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author | zsxwing <zsxwing@gmail.com> | 2015-07-30 15:39:46 -0700 |
---|---|---|
committer | Tathagata Das <tathagata.das1565@gmail.com> | 2015-07-30 15:39:46 -0700 |
commit | 0dbd6963d589a8f6ad344273f3da7df680ada515 (patch) | |
tree | b631e05cb48185de381418dd007494cffe80bd88 | |
parent | 89cda69ecd5ef942a68ad13fc4e1f4184010f087 (diff) | |
download | spark-0dbd6963d589a8f6ad344273f3da7df680ada515.tar.gz spark-0dbd6963d589a8f6ad344273f3da7df680ada515.tar.bz2 spark-0dbd6963d589a8f6ad344273f3da7df680ada515.zip |
[SPARK-9479] [STREAMING] [TESTS] Fix ReceiverTrackerSuite failure for maven build and other potential test failures in Streaming
See https://issues.apache.org/jira/browse/SPARK-9479 for the failure cause.
The PR includes the following changes:
1. Make ReceiverTrackerSuite create StreamingContext in the test body.
2. Fix places that don't stop StreamingContext. I verified no SparkContext was stopped in the shutdown hook locally after this fix.
3. Fix an issue that `ReceiverTracker.endpoint` may be null.
4. Make sure stopping SparkContext in non-main thread won't fail other tests.
Author: zsxwing <zsxwing@gmail.com>
Closes #7797 from zsxwing/fix-ReceiverTrackerSuite and squashes the following commits:
3a4bb98 [zsxwing] Fix another potential NPE
d7497df [zsxwing] Fix ReceiverTrackerSuite; make sure StreamingContext in tests is closed
12 files changed, 183 insertions, 103 deletions
diff --git a/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala index fd653296c9..d7b291d5a6 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/classification/StreamingLogisticRegressionSuite.scala @@ -24,13 +24,22 @@ import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.regression.LabeledPoint import org.apache.spark.mllib.util.TestingUtils._ import org.apache.spark.streaming.dstream.DStream -import org.apache.spark.streaming.TestSuiteBase +import org.apache.spark.streaming.{StreamingContext, TestSuiteBase} class StreamingLogisticRegressionSuite extends SparkFunSuite with TestSuiteBase { // use longer wait time to ensure job completion override def maxWaitTimeMillis: Int = 30000 + var ssc: StreamingContext = _ + + override def afterFunction() { + super.afterFunction() + if (ssc != null) { + ssc.stop() + } + } + // Test if we can accurately learn B for Y = logistic(BX) on streaming data test("parameter accuracy") { @@ -50,7 +59,7 @@ class StreamingLogisticRegressionSuite extends SparkFunSuite with TestSuiteBase } // apply model training to input stream - val ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => { + ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => { model.trainOn(inputDStream) inputDStream.count() }) @@ -84,7 +93,7 @@ class StreamingLogisticRegressionSuite extends SparkFunSuite with TestSuiteBase // apply model training to input stream, storing the intermediate results // (we add a count to ensure the result is a DStream) - val ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => { + ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => { model.trainOn(inputDStream) inputDStream.foreachRDD(x => history.append(math.abs(model.latestModel().weights(0) - B))) inputDStream.count() @@ -118,7 +127,7 @@ class StreamingLogisticRegressionSuite extends SparkFunSuite with TestSuiteBase } // apply model predictions to test stream - val ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => { + ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => { model.predictOnValues(inputDStream.map(x => (x.label, x.features))) }) @@ -147,7 +156,7 @@ class StreamingLogisticRegressionSuite extends SparkFunSuite with TestSuiteBase } // train and predict - val ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => { + ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => { model.trainOn(inputDStream) model.predictOnValues(inputDStream.map(x => (x.label, x.features))) }) @@ -167,7 +176,7 @@ class StreamingLogisticRegressionSuite extends SparkFunSuite with TestSuiteBase .setNumIterations(10) val numBatches = 10 val emptyInput = Seq.empty[Seq[LabeledPoint]] - val ssc = setupStreams(emptyInput, + ssc = setupStreams(emptyInput, (inputDStream: DStream[LabeledPoint]) => { model.trainOn(inputDStream) model.predictOnValues(inputDStream.map(x => (x.label, x.features))) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala index ac01622b8a..3645d29dcc 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/clustering/StreamingKMeansSuite.scala @@ -20,7 +20,7 @@ package org.apache.spark.mllib.clustering import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.{Vector, Vectors} import org.apache.spark.mllib.util.TestingUtils._ -import org.apache.spark.streaming.TestSuiteBase +import org.apache.spark.streaming.{StreamingContext, TestSuiteBase} import org.apache.spark.streaming.dstream.DStream import org.apache.spark.util.random.XORShiftRandom @@ -28,6 +28,15 @@ class StreamingKMeansSuite extends SparkFunSuite with TestSuiteBase { override def maxWaitTimeMillis: Int = 30000 + var ssc: StreamingContext = _ + + override def afterFunction() { + super.afterFunction() + if (ssc != null) { + ssc.stop() + } + } + test("accuracy for single center and equivalence to grand average") { // set parameters val numBatches = 10 @@ -46,7 +55,7 @@ class StreamingKMeansSuite extends SparkFunSuite with TestSuiteBase { val (input, centers) = StreamingKMeansDataGenerator(numPoints, numBatches, k, d, r, 42) // setup and run the model training - val ssc = setupStreams(input, (inputDStream: DStream[Vector]) => { + ssc = setupStreams(input, (inputDStream: DStream[Vector]) => { model.trainOn(inputDStream) inputDStream.count() }) @@ -82,7 +91,7 @@ class StreamingKMeansSuite extends SparkFunSuite with TestSuiteBase { val (input, centers) = StreamingKMeansDataGenerator(numPoints, numBatches, k, d, r, 42) // setup and run the model training - val ssc = setupStreams(input, (inputDStream: DStream[Vector]) => { + ssc = setupStreams(input, (inputDStream: DStream[Vector]) => { kMeans.trainOn(inputDStream) inputDStream.count() }) @@ -114,7 +123,7 @@ class StreamingKMeansSuite extends SparkFunSuite with TestSuiteBase { StreamingKMeansDataGenerator(numPoints, numBatches, k, d, r, 42, Array(Vectors.dense(0.0))) // setup and run the model training - val ssc = setupStreams(input, (inputDStream: DStream[Vector]) => { + ssc = setupStreams(input, (inputDStream: DStream[Vector]) => { kMeans.trainOn(inputDStream) inputDStream.count() }) diff --git a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala index a2a4c5f6b8..34c07ed170 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/regression/StreamingLinearRegressionSuite.scala @@ -22,14 +22,23 @@ import scala.collection.mutable.ArrayBuffer import org.apache.spark.SparkFunSuite import org.apache.spark.mllib.linalg.Vectors import org.apache.spark.mllib.util.LinearDataGenerator +import org.apache.spark.streaming.{StreamingContext, TestSuiteBase} import org.apache.spark.streaming.dstream.DStream -import org.apache.spark.streaming.TestSuiteBase class StreamingLinearRegressionSuite extends SparkFunSuite with TestSuiteBase { // use longer wait time to ensure job completion override def maxWaitTimeMillis: Int = 20000 + var ssc: StreamingContext = _ + + override def afterFunction() { + super.afterFunction() + if (ssc != null) { + ssc.stop() + } + } + // Assert that two values are equal within tolerance epsilon def assertEqual(v1: Double, v2: Double, epsilon: Double) { def errorMessage = v1.toString + " did not equal " + v2.toString @@ -62,7 +71,7 @@ class StreamingLinearRegressionSuite extends SparkFunSuite with TestSuiteBase { } // apply model training to input stream - val ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => { + ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => { model.trainOn(inputDStream) inputDStream.count() }) @@ -98,7 +107,7 @@ class StreamingLinearRegressionSuite extends SparkFunSuite with TestSuiteBase { // apply model training to input stream, storing the intermediate results // (we add a count to ensure the result is a DStream) - val ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => { + ssc = setupStreams(input, (inputDStream: DStream[LabeledPoint]) => { model.trainOn(inputDStream) inputDStream.foreachRDD(x => history.append(math.abs(model.latestModel().weights(0) - 10.0))) inputDStream.count() @@ -129,7 +138,7 @@ class StreamingLinearRegressionSuite extends SparkFunSuite with TestSuiteBase { } // apply model predictions to test stream - val ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => { + ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => { model.predictOnValues(inputDStream.map(x => (x.label, x.features))) }) // collect the output as (true, estimated) tuples @@ -156,7 +165,7 @@ class StreamingLinearRegressionSuite extends SparkFunSuite with TestSuiteBase { } // train and predict - val ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => { + ssc = setupStreams(testInput, (inputDStream: DStream[LabeledPoint]) => { model.trainOn(inputDStream) model.predictOnValues(inputDStream.map(x => (x.label, x.features))) }) @@ -177,7 +186,7 @@ class StreamingLinearRegressionSuite extends SparkFunSuite with TestSuiteBase { val numBatches = 10 val nPoints = 100 val emptyInput = Seq.empty[Seq[LabeledPoint]] - val ssc = setupStreams(emptyInput, + ssc = setupStreams(emptyInput, (inputDStream: DStream[LabeledPoint]) => { model.trainOn(inputDStream) model.predictOnValues(inputDStream.map(x => (x.label, x.features))) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala index 6270137951..e076fb5ea1 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/ReceiverTracker.scala @@ -223,7 +223,11 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false // Signal the receivers to delete old block data if (WriteAheadLogUtils.enableReceiverLog(ssc.conf)) { logInfo(s"Cleanup old received batch data: $cleanupThreshTime") - endpoint.send(CleanupOldBlocks(cleanupThreshTime)) + synchronized { + if (isTrackerStarted) { + endpoint.send(CleanupOldBlocks(cleanupThreshTime)) + } + } } } @@ -285,8 +289,10 @@ class ReceiverTracker(ssc: StreamingContext, skipReceiverLaunch: Boolean = false } /** Update a receiver's maximum ingestion rate */ - def sendRateUpdate(streamUID: Int, newRate: Long): Unit = { - endpoint.send(UpdateReceiverRateLimit(streamUID, newRate)) + def sendRateUpdate(streamUID: Int, newRate: Long): Unit = synchronized { + if (isTrackerStarted) { + endpoint.send(UpdateReceiverRateLimit(streamUID, newRate)) + } } /** Add new blocks for the given stream */ diff --git a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java index a34f234758..e0718f73aa 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java @@ -1735,6 +1735,7 @@ public class JavaAPISuite extends LocalJavaStreamingContext implements Serializa @SuppressWarnings("unchecked") @Test public void testContextGetOrCreate() throws InterruptedException { + ssc.stop(); final SparkConf conf = new SparkConf() .setMaster("local[2]") diff --git a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala index 08faeaa58f..255376807c 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala @@ -81,39 +81,41 @@ class BasicOperationsSuite extends TestSuiteBase { test("repartition (more partitions)") { val input = Seq(1 to 100, 101 to 200, 201 to 300) val operation = (r: DStream[Int]) => r.repartition(5) - val ssc = setupStreams(input, operation, 2) - val output = runStreamsWithPartitions(ssc, 3, 3) - assert(output.size === 3) - val first = output(0) - val second = output(1) - val third = output(2) - - assert(first.size === 5) - assert(second.size === 5) - assert(third.size === 5) - - assert(first.flatten.toSet.equals((1 to 100).toSet) ) - assert(second.flatten.toSet.equals((101 to 200).toSet)) - assert(third.flatten.toSet.equals((201 to 300).toSet)) + withStreamingContext(setupStreams(input, operation, 2)) { ssc => + val output = runStreamsWithPartitions(ssc, 3, 3) + assert(output.size === 3) + val first = output(0) + val second = output(1) + val third = output(2) + + assert(first.size === 5) + assert(second.size === 5) + assert(third.size === 5) + + assert(first.flatten.toSet.equals((1 to 100).toSet)) + assert(second.flatten.toSet.equals((101 to 200).toSet)) + assert(third.flatten.toSet.equals((201 to 300).toSet)) + } } test("repartition (fewer partitions)") { val input = Seq(1 to 100, 101 to 200, 201 to 300) val operation = (r: DStream[Int]) => r.repartition(2) - val ssc = setupStreams(input, operation, 5) - val output = runStreamsWithPartitions(ssc, 3, 3) - assert(output.size === 3) - val first = output(0) - val second = output(1) - val third = output(2) - - assert(first.size === 2) - assert(second.size === 2) - assert(third.size === 2) - - assert(first.flatten.toSet.equals((1 to 100).toSet)) - assert(second.flatten.toSet.equals( (101 to 200).toSet)) - assert(third.flatten.toSet.equals((201 to 300).toSet)) + withStreamingContext(setupStreams(input, operation, 5)) { ssc => + val output = runStreamsWithPartitions(ssc, 3, 3) + assert(output.size === 3) + val first = output(0) + val second = output(1) + val third = output(2) + + assert(first.size === 2) + assert(second.size === 2) + assert(third.size === 2) + + assert(first.flatten.toSet.equals((1 to 100).toSet)) + assert(second.flatten.toSet.equals((101 to 200).toSet)) + assert(third.flatten.toSet.equals((201 to 300).toSet)) + } } test("groupByKey") { diff --git a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala index b74d67c63a..ec2852d9a0 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala @@ -325,27 +325,31 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { } test("test track the number of input stream") { - val ssc = new StreamingContext(conf, batchDuration) + withStreamingContext(new StreamingContext(conf, batchDuration)) { ssc => - class TestInputDStream extends InputDStream[String](ssc) { - def start() { } - def stop() { } - def compute(validTime: Time): Option[RDD[String]] = None - } + class TestInputDStream extends InputDStream[String](ssc) { + def start() {} - class TestReceiverInputDStream extends ReceiverInputDStream[String](ssc) { - def getReceiver: Receiver[String] = null - } + def stop() {} + + def compute(validTime: Time): Option[RDD[String]] = None + } + + class TestReceiverInputDStream extends ReceiverInputDStream[String](ssc) { + def getReceiver: Receiver[String] = null + } - // Register input streams - val receiverInputStreams = Array(new TestReceiverInputDStream, new TestReceiverInputDStream) - val inputStreams = Array(new TestInputDStream, new TestInputDStream, new TestInputDStream) + // Register input streams + val receiverInputStreams = Array(new TestReceiverInputDStream, new TestReceiverInputDStream) + val inputStreams = Array(new TestInputDStream, new TestInputDStream, new TestInputDStream) - assert(ssc.graph.getInputStreams().length == receiverInputStreams.length + inputStreams.length) - assert(ssc.graph.getReceiverInputStreams().length == receiverInputStreams.length) - assert(ssc.graph.getReceiverInputStreams() === receiverInputStreams) - assert(ssc.graph.getInputStreams().map(_.id) === Array.tabulate(5)(i => i)) - assert(receiverInputStreams.map(_.id) === Array(0, 1)) + assert(ssc.graph.getInputStreams().length == + receiverInputStreams.length + inputStreams.length) + assert(ssc.graph.getReceiverInputStreams().length == receiverInputStreams.length) + assert(ssc.graph.getReceiverInputStreams() === receiverInputStreams) + assert(ssc.graph.getInputStreams().map(_.id) === Array.tabulate(5)(i => i)) + assert(receiverInputStreams.map(_.id) === Array(0, 1)) + } } def testFileStream(newFilesOnly: Boolean) { diff --git a/streaming/src/test/scala/org/apache/spark/streaming/MasterFailureTest.scala b/streaming/src/test/scala/org/apache/spark/streaming/MasterFailureTest.scala index 6e9d443109..0e64b57e0f 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/MasterFailureTest.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/MasterFailureTest.scala @@ -244,7 +244,13 @@ object MasterFailureTest extends Logging { } catch { case e: Exception => logError("Error running streaming context", e) } - if (killingThread.isAlive) killingThread.interrupt() + if (killingThread.isAlive) { + killingThread.interrupt() + // SparkContext.stop will set SparkEnv.env to null. We need to make sure SparkContext is + // stopped before running the next test. Otherwise, it's possible that we set SparkEnv.env + // to null after the next test creates the new SparkContext and fail the test. + killingThread.join() + } ssc.stop() logInfo("Has been killed = " + killed) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala index 4bba9691f8..84a5fbb3d9 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingContextSuite.scala @@ -120,7 +120,7 @@ class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeo val myConf = SparkContext.updatedConf(new SparkConf(false), master, appName) myConf.set("spark.streaming.checkpoint.directory", checkpointDirectory) - val ssc = new StreamingContext(myConf, batchDuration) + ssc = new StreamingContext(myConf, batchDuration) assert(ssc.checkpointDir != null) } @@ -369,16 +369,22 @@ class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeo } assert(exception.isInstanceOf[TestFailedDueToTimeoutException], "Did not wait for stop") + var t: Thread = null // test whether wait exits if context is stopped failAfter(10000 millis) { // 10 seconds because spark takes a long time to shutdown - new Thread() { + t = new Thread() { override def run() { Thread.sleep(500) ssc.stop() } - }.start() + } + t.start() ssc.awaitTermination() } + // SparkContext.stop will set SparkEnv.env to null. We need to make sure SparkContext is stopped + // before running the next test. Otherwise, it's possible that we set SparkEnv.env to null after + // the next test creates the new SparkContext and fail the test. + t.join() } test("awaitTermination after stop") { @@ -430,16 +436,22 @@ class StreamingContextSuite extends SparkFunSuite with BeforeAndAfter with Timeo assert(ssc.awaitTerminationOrTimeout(500) === false) } + var t: Thread = null // test whether awaitTerminationOrTimeout() return true if context is stopped failAfter(10000 millis) { // 10 seconds because spark takes a long time to shutdown - new Thread() { + t = new Thread() { override def run() { Thread.sleep(500) ssc.stop() } - }.start() + } + t.start() assert(ssc.awaitTerminationOrTimeout(10000) === true) } + // SparkContext.stop will set SparkEnv.env to null. We need to make sure SparkContext is stopped + // before running the next test. Otherwise, it's possible that we set SparkEnv.env to null after + // the next test creates the new SparkContext and fail the test. + t.join() } test("getOrCreate") { diff --git a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala index 4bc1dd4a30..d840c349bb 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/StreamingListenerSuite.scala @@ -36,13 +36,22 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { val input = (1 to 4).map(Seq(_)).toSeq val operation = (d: DStream[Int]) => d.map(x => x) + var ssc: StreamingContext = _ + + override def afterFunction() { + super.afterFunction() + if (ssc != null) { + ssc.stop() + } + } + // To make sure that the processing start and end times in collected // information are different for successive batches override def batchDuration: Duration = Milliseconds(100) override def actuallyWait: Boolean = true test("batch info reporting") { - val ssc = setupStreams(input, operation) + ssc = setupStreams(input, operation) val collector = new BatchInfoCollector ssc.addStreamingListener(collector) runStreams(ssc, input.size, input.size) @@ -107,7 +116,7 @@ class StreamingListenerSuite extends TestSuiteBase with Matchers { } test("receiver info reporting") { - val ssc = new StreamingContext("local[2]", "test", Milliseconds(1000)) + ssc = new StreamingContext("local[2]", "test", Milliseconds(1000)) val inputStream = ssc.receiverStream(new StreamingListenerSuiteReceiver) inputStream.foreachRDD(_.count) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala index aff8b53f75..afad5f16db 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/scheduler/ReceiverTrackerSuite.scala @@ -29,36 +29,40 @@ import org.apache.spark.storage.StorageLevel /** Testsuite for receiver scheduling */ class ReceiverTrackerSuite extends TestSuiteBase { val sparkConf = new SparkConf().setMaster("local[8]").setAppName("test") - val ssc = new StreamingContext(sparkConf, Milliseconds(100)) - ignore("Receiver tracker - propagates rate limit") { - object ReceiverStartedWaiter extends StreamingListener { - @volatile - var started = false + test("Receiver tracker - propagates rate limit") { + withStreamingContext(new StreamingContext(sparkConf, Milliseconds(100))) { ssc => + object ReceiverStartedWaiter extends StreamingListener { + @volatile + var started = false - override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted): Unit = { - started = true + override def onReceiverStarted(receiverStarted: StreamingListenerReceiverStarted): Unit = { + started = true + } } - } - - ssc.addStreamingListener(ReceiverStartedWaiter) - ssc.scheduler.listenerBus.start(ssc.sc) - SingletonTestRateReceiver.reset() - - val newRateLimit = 100L - val inputDStream = new RateLimitInputDStream(ssc) - val tracker = new ReceiverTracker(ssc) - tracker.start() - // we wait until the Receiver has registered with the tracker, - // otherwise our rate update is lost - eventually(timeout(5 seconds)) { - assert(ReceiverStartedWaiter.started) - } - tracker.sendRateUpdate(inputDStream.id, newRateLimit) - // this is an async message, we need to wait a bit for it to be processed - eventually(timeout(3 seconds)) { - assert(inputDStream.getCurrentRateLimit.get === newRateLimit) + ssc.addStreamingListener(ReceiverStartedWaiter) + ssc.scheduler.listenerBus.start(ssc.sc) + SingletonTestRateReceiver.reset() + + val newRateLimit = 100L + val inputDStream = new RateLimitInputDStream(ssc) + val tracker = new ReceiverTracker(ssc) + tracker.start() + try { + // we wait until the Receiver has registered with the tracker, + // otherwise our rate update is lost + eventually(timeout(5 seconds)) { + assert(ReceiverStartedWaiter.started) + } + tracker.sendRateUpdate(inputDStream.id, newRateLimit) + // this is an async message, we need to wait a bit for it to be processed + eventually(timeout(3 seconds)) { + assert(inputDStream.getCurrentRateLimit.get === newRateLimit) + } + } finally { + tracker.stop(false) + } } } } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala index 0891309f95..995f1197cc 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/ui/StreamingJobProgressListenerSuite.scala @@ -22,15 +22,24 @@ import java.util.Properties import org.scalatest.Matchers import org.apache.spark.scheduler.SparkListenerJobStart +import org.apache.spark.streaming._ import org.apache.spark.streaming.dstream.DStream import org.apache.spark.streaming.scheduler._ -import org.apache.spark.streaming.{Duration, Time, Milliseconds, TestSuiteBase} class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { val input = (1 to 4).map(Seq(_)).toSeq val operation = (d: DStream[Int]) => d.map(x => x) + var ssc: StreamingContext = _ + + override def afterFunction() { + super.afterFunction() + if (ssc != null) { + ssc.stop() + } + } + private def createJobStart( batchTime: Time, outputOpId: Int, jobId: Int): SparkListenerJobStart = { val properties = new Properties() @@ -46,7 +55,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { test("onBatchSubmitted, onBatchStarted, onBatchCompleted, " + "onReceiverStarted, onReceiverError, onReceiverStopped") { - val ssc = setupStreams(input, operation) + ssc = setupStreams(input, operation) val listener = new StreamingJobProgressListener(ssc) val streamIdToInputInfo = Map( @@ -141,7 +150,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { } test("Remove the old completed batches when exceeding the limit") { - val ssc = setupStreams(input, operation) + ssc = setupStreams(input, operation) val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000) val listener = new StreamingJobProgressListener(ssc) @@ -158,7 +167,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { } test("out-of-order onJobStart and onBatchXXX") { - val ssc = setupStreams(input, operation) + ssc = setupStreams(input, operation) val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000) val listener = new StreamingJobProgressListener(ssc) @@ -209,7 +218,7 @@ class StreamingJobProgressListenerSuite extends TestSuiteBase with Matchers { } test("detect memory leak") { - val ssc = setupStreams(input, operation) + ssc = setupStreams(input, operation) val listener = new StreamingJobProgressListener(ssc) val limit = ssc.conf.getInt("spark.streaming.ui.retainedBatches", 1000) |