diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala
index 1484f29525a4eb18cf7af08d98488cb7753bd7e9..debbd8d7c26c9a005ea17288a708efb9bb5abc7d 100644
--- a/core/src/main/scala/org/apache/spark/Partitioner.scala
+++ b/core/src/main/scala/org/apache/spark/Partitioner.scala
@@ -108,11 +108,21 @@ class HashPartitioner(partitions: Int) extends Partitioner {
class RangePartitioner[K : Ordering : ClassTag, V](
partitions: Int,
rdd: RDD[_ <: Product2[K, V]],
- private var ascending: Boolean = true)
+ private var ascending: Boolean = true,
+ val samplePointsPerPartitionHint: Int = 20)
extends Partitioner {
+ // A constructor declared in order to maintain backward compatibility for Java, when we add the
+ // 4th constructor parameter samplePointsPerPartitionHint. See SPARK-22160.
+ // This is added to make sure from a bytecode point of view, there is still a 3-arg ctor.
+ def this(partitions: Int, rdd: RDD[_ <: Product2[K, V]], ascending: Boolean) = {
+ this(partitions, rdd, ascending, samplePointsPerPartitionHint = 20)
+ }
+
// We allow partitions = 0, which happens when sorting an empty RDD under the default settings.
require(partitions >= 0, s"Number of partitions cannot be negative but found $partitions.")
+ require(samplePointsPerPartitionHint > 0,
+ s"Sample points per partition must be greater than 0 but found $samplePointsPerPartitionHint")
private var ordering = implicitly[Ordering[K]]
@@ -122,7 +132,8 @@ class RangePartitioner[K : Ordering : ClassTag, V](
Array.empty
} else {
// This is the sample size we need to have roughly balanced output partitions, capped at 1M.
- val sampleSize = math.min(20.0 * partitions, 1e6)
+ // Cast to double to avoid overflowing ints or longs
+ val sampleSize = math.min(samplePointsPerPartitionHint.toDouble * partitions, 1e6)
// Assume the input partitions are roughly balanced and over-sample a little bit.
val sampleSizePerPartition = math.ceil(3.0 * sampleSize / rdd.partitions.length).toInt
val (numItems, sketched) = RangePartitioner.sketch(rdd.map(_._1), sampleSizePerPartition)
diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
index 358cf621490709f46ebd0e0432e7098a4fdc5191..1a73d168b9b6e85ef65f0826867774c4822a4428 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
@@ -907,6 +907,14 @@ object SQLConf {
.booleanConf
.createWithDefault(false)
+ val RANGE_EXCHANGE_SAMPLE_SIZE_PER_PARTITION =
+ buildConf("spark.sql.execution.rangeExchange.sampleSizePerPartition")
+ .internal()
+ .doc("Number of points to sample per partition in order to determine the range boundaries" +
+ " for range partitioning, typically used in global sorting (without limit).")
+ .intConf
+ .createWithDefault(100)
+
val ARROW_EXECUTION_ENABLE =
buildConf("spark.sql.execution.arrow.enabled")
.internal()
@@ -1199,6 +1207,8 @@ class SQLConf extends Serializable with Logging {
def supportQuotedRegexColumnName: Boolean = getConf(SUPPORT_QUOTED_REGEX_COLUMN_NAME)
+ def rangeExchangeSampleSizePerPartition: Int = getConf(RANGE_EXCHANGE_SAMPLE_SIZE_PER_PARTITION)
+
def arrowEnable: Boolean = getConf(ARROW_EXECUTION_ENABLE)
def arrowMaxRecordsPerBatch: Int = getConf(ARROW_EXECUTION_MAX_RECORDS_PER_BATCH)
diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/ShuffleExchangeExec.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/ShuffleExchangeExec.scala
index 11c4aa9b4acf0f4760822351130542fd162d3c3c..5a1e217082bc2e2d1cbfe7433816dca2101a9992 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/ShuffleExchangeExec.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/exchange/ShuffleExchangeExec.scala
@@ -30,6 +30,7 @@ import org.apache.spark.sql.catalyst.expressions.codegen.LazilyGeneratedOrdering
import org.apache.spark.sql.catalyst.plans.physical._
import org.apache.spark.sql.execution._
import org.apache.spark.sql.execution.metric.SQLMetrics
+import org.apache.spark.sql.internal.SQLConf
import org.apache.spark.util.MutablePair
/**
@@ -218,7 +219,11 @@ object ShuffleExchangeExec {
iter.map(row => mutablePair.update(row.copy(), null))
}
implicit val ordering = new LazilyGeneratedOrdering(sortingExpressions, outputAttributes)
- new RangePartitioner(numPartitions, rddForSampling, ascending = true)
+ new RangePartitioner(
+ numPartitions,
+ rddForSampling,
+ ascending = true,
+ samplePointsPerPartitionHint = SQLConf.get.rangeExchangeSampleSizePerPartition)
case SinglePartition =>
new Partitioner {
override def numPartitions: Int = 1
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/ConfigBehaviorSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/ConfigBehaviorSuite.scala
new file mode 100644
index 0000000000000000000000000000000000000000..2c1e5db5fd9bb043604f7843a23e8e83eefeca20
--- /dev/null
+++ b/sql/core/src/test/scala/org/apache/spark/sql/ConfigBehaviorSuite.scala
@@ -0,0 +1,66 @@
+/*
+ * 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.
+ // Also the whole code path for range partitioning as implemented should be deterministic
+ // (it uses the partition id as the seed), so this test shouldn't be flaky.
+
+ 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)
+ }
+ }
+ }
+
+}