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/*
* 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
import java.util.Properties
import java.util.concurrent.{Callable, CyclicBarrier, Executors, ExecutorService}
import org.scalatest.Matchers
import org.apache.spark.ShuffleSuite.NonJavaSerializableClass
import org.apache.spark.memory.TaskMemoryManager
import org.apache.spark.rdd.{CoGroupedRDD, OrderedRDDFunctions, RDD, ShuffledRDD, SubtractedRDD}
import org.apache.spark.scheduler.{MapStatus, MyRDD, SparkListener, SparkListenerTaskEnd}
import org.apache.spark.serializer.KryoSerializer
import org.apache.spark.shuffle.ShuffleWriter
import org.apache.spark.storage.{ShuffleBlockId, ShuffleDataBlockId}
import org.apache.spark.util.MutablePair
abstract class ShuffleSuite extends SparkFunSuite with Matchers with LocalSparkContext {
val conf = new SparkConf(loadDefaults = false)
// Ensure that the DAGScheduler doesn't retry stages whose fetches fail, so that we accurately
// test that the shuffle works (rather than retrying until all blocks are local to one Executor).
conf.set("spark.test.noStageRetry", "true")
test("groupByKey without compression") {
val myConf = conf.clone().set("spark.shuffle.compress", "false")
sc = new SparkContext("local", "test", myConf)
val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1)), 4)
val groups = pairs.groupByKey(4).collect()
assert(groups.size === 2)
val valuesFor1 = groups.find(_._1 == 1).get._2
assert(valuesFor1.toList.sorted === List(1, 2, 3))
val valuesFor2 = groups.find(_._1 == 2).get._2
assert(valuesFor2.toList.sorted === List(1))
}
test("shuffle non-zero block size") {
sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
val NUM_BLOCKS = 3
val a = sc.parallelize(1 to 10, 2)
val b = a.map { x =>
(x, new NonJavaSerializableClass(x * 2))
}
// If the Kryo serializer is not used correctly, the shuffle would fail because the
// default Java serializer cannot handle the non serializable class.
val c = new ShuffledRDD[Int,
NonJavaSerializableClass,
NonJavaSerializableClass](b, new HashPartitioner(NUM_BLOCKS))
c.setSerializer(new KryoSerializer(conf))
val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]].shuffleId
assert(c.count === 10)
// All blocks must have non-zero size
(0 until NUM_BLOCKS).foreach { id =>
val statuses = SparkEnv.get.mapOutputTracker.getMapSizesByExecutorId(shuffleId, id)
assert(statuses.forall(_._2.forall(blockIdSizePair => blockIdSizePair._2 > 0)))
}
}
test("shuffle serializer") {
// Use a local cluster with 2 processes to make sure there are both local and remote blocks
sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
val a = sc.parallelize(1 to 10, 2)
val b = a.map { x =>
(x, new NonJavaSerializableClass(x * 2))
}
// If the Kryo serializer is not used correctly, the shuffle would fail because the
// default Java serializer cannot handle the non serializable class.
val c = new ShuffledRDD[Int,
NonJavaSerializableClass,
NonJavaSerializableClass](b, new HashPartitioner(3))
c.setSerializer(new KryoSerializer(conf))
assert(c.count === 10)
}
test("zero sized blocks") {
// Use a local cluster with 2 processes to make sure there are both local and remote blocks
sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
// 201 partitions (greater than "spark.shuffle.sort.bypassMergeThreshold") from 4 keys
val NUM_BLOCKS = 201
val a = sc.parallelize(1 to 4, NUM_BLOCKS)
val b = a.map(x => (x, x*2))
// NOTE: The default Java serializer doesn't create zero-sized blocks.
// So, use Kryo
val c = new ShuffledRDD[Int, Int, Int](b, new HashPartitioner(NUM_BLOCKS))
.setSerializer(new KryoSerializer(conf))
val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]].shuffleId
assert(c.count === 4)
val blockSizes = (0 until NUM_BLOCKS).flatMap { id =>
val statuses = SparkEnv.get.mapOutputTracker.getMapSizesByExecutorId(shuffleId, id)
statuses.flatMap(_._2.map(_._2))
}
val nonEmptyBlocks = blockSizes.filter(x => x > 0)
// We should have at most 4 non-zero sized partitions
assert(nonEmptyBlocks.size <= 4)
}
test("zero sized blocks without kryo") {
// Use a local cluster with 2 processes to make sure there are both local and remote blocks
sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
// 201 partitions (greater than "spark.shuffle.sort.bypassMergeThreshold") from 4 keys
val NUM_BLOCKS = 201
val a = sc.parallelize(1 to 4, NUM_BLOCKS)
val b = a.map(x => (x, x*2))
// NOTE: The default Java serializer should create zero-sized blocks
val c = new ShuffledRDD[Int, Int, Int](b, new HashPartitioner(NUM_BLOCKS))
val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[_, _, _]].shuffleId
assert(c.count === 4)
val blockSizes = (0 until NUM_BLOCKS).flatMap { id =>
val statuses = SparkEnv.get.mapOutputTracker.getMapSizesByExecutorId(shuffleId, id)
statuses.flatMap(_._2.map(_._2))
}
val nonEmptyBlocks = blockSizes.filter(x => x > 0)
// We should have at most 4 non-zero sized partitions
assert(nonEmptyBlocks.size <= 4)
}
test("shuffle on mutable pairs") {
// Use a local cluster with 2 processes to make sure there are both local and remote blocks
sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2)
val data = Array(p(1, 1), p(1, 2), p(1, 3), p(2, 1))
val pairs: RDD[MutablePair[Int, Int]] = sc.parallelize(data, 2)
val results = new ShuffledRDD[Int, Int, Int](pairs,
new HashPartitioner(2)).collect()
data.foreach { pair => results should contain ((pair._1, pair._2)) }
}
test("sorting on mutable pairs") {
// This is not in SortingSuite because of the local cluster setup.
// Use a local cluster with 2 processes to make sure there are both local and remote blocks
sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2)
val data = Array(p(1, 11), p(3, 33), p(100, 100), p(2, 22))
val pairs: RDD[MutablePair[Int, Int]] = sc.parallelize(data, 2)
val results = new OrderedRDDFunctions[Int, Int, MutablePair[Int, Int]](pairs)
.sortByKey().collect()
results(0) should be ((1, 11))
results(1) should be ((2, 22))
results(2) should be ((3, 33))
results(3) should be ((100, 100))
}
test("cogroup using mutable pairs") {
// Use a local cluster with 2 processes to make sure there are both local and remote blocks
sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2)
val data1 = Seq(p(1, 1), p(1, 2), p(1, 3), p(2, 1))
val data2 = Seq(p(1, "11"), p(1, "12"), p(2, "22"), p(3, "3"))
val pairs1: RDD[MutablePair[Int, Int]] = sc.parallelize(data1, 2)
val pairs2: RDD[MutablePair[Int, String]] = sc.parallelize(data2, 2)
val results = new CoGroupedRDD[Int](Seq(pairs1, pairs2), new HashPartitioner(2))
.map(p => (p._1, p._2.map(_.toArray)))
.collectAsMap()
assert(results(1)(0).length === 3)
assert(results(1)(0).contains(1))
assert(results(1)(0).contains(2))
assert(results(1)(0).contains(3))
assert(results(1)(1).length === 2)
assert(results(1)(1).contains("11"))
assert(results(1)(1).contains("12"))
assert(results(2)(0).length === 1)
assert(results(2)(0).contains(1))
assert(results(2)(1).length === 1)
assert(results(2)(1).contains("22"))
assert(results(3)(0).length === 0)
assert(results(3)(1).contains("3"))
}
test("subtract mutable pairs") {
// Use a local cluster with 2 processes to make sure there are both local and remote blocks
sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
def p[T1, T2](_1: T1, _2: T2): MutablePair[T1, T2] = MutablePair(_1, _2)
val data1 = Seq(p(1, 1), p(1, 2), p(1, 3), p(2, 1), p(3, 33))
val data2 = Seq(p(1, "11"), p(1, "12"), p(2, "22"))
val pairs1: RDD[MutablePair[Int, Int]] = sc.parallelize(data1, 2)
val pairs2: RDD[MutablePair[Int, String]] = sc.parallelize(data2, 2)
val results = new SubtractedRDD(pairs1, pairs2, new HashPartitioner(2)).collect()
results should have length (1)
// substracted rdd return results as Tuple2
results(0) should be ((3, 33))
}
test("sort with Java non serializable class - Kryo") {
// Use a local cluster with 2 processes to make sure there are both local and remote blocks
val myConf = conf.clone().set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
sc = new SparkContext("local-cluster[2,1,1024]", "test", myConf)
val a = sc.parallelize(1 to 10, 2)
val b = a.map { x =>
(new NonJavaSerializableClass(x), x)
}
// If the Kryo serializer is not used correctly, the shuffle would fail because the
// default Java serializer cannot handle the non serializable class.
val c = b.sortByKey().map(x => x._2)
assert(c.collect() === Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10))
}
test("sort with Java non serializable class - Java") {
// Use a local cluster with 2 processes to make sure there are both local and remote blocks
sc = new SparkContext("local-cluster[2,1,1024]", "test", conf)
val a = sc.parallelize(1 to 10, 2)
val b = a.map { x =>
(new NonJavaSerializableClass(x), x)
}
// default Java serializer cannot handle the non serializable class.
val thrown = intercept[SparkException] {
b.sortByKey().collect()
}
assert(thrown.getClass === classOf[SparkException])
assert(thrown.getMessage.toLowerCase.contains("serializable"))
}
test("shuffle with different compression settings (SPARK-3426)") {
for (
shuffleSpillCompress <- Set(true, false);
shuffleCompress <- Set(true, false)
) {
val myConf = conf.clone()
.setAppName("test")
.setMaster("local")
.set("spark.shuffle.spill.compress", shuffleSpillCompress.toString)
.set("spark.shuffle.compress", shuffleCompress.toString)
resetSparkContext()
sc = new SparkContext(myConf)
val diskBlockManager = sc.env.blockManager.diskBlockManager
try {
assert(diskBlockManager.getAllFiles().isEmpty)
sc.parallelize(0 until 10).map(i => (i / 4, i)).groupByKey().collect()
assert(diskBlockManager.getAllFiles().nonEmpty)
} catch {
case e: Exception =>
val errMsg = s"Failed with spark.shuffle.spill.compress=$shuffleSpillCompress," +
s" spark.shuffle.compress=$shuffleCompress"
throw new Exception(errMsg, e)
}
}
}
test("[SPARK-4085] rerun map stage if reduce stage cannot find its local shuffle file") {
val myConf = conf.clone().set("spark.test.noStageRetry", "false")
sc = new SparkContext("local", "test", myConf)
val rdd = sc.parallelize(1 to 10, 2).map((_, 1)).reduceByKey(_ + _)
rdd.count()
// Delete one of the local shuffle blocks.
val hashFile = sc.env.blockManager.diskBlockManager.getFile(new ShuffleBlockId(0, 0, 0))
val sortFile = sc.env.blockManager.diskBlockManager.getFile(new ShuffleDataBlockId(0, 0, 0))
assert(hashFile.exists() || sortFile.exists())
if (hashFile.exists()) {
hashFile.delete()
}
if (sortFile.exists()) {
sortFile.delete()
}
// This count should retry the execution of the previous stage and rerun shuffle.
rdd.count()
}
test("metrics for shuffle without aggregation") {
sc = new SparkContext("local", "test", conf.clone())
val numRecords = 10000
val metrics = ShuffleSuite.runAndReturnMetrics(sc) {
sc.parallelize(1 to numRecords, 4)
.map(key => (key, 1))
.groupByKey()
.collect()
}
assert(metrics.recordsRead === numRecords)
assert(metrics.recordsWritten === numRecords)
assert(metrics.bytesWritten === metrics.byresRead)
assert(metrics.bytesWritten > 0)
}
test("metrics for shuffle with aggregation") {
sc = new SparkContext("local", "test", conf.clone())
val numRecords = 10000
val metrics = ShuffleSuite.runAndReturnMetrics(sc) {
sc.parallelize(1 to numRecords, 4)
.flatMap(key => Array.fill(100)((key, 1)))
.countByKey()
}
assert(metrics.recordsRead === numRecords)
assert(metrics.recordsWritten === numRecords)
assert(metrics.bytesWritten === metrics.byresRead)
assert(metrics.bytesWritten > 0)
}
test("multiple simultaneous attempts for one task (SPARK-8029)") {
sc = new SparkContext("local", "test", conf)
val mapTrackerMaster = sc.env.mapOutputTracker.asInstanceOf[MapOutputTrackerMaster]
val manager = sc.env.shuffleManager
val taskMemoryManager = new TaskMemoryManager(sc.env.memoryManager, 0L)
val metricsSystem = sc.env.metricsSystem
val shuffleMapRdd = new MyRDD(sc, 1, Nil)
val shuffleDep = new ShuffleDependency(shuffleMapRdd, new HashPartitioner(1))
val shuffleHandle = manager.registerShuffle(0, 1, shuffleDep)
// first attempt -- its successful
val writer1 = manager.getWriter[Int, Int](shuffleHandle, 0,
new TaskContextImpl(0, 0, 0L, 0, taskMemoryManager, new Properties, metricsSystem,
InternalAccumulator.create(sc)))
val data1 = (1 to 10).map { x => x -> x}
// second attempt -- also successful. We'll write out different data,
// just to simulate the fact that the records may get written differently
// depending on what gets spilled, what gets combined, etc.
val writer2 = manager.getWriter[Int, Int](shuffleHandle, 0,
new TaskContextImpl(0, 0, 1L, 0, taskMemoryManager, new Properties, metricsSystem,
InternalAccumulator.create(sc)))
val data2 = (11 to 20).map { x => x -> x}
// interleave writes of both attempts -- we want to test that both attempts can occur
// simultaneously, and everything is still OK
def writeAndClose(
writer: ShuffleWriter[Int, Int])(
iter: Iterator[(Int, Int)]): Option[MapStatus] = {
val files = writer.write(iter)
writer.stop(true)
}
val interleaver = new InterleaveIterators(
data1, writeAndClose(writer1), data2, writeAndClose(writer2))
val (mapOutput1, mapOutput2) = interleaver.run()
// check that we can read the map output and it has the right data
assert(mapOutput1.isDefined)
assert(mapOutput2.isDefined)
assert(mapOutput1.get.location === mapOutput2.get.location)
assert(mapOutput1.get.getSizeForBlock(0) === mapOutput1.get.getSizeForBlock(0))
// register one of the map outputs -- doesn't matter which one
mapOutput1.foreach { case mapStatus =>
mapTrackerMaster.registerMapOutputs(0, Array(mapStatus))
}
val reader = manager.getReader[Int, Int](shuffleHandle, 0, 1,
new TaskContextImpl(1, 0, 2L, 0, taskMemoryManager, new Properties, metricsSystem,
InternalAccumulator.create(sc)))
val readData = reader.read().toIndexedSeq
assert(readData === data1.toIndexedSeq || readData === data2.toIndexedSeq)
manager.unregisterShuffle(0)
}
}
/**
* Utility to help tests make sure that we can process two different iterators simultaneously
* in different threads. This makes sure that in your test, you don't completely process data1 with
* f1 before processing data2 with f2 (or vice versa). It adds a barrier so that the functions only
* process one element, before pausing to wait for the other function to "catch up".
*/
class InterleaveIterators[T, R](
data1: Seq[T],
f1: Iterator[T] => R,
data2: Seq[T],
f2: Iterator[T] => R) {
require(data1.size == data2.size)
val barrier = new CyclicBarrier(2)
class BarrierIterator[E](id: Int, sub: Iterator[E]) extends Iterator[E] {
def hasNext: Boolean = sub.hasNext
def next: E = {
barrier.await()
sub.next()
}
}
val c1 = new Callable[R] {
override def call(): R = f1(new BarrierIterator(1, data1.iterator))
}
val c2 = new Callable[R] {
override def call(): R = f2(new BarrierIterator(2, data2.iterator))
}
val e: ExecutorService = Executors.newFixedThreadPool(2)
def run(): (R, R) = {
val future1 = e.submit(c1)
val future2 = e.submit(c2)
val r1 = future1.get()
val r2 = future2.get()
e.shutdown()
(r1, r2)
}
}
object ShuffleSuite {
def mergeCombineException(x: Int, y: Int): Int = {
throw new SparkException("Exception for map-side combine.")
}
class NonJavaSerializableClass(val value: Int) extends Comparable[NonJavaSerializableClass] {
override def compareTo(o: NonJavaSerializableClass): Int = {
value - o.value
}
}
case class AggregatedShuffleMetrics(
recordsWritten: Long,
recordsRead: Long,
bytesWritten: Long,
byresRead: Long)
def runAndReturnMetrics(sc: SparkContext)(job: => Unit): AggregatedShuffleMetrics = {
@volatile var recordsWritten: Long = 0
@volatile var recordsRead: Long = 0
@volatile var bytesWritten: Long = 0
@volatile var bytesRead: Long = 0
val listener = new SparkListener {
override def onTaskEnd(taskEnd: SparkListenerTaskEnd) {
taskEnd.taskMetrics.shuffleWriteMetrics.foreach { m =>
recordsWritten += m.recordsWritten
bytesWritten += m.bytesWritten
}
taskEnd.taskMetrics.shuffleReadMetrics.foreach { m =>
recordsRead += m.recordsRead
bytesRead += m.totalBytesRead
}
}
}
sc.addSparkListener(listener)
job
sc.listenerBus.waitUntilEmpty(500)
AggregatedShuffleMetrics(recordsWritten, recordsRead, bytesWritten, bytesRead)
}
}
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