[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...

2018-12-04 Thread mgaido91
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. --- -

[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...

2018-11-10 Thread mgaido91
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,

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2018-11-05 Thread mgaido91
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

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2018-09-21 Thread mgaido91
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2018-09-20 Thread cloud-fan
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

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2018-09-20 Thread mgaido91
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

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2018-09-20 Thread cloud-fan
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

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2018-09-20 Thread mgaido91
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

[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...

2018-09-20 Thread cloud-fan
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. ---

[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...

2018-09-20 Thread mgaido91
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

[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...

2018-09-20 Thread cloud-fan
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

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2018-09-20 Thread mgaido91
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

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2018-09-20 Thread dilipbiswal
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

[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...

2018-09-20 Thread mgaido91
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: -

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2018-09-19 Thread cloud-fan
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

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2018-09-19 Thread mgaido91
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.

[GitHub] spark issue #22450: [SPARK-25454][SQL] Avoid precision loss in division with...

2018-09-19 Thread cloud-fan
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

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