[Discuss] Follow ANSI SQL on table insertion

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Re: [Discuss] Follow ANSI SQL on table insertion

Ryan Blue
> you guys seem to be arguing no those users don't know what they are doing and they should not exist.

I'm not arguing that they don't exist. Just that the disproportionate impact of awareness about this behavior is much worse for people that don't know about it. And there are a lot of those people as well.

On Wed, Jul 31, 2019 at 4:48 PM Ryan Blue <[hidden email]> wrote:
> "between a runtime error and an analysis-time error" → I think one of those should be the default.

If you're saying that the default should be an error of some kind, then I think we agree. I'm also fine with having a mode that allows turning off the error and silently replacing values with NULL... as long as it isn't the default and I can set the default for my platform to an analysis-time error.

On Wed, Jul 31, 2019 at 4:42 PM Russell Spitzer <[hidden email]> wrote:
I definitely view it as silently corrupting. If i'm copying over a dataset where some elements are null and some have values, how do I differentiate between my expected nulls and those that were added in silently in the cast? 

On Wed, Jul 31, 2019 at 6:15 PM Reynold Xin <[hidden email]> wrote:
"between a runtime error and an analysis-time error" → I think one of those should be the default.

Maybe we are talking past each other or I wasn't explaining clearly, but I don't think you understand what I said and the use cases out there. I as an end user could very well be fully aware of the consequences of exceptional values but I can choose to ignore them. This is especially common for data scientists who are doing some quick and dirty analysis or exploration. You can't deny this large class of use cases out there (probably makes up half of Spark use cases actually).

Also writing out the exceptional cases as null are not silently corrupting them. The engine is sending an explicit signal that the value is no longer valid given the constraint.

Not sure if this is the best analogy, but think about checked exceptions in Java. It's great for writing low level code in which error handling is paramount, e.g. storage systems, network layers. But in most high level applications people just write boilerplate catches that are no-ops, because they have other priorities and they can tolerate mishandling of exceptions, although often maybe they shouldn't.



On Wed, Jul 31, 2019 at 2:55 PM, Ryan Blue <[hidden email]> wrote:
Another important aspect of this problem is whether a user is conscious of the cast that is inserted by Spark. Most of the time, users are not aware of casts that are implicitly inserted, and that means replacing values with NULL would be a very surprising behavior. The impact of this choice affects users disproportionately: someone that knows about inserted casts is mildly annoyed when required to add an explicit cast, but a user that doesn't know an inserted cast is dropping values is very negatively impacted and may not discover the problem until it is too late.

That disproportionate impact is what makes me think that it is not okay for Spark to silently replace values with NULL, even if that's what ANSI would allow. Other databases also have the ability to reject null values in tables, providing extra insurance against the problem, but Spark doesn't have required columns in its DDL.

So while I agree with Reynold that there is a trade-off, I think that trade-off makes the choice between a runtime error and an analysis-time error. I'm okay with either a runtime error as the default or an analysis error as the default, as long as there is a setting that allows me to choose one for my deployment.


On Wed, Jul 31, 2019 at 10:39 AM Reynold Xin <[hidden email]> wrote:
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 <[hidden email]> 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 <[hidden email]>
Date: Wednesday, July 31, 2019 at 9:58 AM
To: Matt Cheah <[hidden email]>
Cc: Russell Spitzer <[hidden email]>, Takeshi Yamamuro <[hidden email]>, Gengliang Wang <[hidden email]>, Ryan Blue <[hidden email]>, Spark dev list <[hidden email]>, Hyukjin Kwon <[hidden email]>, Wenchen Fan <[hidden email]>
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 <[hidden email]> 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 <[hidden email]>
Date: Monday, July 29, 2019 at 11:33 PM
To: Wenchen Fan <[hidden email]>
Cc: Russell Spitzer <[hidden email]>, Takeshi Yamamuro <[hidden email]>, Gengliang Wang <[hidden email]>, Ryan Blue <[hidden email]>, Spark dev list <[hidden email]>
Subject: Re: [Discuss] Follow ANSI SQL on table insertion

 

From my look, +1 on the proposal, considering ASCI and other DBMSes in general.

 

2019 7 30 () 오후 3:21, Wenchen Fan <[hidden email]>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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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] is disabled by default. This should be fine.

 

 

 

On Sat, Jul 27, 2019 at 10:23 AM Wenchen Fan <[hidden email]> 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] . 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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.

Please let me know if you have any thoughts on this.

 

Regards,

Gengliang


 

--

Ryan Blue

Software Engineer

Netflix


 

--

---
Takeshi Yamamuro




--
Ryan Blue
Software Engineer
Netflix



--
Ryan Blue
Software Engineer
Netflix


--
Ryan Blue
Software Engineer
Netflix
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Re: [Discuss] Follow ANSI SQL on table insertion

RussS
I would argue "null" doesn't have to mean invalid. It could mean missing or deleted record. There is a lot of difference between missing record and invalid record.

I definitely have no problem with two modes, but I think setting a parameter to enable lossy conversions is a fine tradeoff to avoid data loss for others. The impact then for those who don't care about lossy casting is an analysis level message "Types don't match, to enable lossy casting set some parameter" while the impact in the other direction is possibly invisible until it hits something critical downstream.

On Wed, Jul 31, 2019 at 6:50 PM Ryan Blue <[hidden email]> wrote:
> you guys seem to be arguing no those users don't know what they are doing and they should not exist.

I'm not arguing that they don't exist. Just that the disproportionate impact of awareness about this behavior is much worse for people that don't know about it. And there are a lot of those people as well.

On Wed, Jul 31, 2019 at 4:48 PM Ryan Blue <[hidden email]> wrote:
> "between a runtime error and an analysis-time error" → I think one of those should be the default.

If you're saying that the default should be an error of some kind, then I think we agree. I'm also fine with having a mode that allows turning off the error and silently replacing values with NULL... as long as it isn't the default and I can set the default for my platform to an analysis-time error.

On Wed, Jul 31, 2019 at 4:42 PM Russell Spitzer <[hidden email]> wrote:
I definitely view it as silently corrupting. If i'm copying over a dataset where some elements are null and some have values, how do I differentiate between my expected nulls and those that were added in silently in the cast? 

On Wed, Jul 31, 2019 at 6:15 PM Reynold Xin <[hidden email]> wrote:
"between a runtime error and an analysis-time error" → I think one of those should be the default.

Maybe we are talking past each other or I wasn't explaining clearly, but I don't think you understand what I said and the use cases out there. I as an end user could very well be fully aware of the consequences of exceptional values but I can choose to ignore them. This is especially common for data scientists who are doing some quick and dirty analysis or exploration. You can't deny this large class of use cases out there (probably makes up half of Spark use cases actually).

Also writing out the exceptional cases as null are not silently corrupting them. The engine is sending an explicit signal that the value is no longer valid given the constraint.

Not sure if this is the best analogy, but think about checked exceptions in Java. It's great for writing low level code in which error handling is paramount, e.g. storage systems, network layers. But in most high level applications people just write boilerplate catches that are no-ops, because they have other priorities and they can tolerate mishandling of exceptions, although often maybe they shouldn't.



On Wed, Jul 31, 2019 at 2:55 PM, Ryan Blue <[hidden email]> wrote:
Another important aspect of this problem is whether a user is conscious of the cast that is inserted by Spark. Most of the time, users are not aware of casts that are implicitly inserted, and that means replacing values with NULL would be a very surprising behavior. The impact of this choice affects users disproportionately: someone that knows about inserted casts is mildly annoyed when required to add an explicit cast, but a user that doesn't know an inserted cast is dropping values is very negatively impacted and may not discover the problem until it is too late.

That disproportionate impact is what makes me think that it is not okay for Spark to silently replace values with NULL, even if that's what ANSI would allow. Other databases also have the ability to reject null values in tables, providing extra insurance against the problem, but Spark doesn't have required columns in its DDL.

So while I agree with Reynold that there is a trade-off, I think that trade-off makes the choice between a runtime error and an analysis-time error. I'm okay with either a runtime error as the default or an analysis error as the default, as long as there is a setting that allows me to choose one for my deployment.


On Wed, Jul 31, 2019 at 10:39 AM Reynold Xin <[hidden email]> wrote:
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 <[hidden email]> 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 <[hidden email]>
Date: Wednesday, July 31, 2019 at 9:58 AM
To: Matt Cheah <[hidden email]>
Cc: Russell Spitzer <[hidden email]>, Takeshi Yamamuro <[hidden email]>, Gengliang Wang <[hidden email]>, Ryan Blue <[hidden email]>, Spark dev list <[hidden email]>, Hyukjin Kwon <[hidden email]>, Wenchen Fan <[hidden email]>
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 <[hidden email]> 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 <[hidden email]>
Date: Monday, July 29, 2019 at 11:33 PM
To: Wenchen Fan <[hidden email]>
Cc: Russell Spitzer <[hidden email]>, Takeshi Yamamuro <[hidden email]>, Gengliang Wang <[hidden email]>, Ryan Blue <[hidden email]>, Spark dev list <[hidden email]>
Subject: Re: [Discuss] Follow ANSI SQL on table insertion

 

From my look, +1 on the proposal, considering ASCI and other DBMSes in general.

 

2019 7 30 () 오후 3:21, Wenchen Fan <[hidden email]>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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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] is disabled by default. This should be fine.

 

 

 

On Sat, Jul 27, 2019 at 10:23 AM Wenchen Fan <[hidden email]> 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] . 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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.

Please let me know if you have any thoughts on this.

 

Regards,

Gengliang


 

--

Ryan Blue

Software Engineer

Netflix


 

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---
Takeshi Yamamuro




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Netflix


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Re: [Discuss] Follow ANSI SQL on table insertion

RussS
Another solution along those lines that I know we implemented for limited precision types is just to do a loud warning whenever you do such a cast. IE: Warning we are casting X to Y this may result in data loss.

On Wed, Jul 31, 2019 at 7:08 PM Russell Spitzer <[hidden email]> wrote:
I would argue "null" doesn't have to mean invalid. It could mean missing or deleted record. There is a lot of difference between missing record and invalid record.

I definitely have no problem with two modes, but I think setting a parameter to enable lossy conversions is a fine tradeoff to avoid data loss for others. The impact then for those who don't care about lossy casting is an analysis level message "Types don't match, to enable lossy casting set some parameter" while the impact in the other direction is possibly invisible until it hits something critical downstream.

On Wed, Jul 31, 2019 at 6:50 PM Ryan Blue <[hidden email]> wrote:
> you guys seem to be arguing no those users don't know what they are doing and they should not exist.

I'm not arguing that they don't exist. Just that the disproportionate impact of awareness about this behavior is much worse for people that don't know about it. And there are a lot of those people as well.

On Wed, Jul 31, 2019 at 4:48 PM Ryan Blue <[hidden email]> wrote:
> "between a runtime error and an analysis-time error" → I think one of those should be the default.

If you're saying that the default should be an error of some kind, then I think we agree. I'm also fine with having a mode that allows turning off the error and silently replacing values with NULL... as long as it isn't the default and I can set the default for my platform to an analysis-time error.

On Wed, Jul 31, 2019 at 4:42 PM Russell Spitzer <[hidden email]> wrote:
I definitely view it as silently corrupting. If i'm copying over a dataset where some elements are null and some have values, how do I differentiate between my expected nulls and those that were added in silently in the cast? 

On Wed, Jul 31, 2019 at 6:15 PM Reynold Xin <[hidden email]> wrote:
"between a runtime error and an analysis-time error" → I think one of those should be the default.

Maybe we are talking past each other or I wasn't explaining clearly, but I don't think you understand what I said and the use cases out there. I as an end user could very well be fully aware of the consequences of exceptional values but I can choose to ignore them. This is especially common for data scientists who are doing some quick and dirty analysis or exploration. You can't deny this large class of use cases out there (probably makes up half of Spark use cases actually).

Also writing out the exceptional cases as null are not silently corrupting them. The engine is sending an explicit signal that the value is no longer valid given the constraint.

Not sure if this is the best analogy, but think about checked exceptions in Java. It's great for writing low level code in which error handling is paramount, e.g. storage systems, network layers. But in most high level applications people just write boilerplate catches that are no-ops, because they have other priorities and they can tolerate mishandling of exceptions, although often maybe they shouldn't.



On Wed, Jul 31, 2019 at 2:55 PM, Ryan Blue <[hidden email]> wrote:
Another important aspect of this problem is whether a user is conscious of the cast that is inserted by Spark. Most of the time, users are not aware of casts that are implicitly inserted, and that means replacing values with NULL would be a very surprising behavior. The impact of this choice affects users disproportionately: someone that knows about inserted casts is mildly annoyed when required to add an explicit cast, but a user that doesn't know an inserted cast is dropping values is very negatively impacted and may not discover the problem until it is too late.

That disproportionate impact is what makes me think that it is not okay for Spark to silently replace values with NULL, even if that's what ANSI would allow. Other databases also have the ability to reject null values in tables, providing extra insurance against the problem, but Spark doesn't have required columns in its DDL.

So while I agree with Reynold that there is a trade-off, I think that trade-off makes the choice between a runtime error and an analysis-time error. I'm okay with either a runtime error as the default or an analysis error as the default, as long as there is a setting that allows me to choose one for my deployment.


On Wed, Jul 31, 2019 at 10:39 AM Reynold Xin <[hidden email]> wrote:
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 <[hidden email]> 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 <[hidden email]>
Date: Wednesday, July 31, 2019 at 9:58 AM
To: Matt Cheah <[hidden email]>
Cc: Russell Spitzer <[hidden email]>, Takeshi Yamamuro <[hidden email]>, Gengliang Wang <[hidden email]>, Ryan Blue <[hidden email]>, Spark dev list <[hidden email]>, Hyukjin Kwon <[hidden email]>, Wenchen Fan <[hidden email]>
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 <[hidden email]> 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 <[hidden email]>
Date: Monday, July 29, 2019 at 11:33 PM
To: Wenchen Fan <[hidden email]>
Cc: Russell Spitzer <[hidden email]>, Takeshi Yamamuro <[hidden email]>, Gengliang Wang <[hidden email]>, Ryan Blue <[hidden email]>, Spark dev list <[hidden email]>
Subject: Re: [Discuss] Follow ANSI SQL on table insertion

 

From my look, +1 on the proposal, considering ASCI and other DBMSes in general.

 

2019 7 30 () 오후 3:21, Wenchen Fan <[hidden email]>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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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] is disabled by default. This should be fine.

 

 

 

On Sat, Jul 27, 2019 at 10:23 AM Wenchen Fan <[hidden email]> 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] . 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 <[hidden email]> 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 <[hidden email]> 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 <[hidden email]> 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.

Please let me know if you have any thoughts on this.

 

Regards,

Gengliang


 

--

Ryan Blue

Software Engineer

Netflix


 

--

---
Takeshi Yamamuro




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Ryan Blue
Software Engineer
Netflix



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Software Engineer
Netflix


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Re: [Discuss] Follow ANSI SQL on table insertion

Hyukjin Kwon
I am sorry I am asking a question without reading whole discussion after I replied.

But why does Spark specifically needs to to it differently while ANCI standard, other DBMSes and other systems do?
If there isn't a specific issue to Spark, that basically says they are all wrong.


2019년 8월 1일 (목) 오전 9:31, Russell Spitzer <[hidden email]>님이 작성:
Another solution along those lines that I know we implemented for limited precision types is just to do a loud warning whenever you do such a cast. IE: Warning we are casting X to Y this may result in data loss.

On Wed, Jul 31, 2019 at 7:08 PM Russell Spitzer <[hidden email]> wrote:
I would argue "null" doesn't have to mean invalid. It could mean missing or deleted record. There is a lot of difference between missing record and invalid record.

I definitely have no problem with two modes, but I think setting a parameter to enable lossy conversions is a fine tradeoff to avoid data loss for others. The impact then for those who don't care about lossy casting is an analysis level message "Types don't match, to enable lossy casting set some parameter" while the impact in the other direction is possibly invisible until it hits something critical downstream.

On Wed, Jul 31, 2019 at 6:50 PM Ryan Blue <[hidden email]> wrote:
> you guys seem to be arguing no those users don't know what they are doing and they should not exist.

I'm not arguing that they don't exist. Just that the disproportionate impact of awareness about this behavior is much worse for people that don't know about it. And there are a lot of those people as well.

On Wed, Jul 31, 2019 at 4:48 PM Ryan Blue <[hidden email]> wrote:
> "between a runtime error and an analysis-time error" → I think one of those should be the default.

If you're saying that the default should be an error of some kind, then I think we agree. I'm also fine with having a mode that allows turning off the error and silently replacing values with NULL... as long as it isn't the default and I can set the default for my platform to an analysis-time error.

On Wed, Jul 31, 2019 at 4:42 PM Russell Spitzer <[hidden email]> wrote:
I definitely view it as silently corrupting. If i'm copying over a dataset where some elements are null and some have values, how do I differentiate between my expected nulls and those that were added in silently in the cast? 

On Wed, Jul 31, 2019 at 6:15 PM Reynold Xin <[hidden email]> wrote:
"between a runtime error and an analysis-time error" → I think one of those should be the default.

Maybe we are talking past each other or I wasn't explaining clearly, but I don't think you understand what I said and the use cases out there. I as an end user could very well be fully aware of the consequences of exceptional values but I can choose to ignore them. This is especially common for data scientists who are doing some quick and dirty analysis or exploration. You can't deny this large class of use cases out there (probably makes up half of Spark use cases actually).

Also writing out the exceptional cases as null are not silently corrupting them. The engine is sending an explicit signal that the value is no longer valid given the constraint.

Not sure if this is the best analogy, but think about checked exceptions in Java. It's great for writing low level code in which error handling is paramount, e.g. storage systems, network layers. But in most high level applications people just write boilerplate catches that are no-ops, because they have other priorities and they can tolerate mishandling of exceptions, although often maybe they shouldn't.



On Wed, Jul 31, 2019 at 2:55 PM, Ryan Blue <[hidden email]> wrote:
Another important aspect of this problem is whether a user is conscious of the cast that is inserted by Spark. Most of the time, users are not aware of casts that are implicitly inserted, and that means replacing values with NULL would be a very surprising behavior. The impact of this choice affects users disproportionately: someone that knows about inserted casts is mildly annoyed when required to add an explicit cast, but a user that doesn't know an inserted cast is dropping values is very negatively impacted and may not discover the problem until it is too late.

That disproportionate impact is what makes me think that it is not okay for Spark to silently replace values with NULL, even if that's what ANSI would allow. Other databases also have the ability to reject null values in tables, providing extra insurance against the problem, but Spark doesn't have required columns in its DDL.

So while I agree with Reynold that there is a trade-off, I think that trade-off makes the choice between a runtime error and an analysis-time error. I'm okay with either a runtime error as the default or an analysis error as the default, as long as there is a setting that allows me to choose one for my deployment.


On Wed, Jul 31, 2019 at 10:39 AM Reynold Xin <[hidden email]> wrote:
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 <[hidden email]> 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 <[hidden email]>
Date: Wednesday, July 31, 2019 at 9:58 AM
To: Matt Cheah <[hidden email]>
Cc: Russell Spitzer <[hidden email]>, Takeshi Yamamuro <[hidden email]>, Gengliang Wang <[hidden email]>, Ryan Blue <[hidden email]>, Spark dev list <[hidden email]>, Hyukjin Kwon <[hidden email]>, Wenchen Fan <[hidden email]>
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 <[hidden email]> 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 <[hidden email]>
Date: Monday, July 29, 2019 at 11:33 PM
To: Wenchen Fan <[hidden email]>
Cc: Russell Spitzer <[hidden email]>, Takeshi Yamamuro <[hidden email]>, Gengliang Wang <[hidden email]>, Ryan Blue <[hidden email]>, Spark dev list <[hidden email]>
Subject: Re: [Discuss] Follow ANSI SQL on table insertion

 

From my look, +1 on the proposal, considering ASCI and other DBMSes in general.

 

2019 7 30 () 오후 3:21, Wenchen Fan <[hidden email]>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.