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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 scala.collection.mutable.ArrayBuffer
import scala.collection.mutable.HashSet
import org.scalatest.FunSuite
import org.scalatest.matchers.ShouldMatchers
import org.scalatest.prop.Checkers
import org.scalacheck.Arbitrary._
import org.scalacheck.Gen
import org.scalacheck.Prop._
import com.google.common.io.Files
import spark.rdd.ShuffledRDD
import spark.SparkContext._
class ShuffleSuite extends FunSuite with ShouldMatchers with LocalSparkContext {
test("groupByKey with compression") {
try {
System.setProperty("spark.shuffle.compress", "true")
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.blockManager.compress", "false")
}
}
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 ShuffleSuite.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(b, new HashPartitioner(NUM_BLOCKS),
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 ShuffleSuite.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(b, new HashPartitioner(3), 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(b, new HashPartitioner(10), 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(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)
}
}
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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