[GitHub] spark issue #21556: [SPARK-24549][SQL] Support Decimal type push down to the...
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Github user rdblue commented on the issue: https://github.com/apache/spark/pull/21556 I misunderstood how it was safe as well. It was Yuming's clarification that helped. --- - 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 HyukjinKwon commented on the issue: https://github.com/apache/spark/pull/21556 @rdblue, ah, I misunderstood then. thanks for clarifying 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 wangyum commented on the issue: https://github.com/apache/spark/pull/21556 cc @gatorsmile @cloud-fan @gengliangwang @michal-databricks @mswit-databricks --- - 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 rdblue commented on the issue: https://github.com/apache/spark/pull/21556 +1, I think this looks ready to go. --- - 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 rdblue commented on the issue: https://github.com/apache/spark/pull/21556 @HyukjinKwon, even if the values are null, the makeEq function only casts null to Java Integer so the handling is still safe. It just looks odd that `null.asInstanceOf[JInt]` is safe. Thanks to @wangyum for explaining it. Even if the null-safe equality predicate contains a null value, this should be safe. And, passing null in an equals predicate is supported by Parquet. --- - 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 HyukjinKwon commented on the issue: https://github.com/apache/spark/pull/21556 @rdblue, so basically you mean it looks both equality comparison and nullsafe equality comparison are identically pushed down and looks it should be distinguished; otherwise, there could be a potential problem? If so, yup. I agree with it. I think we won't have actually a chance to push down equality comparison or nullsafe equality comparison with actual `null` value by the optimizer. However, sure, I think we shouldn't relay on it. I think actually we should disallow one of both nullsafe equality comparison or equality comparison with `null` in `ParquetFilters`. Thing is, I remember I checked the inside of Parquet's equality comparison API itself is actually nullsafe a long ago like few years ago - this of course should be double checked. Since this PR doesn't change the existing behaviour on this and looks needing some more investigation (e.g., checking if it is still (or it has been) true what I remembered and checked about Parquet's equality comparison), probably, it might be okay to leave it as is. --- - 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 rdblue commented on the issue: https://github.com/apache/spark/pull/21556 @wangyum, can you explain what was happening with the `decimal(9,2)` benchmark more clearly? I asked additional questions, but the thread is on a line that changed so it's collapsed by default. Also, `valueCanMakeFilterOn` returns true for all null values, so I think we still have a problem there. Conversion from EqualNullSafe needs to support null filter values. --- - 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 wangyum commented on the issue: https://github.com/apache/spark/pull/21556 @dongjoon-hyun benchmark code: https://github.com/apache/spark/blob/bf67f70c48881ee99751f7d51fbcbda1e593d90a/sql/core/src/test/scala/org/apache/spark/sql/execution/benchmark/FilterPushdownBenchmark.scala#L284-L314 --- - 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 dongjoon-hyun commented on the issue: https://github.com/apache/spark/pull/21556 Thanks for pinging me. @maropu . I'd like to see the benchmark code. > In the benchmarks above, the results of ORC except for the case decimal(9, 2) have worse performance values as compared to the Parquet ones. Is this expected? --- - 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 maropu commented on the issue: https://github.com/apache/spark/pull/21556 @wangyum Thanks for the benchmarks! @dongjoon-hyun In the benchmarks above, the results of ORC except for the case `decimal(9, 2)` have worse performance values as compared to the Parquet ones. Is this expected? --- - 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 wangyum commented on the issue: https://github.com/apache/spark/pull/21556 Benchmark results: ``` ###[ Pushdown benchmark for Decimal ] Java HotSpot(TM) 64-Bit Server VM 1.8.0_151-b12 on Mac OS X 10.12.6 Intel(R) Core(TM) i7-7820HQ CPU @ 2.90GHz Select 1 decimal(9, 2) row (value = 7864320): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative Parquet Vectorized4004 / 5309 3.9 254.5 1.0X Parquet Vectorized (Pushdown) 1401 / 1431 11.2 89.1 2.9X Native ORC Vectorized 4499 / 4567 3.5 286.0 0.9X Native ORC Vectorized (Pushdown) 899 / 961 17.5 57.2 4.5X Select 10% decimal(9, 2) rows (value < 1572864): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative Parquet Vectorized5376 / 6437 2.9 341.8 1.0X Parquet Vectorized (Pushdown) 2696 / 2754 5.8 171.4 2.0X Native ORC Vectorized 5458 / 5623 2.9 347.0 1.0X Native ORC Vectorized (Pushdown) 2230 / 2255 7.1 141.8 2.4X Select 50% decimal(9, 2) rows (value < 7864320): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative Parquet Vectorized8280 / 8487 1.9 526.4 1.0X Parquet Vectorized (Pushdown) 7716 / 7757 2.0 490.6 1.1X Native ORC Vectorized 9144 / 9495 1.7 581.4 0.9X Native ORC Vectorized (Pushdown) 7918 / 8118 2.0 503.4 1.0X Select 90% decimal(9, 2) rows (value < 14155776): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative Parquet Vectorized9648 / 9676 1.6 613.4 1.0X Parquet Vectorized (Pushdown) 9647 / 9778 1.6 613.3 1.0X Native ORC Vectorized 10782 / 10867 1.5 685.5 0.9X Native ORC Vectorized (Pushdown)10108 / 10269 1.6 642.6 1.0X Select 1 decimal(18, 2) row (value = 7864320): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative Parquet Vectorized4066 / 4147 3.9 258.5 1.0X Parquet Vectorized (Pushdown) 84 / 89188.0 5.3 48.6X Native ORC Vectorized 5430 / 5512 2.9 345.3 0.7X Native ORC Vectorized (Pushdown) 1054 / 1076 14.9 67.0 3.9X Select 10% decimal(18, 2) rows (value < 1572864): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative Parquet Vectorized5028 / 5154 3.1 319.7 1.0X Parquet Vectorized (Pushdown) 1360 / 1421 11.6 86.5 3.7X Native ORC Vectorized 6266 / 6360 2.5 398.4 0.8X Native ORC Vectorized (Pushdown) 2513 / 2550 6.3 159.8 2.0X Select 50% decimal(18, 2) rows (value < 7864320): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative Parquet Vectorized8571 / 8600 1.8 544.9 1.0X Parquet Vectorized (Pushdown) 6455 / 6713 2.4 410.4 1.3X Native ORC Vectorized 10138 / 10353 1.6 644.5 0.8X Native ORC Vectorized (Pushdown) 8166 / 8418 1.9 519.2 1.0X Select 90% decimal(18, 2) rows (value < 14155776): Best/Avg Time(ms) Rate(M/s) Per Row(ns) Relative Parquet Vectorized 12184 / 12253 1.3
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