+1To be honest I don't like the legacy policy. It's too loose and easy for users to make mistakes, especially when Spark returns null if a function hit errors like overflow.The strict policy is not good either. It's too strict and stops valid use cases like writing timestamp values to a date type column. Users do expect truncation to happen without adding cast manually in this case. It's also weird to use a spark specific policy that no other database is using.The ANSI policy is better. It stops invalid use cases like writing string values to an int type column, while keeping valid use cases like timestamp -> date.I think it's no doubt that we should use ANSI policy instead of legacy policy for v1 tables. Except for backward compatibility, ANSI policy is literally better than the legacy policy.The v2 table is arguable here. Although the ANSI policy is better than strict policy to me, this is just the store assignment policy, which only partially controls the table insertion behavior. With Spark's "return null on error" behavior, the table insertion is more likely to insert invalid null values with the ANSI policy compared to the strict policy.I think we should use ANSI policy by default for both v1 and v2 tables, because1. End-users don't care how the table is implemented. Spark should provide consistent table insertion behavior between v1 and v2 tables.2. Data Source V2 is unstable in Spark 2.x so there is no backward compatibility issue. That said, the baseline to judge which policy is better should be the table insertion behavior in Spark 2.x, which is the legacy policy + "return null on error". ANSI policy is better than the baseline.3. We expect more and more uses to migrate their data sources to the V2 API. The strict policy can be a stopper as it's a too big breaking change, which may break many existing queries.Thanks,WenchenOn Wed, Sep 4, 2019 at 1:59 PM Gengliang Wang <gengliang.
wang@ databricks. com> wrote:Hi everyone,
I'd like to call for a vote on SPARK-28885 "Follow ANSI store assignment rules in table insertion by default".
When inserting a value into a column with the different data type, Spark performs type coercion. Currently, we support 3 policies for the type coercion rules: ANSI, legacy and strict, which can be set via the option "spark.sql.storeAssignmentPolicy":
1. ANSI: Spark performs the type coercion 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`.
2. Legacy: Spark allows the type coercion 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.
3. Strict: Spark doesn't allow any possible precision loss or data truncation in type coercion, 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 maintainstream 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.
There was also a DISCUSS thread "Follow ANSI SQL on table insertion" in the dev mailing list.
This vote is open until next Thurs (Sept. 12nd).
[ ] +1: Accept the proposal
[ ] +0
[ ] -1: I don't think this is a good idea because ...Thank you!Gengliang