Github user rxin commented on a diff in the pull request: https://github.com/apache/spark/pull/19387#discussion_r141764415 --- Diff: sql/core/src/test/scala/org/apache/spark/sql/ConfigBehaviorSuite.scala --- @@ -0,0 +1,64 @@ +/* + * 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 org.apache.commons.math3.stat.inference.ChiSquareTest + +import org.apache.spark.sql.internal.SQLConf +import org.apache.spark.sql.test.SharedSQLContext + + +class ConfigBehaviorSuite extends QueryTest with SharedSQLContext { + + import testImplicits._ + + test("SPARK-22160 spark.sql.execution.rangeExchange.sampleSizePerPartition") { + // In this test, we run a sort and compute the histogram for partition size post shuffle. + // With a high sample count, the partition size should be more evenly distributed, and has a + // low chi-sq test value. + + val numPartitions = 4 + + def computeChiSquareTest(): Double = { + val n = 10000 + // Trigger a sort + val data = spark.range(0, n, 1, 1).sort('id) + .selectExpr("SPARK_PARTITION_ID() pid", "id").as[(Int, Long)].collect() + + // Compute histogram for the number of records per partition post sort + val dist = data.groupBy(_._1).map(_._2.length.toLong).toArray + assert(dist.length == 4) + + new ChiSquareTest().chiSquare( + Array.fill(numPartitions) { n.toDouble / numPartitions }, + dist) + } + + withSQLConf(SQLConf.SHUFFLE_PARTITIONS.key -> numPartitions.toString) { + // The default chi-sq value should be low + assert(computeChiSquareTest() < 100) --- End diff -- 100 - which is pretty high the actual value computed on my laptop is around 10, so 1000 is already three orders of magnitude larger
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