+1 Thanks for fixing this!
On Thu, Oct 10, 2019 at 6:30 AM Xiao Li <lix...@databricks.com> wrote: > +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> > -- Ryan Blue Software Engineer Netflix
