OK to push back: "disagreeing with the premise that we can afford to not be maximal on standard 3. The correctness of the data is non-negotiable, and whatever solution we settle on cannot silently adjust the user’s data under any circumstances."
This blanket statement sounds great on surface, but there are a lot of subtleties. "Correctness" is absolutely important, but engineering/prod development are often about tradeoffs, and the industry has consistently traded correctness for performance or convenience, e.g. overflow checks, null pointers, consistency in databases ... It all depends on the use cases and to what degree use cases can tolerate. For example, while I want my data engineering production pipeline to throw any error when the data doesn't match my expectations (e.g. type widening, overflow), if I'm doing some quick and dirty data science, I don't want the job to just fail due to outliers. On Wed, Jul 31, 2019 at 10:13 AM, Matt Cheah < mch...@palantir.com > wrote: > > > > Sorry I meant the current behavior for V2, which fails the query > compilation if the cast is not safe. > > > > > > > > Agreed that a separate discussion about overflow might be warranted. I’m > surprised we don’t throw an error now, but it might be warranted to do so. > > > > > > > > > -Matt Cheah > > > > > > > > *From:* Reynold Xin < r...@databricks.com > > *Date:* Wednesday, July 31, 2019 at 9:58 AM > *To:* Matt Cheah < mch...@palantir.com > > *Cc:* Russell Spitzer < russell.spit...@gmail.com >, Takeshi Yamamuro < > linguin....@gmail.com > >, Gengliang Wang < gengliang.w...@databricks.com >, Ryan Blue < > >rb...@netflix.com > >, Spark dev list < dev@spark.apache.org >, Hyukjin Kwon < gurwls...@gmail.com > >, Wenchen Fan < cloud0...@gmail.com > > *Subject:* Re: [Discuss] Follow ANSI SQL on table insertion > > > > > > > > > > > > > > > Matt what do you mean by maximizing 3, while allowing not throwing errors > when any operations overflow? Those two seem contradicting. > > > > > > > > > > > > > > On Wed, Jul 31, 2019 at 9:55 AM, Matt Cheah < mch...@palantir.com > wrote: > > > >> >> >> I’m -1, simply from disagreeing with the premise that we can afford to not >> be maximal on standard 3. The correctness of the data is non-negotiable, >> and whatever solution we settle on cannot silently adjust the user’s data >> under any circumstances. >> >> >> >> >> >> >> >> I think the existing behavior is fine, or perhaps the behavior can be >> flagged by the destination writer at write time. >> >> >> >> >> >> >> >> -Matt Cheah >> >> >> >> >> >> >> >> *From:* Hyukjin Kwon < gurwls...@gmail.com > >> *Date:* Monday, July 29, 2019 at 11:33 PM >> *To:* Wenchen Fan < cloud0...@gmail.com > >> *Cc:* Russell Spitzer < russell.spit...@gmail.com >, Takeshi Yamamuro < >> linguin....@gmail.com >> >, Gengliang Wang < gengliang.w...@databricks.com >, Ryan Blue < >> >rb...@netflix.com >> >, Spark dev list < dev@spark.apache.org > >> *Subject:* Re: [Discuss] Follow ANSI SQL on table insertion >> >> >> >> >> >> >> >> >> >> From my look, +1 on the proposal, considering ASCI and other DBMSes in >> general. >> >> >> >> >> >> >> >> >> 2019 년 7 월 30 일 ( 화 ) 오후 3:21, Wenchen Fan < cloud0...@gmail.com > 님이 작성 : >> >> >> >> >>> >>> >>> We can add a config for a certain behavior if it makes sense, but the most >>> important thing we want to reach an agreement here is: what should be the >>> default behavior? >>> >>> >>> >>> >>> >>> >>> >>> >>> Let's explore the solution space of table insertion behavior first: >>> >>> >>> >>> >>> At compile time, >>> >>> >>> >>> >>> 1. always add cast >>> >>> >>> >>> >>> 2. add cast following the ASNI SQL store assignment rule (e.g. string to >>> int is forbidden but long to int is allowed) >>> >>> >>> >>> >>> 3. only add cast if it's 100% safe >>> >>> >>> >>> >>> At runtime, >>> >>> >>> >>> >>> 1. return null for invalid operations >>> >>> >>> >>> >>> 2. throw exceptions at runtime for invalid operations >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> The standards to evaluate a solution: >>> >>> >>> >>> >>> 1. How robust the query execution is. For example, users usually don't >>> want to see the query fails midway. >>> >>> >>> >>> >>> 2. how tolerant to user queries. For example, a user would like to write >>> long values to an int column as he knows all the long values won't exceed >>> int range. >>> >>> >>> >>> >>> 3. How clean the result is. For example, users usually don't want to see >>> silently corrupted data (null values). >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> The current Spark behavior for Data Source V1 tables: always add cast and >>> return null for invalid operations. This maximizes standard 1 and 2, but >>> the result is least clean and users are very likely to see silently >>> corrupted data (null values). >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> The current Spark behavior for Data Source V2 tables (new in Spark 3.0): >>> only add cast if it's 100% safe. This maximizes standard 1 and 3, but many >>> queries may fail to compile, even if these queries can run on other SQL >>> systems. Note that, people can still see silently corrupted data because >>> cast is not the only one that can return corrupted data. Simple operations >>> like ADD can also return corrected data if overflow happens. e.g. INSERT >>> INTO t1 (intCol) SELECT anotherIntCol + 100 FROM t2 >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> The proposal here: add cast following ANSI SQL store assignment rule, and >>> return null for invalid operations. This maximizes standard 1, and also >>> fits standard 2 well: if a query can't compile in Spark, it usually can't >>> compile in other mainstream databases as well. I think that's tolerant >>> enough. For standard 3, this proposal doesn't maximize it but can avoid >>> many invalid operations already. >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> Technically we can't make the result 100% clean at compile-time, we have >>> to handle things like overflow at runtime. I think the new proposal makes >>> more sense as the default behavior. >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> >>> On Mon, Jul 29, 2019 at 8:31 PM Russell Spitzer < russell.spit...@gmail.com >>> > wrote: >>> >>> >>> >>>> >>>> >>>> I understand spark is making the decisions, i'm say the actual final >>>> effect of the null decision would be different depending on the insertion >>>> target if the target has different behaviors for null. >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> >>>> On Mon, Jul 29, 2019 at 5:26 AM Wenchen Fan < cloud0...@gmail.com > wrote: >>>> >>>> >>>> >>>> >>>>> >>>>> >>>>> > I'm a big -1 on null values for invalid casts. >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> This is why we want to introduce the ANSI mode, so that invalid cast fails >>>>> at runtime. But we have to keep the null behavior for a while, to keep >>>>> backward compatibility. Spark returns null for invalid cast since the >>>>> first day of Spark SQL, we can't just change it without a way to restore >>>>> to the old behavior. >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> I'm OK with adding a strict mode for the upcast behavior in table >>>>> insertion, but I don't agree with making it the default. The default >>>>> behavior should be either the ANSI SQL behavior or the legacy Spark >>>>> behavior. >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> > other modes should be allowed only with strict warning the behavior will >>>>> be determined by the underlying sink. >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> Seems there is some misunderstanding. The table insertion behavior is >>>>> fully controlled by Spark. Spark decides when to add cast and Spark >>>>> decided whether invalid cast should return null or fail. The sink is only >>>>> responsible for writing data, not the type coercion/cast stuff. >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> On Sun, Jul 28, 2019 at 12:24 AM Russell Spitzer < >>>>> russell.spit...@gmail.com >>>>> > wrote: >>>>> >>>>> >>>>> >>>>>> >>>>>> >>>>>> I'm a big -1 on null values for invalid casts. This can lead to a lot of >>>>>> even more unexpected errors and runtime behavior since null is >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> 1. Not allowed in all schemas (Leading to a runtime error anyway) >>>>>> 2. Is the same as delete in some systems (leading to data loss) >>>>>> >>>>>> And this would be dependent on the sink being used. Spark won't just be >>>>>> interacting with ANSI compliant sinks so I think it makes much more sense >>>>>> to be strict. I think Upcast mode is a sensible default and other modes >>>>>> should be allowed only with strict warning the behavior will be >>>>>> determined >>>>>> by the underlying sink. >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> >>>>>> On Sat, Jul 27, 2019 at 8:05 AM Takeshi Yamamuro < linguin....@gmail.com >>>>>> > >>>>>> wrote: >>>>>> >>>>>> >>>>>> >>>>>>> >>>>>>> >>>>>>> Hi, all >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> +1 for implementing this new store cast mode. >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> From a viewpoint of DBMS users, this cast is pretty common for INSERTs >>>>>>> and >>>>>>> I think this functionality could >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> promote migrations from existing DBMSs to Spark. >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> The most important thing for DBMS users is that they could optionally >>>>>>> choose this mode when inserting data. >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> Therefore, I think it might be okay that the two modes (the current >>>>>>> upcast >>>>>>> mode and the proposed store cast mode) >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> co-exist for INSERTs. (There is a room to discuss which mode is enabled >>>>>>> by default though...) >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> IMHO we'll provide three behaviours below for INSERTs; >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> - upcast mode >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> - ANSI store cast mode and runtime exceptions thrown for invalid values >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> - ANSI store cast mode and null filled for invalid values >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> On Sat, Jul 27, 2019 at 8:03 PM Gengliang Wang < >>>>>>> gengliang.w...@databricks.com >>>>>>> > wrote: >>>>>>> >>>>>>> >>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> Hi Ryan, >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> Thanks for the suggestions on the proposal and doc. >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> Currently, there is no data type validation in table insertion of V1. >>>>>>>> We >>>>>>>> are on the same page that we should improve it. But using UpCast is >>>>>>>> from >>>>>>>> one extreme to another. It is possible that many queries are broken >>>>>>>> after >>>>>>>> upgrading to Spark 3.0. >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> The rules of UpCast are too strict. E.g. it doesn't allow assigning >>>>>>>> Timestamp type to Date Type, as there will be "precision loss". To me, >>>>>>>> the >>>>>>>> type coercion is reasonable and the "precision loss" is under >>>>>>>> expectation. >>>>>>>> This is very common in other SQL engines. >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> As long as Spark is following the ANSI SQL store assignment rules, it >>>>>>>> is >>>>>>>> users' responsibility to take good care of the type coercion in data >>>>>>>> writing. I think it's the right decision. >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> > 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. >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> Eventually, most sources are supposed to be migrated to DataSourceV2 >>>>>>>> V2. I >>>>>>>> think we can discuss and make a decision now. >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> > Fixing the silent corruption by adding a runtime exception is not a >>>>>>>> > good >>>>>>>> option, either. >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> The new optional mode proposed in >>>>>>>> https://issues.apache.org/jira/browse/SPARK-28512 >>>>>>>> [issues.apache.org] ( >>>>>>>> https://urldefense.proofpoint.com/v2/url?u=https-3A__issues.apache.org_jira_browse_SPARK-2D28512&d=DwMFaQ&c=izlc9mHr637UR4lpLEZLFFS3Vn2UXBrZ4tFb6oOnmz8&r=hzwIMNQ9E99EMYGuqHI0kXhVbvX3nU3OSDadUnJxjAs&m=PcMDvxQsrOa4XX4-xjnVwG6ikOifZ3wN1HMMZNx7hfU&s=kluCSIsFxJd_F1ljwRLu_dVRBvOYekEogDH4KWhfkkA&e= >>>>>>>> ) is disabled by default. This should be fine. >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> On Sat, Jul 27, 2019 at 10:23 AM Wenchen Fan < cloud0...@gmail.com > >>>>>>>> wrote: >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> 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 >>>>>>>>> [issues.apache.org] ( >>>>>>>>> https://urldefense.proofpoint.com/v2/url?u=https-3A__issues.apache.org_jira_browse_SPARK-2D26217&d=DwMFaQ&c=izlc9mHr637UR4lpLEZLFFS3Vn2UXBrZ4tFb6oOnmz8&r=hzwIMNQ9E99EMYGuqHI0kXhVbvX3nU3OSDadUnJxjAs&m=PcMDvxQsrOa4XX4-xjnVwG6ikOifZ3wN1HMMZNx7hfU&s=Y9ReXszbCYw7dLvhzbVJD7wYppANZpZtUkoSNJ-MJC4&e= >>>>>>>>> ) . 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 [docs.google.com] ( >>>>>>>>>>>> https://urldefense.proofpoint.com/v2/url?u=https-3A__docs.google.com_document_d_1b9nnWWbKVDRp7lpzhQS1buv1-5FlDzWIZY2ApFs5rBcGI_edit-3Fusp-3Dsharing&d=DwMFaQ&c=izlc9mHr637UR4lpLEZLFFS3Vn2UXBrZ4tFb6oOnmz8&r=hzwIMNQ9E99EMYGuqHI0kXhVbvX3nU3OSDadUnJxjAs&m=PcMDvxQsrOa4XX4-xjnVwG6ikOifZ3wN1HMMZNx7hfU&s=bLJxfpdKatSv5gaYIKVWx29RmEBKz-DTtRwuYd7lYks&e= >>>>>>>>>>>> ) >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> Please let me know if you have any thoughts on this. >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> Regards, >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> Gengliang >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> -- >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> Ryan Blue >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> Software Engineer >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> Netflix >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> >>>>>>> -- >>>>>>> >>>>>>> >>>>>>> >>>>>>> --- >>>>>>> Takeshi Yamamuro >>>>>>> >>>>>>> >>>>>>> >>>>>> >>>>>> >>>>> >>>>> >>>> >>>> >>> >>> >> >> > >
