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[SPARK-24373][SQL] Add AnalysisBarrier to RelationalGroupedDataset's …
…and KeyValueGroupedDataset's child ## What changes were proposed in this pull request? When we create a `RelationalGroupedDataset` or a `KeyValueGroupedDataset` we set its child to the `logicalPlan` of the `DataFrame` we need to aggregate. Since the `logicalPlan` is already analyzed, we should not analyze it again. But this happens when the new plan of the aggregate is analyzed. The current behavior in most of the cases is likely to produce no harm, but in other cases re-analyzing an analyzed plan can change it, since the analysis is not idempotent. This can cause issues like the one described in the JIRA (missing to find a cached plan). The PR adds an `AnalysisBarrier` to the `logicalPlan` which is used as child of `RelationalGroupedDataset` or a `KeyValueGroupedDataset`. ## How was this patch tested? added UT Author: Marco Gaido <[email protected]> Closes #21432 from mgaido91/SPARK-24373.
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sql/core/src/test/scala/org/apache/spark/sql/GroupedDatasetSuite.scala
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| /* | ||
| * Licensed to the Apache Software Foundation (ASF) under one or more | ||
| * contributor license agreements. See the NOTICE file distributed with | ||
| * this work for additional information regarding copyright ownership. | ||
| * The ASF licenses this file to You under the Apache License, Version 2.0 | ||
| * (the "License"); you may not use this file except in compliance with | ||
| * the License. You may obtain a copy of the License at | ||
| * | ||
| * http://www.apache.org/licenses/LICENSE-2.0 | ||
| * | ||
| * Unless required by applicable law or agreed to in writing, software | ||
| * distributed under the License is distributed on an "AS IS" BASIS, | ||
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| * See the License for the specific language governing permissions and | ||
| * limitations under the License. | ||
| */ | ||
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| package org.apache.spark.sql | ||
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| import org.apache.spark.api.python.PythonEvalType | ||
| import org.apache.spark.sql.catalyst.expressions.PythonUDF | ||
| import org.apache.spark.sql.catalyst.plans.logical.AnalysisBarrier | ||
| import org.apache.spark.sql.functions.udf | ||
| import org.apache.spark.sql.test.SharedSQLContext | ||
| import org.apache.spark.sql.types.{LongType, StructField, StructType} | ||
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| class GroupedDatasetSuite extends QueryTest with SharedSQLContext { | ||
| import testImplicits._ | ||
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| private val scalaUDF = udf((x: Long) => { x + 1 }) | ||
| private lazy val datasetWithUDF = spark.range(1).toDF("s").select($"s", scalaUDF($"s")) | ||
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| private def assertContainsAnalysisBarrier(ds: Dataset[_], atLevel: Int = 1): Unit = { | ||
| assert(atLevel >= 0) | ||
| var children = Seq(ds.queryExecution.logical) | ||
| (1 to atLevel).foreach { _ => | ||
| children = children.flatMap(_.children) | ||
| } | ||
| val barriers = children.collect { | ||
| case ab: AnalysisBarrier => ab | ||
| } | ||
| assert(barriers.nonEmpty, s"Plan does not contain AnalysisBarrier at level $atLevel:\n" + | ||
| ds.queryExecution.logical) | ||
| } | ||
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| test("SPARK-24373: avoid running Analyzer rules twice on RelationalGroupedDataset") { | ||
| val groupByDataset = datasetWithUDF.groupBy() | ||
| val rollupDataset = datasetWithUDF.rollup("s") | ||
| val cubeDataset = datasetWithUDF.cube("s") | ||
| val pivotDataset = datasetWithUDF.groupBy().pivot("s", Seq(1, 2)) | ||
| datasetWithUDF.cache() | ||
| Seq(groupByDataset, rollupDataset, cubeDataset, pivotDataset).foreach { rgDS => | ||
| val df = rgDS.count() | ||
| assertContainsAnalysisBarrier(df) | ||
| assertCached(df) | ||
| } | ||
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| val flatMapGroupsInRDF = datasetWithUDF.groupBy().flatMapGroupsInR( | ||
| Array.emptyByteArray, | ||
| Array.emptyByteArray, | ||
| Array.empty, | ||
| StructType(Seq(StructField("s", LongType)))) | ||
| val flatMapGroupsInPandasDF = datasetWithUDF.groupBy().flatMapGroupsInPandas(PythonUDF( | ||
| "pyUDF", | ||
| null, | ||
| StructType(Seq(StructField("s", LongType))), | ||
| Seq.empty, | ||
| PythonEvalType.SQL_GROUPED_MAP_PANDAS_UDF, | ||
| true)) | ||
| Seq(flatMapGroupsInRDF, flatMapGroupsInPandasDF).foreach { df => | ||
| assertContainsAnalysisBarrier(df, 2) | ||
| assertCached(df) | ||
| } | ||
| datasetWithUDF.unpersist(true) | ||
| } | ||
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| test("SPARK-24373: avoid running Analyzer rules twice on KeyValueGroupedDataset") { | ||
| val kvDasaset = datasetWithUDF.groupByKey(_.getLong(0)) | ||
| datasetWithUDF.cache() | ||
| val mapValuesKVDataset = kvDasaset.mapValues(_.getLong(0)).reduceGroups(_ + _) | ||
| val keysKVDataset = kvDasaset.keys | ||
| val flatMapGroupsKVDataset = kvDasaset.flatMapGroups((k, _) => Seq(k)) | ||
| val aggKVDataset = kvDasaset.count() | ||
| val otherKVDataset = spark.range(1).groupByKey(_ + 1) | ||
| val cogroupKVDataset = kvDasaset.cogroup(otherKVDataset)((k, _, _) => Seq(k)) | ||
| Seq((mapValuesKVDataset, 1), | ||
| (keysKVDataset, 2), | ||
| (flatMapGroupsKVDataset, 2), | ||
| (aggKVDataset, 1), | ||
| (cogroupKVDataset, 2)).foreach { case (df, analysisBarrierDepth) => | ||
| assertContainsAnalysisBarrier(df, analysisBarrierDepth) | ||
| assertCached(df) | ||
| } | ||
| datasetWithUDF.unpersist(true) | ||
| } | ||
| } |