+1 On Thu, Oct 10, 2019 at 2:13 AM Hyukjin Kwon <gurwls...@gmail.com> wrote:
> +1 (binding) > > 2019년 10월 10일 (목) 오후 5:11, Takeshi Yamamuro <linguin....@gmail.com>님이 작성: > >> Thanks for the great work, Gengliang! >> >> +1 for that. >> As I said before, the behaviour is pretty common in DBMSs, so the change >> helps for DMBS users. >> >> Bests, >> Takeshi >> >> >> On Mon, Oct 7, 2019 at 5:24 PM 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 >>> >> >> >> -- >> --- >> Takeshi Yamamuro >> > -- [image: Databricks Summit - Watch the talks] <https://databricks.com/sparkaisummit/north-america>
