ShreyeshArangath commented on code in PR #1820: URL: https://github.com/apache/auron/pull/1820#discussion_r2710920527
########## auron-spark-tests/spark33/src/test/scala/org/apache/spark/sql/AuronDataFrameSuite.scala: ########## @@ -0,0 +1,280 @@ +/* + * 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. + */ +package org.apache.spark.sql + +import java.io.ByteArrayOutputStream + +import scala.util.Random + +import org.apache.spark.sql.catalyst.expressions.{EqualTo, Expression} +import org.apache.spark.sql.execution.auron.plan.{NativeAggExec, NativeShuffleExchangeExec} +import org.apache.spark.sql.execution.exchange.ReusedExchangeExec +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.test.SQLTestData.TestData2 + +class AuronDataFrameSuite extends DataFrameSuite with SparkQueryTestsBase { + + testAuron("repartitionByRange") { + val partitionNum = 10 + withSQLConf( + SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> "false", + SQLConf.SHUFFLE_PARTITIONS.key -> partitionNum.toString) { + import testImplicits._ + val data1d = Random.shuffle(0.to(partitionNum - 1)) + val data2d = data1d.map(i => (i, data1d.size - i)) + + checkAnswer( + data1d + .toDF("val") + .repartitionByRange(data1d.size, $"val".asc) + .select(spark_partition_id().as("id"), $"val"), + data1d.map(i => Row(i, i))) + + checkAnswer( + data1d + .toDF("val") + .repartitionByRange(data1d.size, $"val".desc) + .select(spark_partition_id().as("id"), $"val"), + data1d.map(i => Row(i, data1d.size - 1 - i))) + + checkAnswer( + data1d + .toDF("val") + .repartitionByRange(data1d.size, lit(42)) + .select(spark_partition_id().as("id"), $"val"), + data1d.map(i => Row(0, i))) + + checkAnswer( + data1d + .toDF("val") + .repartitionByRange(data1d.size, lit(null), $"val".asc, rand()) + .select(spark_partition_id().as("id"), $"val"), + data1d.map(i => Row(i, i))) + + checkAnswer( + data2d + .toDF("a", "b") + .repartitionByRange(data2d.size, $"a".desc, $"b") + .select(spark_partition_id().as("id"), $"a", $"b"), + data2d + .toDF("a", "b") + .repartitionByRange(data2d.size, $"a".desc, $"b".asc) + .select(spark_partition_id().as("id"), $"a", $"b")) + + intercept[IllegalArgumentException] { + data1d.toDF("val").repartitionByRange(data1d.size) + } + intercept[IllegalArgumentException] { + data1d.toDF("val").repartitionByRange(data1d.size, Seq.empty: _*) + } + } + } + + testAuron("distributeBy and localSort") { + import testImplicits._ + val data = spark.sparkContext.parallelize((1 to 100).map(i => TestData2(i % 10, i))).toDF() + + var partitionNum = 1 + val original = testData.repartition(partitionNum) + assert(original.rdd.partitions.length == partitionNum) + + val df6 = data.repartition(partitionNum, $"a").sortWithinPartitions("b") + df6.rdd.foreachPartition { p => + var previousValue: Int = -1 + var allSequential: Boolean = true + p.foreach { r => + val v: Int = r.getInt(1) + if (previousValue != -1) { + if (previousValue > v) throw new Exception("Partition is not ordered.") + if (v - 1 != previousValue) allSequential = false + } + previousValue = v + } + if (!allSequential) { + throw new Exception("Partition should contain all sequential values") + } + } + + partitionNum = 5 + withSQLConf( + SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> "false", + SQLConf.SHUFFLE_PARTITIONS.key -> partitionNum.toString) { + val df = original.repartition(partitionNum, $"key") + assert(df.rdd.partitions.length == partitionNum) + checkAnswer(original.select(), df.select()) + + val df4 = data.repartition(partitionNum, $"a").sortWithinPartitions($"b".desc) + df4.rdd.foreachPartition { p => + if (p.hasNext) { + var previousValue: Int = -1 + var allSequential: Boolean = true + p.foreach { r => + val v: Int = r.getInt(1) + if (previousValue != -1) { + if (previousValue < v) throw new Exception("Partition is not ordered.") + if (v + 1 != previousValue) allSequential = false + } + previousValue = v + } + if (allSequential) throw new Exception("Partition should not be globally ordered") + } + } + } + + partitionNum = 10 + withSQLConf( + SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> "false", + SQLConf.SHUFFLE_PARTITIONS.key -> partitionNum.toString) { + val df2 = original.repartition(partitionNum, $"key") + assert(df2.rdd.partitions.length == partitionNum) + checkAnswer(original.select(), df2.select()) + } + + val df3 = testData.repartition($"key").groupBy("key").count() + verifyNonExchangingAgg(df3) + verifyNonExchangingAgg( + testData + .repartition($"key", $"value") + .groupBy("key", "value") + .count()) + + verifyExchangingAgg( + testData + .repartition($"key", $"value") + .groupBy("key") + .count()) + + partitionNum = 2 + withSQLConf( + SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> "false", + SQLConf.SHUFFLE_PARTITIONS.key -> partitionNum.toString) { + val df5 = data.repartition(partitionNum, $"a").sortWithinPartitions($"b".asc, $"a".asc) + df5.rdd.foreachPartition { p => + var previousValue: Int = -1 + var allSequential: Boolean = true + p.foreach { r => + val v: Int = r.getInt(1) + if (previousValue != -1) { + if (previousValue > v) throw new Exception("Partition is not ordered.") + if (v - 1 != previousValue) allSequential = false + } + previousValue = v + } + if (allSequential) throw new Exception("Partition should not be all sequential") + } + } + } + + testAuron("reuse exchange") { + withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "2") { + val df = spark.range(100).toDF() + val join = df.join(df, "id") + checkAnswer(join, df) + val shuffleCount = collect(join.queryExecution.executedPlan) { + case e: NativeShuffleExchangeExec => + true + }.size + assert(shuffleCount === 1, s"Expected 1 shuffle exchange, got $shuffleCount") + assert(collect(join.queryExecution.executedPlan) { case e: ReusedExchangeExec => + true + }.size === 1) + val broadcasted = broadcast(join) + val join2 = join.join(broadcasted, "id").join(broadcasted, "id") + checkAnswer(join2, df) + val shuffleCount2 = collect(join2.queryExecution.executedPlan) { + case e: NativeShuffleExchangeExec => + true + }.size + assert(shuffleCount2 == 1, s"Expected 1 shuffle exchange in join2, got $shuffleCount2") + assert(collect(join2.queryExecution.executedPlan) { case e: ReusedExchangeExec => + true + }.size == 4) + } + } + + testAuron("SPARK-22520: support code generation for large CaseWhen") { + import org.apache.spark.sql.catalyst.dsl.expressions.StringToAttributeConversionHelper Review Comment: This needs to exist because of using "$" -- This is an automated message from the Apache Git Service. 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