I don't agree with handling literal values specially. Although Postgres does it, I can't find anything about it in the SQL standard. And it introduces inconsistent behaviors which may be strange to users: * What about something like "INSERT INTO t SELECT float_col + 1.1"? * The same insert with a decimal column as input will fail even when a decimal literal would succeed * Similar insert queries with "literal" inputs can be constructed through layers of indirection via views, inline views, CTEs, unions, etc. Would those decimals be treated as columns and fail or would we attempt to make them succeed as well? Would users find this behavior surprising?
Silently corrupt data is bad, but this is the decision we made at the beginning when design Spark behaviors. Whenever an error occurs, Spark attempts to return null instead of runtime exception. Recently we provide configs to make Spark fail at runtime for overflow, but that's another story. Silently corrupt data is bad, runtime exception is bad, and forbidding all the table insertions that may fail(even with very little possibility) is also bad. We have to make trade-offs. The trade-offs we made in this proposal are: * forbid table insertions that are very like to fail, at compile time. (things like writing string values to int column) * allow table insertions that are not that likely to fail. If the data is wrong, don't fail, insert null. * provide a config to fail the insertion at runtime if the data is wrong. > But the new behavior is only applied in DataSourceV2, so it won’t affect existing jobs until sources move to v2 and break other behavior anyway. When users write SQL queries, they don't care if a table is backed by Data Source V1 or V2. We should make sure the table insertion behavior is consistent and reasonable. Furthermore, users may even not care if the SQL queries are run in Spark or other RDBMS, it's better to follow SQL standard instead of introducing a Spark-specific behavior. We are not talking about a small use case like allowing writing decimal literal to float column, we are talking about a big goal to make Spark compliant to SQL standard, w.r.t. https://issues.apache.org/jira/browse/SPARK-26217 . This proposal is a sub-task of it, to make the table insertion behavior follow SQL standard. On Sat, Jul 27, 2019 at 1:35 AM Ryan Blue <rb...@netflix.com> wrote: > I don’t think this is a good idea. Following the ANSI standard is usually > fine, but here it would *silently corrupt data*. > > From your proposal doc, ANSI allows implicitly casting from long to int > (any numeric type to any other numeric type) and inserts NULL when a value > overflows. That would drop data values and is not safe. > > Fixing the silent corruption by adding a runtime exception is not a good > option, either. That puts off the problem until much of the job has > completed, instead of catching the error at analysis time. It is better to > catch this earlier during analysis than to run most of a job and then fail. > > In addition, part of the justification for using the ANSI standard is to > avoid breaking existing jobs. But the new behavior is only applied in > DataSourceV2, so it won’t affect existing jobs until sources move to v2 and > break other behavior anyway. > > I think that the correct solution is to go with the existing validation > rules that require explicit casts to truncate values. > > That still leaves the use case that motivated this proposal, which is that > floating point literals are parsed as decimals and fail simple insert > statements. We already came up with two alternatives to fix that problem in > the DSv2 sync and I think it is a better idea to go with one of those > instead of “fixing” Spark in a way that will corrupt data or cause runtime > failures. > > On Thu, Jul 25, 2019 at 9:11 AM Wenchen Fan <cloud0...@gmail.com> wrote: > >> I have heard about many complaints about the old table insertion >> behavior. Blindly casting everything will leak the user mistake to a late >> stage of the data pipeline, and make it very hard to debug. When a user >> writes string values to an int column, it's probably a mistake and the >> columns are misordered in the INSERT statement. We should fail the query >> earlier and ask users to fix the mistake. >> >> In the meanwhile, I agree that the new table insertion behavior we >> introduced for Data Source V2 is too strict. It may fail valid queries >> unexpectedly. >> >> In general, I support the direction of following the ANSI SQL standard. >> But I'd like to do it with 2 steps: >> 1. only add cast when the assignment rule is satisfied. This should be >> the default behavior and we should provide a legacy config to restore to >> the old behavior. >> 2. fail the cast operation at runtime if overflow happens. AFAIK Marco >> Gaido is working on it already. This will have a config as well and by >> default we still return null. >> >> After doing this, the default behavior will be slightly different from >> the SQL standard (cast can return null), and users can turn on the ANSI >> mode to fully follow the SQL standard. This is much better than before and >> should prevent a lot of user mistakes. It's also a reasonable choice to me >> to not throw exceptions at runtime by default, as it's usually bad for >> long-running jobs. >> >> Thanks, >> Wenchen >> >> On Thu, Jul 25, 2019 at 11:37 PM Gengliang Wang < >> gengliang.w...@databricks.com> wrote: >> >>> Hi everyone, >>> >>> I would like to discuss the table insertion behavior of Spark. In the >>> current data source V2, only UpCast is allowed for table insertion. I think >>> following ANSI SQL is a better idea. >>> For more information, please read the Discuss: Follow ANSI SQL on table >>> insertion >>> <https://docs.google.com/document/d/1b9nnWWbKVDRp7lpzhQS1buv1_lDzWIZY2ApFs5rBcGI/edit?usp=sharing> >>> Please let me know if you have any thoughts on this. >>> >>> Regards, >>> Gengliang >>> >> > > -- > Ryan Blue > Software Engineer > Netflix >
