaviors of popular DBMS like PostgreSQL/Oracle/Mysql.
For more details, see the discussion on http://apache-spark-developers-list.1001551.n3.nabble.com/Discuss-Follow-ANSI-SQL-on-table-insertion-td27531.html#a27562 and https://github.com/apache/spark/pull/25453 .
This task is to add ANSI store assignment policy as a new option for the configuration "spark.sql.storeAssignmentPolicy“