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Posted to reviews@spark.apache.org by "sunchao (via GitHub)" <gi...@apache.org> on 2023/09/06 20:09:12 UTC
[GitHub] [spark] sunchao opened a new pull request, #42839: [SPARK-45036][FOLLOWUP][SQL] SPJ: Make sure result partitions are sorted according to partition values
sunchao opened a new pull request, #42839:
URL: https://github.com/apache/spark/pull/42839
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### What changes were proposed in this pull request?
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This PR makes sure the result grouped partitions from `DataSourceV2ScanExec#groupPartitions` are sorted according to the partition values. Previously in the #42757 we were assuming Scala would preserve the input ordering but apparently that's not the case.
### Why are the changes needed?
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See https://github.com/apache/spark/pull/42757#discussion_r1316926504 for diagnosis. The partition ordering is a fundamental property for SPJ and thus must be guaranteed.
### Does this PR introduce _any_ user-facing change?
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No
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We have tests in `KeyGroupedPartitioningSuite` to cover this.
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[GitHub] [spark] viirya commented on a diff in pull request #42839: [SPARK-45036][FOLLOWUP][SQL] SPJ: Make sure result partitions are sorted according to partition values
Posted by "viirya (via GitHub)" <gi...@apache.org>.
viirya commented on code in PR #42839:
URL: https://github.com/apache/spark/pull/42839#discussion_r1317860693
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2ScanExecBase.scala:
##########
@@ -143,17 +143,16 @@ trait DataSourceV2ScanExecBase extends LeafExecNode {
// also sort the input partitions according to their partition key order. This ensures
// a canonical order from both sides of a bucketed join, for example.
val partitionDataTypes = expressions.map(_.dataType)
- val partitionOrdering: Ordering[(InternalRow, InputPartition)] = {
- RowOrdering.createNaturalAscendingOrdering(partitionDataTypes).on(_._1)
- }
- val sortedKeyToPartitions = results.sorted(partitionOrdering)
- val groupedPartitions = sortedKeyToPartitions
+ val rowOrdering = RowOrdering.createNaturalAscendingOrdering(partitionDataTypes)
+ val sortedKeyToPartitions = results.sorted(rowOrdering.on(_._1))
Review Comment:
Oh okay, missed it. It is part of `KeyGroupedPartitionInfo`.
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[GitHub] [spark] LuciferYang commented on pull request #42839: [SPARK-45036][FOLLOWUP][SQL] SPJ: Make sure result partitions are sorted according to partition values
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on PR #42839:
URL: https://github.com/apache/spark/pull/42839#issuecomment-1709895599
Merged into master. Thanks @sunchao @viirya @dongjoon-hyun @Hisoka-X
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[GitHub] [spark] viirya commented on a diff in pull request #42839: [SPARK-45036][FOLLOWUP][SQL] SPJ: Make sure result partitions are sorted according to partition values
Posted by "viirya (via GitHub)" <gi...@apache.org>.
viirya commented on code in PR #42839:
URL: https://github.com/apache/spark/pull/42839#discussion_r1317829652
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2ScanExecBase.scala:
##########
@@ -143,17 +143,16 @@ trait DataSourceV2ScanExecBase extends LeafExecNode {
// also sort the input partitions according to their partition key order. This ensures
// a canonical order from both sides of a bucketed join, for example.
val partitionDataTypes = expressions.map(_.dataType)
- val partitionOrdering: Ordering[(InternalRow, InputPartition)] = {
- RowOrdering.createNaturalAscendingOrdering(partitionDataTypes).on(_._1)
- }
- val sortedKeyToPartitions = results.sorted(partitionOrdering)
- val groupedPartitions = sortedKeyToPartitions
+ val rowOrdering = RowOrdering.createNaturalAscendingOrdering(partitionDataTypes)
+ val sortedKeyToPartitions = results.sorted(rowOrdering.on(_._1))
Review Comment:
Then I think we don't need this sorting, if you are going to sort anyway after `groupBy`?
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[GitHub] [spark] sunchao commented on a diff in pull request #42839: [SPARK-45036][FOLLOWUP][SQL] SPJ: Make sure result partitions are sorted according to partition values
Posted by "sunchao (via GitHub)" <gi...@apache.org>.
sunchao commented on code in PR #42839:
URL: https://github.com/apache/spark/pull/42839#discussion_r1317833964
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2ScanExecBase.scala:
##########
@@ -143,17 +143,16 @@ trait DataSourceV2ScanExecBase extends LeafExecNode {
// also sort the input partitions according to their partition key order. This ensures
// a canonical order from both sides of a bucketed join, for example.
val partitionDataTypes = expressions.map(_.dataType)
- val partitionOrdering: Ordering[(InternalRow, InputPartition)] = {
- RowOrdering.createNaturalAscendingOrdering(partitionDataTypes).on(_._1)
- }
- val sortedKeyToPartitions = results.sorted(partitionOrdering)
- val groupedPartitions = sortedKeyToPartitions
+ val rowOrdering = RowOrdering.createNaturalAscendingOrdering(partitionDataTypes)
+ val sortedKeyToPartitions = results.sorted(rowOrdering.on(_._1))
Review Comment:
Hmm this is the partition value -> input split mapping before the `groupBy` though. It also need to be returned.
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[GitHub] [spark] LuciferYang commented on a diff in pull request #42839: [SPARK-45036][FOLLOWUP][SQL] SPJ: Make sure result partitions are sorted according to partition values
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang commented on code in PR #42839:
URL: https://github.com/apache/spark/pull/42839#discussion_r1318042923
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2ScanExecBase.scala:
##########
@@ -143,17 +143,16 @@ trait DataSourceV2ScanExecBase extends LeafExecNode {
// also sort the input partitions according to their partition key order. This ensures
// a canonical order from both sides of a bucketed join, for example.
val partitionDataTypes = expressions.map(_.dataType)
- val partitionOrdering: Ordering[(InternalRow, InputPartition)] = {
- RowOrdering.createNaturalAscendingOrdering(partitionDataTypes).on(_._1)
- }
- val sortedKeyToPartitions = results.sorted(partitionOrdering)
- val groupedPartitions = sortedKeyToPartitions
+ val rowOrdering = RowOrdering.createNaturalAscendingOrdering(partitionDataTypes)
+ val sortedKeyToPartitions = results.sorted(rowOrdering.on(_._1))
+ val sortedGroupedPartitions = sortedKeyToPartitions
.map(t => (InternalRowComparableWrapper(t._1, expressions), t._2))
.groupBy(_._1)
.toSeq
.map { case (key, s) => KeyGroupedPartition(key.row, s.map(_._2)) }
+ .sorted(rowOrdering.on(_.value))
Review Comment:
```suggestion
.sorted(rowOrdering.on((k: KeyGroupedPartition) => k.value))
```
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2ScanExecBase.scala:
##########
@@ -143,17 +143,16 @@ trait DataSourceV2ScanExecBase extends LeafExecNode {
// also sort the input partitions according to their partition key order. This ensures
// a canonical order from both sides of a bucketed join, for example.
val partitionDataTypes = expressions.map(_.dataType)
- val partitionOrdering: Ordering[(InternalRow, InputPartition)] = {
- RowOrdering.createNaturalAscendingOrdering(partitionDataTypes).on(_._1)
- }
- val sortedKeyToPartitions = results.sorted(partitionOrdering)
- val groupedPartitions = sortedKeyToPartitions
+ val rowOrdering = RowOrdering.createNaturalAscendingOrdering(partitionDataTypes)
+ val sortedKeyToPartitions = results.sorted(rowOrdering.on(_._1))
Review Comment:
To fix Scala 2.13 build
```
[error] /home/runner/work/spark/spark/sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2ScanExecBase.scala:147:67: missing parameter type for expanded function ((<x$7: error>) => x$7._1)
[error] val sortedKeyToPartitions = results.sorted(rowOrdering.on(_._1))
```
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2ScanExecBase.scala:
##########
@@ -143,17 +143,16 @@ trait DataSourceV2ScanExecBase extends LeafExecNode {
// also sort the input partitions according to their partition key order. This ensures
// a canonical order from both sides of a bucketed join, for example.
val partitionDataTypes = expressions.map(_.dataType)
- val partitionOrdering: Ordering[(InternalRow, InputPartition)] = {
- RowOrdering.createNaturalAscendingOrdering(partitionDataTypes).on(_._1)
- }
- val sortedKeyToPartitions = results.sorted(partitionOrdering)
- val groupedPartitions = sortedKeyToPartitions
+ val rowOrdering = RowOrdering.createNaturalAscendingOrdering(partitionDataTypes)
+ val sortedKeyToPartitions = results.sorted(rowOrdering.on(_._1))
Review Comment:
```suggestion
val sortedKeyToPartitions = results.sorted(rowOrdering.on((t: (InternalRow, _)) => t._1))
```
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2ScanExecBase.scala:
##########
@@ -143,17 +143,16 @@ trait DataSourceV2ScanExecBase extends LeafExecNode {
// also sort the input partitions according to their partition key order. This ensures
// a canonical order from both sides of a bucketed join, for example.
val partitionDataTypes = expressions.map(_.dataType)
- val partitionOrdering: Ordering[(InternalRow, InputPartition)] = {
- RowOrdering.createNaturalAscendingOrdering(partitionDataTypes).on(_._1)
- }
- val sortedKeyToPartitions = results.sorted(partitionOrdering)
- val groupedPartitions = sortedKeyToPartitions
+ val rowOrdering = RowOrdering.createNaturalAscendingOrdering(partitionDataTypes)
+ val sortedKeyToPartitions = results.sorted(rowOrdering.on(_._1))
+ val sortedGroupedPartitions = sortedKeyToPartitions
.map(t => (InternalRowComparableWrapper(t._1, expressions), t._2))
.groupBy(_._1)
.toSeq
.map { case (key, s) => KeyGroupedPartition(key.row, s.map(_._2)) }
+ .sorted(rowOrdering.on(_.value))
Review Comment:
ditto
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[GitHub] [spark] sunchao commented on a diff in pull request #42839: [SPARK-45036][FOLLOWUP][SQL] SPJ: Make sure result partitions are sorted according to partition values
Posted by "sunchao (via GitHub)" <gi...@apache.org>.
sunchao commented on code in PR #42839:
URL: https://github.com/apache/spark/pull/42839#discussion_r1318064440
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2ScanExecBase.scala:
##########
@@ -143,17 +143,16 @@ trait DataSourceV2ScanExecBase extends LeafExecNode {
// also sort the input partitions according to their partition key order. This ensures
// a canonical order from both sides of a bucketed join, for example.
val partitionDataTypes = expressions.map(_.dataType)
- val partitionOrdering: Ordering[(InternalRow, InputPartition)] = {
- RowOrdering.createNaturalAscendingOrdering(partitionDataTypes).on(_._1)
- }
- val sortedKeyToPartitions = results.sorted(partitionOrdering)
- val groupedPartitions = sortedKeyToPartitions
+ val rowOrdering = RowOrdering.createNaturalAscendingOrdering(partitionDataTypes)
+ val sortedKeyToPartitions = results.sorted(rowOrdering.on(_._1))
Review Comment:
oops didn't realize it doesn't compile this way.
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[GitHub] [spark] dongjoon-hyun commented on pull request #42839: [SPARK-45036][FOLLOWUP][SQL] SPJ: Make sure result partitions are sorted according to partition values
Posted by "dongjoon-hyun (via GitHub)" <gi...@apache.org>.
dongjoon-hyun commented on PR #42839:
URL: https://github.com/apache/spark/pull/42839#issuecomment-1710600124
Thank you, all!
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[GitHub] [spark] LuciferYang closed pull request #42839: [SPARK-45036][FOLLOWUP][SQL] SPJ: Make sure result partitions are sorted according to partition values
Posted by "LuciferYang (via GitHub)" <gi...@apache.org>.
LuciferYang closed pull request #42839: [SPARK-45036][FOLLOWUP][SQL] SPJ: Make sure result partitions are sorted according to partition values
URL: https://github.com/apache/spark/pull/42839
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[GitHub] [spark] sunchao commented on a diff in pull request #42839: [SPARK-45036][FOLLOWUP][SQL] SPJ: Make sure result partitions are sorted according to partition values
Posted by "sunchao (via GitHub)" <gi...@apache.org>.
sunchao commented on code in PR #42839:
URL: https://github.com/apache/spark/pull/42839#discussion_r1318064553
##########
sql/core/src/main/scala/org/apache/spark/sql/execution/datasources/v2/DataSourceV2ScanExecBase.scala:
##########
@@ -143,17 +143,16 @@ trait DataSourceV2ScanExecBase extends LeafExecNode {
// also sort the input partitions according to their partition key order. This ensures
// a canonical order from both sides of a bucketed join, for example.
val partitionDataTypes = expressions.map(_.dataType)
- val partitionOrdering: Ordering[(InternalRow, InputPartition)] = {
- RowOrdering.createNaturalAscendingOrdering(partitionDataTypes).on(_._1)
- }
- val sortedKeyToPartitions = results.sorted(partitionOrdering)
- val groupedPartitions = sortedKeyToPartitions
+ val rowOrdering = RowOrdering.createNaturalAscendingOrdering(partitionDataTypes)
+ val sortedKeyToPartitions = results.sorted(rowOrdering.on(_._1))
+ val sortedGroupedPartitions = sortedKeyToPartitions
.map(t => (InternalRowComparableWrapper(t._1, expressions), t._2))
.groupBy(_._1)
.toSeq
.map { case (key, s) => KeyGroupedPartition(key.row, s.map(_._2)) }
+ .sorted(rowOrdering.on(_.value))
Review Comment:
thanks!
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