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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 spark
import org.scalatest.FunSuite
import org.scalatest.matchers.ShouldMatchers
import spark.SparkContext._
import spark.ShuffleSuite.NonJavaSerializableClass
import spark.rdd.{SubtractedRDD, CoGroupedRDD, OrderedRDDFunctions, ShuffledRDD}
import spark.util.MutablePair
class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext {
test("groupByKey without compression") {
try {
System.setProperty("spark.shuffle.compress", "false")
sc = new SparkContext("local", "test")
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))
} finally {
System.setProperty("spark.shuffle.compress", "true")
}
}
test("shuffle non-zero block size") {
sc = new SparkContext("local-cluster[2,1,512]", "test")
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, (Int, NonJavaSerializableClass)](
b, new HashPartitioner(NUM_BLOCKS)).setSerializer(classOf[spark.KryoSerializer].getName)
val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[Int, Int]].shuffleId
assert(c.count === 10)
// All blocks must have non-zero size
(0 until NUM_BLOCKS).foreach { id =>
val statuses = SparkEnv.get.mapOutputTracker.getServerStatuses(shuffleId, id)
assert(statuses.forall(s => s._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[2,1,512]", "test")
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, (Int, NonJavaSerializableClass)](
b, new HashPartitioner(3)).setSerializer(classOf[spark.KryoSerializer].getName)
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[2,1,512]", "test")
// 10 partitions from 4 keys
val NUM_BLOCKS = 10
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, Int)](b, new HashPartitioner(10))
.setSerializer(classOf[spark.KryoSerializer].getName)
val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[Int, Int]].shuffleId
assert(c.count === 4)
val blockSizes = (0 until NUM_BLOCKS).flatMap { id =>
val statuses = SparkEnv.get.mapOutputTracker.getServerStatuses(shuffleId, id)
statuses.map(x => x._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[2,1,512]", "test")
// 10 partitions from 4 keys
val NUM_BLOCKS = 10
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, Int)](b, new HashPartitioner(10))
val shuffleId = c.dependencies.head.asInstanceOf[ShuffleDependency[Int, Int]].shuffleId
assert(c.count === 4)
val blockSizes = (0 until NUM_BLOCKS).flatMap { id =>
val statuses = SparkEnv.get.mapOutputTracker.getServerStatuses(shuffleId, id)
statuses.map(x => x._2)
}
val nonEmptyBlocks = blockSizes.filter(x => x > 0)
// We should have at most 4 non-zero sized partitions
assert(nonEmptyBlocks.size <= 4)
}
test("shuffle 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[2,1,512]", "test")
def p[T1, T2](_1: T1, _2: 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, MutablePair[Int, Int]](pairs, new HashPartitioner(2))
.collect()
data.foreach { pair => results should contain (pair) }
}
test("sorting using 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[2,1,512]", "test")
def p[T1, T2](_1: T1, _2: 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 (p(1, 11))
results(1) should be (p(2, 22))
results(2) should be (p(3, 33))
results(3) should be (p(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[2,1,512]", "test")
def p[T1, T2](_1: T1, _2: 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)).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[2,1,512]", "test")
def p[T1, T2](_1: T1, _2: 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))
}
}
object ShuffleSuite {
def mergeCombineException(x: Int, y: Int): Int = {
throw new SparkException("Exception for map-side combine.")
x + y
}
class NonJavaSerializableClass(val value: Int)
}
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