upgrade version to 2.2.1-kylin-r10 #110
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eventd
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fishcus
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chenzhx
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### What changes were proposed in this pull request? Use sideBySide to format the log plan in `AdaptiveSparkPlanExec`. Before: ``` 12:08:36.876 ERROR org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec: Plan changed from SortMergeJoin [key#13], [a#23], Inner :- Sort [key#13 ASC NULLS FIRST], false, 0 : +- ShuffleQueryStage 0 : +- Exchange hashpartitioning(key#13, 5), ENSURE_REQUIREMENTS, [id=Kyligence#110] : +- *(1) Filter (isnotnull(value#14) AND (value#14 = 1)) : +- *(1) SerializeFromObject [knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData, true])).key AS key#13, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData, true])).value, true, false, true) AS value#14] : +- Scan[obj#12] +- Sort [a#23 ASC NULLS FIRST], false, 0 +- ShuffleQueryStage 1 +- Exchange hashpartitioning(a#23, 5), ENSURE_REQUIREMENTS, [id=Kyligence#129] +- *(2) SerializeFromObject [knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData2, true])).a AS a#23, knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData2, true])).b AS b#24] +- Scan[obj#22] to BroadcastHashJoin [key#13], [a#23], Inner, BuildLeft, false :- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [id=Kyligence#145] : +- ShuffleQueryStage 0 : +- Exchange hashpartitioning(key#13, 5), ENSURE_REQUIREMENTS, [id=Kyligence#110] : +- *(1) Filter (isnotnull(value#14) AND (value#14 = 1)) : +- *(1) SerializeFromObject [knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData, true])).key AS key#13, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData, true])).value, true, false, true) AS value#14] : +- Scan[obj#12] +- ShuffleQueryStage 1 +- Exchange hashpartitioning(a#23, 5), ENSURE_REQUIREMENTS, [id=Kyligence#129] +- *(2) SerializeFromObject [knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData2, true])).a AS a#23, knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData2, true])).b AS b#24] +- Scan[obj#22] ``` After: ``` 15:57:59.481 ERROR org.apache.spark.sql.execution.adaptive.AdaptiveSparkPlanExec: Plan changed: !SortMergeJoin [key#13], [a#23], Inner BroadcastHashJoin [key#13], [a#23], Inner, BuildLeft, false !:- Sort [key#13 ASC NULLS FIRST], false, 0 :- BroadcastExchange HashedRelationBroadcastMode(List(cast(input[0, int, false] as bigint)),false), [id=Kyligence#145] : +- ShuffleQueryStage 0 : +- ShuffleQueryStage 0 : +- Exchange hashpartitioning(key#13, 5), ENSURE_REQUIREMENTS, [id=Kyligence#110] : +- Exchange hashpartitioning(key#13, 5), ENSURE_REQUIREMENTS, [id=Kyligence#110] : +- *(1) Filter (isnotnull(value#14) AND (value#14 = 1)) : +- *(1) Filter (isnotnull(value#14) AND (value#14 = 1)) : +- *(1) SerializeFromObject [knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData, true])).key AS key#13, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData, true])).value, true, false, true) AS value#14] : +- *(1) SerializeFromObject [knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData, true])).key AS key#13, staticinvoke(class org.apache.spark.unsafe.types.UTF8String, StringType, fromString, knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData, true])).value, true, false, true) AS value#14] : +- Scan[obj#12] : +- Scan[obj#12] !+- Sort [a#23 ASC NULLS FIRST], false, 0 +- ShuffleQueryStage 1 ! +- ShuffleQueryStage 1 +- Exchange hashpartitioning(a#23, 5), ENSURE_REQUIREMENTS, [id=Kyligence#129] ! +- Exchange hashpartitioning(a#23, 5), ENSURE_REQUIREMENTS, [id=Kyligence#129] +- *(2) SerializeFromObject [knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData2, true])).a AS a#23, knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData2, true])).b AS b#24] ! +- *(2) SerializeFromObject [knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData2, true])).a AS a#23, knownnotnull(assertnotnull(input[0, org.apache.spark.sql.test.SQLTestData$TestData2, true])).b AS b#24] +- Scan[obj#22] ! +- Scan[obj#22] ``` ### Why are the changes needed? Enhance readability. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? Manual testing. Closes apache#36045 from wangyum/SPARK-38772. Authored-by: Yuming Wang <[email protected]> Signed-off-by: Yuming Wang <[email protected]>
chenzhx
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May 17, 2022
### What changes were proposed in this pull request? This PR adds a new optimizer rule `MergeScalarSubqueries` to merge multiple non-correlated `ScalarSubquery`s to compute multiple scalar values once. E.g. the following query: ``` SELECT (SELECT avg(a) FROM t), (SELECT sum(b) FROM t) ``` is optimized from: ``` == Optimized Logical Plan == Project [scalar-subquery#242 [] AS scalarsubquery()Kyligence#253, scalar-subquery#243 [] AS scalarsubquery()#254L] : :- Aggregate [avg(a#244) AS avg(a)Kyligence#247] : : +- Project [a#244] : : +- Relation default.t[a#244,b#245] parquet : +- Aggregate [sum(a#251) AS sum(a)#250L] : +- Project [a#251] : +- Relation default.t[a#251,b#252] parquet +- OneRowRelation ``` to: ``` == Optimized Logical Plan == Project [scalar-subquery#242 [].avg(a) AS scalarsubquery()Kyligence#253, scalar-subquery#243 [].sum(a) AS scalarsubquery()#254L] : :- Project [named_struct(avg(a), avg(a)Kyligence#247, sum(a), sum(a)#250L) AS mergedValue#260] : : +- Aggregate [avg(a#244) AS avg(a)Kyligence#247, sum(a#244) AS sum(a)#250L] : : +- Project [a#244] : : +- Relation default.t[a#244,b#245] parquet : +- Project [named_struct(avg(a), avg(a)Kyligence#247, sum(a), sum(a)#250L) AS mergedValue#260] : +- Aggregate [avg(a#244) AS avg(a)Kyligence#247, sum(a#244) AS sum(a)#250L] : +- Project [a#244] : +- Relation default.t[a#244,b#245] parquet +- OneRowRelation ``` and in the physical plan subqueries are reused: ``` == Physical Plan == AdaptiveSparkPlan isFinalPlan=true +- == Final Plan == *(1) Project [Subquery subquery#242, [id=Kyligence#113].avg(a) AS scalarsubquery()Kyligence#253, ReusedSubquery Subquery subquery#242, [id=Kyligence#113].sum(a) AS scalarsubquery()#254L] : :- Subquery subquery#242, [id=Kyligence#113] : : +- AdaptiveSparkPlan isFinalPlan=true +- == Final Plan == *(2) Project [named_struct(avg(a), avg(a)Kyligence#247, sum(a), sum(a)#250L) AS mergedValue#260] +- *(2) HashAggregate(keys=[], functions=[avg(a#244), sum(a#244)], output=[avg(a)Kyligence#247, sum(a)#250L]) +- ShuffleQueryStage 0 +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=Kyligence#158] +- *(1) HashAggregate(keys=[], functions=[partial_avg(a#244), partial_sum(a#244)], output=[sum#262, count#263L, sum#264L]) +- *(1) ColumnarToRow +- FileScan parquet default.t[a#244] Batched: true, DataFilters: [], Format: Parquet, Location: ..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<a:int> +- == Initial Plan == Project [named_struct(avg(a), avg(a)Kyligence#247, sum(a), sum(a)#250L) AS mergedValue#260] +- HashAggregate(keys=[], functions=[avg(a#244), sum(a#244)], output=[avg(a)Kyligence#247, sum(a)#250L]) +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=Kyligence#110] +- HashAggregate(keys=[], functions=[partial_avg(a#244), partial_sum(a#244)], output=[sum#262, count#263L, sum#264L]) +- FileScan parquet default.t[a#244] Batched: true, DataFilters: [], Format: Parquet, Location: ..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<a:int> : +- ReusedSubquery Subquery subquery#242, [id=Kyligence#113] +- *(1) Scan OneRowRelation[] +- == Initial Plan == ... ``` Please note that the above simple example could be easily optimized into a common select expression without reuse node, but this PR can handle more complex queries as well. ### Why are the changes needed? Performance improvement. ``` [info] TPCDS Snappy: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative [info] ------------------------------------------------------------------------------------------------------------------------ [info] q9 - MergeScalarSubqueries off 50798 52521 1423 0.0 Infinity 1.0X [info] q9 - MergeScalarSubqueries on 19484 19675 226 0.0 Infinity 2.6X [info] TPCDS Snappy: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative [info] ------------------------------------------------------------------------------------------------------------------------ [info] q9b - MergeScalarSubqueries off 15430 17803 NaN 0.0 Infinity 1.0X [info] q9b - MergeScalarSubqueries on 3862 4002 196 0.0 Infinity 4.0X ``` Please find `q9b` in the description of SPARK-34079. It is a variant of [q9.sql](https://github.com/apache/spark/blob/master/sql/core/src/test/resources/tpcds/q9.sql) using CTE. The performance improvement in case of `q9` comes from merging 15 subqueries into 5 and in case of `q9b` it comes from merging 5 subqueries into 1. ### Does this PR introduce _any_ user-facing change? No. But this optimization can be disabled with `spark.sql.optimizer.excludedRules` config. ### How was this patch tested? Existing and new UTs. Closes apache#32298 from peter-toth/SPARK-34079-multi-column-scalar-subquery. Lead-authored-by: Peter Toth <[email protected]> Co-authored-by: attilapiros <[email protected]> Signed-off-by: Wenchen Fan <[email protected]>
leejaywei
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Jul 13, 2022
### What changes were proposed in this pull request? This PR adds a new optimizer rule `MergeScalarSubqueries` to merge multiple non-correlated `ScalarSubquery`s to compute multiple scalar values once. E.g. the following query: ``` SELECT (SELECT avg(a) FROM t), (SELECT sum(b) FROM t) ``` is optimized from: ``` == Optimized Logical Plan == Project [scalar-subquery#242 [] AS scalarsubquery()#253, scalar-subquery#243 [] AS scalarsubquery()#254L] : :- Aggregate [avg(a#244) AS avg(a)#247] : : +- Project [a#244] : : +- Relation default.t[a#244,b#245] parquet : +- Aggregate [sum(a#251) AS sum(a)#250L] : +- Project [a#251] : +- Relation default.t[a#251,b#252] parquet +- OneRowRelation ``` to: ``` == Optimized Logical Plan == Project [scalar-subquery#242 [].avg(a) AS scalarsubquery()#253, scalar-subquery#243 [].sum(a) AS scalarsubquery()#254L] : :- Project [named_struct(avg(a), avg(a)#247, sum(a), sum(a)#250L) AS mergedValue#260] : : +- Aggregate [avg(a#244) AS avg(a)#247, sum(a#244) AS sum(a)#250L] : : +- Project [a#244] : : +- Relation default.t[a#244,b#245] parquet : +- Project [named_struct(avg(a), avg(a)#247, sum(a), sum(a)#250L) AS mergedValue#260] : +- Aggregate [avg(a#244) AS avg(a)#247, sum(a#244) AS sum(a)#250L] : +- Project [a#244] : +- Relation default.t[a#244,b#245] parquet +- OneRowRelation ``` and in the physical plan subqueries are reused: ``` == Physical Plan == AdaptiveSparkPlan isFinalPlan=true +- == Final Plan == *(1) Project [Subquery subquery#242, [id=#113].avg(a) AS scalarsubquery()#253, ReusedSubquery Subquery subquery#242, [id=#113].sum(a) AS scalarsubquery()#254L] : :- Subquery subquery#242, [id=#113] : : +- AdaptiveSparkPlan isFinalPlan=true +- == Final Plan == *(2) Project [named_struct(avg(a), avg(a)#247, sum(a), sum(a)#250L) AS mergedValue#260] +- *(2) HashAggregate(keys=[], functions=[avg(a#244), sum(a#244)], output=[avg(a)#247, sum(a)#250L]) +- ShuffleQueryStage 0 +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#158] +- *(1) HashAggregate(keys=[], functions=[partial_avg(a#244), partial_sum(a#244)], output=[sum#262, count#263L, sum#264L]) +- *(1) ColumnarToRow +- FileScan parquet default.t[a#244] Batched: true, DataFilters: [], Format: Parquet, Location: ..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<a:int> +- == Initial Plan == Project [named_struct(avg(a), avg(a)#247, sum(a), sum(a)#250L) AS mergedValue#260] +- HashAggregate(keys=[], functions=[avg(a#244), sum(a#244)], output=[avg(a)#247, sum(a)#250L]) +- Exchange SinglePartition, ENSURE_REQUIREMENTS, [id=#110] +- HashAggregate(keys=[], functions=[partial_avg(a#244), partial_sum(a#244)], output=[sum#262, count#263L, sum#264L]) +- FileScan parquet default.t[a#244] Batched: true, DataFilters: [], Format: Parquet, Location: ..., PartitionFilters: [], PushedFilters: [], ReadSchema: struct<a:int> : +- ReusedSubquery Subquery subquery#242, [id=#113] +- *(1) Scan OneRowRelation[] +- == Initial Plan == ... ``` Please note that the above simple example could be easily optimized into a common select expression without reuse node, but this PR can handle more complex queries as well. ### Why are the changes needed? Performance improvement. ``` [info] TPCDS Snappy: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative [info] ------------------------------------------------------------------------------------------------------------------------ [info] q9 - MergeScalarSubqueries off 50798 52521 1423 0.0 Infinity 1.0X [info] q9 - MergeScalarSubqueries on 19484 19675 226 0.0 Infinity 2.6X [info] TPCDS Snappy: Best Time(ms) Avg Time(ms) Stdev(ms) Rate(M/s) Per Row(ns) Relative [info] ------------------------------------------------------------------------------------------------------------------------ [info] q9b - MergeScalarSubqueries off 15430 17803 NaN 0.0 Infinity 1.0X [info] q9b - MergeScalarSubqueries on 3862 4002 196 0.0 Infinity 4.0X ``` Please find `q9b` in the description of SPARK-34079. It is a variant of [q9.sql](https://github.com/apache/spark/blob/master/sql/core/src/test/resources/tpcds/q9.sql) using CTE. The performance improvement in case of `q9` comes from merging 15 subqueries into 5 and in case of `q9b` it comes from merging 5 subqueries into 1. ### Does this PR introduce _any_ user-facing change? No. But this optimization can be disabled with `spark.sql.optimizer.excludedRules` config. ### How was this patch tested? Existing and new UTs. Closes apache#32298 from peter-toth/SPARK-34079-multi-column-scalar-subquery. Lead-authored-by: Peter Toth <[email protected]> Co-authored-by: attilapiros <[email protected]> Signed-off-by: Wenchen Fan <[email protected]> (cherry picked from commit e00b81e) Signed-off-by: Wenchen Fan <[email protected]>
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