Github user rxin commented on a diff in the pull request:
https://github.com/apache/spark/pull/19387#discussion_r141764431
--- 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)
+
+ withSQLConf(SQLConf.RANGE_EXCHANGE_SAMPLE_SIZE_PER_PARTITION.key ->
"1") {
+ // If we only sample one point, the range boundaries will be
pretty bad and the
+ // chi-sq value would be very high.
+ assert(computeChiSquareTest() > 1000)
--- End diff --
the value i got from my laptop was 1800
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