[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/22450 @cloud-fan this has been stuck for a while now. Is there something blocking this? Is there something I can do? Thanks. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/22450 kindly ping @cloud-fan , thanks --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/22450 @cloud-fan I have seen no more comments on the design doc for a while. I also wrote an email about one week ago to the dev list in order to check if there were further comments but I have seen none. As I don't see any concern on this, shall we go ahead with this PR? Thanks. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/22450 Thank you for your help and guidance. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/22450 totally agree, thanks for looking into it! --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/22450 I think there are 2 separate topics here: - **Handling negative scale in decimal operations** I am writing the design doc and I'll update this PR if needed (anyway I'll add more test cases). I think we can target this work for 2.5 too. - **Handling/parsing of literals and numbers in general** This is way more complex I think and it involves more places in the code-base and more possible breaking changes. On this I 100% agree that we should start designing it ASAP and target the implementation for 3.0. Do you agree on the above distinction? Thanks for your time and help here anyway --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/22450 given how complex it is, I feel we can start the design at 2.5 and implement it at 3.0, what do you think? --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/22450 oh I see now what you mean, thanks. Yes, Hive does the same. We may have to revisit completely our parsing of literals but since it is a breaking change I am not sure it will be possible before 3.0. And if the next release is going to be 2.5, probably this means that we have to wait before doing anything like that. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/22450 @dilipbiswal showed that DB2 and presto treat `1e100` as double instead of decimal. We should consider this option and see what's the consequence of it. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/22450 > seems there are more problems like the data type of literals sorry, I haven't got what you mean here, may you please explain me? > your long explanation makes me think we should have a design doc about it I'll prepare a design doc and attach it to the JIRA asap then. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/22450 @mgaido91 well, your long explanation makes me think we should have a design doc about it, and have more people to review it and ensure we cover all the corner cases. And seems there are more problems like the data type of literals(e.g. `1e3`). I'd suggest we pick a safer option: allow users to fully turn off https://github.com/apache/spark/pull/20023 , which is done by https://github.com/apache/spark/pull/22494 --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/22450 yes @dilipbiswal , Hive does the same. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user dilipbiswal commented on the issue:
https://github.com/apache/spark/pull/22450
@mgaido91
Again, this may be something to think about in 3.0 timeframe. I just
checked two databases, presto and db2. Both of them treat literals such as
`1e26` as double.
db2
```
db2 => describe select 1e26 from cast
Column Information
Number of columns: 1
SQL type Type length Column name Name
length
--- --
---
480 DOUBLE8 1
1
db2 => describe select 1.23 from cast
Column Information
Number of columns: 1
SQL type Type length Column name Name
length
--- --
---
484 DECIMAL3, 2 1
1
```
presto
=
```
presto:default> explain select 2.34E10;
Query Plan
--
- Output[_col0] => [expr:double]
Cost: {rows: 1 (10B), cpu: 10.00, memory: 0.00, network: 0.00}
```
Should spark do the same ? What would be the repercussions if we did that ?
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[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/22450 Let me answer to all your 3 points: > how to prove Divide is the only one having problems of negative scale? You can check the other operations. Let's go though all of them: - Add and Subtract: in add you never use p1, p2 alone, but you do `p1-s1` and `p2-s2`, so having a negative scale is the same as having scale 0 and corresponding precision; - Multiply: this is the easiest to handle. A negative scale can just cause the scale of the result to be negative too. But that's right, in multiply scale just moves the result to the right/left and this is what a negative scale does too. There are no issues with it. - Remainder/Pmod: same reason of Add and Subtract. > how to prove the fix here is corrected and covers all the corner cases? We can add more and more test cases, but let's check the logic of the divide rule. In the precision we have 2 parts: the digits before the comma - ie. intDigits- (`p1-s1+s2`) and the digits after - ie. the scale - (`max(6, s1 + p2 + 1)`) which are summed and then we have the scale itself. The intDigits are fine for the 1st operand as there is a `p1-s1` which natively handles the negative scale, but if the second operand has a negative scale, the number of intDigits can become negative. This is not something we can allow, as we would end up with a precision lower than the scale, which we don't support. Hence, we need to set the intDigits to 0 if they become negative. As far as the scale is regarded, we already have a guard against a negative scale, as we get the max of it and 6. But we have another problem, ie. we are using `p2` alone. So in case `s2` is negative, in order to get the same `p2` we would have avoiding negative scales, we need to adjust it to `p2 - s2`. No other case s are present. > Do you think it makes sense for spark.sql.decimalOperations.allowPrecisionLoss to also toggle how literal promotion happens (the old way vs. the new way)? I think what we can do is forbidding negative scale when handling it always, regardless of the value of that flag. I think this can safely be done, as negative scales in this case were not supported before 2.3. But anyway, this would just reduce the number of cases when this can happen... So I think we can do that, but it is not a definitive solution. If you want, I can do this in scope of this PR or I can create a new one or we can do this in the PR you just closed. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/22450 My major concern is: 1. how to prove `Divide` is the only one having problems of negative scale? 2. how to prove the fix here is corrected and covers all the corner cases? I'm reconsidering this proposal ``` Do you think it makes sense for spark.sql.decimalOperations.allowPrecisionLoss to also toggle how literal promotion happens (the old way vs. the new way)? ``` --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user mgaido91 commented on the issue: https://github.com/apache/spark/pull/22450 @cloud-fan I checked the other operations and they have no issue with negative scale. This is the reason why this fix is only for Divide: it is the only operation which wasn't dealing it properly. I also thought about doing that but I chose not to do in order not to introduce regressions. Anyway I'll argument more in your PR. Thanks. --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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Github user cloud-fan commented on the issue: https://github.com/apache/spark/pull/22450 I feel there are more places we need to fix for negative scale. I couldn't find any design doc for negative scale in Spark and I believe we supported it by accident. That said, fixing division is just fixing the specific case the user reported, which is not ideal. We should either officially support negative scale and fix all the cases, or officially forbid negative scale. However, neither of them can be made into a bug fix for branch 2.3 and 2.4. Instead, I'm proposing a different fix: un-officially forbids negative scale. Users can still create a decimal value with negative scale, but Spark itself should avoid generating such values. See https://github.com/apache/spark/pull/22470 --- - To unsubscribe, e-mail: reviews-unsubscr...@spark.apache.org For additional commands, e-mail: reviews-h...@spark.apache.org
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[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
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[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
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[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
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[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
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[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
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[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
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[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
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[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
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[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
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[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
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[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
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[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...
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