+1 I think this is the most reasonable default behavior among the three.
On Mon, Oct 7, 2019 at 6:06 PM Alessandro Solimando < alessandro.solima...@gmail.com> wrote: > +1 (non-binding) > > I have been following this standardization effort and I think it is sound > and it provides the needed flexibility via the option. > > Best regards, > Alessandro > > On Mon, 7 Oct 2019 at 10:24, Gengliang Wang <gengliang.w...@databricks.com> > wrote: > >> Hi everyone, >> >> I'd like to call for a new vote on SPARK-28885 >> <https://issues.apache.org/jira/browse/SPARK-28885> "Follow ANSI store >> assignment rules in table insertion by default" after revising the ANSI >> store assignment policy(SPARK-29326 >> <https://issues.apache.org/jira/browse/SPARK-29326>). >> When inserting a value into a column with the different data type, Spark >> performs type coercion. Currently, we support 3 policies for the store >> assignment rules: ANSI, legacy and strict, which can be set via the option >> "spark.sql.storeAssignmentPolicy": >> 1. ANSI: Spark performs the store assignment as per ANSI SQL. In >> practice, the behavior is mostly the same as PostgreSQL. It disallows >> certain unreasonable type conversions such as converting `string` to `int` >> and `double` to `boolean`. It will throw a runtime exception if the value >> is out-of-range(overflow). >> 2. Legacy: Spark allows the store assignment as long as it is a valid >> `Cast`, which is very loose. E.g., converting either `string` to `int` or >> `double` to `boolean` is allowed. It is the current behavior in Spark 2.x >> for compatibility with Hive. When inserting an out-of-range value to an >> integral field, the low-order bits of the value is inserted(the same as >> Java/Scala numeric type casting). For example, if 257 is inserted into a >> field of Byte type, the result is 1. >> 3. Strict: Spark doesn't allow any possible precision loss or data >> truncation in store assignment, e.g., converting either `double` to `int` >> or `decimal` to `double` is allowed. The rules are originally for Dataset >> encoder. As far as I know, no mainstream DBMS is using this policy by >> default. >> >> Currently, the V1 data source uses "Legacy" policy by default, while V2 >> uses "Strict". This proposal is to use "ANSI" policy by default for both V1 >> and V2 in Spark 3.0. >> >> This vote is open until Friday (Oct. 11). >> >> [ ] +1: Accept the proposal >> [ ] +0 >> [ ] -1: I don't think this is a good idea because ... >> >> Thank you! >> >> Gengliang >> >
