[SPARK-39915][SQL] Dataset.repartition(N) may not create N partitions Non-AQE part #37706
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cc @cloud-fan |
cloud-fan
reviewed
Aug 29, 2022
| */ | ||
| protected def addTagForRootRepartition(plan: LogicalPlan): LogicalPlan = { | ||
| var isRootRepartition = true | ||
| plan.transformDownWithPruning(_.containsPattern(repartitionTreePattern)) { |
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I think it's better to use a manual traversal here. We can stop traversal once we hit a node that is not repartition/project/filter.
cloud-fan
reviewed
Aug 30, 2022
| @@ -1310,6 +1310,14 @@ class PlannerSuite extends SharedSparkSession with AdaptiveSparkPlanHelper { | |||
| assert(topKs.size == 1) | |||
| assert(sorts.isEmpty) | |||
| } | |||
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| test("SPARK-39915: Dataset.repartition(N) may not create N partitions") { | |||
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This is not a planner bug... We can probably add an end-to-end test in DataFrameSuite
cloud-fan
approved these changes
Aug 30, 2022
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thanks, merging to master! |
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@ulysses-you can you open a backport PR for 3.3? it has conflicts. |
ulysses-you
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Aug 30, 2022
… Non-AQE part
Skip optimize the root user-specified repartition in `PropagateEmptyRelation`.
Spark should preserve the final repatition which can affect the final output partition which is user-specified.
For example:
```scala
spark.sql("select * from values(1) where 1 < rand()").repartition(1)
// before:
== Optimized Logical Plan ==
LocalTableScan <empty>, [col1#0]
// after:
== Optimized Logical Plan ==
Repartition 1, true
+- LocalRelation <empty>, [col1#0]
```
yes, the empty plan may change
add test
Closes apache#37706 from ulysses-you/empty.
Authored-by: ulysses-you <[email protected]>
Signed-off-by: Wenchen Fan <[email protected]>
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thank you @cloud-fan , craeted #37730 |
cloud-fan
reviewed
Aug 31, 2022
| * Add a [[ROOT_REPARTITION]] tag for the root user-specified repartition so this rule can | ||
| * skip optimize it. | ||
| */ | ||
| private def addTagForRootRepartition(plan: LogicalPlan): LogicalPlan = plan match { |
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note: we can skip this earlier with something like if (!plan.containsPattern(REPARTITION))
dongjoon-hyun
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Sep 9, 2022
…tions Non-AQE part ### What changes were proposed in this pull request? backport #37706 for branch-3.3 Skip optimize the root user-specified repartition in `PropagateEmptyRelation`. ### Why are the changes needed? Spark should preserve the final repatition which can affect the final output partition which is user-specified. For example: ```scala spark.sql("select * from values(1) where 1 < rand()").repartition(1) // before: == Optimized Logical Plan == LocalTableScan <empty>, [col1#0] // after: == Optimized Logical Plan == Repartition 1, true +- LocalRelation <empty>, [col1#0] ``` ### Does this PR introduce _any_ user-facing change? yes, the empty plan may change ### How was this patch tested? add test Closes #37730 from ulysses-you/empty-3.3. Authored-by: ulysses-you <[email protected]> Signed-off-by: Dongjoon Hyun <[email protected]>
dongjoon-hyun
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Nov 4, 2023
### What changes were proposed in this pull request? This pr upgrade Apache Arrow from 13.0.0 to 14.0.0. ### Why are the changes needed? The Apache Arrow 14.0.0 release brings a number of enhancements and bug fixes. In terms of bug fixes, the release addresses several critical issues that were causing failures in integration jobs with Spark([GH-36332](apache/arrow#36332)) and problems with importing empty data arrays([GH-37056](apache/arrow#37056)). It also optimizes the process of appending variable length vectors([GH-37829](apache/arrow#37829)) and includes C++ libraries for MacOS AARCH 64 in Java-Jars([GH-38076](apache/arrow#38076)). The new features and improvements focus on enhancing the handling and manipulation of data. This includes the introduction of DefaultVectorComparators for large types([GH-25659](apache/arrow#25659)), support for extended expressions in ScannerBuilder([GH-34252](apache/arrow#34252)), and the exposure of the VectorAppender class([GH-37246](apache/arrow#37246)). The release also brings enhancements to the development and testing process, with the CI environment now using JDK 21([GH-36994](apache/arrow#36994)). In addition, the release introduces vector validation consistent with C++, ensuring consistency across different languages([GH-37702](apache/arrow#37702)). Furthermore, the usability of VarChar writers and binary writers has been improved with the addition of extra input methods([GH-37705](apache/arrow#37705)), and VarCharWriter now supports writing from `Text` and `String`([GH-37706](apache/arrow#37706)). The release also adds typed getters for StructVector, improving the ease of accessing data([GH-37863](apache/arrow#37863)). The full release notes as follows: - https://arrow.apache.org/release/14.0.0.html ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Pass GitHub Actions ### Was this patch authored or co-authored using generative AI tooling? No Closes #43650 from LuciferYang/arrow-14. Lead-authored-by: yangjie01 <[email protected]> Co-authored-by: YangJie <[email protected]> Signed-off-by: Dongjoon Hyun <[email protected]>
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What changes were proposed in this pull request?
Skip optimize the root user-specified repartition in
PropagateEmptyRelation.Why are the changes needed?
Spark should preserve the final repatition which can affect the final output partition which is user-specified.
For example:
Does this PR introduce any user-facing change?
yes, the empty plan may change
How was this patch tested?
add test