Github user andrewor14 commented on a diff in the pull request:
https://github.com/apache/spark/pull/1499#discussion_r15621714
--- Diff:
core/src/test/scala/org/apache/spark/util/collection/ExternalSorterSuite.scala
---
@@ -0,0 +1,566 @@
+/*
+ * 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.util.collection
+
+import scala.collection.mutable.ArrayBuffer
+
+import org.scalatest.FunSuite
+
+import org.apache.spark._
+import org.apache.spark.SparkContext._
+
+class ExternalSorterSuite extends FunSuite with LocalSparkContext {
+ test("empty data stream") {
+ val conf = new SparkConf(false)
+ conf.set("spark.shuffle.memoryFraction", "0.001")
+ conf.set("spark.shuffle.manager",
"org.apache.spark.shuffle.sort.SortShuffleManager")
+ sc = new SparkContext("local", "test", conf)
+
+ val agg = new Aggregator[Int, Int, Int](i => i, (i, j) => i + j, (i,
j) => i + j)
+ val ord = implicitly[Ordering[Int]]
+
+ // Both aggregator and ordering
+ val sorter = new ExternalSorter[Int, Int, Int](
+ Some(agg), Some(new HashPartitioner(3)), Some(ord), None)
+ assert(sorter.iterator.toSeq === Seq())
+ sorter.stop()
+
+ // Only aggregator
+ val sorter2 = new ExternalSorter[Int, Int, Int](
+ Some(agg), Some(new HashPartitioner(3)), None, None)
+ assert(sorter2.iterator.toSeq === Seq())
+ sorter2.stop()
+
+ // Only ordering
+ val sorter3 = new ExternalSorter[Int, Int, Int](
+ None, Some(new HashPartitioner(3)), Some(ord), None)
+ assert(sorter3.iterator.toSeq === Seq())
+ sorter3.stop()
+
+ // Neither aggregator nor ordering
+ val sorter4 = new ExternalSorter[Int, Int, Int](
+ None, Some(new HashPartitioner(3)), None, None)
+ assert(sorter4.iterator.toSeq === Seq())
+ sorter4.stop()
+ }
+
+ test("few elements per partition") {
+ val conf = new SparkConf(false)
+ conf.set("spark.shuffle.memoryFraction", "0.001")
+ conf.set("spark.shuffle.manager",
"org.apache.spark.shuffle.sort.SortShuffleManager")
+ sc = new SparkContext("local", "test", conf)
+
+ val agg = new Aggregator[Int, Int, Int](i => i, (i, j) => i + j, (i,
j) => i + j)
+ val ord = implicitly[Ordering[Int]]
+ val elements = Set((1, 1), (2, 2), (5, 5))
+ val expected = Set(
+ (0, Set()), (1, Set((1, 1))), (2, Set((2, 2))), (3, Set()), (4,
Set()),
+ (5, Set((5, 5))), (6, Set()))
+
+ // Both aggregator and ordering
+ val sorter = new ExternalSorter[Int, Int, Int](
+ Some(agg), Some(new HashPartitioner(7)), Some(ord), None)
+ sorter.write(elements.iterator)
+ assert(sorter.partitionedIterator.map(p => (p._1, p._2.toSet)).toSet
=== expected)
+ sorter.stop()
+
+ // Only aggregator
+ val sorter2 = new ExternalSorter[Int, Int, Int](
+ Some(agg), Some(new HashPartitioner(7)), None, None)
+ sorter2.write(elements.iterator)
+ assert(sorter2.partitionedIterator.map(p => (p._1, p._2.toSet)).toSet
=== expected)
+ sorter2.stop()
+
+ // Only ordering
+ val sorter3 = new ExternalSorter[Int, Int, Int](
+ None, Some(new HashPartitioner(7)), Some(ord), None)
+ sorter3.write(elements.iterator)
+ assert(sorter3.partitionedIterator.map(p => (p._1, p._2.toSet)).toSet
=== expected)
+ sorter3.stop()
+
+ // Neither aggregator nor ordering
+ val sorter4 = new ExternalSorter[Int, Int, Int](
+ None, Some(new HashPartitioner(7)), None, None)
+ sorter4.write(elements.iterator)
+ assert(sorter4.partitionedIterator.map(p => (p._1, p._2.toSet)).toSet
=== expected)
+ sorter4.stop()
+ }
+
+ test("empty partitions with spilling") {
+ val conf = new SparkConf(false)
+ conf.set("spark.shuffle.memoryFraction", "0.001")
+ conf.set("spark.shuffle.manager",
"org.apache.spark.shuffle.sort.SortShuffleManager")
+ sc = new SparkContext("local", "test", conf)
+
+ val agg = new Aggregator[Int, Int, Int](i => i, (i, j) => i + j, (i,
j) => i + j)
+ val ord = implicitly[Ordering[Int]]
+ val elements = Iterator((1, 1), (5, 5)) ++ (0 until
100000).iterator.map(x => (2, 2))
+
+ val sorter = new ExternalSorter[Int, Int, Int](
+ None, Some(new HashPartitioner(7)), None, None)
+ sorter.write(elements)
+ assert(sc.env.blockManager.diskBlockManager.getAllFiles().length > 0)
// Make sure it spilled
+ val iter = sorter.partitionedIterator.map(p => (p._1, p._2.toList))
+ assert(iter.next() === (0, Nil))
+ assert(iter.next() === (1, List((1, 1))))
+ assert(iter.next() === (2, (0 until 100000).map(x => (2, 2)).toList))
+ assert(iter.next() === (3, Nil))
+ assert(iter.next() === (4, Nil))
+ assert(iter.next() === (5, List((5, 5))))
+ assert(iter.next() === (6, Nil))
+ sorter.stop()
+ }
+
+ test("spilling in local cluster") {
+ val conf = new SparkConf(true) // Load defaults, otherwise SPARK_HOME
is not found
+ conf.set("spark.shuffle.memoryFraction", "0.001")
+ conf.set("spark.shuffle.manager",
"org.apache.spark.shuffle.sort.SortShuffleManager")
+ sc = new SparkContext("local-cluster[1,1,512]", "test", conf)
+
+ // reduceByKey - should spill ~8 times
+ val rddA = sc.parallelize(0 until 100000).map(i => (i/2, i))
+ val resultA = rddA.reduceByKey(math.max).collect()
+ assert(resultA.length == 50000)
+ resultA.foreach { case(k, v) =>
+ if (v != k * 2 + 1) {
+ fail(s"Value for ${k} was wrong: expected ${k * 2 + 1}, got ${v}")
+ }
+ }
+
+ // groupByKey - should spill ~17 times
+ val rddB = sc.parallelize(0 until 100000).map(i => (i/4, i))
+ val resultB = rddB.groupByKey().collect()
+ assert(resultB.length == 25000)
+ resultB.foreach { case(i, seq) =>
+ val expected = Set(i * 4, i * 4 + 1, i * 4 + 2, i * 4 + 3)
+ if (seq.toSet != expected) {
+ fail(s"Value for ${i} was wrong: expected ${expected}, got
${seq.toSet}")
+ }
+ }
+
+ // cogroup - should spill ~7 times
+ val rddC1 = sc.parallelize(0 until 10000).map(i => (i, i))
+ val rddC2 = sc.parallelize(0 until 10000).map(i => (i%1000, i))
+ val resultC = rddC1.cogroup(rddC2).collect()
+ assert(resultC.length == 10000)
+ resultC.foreach { case(i, (seq1, seq2)) =>
+ i match {
+ case 0 =>
+ assert(seq1.toSet == Set[Int](0))
+ assert(seq2.toSet == Set[Int](0, 1000, 2000, 3000, 4000, 5000,
6000, 7000, 8000, 9000))
+ case 1 =>
+ assert(seq1.toSet == Set[Int](1))
+ assert(seq2.toSet == Set[Int](1, 1001, 2001, 3001, 4001, 5001,
6001, 7001, 8001, 9001))
+ case 5000 =>
+ assert(seq1.toSet == Set[Int](5000))
+ assert(seq2.toSet == Set[Int]())
+ case 9999 =>
+ assert(seq1.toSet == Set[Int](9999))
+ assert(seq2.toSet == Set[Int]())
+ case _ =>
+ }
+ }
+
+ // larger cogroup - should spill ~7 times
--- End diff --
strange that larger cogroup spills the same number of times than the
previous cogroup
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