Repository: spark Updated Branches: refs/heads/branch-2.3 5b5851cb6 -> eb4fa551e
[SPARK-22951][SQL] fix aggregation after dropDuplicates on empty data frames ## What changes were proposed in this pull request? (courtesy of liancheng) Spark SQL supports both global aggregation and grouping aggregation. Global aggregation always return a single row with the initial aggregation state as the output, even there are zero input rows. Spark implements this by simply checking the number of grouping keys and treats an aggregation as a global aggregation if it has zero grouping keys. However, this simple principle drops the ball in the following case: ```scala spark.emptyDataFrame.dropDuplicates().agg(count($"*") as "c").show() // +---+ // | c | // +---+ // | 1 | // +---+ ``` The reason is that: 1. `df.dropDuplicates()` is roughly translated into something equivalent to: ```scala val allColumns = df.columns.map { col } df.groupBy(allColumns: _*).agg(allColumns.head, allColumns.tail: _*) ``` This translation is implemented in the rule `ReplaceDeduplicateWithAggregate`. 2. `spark.emptyDataFrame` contains zero columns and zero rows. Therefore, rule `ReplaceDeduplicateWithAggregate` makes a confusing transformation roughly equivalent to the following one: ```scala spark.emptyDataFrame.dropDuplicates() => spark.emptyDataFrame.groupBy().agg(Map.empty[String, String]) ``` The above transformation is confusing because the resulting aggregate operator contains no grouping keys (because `emptyDataFrame` contains no columns), and gets recognized as a global aggregation. As a result, Spark SQL allocates a single row filled by the initial aggregation state and uses it as the output, and returns a wrong result. To fix this issue, this PR tweaks `ReplaceDeduplicateWithAggregate` by appending a literal `1` to the grouping key list of the resulting `Aggregate` operator when the input plan contains zero output columns. In this way, `spark.emptyDataFrame.dropDuplicates()` is now translated into a grouping aggregation, roughly depicted as: ```scala spark.emptyDataFrame.dropDuplicates() => spark.emptyDataFrame.groupBy(lit(1)).agg(Map.empty[String, String]) ``` Which is now properly treated as a grouping aggregation and returns the correct answer. ## How was this patch tested? New unit tests added Author: Feng Liu <feng...@databricks.com> Closes #20174 from liufengdb/fix-duplicate. (cherry picked from commit 9b33dfc408de986f4203bb0ac0c3f5c56effd69d) Signed-off-by: Cheng Lian <lian.cs....@gmail.com> Project: http://git-wip-us.apache.org/repos/asf/spark/repo Commit: http://git-wip-us.apache.org/repos/asf/spark/commit/eb4fa551 Tree: http://git-wip-us.apache.org/repos/asf/spark/tree/eb4fa551 Diff: http://git-wip-us.apache.org/repos/asf/spark/diff/eb4fa551 Branch: refs/heads/branch-2.3 Commit: eb4fa551e60800269a939b2c1c0ad69e3a801264 Parents: 5b5851c Author: Feng Liu <feng...@databricks.com> Authored: Wed Jan 10 14:25:04 2018 -0800 Committer: Cheng Lian <lian.cs....@gmail.com> Committed: Wed Jan 10 14:25:33 2018 -0800 ---------------------------------------------------------------------- .../sql/catalyst/optimizer/Optimizer.scala | 8 ++++++- .../optimizer/ReplaceOperatorSuite.scala | 10 +++++++- .../spark/sql/DataFrameAggregateSuite.scala | 24 ++++++++++++++++++-- 3 files changed, 38 insertions(+), 4 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/spark/blob/eb4fa551/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala ---------------------------------------------------------------------- diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala index df0af82..c794ba8 100644 --- a/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala +++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/catalyst/optimizer/Optimizer.scala @@ -1222,7 +1222,13 @@ object ReplaceDeduplicateWithAggregate extends Rule[LogicalPlan] { Alias(new First(attr).toAggregateExpression(), attr.name)(attr.exprId) } } - Aggregate(keys, aggCols, child) + // SPARK-22951: Physical aggregate operators distinguishes global aggregation and grouping + // aggregations by checking the number of grouping keys. The key difference here is that a + // global aggregation always returns at least one row even if there are no input rows. Here + // we append a literal when the grouping key list is empty so that the result aggregate + // operator is properly treated as a grouping aggregation. + val nonemptyKeys = if (keys.isEmpty) Literal(1) :: Nil else keys + Aggregate(nonemptyKeys, aggCols, child) } } http://git-wip-us.apache.org/repos/asf/spark/blob/eb4fa551/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceOperatorSuite.scala ---------------------------------------------------------------------- diff --git a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceOperatorSuite.scala b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceOperatorSuite.scala index 0fa1aae..e9701ff 100644 --- a/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceOperatorSuite.scala +++ b/sql/catalyst/src/test/scala/org/apache/spark/sql/catalyst/optimizer/ReplaceOperatorSuite.scala @@ -20,7 +20,7 @@ package org.apache.spark.sql.catalyst.optimizer import org.apache.spark.sql.Row import org.apache.spark.sql.catalyst.dsl.expressions._ import org.apache.spark.sql.catalyst.dsl.plans._ -import org.apache.spark.sql.catalyst.expressions.{Alias, Not} +import org.apache.spark.sql.catalyst.expressions.{Alias, Literal, Not} import org.apache.spark.sql.catalyst.expressions.aggregate.First import org.apache.spark.sql.catalyst.plans.{LeftAnti, LeftSemi, PlanTest} import org.apache.spark.sql.catalyst.plans.logical._ @@ -198,6 +198,14 @@ class ReplaceOperatorSuite extends PlanTest { comparePlans(optimized, correctAnswer) } + test("add one grouping key if necessary when replace Deduplicate with Aggregate") { + val input = LocalRelation() + val query = Deduplicate(Seq.empty, input) // dropDuplicates() + val optimized = Optimize.execute(query.analyze) + val correctAnswer = Aggregate(Seq(Literal(1)), input.output, input) + comparePlans(optimized, correctAnswer) + } + test("don't replace streaming Deduplicate") { val input = LocalRelation(Seq('a.int, 'b.int), isStreaming = true) val attrA = input.output(0) http://git-wip-us.apache.org/repos/asf/spark/blob/eb4fa551/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala ---------------------------------------------------------------------- diff --git a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala index 06848e4..e7776e3 100644 --- a/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala +++ b/sql/core/src/test/scala/org/apache/spark/sql/DataFrameAggregateSuite.scala @@ -19,6 +19,8 @@ package org.apache.spark.sql import scala.util.Random +import org.apache.spark.sql.catalyst.expressions.{Alias, Literal} +import org.apache.spark.sql.catalyst.expressions.aggregate.Count import org.apache.spark.sql.execution.WholeStageCodegenExec import org.apache.spark.sql.execution.aggregate.{HashAggregateExec, ObjectHashAggregateExec, SortAggregateExec} import org.apache.spark.sql.execution.exchange.ShuffleExchangeExec @@ -27,7 +29,7 @@ import org.apache.spark.sql.functions._ import org.apache.spark.sql.internal.SQLConf import org.apache.spark.sql.test.SharedSQLContext import org.apache.spark.sql.test.SQLTestData.DecimalData -import org.apache.spark.sql.types.{Decimal, DecimalType} +import org.apache.spark.sql.types.DecimalType case class Fact(date: Int, hour: Int, minute: Int, room_name: String, temp: Double) @@ -456,7 +458,6 @@ class DataFrameAggregateSuite extends QueryTest with SharedSQLContext { test("null moments") { val emptyTableData = Seq.empty[(Int, Int)].toDF("a", "b") - checkAnswer( emptyTableData.agg(variance('a), var_samp('a), var_pop('a), skewness('a), kurtosis('a)), Row(null, null, null, null, null)) @@ -666,4 +667,23 @@ class DataFrameAggregateSuite extends QueryTest with SharedSQLContext { assert(exchangePlans.length == 1) } } + + Seq(true, false).foreach { codegen => + test("SPARK-22951: dropDuplicates on empty dataFrames should produce correct aggregate " + + s"results when codegen is enabled: $codegen") { + withSQLConf((SQLConf.WHOLESTAGE_CODEGEN_ENABLED.key, codegen.toString)) { + // explicit global aggregations + val emptyAgg = Map.empty[String, String] + checkAnswer(spark.emptyDataFrame.agg(emptyAgg), Seq(Row())) + checkAnswer(spark.emptyDataFrame.groupBy().agg(emptyAgg), Seq(Row())) + checkAnswer(spark.emptyDataFrame.groupBy().agg(count("*")), Seq(Row(0))) + checkAnswer(spark.emptyDataFrame.dropDuplicates().agg(emptyAgg), Seq(Row())) + checkAnswer(spark.emptyDataFrame.dropDuplicates().groupBy().agg(emptyAgg), Seq(Row())) + checkAnswer(spark.emptyDataFrame.dropDuplicates().groupBy().agg(count("*")), Seq(Row(0))) + + // global aggregation is converted to grouping aggregation: + assert(spark.emptyDataFrame.dropDuplicates().count() == 0) + } + } + } } --------------------------------------------------------------------- To unsubscribe, e-mail: commits-unsubscr...@spark.apache.org For additional commands, e-mail: commits-h...@spark.apache.org