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Diffstat (limited to 'core/src/test/scala/org')
47 files changed, 7732 insertions, 0 deletions
diff --git a/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala new file mode 100644 index 0000000000..4434f3b87c --- /dev/null +++ b/core/src/test/scala/org/apache/spark/AccumulatorSuite.scala @@ -0,0 +1,143 @@ +/* + * 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 org.scalatest.FunSuite +import org.scalatest.matchers.ShouldMatchers +import collection.mutable +import java.util.Random +import scala.math.exp +import scala.math.signum +import org.apache.spark.SparkContext._ + +class AccumulatorSuite extends FunSuite with ShouldMatchers with LocalSparkContext { + + test ("basic accumulation"){ + sc = new SparkContext("local", "test") + val acc : Accumulator[Int] = sc.accumulator(0) + + val d = sc.parallelize(1 to 20) + d.foreach{x => acc += x} + acc.value should be (210) + + + val longAcc = sc.accumulator(0l) + val maxInt = Integer.MAX_VALUE.toLong + d.foreach{x => longAcc += maxInt + x} + longAcc.value should be (210l + maxInt * 20) + } + + test ("value not assignable from tasks") { + sc = new SparkContext("local", "test") + val acc : Accumulator[Int] = sc.accumulator(0) + + val d = sc.parallelize(1 to 20) + evaluating {d.foreach{x => acc.value = x}} should produce [Exception] + } + + test ("add value to collection accumulators") { + import SetAccum._ + val maxI = 1000 + for (nThreads <- List(1, 10)) { //test single & multi-threaded + sc = new SparkContext("local[" + nThreads + "]", "test") + val acc: Accumulable[mutable.Set[Any], Any] = sc.accumulable(new mutable.HashSet[Any]()) + val d = sc.parallelize(1 to maxI) + d.foreach { + x => acc += x + } + val v = acc.value.asInstanceOf[mutable.Set[Int]] + for (i <- 1 to maxI) { + v should contain(i) + } + resetSparkContext() + } + } + + implicit object SetAccum extends AccumulableParam[mutable.Set[Any], Any] { + def addInPlace(t1: mutable.Set[Any], t2: mutable.Set[Any]) : mutable.Set[Any] = { + t1 ++= t2 + t1 + } + def addAccumulator(t1: mutable.Set[Any], t2: Any) : mutable.Set[Any] = { + t1 += t2 + t1 + } + def zero(t: mutable.Set[Any]) : mutable.Set[Any] = { + new mutable.HashSet[Any]() + } + } + + test ("value not readable in tasks") { + import SetAccum._ + val maxI = 1000 + for (nThreads <- List(1, 10)) { //test single & multi-threaded + sc = new SparkContext("local[" + nThreads + "]", "test") + val acc: Accumulable[mutable.Set[Any], Any] = sc.accumulable(new mutable.HashSet[Any]()) + val d = sc.parallelize(1 to maxI) + evaluating { + d.foreach { + x => acc.value += x + } + } should produce [SparkException] + resetSparkContext() + } + } + + test ("collection accumulators") { + val maxI = 1000 + for (nThreads <- List(1, 10)) { + // test single & multi-threaded + sc = new SparkContext("local[" + nThreads + "]", "test") + val setAcc = sc.accumulableCollection(mutable.HashSet[Int]()) + val bufferAcc = sc.accumulableCollection(mutable.ArrayBuffer[Int]()) + val mapAcc = sc.accumulableCollection(mutable.HashMap[Int,String]()) + val d = sc.parallelize((1 to maxI) ++ (1 to maxI)) + d.foreach { + x => {setAcc += x; bufferAcc += x; mapAcc += (x -> x.toString)} + } + + // Note that this is typed correctly -- no casts necessary + setAcc.value.size should be (maxI) + bufferAcc.value.size should be (2 * maxI) + mapAcc.value.size should be (maxI) + for (i <- 1 to maxI) { + setAcc.value should contain(i) + bufferAcc.value should contain(i) + mapAcc.value should contain (i -> i.toString) + } + resetSparkContext() + } + } + + test ("localValue readable in tasks") { + import SetAccum._ + val maxI = 1000 + for (nThreads <- List(1, 10)) { //test single & multi-threaded + sc = new SparkContext("local[" + nThreads + "]", "test") + val acc: Accumulable[mutable.Set[Any], Any] = sc.accumulable(new mutable.HashSet[Any]()) + val groupedInts = (1 to (maxI/20)).map {x => (20 * (x - 1) to 20 * x).toSet} + val d = sc.parallelize(groupedInts) + d.foreach { + x => acc.localValue ++= x + } + acc.value should be ( (0 to maxI).toSet) + resetSparkContext() + } + } + +} diff --git a/core/src/test/scala/org/apache/spark/BroadcastSuite.scala b/core/src/test/scala/org/apache/spark/BroadcastSuite.scala new file mode 100644 index 0000000000..b3a53d928b --- /dev/null +++ b/core/src/test/scala/org/apache/spark/BroadcastSuite.scala @@ -0,0 +1,39 @@ +/* + * 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 org.scalatest.FunSuite + +class BroadcastSuite extends FunSuite with LocalSparkContext { + + test("basic broadcast") { + sc = new SparkContext("local", "test") + val list = List(1, 2, 3, 4) + val listBroadcast = sc.broadcast(list) + val results = sc.parallelize(1 to 2).map(x => (x, listBroadcast.value.sum)) + assert(results.collect.toSet === Set((1, 10), (2, 10))) + } + + test("broadcast variables accessed in multiple threads") { + sc = new SparkContext("local[10]", "test") + val list = List(1, 2, 3, 4) + val listBroadcast = sc.broadcast(list) + val results = sc.parallelize(1 to 10).map(x => (x, listBroadcast.value.sum)) + assert(results.collect.toSet === (1 to 10).map(x => (x, 10)).toSet) + } +} diff --git a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala new file mode 100644 index 0000000000..23b14f4245 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala @@ -0,0 +1,392 @@ +/* + * 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 org.scalatest.FunSuite +import java.io.File +import org.apache.spark.rdd._ +import org.apache.spark.SparkContext._ +import storage.StorageLevel + +class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { + initLogging() + + var checkpointDir: File = _ + val partitioner = new HashPartitioner(2) + + override def beforeEach() { + super.beforeEach() + checkpointDir = File.createTempFile("temp", "") + checkpointDir.delete() + sc = new SparkContext("local", "test") + sc.setCheckpointDir(checkpointDir.toString) + } + + override def afterEach() { + super.afterEach() + if (checkpointDir != null) { + checkpointDir.delete() + } + } + + test("basic checkpointing") { + val parCollection = sc.makeRDD(1 to 4) + val flatMappedRDD = parCollection.flatMap(x => 1 to x) + flatMappedRDD.checkpoint() + assert(flatMappedRDD.dependencies.head.rdd == parCollection) + val result = flatMappedRDD.collect() + assert(flatMappedRDD.dependencies.head.rdd != parCollection) + assert(flatMappedRDD.collect() === result) + } + + test("RDDs with one-to-one dependencies") { + testCheckpointing(_.map(x => x.toString)) + testCheckpointing(_.flatMap(x => 1 to x)) + testCheckpointing(_.filter(_ % 2 == 0)) + testCheckpointing(_.sample(false, 0.5, 0)) + testCheckpointing(_.glom()) + testCheckpointing(_.mapPartitions(_.map(_.toString))) + testCheckpointing(r => new MapPartitionsWithIndexRDD(r, + (i: Int, iter: Iterator[Int]) => iter.map(_.toString), false )) + testCheckpointing(_.map(x => (x % 2, 1)).reduceByKey(_ + _).mapValues(_.toString)) + testCheckpointing(_.map(x => (x % 2, 1)).reduceByKey(_ + _).flatMapValues(x => 1 to x)) + testCheckpointing(_.pipe(Seq("cat"))) + } + + test("ParallelCollection") { + val parCollection = sc.makeRDD(1 to 4, 2) + val numPartitions = parCollection.partitions.size + parCollection.checkpoint() + assert(parCollection.dependencies === Nil) + val result = parCollection.collect() + assert(sc.checkpointFile[Int](parCollection.getCheckpointFile.get).collect() === result) + assert(parCollection.dependencies != Nil) + assert(parCollection.partitions.length === numPartitions) + assert(parCollection.partitions.toList === parCollection.checkpointData.get.getPartitions.toList) + assert(parCollection.collect() === result) + } + + test("BlockRDD") { + val blockId = "id" + val blockManager = SparkEnv.get.blockManager + blockManager.putSingle(blockId, "test", StorageLevel.MEMORY_ONLY) + val blockRDD = new BlockRDD[String](sc, Array(blockId)) + val numPartitions = blockRDD.partitions.size + blockRDD.checkpoint() + val result = blockRDD.collect() + assert(sc.checkpointFile[String](blockRDD.getCheckpointFile.get).collect() === result) + assert(blockRDD.dependencies != Nil) + assert(blockRDD.partitions.length === numPartitions) + assert(blockRDD.partitions.toList === blockRDD.checkpointData.get.getPartitions.toList) + assert(blockRDD.collect() === result) + } + + test("ShuffledRDD") { + testCheckpointing(rdd => { + // Creating ShuffledRDD directly as PairRDDFunctions.combineByKey produces a MapPartitionedRDD + new ShuffledRDD[Int, Int, (Int, Int)](rdd.map(x => (x % 2, 1)), partitioner) + }) + } + + test("UnionRDD") { + def otherRDD = sc.makeRDD(1 to 10, 1) + + // Test whether the size of UnionRDDPartitions reduce in size after parent RDD is checkpointed. + // Current implementation of UnionRDD has transient reference to parent RDDs, + // so only the partitions will reduce in serialized size, not the RDD. + testCheckpointing(_.union(otherRDD), false, true) + testParentCheckpointing(_.union(otherRDD), false, true) + } + + test("CartesianRDD") { + def otherRDD = sc.makeRDD(1 to 10, 1) + testCheckpointing(new CartesianRDD(sc, _, otherRDD)) + + // Test whether size of CoalescedRDD reduce in size after parent RDD is checkpointed + // Current implementation of CoalescedRDDPartition has transient reference to parent RDD, + // so only the RDD will reduce in serialized size, not the partitions. + testParentCheckpointing(new CartesianRDD(sc, _, otherRDD), true, false) + + // Test that the CartesianRDD updates parent partitions (CartesianRDD.s1/s2) after + // the parent RDD has been checkpointed and parent partitions have been changed to HadoopPartitions. + // Note that this test is very specific to the current implementation of CartesianRDD. + val ones = sc.makeRDD(1 to 100, 10).map(x => x) + ones.checkpoint() // checkpoint that MappedRDD + val cartesian = new CartesianRDD(sc, ones, ones) + val splitBeforeCheckpoint = + serializeDeserialize(cartesian.partitions.head.asInstanceOf[CartesianPartition]) + cartesian.count() // do the checkpointing + val splitAfterCheckpoint = + serializeDeserialize(cartesian.partitions.head.asInstanceOf[CartesianPartition]) + assert( + (splitAfterCheckpoint.s1 != splitBeforeCheckpoint.s1) && + (splitAfterCheckpoint.s2 != splitBeforeCheckpoint.s2), + "CartesianRDD.parents not updated after parent RDD checkpointed" + ) + } + + test("CoalescedRDD") { + testCheckpointing(_.coalesce(2)) + + // Test whether size of CoalescedRDD reduce in size after parent RDD is checkpointed + // Current implementation of CoalescedRDDPartition has transient reference to parent RDD, + // so only the RDD will reduce in serialized size, not the partitions. + testParentCheckpointing(_.coalesce(2), true, false) + + // Test that the CoalescedRDDPartition updates parent partitions (CoalescedRDDPartition.parents) after + // the parent RDD has been checkpointed and parent partitions have been changed to HadoopPartitions. + // Note that this test is very specific to the current implementation of CoalescedRDDPartitions + val ones = sc.makeRDD(1 to 100, 10).map(x => x) + ones.checkpoint() // checkpoint that MappedRDD + val coalesced = new CoalescedRDD(ones, 2) + val splitBeforeCheckpoint = + serializeDeserialize(coalesced.partitions.head.asInstanceOf[CoalescedRDDPartition]) + coalesced.count() // do the checkpointing + val splitAfterCheckpoint = + serializeDeserialize(coalesced.partitions.head.asInstanceOf[CoalescedRDDPartition]) + assert( + splitAfterCheckpoint.parents.head != splitBeforeCheckpoint.parents.head, + "CoalescedRDDPartition.parents not updated after parent RDD checkpointed" + ) + } + + test("CoGroupedRDD") { + val longLineageRDD1 = generateLongLineageRDDForCoGroupedRDD() + testCheckpointing(rdd => { + CheckpointSuite.cogroup(longLineageRDD1, rdd.map(x => (x % 2, 1)), partitioner) + }, false, true) + + val longLineageRDD2 = generateLongLineageRDDForCoGroupedRDD() + testParentCheckpointing(rdd => { + CheckpointSuite.cogroup( + longLineageRDD2, sc.makeRDD(1 to 2, 2).map(x => (x % 2, 1)), partitioner) + }, false, true) + } + + test("ZippedRDD") { + testCheckpointing( + rdd => new ZippedRDD(sc, rdd, rdd.map(x => x)), true, false) + + // Test whether size of ZippedRDD reduce in size after parent RDD is checkpointed + // Current implementation of ZippedRDDPartitions has transient references to parent RDDs, + // so only the RDD will reduce in serialized size, not the partitions. + testParentCheckpointing( + rdd => new ZippedRDD(sc, rdd, rdd.map(x => x)), true, false) + } + + test("CheckpointRDD with zero partitions") { + val rdd = new BlockRDD[Int](sc, Array[String]()) + assert(rdd.partitions.size === 0) + assert(rdd.isCheckpointed === false) + rdd.checkpoint() + assert(rdd.count() === 0) + assert(rdd.isCheckpointed === true) + assert(rdd.partitions.size === 0) + } + + /** + * Test checkpointing of the final RDD generated by the given operation. By default, + * this method tests whether the size of serialized RDD has reduced after checkpointing or not. + * It can also test whether the size of serialized RDD partitions has reduced after checkpointing or + * not, but this is not done by default as usually the partitions do not refer to any RDD and + * therefore never store the lineage. + */ + def testCheckpointing[U: ClassManifest]( + op: (RDD[Int]) => RDD[U], + testRDDSize: Boolean = true, + testRDDPartitionSize: Boolean = false + ) { + // Generate the final RDD using given RDD operation + val baseRDD = generateLongLineageRDD() + val operatedRDD = op(baseRDD) + val parentRDD = operatedRDD.dependencies.headOption.orNull + val rddType = operatedRDD.getClass.getSimpleName + val numPartitions = operatedRDD.partitions.length + + // Find serialized sizes before and after the checkpoint + val (rddSizeBeforeCheckpoint, splitSizeBeforeCheckpoint) = getSerializedSizes(operatedRDD) + operatedRDD.checkpoint() + val result = operatedRDD.collect() + val (rddSizeAfterCheckpoint, splitSizeAfterCheckpoint) = getSerializedSizes(operatedRDD) + + // Test whether the checkpoint file has been created + assert(sc.checkpointFile[U](operatedRDD.getCheckpointFile.get).collect() === result) + + // Test whether dependencies have been changed from its earlier parent RDD + assert(operatedRDD.dependencies.head.rdd != parentRDD) + + // Test whether the partitions have been changed to the new Hadoop partitions + assert(operatedRDD.partitions.toList === operatedRDD.checkpointData.get.getPartitions.toList) + + // Test whether the number of partitions is same as before + assert(operatedRDD.partitions.length === numPartitions) + + // Test whether the data in the checkpointed RDD is same as original + assert(operatedRDD.collect() === result) + + // Test whether serialized size of the RDD has reduced. If the RDD + // does not have any dependency to another RDD (e.g., ParallelCollection, + // ShuffleRDD with ShuffleDependency), it may not reduce in size after checkpointing. + if (testRDDSize) { + logInfo("Size of " + rddType + + "[" + rddSizeBeforeCheckpoint + " --> " + rddSizeAfterCheckpoint + "]") + assert( + rddSizeAfterCheckpoint < rddSizeBeforeCheckpoint, + "Size of " + rddType + " did not reduce after checkpointing " + + "[" + rddSizeBeforeCheckpoint + " --> " + rddSizeAfterCheckpoint + "]" + ) + } + + // Test whether serialized size of the partitions has reduced. If the partitions + // do not have any non-transient reference to another RDD or another RDD's partitions, it + // does not refer to a lineage and therefore may not reduce in size after checkpointing. + // However, if the original partitions before checkpointing do refer to a parent RDD, the partitions + // must be forgotten after checkpointing (to remove all reference to parent RDDs) and + // replaced with the HadooPartitions of the checkpointed RDD. + if (testRDDPartitionSize) { + logInfo("Size of " + rddType + " partitions " + + "[" + splitSizeBeforeCheckpoint + " --> " + splitSizeAfterCheckpoint + "]") + assert( + splitSizeAfterCheckpoint < splitSizeBeforeCheckpoint, + "Size of " + rddType + " partitions did not reduce after checkpointing " + + "[" + splitSizeBeforeCheckpoint + " --> " + splitSizeAfterCheckpoint + "]" + ) + } + } + + /** + * Test whether checkpointing of the parent of the generated RDD also + * truncates the lineage or not. Some RDDs like CoGroupedRDD hold on to its parent + * RDDs partitions. So even if the parent RDD is checkpointed and its partitions changed, + * this RDD will remember the partitions and therefore potentially the whole lineage. + */ + def testParentCheckpointing[U: ClassManifest]( + op: (RDD[Int]) => RDD[U], + testRDDSize: Boolean, + testRDDPartitionSize: Boolean + ) { + // Generate the final RDD using given RDD operation + val baseRDD = generateLongLineageRDD() + val operatedRDD = op(baseRDD) + val parentRDD = operatedRDD.dependencies.head.rdd + val rddType = operatedRDD.getClass.getSimpleName + val parentRDDType = parentRDD.getClass.getSimpleName + + // Get the partitions and dependencies of the parent in case they're lazily computed + parentRDD.dependencies + parentRDD.partitions + + // Find serialized sizes before and after the checkpoint + val (rddSizeBeforeCheckpoint, splitSizeBeforeCheckpoint) = getSerializedSizes(operatedRDD) + parentRDD.checkpoint() // checkpoint the parent RDD, not the generated one + val result = operatedRDD.collect() + val (rddSizeAfterCheckpoint, splitSizeAfterCheckpoint) = getSerializedSizes(operatedRDD) + + // Test whether the data in the checkpointed RDD is same as original + assert(operatedRDD.collect() === result) + + // Test whether serialized size of the RDD has reduced because of its parent being + // checkpointed. If this RDD or its parent RDD do not have any dependency + // to another RDD (e.g., ParallelCollection, ShuffleRDD with ShuffleDependency), it may + // not reduce in size after checkpointing. + if (testRDDSize) { + assert( + rddSizeAfterCheckpoint < rddSizeBeforeCheckpoint, + "Size of " + rddType + " did not reduce after checkpointing parent " + parentRDDType + + "[" + rddSizeBeforeCheckpoint + " --> " + rddSizeAfterCheckpoint + "]" + ) + } + + // Test whether serialized size of the partitions has reduced because of its parent being + // checkpointed. If the partitions do not have any non-transient reference to another RDD + // or another RDD's partitions, it does not refer to a lineage and therefore may not reduce + // in size after checkpointing. However, if the partitions do refer to the *partitions* of a parent + // RDD, then these partitions must update reference to the parent RDD partitions as the parent RDD's + // partitions must have changed after checkpointing. + if (testRDDPartitionSize) { + assert( + splitSizeAfterCheckpoint < splitSizeBeforeCheckpoint, + "Size of " + rddType + " partitions did not reduce after checkpointing parent " + parentRDDType + + "[" + splitSizeBeforeCheckpoint + " --> " + splitSizeAfterCheckpoint + "]" + ) + } + + } + + /** + * Generate an RDD with a long lineage of one-to-one dependencies. + */ + def generateLongLineageRDD(): RDD[Int] = { + var rdd = sc.makeRDD(1 to 100, 4) + for (i <- 1 to 50) { + rdd = rdd.map(x => x + 1) + } + rdd + } + + /** + * Generate an RDD with a long lineage specifically for CoGroupedRDD. + * A CoGroupedRDD can have a long lineage only one of its parents have a long lineage + * and narrow dependency with this RDD. This method generate such an RDD by a sequence + * of cogroups and mapValues which creates a long lineage of narrow dependencies. + */ + def generateLongLineageRDDForCoGroupedRDD() = { + val add = (x: (Seq[Int], Seq[Int])) => (x._1 ++ x._2).reduce(_ + _) + + def ones: RDD[(Int, Int)] = sc.makeRDD(1 to 2, 2).map(x => (x % 2, 1)).reduceByKey(partitioner, _ + _) + + var cogrouped: RDD[(Int, (Seq[Int], Seq[Int]))] = ones.cogroup(ones) + for(i <- 1 to 10) { + cogrouped = cogrouped.mapValues(add).cogroup(ones) + } + cogrouped.mapValues(add) + } + + /** + * Get serialized sizes of the RDD and its partitions, in order to test whether the size shrinks + * upon checkpointing. Ignores the checkpointData field, which may grow when we checkpoint. + */ + def getSerializedSizes(rdd: RDD[_]): (Int, Int) = { + (Utils.serialize(rdd).length - Utils.serialize(rdd.checkpointData).length, + Utils.serialize(rdd.partitions).length) + } + + /** + * Serialize and deserialize an object. This is useful to verify the objects + * contents after deserialization (e.g., the contents of an RDD split after + * it is sent to a slave along with a task) + */ + def serializeDeserialize[T](obj: T): T = { + val bytes = Utils.serialize(obj) + Utils.deserialize[T](bytes) + } +} + + +object CheckpointSuite { + // This is a custom cogroup function that does not use mapValues like + // the PairRDDFunctions.cogroup() + def cogroup[K, V](first: RDD[(K, V)], second: RDD[(K, V)], part: Partitioner) = { + //println("First = " + first + ", second = " + second) + new CoGroupedRDD[K]( + Seq(first.asInstanceOf[RDD[(K, _)]], second.asInstanceOf[RDD[(K, _)]]), + part + ).asInstanceOf[RDD[(K, Seq[Seq[V]])]] + } + +} diff --git a/core/src/test/scala/org/apache/spark/ClosureCleanerSuite.scala b/core/src/test/scala/org/apache/spark/ClosureCleanerSuite.scala new file mode 100644 index 0000000000..8494899b98 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/ClosureCleanerSuite.scala @@ -0,0 +1,146 @@ +/* + * 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.io.NotSerializableException + +import org.scalatest.FunSuite +import org.apache.spark.LocalSparkContext._ +import SparkContext._ + +class ClosureCleanerSuite extends FunSuite { + test("closures inside an object") { + assert(TestObject.run() === 30) // 6 + 7 + 8 + 9 + } + + test("closures inside a class") { + val obj = new TestClass + assert(obj.run() === 30) // 6 + 7 + 8 + 9 + } + + test("closures inside a class with no default constructor") { + val obj = new TestClassWithoutDefaultConstructor(5) + assert(obj.run() === 30) // 6 + 7 + 8 + 9 + } + + test("closures that don't use fields of the outer class") { + val obj = new TestClassWithoutFieldAccess + assert(obj.run() === 30) // 6 + 7 + 8 + 9 + } + + test("nested closures inside an object") { + assert(TestObjectWithNesting.run() === 96) // 4 * (1+2+3+4) + 4 * (1+2+3+4) + 16 * 1 + } + + test("nested closures inside a class") { + val obj = new TestClassWithNesting(1) + assert(obj.run() === 96) // 4 * (1+2+3+4) + 4 * (1+2+3+4) + 16 * 1 + } +} + +// A non-serializable class we create in closures to make sure that we aren't +// keeping references to unneeded variables from our outer closures. +class NonSerializable {} + +object TestObject { + def run(): Int = { + var nonSer = new NonSerializable + var x = 5 + return withSpark(new SparkContext("local", "test")) { sc => + val nums = sc.parallelize(Array(1, 2, 3, 4)) + nums.map(_ + x).reduce(_ + _) + } + } +} + +class TestClass extends Serializable { + var x = 5 + + def getX = x + + def run(): Int = { + var nonSer = new NonSerializable + return withSpark(new SparkContext("local", "test")) { sc => + val nums = sc.parallelize(Array(1, 2, 3, 4)) + nums.map(_ + getX).reduce(_ + _) + } + } +} + +class TestClassWithoutDefaultConstructor(x: Int) extends Serializable { + def getX = x + + def run(): Int = { + var nonSer = new NonSerializable + return withSpark(new SparkContext("local", "test")) { sc => + val nums = sc.parallelize(Array(1, 2, 3, 4)) + nums.map(_ + getX).reduce(_ + _) + } + } +} + +// This class is not serializable, but we aren't using any of its fields in our +// closures, so they won't have a $outer pointing to it and should still work. +class TestClassWithoutFieldAccess { + var nonSer = new NonSerializable + + def run(): Int = { + var nonSer2 = new NonSerializable + var x = 5 + return withSpark(new SparkContext("local", "test")) { sc => + val nums = sc.parallelize(Array(1, 2, 3, 4)) + nums.map(_ + x).reduce(_ + _) + } + } +} + + +object TestObjectWithNesting { + def run(): Int = { + var nonSer = new NonSerializable + var answer = 0 + return withSpark(new SparkContext("local", "test")) { sc => + val nums = sc.parallelize(Array(1, 2, 3, 4)) + var y = 1 + for (i <- 1 to 4) { + var nonSer2 = new NonSerializable + var x = i + answer += nums.map(_ + x + y).reduce(_ + _) + } + answer + } + } +} + +class TestClassWithNesting(val y: Int) extends Serializable { + def getY = y + + def run(): Int = { + var nonSer = new NonSerializable + var answer = 0 + return withSpark(new SparkContext("local", "test")) { sc => + val nums = sc.parallelize(Array(1, 2, 3, 4)) + for (i <- 1 to 4) { + var nonSer2 = new NonSerializable + var x = i + answer += nums.map(_ + x + getY).reduce(_ + _) + } + answer + } + } +} diff --git a/core/src/test/scala/org/apache/spark/DistributedSuite.scala b/core/src/test/scala/org/apache/spark/DistributedSuite.scala new file mode 100644 index 0000000000..7a856d4081 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/DistributedSuite.scala @@ -0,0 +1,362 @@ +/* + * 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 network.ConnectionManagerId +import org.scalatest.FunSuite +import org.scalatest.BeforeAndAfter +import org.scalatest.concurrent.Timeouts._ +import org.scalatest.matchers.ShouldMatchers +import org.scalatest.prop.Checkers +import org.scalatest.time.{Span, Millis} +import org.scalacheck.Arbitrary._ +import org.scalacheck.Gen +import org.scalacheck.Prop._ +import org.eclipse.jetty.server.{Server, Request, Handler} + +import com.google.common.io.Files + +import scala.collection.mutable.ArrayBuffer + +import SparkContext._ +import storage.{GetBlock, BlockManagerWorker, StorageLevel} +import ui.JettyUtils + + +class NotSerializableClass +class NotSerializableExn(val notSer: NotSerializableClass) extends Throwable() {} + + +class DistributedSuite extends FunSuite with ShouldMatchers with BeforeAndAfter + with LocalSparkContext { + + val clusterUrl = "local-cluster[2,1,512]" + + after { + System.clearProperty("spark.reducer.maxMbInFlight") + System.clearProperty("spark.storage.memoryFraction") + } + + test("task throws not serializable exception") { + // Ensures that executors do not crash when an exn is not serializable. If executors crash, + // this test will hang. Correct behavior is that executors don't crash but fail tasks + // and the scheduler throws a SparkException. + + // numSlaves must be less than numPartitions + val numSlaves = 3 + val numPartitions = 10 + + sc = new SparkContext("local-cluster[%s,1,512]".format(numSlaves), "test") + val data = sc.parallelize(1 to 100, numPartitions). + map(x => throw new NotSerializableExn(new NotSerializableClass)) + intercept[SparkException] { + data.count() + } + resetSparkContext() + } + + test("local-cluster format") { + sc = new SparkContext("local-cluster[2,1,512]", "test") + assert(sc.parallelize(1 to 2, 2).count() == 2) + resetSparkContext() + sc = new SparkContext("local-cluster[2 , 1 , 512]", "test") + assert(sc.parallelize(1 to 2, 2).count() == 2) + resetSparkContext() + sc = new SparkContext("local-cluster[2, 1, 512]", "test") + assert(sc.parallelize(1 to 2, 2).count() == 2) + resetSparkContext() + sc = new SparkContext("local-cluster[ 2, 1, 512 ]", "test") + assert(sc.parallelize(1 to 2, 2).count() == 2) + resetSparkContext() + } + + test("simple groupByKey") { + sc = new SparkContext(clusterUrl, "test") + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1)), 5) + val groups = pairs.groupByKey(5).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("groupByKey where map output sizes exceed maxMbInFlight") { + System.setProperty("spark.reducer.maxMbInFlight", "1") + sc = new SparkContext(clusterUrl, "test") + // This data should be around 20 MB, so even with 4 mappers and 2 reducers, each map output + // file should be about 2.5 MB + val pairs = sc.parallelize(1 to 2000, 4).map(x => (x % 16, new Array[Byte](10000))) + val groups = pairs.groupByKey(2).map(x => (x._1, x._2.size)).collect() + assert(groups.length === 16) + assert(groups.map(_._2).sum === 2000) + // Note that spark.reducer.maxMbInFlight will be cleared in the test suite's after{} block + } + + test("accumulators") { + sc = new SparkContext(clusterUrl, "test") + val accum = sc.accumulator(0) + sc.parallelize(1 to 10, 10).foreach(x => accum += x) + assert(accum.value === 55) + } + + test("broadcast variables") { + sc = new SparkContext(clusterUrl, "test") + val array = new Array[Int](100) + val bv = sc.broadcast(array) + array(2) = 3 // Change the array -- this should not be seen on workers + val rdd = sc.parallelize(1 to 10, 10) + val sum = rdd.map(x => bv.value.sum).reduce(_ + _) + assert(sum === 0) + } + + test("repeatedly failing task") { + sc = new SparkContext(clusterUrl, "test") + val accum = sc.accumulator(0) + val thrown = intercept[SparkException] { + sc.parallelize(1 to 10, 10).foreach(x => println(x / 0)) + } + assert(thrown.getClass === classOf[SparkException]) + assert(thrown.getMessage.contains("more than 4 times")) + } + + test("caching") { + sc = new SparkContext(clusterUrl, "test") + val data = sc.parallelize(1 to 1000, 10).cache() + assert(data.count() === 1000) + assert(data.count() === 1000) + assert(data.count() === 1000) + } + + test("caching on disk") { + sc = new SparkContext(clusterUrl, "test") + val data = sc.parallelize(1 to 1000, 10).persist(StorageLevel.DISK_ONLY) + assert(data.count() === 1000) + assert(data.count() === 1000) + assert(data.count() === 1000) + } + + test("caching in memory, replicated") { + sc = new SparkContext(clusterUrl, "test") + val data = sc.parallelize(1 to 1000, 10).persist(StorageLevel.MEMORY_ONLY_2) + assert(data.count() === 1000) + assert(data.count() === 1000) + assert(data.count() === 1000) + } + + test("caching in memory, serialized, replicated") { + sc = new SparkContext(clusterUrl, "test") + val data = sc.parallelize(1 to 1000, 10).persist(StorageLevel.MEMORY_ONLY_SER_2) + assert(data.count() === 1000) + assert(data.count() === 1000) + assert(data.count() === 1000) + } + + test("caching on disk, replicated") { + sc = new SparkContext(clusterUrl, "test") + val data = sc.parallelize(1 to 1000, 10).persist(StorageLevel.DISK_ONLY_2) + assert(data.count() === 1000) + assert(data.count() === 1000) + assert(data.count() === 1000) + } + + test("caching in memory and disk, replicated") { + sc = new SparkContext(clusterUrl, "test") + val data = sc.parallelize(1 to 1000, 10).persist(StorageLevel.MEMORY_AND_DISK_2) + assert(data.count() === 1000) + assert(data.count() === 1000) + assert(data.count() === 1000) + } + + test("caching in memory and disk, serialized, replicated") { + sc = new SparkContext(clusterUrl, "test") + val data = sc.parallelize(1 to 1000, 10).persist(StorageLevel.MEMORY_AND_DISK_SER_2) + + assert(data.count() === 1000) + assert(data.count() === 1000) + assert(data.count() === 1000) + + // Get all the locations of the first partition and try to fetch the partitions + // from those locations. + val blockIds = data.partitions.indices.map(index => "rdd_%d_%d".format(data.id, index)).toArray + val blockId = blockIds(0) + val blockManager = SparkEnv.get.blockManager + blockManager.master.getLocations(blockId).foreach(id => { + val bytes = BlockManagerWorker.syncGetBlock( + GetBlock(blockId), ConnectionManagerId(id.host, id.port)) + val deserialized = blockManager.dataDeserialize(blockId, bytes).asInstanceOf[Iterator[Int]].toList + assert(deserialized === (1 to 100).toList) + }) + } + + test("compute without caching when no partitions fit in memory") { + System.setProperty("spark.storage.memoryFraction", "0.0001") + sc = new SparkContext(clusterUrl, "test") + // data will be 4 million * 4 bytes = 16 MB in size, but our memoryFraction set the cache + // to only 50 KB (0.0001 of 512 MB), so no partitions should fit in memory + val data = sc.parallelize(1 to 4000000, 2).persist(StorageLevel.MEMORY_ONLY_SER) + assert(data.count() === 4000000) + assert(data.count() === 4000000) + assert(data.count() === 4000000) + System.clearProperty("spark.storage.memoryFraction") + } + + test("compute when only some partitions fit in memory") { + System.setProperty("spark.storage.memoryFraction", "0.01") + sc = new SparkContext(clusterUrl, "test") + // data will be 4 million * 4 bytes = 16 MB in size, but our memoryFraction set the cache + // to only 5 MB (0.01 of 512 MB), so not all of it will fit in memory; we use 20 partitions + // to make sure that *some* of them do fit though + val data = sc.parallelize(1 to 4000000, 20).persist(StorageLevel.MEMORY_ONLY_SER) + assert(data.count() === 4000000) + assert(data.count() === 4000000) + assert(data.count() === 4000000) + System.clearProperty("spark.storage.memoryFraction") + } + + test("passing environment variables to cluster") { + sc = new SparkContext(clusterUrl, "test", null, Nil, Map("TEST_VAR" -> "TEST_VALUE")) + val values = sc.parallelize(1 to 2, 2).map(x => System.getenv("TEST_VAR")).collect() + assert(values.toSeq === Seq("TEST_VALUE", "TEST_VALUE")) + } + + test("recover from node failures") { + import DistributedSuite.{markNodeIfIdentity, failOnMarkedIdentity} + DistributedSuite.amMaster = true + sc = new SparkContext(clusterUrl, "test") + val data = sc.parallelize(Seq(true, true), 2) + assert(data.count === 2) // force executors to start + assert(data.map(markNodeIfIdentity).collect.size === 2) + assert(data.map(failOnMarkedIdentity).collect.size === 2) + } + + test("recover from repeated node failures during shuffle-map") { + import DistributedSuite.{markNodeIfIdentity, failOnMarkedIdentity} + DistributedSuite.amMaster = true + sc = new SparkContext(clusterUrl, "test") + for (i <- 1 to 3) { + val data = sc.parallelize(Seq(true, false), 2) + assert(data.count === 2) + assert(data.map(markNodeIfIdentity).collect.size === 2) + assert(data.map(failOnMarkedIdentity).map(x => x -> x).groupByKey.count === 2) + } + } + + test("recover from repeated node failures during shuffle-reduce") { + import DistributedSuite.{markNodeIfIdentity, failOnMarkedIdentity} + DistributedSuite.amMaster = true + sc = new SparkContext(clusterUrl, "test") + for (i <- 1 to 3) { + val data = sc.parallelize(Seq(true, true), 2) + assert(data.count === 2) + assert(data.map(markNodeIfIdentity).collect.size === 2) + // This relies on mergeCombiners being used to perform the actual reduce for this + // test to actually be testing what it claims. + val grouped = data.map(x => x -> x).combineByKey( + x => x, + (x: Boolean, y: Boolean) => x, + (x: Boolean, y: Boolean) => failOnMarkedIdentity(x) + ) + assert(grouped.collect.size === 1) + } + } + + test("recover from node failures with replication") { + import DistributedSuite.{markNodeIfIdentity, failOnMarkedIdentity} + DistributedSuite.amMaster = true + // Using more than two nodes so we don't have a symmetric communication pattern and might + // cache a partially correct list of peers. + sc = new SparkContext("local-cluster[3,1,512]", "test") + for (i <- 1 to 3) { + val data = sc.parallelize(Seq(true, false, false, false), 4) + data.persist(StorageLevel.MEMORY_ONLY_2) + + assert(data.count === 4) + assert(data.map(markNodeIfIdentity).collect.size === 4) + assert(data.map(failOnMarkedIdentity).collect.size === 4) + + // Create a new replicated RDD to make sure that cached peer information doesn't cause + // problems. + val data2 = sc.parallelize(Seq(true, true), 2).persist(StorageLevel.MEMORY_ONLY_2) + assert(data2.count === 2) + } + } + + test("unpersist RDDs") { + DistributedSuite.amMaster = true + sc = new SparkContext("local-cluster[3,1,512]", "test") + val data = sc.parallelize(Seq(true, false, false, false), 4) + data.persist(StorageLevel.MEMORY_ONLY_2) + data.count + assert(sc.persistentRdds.isEmpty === false) + data.unpersist() + assert(sc.persistentRdds.isEmpty === true) + + failAfter(Span(3000, Millis)) { + try { + while (! sc.getRDDStorageInfo.isEmpty) { + Thread.sleep(200) + } + } catch { + case _ => { Thread.sleep(10) } + // Do nothing. We might see exceptions because block manager + // is racing this thread to remove entries from the driver. + } + } + } + + test("job should fail if TaskResult exceeds Akka frame size") { + // We must use local-cluster mode since results are returned differently + // when running under LocalScheduler: + sc = new SparkContext("local-cluster[1,1,512]", "test") + val akkaFrameSize = + sc.env.actorSystem.settings.config.getBytes("akka.remote.netty.message-frame-size").toInt + val rdd = sc.parallelize(Seq(1)).map{x => new Array[Byte](akkaFrameSize)} + val exception = intercept[SparkException] { + rdd.reduce((x, y) => x) + } + exception.getMessage should endWith("result exceeded Akka frame size") + } +} + +object DistributedSuite { + // Indicates whether this JVM is marked for failure. + var mark = false + + // Set by test to remember if we are in the driver program so we can assert + // that we are not. + var amMaster = false + + // Act like an identity function, but if the argument is true, set mark to true. + def markNodeIfIdentity(item: Boolean): Boolean = { + if (item) { + assert(!amMaster) + mark = true + } + item + } + + // Act like an identity function, but if mark was set to true previously, fail, + // crashing the entire JVM. + def failOnMarkedIdentity(item: Boolean): Boolean = { + if (mark) { + System.exit(42) + } + item + } +} diff --git a/core/src/test/scala/org/apache/spark/DriverSuite.scala b/core/src/test/scala/org/apache/spark/DriverSuite.scala new file mode 100644 index 0000000000..b08aad1a6f --- /dev/null +++ b/core/src/test/scala/org/apache/spark/DriverSuite.scala @@ -0,0 +1,54 @@ +/* + * 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.io.File + +import org.apache.log4j.Logger +import org.apache.log4j.Level + +import org.scalatest.FunSuite +import org.scalatest.concurrent.Timeouts +import org.scalatest.prop.TableDrivenPropertyChecks._ +import org.scalatest.time.SpanSugar._ + +class DriverSuite extends FunSuite with Timeouts { + test("driver should exit after finishing") { + assert(System.getenv("SPARK_HOME") != null) + // Regression test for SPARK-530: "Spark driver process doesn't exit after finishing" + val masters = Table(("master"), ("local"), ("local-cluster[2,1,512]")) + forAll(masters) { (master: String) => + failAfter(30 seconds) { + Utils.execute(Seq("./spark-class", "org.apache.spark.DriverWithoutCleanup", master), + new File(System.getenv("SPARK_HOME"))) + } + } + } +} + +/** + * Program that creates a Spark driver but doesn't call SparkContext.stop() or + * Sys.exit() after finishing. + */ +object DriverWithoutCleanup { + def main(args: Array[String]) { + Logger.getRootLogger().setLevel(Level.WARN) + val sc = new SparkContext(args(0), "DriverWithoutCleanup") + sc.parallelize(1 to 100, 4).count() + } +} diff --git a/core/src/test/scala/org/apache/spark/FailureSuite.scala b/core/src/test/scala/org/apache/spark/FailureSuite.scala new file mode 100644 index 0000000000..ee89a7a387 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/FailureSuite.scala @@ -0,0 +1,127 @@ +/* + * 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 org.scalatest.FunSuite + +import SparkContext._ + +// Common state shared by FailureSuite-launched tasks. We use a global object +// for this because any local variables used in the task closures will rightfully +// be copied for each task, so there's no other way for them to share state. +object FailureSuiteState { + var tasksRun = 0 + var tasksFailed = 0 + + def clear() { + synchronized { + tasksRun = 0 + tasksFailed = 0 + } + } +} + +class FailureSuite extends FunSuite with LocalSparkContext { + + // Run a 3-task map job in which task 1 deterministically fails once, and check + // whether the job completes successfully and we ran 4 tasks in total. + test("failure in a single-stage job") { + sc = new SparkContext("local[1,1]", "test") + val results = sc.makeRDD(1 to 3, 3).map { x => + FailureSuiteState.synchronized { + FailureSuiteState.tasksRun += 1 + if (x == 1 && FailureSuiteState.tasksFailed == 0) { + FailureSuiteState.tasksFailed += 1 + throw new Exception("Intentional task failure") + } + } + x * x + }.collect() + FailureSuiteState.synchronized { + assert(FailureSuiteState.tasksRun === 4) + } + assert(results.toList === List(1,4,9)) + FailureSuiteState.clear() + } + + // Run a map-reduce job in which a reduce task deterministically fails once. + test("failure in a two-stage job") { + sc = new SparkContext("local[1,1]", "test") + val results = sc.makeRDD(1 to 3).map(x => (x, x)).groupByKey(3).map { + case (k, v) => + FailureSuiteState.synchronized { + FailureSuiteState.tasksRun += 1 + if (k == 1 && FailureSuiteState.tasksFailed == 0) { + FailureSuiteState.tasksFailed += 1 + throw new Exception("Intentional task failure") + } + } + (k, v(0) * v(0)) + }.collect() + FailureSuiteState.synchronized { + assert(FailureSuiteState.tasksRun === 4) + } + assert(results.toSet === Set((1, 1), (2, 4), (3, 9))) + FailureSuiteState.clear() + } + + test("failure because task results are not serializable") { + sc = new SparkContext("local[1,1]", "test") + val results = sc.makeRDD(1 to 3).map(x => new NonSerializable) + + val thrown = intercept[SparkException] { + results.collect() + } + assert(thrown.getClass === classOf[SparkException]) + assert(thrown.getMessage.contains("NotSerializableException")) + + FailureSuiteState.clear() + } + + test("failure because task closure is not serializable") { + sc = new SparkContext("local[1,1]", "test") + val a = new NonSerializable + + // Non-serializable closure in the final result stage + val thrown = intercept[SparkException] { + sc.parallelize(1 to 10, 2).map(x => a).count() + } + assert(thrown.getClass === classOf[SparkException]) + assert(thrown.getMessage.contains("NotSerializableException")) + + // Non-serializable closure in an earlier stage + val thrown1 = intercept[SparkException] { + sc.parallelize(1 to 10, 2).map(x => (x, a)).partitionBy(new HashPartitioner(3)).count() + } + assert(thrown1.getClass === classOf[SparkException]) + assert(thrown1.getMessage.contains("NotSerializableException")) + + // Non-serializable closure in foreach function + val thrown2 = intercept[SparkException] { + sc.parallelize(1 to 10, 2).foreach(x => println(a)) + } + assert(thrown2.getClass === classOf[SparkException]) + assert(thrown2.getMessage.contains("NotSerializableException")) + + FailureSuiteState.clear() + } + + // TODO: Need to add tests with shuffle fetch failures. +} + + diff --git a/core/src/test/scala/org/apache/spark/FileServerSuite.scala b/core/src/test/scala/org/apache/spark/FileServerSuite.scala new file mode 100644 index 0000000000..35d1d41af1 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/FileServerSuite.scala @@ -0,0 +1,123 @@ +/* + * 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 com.google.common.io.Files +import org.scalatest.FunSuite +import java.io.{File, PrintWriter, FileReader, BufferedReader} +import SparkContext._ + +class FileServerSuite extends FunSuite with LocalSparkContext { + + @transient var tmpFile: File = _ + @transient var testJarFile: File = _ + + override def beforeEach() { + super.beforeEach() + // Create a sample text file + val tmpdir = new File(Files.createTempDir(), "test") + tmpdir.mkdir() + tmpFile = new File(tmpdir, "FileServerSuite.txt") + val pw = new PrintWriter(tmpFile) + pw.println("100") + pw.close() + } + + override def afterEach() { + super.afterEach() + // Clean up downloaded file + if (tmpFile.exists) { + tmpFile.delete() + } + } + + test("Distributing files locally") { + sc = new SparkContext("local[4]", "test") + sc.addFile(tmpFile.toString) + val testData = Array((1,1), (1,1), (2,1), (3,5), (2,2), (3,0)) + val result = sc.parallelize(testData).reduceByKey { + val path = SparkFiles.get("FileServerSuite.txt") + val in = new BufferedReader(new FileReader(path)) + val fileVal = in.readLine().toInt + in.close() + _ * fileVal + _ * fileVal + }.collect() + assert(result.toSet === Set((1,200), (2,300), (3,500))) + } + + test("Distributing files locally using URL as input") { + // addFile("file:///....") + sc = new SparkContext("local[4]", "test") + sc.addFile(new File(tmpFile.toString).toURI.toString) + val testData = Array((1,1), (1,1), (2,1), (3,5), (2,2), (3,0)) + val result = sc.parallelize(testData).reduceByKey { + val path = SparkFiles.get("FileServerSuite.txt") + val in = new BufferedReader(new FileReader(path)) + val fileVal = in.readLine().toInt + in.close() + _ * fileVal + _ * fileVal + }.collect() + assert(result.toSet === Set((1,200), (2,300), (3,500))) + } + + test ("Dynamically adding JARS locally") { + sc = new SparkContext("local[4]", "test") + val sampleJarFile = getClass.getClassLoader.getResource("uncommons-maths-1.2.2.jar").getFile() + sc.addJar(sampleJarFile) + val testData = Array((1,1), (1,1), (2,1), (3,5), (2,3), (3,0)) + val result = sc.parallelize(testData).reduceByKey { (x,y) => + val fac = Thread.currentThread.getContextClassLoader() + .loadClass("org.uncommons.maths.Maths") + .getDeclaredMethod("factorial", classOf[Int]) + val a = fac.invoke(null, x.asInstanceOf[java.lang.Integer]).asInstanceOf[Long].toInt + val b = fac.invoke(null, y.asInstanceOf[java.lang.Integer]).asInstanceOf[Long].toInt + a + b + }.collect() + assert(result.toSet === Set((1,2), (2,7), (3,121))) + } + + test("Distributing files on a standalone cluster") { + sc = new SparkContext("local-cluster[1,1,512]", "test") + sc.addFile(tmpFile.toString) + val testData = Array((1,1), (1,1), (2,1), (3,5), (2,2), (3,0)) + val result = sc.parallelize(testData).reduceByKey { + val path = SparkFiles.get("FileServerSuite.txt") + val in = new BufferedReader(new FileReader(path)) + val fileVal = in.readLine().toInt + in.close() + _ * fileVal + _ * fileVal + }.collect() + assert(result.toSet === Set((1,200), (2,300), (3,500))) + } + + test ("Dynamically adding JARS on a standalone cluster") { + sc = new SparkContext("local-cluster[1,1,512]", "test") + val sampleJarFile = getClass.getClassLoader.getResource("uncommons-maths-1.2.2.jar").getFile() + sc.addJar(sampleJarFile) + val testData = Array((1,1), (1,1), (2,1), (3,5), (2,3), (3,0)) + val result = sc.parallelize(testData).reduceByKey { (x,y) => + val fac = Thread.currentThread.getContextClassLoader() + .loadClass("org.uncommons.maths.Maths") + .getDeclaredMethod("factorial", classOf[Int]) + val a = fac.invoke(null, x.asInstanceOf[java.lang.Integer]).asInstanceOf[Long].toInt + val b = fac.invoke(null, y.asInstanceOf[java.lang.Integer]).asInstanceOf[Long].toInt + a + b + }.collect() + assert(result.toSet === Set((1,2), (2,7), (3,121))) + } +} diff --git a/core/src/test/scala/org/apache/spark/FileSuite.scala b/core/src/test/scala/org/apache/spark/FileSuite.scala new file mode 100644 index 0000000000..7b82a4cdd9 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/FileSuite.scala @@ -0,0 +1,212 @@ +/* + * 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.io.{FileWriter, PrintWriter, File} + +import scala.io.Source + +import com.google.common.io.Files +import org.scalatest.FunSuite +import org.apache.hadoop.io._ +import org.apache.hadoop.io.compress.{DefaultCodec, CompressionCodec, GzipCodec} + + +import SparkContext._ + +class FileSuite extends FunSuite with LocalSparkContext { + + test("text files") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val outputDir = new File(tempDir, "output").getAbsolutePath + val nums = sc.makeRDD(1 to 4) + nums.saveAsTextFile(outputDir) + // Read the plain text file and check it's OK + val outputFile = new File(outputDir, "part-00000") + val content = Source.fromFile(outputFile).mkString + assert(content === "1\n2\n3\n4\n") + // Also try reading it in as a text file RDD + assert(sc.textFile(outputDir).collect().toList === List("1", "2", "3", "4")) + } + + test("text files (compressed)") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val normalDir = new File(tempDir, "output_normal").getAbsolutePath + val compressedOutputDir = new File(tempDir, "output_compressed").getAbsolutePath + val codec = new DefaultCodec() + + val data = sc.parallelize("a" * 10000, 1) + data.saveAsTextFile(normalDir) + data.saveAsTextFile(compressedOutputDir, classOf[DefaultCodec]) + + val normalFile = new File(normalDir, "part-00000") + val normalContent = sc.textFile(normalDir).collect + assert(normalContent === Array.fill(10000)("a")) + + val compressedFile = new File(compressedOutputDir, "part-00000" + codec.getDefaultExtension) + val compressedContent = sc.textFile(compressedOutputDir).collect + assert(compressedContent === Array.fill(10000)("a")) + + assert(compressedFile.length < normalFile.length) + } + + test("SequenceFiles") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val outputDir = new File(tempDir, "output").getAbsolutePath + val nums = sc.makeRDD(1 to 3).map(x => (x, "a" * x)) // (1,a), (2,aa), (3,aaa) + nums.saveAsSequenceFile(outputDir) + // Try reading the output back as a SequenceFile + val output = sc.sequenceFile[IntWritable, Text](outputDir) + assert(output.map(_.toString).collect().toList === List("(1,a)", "(2,aa)", "(3,aaa)")) + } + + test("SequenceFile (compressed)") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val normalDir = new File(tempDir, "output_normal").getAbsolutePath + val compressedOutputDir = new File(tempDir, "output_compressed").getAbsolutePath + val codec = new DefaultCodec() + + val data = sc.parallelize(Seq.fill(100)("abc"), 1).map(x => (x, x)) + data.saveAsSequenceFile(normalDir) + data.saveAsSequenceFile(compressedOutputDir, Some(classOf[DefaultCodec])) + + val normalFile = new File(normalDir, "part-00000") + val normalContent = sc.sequenceFile[String, String](normalDir).collect + assert(normalContent === Array.fill(100)("abc", "abc")) + + val compressedFile = new File(compressedOutputDir, "part-00000" + codec.getDefaultExtension) + val compressedContent = sc.sequenceFile[String, String](compressedOutputDir).collect + assert(compressedContent === Array.fill(100)("abc", "abc")) + + assert(compressedFile.length < normalFile.length) + } + + test("SequenceFile with writable key") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val outputDir = new File(tempDir, "output").getAbsolutePath + val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), "a" * x)) + nums.saveAsSequenceFile(outputDir) + // Try reading the output back as a SequenceFile + val output = sc.sequenceFile[IntWritable, Text](outputDir) + assert(output.map(_.toString).collect().toList === List("(1,a)", "(2,aa)", "(3,aaa)")) + } + + test("SequenceFile with writable value") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val outputDir = new File(tempDir, "output").getAbsolutePath + val nums = sc.makeRDD(1 to 3).map(x => (x, new Text("a" * x))) + nums.saveAsSequenceFile(outputDir) + // Try reading the output back as a SequenceFile + val output = sc.sequenceFile[IntWritable, Text](outputDir) + assert(output.map(_.toString).collect().toList === List("(1,a)", "(2,aa)", "(3,aaa)")) + } + + test("SequenceFile with writable key and value") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val outputDir = new File(tempDir, "output").getAbsolutePath + val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), new Text("a" * x))) + nums.saveAsSequenceFile(outputDir) + // Try reading the output back as a SequenceFile + val output = sc.sequenceFile[IntWritable, Text](outputDir) + assert(output.map(_.toString).collect().toList === List("(1,a)", "(2,aa)", "(3,aaa)")) + } + + test("implicit conversions in reading SequenceFiles") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val outputDir = new File(tempDir, "output").getAbsolutePath + val nums = sc.makeRDD(1 to 3).map(x => (x, "a" * x)) // (1,a), (2,aa), (3,aaa) + nums.saveAsSequenceFile(outputDir) + // Similar to the tests above, we read a SequenceFile, but this time we pass type params + // that are convertable to Writable instead of calling sequenceFile[IntWritable, Text] + val output1 = sc.sequenceFile[Int, String](outputDir) + assert(output1.collect().toList === List((1, "a"), (2, "aa"), (3, "aaa"))) + // Also try having one type be a subclass of Writable and one not + val output2 = sc.sequenceFile[Int, Text](outputDir) + assert(output2.map(_.toString).collect().toList === List("(1,a)", "(2,aa)", "(3,aaa)")) + val output3 = sc.sequenceFile[IntWritable, String](outputDir) + assert(output3.map(_.toString).collect().toList === List("(1,a)", "(2,aa)", "(3,aaa)")) + } + + test("object files of ints") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val outputDir = new File(tempDir, "output").getAbsolutePath + val nums = sc.makeRDD(1 to 4) + nums.saveAsObjectFile(outputDir) + // Try reading the output back as an object file + val output = sc.objectFile[Int](outputDir) + assert(output.collect().toList === List(1, 2, 3, 4)) + } + + test("object files of complex types") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val outputDir = new File(tempDir, "output").getAbsolutePath + val nums = sc.makeRDD(1 to 3).map(x => (x, "a" * x)) + nums.saveAsObjectFile(outputDir) + // Try reading the output back as an object file + val output = sc.objectFile[(Int, String)](outputDir) + assert(output.collect().toList === List((1, "a"), (2, "aa"), (3, "aaa"))) + } + + test("write SequenceFile using new Hadoop API") { + import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val outputDir = new File(tempDir, "output").getAbsolutePath + val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), new Text("a" * x))) + nums.saveAsNewAPIHadoopFile[SequenceFileOutputFormat[IntWritable, Text]]( + outputDir) + val output = sc.sequenceFile[IntWritable, Text](outputDir) + assert(output.map(_.toString).collect().toList === List("(1,a)", "(2,aa)", "(3,aaa)")) + } + + test("read SequenceFile using new Hadoop API") { + import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val outputDir = new File(tempDir, "output").getAbsolutePath + val nums = sc.makeRDD(1 to 3).map(x => (new IntWritable(x), new Text("a" * x))) + nums.saveAsSequenceFile(outputDir) + val output = + sc.newAPIHadoopFile[IntWritable, Text, SequenceFileInputFormat[IntWritable, Text]](outputDir) + assert(output.map(_.toString).collect().toList === List("(1,a)", "(2,aa)", "(3,aaa)")) + } + + test("file caching") { + sc = new SparkContext("local", "test") + val tempDir = Files.createTempDir() + val out = new FileWriter(tempDir + "/input") + out.write("Hello world!\n") + out.write("What's up?\n") + out.write("Goodbye\n") + out.close() + val rdd = sc.textFile(tempDir + "/input").cache() + assert(rdd.count() === 3) + assert(rdd.count() === 3) + assert(rdd.count() === 3) + } +} diff --git a/core/src/test/scala/org/apache/spark/JavaAPISuite.java b/core/src/test/scala/org/apache/spark/JavaAPISuite.java new file mode 100644 index 0000000000..8a869c9005 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/JavaAPISuite.java @@ -0,0 +1,865 @@ +/* + * 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.io.File; +import java.io.IOException; +import java.io.Serializable; +import java.util.*; + +import com.google.common.base.Optional; +import scala.Tuple2; + +import com.google.common.base.Charsets; +import org.apache.hadoop.io.compress.DefaultCodec; +import com.google.common.io.Files; +import org.apache.hadoop.io.IntWritable; +import org.apache.hadoop.io.Text; +import org.apache.hadoop.mapred.SequenceFileInputFormat; +import org.apache.hadoop.mapred.SequenceFileOutputFormat; +import org.apache.hadoop.mapreduce.Job; +import org.junit.After; +import org.junit.Assert; +import org.junit.Before; +import org.junit.Test; + +import org.apache.spark.api.java.JavaDoubleRDD; +import org.apache.spark.api.java.JavaPairRDD; +import org.apache.spark.api.java.JavaRDD; +import org.apache.spark.api.java.JavaSparkContext; +import org.apache.spark.api.java.function.*; +import org.apache.spark.partial.BoundedDouble; +import org.apache.spark.partial.PartialResult; +import org.apache.spark.storage.StorageLevel; +import org.apache.spark.util.StatCounter; + + +// The test suite itself is Serializable so that anonymous Function implementations can be +// serialized, as an alternative to converting these anonymous classes to static inner classes; +// see http://stackoverflow.com/questions/758570/. +public class JavaAPISuite implements Serializable { + private transient JavaSparkContext sc; + + @Before + public void setUp() { + sc = new JavaSparkContext("local", "JavaAPISuite"); + } + + @After + public void tearDown() { + sc.stop(); + sc = null; + // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown + System.clearProperty("spark.driver.port"); + } + + static class ReverseIntComparator implements Comparator<Integer>, Serializable { + + @Override + public int compare(Integer a, Integer b) { + if (a > b) return -1; + else if (a < b) return 1; + else return 0; + } + }; + + @Test + public void sparkContextUnion() { + // Union of non-specialized JavaRDDs + List<String> strings = Arrays.asList("Hello", "World"); + JavaRDD<String> s1 = sc.parallelize(strings); + JavaRDD<String> s2 = sc.parallelize(strings); + // Varargs + JavaRDD<String> sUnion = sc.union(s1, s2); + Assert.assertEquals(4, sUnion.count()); + // List + List<JavaRDD<String>> list = new ArrayList<JavaRDD<String>>(); + list.add(s2); + sUnion = sc.union(s1, list); + Assert.assertEquals(4, sUnion.count()); + + // Union of JavaDoubleRDDs + List<Double> doubles = Arrays.asList(1.0, 2.0); + JavaDoubleRDD d1 = sc.parallelizeDoubles(doubles); + JavaDoubleRDD d2 = sc.parallelizeDoubles(doubles); + JavaDoubleRDD dUnion = sc.union(d1, d2); + Assert.assertEquals(4, dUnion.count()); + + // Union of JavaPairRDDs + List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>(); + pairs.add(new Tuple2<Integer, Integer>(1, 2)); + pairs.add(new Tuple2<Integer, Integer>(3, 4)); + JavaPairRDD<Integer, Integer> p1 = sc.parallelizePairs(pairs); + JavaPairRDD<Integer, Integer> p2 = sc.parallelizePairs(pairs); + JavaPairRDD<Integer, Integer> pUnion = sc.union(p1, p2); + Assert.assertEquals(4, pUnion.count()); + } + + @Test + public void sortByKey() { + List<Tuple2<Integer, Integer>> pairs = new ArrayList<Tuple2<Integer, Integer>>(); + pairs.add(new Tuple2<Integer, Integer>(0, 4)); + pairs.add(new Tuple2<Integer, Integer>(3, 2)); + pairs.add(new Tuple2<Integer, Integer>(-1, 1)); + + JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs); + + // Default comparator + JavaPairRDD<Integer, Integer> sortedRDD = rdd.sortByKey(); + Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first()); + List<Tuple2<Integer, Integer>> sortedPairs = sortedRDD.collect(); + Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(1)); + Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2)); + + // Custom comparator + sortedRDD = rdd.sortByKey(new ReverseIntComparator(), false); + Assert.assertEquals(new Tuple2<Integer, Integer>(-1, 1), sortedRDD.first()); + sortedPairs = sortedRDD.collect(); + Assert.assertEquals(new Tuple2<Integer, Integer>(0, 4), sortedPairs.get(1)); + Assert.assertEquals(new Tuple2<Integer, Integer>(3, 2), sortedPairs.get(2)); + } + + static int foreachCalls = 0; + + @Test + public void foreach() { + foreachCalls = 0; + JavaRDD<String> rdd = sc.parallelize(Arrays.asList("Hello", "World")); + rdd.foreach(new VoidFunction<String>() { + @Override + public void call(String s) { + foreachCalls++; + } + }); + Assert.assertEquals(2, foreachCalls); + } + + @Test + public void lookup() { + JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList( + new Tuple2<String, String>("Apples", "Fruit"), + new Tuple2<String, String>("Oranges", "Fruit"), + new Tuple2<String, String>("Oranges", "Citrus") + )); + Assert.assertEquals(2, categories.lookup("Oranges").size()); + Assert.assertEquals(2, categories.groupByKey().lookup("Oranges").get(0).size()); + } + + @Test + public void groupBy() { + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 1, 2, 3, 5, 8, 13)); + Function<Integer, Boolean> isOdd = new Function<Integer, Boolean>() { + @Override + public Boolean call(Integer x) { + return x % 2 == 0; + } + }; + JavaPairRDD<Boolean, List<Integer>> oddsAndEvens = rdd.groupBy(isOdd); + Assert.assertEquals(2, oddsAndEvens.count()); + Assert.assertEquals(2, oddsAndEvens.lookup(true).get(0).size()); // Evens + Assert.assertEquals(5, oddsAndEvens.lookup(false).get(0).size()); // Odds + + oddsAndEvens = rdd.groupBy(isOdd, 1); + Assert.assertEquals(2, oddsAndEvens.count()); + Assert.assertEquals(2, oddsAndEvens.lookup(true).get(0).size()); // Evens + Assert.assertEquals(5, oddsAndEvens.lookup(false).get(0).size()); // Odds + } + + @Test + public void cogroup() { + JavaPairRDD<String, String> categories = sc.parallelizePairs(Arrays.asList( + new Tuple2<String, String>("Apples", "Fruit"), + new Tuple2<String, String>("Oranges", "Fruit"), + new Tuple2<String, String>("Oranges", "Citrus") + )); + JavaPairRDD<String, Integer> prices = sc.parallelizePairs(Arrays.asList( + new Tuple2<String, Integer>("Oranges", 2), + new Tuple2<String, Integer>("Apples", 3) + )); + JavaPairRDD<String, Tuple2<List<String>, List<Integer>>> cogrouped = categories.cogroup(prices); + Assert.assertEquals("[Fruit, Citrus]", cogrouped.lookup("Oranges").get(0)._1().toString()); + Assert.assertEquals("[2]", cogrouped.lookup("Oranges").get(0)._2().toString()); + + cogrouped.collect(); + } + + @Test + public void leftOuterJoin() { + JavaPairRDD<Integer, Integer> rdd1 = sc.parallelizePairs(Arrays.asList( + new Tuple2<Integer, Integer>(1, 1), + new Tuple2<Integer, Integer>(1, 2), + new Tuple2<Integer, Integer>(2, 1), + new Tuple2<Integer, Integer>(3, 1) + )); + JavaPairRDD<Integer, Character> rdd2 = sc.parallelizePairs(Arrays.asList( + new Tuple2<Integer, Character>(1, 'x'), + new Tuple2<Integer, Character>(2, 'y'), + new Tuple2<Integer, Character>(2, 'z'), + new Tuple2<Integer, Character>(4, 'w') + )); + List<Tuple2<Integer,Tuple2<Integer,Optional<Character>>>> joined = + rdd1.leftOuterJoin(rdd2).collect(); + Assert.assertEquals(5, joined.size()); + Tuple2<Integer,Tuple2<Integer,Optional<Character>>> firstUnmatched = + rdd1.leftOuterJoin(rdd2).filter( + new Function<Tuple2<Integer, Tuple2<Integer, Optional<Character>>>, Boolean>() { + @Override + public Boolean call(Tuple2<Integer, Tuple2<Integer, Optional<Character>>> tup) + throws Exception { + return !tup._2()._2().isPresent(); + } + }).first(); + Assert.assertEquals(3, firstUnmatched._1().intValue()); + } + + @Test + public void foldReduce() { + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 1, 2, 3, 5, 8, 13)); + Function2<Integer, Integer, Integer> add = new Function2<Integer, Integer, Integer>() { + @Override + public Integer call(Integer a, Integer b) { + return a + b; + } + }; + + int sum = rdd.fold(0, add); + Assert.assertEquals(33, sum); + + sum = rdd.reduce(add); + Assert.assertEquals(33, sum); + } + + @Test + public void foldByKey() { + List<Tuple2<Integer, Integer>> pairs = Arrays.asList( + new Tuple2<Integer, Integer>(2, 1), + new Tuple2<Integer, Integer>(2, 1), + new Tuple2<Integer, Integer>(1, 1), + new Tuple2<Integer, Integer>(3, 2), + new Tuple2<Integer, Integer>(3, 1) + ); + JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs); + JavaPairRDD<Integer, Integer> sums = rdd.foldByKey(0, + new Function2<Integer, Integer, Integer>() { + @Override + public Integer call(Integer a, Integer b) { + return a + b; + } + }); + Assert.assertEquals(1, sums.lookup(1).get(0).intValue()); + Assert.assertEquals(2, sums.lookup(2).get(0).intValue()); + Assert.assertEquals(3, sums.lookup(3).get(0).intValue()); + } + + @Test + public void reduceByKey() { + List<Tuple2<Integer, Integer>> pairs = Arrays.asList( + new Tuple2<Integer, Integer>(2, 1), + new Tuple2<Integer, Integer>(2, 1), + new Tuple2<Integer, Integer>(1, 1), + new Tuple2<Integer, Integer>(3, 2), + new Tuple2<Integer, Integer>(3, 1) + ); + JavaPairRDD<Integer, Integer> rdd = sc.parallelizePairs(pairs); + JavaPairRDD<Integer, Integer> counts = rdd.reduceByKey( + new Function2<Integer, Integer, Integer>() { + @Override + public Integer call(Integer a, Integer b) { + return a + b; + } + }); + Assert.assertEquals(1, counts.lookup(1).get(0).intValue()); + Assert.assertEquals(2, counts.lookup(2).get(0).intValue()); + Assert.assertEquals(3, counts.lookup(3).get(0).intValue()); + + Map<Integer, Integer> localCounts = counts.collectAsMap(); + Assert.assertEquals(1, localCounts.get(1).intValue()); + Assert.assertEquals(2, localCounts.get(2).intValue()); + Assert.assertEquals(3, localCounts.get(3).intValue()); + + localCounts = rdd.reduceByKeyLocally(new Function2<Integer, Integer, + Integer>() { + @Override + public Integer call(Integer a, Integer b) { + return a + b; + } + }); + Assert.assertEquals(1, localCounts.get(1).intValue()); + Assert.assertEquals(2, localCounts.get(2).intValue()); + Assert.assertEquals(3, localCounts.get(3).intValue()); + } + + @Test + public void approximateResults() { + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 1, 2, 3, 5, 8, 13)); + Map<Integer, Long> countsByValue = rdd.countByValue(); + Assert.assertEquals(2, countsByValue.get(1).longValue()); + Assert.assertEquals(1, countsByValue.get(13).longValue()); + + PartialResult<Map<Integer, BoundedDouble>> approx = rdd.countByValueApprox(1); + Map<Integer, BoundedDouble> finalValue = approx.getFinalValue(); + Assert.assertEquals(2.0, finalValue.get(1).mean(), 0.01); + Assert.assertEquals(1.0, finalValue.get(13).mean(), 0.01); + } + + @Test + public void take() { + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 1, 2, 3, 5, 8, 13)); + Assert.assertEquals(1, rdd.first().intValue()); + List<Integer> firstTwo = rdd.take(2); + List<Integer> sample = rdd.takeSample(false, 2, 42); + } + + @Test + public void cartesian() { + JavaDoubleRDD doubleRDD = sc.parallelizeDoubles(Arrays.asList(1.0, 1.0, 2.0, 3.0, 5.0, 8.0)); + JavaRDD<String> stringRDD = sc.parallelize(Arrays.asList("Hello", "World")); + JavaPairRDD<String, Double> cartesian = stringRDD.cartesian(doubleRDD); + Assert.assertEquals(new Tuple2<String, Double>("Hello", 1.0), cartesian.first()); + } + + @Test + public void javaDoubleRDD() { + JavaDoubleRDD rdd = sc.parallelizeDoubles(Arrays.asList(1.0, 1.0, 2.0, 3.0, 5.0, 8.0)); + JavaDoubleRDD distinct = rdd.distinct(); + Assert.assertEquals(5, distinct.count()); + JavaDoubleRDD filter = rdd.filter(new Function<Double, Boolean>() { + @Override + public Boolean call(Double x) { + return x > 2.0; + } + }); + Assert.assertEquals(3, filter.count()); + JavaDoubleRDD union = rdd.union(rdd); + Assert.assertEquals(12, union.count()); + union = union.cache(); + Assert.assertEquals(12, union.count()); + + Assert.assertEquals(20, rdd.sum(), 0.01); + StatCounter stats = rdd.stats(); + Assert.assertEquals(20, stats.sum(), 0.01); + Assert.assertEquals(20/6.0, rdd.mean(), 0.01); + Assert.assertEquals(20/6.0, rdd.mean(), 0.01); + Assert.assertEquals(6.22222, rdd.variance(), 0.01); + Assert.assertEquals(7.46667, rdd.sampleVariance(), 0.01); + Assert.assertEquals(2.49444, rdd.stdev(), 0.01); + Assert.assertEquals(2.73252, rdd.sampleStdev(), 0.01); + + Double first = rdd.first(); + List<Double> take = rdd.take(5); + } + + @Test + public void map() { + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5)); + JavaDoubleRDD doubles = rdd.map(new DoubleFunction<Integer>() { + @Override + public Double call(Integer x) { + return 1.0 * x; + } + }).cache(); + JavaPairRDD<Integer, Integer> pairs = rdd.map(new PairFunction<Integer, Integer, Integer>() { + @Override + public Tuple2<Integer, Integer> call(Integer x) { + return new Tuple2<Integer, Integer>(x, x); + } + }).cache(); + JavaRDD<String> strings = rdd.map(new Function<Integer, String>() { + @Override + public String call(Integer x) { + return x.toString(); + } + }).cache(); + } + + @Test + public void flatMap() { + JavaRDD<String> rdd = sc.parallelize(Arrays.asList("Hello World!", + "The quick brown fox jumps over the lazy dog.")); + JavaRDD<String> words = rdd.flatMap(new FlatMapFunction<String, String>() { + @Override + public Iterable<String> call(String x) { + return Arrays.asList(x.split(" ")); + } + }); + Assert.assertEquals("Hello", words.first()); + Assert.assertEquals(11, words.count()); + + JavaPairRDD<String, String> pairs = rdd.flatMap( + new PairFlatMapFunction<String, String, String>() { + + @Override + public Iterable<Tuple2<String, String>> call(String s) { + List<Tuple2<String, String>> pairs = new LinkedList<Tuple2<String, String>>(); + for (String word : s.split(" ")) pairs.add(new Tuple2<String, String>(word, word)); + return pairs; + } + } + ); + Assert.assertEquals(new Tuple2<String, String>("Hello", "Hello"), pairs.first()); + Assert.assertEquals(11, pairs.count()); + + JavaDoubleRDD doubles = rdd.flatMap(new DoubleFlatMapFunction<String>() { + @Override + public Iterable<Double> call(String s) { + List<Double> lengths = new LinkedList<Double>(); + for (String word : s.split(" ")) lengths.add(word.length() * 1.0); + return lengths; + } + }); + Double x = doubles.first(); + Assert.assertEquals(5.0, doubles.first().doubleValue(), 0.01); + Assert.assertEquals(11, pairs.count()); + } + + @Test + public void mapsFromPairsToPairs() { + List<Tuple2<Integer, String>> pairs = Arrays.asList( + new Tuple2<Integer, String>(1, "a"), + new Tuple2<Integer, String>(2, "aa"), + new Tuple2<Integer, String>(3, "aaa") + ); + JavaPairRDD<Integer, String> pairRDD = sc.parallelizePairs(pairs); + + // Regression test for SPARK-668: + JavaPairRDD<String, Integer> swapped = pairRDD.flatMap( + new PairFlatMapFunction<Tuple2<Integer, String>, String, Integer>() { + @Override + public Iterable<Tuple2<String, Integer>> call(Tuple2<Integer, String> item) throws Exception { + return Collections.singletonList(item.swap()); + } + }); + swapped.collect(); + + // There was never a bug here, but it's worth testing: + pairRDD.map(new PairFunction<Tuple2<Integer, String>, String, Integer>() { + @Override + public Tuple2<String, Integer> call(Tuple2<Integer, String> item) throws Exception { + return item.swap(); + } + }).collect(); + } + + @Test + public void mapPartitions() { + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4), 2); + JavaRDD<Integer> partitionSums = rdd.mapPartitions( + new FlatMapFunction<Iterator<Integer>, Integer>() { + @Override + public Iterable<Integer> call(Iterator<Integer> iter) { + int sum = 0; + while (iter.hasNext()) { + sum += iter.next(); + } + return Collections.singletonList(sum); + } + }); + Assert.assertEquals("[3, 7]", partitionSums.collect().toString()); + } + + @Test + public void persist() { + JavaDoubleRDD doubleRDD = sc.parallelizeDoubles(Arrays.asList(1.0, 1.0, 2.0, 3.0, 5.0, 8.0)); + doubleRDD = doubleRDD.persist(StorageLevel.DISK_ONLY()); + Assert.assertEquals(20, doubleRDD.sum(), 0.1); + + List<Tuple2<Integer, String>> pairs = Arrays.asList( + new Tuple2<Integer, String>(1, "a"), + new Tuple2<Integer, String>(2, "aa"), + new Tuple2<Integer, String>(3, "aaa") + ); + JavaPairRDD<Integer, String> pairRDD = sc.parallelizePairs(pairs); + pairRDD = pairRDD.persist(StorageLevel.DISK_ONLY()); + Assert.assertEquals("a", pairRDD.first()._2()); + + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5)); + rdd = rdd.persist(StorageLevel.DISK_ONLY()); + Assert.assertEquals(1, rdd.first().intValue()); + } + + @Test + public void iterator() { + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5), 2); + TaskContext context = new TaskContext(0, 0, 0, null); + Assert.assertEquals(1, rdd.iterator(rdd.splits().get(0), context).next().intValue()); + } + + @Test + public void glom() { + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4), 2); + Assert.assertEquals("[1, 2]", rdd.glom().first().toString()); + } + + // File input / output tests are largely adapted from FileSuite: + + @Test + public void textFiles() throws IOException { + File tempDir = Files.createTempDir(); + String outputDir = new File(tempDir, "output").getAbsolutePath(); + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4)); + rdd.saveAsTextFile(outputDir); + // Read the plain text file and check it's OK + File outputFile = new File(outputDir, "part-00000"); + String content = Files.toString(outputFile, Charsets.UTF_8); + Assert.assertEquals("1\n2\n3\n4\n", content); + // Also try reading it in as a text file RDD + List<String> expected = Arrays.asList("1", "2", "3", "4"); + JavaRDD<String> readRDD = sc.textFile(outputDir); + Assert.assertEquals(expected, readRDD.collect()); + } + + @Test + public void textFilesCompressed() throws IOException { + File tempDir = Files.createTempDir(); + String outputDir = new File(tempDir, "output").getAbsolutePath(); + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4)); + rdd.saveAsTextFile(outputDir, DefaultCodec.class); + + // Try reading it in as a text file RDD + List<String> expected = Arrays.asList("1", "2", "3", "4"); + JavaRDD<String> readRDD = sc.textFile(outputDir); + Assert.assertEquals(expected, readRDD.collect()); + } + + @Test + public void sequenceFile() { + File tempDir = Files.createTempDir(); + String outputDir = new File(tempDir, "output").getAbsolutePath(); + List<Tuple2<Integer, String>> pairs = Arrays.asList( + new Tuple2<Integer, String>(1, "a"), + new Tuple2<Integer, String>(2, "aa"), + new Tuple2<Integer, String>(3, "aaa") + ); + JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs); + + rdd.map(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() { + @Override + public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) { + return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2())); + } + }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class); + + // Try reading the output back as an object file + JavaPairRDD<Integer, String> readRDD = sc.sequenceFile(outputDir, IntWritable.class, + Text.class).map(new PairFunction<Tuple2<IntWritable, Text>, Integer, String>() { + @Override + public Tuple2<Integer, String> call(Tuple2<IntWritable, Text> pair) { + return new Tuple2<Integer, String>(pair._1().get(), pair._2().toString()); + } + }); + Assert.assertEquals(pairs, readRDD.collect()); + } + + @Test + public void writeWithNewAPIHadoopFile() { + File tempDir = Files.createTempDir(); + String outputDir = new File(tempDir, "output").getAbsolutePath(); + List<Tuple2<Integer, String>> pairs = Arrays.asList( + new Tuple2<Integer, String>(1, "a"), + new Tuple2<Integer, String>(2, "aa"), + new Tuple2<Integer, String>(3, "aaa") + ); + JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs); + + rdd.map(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() { + @Override + public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) { + return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2())); + } + }).saveAsNewAPIHadoopFile(outputDir, IntWritable.class, Text.class, + org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat.class); + + JavaPairRDD<IntWritable, Text> output = sc.sequenceFile(outputDir, IntWritable.class, + Text.class); + Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>, + String>() { + @Override + public String call(Tuple2<IntWritable, Text> x) { + return x.toString(); + } + }).collect().toString()); + } + + @Test + public void readWithNewAPIHadoopFile() throws IOException { + File tempDir = Files.createTempDir(); + String outputDir = new File(tempDir, "output").getAbsolutePath(); + List<Tuple2<Integer, String>> pairs = Arrays.asList( + new Tuple2<Integer, String>(1, "a"), + new Tuple2<Integer, String>(2, "aa"), + new Tuple2<Integer, String>(3, "aaa") + ); + JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs); + + rdd.map(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() { + @Override + public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) { + return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2())); + } + }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class); + + JavaPairRDD<IntWritable, Text> output = sc.newAPIHadoopFile(outputDir, + org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat.class, IntWritable.class, + Text.class, new Job().getConfiguration()); + Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>, + String>() { + @Override + public String call(Tuple2<IntWritable, Text> x) { + return x.toString(); + } + }).collect().toString()); + } + + @Test + public void objectFilesOfInts() { + File tempDir = Files.createTempDir(); + String outputDir = new File(tempDir, "output").getAbsolutePath(); + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4)); + rdd.saveAsObjectFile(outputDir); + // Try reading the output back as an object file + List<Integer> expected = Arrays.asList(1, 2, 3, 4); + JavaRDD<Integer> readRDD = sc.objectFile(outputDir); + Assert.assertEquals(expected, readRDD.collect()); + } + + @Test + public void objectFilesOfComplexTypes() { + File tempDir = Files.createTempDir(); + String outputDir = new File(tempDir, "output").getAbsolutePath(); + List<Tuple2<Integer, String>> pairs = Arrays.asList( + new Tuple2<Integer, String>(1, "a"), + new Tuple2<Integer, String>(2, "aa"), + new Tuple2<Integer, String>(3, "aaa") + ); + JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs); + rdd.saveAsObjectFile(outputDir); + // Try reading the output back as an object file + JavaRDD<Tuple2<Integer, String>> readRDD = sc.objectFile(outputDir); + Assert.assertEquals(pairs, readRDD.collect()); + } + + @Test + public void hadoopFile() { + File tempDir = Files.createTempDir(); + String outputDir = new File(tempDir, "output").getAbsolutePath(); + List<Tuple2<Integer, String>> pairs = Arrays.asList( + new Tuple2<Integer, String>(1, "a"), + new Tuple2<Integer, String>(2, "aa"), + new Tuple2<Integer, String>(3, "aaa") + ); + JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs); + + rdd.map(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() { + @Override + public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) { + return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2())); + } + }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class); + + JavaPairRDD<IntWritable, Text> output = sc.hadoopFile(outputDir, + SequenceFileInputFormat.class, IntWritable.class, Text.class); + Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>, + String>() { + @Override + public String call(Tuple2<IntWritable, Text> x) { + return x.toString(); + } + }).collect().toString()); + } + + @Test + public void hadoopFileCompressed() { + File tempDir = Files.createTempDir(); + String outputDir = new File(tempDir, "output_compressed").getAbsolutePath(); + List<Tuple2<Integer, String>> pairs = Arrays.asList( + new Tuple2<Integer, String>(1, "a"), + new Tuple2<Integer, String>(2, "aa"), + new Tuple2<Integer, String>(3, "aaa") + ); + JavaPairRDD<Integer, String> rdd = sc.parallelizePairs(pairs); + + rdd.map(new PairFunction<Tuple2<Integer, String>, IntWritable, Text>() { + @Override + public Tuple2<IntWritable, Text> call(Tuple2<Integer, String> pair) { + return new Tuple2<IntWritable, Text>(new IntWritable(pair._1()), new Text(pair._2())); + } + }).saveAsHadoopFile(outputDir, IntWritable.class, Text.class, SequenceFileOutputFormat.class, + DefaultCodec.class); + + JavaPairRDD<IntWritable, Text> output = sc.hadoopFile(outputDir, + SequenceFileInputFormat.class, IntWritable.class, Text.class); + + Assert.assertEquals(pairs.toString(), output.map(new Function<Tuple2<IntWritable, Text>, + String>() { + @Override + public String call(Tuple2<IntWritable, Text> x) { + return x.toString(); + } + }).collect().toString()); + } + + @Test + public void zip() { + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5)); + JavaDoubleRDD doubles = rdd.map(new DoubleFunction<Integer>() { + @Override + public Double call(Integer x) { + return 1.0 * x; + } + }); + JavaPairRDD<Integer, Double> zipped = rdd.zip(doubles); + zipped.count(); + } + + @Test + public void zipPartitions() { + JavaRDD<Integer> rdd1 = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5, 6), 2); + JavaRDD<String> rdd2 = sc.parallelize(Arrays.asList("1", "2", "3", "4"), 2); + FlatMapFunction2<Iterator<Integer>, Iterator<String>, Integer> sizesFn = + new FlatMapFunction2<Iterator<Integer>, Iterator<String>, Integer>() { + @Override + public Iterable<Integer> call(Iterator<Integer> i, Iterator<String> s) { + int sizeI = 0; + int sizeS = 0; + while (i.hasNext()) { + sizeI += 1; + i.next(); + } + while (s.hasNext()) { + sizeS += 1; + s.next(); + } + return Arrays.asList(sizeI, sizeS); + } + }; + + JavaRDD<Integer> sizes = rdd1.zipPartitions(rdd2, sizesFn); + Assert.assertEquals("[3, 2, 3, 2]", sizes.collect().toString()); + } + + @Test + public void accumulators() { + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5)); + + final Accumulator<Integer> intAccum = sc.intAccumulator(10); + rdd.foreach(new VoidFunction<Integer>() { + public void call(Integer x) { + intAccum.add(x); + } + }); + Assert.assertEquals((Integer) 25, intAccum.value()); + + final Accumulator<Double> doubleAccum = sc.doubleAccumulator(10.0); + rdd.foreach(new VoidFunction<Integer>() { + public void call(Integer x) { + doubleAccum.add((double) x); + } + }); + Assert.assertEquals((Double) 25.0, doubleAccum.value()); + + // Try a custom accumulator type + AccumulatorParam<Float> floatAccumulatorParam = new AccumulatorParam<Float>() { + public Float addInPlace(Float r, Float t) { + return r + t; + } + + public Float addAccumulator(Float r, Float t) { + return r + t; + } + + public Float zero(Float initialValue) { + return 0.0f; + } + }; + + final Accumulator<Float> floatAccum = sc.accumulator((Float) 10.0f, floatAccumulatorParam); + rdd.foreach(new VoidFunction<Integer>() { + public void call(Integer x) { + floatAccum.add((float) x); + } + }); + Assert.assertEquals((Float) 25.0f, floatAccum.value()); + + // Test the setValue method + floatAccum.setValue(5.0f); + Assert.assertEquals((Float) 5.0f, floatAccum.value()); + } + + @Test + public void keyBy() { + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2)); + List<Tuple2<String, Integer>> s = rdd.keyBy(new Function<Integer, String>() { + public String call(Integer t) throws Exception { + return t.toString(); + } + }).collect(); + Assert.assertEquals(new Tuple2<String, Integer>("1", 1), s.get(0)); + Assert.assertEquals(new Tuple2<String, Integer>("2", 2), s.get(1)); + } + + @Test + public void checkpointAndComputation() { + File tempDir = Files.createTempDir(); + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5)); + sc.setCheckpointDir(tempDir.getAbsolutePath(), true); + Assert.assertEquals(false, rdd.isCheckpointed()); + rdd.checkpoint(); + rdd.count(); // Forces the DAG to cause a checkpoint + Assert.assertEquals(true, rdd.isCheckpointed()); + Assert.assertEquals(Arrays.asList(1, 2, 3, 4, 5), rdd.collect()); + } + + @Test + public void checkpointAndRestore() { + File tempDir = Files.createTempDir(); + JavaRDD<Integer> rdd = sc.parallelize(Arrays.asList(1, 2, 3, 4, 5)); + sc.setCheckpointDir(tempDir.getAbsolutePath(), true); + Assert.assertEquals(false, rdd.isCheckpointed()); + rdd.checkpoint(); + rdd.count(); // Forces the DAG to cause a checkpoint + Assert.assertEquals(true, rdd.isCheckpointed()); + + Assert.assertTrue(rdd.getCheckpointFile().isPresent()); + JavaRDD<Integer> recovered = sc.checkpointFile(rdd.getCheckpointFile().get()); + Assert.assertEquals(Arrays.asList(1, 2, 3, 4, 5), recovered.collect()); + } + + @Test + public void mapOnPairRDD() { + JavaRDD<Integer> rdd1 = sc.parallelize(Arrays.asList(1,2,3,4)); + JavaPairRDD<Integer, Integer> rdd2 = rdd1.map(new PairFunction<Integer, Integer, Integer>() { + @Override + public Tuple2<Integer, Integer> call(Integer i) throws Exception { + return new Tuple2<Integer, Integer>(i, i % 2); + } + }); + JavaPairRDD<Integer, Integer> rdd3 = rdd2.map( + new PairFunction<Tuple2<Integer, Integer>, Integer, Integer>() { + @Override + public Tuple2<Integer, Integer> call(Tuple2<Integer, Integer> in) throws Exception { + return new Tuple2<Integer, Integer>(in._2(), in._1()); + } + }); + Assert.assertEquals(Arrays.asList( + new Tuple2<Integer, Integer>(1, 1), + new Tuple2<Integer, Integer>(0, 2), + new Tuple2<Integer, Integer>(1, 3), + new Tuple2<Integer, Integer>(0, 4)), rdd3.collect()); + + } +} diff --git a/core/src/test/scala/org/apache/spark/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/KryoSerializerSuite.scala new file mode 100644 index 0000000000..d7b23c93fe --- /dev/null +++ b/core/src/test/scala/org/apache/spark/KryoSerializerSuite.scala @@ -0,0 +1,208 @@ +/* + * 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 scala.collection.mutable + +import org.scalatest.FunSuite +import com.esotericsoftware.kryo._ + +import KryoTest._ + +class KryoSerializerSuite extends FunSuite with SharedSparkContext { + test("basic types") { + val ser = (new KryoSerializer).newInstance() + def check[T](t: T) { + assert(ser.deserialize[T](ser.serialize(t)) === t) + } + check(1) + check(1L) + check(1.0f) + check(1.0) + check(1.toByte) + check(1.toShort) + check("") + check("hello") + check(Integer.MAX_VALUE) + check(Integer.MIN_VALUE) + check(java.lang.Long.MAX_VALUE) + check(java.lang.Long.MIN_VALUE) + check[String](null) + check(Array(1, 2, 3)) + check(Array(1L, 2L, 3L)) + check(Array(1.0, 2.0, 3.0)) + check(Array(1.0f, 2.9f, 3.9f)) + check(Array("aaa", "bbb", "ccc")) + check(Array("aaa", "bbb", null)) + check(Array(true, false, true)) + check(Array('a', 'b', 'c')) + check(Array[Int]()) + check(Array(Array("1", "2"), Array("1", "2", "3", "4"))) + } + + test("pairs") { + val ser = (new KryoSerializer).newInstance() + def check[T](t: T) { + assert(ser.deserialize[T](ser.serialize(t)) === t) + } + check((1, 1)) + check((1, 1L)) + check((1L, 1)) + check((1L, 1L)) + check((1.0, 1)) + check((1, 1.0)) + check((1.0, 1.0)) + check((1.0, 1L)) + check((1L, 1.0)) + check((1.0, 1L)) + check(("x", 1)) + check(("x", 1.0)) + check(("x", 1L)) + check((1, "x")) + check((1.0, "x")) + check((1L, "x")) + check(("x", "x")) + } + + test("Scala data structures") { + val ser = (new KryoSerializer).newInstance() + def check[T](t: T) { + assert(ser.deserialize[T](ser.serialize(t)) === t) + } + check(List[Int]()) + check(List[Int](1, 2, 3)) + check(List[String]()) + check(List[String]("x", "y", "z")) + check(None) + check(Some(1)) + check(Some("hi")) + check(mutable.ArrayBuffer(1, 2, 3)) + check(mutable.ArrayBuffer("1", "2", "3")) + check(mutable.Map()) + check(mutable.Map(1 -> "one", 2 -> "two")) + check(mutable.Map("one" -> 1, "two" -> 2)) + check(mutable.HashMap(1 -> "one", 2 -> "two")) + check(mutable.HashMap("one" -> 1, "two" -> 2)) + check(List(Some(mutable.HashMap(1->1, 2->2)), None, Some(mutable.HashMap(3->4)))) + check(List(mutable.HashMap("one" -> 1, "two" -> 2),mutable.HashMap(1->"one",2->"two",3->"three"))) + } + + test("custom registrator") { + import KryoTest._ + System.setProperty("spark.kryo.registrator", classOf[MyRegistrator].getName) + + val ser = (new KryoSerializer).newInstance() + def check[T](t: T) { + assert(ser.deserialize[T](ser.serialize(t)) === t) + } + + check(CaseClass(17, "hello")) + + val c1 = new ClassWithNoArgConstructor + c1.x = 32 + check(c1) + + val c2 = new ClassWithoutNoArgConstructor(47) + check(c2) + + val hashMap = new java.util.HashMap[String, String] + hashMap.put("foo", "bar") + check(hashMap) + + System.clearProperty("spark.kryo.registrator") + } + + test("kryo with collect") { + val control = 1 :: 2 :: Nil + val result = sc.parallelize(control, 2).map(new ClassWithoutNoArgConstructor(_)).collect().map(_.x) + assert(control === result.toSeq) + } + + test("kryo with parallelize") { + val control = 1 :: 2 :: Nil + val result = sc.parallelize(control.map(new ClassWithoutNoArgConstructor(_))).map(_.x).collect() + assert (control === result.toSeq) + } + + test("kryo with parallelize for specialized tuples") { + assert (sc.parallelize( Array((1, 11), (2, 22), (3, 33)) ).count === 3) + } + + test("kryo with parallelize for primitive arrays") { + assert (sc.parallelize( Array(1, 2, 3) ).count === 3) + } + + test("kryo with collect for specialized tuples") { + assert (sc.parallelize( Array((1, 11), (2, 22), (3, 33)) ).collect().head === (1, 11)) + } + + test("kryo with reduce") { + val control = 1 :: 2 :: Nil + val result = sc.parallelize(control, 2).map(new ClassWithoutNoArgConstructor(_)) + .reduce((t1, t2) => new ClassWithoutNoArgConstructor(t1.x + t2.x)).x + assert(control.sum === result) + } + + // TODO: this still doesn't work + ignore("kryo with fold") { + val control = 1 :: 2 :: Nil + val result = sc.parallelize(control, 2).map(new ClassWithoutNoArgConstructor(_)) + .fold(new ClassWithoutNoArgConstructor(10))((t1, t2) => new ClassWithoutNoArgConstructor(t1.x + t2.x)).x + assert(10 + control.sum === result) + } + + override def beforeAll() { + System.setProperty("spark.serializer", "org.apache.spark.KryoSerializer") + System.setProperty("spark.kryo.registrator", classOf[MyRegistrator].getName) + super.beforeAll() + } + + override def afterAll() { + super.afterAll() + System.clearProperty("spark.kryo.registrator") + System.clearProperty("spark.serializer") + } +} + +object KryoTest { + case class CaseClass(i: Int, s: String) {} + + class ClassWithNoArgConstructor { + var x: Int = 0 + override def equals(other: Any) = other match { + case c: ClassWithNoArgConstructor => x == c.x + case _ => false + } + } + + class ClassWithoutNoArgConstructor(val x: Int) { + override def equals(other: Any) = other match { + case c: ClassWithoutNoArgConstructor => x == c.x + case _ => false + } + } + + class MyRegistrator extends KryoRegistrator { + override def registerClasses(k: Kryo) { + k.register(classOf[CaseClass]) + k.register(classOf[ClassWithNoArgConstructor]) + k.register(classOf[ClassWithoutNoArgConstructor]) + k.register(classOf[java.util.HashMap[_, _]]) + } + } +} diff --git a/core/src/test/scala/org/apache/spark/LocalSparkContext.scala b/core/src/test/scala/org/apache/spark/LocalSparkContext.scala new file mode 100644 index 0000000000..6ec124da9c --- /dev/null +++ b/core/src/test/scala/org/apache/spark/LocalSparkContext.scala @@ -0,0 +1,68 @@ +/* + * 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 org.scalatest.Suite +import org.scalatest.BeforeAndAfterEach +import org.scalatest.BeforeAndAfterAll + +import org.jboss.netty.logging.InternalLoggerFactory +import org.jboss.netty.logging.Slf4JLoggerFactory + +/** Manages a local `sc` {@link SparkContext} variable, correctly stopping it after each test. */ +trait LocalSparkContext extends BeforeAndAfterEach with BeforeAndAfterAll { self: Suite => + + @transient var sc: SparkContext = _ + + override def beforeAll() { + InternalLoggerFactory.setDefaultFactory(new Slf4JLoggerFactory()); + super.beforeAll() + } + + override def afterEach() { + resetSparkContext() + super.afterEach() + } + + def resetSparkContext() = { + if (sc != null) { + LocalSparkContext.stop(sc) + sc = null + } + } + +} + +object LocalSparkContext { + def stop(sc: SparkContext) { + sc.stop() + // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown + System.clearProperty("spark.driver.port") + System.clearProperty("spark.hostPort") + } + + /** Runs `f` by passing in `sc` and ensures that `sc` is stopped. */ + def withSpark[T](sc: SparkContext)(f: SparkContext => T) = { + try { + f(sc) + } finally { + stop(sc) + } + } + +} diff --git a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala new file mode 100644 index 0000000000..6013320eaa --- /dev/null +++ b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala @@ -0,0 +1,136 @@ +/* + * 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 org.scalatest.FunSuite + +import akka.actor._ +import org.apache.spark.scheduler.MapStatus +import org.apache.spark.storage.BlockManagerId +import org.apache.spark.util.AkkaUtils + +class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { + + test("compressSize") { + assert(MapOutputTracker.compressSize(0L) === 0) + assert(MapOutputTracker.compressSize(1L) === 1) + assert(MapOutputTracker.compressSize(2L) === 8) + assert(MapOutputTracker.compressSize(10L) === 25) + assert((MapOutputTracker.compressSize(1000000L) & 0xFF) === 145) + assert((MapOutputTracker.compressSize(1000000000L) & 0xFF) === 218) + // This last size is bigger than we can encode in a byte, so check that we just return 255 + assert((MapOutputTracker.compressSize(1000000000000000000L) & 0xFF) === 255) + } + + test("decompressSize") { + assert(MapOutputTracker.decompressSize(0) === 0) + for (size <- Seq(2L, 10L, 100L, 50000L, 1000000L, 1000000000L)) { + val size2 = MapOutputTracker.decompressSize(MapOutputTracker.compressSize(size)) + assert(size2 >= 0.99 * size && size2 <= 1.11 * size, + "size " + size + " decompressed to " + size2 + ", which is out of range") + } + } + + test("master start and stop") { + val actorSystem = ActorSystem("test") + val tracker = new MapOutputTracker() + tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerActor(tracker))) + tracker.stop() + } + + test("master register and fetch") { + val actorSystem = ActorSystem("test") + val tracker = new MapOutputTracker() + tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerActor(tracker))) + tracker.registerShuffle(10, 2) + val compressedSize1000 = MapOutputTracker.compressSize(1000L) + val compressedSize10000 = MapOutputTracker.compressSize(10000L) + val size1000 = MapOutputTracker.decompressSize(compressedSize1000) + val size10000 = MapOutputTracker.decompressSize(compressedSize10000) + tracker.registerMapOutput(10, 0, new MapStatus(BlockManagerId("a", "hostA", 1000, 0), + Array(compressedSize1000, compressedSize10000))) + tracker.registerMapOutput(10, 1, new MapStatus(BlockManagerId("b", "hostB", 1000, 0), + Array(compressedSize10000, compressedSize1000))) + val statuses = tracker.getServerStatuses(10, 0) + assert(statuses.toSeq === Seq((BlockManagerId("a", "hostA", 1000, 0), size1000), + (BlockManagerId("b", "hostB", 1000, 0), size10000))) + tracker.stop() + } + + test("master register and unregister and fetch") { + val actorSystem = ActorSystem("test") + val tracker = new MapOutputTracker() + tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerActor(tracker))) + tracker.registerShuffle(10, 2) + val compressedSize1000 = MapOutputTracker.compressSize(1000L) + val compressedSize10000 = MapOutputTracker.compressSize(10000L) + val size1000 = MapOutputTracker.decompressSize(compressedSize1000) + val size10000 = MapOutputTracker.decompressSize(compressedSize10000) + tracker.registerMapOutput(10, 0, new MapStatus(BlockManagerId("a", "hostA", 1000, 0), + Array(compressedSize1000, compressedSize1000, compressedSize1000))) + tracker.registerMapOutput(10, 1, new MapStatus(BlockManagerId("b", "hostB", 1000, 0), + Array(compressedSize10000, compressedSize1000, compressedSize1000))) + + // As if we had two simulatenous fetch failures + tracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000, 0)) + tracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000, 0)) + + // The remaining reduce task might try to grab the output despite the shuffle failure; + // this should cause it to fail, and the scheduler will ignore the failure due to the + // stage already being aborted. + intercept[FetchFailedException] { tracker.getServerStatuses(10, 1) } + } + + test("remote fetch") { + val hostname = "localhost" + val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0) + System.setProperty("spark.driver.port", boundPort.toString) // Will be cleared by LocalSparkContext + System.setProperty("spark.hostPort", hostname + ":" + boundPort) + + val masterTracker = new MapOutputTracker() + masterTracker.trackerActor = actorSystem.actorOf( + Props(new MapOutputTrackerActor(masterTracker)), "MapOutputTracker") + + val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0) + val slaveTracker = new MapOutputTracker() + slaveTracker.trackerActor = slaveSystem.actorFor( + "akka://spark@localhost:" + boundPort + "/user/MapOutputTracker") + + masterTracker.registerShuffle(10, 1) + masterTracker.incrementEpoch() + slaveTracker.updateEpoch(masterTracker.getEpoch) + intercept[FetchFailedException] { slaveTracker.getServerStatuses(10, 0) } + + val compressedSize1000 = MapOutputTracker.compressSize(1000L) + val size1000 = MapOutputTracker.decompressSize(compressedSize1000) + masterTracker.registerMapOutput(10, 0, new MapStatus( + BlockManagerId("a", "hostA", 1000, 0), Array(compressedSize1000))) + masterTracker.incrementEpoch() + slaveTracker.updateEpoch(masterTracker.getEpoch) + assert(slaveTracker.getServerStatuses(10, 0).toSeq === + Seq((BlockManagerId("a", "hostA", 1000, 0), size1000))) + + masterTracker.unregisterMapOutput(10, 0, BlockManagerId("a", "hostA", 1000, 0)) + masterTracker.incrementEpoch() + slaveTracker.updateEpoch(masterTracker.getEpoch) + intercept[FetchFailedException] { slaveTracker.getServerStatuses(10, 0) } + + // failure should be cached + intercept[FetchFailedException] { slaveTracker.getServerStatuses(10, 0) } + } +} diff --git a/core/src/test/scala/org/apache/spark/PairRDDFunctionsSuite.scala b/core/src/test/scala/org/apache/spark/PairRDDFunctionsSuite.scala new file mode 100644 index 0000000000..f79752b34e --- /dev/null +++ b/core/src/test/scala/org/apache/spark/PairRDDFunctionsSuite.scala @@ -0,0 +1,299 @@ +/* + * 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 scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.HashSet + +import org.scalatest.FunSuite + +import com.google.common.io.Files +import org.apache.spark.SparkContext._ + + +class PairRDDFunctionsSuite extends FunSuite with SharedSparkContext { + test("groupByKey") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1))) + val groups = pairs.groupByKey().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("groupByKey with duplicates") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) + val groups = pairs.groupByKey().collect() + assert(groups.size === 2) + val valuesFor1 = groups.find(_._1 == 1).get._2 + assert(valuesFor1.toList.sorted === List(1, 1, 2, 3)) + val valuesFor2 = groups.find(_._1 == 2).get._2 + assert(valuesFor2.toList.sorted === List(1)) + } + + test("groupByKey with negative key hash codes") { + val pairs = sc.parallelize(Array((-1, 1), (-1, 2), (-1, 3), (2, 1))) + val groups = pairs.groupByKey().collect() + assert(groups.size === 2) + val valuesForMinus1 = groups.find(_._1 == -1).get._2 + assert(valuesForMinus1.toList.sorted === List(1, 2, 3)) + val valuesFor2 = groups.find(_._1 == 2).get._2 + assert(valuesFor2.toList.sorted === List(1)) + } + + test("groupByKey with many output partitions") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (2, 1))) + val groups = pairs.groupByKey(10).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("reduceByKey") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) + val sums = pairs.reduceByKey(_+_).collect() + assert(sums.toSet === Set((1, 7), (2, 1))) + } + + test("reduceByKey with collectAsMap") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) + val sums = pairs.reduceByKey(_+_).collectAsMap() + assert(sums.size === 2) + assert(sums(1) === 7) + assert(sums(2) === 1) + } + + test("reduceByKey with many output partitons") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) + val sums = pairs.reduceByKey(_+_, 10).collect() + assert(sums.toSet === Set((1, 7), (2, 1))) + } + + test("reduceByKey with partitioner") { + val p = new Partitioner() { + def numPartitions = 2 + def getPartition(key: Any) = key.asInstanceOf[Int] + } + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 1), (0, 1))).partitionBy(p) + val sums = pairs.reduceByKey(_+_) + assert(sums.collect().toSet === Set((1, 4), (0, 1))) + assert(sums.partitioner === Some(p)) + // count the dependencies to make sure there is only 1 ShuffledRDD + val deps = new HashSet[RDD[_]]() + def visit(r: RDD[_]) { + for (dep <- r.dependencies) { + deps += dep.rdd + visit(dep.rdd) + } + } + visit(sums) + assert(deps.size === 2) // ShuffledRDD, ParallelCollection + } + + test("join") { + val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) + val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) + val joined = rdd1.join(rdd2).collect() + assert(joined.size === 4) + assert(joined.toSet === Set( + (1, (1, 'x')), + (1, (2, 'x')), + (2, (1, 'y')), + (2, (1, 'z')) + )) + } + + test("join all-to-all") { + val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (1, 3))) + val rdd2 = sc.parallelize(Array((1, 'x'), (1, 'y'))) + val joined = rdd1.join(rdd2).collect() + assert(joined.size === 6) + assert(joined.toSet === Set( + (1, (1, 'x')), + (1, (1, 'y')), + (1, (2, 'x')), + (1, (2, 'y')), + (1, (3, 'x')), + (1, (3, 'y')) + )) + } + + test("leftOuterJoin") { + val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) + val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) + val joined = rdd1.leftOuterJoin(rdd2).collect() + assert(joined.size === 5) + assert(joined.toSet === Set( + (1, (1, Some('x'))), + (1, (2, Some('x'))), + (2, (1, Some('y'))), + (2, (1, Some('z'))), + (3, (1, None)) + )) + } + + test("rightOuterJoin") { + val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) + val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) + val joined = rdd1.rightOuterJoin(rdd2).collect() + assert(joined.size === 5) + assert(joined.toSet === Set( + (1, (Some(1), 'x')), + (1, (Some(2), 'x')), + (2, (Some(1), 'y')), + (2, (Some(1), 'z')), + (4, (None, 'w')) + )) + } + + test("join with no matches") { + val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) + val rdd2 = sc.parallelize(Array((4, 'x'), (5, 'y'), (5, 'z'), (6, 'w'))) + val joined = rdd1.join(rdd2).collect() + assert(joined.size === 0) + } + + test("join with many output partitions") { + val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) + val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) + val joined = rdd1.join(rdd2, 10).collect() + assert(joined.size === 4) + assert(joined.toSet === Set( + (1, (1, 'x')), + (1, (2, 'x')), + (2, (1, 'y')), + (2, (1, 'z')) + )) + } + + test("groupWith") { + val rdd1 = sc.parallelize(Array((1, 1), (1, 2), (2, 1), (3, 1))) + val rdd2 = sc.parallelize(Array((1, 'x'), (2, 'y'), (2, 'z'), (4, 'w'))) + val joined = rdd1.groupWith(rdd2).collect() + assert(joined.size === 4) + assert(joined.toSet === Set( + (1, (ArrayBuffer(1, 2), ArrayBuffer('x'))), + (2, (ArrayBuffer(1), ArrayBuffer('y', 'z'))), + (3, (ArrayBuffer(1), ArrayBuffer())), + (4, (ArrayBuffer(), ArrayBuffer('w'))) + )) + } + + test("zero-partition RDD") { + val emptyDir = Files.createTempDir() + val file = sc.textFile(emptyDir.getAbsolutePath) + assert(file.partitions.size == 0) + assert(file.collect().toList === Nil) + // Test that a shuffle on the file works, because this used to be a bug + assert(file.map(line => (line, 1)).reduceByKey(_ + _).collect().toList === Nil) + } + + test("keys and values") { + val rdd = sc.parallelize(Array((1, "a"), (2, "b"))) + assert(rdd.keys.collect().toList === List(1, 2)) + assert(rdd.values.collect().toList === List("a", "b")) + } + + test("default partitioner uses partition size") { + // specify 2000 partitions + val a = sc.makeRDD(Array(1, 2, 3, 4), 2000) + // do a map, which loses the partitioner + val b = a.map(a => (a, (a * 2).toString)) + // then a group by, and see we didn't revert to 2 partitions + val c = b.groupByKey() + assert(c.partitions.size === 2000) + } + + test("default partitioner uses largest partitioner") { + val a = sc.makeRDD(Array((1, "a"), (2, "b")), 2) + val b = sc.makeRDD(Array((1, "a"), (2, "b")), 2000) + val c = a.join(b) + assert(c.partitions.size === 2000) + } + + test("subtract") { + val a = sc.parallelize(Array(1, 2, 3), 2) + val b = sc.parallelize(Array(2, 3, 4), 4) + val c = a.subtract(b) + assert(c.collect().toSet === Set(1)) + assert(c.partitions.size === a.partitions.size) + } + + test("subtract with narrow dependency") { + // use a deterministic partitioner + val p = new Partitioner() { + def numPartitions = 5 + def getPartition(key: Any) = key.asInstanceOf[Int] + } + // partitionBy so we have a narrow dependency + val a = sc.parallelize(Array((1, "a"), (2, "b"), (3, "c"))).partitionBy(p) + // more partitions/no partitioner so a shuffle dependency + val b = sc.parallelize(Array((2, "b"), (3, "cc"), (4, "d")), 4) + val c = a.subtract(b) + assert(c.collect().toSet === Set((1, "a"), (3, "c"))) + // Ideally we could keep the original partitioner... + assert(c.partitioner === None) + } + + test("subtractByKey") { + val a = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c")), 2) + val b = sc.parallelize(Array((2, 20), (3, 30), (4, 40)), 4) + val c = a.subtractByKey(b) + assert(c.collect().toSet === Set((1, "a"), (1, "a"))) + assert(c.partitions.size === a.partitions.size) + } + + test("subtractByKey with narrow dependency") { + // use a deterministic partitioner + val p = new Partitioner() { + def numPartitions = 5 + def getPartition(key: Any) = key.asInstanceOf[Int] + } + // partitionBy so we have a narrow dependency + val a = sc.parallelize(Array((1, "a"), (1, "a"), (2, "b"), (3, "c"))).partitionBy(p) + // more partitions/no partitioner so a shuffle dependency + val b = sc.parallelize(Array((2, "b"), (3, "cc"), (4, "d")), 4) + val c = a.subtractByKey(b) + assert(c.collect().toSet === Set((1, "a"), (1, "a"))) + assert(c.partitioner.get === p) + } + + test("foldByKey") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) + val sums = pairs.foldByKey(0)(_+_).collect() + assert(sums.toSet === Set((1, 7), (2, 1))) + } + + test("foldByKey with mutable result type") { + val pairs = sc.parallelize(Array((1, 1), (1, 2), (1, 3), (1, 1), (2, 1))) + val bufs = pairs.mapValues(v => ArrayBuffer(v)).cache() + // Fold the values using in-place mutation + val sums = bufs.foldByKey(new ArrayBuffer[Int])(_ ++= _).collect() + assert(sums.toSet === Set((1, ArrayBuffer(1, 2, 3, 1)), (2, ArrayBuffer(1)))) + // Check that the mutable objects in the original RDD were not changed + assert(bufs.collect().toSet === Set( + (1, ArrayBuffer(1)), + (1, ArrayBuffer(2)), + (1, ArrayBuffer(3)), + (1, ArrayBuffer(1)), + (2, ArrayBuffer(1)))) + } +} diff --git a/core/src/test/scala/org/apache/spark/PartitionPruningRDDSuite.scala b/core/src/test/scala/org/apache/spark/PartitionPruningRDDSuite.scala new file mode 100644 index 0000000000..adbe805916 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/PartitionPruningRDDSuite.scala @@ -0,0 +1,28 @@ +package org.apache.spark + +import org.scalatest.FunSuite +import org.apache.spark.SparkContext._ +import org.apache.spark.rdd.PartitionPruningRDD + + +class PartitionPruningRDDSuite extends FunSuite with SharedSparkContext { + + test("Pruned Partitions inherit locality prefs correctly") { + class TestPartition(i: Int) extends Partition { + def index = i + } + val rdd = new RDD[Int](sc, Nil) { + override protected def getPartitions = { + Array[Partition]( + new TestPartition(1), + new TestPartition(2), + new TestPartition(3)) + } + def compute(split: Partition, context: TaskContext) = {Iterator()} + } + val prunedRDD = PartitionPruningRDD.create(rdd, {x => if (x==2) true else false}) + val p = prunedRDD.partitions(0) + assert(p.index == 2) + assert(prunedRDD.partitions.length == 1) + } +} diff --git a/core/src/test/scala/org/apache/spark/PartitioningSuite.scala b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala new file mode 100644 index 0000000000..7669cf6fb1 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/PartitioningSuite.scala @@ -0,0 +1,150 @@ +/* + * 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 org.scalatest.FunSuite +import scala.collection.mutable.ArrayBuffer +import SparkContext._ +import org.apache.spark.util.StatCounter +import scala.math.abs + +class PartitioningSuite extends FunSuite with SharedSparkContext { + + test("HashPartitioner equality") { + val p2 = new HashPartitioner(2) + val p4 = new HashPartitioner(4) + val anotherP4 = new HashPartitioner(4) + assert(p2 === p2) + assert(p4 === p4) + assert(p2 != p4) + assert(p4 != p2) + assert(p4 === anotherP4) + assert(anotherP4 === p4) + } + + test("RangePartitioner equality") { + // Make an RDD where all the elements are the same so that the partition range bounds + // are deterministically all the same. + val rdd = sc.parallelize(Seq(1, 1, 1, 1)).map(x => (x, x)) + + val p2 = new RangePartitioner(2, rdd) + val p4 = new RangePartitioner(4, rdd) + val anotherP4 = new RangePartitioner(4, rdd) + val descendingP2 = new RangePartitioner(2, rdd, false) + val descendingP4 = new RangePartitioner(4, rdd, false) + + assert(p2 === p2) + assert(p4 === p4) + assert(p2 != p4) + assert(p4 != p2) + assert(p4 === anotherP4) + assert(anotherP4 === p4) + assert(descendingP2 === descendingP2) + assert(descendingP4 === descendingP4) + assert(descendingP2 != descendingP4) + assert(descendingP4 != descendingP2) + assert(p2 != descendingP2) + assert(p4 != descendingP4) + assert(descendingP2 != p2) + assert(descendingP4 != p4) + } + + test("HashPartitioner not equal to RangePartitioner") { + val rdd = sc.parallelize(1 to 10).map(x => (x, x)) + val rangeP2 = new RangePartitioner(2, rdd) + val hashP2 = new HashPartitioner(2) + assert(rangeP2 === rangeP2) + assert(hashP2 === hashP2) + assert(hashP2 != rangeP2) + assert(rangeP2 != hashP2) + } + + test("partitioner preservation") { + val rdd = sc.parallelize(1 to 10, 4).map(x => (x, x)) + + val grouped2 = rdd.groupByKey(2) + val grouped4 = rdd.groupByKey(4) + val reduced2 = rdd.reduceByKey(_ + _, 2) + val reduced4 = rdd.reduceByKey(_ + _, 4) + + assert(rdd.partitioner === None) + + assert(grouped2.partitioner === Some(new HashPartitioner(2))) + assert(grouped4.partitioner === Some(new HashPartitioner(4))) + assert(reduced2.partitioner === Some(new HashPartitioner(2))) + assert(reduced4.partitioner === Some(new HashPartitioner(4))) + + assert(grouped2.groupByKey().partitioner === grouped2.partitioner) + assert(grouped2.groupByKey(3).partitioner != grouped2.partitioner) + assert(grouped2.groupByKey(2).partitioner === grouped2.partitioner) + assert(grouped4.groupByKey().partitioner === grouped4.partitioner) + assert(grouped4.groupByKey(3).partitioner != grouped4.partitioner) + assert(grouped4.groupByKey(4).partitioner === grouped4.partitioner) + + assert(grouped2.join(grouped4).partitioner === grouped4.partitioner) + assert(grouped2.leftOuterJoin(grouped4).partitioner === grouped4.partitioner) + assert(grouped2.rightOuterJoin(grouped4).partitioner === grouped4.partitioner) + assert(grouped2.cogroup(grouped4).partitioner === grouped4.partitioner) + + assert(grouped2.join(reduced2).partitioner === grouped2.partitioner) + assert(grouped2.leftOuterJoin(reduced2).partitioner === grouped2.partitioner) + assert(grouped2.rightOuterJoin(reduced2).partitioner === grouped2.partitioner) + assert(grouped2.cogroup(reduced2).partitioner === grouped2.partitioner) + + assert(grouped2.map(_ => 1).partitioner === None) + assert(grouped2.mapValues(_ => 1).partitioner === grouped2.partitioner) + assert(grouped2.flatMapValues(_ => Seq(1)).partitioner === grouped2.partitioner) + assert(grouped2.filter(_._1 > 4).partitioner === grouped2.partitioner) + } + + test("partitioning Java arrays should fail") { + val arrs: RDD[Array[Int]] = sc.parallelize(Array(1, 2, 3, 4), 2).map(x => Array(x)) + val arrPairs: RDD[(Array[Int], Int)] = + sc.parallelize(Array(1, 2, 3, 4), 2).map(x => (Array(x), x)) + + assert(intercept[SparkException]{ arrs.distinct() }.getMessage.contains("array")) + // We can't catch all usages of arrays, since they might occur inside other collections: + //assert(fails { arrPairs.distinct() }) + assert(intercept[SparkException]{ arrPairs.partitionBy(new HashPartitioner(2)) }.getMessage.contains("array")) + assert(intercept[SparkException]{ arrPairs.join(arrPairs) }.getMessage.contains("array")) + assert(intercept[SparkException]{ arrPairs.leftOuterJoin(arrPairs) }.getMessage.contains("array")) + assert(intercept[SparkException]{ arrPairs.rightOuterJoin(arrPairs) }.getMessage.contains("array")) + assert(intercept[SparkException]{ arrPairs.groupByKey() }.getMessage.contains("array")) + assert(intercept[SparkException]{ arrPairs.countByKey() }.getMessage.contains("array")) + assert(intercept[SparkException]{ arrPairs.countByKeyApprox(1) }.getMessage.contains("array")) + assert(intercept[SparkException]{ arrPairs.cogroup(arrPairs) }.getMessage.contains("array")) + assert(intercept[SparkException]{ arrPairs.reduceByKeyLocally(_ + _) }.getMessage.contains("array")) + assert(intercept[SparkException]{ arrPairs.reduceByKey(_ + _) }.getMessage.contains("array")) + } + + test("zero-length partitions should be correctly handled") { + // Create RDD with some consecutive empty partitions (including the "first" one) + val rdd: RDD[Double] = sc + .parallelize(Array(-1.0, -1.0, -1.0, -1.0, 2.0, 4.0, -1.0, -1.0), 8) + .filter(_ >= 0.0) + + // Run the partitions, including the consecutive empty ones, through StatCounter + val stats: StatCounter = rdd.stats(); + assert(abs(6.0 - stats.sum) < 0.01); + assert(abs(6.0/2 - rdd.mean) < 0.01); + assert(abs(1.0 - rdd.variance) < 0.01); + assert(abs(1.0 - rdd.stdev) < 0.01); + + // Add other tests here for classes that should be able to handle empty partitions correctly + } +} diff --git a/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala b/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala new file mode 100644 index 0000000000..2e851d892d --- /dev/null +++ b/core/src/test/scala/org/apache/spark/PipedRDDSuite.scala @@ -0,0 +1,93 @@ +/* + * 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 org.scalatest.FunSuite +import SparkContext._ + +class PipedRDDSuite extends FunSuite with SharedSparkContext { + + test("basic pipe") { + val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) + + val piped = nums.pipe(Seq("cat")) + + val c = piped.collect() + assert(c.size === 4) + assert(c(0) === "1") + assert(c(1) === "2") + assert(c(2) === "3") + assert(c(3) === "4") + } + + test("advanced pipe") { + val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) + val bl = sc.broadcast(List("0")) + + val piped = nums.pipe(Seq("cat"), + Map[String, String](), + (f: String => Unit) => {bl.value.map(f(_));f("\u0001")}, + (i:Int, f: String=> Unit) => f(i + "_")) + + val c = piped.collect() + + assert(c.size === 8) + assert(c(0) === "0") + assert(c(1) === "\u0001") + assert(c(2) === "1_") + assert(c(3) === "2_") + assert(c(4) === "0") + assert(c(5) === "\u0001") + assert(c(6) === "3_") + assert(c(7) === "4_") + + val nums1 = sc.makeRDD(Array("a\t1", "b\t2", "a\t3", "b\t4"), 2) + val d = nums1.groupBy(str=>str.split("\t")(0)). + pipe(Seq("cat"), + Map[String, String](), + (f: String => Unit) => {bl.value.map(f(_));f("\u0001")}, + (i:Tuple2[String, Seq[String]], f: String=> Unit) => {for (e <- i._2){ f(e + "_")}}).collect() + assert(d.size === 8) + assert(d(0) === "0") + assert(d(1) === "\u0001") + assert(d(2) === "b\t2_") + assert(d(3) === "b\t4_") + assert(d(4) === "0") + assert(d(5) === "\u0001") + assert(d(6) === "a\t1_") + assert(d(7) === "a\t3_") + } + + test("pipe with env variable") { + val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) + val piped = nums.pipe(Seq("printenv", "MY_TEST_ENV"), Map("MY_TEST_ENV" -> "LALALA")) + val c = piped.collect() + assert(c.size === 2) + assert(c(0) === "LALALA") + assert(c(1) === "LALALA") + } + + test("pipe with non-zero exit status") { + val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) + val piped = nums.pipe(Seq("cat nonexistent_file", "2>", "/dev/null")) + intercept[SparkException] { + piped.collect() + } + } + +} diff --git a/core/src/test/scala/org/apache/spark/RDDSuite.scala b/core/src/test/scala/org/apache/spark/RDDSuite.scala new file mode 100644 index 0000000000..342ba8adb2 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/RDDSuite.scala @@ -0,0 +1,389 @@ +/* + * 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 scala.collection.mutable.HashMap +import org.scalatest.FunSuite +import org.scalatest.concurrent.Timeouts._ +import org.scalatest.time.{Span, Millis} +import org.apache.spark.SparkContext._ +import org.apache.spark.rdd._ +import scala.collection.parallel.mutable + +class RDDSuite extends FunSuite with SharedSparkContext { + + test("basic operations") { + val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) + assert(nums.collect().toList === List(1, 2, 3, 4)) + val dups = sc.makeRDD(Array(1, 1, 2, 2, 3, 3, 4, 4), 2) + assert(dups.distinct().count() === 4) + assert(dups.distinct.count === 4) // Can distinct and count be called without parentheses? + assert(dups.distinct.collect === dups.distinct().collect) + assert(dups.distinct(2).collect === dups.distinct().collect) + assert(nums.reduce(_ + _) === 10) + assert(nums.fold(0)(_ + _) === 10) + assert(nums.map(_.toString).collect().toList === List("1", "2", "3", "4")) + assert(nums.filter(_ > 2).collect().toList === List(3, 4)) + assert(nums.flatMap(x => 1 to x).collect().toList === List(1, 1, 2, 1, 2, 3, 1, 2, 3, 4)) + assert(nums.union(nums).collect().toList === List(1, 2, 3, 4, 1, 2, 3, 4)) + assert(nums.glom().map(_.toList).collect().toList === List(List(1, 2), List(3, 4))) + assert(nums.collect({ case i if i >= 3 => i.toString }).collect().toList === List("3", "4")) + assert(nums.keyBy(_.toString).collect().toList === List(("1", 1), ("2", 2), ("3", 3), ("4", 4))) + val partitionSums = nums.mapPartitions(iter => Iterator(iter.reduceLeft(_ + _))) + assert(partitionSums.collect().toList === List(3, 7)) + + val partitionSumsWithSplit = nums.mapPartitionsWithSplit { + case(split, iter) => Iterator((split, iter.reduceLeft(_ + _))) + } + assert(partitionSumsWithSplit.collect().toList === List((0, 3), (1, 7))) + + val partitionSumsWithIndex = nums.mapPartitionsWithIndex { + case(split, iter) => Iterator((split, iter.reduceLeft(_ + _))) + } + assert(partitionSumsWithIndex.collect().toList === List((0, 3), (1, 7))) + + intercept[UnsupportedOperationException] { + nums.filter(_ > 5).reduce(_ + _) + } + } + + test("SparkContext.union") { + val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) + assert(sc.union(nums).collect().toList === List(1, 2, 3, 4)) + assert(sc.union(nums, nums).collect().toList === List(1, 2, 3, 4, 1, 2, 3, 4)) + assert(sc.union(Seq(nums)).collect().toList === List(1, 2, 3, 4)) + assert(sc.union(Seq(nums, nums)).collect().toList === List(1, 2, 3, 4, 1, 2, 3, 4)) + } + + test("aggregate") { + val pairs = sc.makeRDD(Array(("a", 1), ("b", 2), ("a", 2), ("c", 5), ("a", 3))) + type StringMap = HashMap[String, Int] + val emptyMap = new StringMap { + override def default(key: String): Int = 0 + } + val mergeElement: (StringMap, (String, Int)) => StringMap = (map, pair) => { + map(pair._1) += pair._2 + map + } + val mergeMaps: (StringMap, StringMap) => StringMap = (map1, map2) => { + for ((key, value) <- map2) { + map1(key) += value + } + map1 + } + val result = pairs.aggregate(emptyMap)(mergeElement, mergeMaps) + assert(result.toSet === Set(("a", 6), ("b", 2), ("c", 5))) + } + + test("basic caching") { + val rdd = sc.makeRDD(Array(1, 2, 3, 4), 2).cache() + assert(rdd.collect().toList === List(1, 2, 3, 4)) + assert(rdd.collect().toList === List(1, 2, 3, 4)) + assert(rdd.collect().toList === List(1, 2, 3, 4)) + } + + test("caching with failures") { + val onlySplit = new Partition { override def index: Int = 0 } + var shouldFail = true + val rdd = new RDD[Int](sc, Nil) { + override def getPartitions: Array[Partition] = Array(onlySplit) + override val getDependencies = List[Dependency[_]]() + override def compute(split: Partition, context: TaskContext): Iterator[Int] = { + if (shouldFail) { + throw new Exception("injected failure") + } else { + return Array(1, 2, 3, 4).iterator + } + } + }.cache() + val thrown = intercept[Exception]{ + rdd.collect() + } + assert(thrown.getMessage.contains("injected failure")) + shouldFail = false + assert(rdd.collect().toList === List(1, 2, 3, 4)) + } + + test("empty RDD") { + val empty = new EmptyRDD[Int](sc) + assert(empty.count === 0) + assert(empty.collect().size === 0) + + val thrown = intercept[UnsupportedOperationException]{ + empty.reduce(_+_) + } + assert(thrown.getMessage.contains("empty")) + + val emptyKv = new EmptyRDD[(Int, Int)](sc) + val rdd = sc.parallelize(1 to 2, 2).map(x => (x, x)) + assert(rdd.join(emptyKv).collect().size === 0) + assert(rdd.rightOuterJoin(emptyKv).collect().size === 0) + assert(rdd.leftOuterJoin(emptyKv).collect().size === 2) + assert(rdd.cogroup(emptyKv).collect().size === 2) + assert(rdd.union(emptyKv).collect().size === 2) + } + + test("cogrouped RDDs") { + val data = sc.parallelize(1 to 10, 10) + + val coalesced1 = data.coalesce(2) + assert(coalesced1.collect().toList === (1 to 10).toList) + assert(coalesced1.glom().collect().map(_.toList).toList === + List(List(1, 2, 3, 4, 5), List(6, 7, 8, 9, 10))) + + // Check that the narrow dependency is also specified correctly + assert(coalesced1.dependencies.head.asInstanceOf[NarrowDependency[_]].getParents(0).toList === + List(0, 1, 2, 3, 4)) + assert(coalesced1.dependencies.head.asInstanceOf[NarrowDependency[_]].getParents(1).toList === + List(5, 6, 7, 8, 9)) + + val coalesced2 = data.coalesce(3) + assert(coalesced2.collect().toList === (1 to 10).toList) + assert(coalesced2.glom().collect().map(_.toList).toList === + List(List(1, 2, 3), List(4, 5, 6), List(7, 8, 9, 10))) + + val coalesced3 = data.coalesce(10) + assert(coalesced3.collect().toList === (1 to 10).toList) + assert(coalesced3.glom().collect().map(_.toList).toList === + (1 to 10).map(x => List(x)).toList) + + // If we try to coalesce into more partitions than the original RDD, it should just + // keep the original number of partitions. + val coalesced4 = data.coalesce(20) + assert(coalesced4.collect().toList === (1 to 10).toList) + assert(coalesced4.glom().collect().map(_.toList).toList === + (1 to 10).map(x => List(x)).toList) + + // we can optionally shuffle to keep the upstream parallel + val coalesced5 = data.coalesce(1, shuffle = true) + assert(coalesced5.dependencies.head.rdd.dependencies.head.rdd.asInstanceOf[ShuffledRDD[_, _, _]] != + null) + } + test("cogrouped RDDs with locality") { + val data3 = sc.makeRDD(List((1,List("a","c")), (2,List("a","b","c")), (3,List("b")))) + val coal3 = data3.coalesce(3) + val list3 = coal3.partitions.map(p => p.asInstanceOf[CoalescedRDDPartition].preferredLocation) + assert(list3.sorted === Array("a","b","c"), "Locality preferences are dropped") + + // RDD with locality preferences spread (non-randomly) over 6 machines, m0 through m5 + val data = sc.makeRDD((1 to 9).map(i => (i, (i to (i+2)).map{ j => "m" + (j%6)}))) + val coalesced1 = data.coalesce(3) + assert(coalesced1.collect().toList.sorted === (1 to 9).toList, "Data got *lost* in coalescing") + + val splits = coalesced1.glom().collect().map(_.toList).toList + assert(splits.length === 3, "Supposed to coalesce to 3 but got " + splits.length) + + assert(splits.forall(_.length >= 1) === true, "Some partitions were empty") + + // If we try to coalesce into more partitions than the original RDD, it should just + // keep the original number of partitions. + val coalesced4 = data.coalesce(20) + val listOfLists = coalesced4.glom().collect().map(_.toList).toList + val sortedList = listOfLists.sortWith{ (x, y) => !x.isEmpty && (y.isEmpty || (x(0) < y(0))) } + assert( sortedList === (1 to 9). + map{x => List(x)}.toList, "Tried coalescing 9 partitions to 20 but didn't get 9 back") + } + + test("cogrouped RDDs with locality, large scale (10K partitions)") { + // large scale experiment + import collection.mutable + val rnd = scala.util.Random + val partitions = 10000 + val numMachines = 50 + val machines = mutable.ListBuffer[String]() + (1 to numMachines).foreach(machines += "m"+_) + + val blocks = (1 to partitions).map(i => + { (i, Array.fill(3)(machines(rnd.nextInt(machines.size))).toList) } ) + + val data2 = sc.makeRDD(blocks) + val coalesced2 = data2.coalesce(numMachines*2) + + // test that you get over 90% locality in each group + val minLocality = coalesced2.partitions + .map(part => part.asInstanceOf[CoalescedRDDPartition].localFraction) + .foldLeft(1.)((perc, loc) => math.min(perc,loc)) + assert(minLocality >= 0.90, "Expected 90% locality but got " + (minLocality*100.).toInt + "%") + + // test that the groups are load balanced with 100 +/- 20 elements in each + val maxImbalance = coalesced2.partitions + .map(part => part.asInstanceOf[CoalescedRDDPartition].parents.size) + .foldLeft(0)((dev, curr) => math.max(math.abs(100-curr),dev)) + assert(maxImbalance <= 20, "Expected 100 +/- 20 per partition, but got " + maxImbalance) + + val data3 = sc.makeRDD(blocks).map(i => i*2) // derived RDD to test *current* pref locs + val coalesced3 = data3.coalesce(numMachines*2) + val minLocality2 = coalesced3.partitions + .map(part => part.asInstanceOf[CoalescedRDDPartition].localFraction) + .foldLeft(1.)((perc, loc) => math.min(perc,loc)) + assert(minLocality2 >= 0.90, "Expected 90% locality for derived RDD but got " + + (minLocality2*100.).toInt + "%") + } + + test("zipped RDDs") { + val nums = sc.makeRDD(Array(1, 2, 3, 4), 2) + val zipped = nums.zip(nums.map(_ + 1.0)) + assert(zipped.glom().map(_.toList).collect().toList === + List(List((1, 2.0), (2, 3.0)), List((3, 4.0), (4, 5.0)))) + + intercept[IllegalArgumentException] { + nums.zip(sc.parallelize(1 to 4, 1)).collect() + } + } + + test("partition pruning") { + val data = sc.parallelize(1 to 10, 10) + // Note that split number starts from 0, so > 8 means only 10th partition left. + val prunedRdd = new PartitionPruningRDD(data, splitNum => splitNum > 8) + assert(prunedRdd.partitions.size === 1) + val prunedData = prunedRdd.collect() + assert(prunedData.size === 1) + assert(prunedData(0) === 10) + } + + test("mapWith") { + import java.util.Random + val ones = sc.makeRDD(Array(1, 1, 1, 1, 1, 1), 2) + val randoms = ones.mapWith( + (index: Int) => new Random(index + 42)) + {(t: Int, prng: Random) => prng.nextDouble * t}.collect() + val prn42_3 = { + val prng42 = new Random(42) + prng42.nextDouble(); prng42.nextDouble(); prng42.nextDouble() + } + val prn43_3 = { + val prng43 = new Random(43) + prng43.nextDouble(); prng43.nextDouble(); prng43.nextDouble() + } + assert(randoms(2) === prn42_3) + assert(randoms(5) === prn43_3) + } + + test("flatMapWith") { + import java.util.Random + val ones = sc.makeRDD(Array(1, 1, 1, 1, 1, 1), 2) + val randoms = ones.flatMapWith( + (index: Int) => new Random(index + 42)) + {(t: Int, prng: Random) => + val random = prng.nextDouble() + Seq(random * t, random * t * 10)}. + collect() + val prn42_3 = { + val prng42 = new Random(42) + prng42.nextDouble(); prng42.nextDouble(); prng42.nextDouble() + } + val prn43_3 = { + val prng43 = new Random(43) + prng43.nextDouble(); prng43.nextDouble(); prng43.nextDouble() + } + assert(randoms(5) === prn42_3 * 10) + assert(randoms(11) === prn43_3 * 10) + } + + test("filterWith") { + import java.util.Random + val ints = sc.makeRDD(Array(1, 2, 3, 4, 5, 6), 2) + val sample = ints.filterWith( + (index: Int) => new Random(index + 42)) + {(t: Int, prng: Random) => prng.nextInt(3) == 0}. + collect() + val checkSample = { + val prng42 = new Random(42) + val prng43 = new Random(43) + Array(1, 2, 3, 4, 5, 6).filter{i => + if (i < 4) 0 == prng42.nextInt(3) + else 0 == prng43.nextInt(3)} + } + assert(sample.size === checkSample.size) + for (i <- 0 until sample.size) assert(sample(i) === checkSample(i)) + } + + test("top with predefined ordering") { + val nums = Array.range(1, 100000) + val ints = sc.makeRDD(scala.util.Random.shuffle(nums), 2) + val topK = ints.top(5) + assert(topK.size === 5) + assert(topK === nums.reverse.take(5)) + } + + test("top with custom ordering") { + val words = Vector("a", "b", "c", "d") + implicit val ord = implicitly[Ordering[String]].reverse + val rdd = sc.makeRDD(words, 2) + val topK = rdd.top(2) + assert(topK.size === 2) + assert(topK.sorted === Array("b", "a")) + } + + test("takeOrdered with predefined ordering") { + val nums = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) + val rdd = sc.makeRDD(nums, 2) + val sortedLowerK = rdd.takeOrdered(5) + assert(sortedLowerK.size === 5) + assert(sortedLowerK === Array(1, 2, 3, 4, 5)) + } + + test("takeOrdered with custom ordering") { + val nums = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) + implicit val ord = implicitly[Ordering[Int]].reverse + val rdd = sc.makeRDD(nums, 2) + val sortedTopK = rdd.takeOrdered(5) + assert(sortedTopK.size === 5) + assert(sortedTopK === Array(10, 9, 8, 7, 6)) + assert(sortedTopK === nums.sorted(ord).take(5)) + } + + test("takeSample") { + val data = sc.parallelize(1 to 100, 2) + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=false, 20, seed) + assert(sample.size === 20) // Got exactly 20 elements + assert(sample.toSet.size === 20) // Elements are distinct + assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=false, 200, seed) + assert(sample.size === 100) // Got only 100 elements + assert(sample.toSet.size === 100) // Elements are distinct + assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=true, 20, seed) + assert(sample.size === 20) // Got exactly 20 elements + assert(sample.forall(x => 1 <= x && x <= 100), "elements not in [1, 100]") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=true, 100, seed) + assert(sample.size === 100) // Got exactly 100 elements + // Chance of getting all distinct elements is astronomically low, so test we got < 100 + assert(sample.toSet.size < 100, "sampling with replacement returned all distinct elements") + } + for (seed <- 1 to 5) { + val sample = data.takeSample(withReplacement=true, 200, seed) + assert(sample.size === 200) // Got exactly 200 elements + // Chance of getting all distinct elements is still quite low, so test we got < 100 + assert(sample.toSet.size < 100, "sampling with replacement returned all distinct elements") + } + } + + test("runJob on an invalid partition") { + intercept[IllegalArgumentException] { + sc.runJob(sc.parallelize(1 to 10, 2), {iter: Iterator[Int] => iter.size}, Seq(0, 1, 2), false) + } + } +} diff --git a/core/src/test/scala/org/apache/spark/SharedSparkContext.scala b/core/src/test/scala/org/apache/spark/SharedSparkContext.scala new file mode 100644 index 0000000000..97cbca09bf --- /dev/null +++ b/core/src/test/scala/org/apache/spark/SharedSparkContext.scala @@ -0,0 +1,42 @@ +/* + * 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 org.scalatest.Suite +import org.scalatest.BeforeAndAfterAll + +/** Shares a local `SparkContext` between all tests in a suite and closes it at the end */ +trait SharedSparkContext extends BeforeAndAfterAll { self: Suite => + + @transient private var _sc: SparkContext = _ + + def sc: SparkContext = _sc + + override def beforeAll() { + _sc = new SparkContext("local", "test") + super.beforeAll() + } + + override def afterAll() { + if (_sc != null) { + LocalSparkContext.stop(_sc) + _sc = null + } + super.afterAll() + } +} diff --git a/core/src/test/scala/org/apache/spark/ShuffleNettySuite.scala b/core/src/test/scala/org/apache/spark/ShuffleNettySuite.scala new file mode 100644 index 0000000000..e121b162ad --- /dev/null +++ b/core/src/test/scala/org/apache/spark/ShuffleNettySuite.scala @@ -0,0 +1,34 @@ +/* + * 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 org.scalatest.BeforeAndAfterAll + + +class ShuffleNettySuite extends ShuffleSuite with BeforeAndAfterAll { + + // This test suite should run all tests in ShuffleSuite with Netty shuffle mode. + + override def beforeAll(configMap: Map[String, Any]) { + System.setProperty("spark.shuffle.use.netty", "true") + } + + override def afterAll(configMap: Map[String, Any]) { + System.setProperty("spark.shuffle.use.netty", "false") + } +} diff --git a/core/src/test/scala/org/apache/spark/ShuffleSuite.scala b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala new file mode 100644 index 0000000000..357175e89e --- /dev/null +++ b/core/src/test/scala/org/apache/spark/ShuffleSuite.scala @@ -0,0 +1,210 @@ +/* + * 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 org.scalatest.FunSuite +import org.scalatest.matchers.ShouldMatchers + +import org.apache.spark.SparkContext._ +import org.apache.spark.ShuffleSuite.NonJavaSerializableClass +import org.apache.spark.rdd.{SubtractedRDD, CoGroupedRDD, OrderedRDDFunctions, ShuffledRDD} +import org.apache.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[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[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[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) +} diff --git a/core/src/test/scala/org/apache/spark/SizeEstimatorSuite.scala b/core/src/test/scala/org/apache/spark/SizeEstimatorSuite.scala new file mode 100644 index 0000000000..214ac74898 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/SizeEstimatorSuite.scala @@ -0,0 +1,164 @@ +/* + * 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 org.scalatest.FunSuite +import org.scalatest.BeforeAndAfterAll +import org.scalatest.PrivateMethodTester + +class DummyClass1 {} + +class DummyClass2 { + val x: Int = 0 +} + +class DummyClass3 { + val x: Int = 0 + val y: Double = 0.0 +} + +class DummyClass4(val d: DummyClass3) { + val x: Int = 0 +} + +object DummyString { + def apply(str: String) : DummyString = new DummyString(str.toArray) +} +class DummyString(val arr: Array[Char]) { + override val hashCode: Int = 0 + // JDK-7 has an extra hash32 field http://hg.openjdk.java.net/jdk7u/jdk7u6/jdk/rev/11987e85555f + @transient val hash32: Int = 0 +} + +class SizeEstimatorSuite + extends FunSuite with BeforeAndAfterAll with PrivateMethodTester { + + var oldArch: String = _ + var oldOops: String = _ + + override def beforeAll() { + // Set the arch to 64-bit and compressedOops to true to get a deterministic test-case + oldArch = System.setProperty("os.arch", "amd64") + oldOops = System.setProperty("spark.test.useCompressedOops", "true") + } + + override def afterAll() { + resetOrClear("os.arch", oldArch) + resetOrClear("spark.test.useCompressedOops", oldOops) + } + + test("simple classes") { + assert(SizeEstimator.estimate(new DummyClass1) === 16) + assert(SizeEstimator.estimate(new DummyClass2) === 16) + assert(SizeEstimator.estimate(new DummyClass3) === 24) + assert(SizeEstimator.estimate(new DummyClass4(null)) === 24) + assert(SizeEstimator.estimate(new DummyClass4(new DummyClass3)) === 48) + } + + // NOTE: The String class definition varies across JDK versions (1.6 vs. 1.7) and vendors + // (Sun vs IBM). Use a DummyString class to make tests deterministic. + test("strings") { + assert(SizeEstimator.estimate(DummyString("")) === 40) + assert(SizeEstimator.estimate(DummyString("a")) === 48) + assert(SizeEstimator.estimate(DummyString("ab")) === 48) + assert(SizeEstimator.estimate(DummyString("abcdefgh")) === 56) + } + + test("primitive arrays") { + assert(SizeEstimator.estimate(new Array[Byte](10)) === 32) + assert(SizeEstimator.estimate(new Array[Char](10)) === 40) + assert(SizeEstimator.estimate(new Array[Short](10)) === 40) + assert(SizeEstimator.estimate(new Array[Int](10)) === 56) + assert(SizeEstimator.estimate(new Array[Long](10)) === 96) + assert(SizeEstimator.estimate(new Array[Float](10)) === 56) + assert(SizeEstimator.estimate(new Array[Double](10)) === 96) + assert(SizeEstimator.estimate(new Array[Int](1000)) === 4016) + assert(SizeEstimator.estimate(new Array[Long](1000)) === 8016) + } + + test("object arrays") { + // Arrays containing nulls should just have one pointer per element + assert(SizeEstimator.estimate(new Array[String](10)) === 56) + assert(SizeEstimator.estimate(new Array[AnyRef](10)) === 56) + + // For object arrays with non-null elements, each object should take one pointer plus + // however many bytes that class takes. (Note that Array.fill calls the code in its + // second parameter separately for each object, so we get distinct objects.) + assert(SizeEstimator.estimate(Array.fill(10)(new DummyClass1)) === 216) + assert(SizeEstimator.estimate(Array.fill(10)(new DummyClass2)) === 216) + assert(SizeEstimator.estimate(Array.fill(10)(new DummyClass3)) === 296) + assert(SizeEstimator.estimate(Array(new DummyClass1, new DummyClass2)) === 56) + + // Past size 100, our samples 100 elements, but we should still get the right size. + assert(SizeEstimator.estimate(Array.fill(1000)(new DummyClass3)) === 28016) + + // If an array contains the *same* element many times, we should only count it once. + val d1 = new DummyClass1 + assert(SizeEstimator.estimate(Array.fill(10)(d1)) === 72) // 10 pointers plus 8-byte object + assert(SizeEstimator.estimate(Array.fill(100)(d1)) === 432) // 100 pointers plus 8-byte object + + // Same thing with huge array containing the same element many times. Note that this won't + // return exactly 4032 because it can't tell that *all* the elements will equal the first + // one it samples, but it should be close to that. + + // TODO: If we sample 100 elements, this should always be 4176 ? + val estimatedSize = SizeEstimator.estimate(Array.fill(1000)(d1)) + assert(estimatedSize >= 4000, "Estimated size " + estimatedSize + " should be more than 4000") + assert(estimatedSize <= 4200, "Estimated size " + estimatedSize + " should be less than 4100") + } + + test("32-bit arch") { + val arch = System.setProperty("os.arch", "x86") + + val initialize = PrivateMethod[Unit]('initialize) + SizeEstimator invokePrivate initialize() + + assert(SizeEstimator.estimate(DummyString("")) === 40) + assert(SizeEstimator.estimate(DummyString("a")) === 48) + assert(SizeEstimator.estimate(DummyString("ab")) === 48) + assert(SizeEstimator.estimate(DummyString("abcdefgh")) === 56) + + resetOrClear("os.arch", arch) + } + + // NOTE: The String class definition varies across JDK versions (1.6 vs. 1.7) and vendors + // (Sun vs IBM). Use a DummyString class to make tests deterministic. + test("64-bit arch with no compressed oops") { + val arch = System.setProperty("os.arch", "amd64") + val oops = System.setProperty("spark.test.useCompressedOops", "false") + + val initialize = PrivateMethod[Unit]('initialize) + SizeEstimator invokePrivate initialize() + + assert(SizeEstimator.estimate(DummyString("")) === 56) + assert(SizeEstimator.estimate(DummyString("a")) === 64) + assert(SizeEstimator.estimate(DummyString("ab")) === 64) + assert(SizeEstimator.estimate(DummyString("abcdefgh")) === 72) + + resetOrClear("os.arch", arch) + resetOrClear("spark.test.useCompressedOops", oops) + } + + def resetOrClear(prop: String, oldValue: String) { + if (oldValue != null) { + System.setProperty(prop, oldValue) + } else { + System.clearProperty(prop) + } + } +} diff --git a/core/src/test/scala/org/apache/spark/SortingSuite.scala b/core/src/test/scala/org/apache/spark/SortingSuite.scala new file mode 100644 index 0000000000..f4fa9511dd --- /dev/null +++ b/core/src/test/scala/org/apache/spark/SortingSuite.scala @@ -0,0 +1,123 @@ +/* + * 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 org.scalatest.FunSuite +import org.scalatest.BeforeAndAfter +import org.scalatest.matchers.ShouldMatchers +import SparkContext._ + +class SortingSuite extends FunSuite with SharedSparkContext with ShouldMatchers with Logging { + + test("sortByKey") { + val pairs = sc.parallelize(Array((1, 0), (2, 0), (0, 0), (3, 0)), 2) + assert(pairs.sortByKey().collect() === Array((0,0), (1,0), (2,0), (3,0))) + } + + test("large array") { + val rand = new scala.util.Random() + val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) } + val pairs = sc.parallelize(pairArr, 2) + val sorted = pairs.sortByKey() + assert(sorted.partitions.size === 2) + assert(sorted.collect() === pairArr.sortBy(_._1)) + } + + test("large array with one split") { + val rand = new scala.util.Random() + val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) } + val pairs = sc.parallelize(pairArr, 2) + val sorted = pairs.sortByKey(true, 1) + assert(sorted.partitions.size === 1) + assert(sorted.collect() === pairArr.sortBy(_._1)) + } + + test("large array with many partitions") { + val rand = new scala.util.Random() + val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) } + val pairs = sc.parallelize(pairArr, 2) + val sorted = pairs.sortByKey(true, 20) + assert(sorted.partitions.size === 20) + assert(sorted.collect() === pairArr.sortBy(_._1)) + } + + test("sort descending") { + val rand = new scala.util.Random() + val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) } + val pairs = sc.parallelize(pairArr, 2) + assert(pairs.sortByKey(false).collect() === pairArr.sortWith((x, y) => x._1 > y._1)) + } + + test("sort descending with one split") { + val rand = new scala.util.Random() + val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) } + val pairs = sc.parallelize(pairArr, 1) + assert(pairs.sortByKey(false, 1).collect() === pairArr.sortWith((x, y) => x._1 > y._1)) + } + + test("sort descending with many partitions") { + val rand = new scala.util.Random() + val pairArr = Array.fill(1000) { (rand.nextInt(), rand.nextInt()) } + val pairs = sc.parallelize(pairArr, 2) + assert(pairs.sortByKey(false, 20).collect() === pairArr.sortWith((x, y) => x._1 > y._1)) + } + + test("more partitions than elements") { + val rand = new scala.util.Random() + val pairArr = Array.fill(10) { (rand.nextInt(), rand.nextInt()) } + val pairs = sc.parallelize(pairArr, 30) + assert(pairs.sortByKey().collect() === pairArr.sortBy(_._1)) + } + + test("empty RDD") { + val pairArr = new Array[(Int, Int)](0) + val pairs = sc.parallelize(pairArr, 2) + assert(pairs.sortByKey().collect() === pairArr.sortBy(_._1)) + } + + test("partition balancing") { + val pairArr = (1 to 1000).map(x => (x, x)).toArray + val sorted = sc.parallelize(pairArr, 4).sortByKey() + assert(sorted.collect() === pairArr.sortBy(_._1)) + val partitions = sorted.collectPartitions() + logInfo("Partition lengths: " + partitions.map(_.length).mkString(", ")) + partitions(0).length should be > 180 + partitions(1).length should be > 180 + partitions(2).length should be > 180 + partitions(3).length should be > 180 + partitions(0).last should be < partitions(1).head + partitions(1).last should be < partitions(2).head + partitions(2).last should be < partitions(3).head + } + + test("partition balancing for descending sort") { + val pairArr = (1 to 1000).map(x => (x, x)).toArray + val sorted = sc.parallelize(pairArr, 4).sortByKey(false) + assert(sorted.collect() === pairArr.sortBy(_._1).reverse) + val partitions = sorted.collectPartitions() + logInfo("partition lengths: " + partitions.map(_.length).mkString(", ")) + partitions(0).length should be > 180 + partitions(1).length should be > 180 + partitions(2).length should be > 180 + partitions(3).length should be > 180 + partitions(0).last should be > partitions(1).head + partitions(1).last should be > partitions(2).head + partitions(2).last should be > partitions(3).head + } +} + diff --git a/core/src/test/scala/org/apache/spark/SparkContextInfoSuite.scala b/core/src/test/scala/org/apache/spark/SparkContextInfoSuite.scala new file mode 100644 index 0000000000..939fe51801 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/SparkContextInfoSuite.scala @@ -0,0 +1,60 @@ +/* + * 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 org.scalatest.FunSuite +import org.apache.spark.SparkContext._ + +class SparkContextInfoSuite extends FunSuite with LocalSparkContext { + test("getPersistentRDDs only returns RDDs that are marked as cached") { + sc = new SparkContext("local", "test") + assert(sc.getPersistentRDDs.isEmpty === true) + + val rdd = sc.makeRDD(Array(1, 2, 3, 4), 2) + assert(sc.getPersistentRDDs.isEmpty === true) + + rdd.cache() + assert(sc.getPersistentRDDs.size === 1) + assert(sc.getPersistentRDDs.values.head === rdd) + } + + test("getPersistentRDDs returns an immutable map") { + sc = new SparkContext("local", "test") + val rdd1 = sc.makeRDD(Array(1, 2, 3, 4), 2).cache() + + val myRdds = sc.getPersistentRDDs + assert(myRdds.size === 1) + assert(myRdds.values.head === rdd1) + + val rdd2 = sc.makeRDD(Array(5, 6, 7, 8), 1).cache() + + // getPersistentRDDs should have 2 RDDs, but myRdds should not change + assert(sc.getPersistentRDDs.size === 2) + assert(myRdds.size === 1) + } + + test("getRDDStorageInfo only reports on RDDs that actually persist data") { + sc = new SparkContext("local", "test") + val rdd = sc.makeRDD(Array(1, 2, 3, 4), 2).cache() + + assert(sc.getRDDStorageInfo.size === 0) + + rdd.collect() + assert(sc.getRDDStorageInfo.size === 1) + } +} diff --git a/core/src/test/scala/org/apache/spark/ThreadingSuite.scala b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala new file mode 100644 index 0000000000..69383ddfb8 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/ThreadingSuite.scala @@ -0,0 +1,152 @@ +/* + * 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.concurrent.Semaphore +import java.util.concurrent.atomic.AtomicBoolean +import java.util.concurrent.atomic.AtomicInteger + +import org.scalatest.FunSuite +import org.scalatest.BeforeAndAfter + +import SparkContext._ + +/** + * Holds state shared across task threads in some ThreadingSuite tests. + */ +object ThreadingSuiteState { + val runningThreads = new AtomicInteger + val failed = new AtomicBoolean + + def clear() { + runningThreads.set(0) + failed.set(false) + } +} + +class ThreadingSuite extends FunSuite with LocalSparkContext { + + test("accessing SparkContext form a different thread") { + sc = new SparkContext("local", "test") + val nums = sc.parallelize(1 to 10, 2) + val sem = new Semaphore(0) + @volatile var answer1: Int = 0 + @volatile var answer2: Int = 0 + new Thread { + override def run() { + answer1 = nums.reduce(_ + _) + answer2 = nums.first() // This will run "locally" in the current thread + sem.release() + } + }.start() + sem.acquire() + assert(answer1 === 55) + assert(answer2 === 1) + } + + test("accessing SparkContext form multiple threads") { + sc = new SparkContext("local", "test") + val nums = sc.parallelize(1 to 10, 2) + val sem = new Semaphore(0) + @volatile var ok = true + for (i <- 0 until 10) { + new Thread { + override def run() { + val answer1 = nums.reduce(_ + _) + if (answer1 != 55) { + printf("In thread %d: answer1 was %d\n", i, answer1) + ok = false + } + val answer2 = nums.first() // This will run "locally" in the current thread + if (answer2 != 1) { + printf("In thread %d: answer2 was %d\n", i, answer2) + ok = false + } + sem.release() + } + }.start() + } + sem.acquire(10) + if (!ok) { + fail("One or more threads got the wrong answer from an RDD operation") + } + } + + test("accessing multi-threaded SparkContext form multiple threads") { + sc = new SparkContext("local[4]", "test") + val nums = sc.parallelize(1 to 10, 2) + val sem = new Semaphore(0) + @volatile var ok = true + for (i <- 0 until 10) { + new Thread { + override def run() { + val answer1 = nums.reduce(_ + _) + if (answer1 != 55) { + printf("In thread %d: answer1 was %d\n", i, answer1) + ok = false + } + val answer2 = nums.first() // This will run "locally" in the current thread + if (answer2 != 1) { + printf("In thread %d: answer2 was %d\n", i, answer2) + ok = false + } + sem.release() + } + }.start() + } + sem.acquire(10) + if (!ok) { + fail("One or more threads got the wrong answer from an RDD operation") + } + } + + test("parallel job execution") { + // This test launches two jobs with two threads each on a 4-core local cluster. Each thread + // waits until there are 4 threads running at once, to test that both jobs have been launched. + sc = new SparkContext("local[4]", "test") + val nums = sc.parallelize(1 to 2, 2) + val sem = new Semaphore(0) + ThreadingSuiteState.clear() + for (i <- 0 until 2) { + new Thread { + override def run() { + val ans = nums.map(number => { + val running = ThreadingSuiteState.runningThreads + running.getAndIncrement() + val time = System.currentTimeMillis() + while (running.get() != 4 && System.currentTimeMillis() < time + 1000) { + Thread.sleep(100) + } + if (running.get() != 4) { + println("Waited 1 second without seeing runningThreads = 4 (it was " + + running.get() + "); failing test") + ThreadingSuiteState.failed.set(true) + } + number + }).collect() + assert(ans.toList === List(1, 2)) + sem.release() + } + }.start() + } + sem.acquire(2) + if (ThreadingSuiteState.failed.get()) { + fail("One or more threads didn't see runningThreads = 4") + } + } +} diff --git a/core/src/test/scala/org/apache/spark/UnpersistSuite.scala b/core/src/test/scala/org/apache/spark/UnpersistSuite.scala new file mode 100644 index 0000000000..46a2da1724 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/UnpersistSuite.scala @@ -0,0 +1,47 @@ +/* + * 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 org.scalatest.FunSuite +import org.scalatest.concurrent.Timeouts._ +import org.scalatest.time.{Span, Millis} +import org.apache.spark.SparkContext._ + +class UnpersistSuite extends FunSuite with LocalSparkContext { + test("unpersist RDD") { + sc = new SparkContext("local", "test") + val rdd = sc.makeRDD(Array(1, 2, 3, 4), 2).cache() + rdd.count + assert(sc.persistentRdds.isEmpty === false) + rdd.unpersist() + assert(sc.persistentRdds.isEmpty === true) + + failAfter(Span(3000, Millis)) { + try { + while (! sc.getRDDStorageInfo.isEmpty) { + Thread.sleep(200) + } + } catch { + case _ => { Thread.sleep(10) } + // Do nothing. We might see exceptions because block manager + // is racing this thread to remove entries from the driver. + } + } + assert(sc.getRDDStorageInfo.isEmpty === true) + } +} diff --git a/core/src/test/scala/org/apache/spark/UtilsSuite.scala b/core/src/test/scala/org/apache/spark/UtilsSuite.scala new file mode 100644 index 0000000000..3a908720a8 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/UtilsSuite.scala @@ -0,0 +1,139 @@ +/* + * 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 com.google.common.base.Charsets +import com.google.common.io.Files +import java.io.{ByteArrayOutputStream, ByteArrayInputStream, FileOutputStream, File} +import org.scalatest.FunSuite +import org.apache.commons.io.FileUtils +import scala.util.Random + +class UtilsSuite extends FunSuite { + + test("bytesToString") { + assert(Utils.bytesToString(10) === "10.0 B") + assert(Utils.bytesToString(1500) === "1500.0 B") + assert(Utils.bytesToString(2000000) === "1953.1 KB") + assert(Utils.bytesToString(2097152) === "2.0 MB") + assert(Utils.bytesToString(2306867) === "2.2 MB") + assert(Utils.bytesToString(5368709120L) === "5.0 GB") + assert(Utils.bytesToString(5L * 1024L * 1024L * 1024L * 1024L) === "5.0 TB") + } + + test("copyStream") { + //input array initialization + val bytes = Array.ofDim[Byte](9000) + Random.nextBytes(bytes) + + val os = new ByteArrayOutputStream() + Utils.copyStream(new ByteArrayInputStream(bytes), os) + + assert(os.toByteArray.toList.equals(bytes.toList)) + } + + test("memoryStringToMb") { + assert(Utils.memoryStringToMb("1") === 0) + assert(Utils.memoryStringToMb("1048575") === 0) + assert(Utils.memoryStringToMb("3145728") === 3) + + assert(Utils.memoryStringToMb("1024k") === 1) + assert(Utils.memoryStringToMb("5000k") === 4) + assert(Utils.memoryStringToMb("4024k") === Utils.memoryStringToMb("4024K")) + + assert(Utils.memoryStringToMb("1024m") === 1024) + assert(Utils.memoryStringToMb("5000m") === 5000) + assert(Utils.memoryStringToMb("4024m") === Utils.memoryStringToMb("4024M")) + + assert(Utils.memoryStringToMb("2g") === 2048) + assert(Utils.memoryStringToMb("3g") === Utils.memoryStringToMb("3G")) + + assert(Utils.memoryStringToMb("2t") === 2097152) + assert(Utils.memoryStringToMb("3t") === Utils.memoryStringToMb("3T")) + } + + test("splitCommandString") { + assert(Utils.splitCommandString("") === Seq()) + assert(Utils.splitCommandString("a") === Seq("a")) + assert(Utils.splitCommandString("aaa") === Seq("aaa")) + assert(Utils.splitCommandString("a b c") === Seq("a", "b", "c")) + assert(Utils.splitCommandString(" a b\t c ") === Seq("a", "b", "c")) + assert(Utils.splitCommandString("a 'b c'") === Seq("a", "b c")) + assert(Utils.splitCommandString("a 'b c' d") === Seq("a", "b c", "d")) + assert(Utils.splitCommandString("'b c'") === Seq("b c")) + assert(Utils.splitCommandString("a \"b c\"") === Seq("a", "b c")) + assert(Utils.splitCommandString("a \"b c\" d") === Seq("a", "b c", "d")) + assert(Utils.splitCommandString("\"b c\"") === Seq("b c")) + assert(Utils.splitCommandString("a 'b\" c' \"d' e\"") === Seq("a", "b\" c", "d' e")) + assert(Utils.splitCommandString("a\t'b\nc'\nd") === Seq("a", "b\nc", "d")) + assert(Utils.splitCommandString("a \"b\\\\c\"") === Seq("a", "b\\c")) + assert(Utils.splitCommandString("a \"b\\\"c\"") === Seq("a", "b\"c")) + assert(Utils.splitCommandString("a 'b\\\"c'") === Seq("a", "b\\\"c")) + assert(Utils.splitCommandString("'a'b") === Seq("ab")) + assert(Utils.splitCommandString("'a''b'") === Seq("ab")) + assert(Utils.splitCommandString("\"a\"b") === Seq("ab")) + assert(Utils.splitCommandString("\"a\"\"b\"") === Seq("ab")) + assert(Utils.splitCommandString("''") === Seq("")) + assert(Utils.splitCommandString("\"\"") === Seq("")) + } + + test("string formatting of time durations") { + val second = 1000 + val minute = second * 60 + val hour = minute * 60 + def str = Utils.msDurationToString(_) + + assert(str(123) === "123 ms") + assert(str(second) === "1.0 s") + assert(str(second + 462) === "1.5 s") + assert(str(hour) === "1.00 h") + assert(str(minute) === "1.0 m") + assert(str(minute + 4 * second + 34) === "1.1 m") + assert(str(10 * hour + minute + 4 * second) === "10.02 h") + assert(str(10 * hour + 59 * minute + 59 * second + 999) === "11.00 h") + } + + test("reading offset bytes of a file") { + val tmpDir2 = Files.createTempDir() + val f1Path = tmpDir2 + "/f1" + val f1 = new FileOutputStream(f1Path) + f1.write("1\n2\n3\n4\n5\n6\n7\n8\n9\n".getBytes(Charsets.UTF_8)) + f1.close() + + // Read first few bytes + assert(Utils.offsetBytes(f1Path, 0, 5) === "1\n2\n3") + + // Read some middle bytes + assert(Utils.offsetBytes(f1Path, 4, 11) === "3\n4\n5\n6") + + // Read last few bytes + assert(Utils.offsetBytes(f1Path, 12, 18) === "7\n8\n9\n") + + // Read some nonexistent bytes in the beginning + assert(Utils.offsetBytes(f1Path, -5, 5) === "1\n2\n3") + + // Read some nonexistent bytes at the end + assert(Utils.offsetBytes(f1Path, 12, 22) === "7\n8\n9\n") + + // Read some nonexistent bytes on both ends + assert(Utils.offsetBytes(f1Path, -3, 25) === "1\n2\n3\n4\n5\n6\n7\n8\n9\n") + + FileUtils.deleteDirectory(tmpDir2) + } +} + diff --git a/core/src/test/scala/org/apache/spark/ZippedPartitionsSuite.scala b/core/src/test/scala/org/apache/spark/ZippedPartitionsSuite.scala new file mode 100644 index 0000000000..618b9c113b --- /dev/null +++ b/core/src/test/scala/org/apache/spark/ZippedPartitionsSuite.scala @@ -0,0 +1,50 @@ +/* + * 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 scala.collection.immutable.NumericRange + +import org.scalatest.FunSuite +import org.scalatest.prop.Checkers +import org.scalacheck.Arbitrary._ +import org.scalacheck.Gen +import org.scalacheck.Prop._ + +import SparkContext._ + + +object ZippedPartitionsSuite { + def procZippedData(i: Iterator[Int], s: Iterator[String], d: Iterator[Double]) : Iterator[Int] = { + Iterator(i.toArray.size, s.toArray.size, d.toArray.size) + } +} + +class ZippedPartitionsSuite extends FunSuite with SharedSparkContext { + test("print sizes") { + val data1 = sc.makeRDD(Array(1, 2, 3, 4), 2) + val data2 = sc.makeRDD(Array("1", "2", "3", "4", "5", "6"), 2) + val data3 = sc.makeRDD(Array(1.0, 2.0), 2) + + val zippedRDD = data1.zipPartitions(data2, data3)(ZippedPartitionsSuite.procZippedData) + + val obtainedSizes = zippedRDD.collect() + val expectedSizes = Array(2, 3, 1, 2, 3, 1) + assert(obtainedSizes.size == 6) + assert(obtainedSizes.zip(expectedSizes).forall(x => x._1 == x._2)) + } +} diff --git a/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala b/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala new file mode 100644 index 0000000000..fd6f69041a --- /dev/null +++ b/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala @@ -0,0 +1,62 @@ +/* + * 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.io + +import java.io.{ByteArrayInputStream, ByteArrayOutputStream} + +import org.scalatest.FunSuite + + +class CompressionCodecSuite extends FunSuite { + + def testCodec(codec: CompressionCodec) { + // Write 1000 integers to the output stream, compressed. + val outputStream = new ByteArrayOutputStream() + val out = codec.compressedOutputStream(outputStream) + for (i <- 1 until 1000) { + out.write(i % 256) + } + out.close() + + // Read the 1000 integers back. + val inputStream = new ByteArrayInputStream(outputStream.toByteArray) + val in = codec.compressedInputStream(inputStream) + for (i <- 1 until 1000) { + assert(in.read() === i % 256) + } + in.close() + } + + test("default compression codec") { + val codec = CompressionCodec.createCodec() + assert(codec.getClass === classOf[SnappyCompressionCodec]) + testCodec(codec) + } + + test("lzf compression codec") { + val codec = CompressionCodec.createCodec(classOf[LZFCompressionCodec].getName) + assert(codec.getClass === classOf[LZFCompressionCodec]) + testCodec(codec) + } + + test("snappy compression codec") { + val codec = CompressionCodec.createCodec(classOf[SnappyCompressionCodec].getName) + assert(codec.getClass === classOf[SnappyCompressionCodec]) + testCodec(codec) + } +} diff --git a/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala b/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala new file mode 100644 index 0000000000..58c94a162d --- /dev/null +++ b/core/src/test/scala/org/apache/spark/metrics/MetricsConfigSuite.scala @@ -0,0 +1,89 @@ +/* + * 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.metrics + +import org.scalatest.{BeforeAndAfter, FunSuite} + +class MetricsConfigSuite extends FunSuite with BeforeAndAfter { + var filePath: String = _ + + before { + filePath = getClass.getClassLoader.getResource("test_metrics_config.properties").getFile() + } + + test("MetricsConfig with default properties") { + val conf = new MetricsConfig(Option("dummy-file")) + conf.initialize() + + assert(conf.properties.size() === 5) + assert(conf.properties.getProperty("test-for-dummy") === null) + + val property = conf.getInstance("random") + assert(property.size() === 3) + assert(property.getProperty("sink.servlet.class") === "org.apache.spark.metrics.sink.MetricsServlet") + assert(property.getProperty("sink.servlet.uri") === "/metrics/json") + assert(property.getProperty("sink.servlet.sample") === "false") + } + + test("MetricsConfig with properties set") { + val conf = new MetricsConfig(Option(filePath)) + conf.initialize() + + val masterProp = conf.getInstance("master") + assert(masterProp.size() === 6) + assert(masterProp.getProperty("sink.console.period") === "20") + assert(masterProp.getProperty("sink.console.unit") === "minutes") + assert(masterProp.getProperty("source.jvm.class") === "org.apache.spark.metrics.source.JvmSource") + assert(masterProp.getProperty("sink.servlet.class") === "org.apache.spark.metrics.sink.MetricsServlet") + assert(masterProp.getProperty("sink.servlet.uri") === "/metrics/master/json") + assert(masterProp.getProperty("sink.servlet.sample") === "false") + + val workerProp = conf.getInstance("worker") + assert(workerProp.size() === 6) + assert(workerProp.getProperty("sink.console.period") === "10") + assert(workerProp.getProperty("sink.console.unit") === "seconds") + assert(workerProp.getProperty("source.jvm.class") === "org.apache.spark.metrics.source.JvmSource") + assert(workerProp.getProperty("sink.servlet.class") === "org.apache.spark.metrics.sink.MetricsServlet") + assert(workerProp.getProperty("sink.servlet.uri") === "/metrics/json") + assert(workerProp.getProperty("sink.servlet.sample") === "false") + } + + test("MetricsConfig with subProperties") { + val conf = new MetricsConfig(Option(filePath)) + conf.initialize() + + val propCategories = conf.propertyCategories + assert(propCategories.size === 3) + + val masterProp = conf.getInstance("master") + val sourceProps = conf.subProperties(masterProp, MetricsSystem.SOURCE_REGEX) + assert(sourceProps.size === 1) + assert(sourceProps("jvm").getProperty("class") === "org.apache.spark.metrics.source.JvmSource") + + val sinkProps = conf.subProperties(masterProp, MetricsSystem.SINK_REGEX) + assert(sinkProps.size === 2) + assert(sinkProps.contains("console")) + assert(sinkProps.contains("servlet")) + + val consoleProps = sinkProps("console") + assert(consoleProps.size() === 2) + + val servletProps = sinkProps("servlet") + assert(servletProps.size() === 3) + } +} diff --git a/core/src/test/scala/org/apache/spark/metrics/MetricsSystemSuite.scala b/core/src/test/scala/org/apache/spark/metrics/MetricsSystemSuite.scala new file mode 100644 index 0000000000..7181333adf --- /dev/null +++ b/core/src/test/scala/org/apache/spark/metrics/MetricsSystemSuite.scala @@ -0,0 +1,54 @@ +/* + * 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.metrics + +import org.scalatest.{BeforeAndAfter, FunSuite} +import org.apache.spark.deploy.master.MasterSource + +class MetricsSystemSuite extends FunSuite with BeforeAndAfter { + var filePath: String = _ + + before { + filePath = getClass.getClassLoader.getResource("test_metrics_system.properties").getFile() + System.setProperty("spark.metrics.conf", filePath) + } + + test("MetricsSystem with default config") { + val metricsSystem = MetricsSystem.createMetricsSystem("default") + val sources = metricsSystem.sources + val sinks = metricsSystem.sinks + + assert(sources.length === 0) + assert(sinks.length === 0) + assert(!metricsSystem.getServletHandlers.isEmpty) + } + + test("MetricsSystem with sources add") { + val metricsSystem = MetricsSystem.createMetricsSystem("test") + val sources = metricsSystem.sources + val sinks = metricsSystem.sinks + + assert(sources.length === 0) + assert(sinks.length === 1) + assert(!metricsSystem.getServletHandlers.isEmpty) + + val source = new MasterSource(null) + metricsSystem.registerSource(source) + assert(sources.length === 1) + } +} diff --git a/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala new file mode 100644 index 0000000000..3d39a31252 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/rdd/JdbcRDDSuite.scala @@ -0,0 +1,73 @@ +/* + * 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 org.scalatest.{ BeforeAndAfter, FunSuite } +import org.apache.spark.SparkContext._ +import org.apache.spark.rdd.JdbcRDD +import java.sql._ + +class JdbcRDDSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { + + before { + Class.forName("org.apache.derby.jdbc.EmbeddedDriver") + val conn = DriverManager.getConnection("jdbc:derby:target/JdbcRDDSuiteDb;create=true") + try { + val create = conn.createStatement + create.execute(""" + CREATE TABLE FOO( + ID INTEGER NOT NULL GENERATED ALWAYS AS IDENTITY (START WITH 1, INCREMENT BY 1), + DATA INTEGER + )""") + create.close + val insert = conn.prepareStatement("INSERT INTO FOO(DATA) VALUES(?)") + (1 to 100).foreach { i => + insert.setInt(1, i * 2) + insert.executeUpdate + } + insert.close + } catch { + case e: SQLException if e.getSQLState == "X0Y32" => + // table exists + } finally { + conn.close + } + } + + test("basic functionality") { + sc = new SparkContext("local", "test") + val rdd = new JdbcRDD( + sc, + () => { DriverManager.getConnection("jdbc:derby:target/JdbcRDDSuiteDb") }, + "SELECT DATA FROM FOO WHERE ? <= ID AND ID <= ?", + 1, 100, 3, + (r: ResultSet) => { r.getInt(1) } ).cache + + assert(rdd.count === 100) + assert(rdd.reduce(_+_) === 10100) + } + + after { + try { + DriverManager.getConnection("jdbc:derby:;shutdown=true") + } catch { + case se: SQLException if se.getSQLState == "XJ015" => + // normal shutdown + } + } +} diff --git a/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala b/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala new file mode 100644 index 0000000000..a80afdee7e --- /dev/null +++ b/core/src/test/scala/org/apache/spark/rdd/ParallelCollectionSplitSuite.scala @@ -0,0 +1,212 @@ +/* + * 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.rdd + +import scala.collection.immutable.NumericRange + +import org.scalatest.FunSuite +import org.scalatest.prop.Checkers +import org.scalacheck.Arbitrary._ +import org.scalacheck.Gen +import org.scalacheck.Prop._ + +class ParallelCollectionSplitSuite extends FunSuite with Checkers { + test("one element per slice") { + val data = Array(1, 2, 3) + val slices = ParallelCollectionRDD.slice(data, 3) + assert(slices.size === 3) + assert(slices(0).mkString(",") === "1") + assert(slices(1).mkString(",") === "2") + assert(slices(2).mkString(",") === "3") + } + + test("one slice") { + val data = Array(1, 2, 3) + val slices = ParallelCollectionRDD.slice(data, 1) + assert(slices.size === 1) + assert(slices(0).mkString(",") === "1,2,3") + } + + test("equal slices") { + val data = Array(1, 2, 3, 4, 5, 6, 7, 8, 9) + val slices = ParallelCollectionRDD.slice(data, 3) + assert(slices.size === 3) + assert(slices(0).mkString(",") === "1,2,3") + assert(slices(1).mkString(",") === "4,5,6") + assert(slices(2).mkString(",") === "7,8,9") + } + + test("non-equal slices") { + val data = Array(1, 2, 3, 4, 5, 6, 7, 8, 9, 10) + val slices = ParallelCollectionRDD.slice(data, 3) + assert(slices.size === 3) + assert(slices(0).mkString(",") === "1,2,3") + assert(slices(1).mkString(",") === "4,5,6") + assert(slices(2).mkString(",") === "7,8,9,10") + } + + test("splitting exclusive range") { + val data = 0 until 100 + val slices = ParallelCollectionRDD.slice(data, 3) + assert(slices.size === 3) + assert(slices(0).mkString(",") === (0 to 32).mkString(",")) + assert(slices(1).mkString(",") === (33 to 65).mkString(",")) + assert(slices(2).mkString(",") === (66 to 99).mkString(",")) + } + + test("splitting inclusive range") { + val data = 0 to 100 + val slices = ParallelCollectionRDD.slice(data, 3) + assert(slices.size === 3) + assert(slices(0).mkString(",") === (0 to 32).mkString(",")) + assert(slices(1).mkString(",") === (33 to 66).mkString(",")) + assert(slices(2).mkString(",") === (67 to 100).mkString(",")) + } + + test("empty data") { + val data = new Array[Int](0) + val slices = ParallelCollectionRDD.slice(data, 5) + assert(slices.size === 5) + for (slice <- slices) assert(slice.size === 0) + } + + test("zero slices") { + val data = Array(1, 2, 3) + intercept[IllegalArgumentException] { ParallelCollectionRDD.slice(data, 0) } + } + + test("negative number of slices") { + val data = Array(1, 2, 3) + intercept[IllegalArgumentException] { ParallelCollectionRDD.slice(data, -5) } + } + + test("exclusive ranges sliced into ranges") { + val data = 1 until 100 + val slices = ParallelCollectionRDD.slice(data, 3) + assert(slices.size === 3) + assert(slices.map(_.size).reduceLeft(_+_) === 99) + assert(slices.forall(_.isInstanceOf[Range])) + } + + test("inclusive ranges sliced into ranges") { + val data = 1 to 100 + val slices = ParallelCollectionRDD.slice(data, 3) + assert(slices.size === 3) + assert(slices.map(_.size).reduceLeft(_+_) === 100) + assert(slices.forall(_.isInstanceOf[Range])) + } + + test("large ranges don't overflow") { + val N = 100 * 1000 * 1000 + val data = 0 until N + val slices = ParallelCollectionRDD.slice(data, 40) + assert(slices.size === 40) + for (i <- 0 until 40) { + assert(slices(i).isInstanceOf[Range]) + val range = slices(i).asInstanceOf[Range] + assert(range.start === i * (N / 40), "slice " + i + " start") + assert(range.end === (i+1) * (N / 40), "slice " + i + " end") + assert(range.step === 1, "slice " + i + " step") + } + } + + test("random array tests") { + val gen = for { + d <- arbitrary[List[Int]] + n <- Gen.choose(1, 100) + } yield (d, n) + val prop = forAll(gen) { + (tuple: (List[Int], Int)) => + val d = tuple._1 + val n = tuple._2 + val slices = ParallelCollectionRDD.slice(d, n) + ("n slices" |: slices.size == n) && + ("concat to d" |: Seq.concat(slices: _*).mkString(",") == d.mkString(",")) && + ("equal sizes" |: slices.map(_.size).forall(x => x==d.size/n || x==d.size/n+1)) + } + check(prop) + } + + test("random exclusive range tests") { + val gen = for { + a <- Gen.choose(-100, 100) + b <- Gen.choose(-100, 100) + step <- Gen.choose(-5, 5) suchThat (_ != 0) + n <- Gen.choose(1, 100) + } yield (a until b by step, n) + val prop = forAll(gen) { + case (d: Range, n: Int) => + val slices = ParallelCollectionRDD.slice(d, n) + ("n slices" |: slices.size == n) && + ("all ranges" |: slices.forall(_.isInstanceOf[Range])) && + ("concat to d" |: Seq.concat(slices: _*).mkString(",") == d.mkString(",")) && + ("equal sizes" |: slices.map(_.size).forall(x => x==d.size/n || x==d.size/n+1)) + } + check(prop) + } + + test("random inclusive range tests") { + val gen = for { + a <- Gen.choose(-100, 100) + b <- Gen.choose(-100, 100) + step <- Gen.choose(-5, 5) suchThat (_ != 0) + n <- Gen.choose(1, 100) + } yield (a to b by step, n) + val prop = forAll(gen) { + case (d: Range, n: Int) => + val slices = ParallelCollectionRDD.slice(d, n) + ("n slices" |: slices.size == n) && + ("all ranges" |: slices.forall(_.isInstanceOf[Range])) && + ("concat to d" |: Seq.concat(slices: _*).mkString(",") == d.mkString(",")) && + ("equal sizes" |: slices.map(_.size).forall(x => x==d.size/n || x==d.size/n+1)) + } + check(prop) + } + + test("exclusive ranges of longs") { + val data = 1L until 100L + val slices = ParallelCollectionRDD.slice(data, 3) + assert(slices.size === 3) + assert(slices.map(_.size).reduceLeft(_+_) === 99) + assert(slices.forall(_.isInstanceOf[NumericRange[_]])) + } + + test("inclusive ranges of longs") { + val data = 1L to 100L + val slices = ParallelCollectionRDD.slice(data, 3) + assert(slices.size === 3) + assert(slices.map(_.size).reduceLeft(_+_) === 100) + assert(slices.forall(_.isInstanceOf[NumericRange[_]])) + } + + test("exclusive ranges of doubles") { + val data = 1.0 until 100.0 by 1.0 + val slices = ParallelCollectionRDD.slice(data, 3) + assert(slices.size === 3) + assert(slices.map(_.size).reduceLeft(_+_) === 99) + assert(slices.forall(_.isInstanceOf[NumericRange[_]])) + } + + test("inclusive ranges of doubles") { + val data = 1.0 to 100.0 by 1.0 + val slices = ParallelCollectionRDD.slice(data, 3) + assert(slices.size === 3) + assert(slices.map(_.size).reduceLeft(_+_) === 100) + assert(slices.forall(_.isInstanceOf[NumericRange[_]])) + } +} diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala new file mode 100644 index 0000000000..94df282b28 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala @@ -0,0 +1,421 @@ +/* + * 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.scheduler + +import scala.collection.mutable.{Map, HashMap} + +import org.scalatest.FunSuite +import org.scalatest.BeforeAndAfter + +import org.apache.spark.LocalSparkContext +import org.apache.spark.MapOutputTracker +import org.apache.spark.RDD +import org.apache.spark.SparkContext +import org.apache.spark.Partition +import org.apache.spark.TaskContext +import org.apache.spark.{Dependency, ShuffleDependency, OneToOneDependency} +import org.apache.spark.{FetchFailed, Success, TaskEndReason} +import org.apache.spark.storage.{BlockManagerId, BlockManagerMaster} + +import org.apache.spark.scheduler.cluster.Pool +import org.apache.spark.scheduler.cluster.SchedulingMode +import org.apache.spark.scheduler.cluster.SchedulingMode.SchedulingMode + +/** + * Tests for DAGScheduler. These tests directly call the event processing functions in DAGScheduler + * rather than spawning an event loop thread as happens in the real code. They use EasyMock + * to mock out two classes that DAGScheduler interacts with: TaskScheduler (to which TaskSets are + * submitted) and BlockManagerMaster (from which cache locations are retrieved and to which dead + * host notifications are sent). In addition, tests may check for side effects on a non-mocked + * MapOutputTracker instance. + * + * Tests primarily consist of running DAGScheduler#processEvent and + * DAGScheduler#submitWaitingStages (via test utility functions like runEvent or respondToTaskSet) + * and capturing the resulting TaskSets from the mock TaskScheduler. + */ +class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { + + /** Set of TaskSets the DAGScheduler has requested executed. */ + val taskSets = scala.collection.mutable.Buffer[TaskSet]() + val taskScheduler = new TaskScheduler() { + override def rootPool: Pool = null + override def schedulingMode: SchedulingMode = SchedulingMode.NONE + override def start() = {} + override def stop() = {} + override def submitTasks(taskSet: TaskSet) = { + // normally done by TaskSetManager + taskSet.tasks.foreach(_.epoch = mapOutputTracker.getEpoch) + taskSets += taskSet + } + override def setListener(listener: TaskSchedulerListener) = {} + override def defaultParallelism() = 2 + } + + var mapOutputTracker: MapOutputTracker = null + var scheduler: DAGScheduler = null + + /** + * Set of cache locations to return from our mock BlockManagerMaster. + * Keys are (rdd ID, partition ID). Anything not present will return an empty + * list of cache locations silently. + */ + val cacheLocations = new HashMap[(Int, Int), Seq[BlockManagerId]] + // stub out BlockManagerMaster.getLocations to use our cacheLocations + val blockManagerMaster = new BlockManagerMaster(null) { + override def getLocations(blockIds: Array[String]): Seq[Seq[BlockManagerId]] = { + blockIds.map { name => + val pieces = name.split("_") + if (pieces(0) == "rdd") { + val key = pieces(1).toInt -> pieces(2).toInt + cacheLocations.getOrElse(key, Seq()) + } else { + Seq() + } + }.toSeq + } + override def removeExecutor(execId: String) { + // don't need to propagate to the driver, which we don't have + } + } + + /** The list of results that DAGScheduler has collected. */ + val results = new HashMap[Int, Any]() + var failure: Exception = _ + val listener = new JobListener() { + override def taskSucceeded(index: Int, result: Any) = results.put(index, result) + override def jobFailed(exception: Exception) = { failure = exception } + } + + before { + sc = new SparkContext("local", "DAGSchedulerSuite") + taskSets.clear() + cacheLocations.clear() + results.clear() + mapOutputTracker = new MapOutputTracker() + scheduler = new DAGScheduler(taskScheduler, mapOutputTracker, blockManagerMaster, null) { + override def runLocally(job: ActiveJob) { + // don't bother with the thread while unit testing + runLocallyWithinThread(job) + } + } + } + + after { + scheduler.stop() + } + + /** + * Type of RDD we use for testing. Note that we should never call the real RDD compute methods. + * This is a pair RDD type so it can always be used in ShuffleDependencies. + */ + type MyRDD = RDD[(Int, Int)] + + /** + * Create an RDD for passing to DAGScheduler. These RDDs will use the dependencies and + * preferredLocations (if any) that are passed to them. They are deliberately not executable + * so we can test that DAGScheduler does not try to execute RDDs locally. + */ + private def makeRdd( + numPartitions: Int, + dependencies: List[Dependency[_]], + locations: Seq[Seq[String]] = Nil + ): MyRDD = { + val maxPartition = numPartitions - 1 + return new MyRDD(sc, dependencies) { + override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] = + throw new RuntimeException("should not be reached") + override def getPartitions = (0 to maxPartition).map(i => new Partition { + override def index = i + }).toArray + override def getPreferredLocations(split: Partition): Seq[String] = + if (locations.isDefinedAt(split.index)) + locations(split.index) + else + Nil + override def toString: String = "DAGSchedulerSuiteRDD " + id + } + } + + /** + * Process the supplied event as if it were the top of the DAGScheduler event queue, expecting + * the scheduler not to exit. + * + * After processing the event, submit waiting stages as is done on most iterations of the + * DAGScheduler event loop. + */ + private def runEvent(event: DAGSchedulerEvent) { + assert(!scheduler.processEvent(event)) + scheduler.submitWaitingStages() + } + + /** + * When we submit dummy Jobs, this is the compute function we supply. Except in a local test + * below, we do not expect this function to ever be executed; instead, we will return results + * directly through CompletionEvents. + */ + private val jobComputeFunc = (context: TaskContext, it: Iterator[(_)]) => + it.next.asInstanceOf[Tuple2[_, _]]._1 + + /** Send the given CompletionEvent messages for the tasks in the TaskSet. */ + private def complete(taskSet: TaskSet, results: Seq[(TaskEndReason, Any)]) { + assert(taskSet.tasks.size >= results.size) + for ((result, i) <- results.zipWithIndex) { + if (i < taskSet.tasks.size) { + runEvent(CompletionEvent(taskSet.tasks(i), result._1, result._2, Map[Long, Any](), null, null)) + } + } + } + + /** Sends the rdd to the scheduler for scheduling. */ + private def submit( + rdd: RDD[_], + partitions: Array[Int], + func: (TaskContext, Iterator[_]) => _ = jobComputeFunc, + allowLocal: Boolean = false, + listener: JobListener = listener) { + runEvent(JobSubmitted(rdd, func, partitions, allowLocal, null, listener)) + } + + /** Sends TaskSetFailed to the scheduler. */ + private def failed(taskSet: TaskSet, message: String) { + runEvent(TaskSetFailed(taskSet, message)) + } + + test("zero split job") { + val rdd = makeRdd(0, Nil) + var numResults = 0 + val fakeListener = new JobListener() { + override def taskSucceeded(partition: Int, value: Any) = numResults += 1 + override def jobFailed(exception: Exception) = throw exception + } + submit(rdd, Array(), listener = fakeListener) + assert(numResults === 0) + } + + test("run trivial job") { + val rdd = makeRdd(1, Nil) + submit(rdd, Array(0)) + complete(taskSets(0), List((Success, 42))) + assert(results === Map(0 -> 42)) + } + + test("local job") { + val rdd = new MyRDD(sc, Nil) { + override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] = + Array(42 -> 0).iterator + override def getPartitions = Array( new Partition { override def index = 0 } ) + override def getPreferredLocations(split: Partition) = Nil + override def toString = "DAGSchedulerSuite Local RDD" + } + runEvent(JobSubmitted(rdd, jobComputeFunc, Array(0), true, null, listener)) + assert(results === Map(0 -> 42)) + } + + test("run trivial job w/ dependency") { + val baseRdd = makeRdd(1, Nil) + val finalRdd = makeRdd(1, List(new OneToOneDependency(baseRdd))) + submit(finalRdd, Array(0)) + complete(taskSets(0), Seq((Success, 42))) + assert(results === Map(0 -> 42)) + } + + test("cache location preferences w/ dependency") { + val baseRdd = makeRdd(1, Nil) + val finalRdd = makeRdd(1, List(new OneToOneDependency(baseRdd))) + cacheLocations(baseRdd.id -> 0) = + Seq(makeBlockManagerId("hostA"), makeBlockManagerId("hostB")) + submit(finalRdd, Array(0)) + val taskSet = taskSets(0) + assertLocations(taskSet, Seq(Seq("hostA", "hostB"))) + complete(taskSet, Seq((Success, 42))) + assert(results === Map(0 -> 42)) + } + + test("trivial job failure") { + submit(makeRdd(1, Nil), Array(0)) + failed(taskSets(0), "some failure") + assert(failure.getMessage === "Job failed: some failure") + } + + test("run trivial shuffle") { + val shuffleMapRdd = makeRdd(2, Nil) + val shuffleDep = new ShuffleDependency(shuffleMapRdd, null) + val shuffleId = shuffleDep.shuffleId + val reduceRdd = makeRdd(1, List(shuffleDep)) + submit(reduceRdd, Array(0)) + complete(taskSets(0), Seq( + (Success, makeMapStatus("hostA", 1)), + (Success, makeMapStatus("hostB", 1)))) + assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1) === + Array(makeBlockManagerId("hostA"), makeBlockManagerId("hostB"))) + complete(taskSets(1), Seq((Success, 42))) + assert(results === Map(0 -> 42)) + } + + test("run trivial shuffle with fetch failure") { + val shuffleMapRdd = makeRdd(2, Nil) + val shuffleDep = new ShuffleDependency(shuffleMapRdd, null) + val shuffleId = shuffleDep.shuffleId + val reduceRdd = makeRdd(2, List(shuffleDep)) + submit(reduceRdd, Array(0, 1)) + complete(taskSets(0), Seq( + (Success, makeMapStatus("hostA", 1)), + (Success, makeMapStatus("hostB", 1)))) + // the 2nd ResultTask failed + complete(taskSets(1), Seq( + (Success, 42), + (FetchFailed(makeBlockManagerId("hostA"), shuffleId, 0, 0), null))) + // this will get called + // blockManagerMaster.removeExecutor("exec-hostA") + // ask the scheduler to try it again + scheduler.resubmitFailedStages() + // have the 2nd attempt pass + complete(taskSets(2), Seq((Success, makeMapStatus("hostA", 1)))) + // we can see both result blocks now + assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1.host) === Array("hostA", "hostB")) + complete(taskSets(3), Seq((Success, 43))) + assert(results === Map(0 -> 42, 1 -> 43)) + } + + test("ignore late map task completions") { + val shuffleMapRdd = makeRdd(2, Nil) + val shuffleDep = new ShuffleDependency(shuffleMapRdd, null) + val shuffleId = shuffleDep.shuffleId + val reduceRdd = makeRdd(2, List(shuffleDep)) + submit(reduceRdd, Array(0, 1)) + // pretend we were told hostA went away + val oldEpoch = mapOutputTracker.getEpoch + runEvent(ExecutorLost("exec-hostA")) + val newEpoch = mapOutputTracker.getEpoch + assert(newEpoch > oldEpoch) + val noAccum = Map[Long, Any]() + val taskSet = taskSets(0) + // should be ignored for being too old + runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostA", 1), noAccum, null, null)) + // should work because it's a non-failed host + runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostB", 1), noAccum, null, null)) + // should be ignored for being too old + runEvent(CompletionEvent(taskSet.tasks(0), Success, makeMapStatus("hostA", 1), noAccum, null, null)) + // should work because it's a new epoch + taskSet.tasks(1).epoch = newEpoch + runEvent(CompletionEvent(taskSet.tasks(1), Success, makeMapStatus("hostA", 1), noAccum, null, null)) + assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1) === + Array(makeBlockManagerId("hostB"), makeBlockManagerId("hostA"))) + complete(taskSets(1), Seq((Success, 42), (Success, 43))) + assert(results === Map(0 -> 42, 1 -> 43)) + } + + test("run trivial shuffle with out-of-band failure and retry") { + val shuffleMapRdd = makeRdd(2, Nil) + val shuffleDep = new ShuffleDependency(shuffleMapRdd, null) + val shuffleId = shuffleDep.shuffleId + val reduceRdd = makeRdd(1, List(shuffleDep)) + submit(reduceRdd, Array(0)) + // blockManagerMaster.removeExecutor("exec-hostA") + // pretend we were told hostA went away + runEvent(ExecutorLost("exec-hostA")) + // DAGScheduler will immediately resubmit the stage after it appears to have no pending tasks + // rather than marking it is as failed and waiting. + complete(taskSets(0), Seq( + (Success, makeMapStatus("hostA", 1)), + (Success, makeMapStatus("hostB", 1)))) + // have hostC complete the resubmitted task + complete(taskSets(1), Seq((Success, makeMapStatus("hostC", 1)))) + assert(mapOutputTracker.getServerStatuses(shuffleId, 0).map(_._1) === + Array(makeBlockManagerId("hostC"), makeBlockManagerId("hostB"))) + complete(taskSets(2), Seq((Success, 42))) + assert(results === Map(0 -> 42)) + } + + test("recursive shuffle failures") { + val shuffleOneRdd = makeRdd(2, Nil) + val shuffleDepOne = new ShuffleDependency(shuffleOneRdd, null) + val shuffleTwoRdd = makeRdd(2, List(shuffleDepOne)) + val shuffleDepTwo = new ShuffleDependency(shuffleTwoRdd, null) + val finalRdd = makeRdd(1, List(shuffleDepTwo)) + submit(finalRdd, Array(0)) + // have the first stage complete normally + complete(taskSets(0), Seq( + (Success, makeMapStatus("hostA", 2)), + (Success, makeMapStatus("hostB", 2)))) + // have the second stage complete normally + complete(taskSets(1), Seq( + (Success, makeMapStatus("hostA", 1)), + (Success, makeMapStatus("hostC", 1)))) + // fail the third stage because hostA went down + complete(taskSets(2), Seq( + (FetchFailed(makeBlockManagerId("hostA"), shuffleDepTwo.shuffleId, 0, 0), null))) + // TODO assert this: + // blockManagerMaster.removeExecutor("exec-hostA") + // have DAGScheduler try again + scheduler.resubmitFailedStages() + complete(taskSets(3), Seq((Success, makeMapStatus("hostA", 2)))) + complete(taskSets(4), Seq((Success, makeMapStatus("hostA", 1)))) + complete(taskSets(5), Seq((Success, 42))) + assert(results === Map(0 -> 42)) + } + + test("cached post-shuffle") { + val shuffleOneRdd = makeRdd(2, Nil) + val shuffleDepOne = new ShuffleDependency(shuffleOneRdd, null) + val shuffleTwoRdd = makeRdd(2, List(shuffleDepOne)) + val shuffleDepTwo = new ShuffleDependency(shuffleTwoRdd, null) + val finalRdd = makeRdd(1, List(shuffleDepTwo)) + submit(finalRdd, Array(0)) + cacheLocations(shuffleTwoRdd.id -> 0) = Seq(makeBlockManagerId("hostD")) + cacheLocations(shuffleTwoRdd.id -> 1) = Seq(makeBlockManagerId("hostC")) + // complete stage 2 + complete(taskSets(0), Seq( + (Success, makeMapStatus("hostA", 2)), + (Success, makeMapStatus("hostB", 2)))) + // complete stage 1 + complete(taskSets(1), Seq( + (Success, makeMapStatus("hostA", 1)), + (Success, makeMapStatus("hostB", 1)))) + // pretend stage 0 failed because hostA went down + complete(taskSets(2), Seq( + (FetchFailed(makeBlockManagerId("hostA"), shuffleDepTwo.shuffleId, 0, 0), null))) + // TODO assert this: + // blockManagerMaster.removeExecutor("exec-hostA") + // DAGScheduler should notice the cached copy of the second shuffle and try to get it rerun. + scheduler.resubmitFailedStages() + assertLocations(taskSets(3), Seq(Seq("hostD"))) + // allow hostD to recover + complete(taskSets(3), Seq((Success, makeMapStatus("hostD", 1)))) + complete(taskSets(4), Seq((Success, 42))) + assert(results === Map(0 -> 42)) + } + + /** + * Assert that the supplied TaskSet has exactly the given hosts as its preferred locations. + * Note that this checks only the host and not the executor ID. + */ + private def assertLocations(taskSet: TaskSet, hosts: Seq[Seq[String]]) { + assert(hosts.size === taskSet.tasks.size) + for ((taskLocs, expectedLocs) <- taskSet.tasks.map(_.preferredLocations).zip(hosts)) { + assert(taskLocs.map(_.host) === expectedLocs) + } + } + + private def makeMapStatus(host: String, reduces: Int): MapStatus = + new MapStatus(makeBlockManagerId(host), Array.fill[Byte](reduces)(2)) + + private def makeBlockManagerId(host: String): BlockManagerId = + BlockManagerId("exec-" + host, host, 12345, 0) + +} diff --git a/core/src/test/scala/org/apache/spark/scheduler/JobLoggerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/JobLoggerSuite.scala new file mode 100644 index 0000000000..f5b3e97222 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/scheduler/JobLoggerSuite.scala @@ -0,0 +1,121 @@ +/* + * 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.scheduler + +import java.util.Properties +import java.util.concurrent.LinkedBlockingQueue +import org.scalatest.FunSuite +import org.scalatest.matchers.ShouldMatchers +import scala.collection.mutable +import org.apache.spark._ +import org.apache.spark.SparkContext._ + + +class JobLoggerSuite extends FunSuite with LocalSparkContext with ShouldMatchers { + + test("inner method") { + sc = new SparkContext("local", "joblogger") + val joblogger = new JobLogger { + def createLogWriterTest(jobID: Int) = createLogWriter(jobID) + def closeLogWriterTest(jobID: Int) = closeLogWriter(jobID) + def getRddNameTest(rdd: RDD[_]) = getRddName(rdd) + def buildJobDepTest(jobID: Int, stage: Stage) = buildJobDep(jobID, stage) + } + type MyRDD = RDD[(Int, Int)] + def makeRdd( + numPartitions: Int, + dependencies: List[Dependency[_]] + ): MyRDD = { + val maxPartition = numPartitions - 1 + return new MyRDD(sc, dependencies) { + override def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] = + throw new RuntimeException("should not be reached") + override def getPartitions = (0 to maxPartition).map(i => new Partition { + override def index = i + }).toArray + } + } + val jobID = 5 + val parentRdd = makeRdd(4, Nil) + val shuffleDep = new ShuffleDependency(parentRdd, null) + val rootRdd = makeRdd(4, List(shuffleDep)) + val shuffleMapStage = new Stage(1, parentRdd, Some(shuffleDep), Nil, jobID, None) + val rootStage = new Stage(0, rootRdd, None, List(shuffleMapStage), jobID, None) + + joblogger.onStageSubmitted(SparkListenerStageSubmitted(rootStage, 4, null)) + joblogger.getRddNameTest(parentRdd) should be (parentRdd.getClass.getName) + parentRdd.setName("MyRDD") + joblogger.getRddNameTest(parentRdd) should be ("MyRDD") + joblogger.createLogWriterTest(jobID) + joblogger.getJobIDtoPrintWriter.size should be (1) + joblogger.buildJobDepTest(jobID, rootStage) + joblogger.getJobIDToStages.get(jobID).get.size should be (2) + joblogger.getStageIDToJobID.get(0) should be (Some(jobID)) + joblogger.getStageIDToJobID.get(1) should be (Some(jobID)) + joblogger.closeLogWriterTest(jobID) + joblogger.getStageIDToJobID.size should be (0) + joblogger.getJobIDToStages.size should be (0) + joblogger.getJobIDtoPrintWriter.size should be (0) + } + + test("inner variables") { + sc = new SparkContext("local[4]", "joblogger") + val joblogger = new JobLogger { + override protected def closeLogWriter(jobID: Int) = + getJobIDtoPrintWriter.get(jobID).foreach { fileWriter => + fileWriter.close() + } + } + sc.addSparkListener(joblogger) + val rdd = sc.parallelize(1 to 1e2.toInt, 4).map{ i => (i % 12, 2 * i) } + rdd.reduceByKey(_+_).collect() + + joblogger.getLogDir should be ("/tmp/spark") + joblogger.getJobIDtoPrintWriter.size should be (1) + joblogger.getStageIDToJobID.size should be (2) + joblogger.getStageIDToJobID.get(0) should be (Some(0)) + joblogger.getStageIDToJobID.get(1) should be (Some(0)) + joblogger.getJobIDToStages.size should be (1) + } + + + test("interface functions") { + sc = new SparkContext("local[4]", "joblogger") + val joblogger = new JobLogger { + var onTaskEndCount = 0 + var onJobEndCount = 0 + var onJobStartCount = 0 + var onStageCompletedCount = 0 + var onStageSubmittedCount = 0 + override def onTaskEnd(taskEnd: SparkListenerTaskEnd) = onTaskEndCount += 1 + override def onJobEnd(jobEnd: SparkListenerJobEnd) = onJobEndCount += 1 + override def onJobStart(jobStart: SparkListenerJobStart) = onJobStartCount += 1 + override def onStageCompleted(stageCompleted: StageCompleted) = onStageCompletedCount += 1 + override def onStageSubmitted(stageSubmitted: SparkListenerStageSubmitted) = onStageSubmittedCount += 1 + } + sc.addSparkListener(joblogger) + val rdd = sc.parallelize(1 to 1e2.toInt, 4).map{ i => (i % 12, 2 * i) } + rdd.reduceByKey(_+_).collect() + + joblogger.onJobStartCount should be (1) + joblogger.onJobEndCount should be (1) + joblogger.onTaskEndCount should be (8) + joblogger.onStageSubmittedCount should be (2) + joblogger.onStageCompletedCount should be (2) + } +} diff --git a/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala new file mode 100644 index 0000000000..aac7c207cb --- /dev/null +++ b/core/src/test/scala/org/apache/spark/scheduler/SparkListenerSuite.scala @@ -0,0 +1,102 @@ +/* + * 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.scheduler + +import org.scalatest.FunSuite +import org.apache.spark.{SparkContext, LocalSparkContext} +import scala.collection.mutable +import org.scalatest.matchers.ShouldMatchers +import org.apache.spark.SparkContext._ + +/** + * + */ + +class SparkListenerSuite extends FunSuite with LocalSparkContext with ShouldMatchers { + + test("local metrics") { + sc = new SparkContext("local[4]", "test") + val listener = new SaveStageInfo + sc.addSparkListener(listener) + sc.addSparkListener(new StatsReportListener) + //just to make sure some of the tasks take a noticeable amount of time + val w = {i:Int => + if (i == 0) + Thread.sleep(100) + i + } + + val d = sc.parallelize(1 to 1e4.toInt, 64).map{i => w(i)} + d.count + listener.stageInfos.size should be (1) + + val d2 = d.map{i => w(i) -> i * 2}.setName("shuffle input 1") + + val d3 = d.map{i => w(i) -> (0 to (i % 5))}.setName("shuffle input 2") + + val d4 = d2.cogroup(d3, 64).map{case(k,(v1,v2)) => w(k) -> (v1.size, v2.size)} + d4.setName("A Cogroup") + + d4.collectAsMap + + listener.stageInfos.size should be (4) + listener.stageInfos.foreach {stageInfo => + //small test, so some tasks might take less than 1 millisecond, but average should be greater than 1 ms + checkNonZeroAvg(stageInfo.taskInfos.map{_._1.duration}, stageInfo + " duration") + checkNonZeroAvg(stageInfo.taskInfos.map{_._2.executorRunTime.toLong}, stageInfo + " executorRunTime") + checkNonZeroAvg(stageInfo.taskInfos.map{_._2.executorDeserializeTime.toLong}, stageInfo + " executorDeserializeTime") + if (stageInfo.stage.rdd.name == d4.name) { + checkNonZeroAvg(stageInfo.taskInfos.map{_._2.shuffleReadMetrics.get.fetchWaitTime}, stageInfo + " fetchWaitTime") + } + + stageInfo.taskInfos.foreach{case (taskInfo, taskMetrics) => + taskMetrics.resultSize should be > (0l) + if (isStage(stageInfo, Set(d2.name, d3.name), Set(d4.name))) { + taskMetrics.shuffleWriteMetrics should be ('defined) + taskMetrics.shuffleWriteMetrics.get.shuffleBytesWritten should be > (0l) + } + if (stageInfo.stage.rdd.name == d4.name) { + taskMetrics.shuffleReadMetrics should be ('defined) + val sm = taskMetrics.shuffleReadMetrics.get + sm.totalBlocksFetched should be > (0) + sm.localBlocksFetched should be > (0) + sm.remoteBlocksFetched should be (0) + sm.remoteBytesRead should be (0l) + sm.remoteFetchTime should be (0l) + } + } + } + } + + def checkNonZeroAvg(m: Traversable[Long], msg: String) { + assert(m.sum / m.size.toDouble > 0.0, msg) + } + + def isStage(stageInfo: StageInfo, rddNames: Set[String], excludedNames: Set[String]) = { + val names = Set(stageInfo.stage.rdd.name) ++ stageInfo.stage.rdd.dependencies.map{_.rdd.name} + !names.intersect(rddNames).isEmpty && names.intersect(excludedNames).isEmpty + } + + class SaveStageInfo extends SparkListener { + val stageInfos = mutable.Buffer[StageInfo]() + override def onStageCompleted(stage: StageCompleted) { + stageInfos += stage.stageInfo + } + } + +} diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala new file mode 100644 index 0000000000..0347cc02d7 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskContextSuite.scala @@ -0,0 +1,49 @@ +/* + * 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.scheduler + +import org.scalatest.FunSuite +import org.scalatest.BeforeAndAfter +import org.apache.spark.TaskContext +import org.apache.spark.RDD +import org.apache.spark.SparkContext +import org.apache.spark.Partition +import org.apache.spark.LocalSparkContext + +class TaskContextSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { + + test("Calls executeOnCompleteCallbacks after failure") { + var completed = false + sc = new SparkContext("local", "test") + val rdd = new RDD[String](sc, List()) { + override def getPartitions = Array[Partition](StubPartition(0)) + override def compute(split: Partition, context: TaskContext) = { + context.addOnCompleteCallback(() => completed = true) + sys.error("failed") + } + } + val func = (c: TaskContext, i: Iterator[String]) => i.next + val task = new ResultTask[String, String](0, rdd, func, 0, Seq(), 0) + intercept[RuntimeException] { + task.run(0) + } + assert(completed === true) + } + + case class StubPartition(val index: Int) extends Partition +} diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/ClusterSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/ClusterSchedulerSuite.scala new file mode 100644 index 0000000000..92ad9f09b2 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/ClusterSchedulerSuite.scala @@ -0,0 +1,266 @@ +/* + * 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.scheduler.cluster + +import org.scalatest.FunSuite +import org.scalatest.BeforeAndAfter + +import org.apache.spark._ +import org.apache.spark.scheduler._ +import org.apache.spark.scheduler.cluster._ +import scala.collection.mutable.ArrayBuffer + +import java.util.Properties + +class FakeTaskSetManager( + initPriority: Int, + initStageId: Int, + initNumTasks: Int, + clusterScheduler: ClusterScheduler, + taskSet: TaskSet) + extends ClusterTaskSetManager(clusterScheduler, taskSet) { + + parent = null + weight = 1 + minShare = 2 + runningTasks = 0 + priority = initPriority + stageId = initStageId + name = "TaskSet_"+stageId + override val numTasks = initNumTasks + tasksFinished = 0 + + override def increaseRunningTasks(taskNum: Int) { + runningTasks += taskNum + if (parent != null) { + parent.increaseRunningTasks(taskNum) + } + } + + override def decreaseRunningTasks(taskNum: Int) { + runningTasks -= taskNum + if (parent != null) { + parent.decreaseRunningTasks(taskNum) + } + } + + override def addSchedulable(schedulable: Schedulable) { + } + + override def removeSchedulable(schedulable: Schedulable) { + } + + override def getSchedulableByName(name: String): Schedulable = { + return null + } + + override def executorLost(executorId: String, host: String): Unit = { + } + + override def resourceOffer( + execId: String, + host: String, + availableCpus: Int, + maxLocality: TaskLocality.TaskLocality) + : Option[TaskDescription] = + { + if (tasksFinished + runningTasks < numTasks) { + increaseRunningTasks(1) + return Some(new TaskDescription(0, execId, "task 0:0", 0, null)) + } + return None + } + + override def checkSpeculatableTasks(): Boolean = { + return true + } + + def taskFinished() { + decreaseRunningTasks(1) + tasksFinished +=1 + if (tasksFinished == numTasks) { + parent.removeSchedulable(this) + } + } + + def abort() { + decreaseRunningTasks(runningTasks) + parent.removeSchedulable(this) + } +} + +class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging { + + def createDummyTaskSetManager(priority: Int, stage: Int, numTasks: Int, cs: ClusterScheduler, taskSet: TaskSet): FakeTaskSetManager = { + new FakeTaskSetManager(priority, stage, numTasks, cs , taskSet) + } + + def resourceOffer(rootPool: Pool): Int = { + val taskSetQueue = rootPool.getSortedTaskSetQueue() + /* Just for Test*/ + for (manager <- taskSetQueue) { + logInfo("parentName:%s, parent running tasks:%d, name:%s,runningTasks:%d".format(manager.parent.name, manager.parent.runningTasks, manager.name, manager.runningTasks)) + } + for (taskSet <- taskSetQueue) { + taskSet.resourceOffer("execId_1", "hostname_1", 1, TaskLocality.ANY) match { + case Some(task) => + return taskSet.stageId + case None => {} + } + } + -1 + } + + def checkTaskSetId(rootPool: Pool, expectedTaskSetId: Int) { + assert(resourceOffer(rootPool) === expectedTaskSetId) + } + + test("FIFO Scheduler Test") { + sc = new SparkContext("local", "ClusterSchedulerSuite") + val clusterScheduler = new ClusterScheduler(sc) + var tasks = ArrayBuffer[Task[_]]() + val task = new FakeTask(0) + tasks += task + val taskSet = new TaskSet(tasks.toArray,0,0,0,null) + + val rootPool = new Pool("", SchedulingMode.FIFO, 0, 0) + val schedulableBuilder = new FIFOSchedulableBuilder(rootPool) + schedulableBuilder.buildPools() + + val taskSetManager0 = createDummyTaskSetManager(0, 0, 2, clusterScheduler, taskSet) + val taskSetManager1 = createDummyTaskSetManager(0, 1, 2, clusterScheduler, taskSet) + val taskSetManager2 = createDummyTaskSetManager(0, 2, 2, clusterScheduler, taskSet) + schedulableBuilder.addTaskSetManager(taskSetManager0, null) + schedulableBuilder.addTaskSetManager(taskSetManager1, null) + schedulableBuilder.addTaskSetManager(taskSetManager2, null) + + checkTaskSetId(rootPool, 0) + resourceOffer(rootPool) + checkTaskSetId(rootPool, 1) + resourceOffer(rootPool) + taskSetManager1.abort() + checkTaskSetId(rootPool, 2) + } + + test("Fair Scheduler Test") { + sc = new SparkContext("local", "ClusterSchedulerSuite") + val clusterScheduler = new ClusterScheduler(sc) + var tasks = ArrayBuffer[Task[_]]() + val task = new FakeTask(0) + tasks += task + val taskSet = new TaskSet(tasks.toArray,0,0,0,null) + + val xmlPath = getClass.getClassLoader.getResource("fairscheduler.xml").getFile() + System.setProperty("spark.fairscheduler.allocation.file", xmlPath) + val rootPool = new Pool("", SchedulingMode.FAIR, 0, 0) + val schedulableBuilder = new FairSchedulableBuilder(rootPool) + schedulableBuilder.buildPools() + + assert(rootPool.getSchedulableByName("default") != null) + assert(rootPool.getSchedulableByName("1") != null) + assert(rootPool.getSchedulableByName("2") != null) + assert(rootPool.getSchedulableByName("3") != null) + assert(rootPool.getSchedulableByName("1").minShare === 2) + assert(rootPool.getSchedulableByName("1").weight === 1) + assert(rootPool.getSchedulableByName("2").minShare === 3) + assert(rootPool.getSchedulableByName("2").weight === 1) + assert(rootPool.getSchedulableByName("3").minShare === 2) + assert(rootPool.getSchedulableByName("3").weight === 1) + + val properties1 = new Properties() + properties1.setProperty("spark.scheduler.cluster.fair.pool","1") + val properties2 = new Properties() + properties2.setProperty("spark.scheduler.cluster.fair.pool","2") + + val taskSetManager10 = createDummyTaskSetManager(1, 0, 1, clusterScheduler, taskSet) + val taskSetManager11 = createDummyTaskSetManager(1, 1, 1, clusterScheduler, taskSet) + val taskSetManager12 = createDummyTaskSetManager(1, 2, 2, clusterScheduler, taskSet) + schedulableBuilder.addTaskSetManager(taskSetManager10, properties1) + schedulableBuilder.addTaskSetManager(taskSetManager11, properties1) + schedulableBuilder.addTaskSetManager(taskSetManager12, properties1) + + val taskSetManager23 = createDummyTaskSetManager(2, 3, 2, clusterScheduler, taskSet) + val taskSetManager24 = createDummyTaskSetManager(2, 4, 2, clusterScheduler, taskSet) + schedulableBuilder.addTaskSetManager(taskSetManager23, properties2) + schedulableBuilder.addTaskSetManager(taskSetManager24, properties2) + + checkTaskSetId(rootPool, 0) + checkTaskSetId(rootPool, 3) + checkTaskSetId(rootPool, 3) + checkTaskSetId(rootPool, 1) + checkTaskSetId(rootPool, 4) + checkTaskSetId(rootPool, 2) + checkTaskSetId(rootPool, 2) + checkTaskSetId(rootPool, 4) + + taskSetManager12.taskFinished() + assert(rootPool.getSchedulableByName("1").runningTasks === 3) + taskSetManager24.abort() + assert(rootPool.getSchedulableByName("2").runningTasks === 2) + } + + test("Nested Pool Test") { + sc = new SparkContext("local", "ClusterSchedulerSuite") + val clusterScheduler = new ClusterScheduler(sc) + var tasks = ArrayBuffer[Task[_]]() + val task = new FakeTask(0) + tasks += task + val taskSet = new TaskSet(tasks.toArray,0,0,0,null) + + val rootPool = new Pool("", SchedulingMode.FAIR, 0, 0) + val pool0 = new Pool("0", SchedulingMode.FAIR, 3, 1) + val pool1 = new Pool("1", SchedulingMode.FAIR, 4, 1) + rootPool.addSchedulable(pool0) + rootPool.addSchedulable(pool1) + + val pool00 = new Pool("00", SchedulingMode.FAIR, 2, 2) + val pool01 = new Pool("01", SchedulingMode.FAIR, 1, 1) + pool0.addSchedulable(pool00) + pool0.addSchedulable(pool01) + + val pool10 = new Pool("10", SchedulingMode.FAIR, 2, 2) + val pool11 = new Pool("11", SchedulingMode.FAIR, 2, 1) + pool1.addSchedulable(pool10) + pool1.addSchedulable(pool11) + + val taskSetManager000 = createDummyTaskSetManager(0, 0, 5, clusterScheduler, taskSet) + val taskSetManager001 = createDummyTaskSetManager(0, 1, 5, clusterScheduler, taskSet) + pool00.addSchedulable(taskSetManager000) + pool00.addSchedulable(taskSetManager001) + + val taskSetManager010 = createDummyTaskSetManager(1, 2, 5, clusterScheduler, taskSet) + val taskSetManager011 = createDummyTaskSetManager(1, 3, 5, clusterScheduler, taskSet) + pool01.addSchedulable(taskSetManager010) + pool01.addSchedulable(taskSetManager011) + + val taskSetManager100 = createDummyTaskSetManager(2, 4, 5, clusterScheduler, taskSet) + val taskSetManager101 = createDummyTaskSetManager(2, 5, 5, clusterScheduler, taskSet) + pool10.addSchedulable(taskSetManager100) + pool10.addSchedulable(taskSetManager101) + + val taskSetManager110 = createDummyTaskSetManager(3, 6, 5, clusterScheduler, taskSet) + val taskSetManager111 = createDummyTaskSetManager(3, 7, 5, clusterScheduler, taskSet) + pool11.addSchedulable(taskSetManager110) + pool11.addSchedulable(taskSetManager111) + + checkTaskSetId(rootPool, 0) + checkTaskSetId(rootPool, 4) + checkTaskSetId(rootPool, 6) + checkTaskSetId(rootPool, 2) + } +} diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManagerSuite.scala new file mode 100644 index 0000000000..a4f63baf3d --- /dev/null +++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/ClusterTaskSetManagerSuite.scala @@ -0,0 +1,273 @@ +/* + * 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.scheduler.cluster + +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable + +import org.scalatest.FunSuite + +import org.apache.spark._ +import org.apache.spark.scheduler._ +import org.apache.spark.executor.TaskMetrics +import java.nio.ByteBuffer +import org.apache.spark.util.FakeClock + +/** + * A mock ClusterScheduler implementation that just remembers information about tasks started and + * feedback received from the TaskSetManagers. Note that it's important to initialize this with + * a list of "live" executors and their hostnames for isExecutorAlive and hasExecutorsAliveOnHost + * to work, and these are required for locality in ClusterTaskSetManager. + */ +class FakeClusterScheduler(sc: SparkContext, liveExecutors: (String, String)* /* execId, host */) + extends ClusterScheduler(sc) +{ + val startedTasks = new ArrayBuffer[Long] + val endedTasks = new mutable.HashMap[Long, TaskEndReason] + val finishedManagers = new ArrayBuffer[TaskSetManager] + + val executors = new mutable.HashMap[String, String] ++ liveExecutors + + listener = new TaskSchedulerListener { + def taskStarted(task: Task[_], taskInfo: TaskInfo) { + startedTasks += taskInfo.index + } + + def taskEnded( + task: Task[_], + reason: TaskEndReason, + result: Any, + accumUpdates: mutable.Map[Long, Any], + taskInfo: TaskInfo, + taskMetrics: TaskMetrics) + { + endedTasks(taskInfo.index) = reason + } + + def executorGained(execId: String, host: String) {} + + def executorLost(execId: String) {} + + def taskSetFailed(taskSet: TaskSet, reason: String) {} + } + + def removeExecutor(execId: String): Unit = executors -= execId + + override def taskSetFinished(manager: TaskSetManager): Unit = finishedManagers += manager + + override def isExecutorAlive(execId: String): Boolean = executors.contains(execId) + + override def hasExecutorsAliveOnHost(host: String): Boolean = executors.values.exists(_ == host) +} + +class ClusterTaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { + import TaskLocality.{ANY, PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL} + + val LOCALITY_WAIT = System.getProperty("spark.locality.wait", "3000").toLong + + test("TaskSet with no preferences") { + sc = new SparkContext("local", "test") + val sched = new FakeClusterScheduler(sc, ("exec1", "host1")) + val taskSet = createTaskSet(1) + val manager = new ClusterTaskSetManager(sched, taskSet) + + // Offer a host with no CPUs + assert(manager.resourceOffer("exec1", "host1", 0, ANY) === None) + + // Offer a host with process-local as the constraint; this should work because the TaskSet + // above won't have any locality preferences + val taskOption = manager.resourceOffer("exec1", "host1", 2, TaskLocality.PROCESS_LOCAL) + assert(taskOption.isDefined) + val task = taskOption.get + assert(task.executorId === "exec1") + assert(sched.startedTasks.contains(0)) + + // Re-offer the host -- now we should get no more tasks + assert(manager.resourceOffer("exec1", "host1", 2, PROCESS_LOCAL) === None) + + // Tell it the task has finished + manager.statusUpdate(0, TaskState.FINISHED, createTaskResult(0)) + assert(sched.endedTasks(0) === Success) + assert(sched.finishedManagers.contains(manager)) + } + + test("multiple offers with no preferences") { + sc = new SparkContext("local", "test") + val sched = new FakeClusterScheduler(sc, ("exec1", "host1")) + val taskSet = createTaskSet(3) + val manager = new ClusterTaskSetManager(sched, taskSet) + + // First three offers should all find tasks + for (i <- 0 until 3) { + val taskOption = manager.resourceOffer("exec1", "host1", 1, PROCESS_LOCAL) + assert(taskOption.isDefined) + val task = taskOption.get + assert(task.executorId === "exec1") + } + assert(sched.startedTasks.toSet === Set(0, 1, 2)) + + // Re-offer the host -- now we should get no more tasks + assert(manager.resourceOffer("exec1", "host1", 1, PROCESS_LOCAL) === None) + + // Finish the first two tasks + manager.statusUpdate(0, TaskState.FINISHED, createTaskResult(0)) + manager.statusUpdate(1, TaskState.FINISHED, createTaskResult(1)) + assert(sched.endedTasks(0) === Success) + assert(sched.endedTasks(1) === Success) + assert(!sched.finishedManagers.contains(manager)) + + // Finish the last task + manager.statusUpdate(2, TaskState.FINISHED, createTaskResult(2)) + assert(sched.endedTasks(2) === Success) + assert(sched.finishedManagers.contains(manager)) + } + + test("basic delay scheduling") { + sc = new SparkContext("local", "test") + val sched = new FakeClusterScheduler(sc, ("exec1", "host1"), ("exec2", "host2")) + val taskSet = createTaskSet(4, + Seq(TaskLocation("host1", "exec1")), + Seq(TaskLocation("host2", "exec2")), + Seq(TaskLocation("host1"), TaskLocation("host2", "exec2")), + Seq() // Last task has no locality prefs + ) + val clock = new FakeClock + val manager = new ClusterTaskSetManager(sched, taskSet, clock) + + // First offer host1, exec1: first task should be chosen + assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 0) + + // Offer host1, exec1 again: the last task, which has no prefs, should be chosen + assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 3) + + // Offer host1, exec1 again, at PROCESS_LOCAL level: nothing should get chosen + assert(manager.resourceOffer("exec1", "host1", 1, PROCESS_LOCAL) === None) + + clock.advance(LOCALITY_WAIT) + + // Offer host1, exec1 again, at PROCESS_LOCAL level: nothing should get chosen + assert(manager.resourceOffer("exec1", "host1", 1, PROCESS_LOCAL) === None) + + // Offer host1, exec1 again, at NODE_LOCAL level: we should choose task 2 + assert(manager.resourceOffer("exec1", "host1", 1, NODE_LOCAL).get.index == 2) + + // Offer host1, exec1 again, at NODE_LOCAL level: nothing should get chosen + assert(manager.resourceOffer("exec1", "host1", 1, NODE_LOCAL) === None) + + // Offer host1, exec1 again, at ANY level: nothing should get chosen + assert(manager.resourceOffer("exec1", "host1", 1, ANY) === None) + + clock.advance(LOCALITY_WAIT) + + // Offer host1, exec1 again, at ANY level: task 1 should get chosen + assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 1) + + // Offer host1, exec1 again, at ANY level: nothing should be chosen as we've launched all tasks + assert(manager.resourceOffer("exec1", "host1", 1, ANY) === None) + } + + test("delay scheduling with fallback") { + sc = new SparkContext("local", "test") + val sched = new FakeClusterScheduler(sc, + ("exec1", "host1"), ("exec2", "host2"), ("exec3", "host3")) + val taskSet = createTaskSet(5, + Seq(TaskLocation("host1")), + Seq(TaskLocation("host2")), + Seq(TaskLocation("host2")), + Seq(TaskLocation("host3")), + Seq(TaskLocation("host2")) + ) + val clock = new FakeClock + val manager = new ClusterTaskSetManager(sched, taskSet, clock) + + // First offer host1: first task should be chosen + assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 0) + + // Offer host1 again: nothing should get chosen + assert(manager.resourceOffer("exec1", "host1", 1, ANY) === None) + + clock.advance(LOCALITY_WAIT) + + // Offer host1 again: second task (on host2) should get chosen + assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 1) + + // Offer host1 again: third task (on host2) should get chosen + assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 2) + + // Offer host2: fifth task (also on host2) should get chosen + assert(manager.resourceOffer("exec2", "host2", 1, ANY).get.index === 4) + + // Now that we've launched a local task, we should no longer launch the task for host3 + assert(manager.resourceOffer("exec2", "host2", 1, ANY) === None) + + clock.advance(LOCALITY_WAIT) + + // After another delay, we can go ahead and launch that task non-locally + assert(manager.resourceOffer("exec2", "host2", 1, ANY).get.index === 3) + } + + test("delay scheduling with failed hosts") { + sc = new SparkContext("local", "test") + val sched = new FakeClusterScheduler(sc, ("exec1", "host1"), ("exec2", "host2")) + val taskSet = createTaskSet(3, + Seq(TaskLocation("host1")), + Seq(TaskLocation("host2")), + Seq(TaskLocation("host3")) + ) + val clock = new FakeClock + val manager = new ClusterTaskSetManager(sched, taskSet, clock) + + // First offer host1: first task should be chosen + assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 0) + + // Offer host1 again: third task should be chosen immediately because host3 is not up + assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 2) + + // After this, nothing should get chosen + assert(manager.resourceOffer("exec1", "host1", 1, ANY) === None) + + // Now mark host2 as dead + sched.removeExecutor("exec2") + manager.executorLost("exec2", "host2") + + // Task 1 should immediately be launched on host1 because its original host is gone + assert(manager.resourceOffer("exec1", "host1", 1, ANY).get.index === 1) + + // Now that all tasks have launched, nothing new should be launched anywhere else + assert(manager.resourceOffer("exec1", "host1", 1, ANY) === None) + assert(manager.resourceOffer("exec2", "host2", 1, ANY) === None) + } + + /** + * Utility method to create a TaskSet, potentially setting a particular sequence of preferred + * locations for each task (given as varargs) if this sequence is not empty. + */ + def createTaskSet(numTasks: Int, prefLocs: Seq[TaskLocation]*): TaskSet = { + if (prefLocs.size != 0 && prefLocs.size != numTasks) { + throw new IllegalArgumentException("Wrong number of task locations") + } + val tasks = Array.tabulate[Task[_]](numTasks) { i => + new FakeTask(i, if (prefLocs.size != 0) prefLocs(i) else Nil) + } + new TaskSet(tasks, 0, 0, 0, null) + } + + def createTaskResult(id: Int): ByteBuffer = { + ByteBuffer.wrap(Utils.serialize(new TaskResult[Int](id, mutable.Map.empty, new TaskMetrics))) + } +} diff --git a/core/src/test/scala/org/apache/spark/scheduler/cluster/FakeTask.scala b/core/src/test/scala/org/apache/spark/scheduler/cluster/FakeTask.scala new file mode 100644 index 0000000000..2f12aaed18 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/scheduler/cluster/FakeTask.scala @@ -0,0 +1,26 @@ +/* + * 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.scheduler.cluster + +import org.apache.spark.scheduler.{TaskLocation, Task} + +class FakeTask(stageId: Int, prefLocs: Seq[TaskLocation] = Nil) extends Task[Int](stageId) { + override def run(attemptId: Long): Int = 0 + + override def preferredLocations: Seq[TaskLocation] = prefLocs +} diff --git a/core/src/test/scala/org/apache/spark/scheduler/local/LocalSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/local/LocalSchedulerSuite.scala new file mode 100644 index 0000000000..111340a65c --- /dev/null +++ b/core/src/test/scala/org/apache/spark/scheduler/local/LocalSchedulerSuite.scala @@ -0,0 +1,223 @@ +/* + * 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.scheduler.local + +import org.scalatest.FunSuite +import org.scalatest.BeforeAndAfter + +import org.apache.spark._ +import org.apache.spark.scheduler._ +import org.apache.spark.scheduler.cluster._ +import scala.collection.mutable.ArrayBuffer +import scala.collection.mutable.{ConcurrentMap, HashMap} +import java.util.concurrent.Semaphore +import java.util.concurrent.CountDownLatch +import java.util.Properties + +class Lock() { + var finished = false + def jobWait() = { + synchronized { + while(!finished) { + this.wait() + } + } + } + + def jobFinished() = { + synchronized { + finished = true + this.notifyAll() + } + } +} + +object TaskThreadInfo { + val threadToLock = HashMap[Int, Lock]() + val threadToRunning = HashMap[Int, Boolean]() + val threadToStarted = HashMap[Int, CountDownLatch]() +} + +/* + * 1. each thread contains one job. + * 2. each job contains one stage. + * 3. each stage only contains one task. + * 4. each task(launched) must be lanched orderly(using threadToStarted) to make sure + * it will get cpu core resource, and will wait to finished after user manually + * release "Lock" and then cluster will contain another free cpu cores. + * 5. each task(pending) must use "sleep" to make sure it has been added to taskSetManager queue, + * thus it will be scheduled later when cluster has free cpu cores. + */ +class LocalSchedulerSuite extends FunSuite with LocalSparkContext { + + def createThread(threadIndex: Int, poolName: String, sc: SparkContext, sem: Semaphore) { + + TaskThreadInfo.threadToRunning(threadIndex) = false + val nums = sc.parallelize(threadIndex to threadIndex, 1) + TaskThreadInfo.threadToLock(threadIndex) = new Lock() + TaskThreadInfo.threadToStarted(threadIndex) = new CountDownLatch(1) + new Thread { + if (poolName != null) { + sc.setLocalProperty("spark.scheduler.cluster.fair.pool", poolName) + } + override def run() { + val ans = nums.map(number => { + TaskThreadInfo.threadToRunning(number) = true + TaskThreadInfo.threadToStarted(number).countDown() + TaskThreadInfo.threadToLock(number).jobWait() + TaskThreadInfo.threadToRunning(number) = false + number + }).collect() + assert(ans.toList === List(threadIndex)) + sem.release() + } + }.start() + } + + test("Local FIFO scheduler end-to-end test") { + System.setProperty("spark.cluster.schedulingmode", "FIFO") + sc = new SparkContext("local[4]", "test") + val sem = new Semaphore(0) + + createThread(1,null,sc,sem) + TaskThreadInfo.threadToStarted(1).await() + createThread(2,null,sc,sem) + TaskThreadInfo.threadToStarted(2).await() + createThread(3,null,sc,sem) + TaskThreadInfo.threadToStarted(3).await() + createThread(4,null,sc,sem) + TaskThreadInfo.threadToStarted(4).await() + // thread 5 and 6 (stage pending)must meet following two points + // 1. stages (taskSetManager) of jobs in thread 5 and 6 should be add to taskSetManager + // queue before executing TaskThreadInfo.threadToLock(1).jobFinished() + // 2. priority of stage in thread 5 should be prior to priority of stage in thread 6 + // So I just use "sleep" 1s here for each thread. + // TODO: any better solution? + createThread(5,null,sc,sem) + Thread.sleep(1000) + createThread(6,null,sc,sem) + Thread.sleep(1000) + + assert(TaskThreadInfo.threadToRunning(1) === true) + assert(TaskThreadInfo.threadToRunning(2) === true) + assert(TaskThreadInfo.threadToRunning(3) === true) + assert(TaskThreadInfo.threadToRunning(4) === true) + assert(TaskThreadInfo.threadToRunning(5) === false) + assert(TaskThreadInfo.threadToRunning(6) === false) + + TaskThreadInfo.threadToLock(1).jobFinished() + TaskThreadInfo.threadToStarted(5).await() + + assert(TaskThreadInfo.threadToRunning(1) === false) + assert(TaskThreadInfo.threadToRunning(2) === true) + assert(TaskThreadInfo.threadToRunning(3) === true) + assert(TaskThreadInfo.threadToRunning(4) === true) + assert(TaskThreadInfo.threadToRunning(5) === true) + assert(TaskThreadInfo.threadToRunning(6) === false) + + TaskThreadInfo.threadToLock(3).jobFinished() + TaskThreadInfo.threadToStarted(6).await() + + assert(TaskThreadInfo.threadToRunning(1) === false) + assert(TaskThreadInfo.threadToRunning(2) === true) + assert(TaskThreadInfo.threadToRunning(3) === false) + assert(TaskThreadInfo.threadToRunning(4) === true) + assert(TaskThreadInfo.threadToRunning(5) === true) + assert(TaskThreadInfo.threadToRunning(6) === true) + + TaskThreadInfo.threadToLock(2).jobFinished() + TaskThreadInfo.threadToLock(4).jobFinished() + TaskThreadInfo.threadToLock(5).jobFinished() + TaskThreadInfo.threadToLock(6).jobFinished() + sem.acquire(6) + } + + test("Local fair scheduler end-to-end test") { + sc = new SparkContext("local[8]", "LocalSchedulerSuite") + val sem = new Semaphore(0) + System.setProperty("spark.cluster.schedulingmode", "FAIR") + val xmlPath = getClass.getClassLoader.getResource("fairscheduler.xml").getFile() + System.setProperty("spark.fairscheduler.allocation.file", xmlPath) + + createThread(10,"1",sc,sem) + TaskThreadInfo.threadToStarted(10).await() + createThread(20,"2",sc,sem) + TaskThreadInfo.threadToStarted(20).await() + createThread(30,"3",sc,sem) + TaskThreadInfo.threadToStarted(30).await() + + assert(TaskThreadInfo.threadToRunning(10) === true) + assert(TaskThreadInfo.threadToRunning(20) === true) + assert(TaskThreadInfo.threadToRunning(30) === true) + + createThread(11,"1",sc,sem) + TaskThreadInfo.threadToStarted(11).await() + createThread(21,"2",sc,sem) + TaskThreadInfo.threadToStarted(21).await() + createThread(31,"3",sc,sem) + TaskThreadInfo.threadToStarted(31).await() + + assert(TaskThreadInfo.threadToRunning(11) === true) + assert(TaskThreadInfo.threadToRunning(21) === true) + assert(TaskThreadInfo.threadToRunning(31) === true) + + createThread(12,"1",sc,sem) + TaskThreadInfo.threadToStarted(12).await() + createThread(22,"2",sc,sem) + TaskThreadInfo.threadToStarted(22).await() + createThread(32,"3",sc,sem) + + assert(TaskThreadInfo.threadToRunning(12) === true) + assert(TaskThreadInfo.threadToRunning(22) === true) + assert(TaskThreadInfo.threadToRunning(32) === false) + + TaskThreadInfo.threadToLock(10).jobFinished() + TaskThreadInfo.threadToStarted(32).await() + + assert(TaskThreadInfo.threadToRunning(32) === true) + + //1. Similar with above scenario, sleep 1s for stage of 23 and 33 to be added to taskSetManager + // queue so that cluster will assign free cpu core to stage 23 after stage 11 finished. + //2. priority of 23 and 33 will be meaningless as using fair scheduler here. + createThread(23,"2",sc,sem) + createThread(33,"3",sc,sem) + Thread.sleep(1000) + + TaskThreadInfo.threadToLock(11).jobFinished() + TaskThreadInfo.threadToStarted(23).await() + + assert(TaskThreadInfo.threadToRunning(23) === true) + assert(TaskThreadInfo.threadToRunning(33) === false) + + TaskThreadInfo.threadToLock(12).jobFinished() + TaskThreadInfo.threadToStarted(33).await() + + assert(TaskThreadInfo.threadToRunning(33) === true) + + TaskThreadInfo.threadToLock(20).jobFinished() + TaskThreadInfo.threadToLock(21).jobFinished() + TaskThreadInfo.threadToLock(22).jobFinished() + TaskThreadInfo.threadToLock(23).jobFinished() + TaskThreadInfo.threadToLock(30).jobFinished() + TaskThreadInfo.threadToLock(31).jobFinished() + TaskThreadInfo.threadToLock(32).jobFinished() + TaskThreadInfo.threadToLock(33).jobFinished() + + sem.acquire(11) + } +} diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala new file mode 100644 index 0000000000..88ba10f2f2 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -0,0 +1,666 @@ +/* + * 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.storage + +import java.nio.ByteBuffer + +import akka.actor._ + +import org.scalatest.FunSuite +import org.scalatest.BeforeAndAfter +import org.scalatest.PrivateMethodTester +import org.scalatest.concurrent.Eventually._ +import org.scalatest.concurrent.Timeouts._ +import org.scalatest.matchers.ShouldMatchers._ +import org.scalatest.time.SpanSugar._ + +import org.apache.spark.JavaSerializer +import org.apache.spark.KryoSerializer +import org.apache.spark.SizeEstimator +import org.apache.spark.Utils +import org.apache.spark.util.AkkaUtils +import org.apache.spark.util.ByteBufferInputStream + + +class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodTester { + var store: BlockManager = null + var store2: BlockManager = null + var actorSystem: ActorSystem = null + var master: BlockManagerMaster = null + var oldArch: String = null + var oldOops: String = null + var oldHeartBeat: String = null + + // Reuse a serializer across tests to avoid creating a new thread-local buffer on each test + System.setProperty("spark.kryoserializer.buffer.mb", "1") + val serializer = new KryoSerializer + + before { + val (actorSystem, boundPort) = AkkaUtils.createActorSystem("test", "localhost", 0) + this.actorSystem = actorSystem + System.setProperty("spark.driver.port", boundPort.toString) + System.setProperty("spark.hostPort", "localhost:" + boundPort) + + master = new BlockManagerMaster( + actorSystem.actorOf(Props(new BlockManagerMasterActor(true)))) + + // Set the arch to 64-bit and compressedOops to true to get a deterministic test-case + oldArch = System.setProperty("os.arch", "amd64") + oldOops = System.setProperty("spark.test.useCompressedOops", "true") + oldHeartBeat = System.setProperty("spark.storage.disableBlockManagerHeartBeat", "true") + val initialize = PrivateMethod[Unit]('initialize) + SizeEstimator invokePrivate initialize() + // Set some value ... + System.setProperty("spark.hostPort", Utils.localHostName() + ":" + 1111) + } + + after { + System.clearProperty("spark.driver.port") + System.clearProperty("spark.hostPort") + + if (store != null) { + store.stop() + store = null + } + if (store2 != null) { + store2.stop() + store2 = null + } + actorSystem.shutdown() + actorSystem.awaitTermination() + actorSystem = null + master = null + + if (oldArch != null) { + System.setProperty("os.arch", oldArch) + } else { + System.clearProperty("os.arch") + } + + if (oldOops != null) { + System.setProperty("spark.test.useCompressedOops", oldOops) + } else { + System.clearProperty("spark.test.useCompressedOops") + } + } + + test("StorageLevel object caching") { + val level1 = StorageLevel(false, false, false, 3) + val level2 = StorageLevel(false, false, false, 3) // this should return the same object as level1 + val level3 = StorageLevel(false, false, false, 2) // this should return a different object + assert(level2 === level1, "level2 is not same as level1") + assert(level2.eq(level1), "level2 is not the same object as level1") + assert(level3 != level1, "level3 is same as level1") + val bytes1 = Utils.serialize(level1) + val level1_ = Utils.deserialize[StorageLevel](bytes1) + val bytes2 = Utils.serialize(level2) + val level2_ = Utils.deserialize[StorageLevel](bytes2) + assert(level1_ === level1, "Deserialized level1 not same as original level1") + assert(level1_.eq(level1), "Deserialized level1 not the same object as original level2") + assert(level2_ === level2, "Deserialized level2 not same as original level2") + assert(level2_.eq(level1), "Deserialized level2 not the same object as original level1") + } + + test("BlockManagerId object caching") { + val id1 = BlockManagerId("e1", "XXX", 1, 0) + val id2 = BlockManagerId("e1", "XXX", 1, 0) // this should return the same object as id1 + val id3 = BlockManagerId("e1", "XXX", 2, 0) // this should return a different object + assert(id2 === id1, "id2 is not same as id1") + assert(id2.eq(id1), "id2 is not the same object as id1") + assert(id3 != id1, "id3 is same as id1") + val bytes1 = Utils.serialize(id1) + val id1_ = Utils.deserialize[BlockManagerId](bytes1) + val bytes2 = Utils.serialize(id2) + val id2_ = Utils.deserialize[BlockManagerId](bytes2) + assert(id1_ === id1, "Deserialized id1 is not same as original id1") + assert(id1_.eq(id1), "Deserialized id1 is not the same object as original id1") + assert(id2_ === id2, "Deserialized id2 is not same as original id2") + assert(id2_.eq(id1), "Deserialized id2 is not the same object as original id1") + } + + test("master + 1 manager interaction") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + val a3 = new Array[Byte](400) + + // Putting a1, a2 and a3 in memory and telling master only about a1 and a2 + store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY) + store.putSingle("a2", a2, StorageLevel.MEMORY_ONLY) + store.putSingle("a3", a3, StorageLevel.MEMORY_ONLY, tellMaster = false) + + // Checking whether blocks are in memory + assert(store.getSingle("a1") != None, "a1 was not in store") + assert(store.getSingle("a2") != None, "a2 was not in store") + assert(store.getSingle("a3") != None, "a3 was not in store") + + // Checking whether master knows about the blocks or not + assert(master.getLocations("a1").size > 0, "master was not told about a1") + assert(master.getLocations("a2").size > 0, "master was not told about a2") + assert(master.getLocations("a3").size === 0, "master was told about a3") + + // Drop a1 and a2 from memory; this should be reported back to the master + store.dropFromMemory("a1", null) + store.dropFromMemory("a2", null) + assert(store.getSingle("a1") === None, "a1 not removed from store") + assert(store.getSingle("a2") === None, "a2 not removed from store") + assert(master.getLocations("a1").size === 0, "master did not remove a1") + assert(master.getLocations("a2").size === 0, "master did not remove a2") + } + + test("master + 2 managers interaction") { + store = new BlockManager("exec1", actorSystem, master, serializer, 2000) + store2 = new BlockManager("exec2", actorSystem, master, new KryoSerializer, 2000) + + val peers = master.getPeers(store.blockManagerId, 1) + assert(peers.size === 1, "master did not return the other manager as a peer") + assert(peers.head === store2.blockManagerId, "peer returned by master is not the other manager") + + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY_2) + store2.putSingle("a2", a2, StorageLevel.MEMORY_ONLY_2) + assert(master.getLocations("a1").size === 2, "master did not report 2 locations for a1") + assert(master.getLocations("a2").size === 2, "master did not report 2 locations for a2") + } + + test("removing block") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + val a3 = new Array[Byte](400) + + // Putting a1, a2 and a3 in memory and telling master only about a1 and a2 + store.putSingle("a1-to-remove", a1, StorageLevel.MEMORY_ONLY) + store.putSingle("a2-to-remove", a2, StorageLevel.MEMORY_ONLY) + store.putSingle("a3-to-remove", a3, StorageLevel.MEMORY_ONLY, tellMaster = false) + + // Checking whether blocks are in memory and memory size + val memStatus = master.getMemoryStatus.head._2 + assert(memStatus._1 == 2000L, "total memory " + memStatus._1 + " should equal 2000") + assert(memStatus._2 <= 1200L, "remaining memory " + memStatus._2 + " should <= 1200") + assert(store.getSingle("a1-to-remove") != None, "a1 was not in store") + assert(store.getSingle("a2-to-remove") != None, "a2 was not in store") + assert(store.getSingle("a3-to-remove") != None, "a3 was not in store") + + // Checking whether master knows about the blocks or not + assert(master.getLocations("a1-to-remove").size > 0, "master was not told about a1") + assert(master.getLocations("a2-to-remove").size > 0, "master was not told about a2") + assert(master.getLocations("a3-to-remove").size === 0, "master was told about a3") + + // Remove a1 and a2 and a3. Should be no-op for a3. + master.removeBlock("a1-to-remove") + master.removeBlock("a2-to-remove") + master.removeBlock("a3-to-remove") + + eventually(timeout(1000 milliseconds), interval(10 milliseconds)) { + store.getSingle("a1-to-remove") should be (None) + master.getLocations("a1-to-remove") should have size 0 + } + eventually(timeout(1000 milliseconds), interval(10 milliseconds)) { + store.getSingle("a2-to-remove") should be (None) + master.getLocations("a2-to-remove") should have size 0 + } + eventually(timeout(1000 milliseconds), interval(10 milliseconds)) { + store.getSingle("a3-to-remove") should not be (None) + master.getLocations("a3-to-remove") should have size 0 + } + eventually(timeout(1000 milliseconds), interval(10 milliseconds)) { + val memStatus = master.getMemoryStatus.head._2 + memStatus._1 should equal (2000L) + memStatus._2 should equal (2000L) + } + } + + test("removing rdd") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + val a3 = new Array[Byte](400) + // Putting a1, a2 and a3 in memory. + store.putSingle("rdd_0_0", a1, StorageLevel.MEMORY_ONLY) + store.putSingle("rdd_0_1", a2, StorageLevel.MEMORY_ONLY) + store.putSingle("nonrddblock", a3, StorageLevel.MEMORY_ONLY) + master.removeRdd(0, blocking = false) + + eventually(timeout(1000 milliseconds), interval(10 milliseconds)) { + store.getSingle("rdd_0_0") should be (None) + master.getLocations("rdd_0_0") should have size 0 + } + eventually(timeout(1000 milliseconds), interval(10 milliseconds)) { + store.getSingle("rdd_0_1") should be (None) + master.getLocations("rdd_0_1") should have size 0 + } + eventually(timeout(1000 milliseconds), interval(10 milliseconds)) { + store.getSingle("nonrddblock") should not be (None) + master.getLocations("nonrddblock") should have size (1) + } + + store.putSingle("rdd_0_0", a1, StorageLevel.MEMORY_ONLY) + store.putSingle("rdd_0_1", a2, StorageLevel.MEMORY_ONLY) + master.removeRdd(0, blocking = true) + store.getSingle("rdd_0_0") should be (None) + master.getLocations("rdd_0_0") should have size 0 + store.getSingle("rdd_0_1") should be (None) + master.getLocations("rdd_0_1") should have size 0 + } + + test("reregistration on heart beat") { + val heartBeat = PrivateMethod[Unit]('heartBeat) + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + val a1 = new Array[Byte](400) + + store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY) + + assert(store.getSingle("a1") != None, "a1 was not in store") + assert(master.getLocations("a1").size > 0, "master was not told about a1") + + master.removeExecutor(store.blockManagerId.executorId) + assert(master.getLocations("a1").size == 0, "a1 was not removed from master") + + store invokePrivate heartBeat() + assert(master.getLocations("a1").size > 0, "a1 was not reregistered with master") + } + + test("reregistration on block update") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + + store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY) + assert(master.getLocations("a1").size > 0, "master was not told about a1") + + master.removeExecutor(store.blockManagerId.executorId) + assert(master.getLocations("a1").size == 0, "a1 was not removed from master") + + store.putSingle("a2", a2, StorageLevel.MEMORY_ONLY) + store.waitForAsyncReregister() + + assert(master.getLocations("a1").size > 0, "a1 was not reregistered with master") + assert(master.getLocations("a2").size > 0, "master was not told about a2") + } + + test("reregistration doesn't dead lock") { + val heartBeat = PrivateMethod[Unit]('heartBeat) + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + val a1 = new Array[Byte](400) + val a2 = List(new Array[Byte](400)) + + // try many times to trigger any deadlocks + for (i <- 1 to 100) { + master.removeExecutor(store.blockManagerId.executorId) + val t1 = new Thread { + override def run() { + store.put("a2", a2.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true) + } + } + val t2 = new Thread { + override def run() { + store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY) + } + } + val t3 = new Thread { + override def run() { + store invokePrivate heartBeat() + } + } + + t1.start() + t2.start() + t3.start() + t1.join() + t2.join() + t3.join() + + store.dropFromMemory("a1", null) + store.dropFromMemory("a2", null) + store.waitForAsyncReregister() + } + } + + test("in-memory LRU storage") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + val a3 = new Array[Byte](400) + store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY) + store.putSingle("a2", a2, StorageLevel.MEMORY_ONLY) + store.putSingle("a3", a3, StorageLevel.MEMORY_ONLY) + assert(store.getSingle("a2") != None, "a2 was not in store") + assert(store.getSingle("a3") != None, "a3 was not in store") + assert(store.getSingle("a1") === None, "a1 was in store") + assert(store.getSingle("a2") != None, "a2 was not in store") + // At this point a2 was gotten last, so LRU will getSingle rid of a3 + store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY) + assert(store.getSingle("a1") != None, "a1 was not in store") + assert(store.getSingle("a2") != None, "a2 was not in store") + assert(store.getSingle("a3") === None, "a3 was in store") + } + + test("in-memory LRU storage with serialization") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + val a3 = new Array[Byte](400) + store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY_SER) + store.putSingle("a2", a2, StorageLevel.MEMORY_ONLY_SER) + store.putSingle("a3", a3, StorageLevel.MEMORY_ONLY_SER) + assert(store.getSingle("a2") != None, "a2 was not in store") + assert(store.getSingle("a3") != None, "a3 was not in store") + assert(store.getSingle("a1") === None, "a1 was in store") + assert(store.getSingle("a2") != None, "a2 was not in store") + // At this point a2 was gotten last, so LRU will getSingle rid of a3 + store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY_SER) + assert(store.getSingle("a1") != None, "a1 was not in store") + assert(store.getSingle("a2") != None, "a2 was not in store") + assert(store.getSingle("a3") === None, "a3 was in store") + } + + test("in-memory LRU for partitions of same RDD") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + val a3 = new Array[Byte](400) + store.putSingle("rdd_0_1", a1, StorageLevel.MEMORY_ONLY) + store.putSingle("rdd_0_2", a2, StorageLevel.MEMORY_ONLY) + store.putSingle("rdd_0_3", a3, StorageLevel.MEMORY_ONLY) + // Even though we accessed rdd_0_3 last, it should not have replaced partitions 1 and 2 + // from the same RDD + assert(store.getSingle("rdd_0_3") === None, "rdd_0_3 was in store") + assert(store.getSingle("rdd_0_2") != None, "rdd_0_2 was not in store") + assert(store.getSingle("rdd_0_1") != None, "rdd_0_1 was not in store") + // Check that rdd_0_3 doesn't replace them even after further accesses + assert(store.getSingle("rdd_0_3") === None, "rdd_0_3 was in store") + assert(store.getSingle("rdd_0_3") === None, "rdd_0_3 was in store") + assert(store.getSingle("rdd_0_3") === None, "rdd_0_3 was in store") + } + + test("in-memory LRU for partitions of multiple RDDs") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store.putSingle("rdd_0_1", new Array[Byte](400), StorageLevel.MEMORY_ONLY) + store.putSingle("rdd_0_2", new Array[Byte](400), StorageLevel.MEMORY_ONLY) + store.putSingle("rdd_1_1", new Array[Byte](400), StorageLevel.MEMORY_ONLY) + // At this point rdd_1_1 should've replaced rdd_0_1 + assert(store.memoryStore.contains("rdd_1_1"), "rdd_1_1 was not in store") + assert(!store.memoryStore.contains("rdd_0_1"), "rdd_0_1 was in store") + assert(store.memoryStore.contains("rdd_0_2"), "rdd_0_2 was not in store") + // Do a get() on rdd_0_2 so that it is the most recently used item + assert(store.getSingle("rdd_0_2") != None, "rdd_0_2 was not in store") + // Put in more partitions from RDD 0; they should replace rdd_1_1 + store.putSingle("rdd_0_3", new Array[Byte](400), StorageLevel.MEMORY_ONLY) + store.putSingle("rdd_0_4", new Array[Byte](400), StorageLevel.MEMORY_ONLY) + // Now rdd_1_1 should be dropped to add rdd_0_3, but then rdd_0_2 should *not* be dropped + // when we try to add rdd_0_4. + assert(!store.memoryStore.contains("rdd_1_1"), "rdd_1_1 was in store") + assert(!store.memoryStore.contains("rdd_0_1"), "rdd_0_1 was in store") + assert(!store.memoryStore.contains("rdd_0_4"), "rdd_0_4 was in store") + assert(store.memoryStore.contains("rdd_0_2"), "rdd_0_2 was not in store") + assert(store.memoryStore.contains("rdd_0_3"), "rdd_0_3 was not in store") + } + + test("on-disk storage") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + val a3 = new Array[Byte](400) + store.putSingle("a1", a1, StorageLevel.DISK_ONLY) + store.putSingle("a2", a2, StorageLevel.DISK_ONLY) + store.putSingle("a3", a3, StorageLevel.DISK_ONLY) + assert(store.getSingle("a2") != None, "a2 was in store") + assert(store.getSingle("a3") != None, "a3 was in store") + assert(store.getSingle("a1") != None, "a1 was in store") + } + + test("disk and memory storage") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + val a3 = new Array[Byte](400) + store.putSingle("a1", a1, StorageLevel.MEMORY_AND_DISK) + store.putSingle("a2", a2, StorageLevel.MEMORY_AND_DISK) + store.putSingle("a3", a3, StorageLevel.MEMORY_AND_DISK) + assert(store.getSingle("a2") != None, "a2 was not in store") + assert(store.getSingle("a3") != None, "a3 was not in store") + assert(store.memoryStore.getValues("a1") == None, "a1 was in memory store") + assert(store.getSingle("a1") != None, "a1 was not in store") + assert(store.memoryStore.getValues("a1") != None, "a1 was not in memory store") + } + + test("disk and memory storage with getLocalBytes") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + val a3 = new Array[Byte](400) + store.putSingle("a1", a1, StorageLevel.MEMORY_AND_DISK) + store.putSingle("a2", a2, StorageLevel.MEMORY_AND_DISK) + store.putSingle("a3", a3, StorageLevel.MEMORY_AND_DISK) + assert(store.getLocalBytes("a2") != None, "a2 was not in store") + assert(store.getLocalBytes("a3") != None, "a3 was not in store") + assert(store.memoryStore.getValues("a1") == None, "a1 was in memory store") + assert(store.getLocalBytes("a1") != None, "a1 was not in store") + assert(store.memoryStore.getValues("a1") != None, "a1 was not in memory store") + } + + test("disk and memory storage with serialization") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + val a3 = new Array[Byte](400) + store.putSingle("a1", a1, StorageLevel.MEMORY_AND_DISK_SER) + store.putSingle("a2", a2, StorageLevel.MEMORY_AND_DISK_SER) + store.putSingle("a3", a3, StorageLevel.MEMORY_AND_DISK_SER) + assert(store.getSingle("a2") != None, "a2 was not in store") + assert(store.getSingle("a3") != None, "a3 was not in store") + assert(store.memoryStore.getValues("a1") == None, "a1 was in memory store") + assert(store.getSingle("a1") != None, "a1 was not in store") + assert(store.memoryStore.getValues("a1") != None, "a1 was not in memory store") + } + + test("disk and memory storage with serialization and getLocalBytes") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + val a3 = new Array[Byte](400) + store.putSingle("a1", a1, StorageLevel.MEMORY_AND_DISK_SER) + store.putSingle("a2", a2, StorageLevel.MEMORY_AND_DISK_SER) + store.putSingle("a3", a3, StorageLevel.MEMORY_AND_DISK_SER) + assert(store.getLocalBytes("a2") != None, "a2 was not in store") + assert(store.getLocalBytes("a3") != None, "a3 was not in store") + assert(store.memoryStore.getValues("a1") == None, "a1 was in memory store") + assert(store.getLocalBytes("a1") != None, "a1 was not in store") + assert(store.memoryStore.getValues("a1") != None, "a1 was not in memory store") + } + + test("LRU with mixed storage levels") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + val a1 = new Array[Byte](400) + val a2 = new Array[Byte](400) + val a3 = new Array[Byte](400) + val a4 = new Array[Byte](400) + // First store a1 and a2, both in memory, and a3, on disk only + store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY_SER) + store.putSingle("a2", a2, StorageLevel.MEMORY_ONLY_SER) + store.putSingle("a3", a3, StorageLevel.DISK_ONLY) + // At this point LRU should not kick in because a3 is only on disk + assert(store.getSingle("a1") != None, "a2 was not in store") + assert(store.getSingle("a2") != None, "a3 was not in store") + assert(store.getSingle("a3") != None, "a1 was not in store") + assert(store.getSingle("a1") != None, "a2 was not in store") + assert(store.getSingle("a2") != None, "a3 was not in store") + assert(store.getSingle("a3") != None, "a1 was not in store") + // Now let's add in a4, which uses both disk and memory; a1 should drop out + store.putSingle("a4", a4, StorageLevel.MEMORY_AND_DISK_SER) + assert(store.getSingle("a1") == None, "a1 was in store") + assert(store.getSingle("a2") != None, "a2 was not in store") + assert(store.getSingle("a3") != None, "a3 was not in store") + assert(store.getSingle("a4") != None, "a4 was not in store") + } + + test("in-memory LRU with streams") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + val list1 = List(new Array[Byte](200), new Array[Byte](200)) + val list2 = List(new Array[Byte](200), new Array[Byte](200)) + val list3 = List(new Array[Byte](200), new Array[Byte](200)) + store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true) + store.put("list2", list2.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true) + store.put("list3", list3.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true) + assert(store.get("list2") != None, "list2 was not in store") + assert(store.get("list2").get.size == 2) + assert(store.get("list3") != None, "list3 was not in store") + assert(store.get("list3").get.size == 2) + assert(store.get("list1") === None, "list1 was in store") + assert(store.get("list2") != None, "list2 was not in store") + assert(store.get("list2").get.size == 2) + // At this point list2 was gotten last, so LRU will getSingle rid of list3 + store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY, tellMaster = true) + assert(store.get("list1") != None, "list1 was not in store") + assert(store.get("list1").get.size == 2) + assert(store.get("list2") != None, "list2 was not in store") + assert(store.get("list2").get.size == 2) + assert(store.get("list3") === None, "list1 was in store") + } + + test("LRU with mixed storage levels and streams") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + val list1 = List(new Array[Byte](200), new Array[Byte](200)) + val list2 = List(new Array[Byte](200), new Array[Byte](200)) + val list3 = List(new Array[Byte](200), new Array[Byte](200)) + val list4 = List(new Array[Byte](200), new Array[Byte](200)) + // First store list1 and list2, both in memory, and list3, on disk only + store.put("list1", list1.iterator, StorageLevel.MEMORY_ONLY_SER, tellMaster = true) + store.put("list2", list2.iterator, StorageLevel.MEMORY_ONLY_SER, tellMaster = true) + store.put("list3", list3.iterator, StorageLevel.DISK_ONLY, tellMaster = true) + // At this point LRU should not kick in because list3 is only on disk + assert(store.get("list1") != None, "list2 was not in store") + assert(store.get("list1").get.size === 2) + assert(store.get("list2") != None, "list3 was not in store") + assert(store.get("list2").get.size === 2) + assert(store.get("list3") != None, "list1 was not in store") + assert(store.get("list3").get.size === 2) + assert(store.get("list1") != None, "list2 was not in store") + assert(store.get("list1").get.size === 2) + assert(store.get("list2") != None, "list3 was not in store") + assert(store.get("list2").get.size === 2) + assert(store.get("list3") != None, "list1 was not in store") + assert(store.get("list3").get.size === 2) + // Now let's add in list4, which uses both disk and memory; list1 should drop out + store.put("list4", list4.iterator, StorageLevel.MEMORY_AND_DISK_SER, tellMaster = true) + assert(store.get("list1") === None, "list1 was in store") + assert(store.get("list2") != None, "list3 was not in store") + assert(store.get("list2").get.size === 2) + assert(store.get("list3") != None, "list1 was not in store") + assert(store.get("list3").get.size === 2) + assert(store.get("list4") != None, "list4 was not in store") + assert(store.get("list4").get.size === 2) + } + + test("negative byte values in ByteBufferInputStream") { + val buffer = ByteBuffer.wrap(Array[Int](254, 255, 0, 1, 2).map(_.toByte).toArray) + val stream = new ByteBufferInputStream(buffer) + val temp = new Array[Byte](10) + assert(stream.read() === 254, "unexpected byte read") + assert(stream.read() === 255, "unexpected byte read") + assert(stream.read() === 0, "unexpected byte read") + assert(stream.read(temp, 0, temp.length) === 2, "unexpected number of bytes read") + assert(stream.read() === -1, "end of stream not signalled") + assert(stream.read(temp, 0, temp.length) === -1, "end of stream not signalled") + } + + test("overly large block") { + store = new BlockManager("<driver>", actorSystem, master, serializer, 500) + store.putSingle("a1", new Array[Byte](1000), StorageLevel.MEMORY_ONLY) + assert(store.getSingle("a1") === None, "a1 was in store") + store.putSingle("a2", new Array[Byte](1000), StorageLevel.MEMORY_AND_DISK) + assert(store.memoryStore.getValues("a2") === None, "a2 was in memory store") + assert(store.getSingle("a2") != None, "a2 was not in store") + } + + test("block compression") { + try { + System.setProperty("spark.shuffle.compress", "true") + store = new BlockManager("exec1", actorSystem, master, serializer, 2000) + store.putSingle("shuffle_0_0_0", new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) + assert(store.memoryStore.getSize("shuffle_0_0_0") <= 100, "shuffle_0_0_0 was not compressed") + store.stop() + store = null + + System.setProperty("spark.shuffle.compress", "false") + store = new BlockManager("exec2", actorSystem, master, serializer, 2000) + store.putSingle("shuffle_0_0_0", new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) + assert(store.memoryStore.getSize("shuffle_0_0_0") >= 1000, "shuffle_0_0_0 was compressed") + store.stop() + store = null + + System.setProperty("spark.broadcast.compress", "true") + store = new BlockManager("exec3", actorSystem, master, serializer, 2000) + store.putSingle("broadcast_0", new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) + assert(store.memoryStore.getSize("broadcast_0") <= 100, "broadcast_0 was not compressed") + store.stop() + store = null + + System.setProperty("spark.broadcast.compress", "false") + store = new BlockManager("exec4", actorSystem, master, serializer, 2000) + store.putSingle("broadcast_0", new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) + assert(store.memoryStore.getSize("broadcast_0") >= 1000, "broadcast_0 was compressed") + store.stop() + store = null + + System.setProperty("spark.rdd.compress", "true") + store = new BlockManager("exec5", actorSystem, master, serializer, 2000) + store.putSingle("rdd_0_0", new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) + assert(store.memoryStore.getSize("rdd_0_0") <= 100, "rdd_0_0 was not compressed") + store.stop() + store = null + + System.setProperty("spark.rdd.compress", "false") + store = new BlockManager("exec6", actorSystem, master, serializer, 2000) + store.putSingle("rdd_0_0", new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) + assert(store.memoryStore.getSize("rdd_0_0") >= 1000, "rdd_0_0 was compressed") + store.stop() + store = null + + // Check that any other block types are also kept uncompressed + store = new BlockManager("exec7", actorSystem, master, serializer, 2000) + store.putSingle("other_block", new Array[Byte](1000), StorageLevel.MEMORY_ONLY) + assert(store.memoryStore.getSize("other_block") >= 1000, "other_block was compressed") + store.stop() + store = null + } finally { + System.clearProperty("spark.shuffle.compress") + System.clearProperty("spark.broadcast.compress") + System.clearProperty("spark.rdd.compress") + } + } + + test("block store put failure") { + // Use Java serializer so we can create an unserializable error. + store = new BlockManager("<driver>", actorSystem, master, new JavaSerializer, 1200) + + // The put should fail since a1 is not serializable. + class UnserializableClass + val a1 = new UnserializableClass + intercept[java.io.NotSerializableException] { + store.putSingle("a1", a1, StorageLevel.DISK_ONLY) + } + + // Make sure get a1 doesn't hang and returns None. + failAfter(1 second) { + assert(store.getSingle("a1") == None, "a1 should not be in store") + } + } +} diff --git a/core/src/test/scala/org/apache/spark/ui/UISuite.scala b/core/src/test/scala/org/apache/spark/ui/UISuite.scala new file mode 100644 index 0000000000..3321fb5eb7 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/ui/UISuite.scala @@ -0,0 +1,47 @@ +/* + * 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.ui + +import scala.util.{Failure, Success, Try} +import java.net.ServerSocket +import org.scalatest.FunSuite +import org.eclipse.jetty.server.Server + +class UISuite extends FunSuite { + test("jetty port increases under contention") { + val startPort = 3030 + val server = new Server(startPort) + server.start() + val (jettyServer1, boundPort1) = JettyUtils.startJettyServer("localhost", startPort, Seq()) + val (jettyServer2, boundPort2) = JettyUtils.startJettyServer("localhost", startPort, Seq()) + + // Allow some wiggle room in case ports on the machine are under contention + assert(boundPort1 > startPort && boundPort1 < startPort + 10) + assert(boundPort2 > boundPort1 && boundPort2 < boundPort1 + 10) + } + + test("jetty binds to port 0 correctly") { + val (jettyServer, boundPort) = JettyUtils.startJettyServer("localhost", 0, Seq()) + assert(jettyServer.getState === "STARTED") + assert(boundPort != 0) + Try {new ServerSocket(boundPort)} match { + case Success(s) => fail("Port %s doesn't seem used by jetty server".format(boundPort)) + case Failure (e) => + } + } +} diff --git a/core/src/test/scala/org/apache/spark/util/DistributionSuite.scala b/core/src/test/scala/org/apache/spark/util/DistributionSuite.scala new file mode 100644 index 0000000000..63642461e4 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/DistributionSuite.scala @@ -0,0 +1,42 @@ +/* + * 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 + +import org.scalatest.FunSuite +import org.scalatest.matchers.ShouldMatchers + +/** + * + */ + +class DistributionSuite extends FunSuite with ShouldMatchers { + test("summary") { + val d = new Distribution((1 to 100).toArray.map{_.toDouble}) + val stats = d.statCounter + stats.count should be (100) + stats.mean should be (50.5) + stats.sum should be (50 * 101) + + val quantiles = d.getQuantiles() + quantiles(0) should be (1) + quantiles(1) should be (26) + quantiles(2) should be (51) + quantiles(3) should be (76) + quantiles(4) should be (100) + } +} diff --git a/core/src/test/scala/org/apache/spark/util/FakeClock.scala b/core/src/test/scala/org/apache/spark/util/FakeClock.scala new file mode 100644 index 0000000000..0a45917b08 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/FakeClock.scala @@ -0,0 +1,26 @@ +/* + * 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 + +class FakeClock extends Clock { + private var time = 0L + + def advance(millis: Long): Unit = time += millis + + def getTime(): Long = time +} diff --git a/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala b/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala new file mode 100644 index 0000000000..45867463a5 --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/NextIteratorSuite.scala @@ -0,0 +1,85 @@ +/* + * 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 + +import org.scalatest.FunSuite +import org.scalatest.matchers.ShouldMatchers +import scala.collection.mutable.Buffer +import java.util.NoSuchElementException + +class NextIteratorSuite extends FunSuite with ShouldMatchers { + test("one iteration") { + val i = new StubIterator(Buffer(1)) + i.hasNext should be === true + i.next should be === 1 + i.hasNext should be === false + intercept[NoSuchElementException] { i.next() } + } + + test("two iterations") { + val i = new StubIterator(Buffer(1, 2)) + i.hasNext should be === true + i.next should be === 1 + i.hasNext should be === true + i.next should be === 2 + i.hasNext should be === false + intercept[NoSuchElementException] { i.next() } + } + + test("empty iteration") { + val i = new StubIterator(Buffer()) + i.hasNext should be === false + intercept[NoSuchElementException] { i.next() } + } + + test("close is called once for empty iterations") { + val i = new StubIterator(Buffer()) + i.hasNext should be === false + i.hasNext should be === false + i.closeCalled should be === 1 + } + + test("close is called once for non-empty iterations") { + val i = new StubIterator(Buffer(1, 2)) + i.next should be === 1 + i.next should be === 2 + // close isn't called until we check for the next element + i.closeCalled should be === 0 + i.hasNext should be === false + i.closeCalled should be === 1 + i.hasNext should be === false + i.closeCalled should be === 1 + } + + class StubIterator(ints: Buffer[Int]) extends NextIterator[Int] { + var closeCalled = 0 + + override def getNext() = { + if (ints.size == 0) { + finished = true + 0 + } else { + ints.remove(0) + } + } + + override def close() { + closeCalled += 1 + } + } +} diff --git a/core/src/test/scala/org/apache/spark/util/RateLimitedOutputStreamSuite.scala b/core/src/test/scala/org/apache/spark/util/RateLimitedOutputStreamSuite.scala new file mode 100644 index 0000000000..a9dd0b1a5b --- /dev/null +++ b/core/src/test/scala/org/apache/spark/util/RateLimitedOutputStreamSuite.scala @@ -0,0 +1,40 @@ +/* + * 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 + +import org.scalatest.FunSuite +import java.io.ByteArrayOutputStream +import java.util.concurrent.TimeUnit._ + +class RateLimitedOutputStreamSuite extends FunSuite { + + private def benchmark[U](f: => U): Long = { + val start = System.nanoTime + f + System.nanoTime - start + } + + test("write") { + val underlying = new ByteArrayOutputStream + val data = "X" * 41000 + val stream = new RateLimitedOutputStream(underlying, 10000) + val elapsedNs = benchmark { stream.write(data.getBytes("UTF-8")) } + assert(SECONDS.convert(elapsedNs, NANOSECONDS) == 4) + assert(underlying.toString("UTF-8") == data) + } +} |