diff options
155 files changed, 4346 insertions, 1195 deletions
@@ -40,7 +40,7 @@ locally with one thread, or "local[N]" to run locally with N threads. ## Running tests -Testing first requires [Building](#Building) Spark. Once Spark is built, tests +Testing first requires [Building](#building) Spark. Once Spark is built, tests can be run using: `./sbt/sbt test` diff --git a/bin/run-example b/bin/run-example index 6c5d4a6a8f..2e9d51440b 100755 --- a/bin/run-example +++ b/bin/run-example @@ -45,20 +45,15 @@ fi EXAMPLES_DIR="$FWDIR"/examples SPARK_EXAMPLES_JAR="" if [ -e "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/*assembly*[0-9Tg].jar ]; then - # Use the JAR from the SBT build export SPARK_EXAMPLES_JAR=`ls "$EXAMPLES_DIR"/target/scala-$SCALA_VERSION/*assembly*[0-9Tg].jar` fi -if [ -e "$EXAMPLES_DIR"/target/spark-examples*[0-9Tg].jar ]; then - # Use the JAR from the Maven build - # TODO: this also needs to become an assembly! - export SPARK_EXAMPLES_JAR=`ls "$EXAMPLES_DIR"/target/spark-examples*[0-9Tg].jar` -fi if [[ -z $SPARK_EXAMPLES_JAR ]]; then echo "Failed to find Spark examples assembly in $FWDIR/examples/target" >&2 echo "You need to build Spark with sbt/sbt assembly before running this program" >&2 exit 1 fi + # Since the examples JAR ideally shouldn't include spark-core (that dependency should be # "provided"), also add our standard Spark classpath, built using compute-classpath.sh. CLASSPATH=`$FWDIR/bin/compute-classpath.sh` diff --git a/conf/log4j.properties.template b/conf/log4j.properties.template index d72dbadc39..17d1978dde 100644 --- a/conf/log4j.properties.template +++ b/conf/log4j.properties.template @@ -1,6 +1,7 @@ # Set everything to be logged to the console log4j.rootCategory=INFO, console log4j.appender.console=org.apache.log4j.ConsoleAppender +log4j.appender.console.target=System.err log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n diff --git a/core/pom.xml b/core/pom.xml index aac0a9d11e..9e5a450d57 100644 --- a/core/pom.xml +++ b/core/pom.xml @@ -99,6 +99,11 @@ <artifactId>akka-slf4j_${scala.binary.version}</artifactId> </dependency> <dependency> + <groupId>${akka.group}</groupId> + <artifactId>akka-testkit_${scala.binary.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-library</artifactId> </dependency> @@ -166,6 +171,11 @@ <scope>test</scope> </dependency> <dependency> + <groupId>org.mockito</groupId> + <artifactId>mockito-all</artifactId> + <scope>test</scope> + </dependency> + <dependency> <groupId>org.scalacheck</groupId> <artifactId>scalacheck_${scala.binary.version}</artifactId> <scope>test</scope> diff --git a/core/src/main/resources/org/apache/spark/log4j-defaults.properties b/core/src/main/resources/org/apache/spark/log4j-defaults.properties index d72dbadc39..17d1978dde 100644 --- a/core/src/main/resources/org/apache/spark/log4j-defaults.properties +++ b/core/src/main/resources/org/apache/spark/log4j-defaults.properties @@ -1,6 +1,7 @@ # Set everything to be logged to the console log4j.rootCategory=INFO, console log4j.appender.console=org.apache.log4j.ConsoleAppender +log4j.appender.console.target=System.err log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala index cdae167aef..77b8ca1cce 100644 --- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala +++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala @@ -55,7 +55,7 @@ private[spark] class MapOutputTracker(conf: SparkConf) extends Logging { private val timeout = AkkaUtils.askTimeout(conf) // Set to the MapOutputTrackerActor living on the driver - var trackerActor: Either[ActorRef, ActorSelection] = _ + var trackerActor: ActorRef = _ protected val mapStatuses = new TimeStampedHashMap[Int, Array[MapStatus]] @@ -71,17 +71,7 @@ private[spark] class MapOutputTracker(conf: SparkConf) extends Logging { // throw a SparkException if this fails. private def askTracker(message: Any): Any = { try { - /* - The difference between ActorRef and ActorSelection is well explained here: - http://doc.akka.io/docs/akka/2.2.3/project/migration-guide-2.1.x-2.2.x.html#Use_actorSelection_instead_of_actorFor - In spark a map output tracker can be either started on Driver where it is created which - is an ActorRef or it can be on executor from where it is looked up which is an - actorSelection. - */ - val future = trackerActor match { - case Left(a: ActorRef) => a.ask(message)(timeout) - case Right(b: ActorSelection) => b.ask(message)(timeout) - } + val future = trackerActor.ask(message)(timeout) Await.result(future, timeout) } catch { case e: Exception => diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala index 31b0773bfe..9b043f06dd 100644 --- a/core/src/main/scala/org/apache/spark/Partitioner.scala +++ b/core/src/main/scala/org/apache/spark/Partitioner.scala @@ -61,7 +61,8 @@ object Partitioner { } /** - * A [[org.apache.spark.Partitioner]] that implements hash-based partitioning using Java's `Object.hashCode`. + * A [[org.apache.spark.Partitioner]] that implements hash-based partitioning using + * Java's `Object.hashCode`. * * Java arrays have hashCodes that are based on the arrays' identities rather than their contents, * so attempting to partition an RDD[Array[_]] or RDD[(Array[_], _)] using a HashPartitioner will @@ -84,8 +85,8 @@ class HashPartitioner(partitions: Int) extends Partitioner { } /** - * A [[org.apache.spark.Partitioner]] that partitions sortable records by range into roughly equal ranges. - * Determines the ranges by sampling the RDD passed in. + * A [[org.apache.spark.Partitioner]] that partitions sortable records by range into roughly + * equal ranges. The ranges are determined by sampling the content of the RDD passed in. */ class RangePartitioner[K <% Ordered[K]: ClassTag, V]( partitions: Int, diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala index b166527614..2de32231e8 100644 --- a/core/src/main/scala/org/apache/spark/SparkConf.scala +++ b/core/src/main/scala/org/apache/spark/SparkConf.scala @@ -67,7 +67,7 @@ class SparkConf(loadDefaults: Boolean) extends Serializable with Cloneable with /** Set JAR files to distribute to the cluster. */ def setJars(jars: Seq[String]): SparkConf = { - for (jar <- jars if (jar == null)) logWarning("null jar passed to SparkContext constructor") + for (jar <- jars if (jar == null)) logWarning("null jar passed to SparkContext constructor") set("spark.jars", jars.filter(_ != null).mkString(",")) } @@ -165,6 +165,11 @@ class SparkConf(loadDefaults: Boolean) extends Serializable with Cloneable with getOption(key).map(_.toDouble).getOrElse(defaultValue) } + /** Get a parameter as a boolean, falling back to a default if not set */ + def getBoolean(key: String, defaultValue: Boolean): Boolean = { + getOption(key).map(_.toBoolean).getOrElse(defaultValue) + } + /** Get all executor environment variables set on this SparkConf */ def getExecutorEnv: Seq[(String, String)] = { val prefix = "spark.executorEnv." diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 99dcced7d7..66c226e491 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -31,9 +31,9 @@ import scala.reflect.{ClassTag, classTag} import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.io.{ArrayWritable, BooleanWritable, BytesWritable, DoubleWritable, -FloatWritable, IntWritable, LongWritable, NullWritable, Text, Writable} + FloatWritable, IntWritable, LongWritable, NullWritable, Text, Writable} import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf, SequenceFileInputFormat, -TextInputFormat} + TextInputFormat} import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat, Job => NewHadoopJob} import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat} import org.apache.mesos.MesosNativeLibrary @@ -49,7 +49,7 @@ import org.apache.spark.scheduler.local.LocalBackend import org.apache.spark.storage.{BlockManagerSource, RDDInfo, StorageStatus, StorageUtils} import org.apache.spark.ui.SparkUI import org.apache.spark.util.{Utils, TimeStampedHashMap, MetadataCleaner, MetadataCleanerType, -ClosureCleaner} + ClosureCleaner} /** * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark @@ -116,7 +116,7 @@ class SparkContext( throw new SparkException("An application must be set in your configuration") } - if (conf.get("spark.log-conf", "false").toBoolean) { + if (conf.getBoolean("spark.logConf", false)) { logInfo("Spark configuration:\n" + conf.toDebugString) } @@ -244,6 +244,10 @@ class SparkContext( localProperties.set(new Properties()) } + /** + * Set a local property that affects jobs submitted from this thread, such as the + * Spark fair scheduler pool. + */ def setLocalProperty(key: String, value: String) { if (localProperties.get() == null) { localProperties.set(new Properties()) @@ -255,6 +259,10 @@ class SparkContext( } } + /** + * Get a local property set in this thread, or null if it is missing. See + * [[org.apache.spark.SparkContext.setLocalProperty]]. + */ def getLocalProperty(key: String): String = Option(localProperties.get).map(_.getProperty(key)).getOrElse(null) @@ -265,7 +273,7 @@ class SparkContext( } /** - * Assigns a group id to all the jobs started by this thread until the group id is set to a + * Assigns a group ID to all the jobs started by this thread until the group ID is set to a * different value or cleared. * * Often, a unit of execution in an application consists of multiple Spark actions or jobs. @@ -288,7 +296,7 @@ class SparkContext( setLocalProperty(SparkContext.SPARK_JOB_GROUP_ID, groupId) } - /** Clear the job group id and its description. */ + /** Clear the current thread's job group ID and its description. */ def clearJobGroup() { setLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, null) setLocalProperty(SparkContext.SPARK_JOB_GROUP_ID, null) @@ -517,15 +525,15 @@ class SparkContext( // Methods for creating shared variables /** - * Create an [[org.apache.spark.Accumulator]] variable of a given type, which tasks can "add" values - * to using the `+=` method. Only the driver can access the accumulator's `value`. + * Create an [[org.apache.spark.Accumulator]] variable of a given type, which tasks can "add" + * values to using the `+=` method. Only the driver can access the accumulator's `value`. */ def accumulator[T](initialValue: T)(implicit param: AccumulatorParam[T]) = new Accumulator(initialValue, param) /** - * Create an [[org.apache.spark.Accumulable]] shared variable, to which tasks can add values with `+=`. - * Only the driver can access the accumuable's `value`. + * Create an [[org.apache.spark.Accumulable]] shared variable, to which tasks can add values + * with `+=`. Only the driver can access the accumuable's `value`. * @tparam T accumulator type * @tparam R type that can be added to the accumulator */ @@ -538,14 +546,16 @@ class SparkContext( * Growable and TraversableOnce are the standard APIs that guarantee += and ++=, implemented by * standard mutable collections. So you can use this with mutable Map, Set, etc. */ - def accumulableCollection[R <% Growable[T] with TraversableOnce[T] with Serializable, T](initialValue: R) = { + def accumulableCollection[R <% Growable[T] with TraversableOnce[T] with Serializable, T] + (initialValue: R) = { val param = new GrowableAccumulableParam[R,T] new Accumulable(initialValue, param) } /** - * Broadcast a read-only variable to the cluster, returning a [[org.apache.spark.broadcast.Broadcast]] object for - * reading it in distributed functions. The variable will be sent to each cluster only once. + * Broadcast a read-only variable to the cluster, returning a + * [[org.apache.spark.broadcast.Broadcast]] object for reading it in distributed functions. + * The variable will be sent to each cluster only once. */ def broadcast[T](value: T) = env.broadcastManager.newBroadcast[T](value, isLocal) @@ -1010,7 +1020,8 @@ object SparkContext { implicit def stringToText(s: String) = new Text(s) - private implicit def arrayToArrayWritable[T <% Writable: ClassTag](arr: Traversable[T]): ArrayWritable = { + private implicit def arrayToArrayWritable[T <% Writable: ClassTag](arr: Traversable[T]) + : ArrayWritable = { def anyToWritable[U <% Writable](u: U): Writable = u new ArrayWritable(classTag[T].runtimeClass.asInstanceOf[Class[Writable]], @@ -1033,7 +1044,9 @@ object SparkContext { implicit def booleanWritableConverter() = simpleWritableConverter[Boolean, BooleanWritable](_.get) - implicit def bytesWritableConverter() = simpleWritableConverter[Array[Byte], BytesWritable](_.getBytes) + implicit def bytesWritableConverter() = { + simpleWritableConverter[Array[Byte], BytesWritable](_.getBytes) + } implicit def stringWritableConverter() = simpleWritableConverter[String, Text](_.toString) @@ -1049,7 +1062,8 @@ object SparkContext { if (uri != null) { val uriStr = uri.toString if (uriStr.startsWith("jar:file:")) { - // URI will be of the form "jar:file:/path/foo.jar!/package/cls.class", so pull out the /path/foo.jar + // URI will be of the form "jar:file:/path/foo.jar!/package/cls.class", + // so pull out the /path/foo.jar List(uriStr.substring("jar:file:".length, uriStr.indexOf('!'))) } else { Nil @@ -1203,7 +1217,7 @@ object SparkContext { case mesosUrl @ MESOS_REGEX(_) => MesosNativeLibrary.load() val scheduler = new TaskSchedulerImpl(sc) - val coarseGrained = sc.conf.get("spark.mesos.coarse", "false").toBoolean + val coarseGrained = sc.conf.getBoolean("spark.mesos.coarse", false) val url = mesosUrl.stripPrefix("mesos://") // strip scheme from raw Mesos URLs val backend = if (coarseGrained) { new CoarseMesosSchedulerBackend(scheduler, sc, url, appName) diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala index 634a94f0a7..e093e2f162 100644 --- a/core/src/main/scala/org/apache/spark/SparkEnv.scala +++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala @@ -17,11 +17,10 @@ package org.apache.spark -import collection.mutable -import serializer.Serializer +import scala.collection.mutable +import scala.concurrent.Await import akka.actor._ -import akka.remote.RemoteActorRefProvider import org.apache.spark.broadcast.BroadcastManager import org.apache.spark.metrics.MetricsSystem @@ -157,17 +156,18 @@ object SparkEnv extends Logging { conf.get("spark.closure.serializer", "org.apache.spark.serializer.JavaSerializer"), conf) - def registerOrLookup(name: String, newActor: => Actor): Either[ActorRef, ActorSelection] = { + def registerOrLookup(name: String, newActor: => Actor): ActorRef = { if (isDriver) { logInfo("Registering " + name) - Left(actorSystem.actorOf(Props(newActor), name = name)) + actorSystem.actorOf(Props(newActor), name = name) } else { val driverHost: String = conf.get("spark.driver.host", "localhost") - val driverPort: Int = conf.get("spark.driver.port", "7077").toInt + val driverPort: Int = conf.getInt("spark.driver.port", 7077) Utils.checkHost(driverHost, "Expected hostname") - val url = "akka.tcp://spark@%s:%s/user/%s".format(driverHost, driverPort, name) - logInfo("Connecting to " + name + ": " + url) - Right(actorSystem.actorSelection(url)) + val url = s"akka.tcp://spark@$driverHost:$driverPort/user/$name" + val timeout = AkkaUtils.lookupTimeout(conf) + logInfo(s"Connecting to $name: $url") + Await.result(actorSystem.actorSelection(url).resolveOne(timeout), timeout) } } diff --git a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala index 103a1c2051..618d95015f 100644 --- a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala +++ b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala @@ -127,10 +127,6 @@ class SparkHadoopWriter(@transient jobConf: JobConf) cmtr.commitJob(getJobContext()) } - def cleanup() { - getOutputCommitter().cleanupJob(getJobContext()) - } - // ********* Private Functions ********* private def getOutputFormat(): OutputFormat[AnyRef,AnyRef] = { diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala index da30cf619a..b0dedc6f4e 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala @@ -207,13 +207,13 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[Double, Jav * e.g. for the array * [1,10,20,50] the buckets are [1,10) [10,20) [20,50] * e.g 1<=x<10 , 10<=x<20, 20<=x<50 - * And on the input of 1 and 50 we would have a histogram of 1,0,0 - * + * And on the input of 1 and 50 we would have a histogram of 1,0,0 + * * Note: if your histogram is evenly spaced (e.g. [0, 10, 20, 30]) this can be switched * from an O(log n) inseration to O(1) per element. (where n = # buckets) if you set evenBuckets * to true. * buckets must be sorted and not contain any duplicates. - * buckets array must be at least two elements + * buckets array must be at least two elements * All NaN entries are treated the same. If you have a NaN bucket it must be * the maximum value of the last position and all NaN entries will be counted * in that bucket. @@ -225,6 +225,12 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[Double, Jav def histogram(buckets: Array[Double], evenBuckets: Boolean): Array[Long] = { srdd.histogram(buckets.map(_.toDouble), evenBuckets) } + + /** Assign a name to this RDD */ + def setName(name: String): JavaDoubleRDD = { + srdd.setName(name) + this + } } object JavaDoubleRDD { diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala index 55c87450ac..0fb7e195b3 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala @@ -647,6 +647,12 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kClassTag: ClassTag[K def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): JavaRDD[(K, Long)] = { rdd.countApproxDistinctByKey(relativeSD, numPartitions) } + + /** Assign a name to this RDD */ + def setName(name: String): JavaPairRDD[K, V] = { + rdd.setName(name) + this + } } object JavaPairRDD { diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala index 037cd1c774..7d48ce01cf 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala @@ -127,6 +127,12 @@ JavaRDDLike[T, JavaRDD[T]] { wrapRDD(rdd.subtract(other, p)) override def toString = rdd.toString + + /** Assign a name to this RDD */ + def setName(name: String): JavaRDD[T] = { + rdd.setName(name) + this + } } object JavaRDD { diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala index 924d8af060..ebbbbd8806 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala @@ -245,6 +245,11 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { } /** + * Return an array that contains all of the elements in this RDD. + */ + def toArray(): JList[T] = collect() + + /** * Return an array that contains all of the elements in a specific partition of this RDD. */ def collectPartitions(partitionIds: Array[Int]): Array[JList[T]] = { @@ -455,4 +460,5 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def countApproxDistinct(relativeSD: Double = 0.05): Long = rdd.countApproxDistinct(relativeSD) + def name(): String = rdd.name } diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala index e93b10fd7e..7a6f044965 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala @@ -425,6 +425,51 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork def clearCallSite() { sc.clearCallSite() } + + /** + * Set a local property that affects jobs submitted from this thread, such as the + * Spark fair scheduler pool. + */ + def setLocalProperty(key: String, value: String): Unit = sc.setLocalProperty(key, value) + + /** + * Get a local property set in this thread, or null if it is missing. See + * [[org.apache.spark.api.java.JavaSparkContext.setLocalProperty]]. + */ + def getLocalProperty(key: String): String = sc.getLocalProperty(key) + + /** + * Assigns a group ID to all the jobs started by this thread until the group ID is set to a + * different value or cleared. + * + * Often, a unit of execution in an application consists of multiple Spark actions or jobs. + * Application programmers can use this method to group all those jobs together and give a + * group description. Once set, the Spark web UI will associate such jobs with this group. + * + * The application can also use [[org.apache.spark.api.java.JavaSparkContext.cancelJobGroup]] + * to cancel all running jobs in this group. For example, + * {{{ + * // In the main thread: + * sc.setJobGroup("some_job_to_cancel", "some job description"); + * rdd.map(...).count(); + * + * // In a separate thread: + * sc.cancelJobGroup("some_job_to_cancel"); + * }}} + */ + def setJobGroup(groupId: String, description: String): Unit = sc.setJobGroup(groupId, description) + + /** Clear the current thread's job group ID and its description. */ + def clearJobGroup(): Unit = sc.clearJobGroup() + + /** + * Cancel active jobs for the specified group. See + * [[org.apache.spark.api.java.JavaSparkContext.setJobGroup]] for more information. + */ + def cancelJobGroup(groupId: String): Unit = sc.cancelJobGroup(groupId) + + /** Cancel all jobs that have been scheduled or are running. */ + def cancelAllJobs(): Unit = sc.cancelAllJobs() } object JavaSparkContext { @@ -436,5 +481,12 @@ object JavaSparkContext { * Find the JAR from which a given class was loaded, to make it easy for users to pass * their JARs to SparkContext. */ - def jarOfClass(cls: Class[_]) = SparkContext.jarOfClass(cls).toArray + def jarOfClass(cls: Class[_]): Array[String] = SparkContext.jarOfClass(cls).toArray + + /** + * Find the JAR that contains the class of a particular object, to make it easy for users + * to pass their JARs to SparkContext. In most cases you can call jarOfObject(this) in + * your driver program. + */ + def jarOfObject(obj: AnyRef): Array[String] = SparkContext.jarOfObject(obj).toArray } diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala index 32cc70e8c9..40c519b5bd 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala @@ -41,7 +41,7 @@ private[spark] class PythonRDD[T: ClassTag]( accumulator: Accumulator[JList[Array[Byte]]]) extends RDD[Array[Byte]](parent) { - val bufferSize = conf.get("spark.buffer.size", "65536").toInt + val bufferSize = conf.getInt("spark.buffer.size", 65536) override def getPartitions = parent.partitions @@ -250,7 +250,7 @@ private class PythonAccumulatorParam(@transient serverHost: String, serverPort: Utils.checkHost(serverHost, "Expected hostname") - val bufferSize = SparkEnv.get.conf.get("spark.buffer.size", "65536").toInt + val bufferSize = SparkEnv.get.conf.getInt("spark.buffer.size", 65536) override def zero(value: JList[Array[Byte]]): JList[Array[Byte]] = new JArrayList diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala index db596d5fcc..0eacda3d7d 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala @@ -92,8 +92,8 @@ private object HttpBroadcast extends Logging { def initialize(isDriver: Boolean, conf: SparkConf) { synchronized { if (!initialized) { - bufferSize = conf.get("spark.buffer.size", "65536").toInt - compress = conf.get("spark.broadcast.compress", "true").toBoolean + bufferSize = conf.getInt("spark.buffer.size", 65536) + compress = conf.getBoolean("spark.broadcast.compress", true) if (isDriver) { createServer(conf) conf.set("spark.httpBroadcast.uri", serverUri) diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala index 9530938278..fdf92eca4f 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala @@ -180,7 +180,7 @@ extends Logging { initialized = false } - lazy val BLOCK_SIZE = conf.get("spark.broadcast.blockSize", "4096").toInt * 1024 + lazy val BLOCK_SIZE = conf.getInt("spark.broadcast.blockSize", 4096) * 1024 def blockifyObject[T](obj: T): TorrentInfo = { val byteArray = Utils.serialize[T](obj) diff --git a/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala b/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala index 19d393a0db..e38459b883 100644 --- a/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala +++ b/core/src/main/scala/org/apache/spark/deploy/ApplicationDescription.scala @@ -19,7 +19,7 @@ package org.apache.spark.deploy private[spark] class ApplicationDescription( val name: String, - val maxCores: Int, /* Integer.MAX_VALUE denotes an unlimited number of cores */ + val maxCores: Option[Int], val memoryPerSlave: Int, val command: Command, val sparkHome: String, diff --git a/core/src/main/scala/org/apache/spark/deploy/Client.scala b/core/src/main/scala/org/apache/spark/deploy/Client.scala new file mode 100644 index 0000000000..e133893f6c --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/Client.scala @@ -0,0 +1,151 @@ +/* + * 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.deploy + +import scala.collection.JavaConversions._ +import scala.collection.mutable.Map +import scala.concurrent._ + +import akka.actor._ +import akka.pattern.ask +import org.apache.log4j.{Level, Logger} + +import org.apache.spark.{Logging, SparkConf} +import org.apache.spark.deploy.DeployMessages._ +import org.apache.spark.deploy.master.{DriverState, Master} +import org.apache.spark.util.{AkkaUtils, Utils} +import akka.actor.Actor.emptyBehavior +import akka.remote.{AssociationErrorEvent, DisassociatedEvent, RemotingLifecycleEvent} + +/** + * Proxy that relays messages to the driver. + */ +class ClientActor(driverArgs: ClientArguments, conf: SparkConf) extends Actor with Logging { + var masterActor: ActorSelection = _ + val timeout = AkkaUtils.askTimeout(conf) + + override def preStart() = { + masterActor = context.actorSelection(Master.toAkkaUrl(driverArgs.master)) + + context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) + + println(s"Sending ${driverArgs.cmd} command to ${driverArgs.master}") + + driverArgs.cmd match { + case "launch" => + // TODO: We could add an env variable here and intercept it in `sc.addJar` that would + // truncate filesystem paths similar to what YARN does. For now, we just require + // people call `addJar` assuming the jar is in the same directory. + val env = Map[String, String]() + System.getenv().foreach{case (k, v) => env(k) = v} + + val mainClass = "org.apache.spark.deploy.worker.DriverWrapper" + val command = new Command(mainClass, Seq("{{WORKER_URL}}", driverArgs.mainClass) ++ + driverArgs.driverOptions, env) + + val driverDescription = new DriverDescription( + driverArgs.jarUrl, + driverArgs.memory, + driverArgs.cores, + driverArgs.supervise, + command) + + masterActor ! RequestSubmitDriver(driverDescription) + + case "kill" => + val driverId = driverArgs.driverId + val killFuture = masterActor ! RequestKillDriver(driverId) + } + } + + /* Find out driver status then exit the JVM */ + def pollAndReportStatus(driverId: String) { + println(s"... waiting before polling master for driver state") + Thread.sleep(5000) + println("... polling master for driver state") + val statusFuture = (masterActor ? RequestDriverStatus(driverId))(timeout) + .mapTo[DriverStatusResponse] + val statusResponse = Await.result(statusFuture, timeout) + + statusResponse.found match { + case false => + println(s"ERROR: Cluster master did not recognize $driverId") + System.exit(-1) + case true => + println(s"State of $driverId is ${statusResponse.state.get}") + // Worker node, if present + (statusResponse.workerId, statusResponse.workerHostPort, statusResponse.state) match { + case (Some(id), Some(hostPort), Some(DriverState.RUNNING)) => + println(s"Driver running on $hostPort ($id)") + case _ => + } + // Exception, if present + statusResponse.exception.map { e => + println(s"Exception from cluster was: $e") + System.exit(-1) + } + System.exit(0) + } + } + + override def receive = { + + case SubmitDriverResponse(success, driverId, message) => + println(message) + if (success) pollAndReportStatus(driverId.get) else System.exit(-1) + + case KillDriverResponse(driverId, success, message) => + println(message) + if (success) pollAndReportStatus(driverId) else System.exit(-1) + + case DisassociatedEvent(_, remoteAddress, _) => + println(s"Error connecting to master ${driverArgs.master} ($remoteAddress), exiting.") + System.exit(-1) + + case AssociationErrorEvent(cause, _, remoteAddress, _) => + println(s"Error connecting to master ${driverArgs.master} ($remoteAddress), exiting.") + println(s"Cause was: $cause") + System.exit(-1) + } +} + +/** + * Executable utility for starting and terminating drivers inside of a standalone cluster. + */ +object Client { + def main(args: Array[String]) { + val conf = new SparkConf() + val driverArgs = new ClientArguments(args) + + if (!driverArgs.logLevel.isGreaterOrEqual(Level.WARN)) { + conf.set("spark.akka.logLifecycleEvents", "true") + } + conf.set("spark.akka.askTimeout", "10") + conf.set("akka.loglevel", driverArgs.logLevel.toString.replace("WARN", "WARNING")) + Logger.getRootLogger.setLevel(driverArgs.logLevel) + + // TODO: See if we can initialize akka so return messages are sent back using the same TCP + // flow. Else, this (sadly) requires the DriverClient be routable from the Master. + val (actorSystem, _) = AkkaUtils.createActorSystem( + "driverClient", Utils.localHostName(), 0, false, conf) + + actorSystem.actorOf(Props(classOf[ClientActor], driverArgs, conf)) + + actorSystem.awaitTermination() + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala new file mode 100644 index 0000000000..db67c6d1bb --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/ClientArguments.scala @@ -0,0 +1,117 @@ +/* + * 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.deploy + +import java.net.URL + +import scala.collection.mutable.ListBuffer + +import org.apache.log4j.Level + +/** + * Command-line parser for the driver client. + */ +private[spark] class ClientArguments(args: Array[String]) { + val defaultCores = 1 + val defaultMemory = 512 + + var cmd: String = "" // 'launch' or 'kill' + var logLevel = Level.WARN + + // launch parameters + var master: String = "" + var jarUrl: String = "" + var mainClass: String = "" + var supervise: Boolean = false + var memory: Int = defaultMemory + var cores: Int = defaultCores + private var _driverOptions = ListBuffer[String]() + def driverOptions = _driverOptions.toSeq + + // kill parameters + var driverId: String = "" + + parse(args.toList) + + def parse(args: List[String]): Unit = args match { + case ("--cores" | "-c") :: value :: tail => + cores = value.toInt + parse(tail) + + case ("--memory" | "-m") :: value :: tail => + memory = value.toInt + parse(tail) + + case ("--supervise" | "-s") :: tail => + supervise = true + parse(tail) + + case ("--help" | "-h") :: tail => + printUsageAndExit(0) + + case ("--verbose" | "-v") :: tail => + logLevel = Level.INFO + parse(tail) + + case "launch" :: _master :: _jarUrl :: _mainClass :: tail => + cmd = "launch" + + try { + new URL(_jarUrl) + } catch { + case e: Exception => + println(s"Jar url '${_jarUrl}' is not a valid URL.") + println(s"Jar must be in URL format (e.g. hdfs://XX, file://XX)") + printUsageAndExit(-1) + } + + jarUrl = _jarUrl + master = _master + mainClass = _mainClass + _driverOptions ++= tail + + case "kill" :: _master :: _driverId :: tail => + cmd = "kill" + master = _master + driverId = _driverId + + case _ => + printUsageAndExit(1) + } + + /** + * Print usage and exit JVM with the given exit code. + */ + def printUsageAndExit(exitCode: Int) { + // TODO: It wouldn't be too hard to allow users to submit their app and dependency jars + // separately similar to in the YARN client. + val usage = + s""" + |Usage: DriverClient [options] launch <active-master> <jar-url> <main-class> [driver options] + |Usage: DriverClient kill <active-master> <driver-id> + | + |Options: + | -c CORES, --cores CORES Number of cores to request (default: $defaultCores) + | -m MEMORY, --memory MEMORY Megabytes of memory to request (default: $defaultMemory) + | -s, --supervise Whether to restart the driver on failure + | -v, --verbose Print more debugging output + """.stripMargin + System.err.println(usage) + System.exit(exitCode) + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala index 275331724a..5e824e1a67 100644 --- a/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/DeployMessage.scala @@ -20,12 +20,12 @@ package org.apache.spark.deploy import scala.collection.immutable.List import org.apache.spark.deploy.ExecutorState.ExecutorState -import org.apache.spark.deploy.master.{WorkerInfo, ApplicationInfo} +import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, WorkerInfo} +import org.apache.spark.deploy.master.DriverState.DriverState import org.apache.spark.deploy.master.RecoveryState.MasterState -import org.apache.spark.deploy.worker.ExecutorRunner +import org.apache.spark.deploy.worker.{DriverRunner, ExecutorRunner} import org.apache.spark.util.Utils - private[deploy] sealed trait DeployMessage extends Serializable /** Contains messages sent between Scheduler actor nodes. */ @@ -54,7 +54,14 @@ private[deploy] object DeployMessages { exitStatus: Option[Int]) extends DeployMessage - case class WorkerSchedulerStateResponse(id: String, executors: List[ExecutorDescription]) + case class DriverStateChanged( + driverId: String, + state: DriverState, + exception: Option[Exception]) + extends DeployMessage + + case class WorkerSchedulerStateResponse(id: String, executors: List[ExecutorDescription], + driverIds: Seq[String]) case class Heartbeat(workerId: String) extends DeployMessage @@ -76,14 +83,18 @@ private[deploy] object DeployMessages { sparkHome: String) extends DeployMessage - // Client to Master + case class LaunchDriver(driverId: String, driverDesc: DriverDescription) extends DeployMessage + + case class KillDriver(driverId: String) extends DeployMessage + + // AppClient to Master case class RegisterApplication(appDescription: ApplicationDescription) extends DeployMessage case class MasterChangeAcknowledged(appId: String) - // Master to Client + // Master to AppClient case class RegisteredApplication(appId: String, masterUrl: String) extends DeployMessage @@ -97,11 +108,28 @@ private[deploy] object DeployMessages { case class ApplicationRemoved(message: String) - // Internal message in Client + // DriverClient <-> Master + + case class RequestSubmitDriver(driverDescription: DriverDescription) extends DeployMessage + + case class SubmitDriverResponse(success: Boolean, driverId: Option[String], message: String) + extends DeployMessage + + case class RequestKillDriver(driverId: String) extends DeployMessage + + case class KillDriverResponse(driverId: String, success: Boolean, message: String) + extends DeployMessage + + case class RequestDriverStatus(driverId: String) extends DeployMessage + + case class DriverStatusResponse(found: Boolean, state: Option[DriverState], + workerId: Option[String], workerHostPort: Option[String], exception: Option[Exception]) + + // Internal message in AppClient - case object StopClient + case object StopAppClient - // Master to Worker & Client + // Master to Worker & AppClient case class MasterChanged(masterUrl: String, masterWebUiUrl: String) @@ -113,6 +141,7 @@ private[deploy] object DeployMessages { case class MasterStateResponse(host: String, port: Int, workers: Array[WorkerInfo], activeApps: Array[ApplicationInfo], completedApps: Array[ApplicationInfo], + activeDrivers: Array[DriverInfo], completedDrivers: Array[DriverInfo], status: MasterState) { Utils.checkHost(host, "Required hostname") @@ -128,14 +157,15 @@ private[deploy] object DeployMessages { // Worker to WorkerWebUI case class WorkerStateResponse(host: String, port: Int, workerId: String, - executors: List[ExecutorRunner], finishedExecutors: List[ExecutorRunner], masterUrl: String, + executors: List[ExecutorRunner], finishedExecutors: List[ExecutorRunner], + drivers: List[DriverRunner], finishedDrivers: List[DriverRunner], masterUrl: String, cores: Int, memory: Int, coresUsed: Int, memoryUsed: Int, masterWebUiUrl: String) { Utils.checkHost(host, "Required hostname") assert (port > 0) } - // Actor System to Worker + // Liveness checks in various places case object SendHeartbeat } diff --git a/core/src/main/scala/org/apache/spark/deploy/DriverDescription.scala b/core/src/main/scala/org/apache/spark/deploy/DriverDescription.scala new file mode 100644 index 0000000000..58c95dc4f9 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/DriverDescription.scala @@ -0,0 +1,29 @@ +/* + * 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.deploy + +private[spark] class DriverDescription( + val jarUrl: String, + val mem: Int, + val cores: Int, + val supervise: Boolean, + val command: Command) + extends Serializable { + + override def toString: String = s"DriverDescription (${command.mainClass})" +} diff --git a/core/src/main/scala/org/apache/spark/deploy/client/Client.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala index 481026eaa2..1415e2f3d1 100644 --- a/core/src/main/scala/org/apache/spark/deploy/client/Client.scala +++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClient.scala @@ -33,16 +33,17 @@ import org.apache.spark.deploy.master.Master import org.apache.spark.util.AkkaUtils /** - * The main class used to talk to a Spark deploy cluster. Takes a master URL, an app description, - * and a listener for cluster events, and calls back the listener when various events occur. + * Interface allowing applications to speak with a Spark deploy cluster. Takes a master URL, + * an app description, and a listener for cluster events, and calls back the listener when various + * events occur. * * @param masterUrls Each url should look like spark://host:port. */ -private[spark] class Client( +private[spark] class AppClient( actorSystem: ActorSystem, masterUrls: Array[String], appDescription: ApplicationDescription, - listener: ClientListener, + listener: AppClientListener, conf: SparkConf) extends Logging { @@ -155,7 +156,7 @@ private[spark] class Client( case AssociationErrorEvent(cause, _, address, _) if isPossibleMaster(address) => logWarning(s"Could not connect to $address: $cause") - case StopClient => + case StopAppClient => markDead() sender ! true context.stop(self) @@ -188,7 +189,7 @@ private[spark] class Client( if (actor != null) { try { val timeout = AkkaUtils.askTimeout(conf) - val future = actor.ask(StopClient)(timeout) + val future = actor.ask(StopAppClient)(timeout) Await.result(future, timeout) } catch { case e: TimeoutException => diff --git a/core/src/main/scala/org/apache/spark/deploy/client/ClientListener.scala b/core/src/main/scala/org/apache/spark/deploy/client/AppClientListener.scala index be7a11bd15..55d4ef1b31 100644 --- a/core/src/main/scala/org/apache/spark/deploy/client/ClientListener.scala +++ b/core/src/main/scala/org/apache/spark/deploy/client/AppClientListener.scala @@ -24,7 +24,7 @@ package org.apache.spark.deploy.client * * Users of this API should *not* block inside the callback methods. */ -private[spark] trait ClientListener { +private[spark] trait AppClientListener { def connected(appId: String): Unit /** Disconnection may be a temporary state, as we fail over to a new Master. */ diff --git a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala index ef649fd80c..ffa909c26b 100644 --- a/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala +++ b/core/src/main/scala/org/apache/spark/deploy/client/TestClient.scala @@ -23,7 +23,7 @@ import org.apache.spark.deploy.{Command, ApplicationDescription} private[spark] object TestClient { - class TestListener extends ClientListener with Logging { + class TestListener extends AppClientListener with Logging { def connected(id: String) { logInfo("Connected to master, got app ID " + id) } @@ -48,10 +48,10 @@ private[spark] object TestClient { val (actorSystem, port) = AkkaUtils.createActorSystem("spark", Utils.localIpAddress, 0, conf = new SparkConf) val desc = new ApplicationDescription( - "TestClient", 1, 512, Command("spark.deploy.client.TestExecutor", Seq(), Map()), + "TestClient", Some(1), 512, Command("spark.deploy.client.TestExecutor", Seq(), Map()), "dummy-spark-home", "ignored") val listener = new TestListener - val client = new Client(actorSystem, Array(url), desc, listener, new SparkConf) + val client = new AppClient(actorSystem, Array(url), desc, listener, new SparkConf) client.start() actorSystem.awaitTermination() } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala index 5150b7c7de..3e26379166 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ApplicationInfo.scala @@ -28,7 +28,8 @@ private[spark] class ApplicationInfo( val desc: ApplicationDescription, val submitDate: Date, val driver: ActorRef, - val appUiUrl: String) + val appUiUrl: String, + defaultCores: Int) extends Serializable { @transient var state: ApplicationState.Value = _ @@ -81,7 +82,9 @@ private[spark] class ApplicationInfo( } } - def coresLeft: Int = desc.maxCores - coresGranted + private val myMaxCores = desc.maxCores.getOrElse(defaultCores) + + def coresLeft: Int = myMaxCores - coresGranted private var _retryCount = 0 diff --git a/core/src/main/scala/org/apache/spark/deploy/master/DriverInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/DriverInfo.scala new file mode 100644 index 0000000000..33377931d6 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/master/DriverInfo.scala @@ -0,0 +1,36 @@ +/* + * 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.deploy.master + +import java.util.Date + +import org.apache.spark.deploy.DriverDescription + +private[spark] class DriverInfo( + val startTime: Long, + val id: String, + val desc: DriverDescription, + val submitDate: Date) + extends Serializable { + + @transient var state: DriverState.Value = DriverState.SUBMITTED + /* If we fail when launching the driver, the exception is stored here. */ + @transient var exception: Option[Exception] = None + /* Most recent worker assigned to this driver */ + @transient var worker: Option[WorkerInfo] = None +} diff --git a/core/src/main/scala/org/apache/spark/deploy/master/DriverState.scala b/core/src/main/scala/org/apache/spark/deploy/master/DriverState.scala new file mode 100644 index 0000000000..26a68bade3 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/master/DriverState.scala @@ -0,0 +1,33 @@ +/* + * 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.deploy.master + +private[spark] object DriverState extends Enumeration { + + type DriverState = Value + + // SUBMITTED: Submitted but not yet scheduled on a worker + // RUNNING: Has been allocated to a worker to run + // FINISHED: Previously ran and exited cleanly + // RELAUNCHING: Exited non-zero or due to worker failure, but has not yet started running again + // UNKNOWN: The state of the driver is temporarily not known due to master failure recovery + // KILLED: A user manually killed this driver + // FAILED: The driver exited non-zero and was not supervised + // ERROR: Unable to run or restart due to an unrecoverable error (e.g. missing jar file) + val SUBMITTED, RUNNING, FINISHED, RELAUNCHING, UNKNOWN, KILLED, FAILED, ERROR = Value +} diff --git a/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala index 043945a211..74bb9ebf1d 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/FileSystemPersistenceEngine.scala @@ -19,8 +19,6 @@ package org.apache.spark.deploy.master import java.io._ -import scala.Serializable - import akka.serialization.Serialization import org.apache.spark.Logging @@ -47,6 +45,15 @@ private[spark] class FileSystemPersistenceEngine( new File(dir + File.separator + "app_" + app.id).delete() } + override def addDriver(driver: DriverInfo) { + val driverFile = new File(dir + File.separator + "driver_" + driver.id) + serializeIntoFile(driverFile, driver) + } + + override def removeDriver(driver: DriverInfo) { + new File(dir + File.separator + "driver_" + driver.id).delete() + } + override def addWorker(worker: WorkerInfo) { val workerFile = new File(dir + File.separator + "worker_" + worker.id) serializeIntoFile(workerFile, worker) @@ -56,13 +63,15 @@ private[spark] class FileSystemPersistenceEngine( new File(dir + File.separator + "worker_" + worker.id).delete() } - override def readPersistedData(): (Seq[ApplicationInfo], Seq[WorkerInfo]) = { + override def readPersistedData(): (Seq[ApplicationInfo], Seq[DriverInfo], Seq[WorkerInfo]) = { val sortedFiles = new File(dir).listFiles().sortBy(_.getName) val appFiles = sortedFiles.filter(_.getName.startsWith("app_")) val apps = appFiles.map(deserializeFromFile[ApplicationInfo]) + val driverFiles = sortedFiles.filter(_.getName.startsWith("driver_")) + val drivers = driverFiles.map(deserializeFromFile[DriverInfo]) val workerFiles = sortedFiles.filter(_.getName.startsWith("worker_")) val workers = workerFiles.map(deserializeFromFile[WorkerInfo]) - (apps, workers) + (apps, drivers, workers) } private def serializeIntoFile(file: File, value: AnyRef) { diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala index 7b696cfcca..d9ea96afcf 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala @@ -23,19 +23,22 @@ import java.util.Date import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} import scala.concurrent.Await import scala.concurrent.duration._ +import scala.util.Random import akka.actor._ import akka.pattern.ask import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent} import akka.serialization.SerializationExtension -import org.apache.spark.{SparkConf, SparkContext, Logging, SparkException} -import org.apache.spark.deploy.{ApplicationDescription, ExecutorState} + +import org.apache.spark.{SparkConf, Logging, SparkException} +import org.apache.spark.deploy.{ApplicationDescription, DriverDescription, ExecutorState} import org.apache.spark.deploy.DeployMessages._ import org.apache.spark.deploy.master.MasterMessages._ import org.apache.spark.deploy.master.ui.MasterWebUI import org.apache.spark.metrics.MetricsSystem import org.apache.spark.util.{AkkaUtils, Utils} +import org.apache.spark.deploy.master.DriverState.DriverState private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Actor with Logging { import context.dispatcher // to use Akka's scheduler.schedule() @@ -43,13 +46,12 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act val conf = new SparkConf val DATE_FORMAT = new SimpleDateFormat("yyyyMMddHHmmss") // For application IDs - val WORKER_TIMEOUT = conf.get("spark.worker.timeout", "60").toLong * 1000 - val RETAINED_APPLICATIONS = conf.get("spark.deploy.retainedApplications", "200").toInt - val REAPER_ITERATIONS = conf.get("spark.dead.worker.persistence", "15").toInt + val WORKER_TIMEOUT = conf.getLong("spark.worker.timeout", 60) * 1000 + val RETAINED_APPLICATIONS = conf.getInt("spark.deploy.retainedApplications", 200) + val REAPER_ITERATIONS = conf.getInt("spark.dead.worker.persistence", 15) val RECOVERY_DIR = conf.get("spark.deploy.recoveryDirectory", "") val RECOVERY_MODE = conf.get("spark.deploy.recoveryMode", "NONE") - var nextAppNumber = 0 val workers = new HashSet[WorkerInfo] val idToWorker = new HashMap[String, WorkerInfo] val actorToWorker = new HashMap[ActorRef, WorkerInfo] @@ -59,9 +61,14 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act val idToApp = new HashMap[String, ApplicationInfo] val actorToApp = new HashMap[ActorRef, ApplicationInfo] val addressToApp = new HashMap[Address, ApplicationInfo] - val waitingApps = new ArrayBuffer[ApplicationInfo] val completedApps = new ArrayBuffer[ApplicationInfo] + var nextAppNumber = 0 + + val drivers = new HashSet[DriverInfo] + val completedDrivers = new ArrayBuffer[DriverInfo] + val waitingDrivers = new ArrayBuffer[DriverInfo] // Drivers currently spooled for scheduling + var nextDriverNumber = 0 Utils.checkHost(host, "Expected hostname") @@ -88,7 +95,13 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act // As a temporary workaround before better ways of configuring memory, we allow users to set // a flag that will perform round-robin scheduling across the nodes (spreading out each app // among all the nodes) instead of trying to consolidate each app onto a small # of nodes. - val spreadOutApps = conf.get("spark.deploy.spreadOut", "true").toBoolean + val spreadOutApps = conf.getBoolean("spark.deploy.spreadOut", true) + + // Default maxCores for applications that don't specify it (i.e. pass Int.MaxValue) + val defaultCores = conf.getInt("spark.deploy.defaultCores", Int.MaxValue) + if (defaultCores < 1) { + throw new SparkException("spark.deploy.defaultCores must be positive") + } override def preStart() { logInfo("Starting Spark master at " + masterUrl) @@ -136,14 +149,14 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act override def receive = { case ElectedLeader => { - val (storedApps, storedWorkers) = persistenceEngine.readPersistedData() - state = if (storedApps.isEmpty && storedWorkers.isEmpty) + val (storedApps, storedDrivers, storedWorkers) = persistenceEngine.readPersistedData() + state = if (storedApps.isEmpty && storedDrivers.isEmpty && storedWorkers.isEmpty) RecoveryState.ALIVE else RecoveryState.RECOVERING logInfo("I have been elected leader! New state: " + state) if (state == RecoveryState.RECOVERING) { - beginRecovery(storedApps, storedWorkers) + beginRecovery(storedApps, storedDrivers, storedWorkers) context.system.scheduler.scheduleOnce(WORKER_TIMEOUT millis) { completeRecovery() } } } @@ -170,6 +183,69 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act } } + case RequestSubmitDriver(description) => { + if (state != RecoveryState.ALIVE) { + val msg = s"Can only accept driver submissions in ALIVE state. Current state: $state." + sender ! SubmitDriverResponse(false, None, msg) + } else { + logInfo("Driver submitted " + description.command.mainClass) + val driver = createDriver(description) + persistenceEngine.addDriver(driver) + waitingDrivers += driver + drivers.add(driver) + schedule() + + // TODO: It might be good to instead have the submission client poll the master to determine + // the current status of the driver. For now it's simply "fire and forget". + + sender ! SubmitDriverResponse(true, Some(driver.id), + s"Driver successfully submitted as ${driver.id}") + } + } + + case RequestKillDriver(driverId) => { + if (state != RecoveryState.ALIVE) { + val msg = s"Can only kill drivers in ALIVE state. Current state: $state." + sender ! KillDriverResponse(driverId, success = false, msg) + } else { + logInfo("Asked to kill driver " + driverId) + val driver = drivers.find(_.id == driverId) + driver match { + case Some(d) => + if (waitingDrivers.contains(d)) { + waitingDrivers -= d + self ! DriverStateChanged(driverId, DriverState.KILLED, None) + } + else { + // We just notify the worker to kill the driver here. The final bookkeeping occurs + // on the return path when the worker submits a state change back to the master + // to notify it that the driver was successfully killed. + d.worker.foreach { w => + w.actor ! KillDriver(driverId) + } + } + // TODO: It would be nice for this to be a synchronous response + val msg = s"Kill request for $driverId submitted" + logInfo(msg) + sender ! KillDriverResponse(driverId, success = true, msg) + case None => + val msg = s"Driver $driverId has already finished or does not exist" + logWarning(msg) + sender ! KillDriverResponse(driverId, success = false, msg) + } + } + } + + case RequestDriverStatus(driverId) => { + (drivers ++ completedDrivers).find(_.id == driverId) match { + case Some(driver) => + sender ! DriverStatusResponse(found = true, Some(driver.state), + driver.worker.map(_.id), driver.worker.map(_.hostPort), driver.exception) + case None => + sender ! DriverStatusResponse(found = false, None, None, None, None) + } + } + case RegisterApplication(description) => { if (state == RecoveryState.STANDBY) { // ignore, don't send response @@ -212,6 +288,15 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act } } + case DriverStateChanged(driverId, state, exception) => { + state match { + case DriverState.ERROR | DriverState.FINISHED | DriverState.KILLED | DriverState.FAILED => + removeDriver(driverId, state, exception) + case _ => + throw new Exception(s"Received unexpected state update for driver $driverId: $state") + } + } + case Heartbeat(workerId) => { idToWorker.get(workerId) match { case Some(workerInfo) => @@ -233,7 +318,7 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act if (canCompleteRecovery) { completeRecovery() } } - case WorkerSchedulerStateResponse(workerId, executors) => { + case WorkerSchedulerStateResponse(workerId, executors, driverIds) => { idToWorker.get(workerId) match { case Some(worker) => logInfo("Worker has been re-registered: " + workerId) @@ -246,6 +331,14 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act worker.addExecutor(execInfo) execInfo.copyState(exec) } + + for (driverId <- driverIds) { + drivers.find(_.id == driverId).foreach { driver => + driver.worker = Some(worker) + driver.state = DriverState.RUNNING + worker.drivers(driverId) = driver + } + } case None => logWarning("Scheduler state from unknown worker: " + workerId) } @@ -263,7 +356,7 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act case RequestMasterState => { sender ! MasterStateResponse(host, port, workers.toArray, apps.toArray, completedApps.toArray, - state) + drivers.toArray, completedDrivers.toArray, state) } case CheckForWorkerTimeOut => { @@ -279,7 +372,8 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act workers.count(_.state == WorkerState.UNKNOWN) == 0 && apps.count(_.state == ApplicationState.UNKNOWN) == 0 - def beginRecovery(storedApps: Seq[ApplicationInfo], storedWorkers: Seq[WorkerInfo]) { + def beginRecovery(storedApps: Seq[ApplicationInfo], storedDrivers: Seq[DriverInfo], + storedWorkers: Seq[WorkerInfo]) { for (app <- storedApps) { logInfo("Trying to recover app: " + app.id) try { @@ -291,6 +385,12 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act } } + for (driver <- storedDrivers) { + // Here we just read in the list of drivers. Any drivers associated with now-lost workers + // will be re-launched when we detect that the worker is missing. + drivers += driver + } + for (worker <- storedWorkers) { logInfo("Trying to recover worker: " + worker.id) try { @@ -314,6 +414,18 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act workers.filter(_.state == WorkerState.UNKNOWN).foreach(removeWorker) apps.filter(_.state == ApplicationState.UNKNOWN).foreach(finishApplication) + // Reschedule drivers which were not claimed by any workers + drivers.filter(_.worker.isEmpty).foreach { d => + logWarning(s"Driver ${d.id} was not found after master recovery") + if (d.desc.supervise) { + logWarning(s"Re-launching ${d.id}") + relaunchDriver(d) + } else { + removeDriver(d.id, DriverState.ERROR, None) + logWarning(s"Did not re-launch ${d.id} because it was not supervised") + } + } + state = RecoveryState.ALIVE schedule() logInfo("Recovery complete - resuming operations!") @@ -334,6 +446,18 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act */ def schedule() { if (state != RecoveryState.ALIVE) { return } + + // First schedule drivers, they take strict precedence over applications + val shuffledWorkers = Random.shuffle(workers) // Randomization helps balance drivers + for (worker <- shuffledWorkers if worker.state == WorkerState.ALIVE) { + for (driver <- waitingDrivers) { + if (worker.memoryFree >= driver.desc.mem && worker.coresFree >= driver.desc.cores) { + launchDriver(worker, driver) + waitingDrivers -= driver + } + } + } + // Right now this is a very simple FIFO scheduler. We keep trying to fit in the first app // in the queue, then the second app, etc. if (spreadOutApps) { @@ -420,13 +544,30 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act exec.id, ExecutorState.LOST, Some("worker lost"), None) exec.application.removeExecutor(exec) } + for (driver <- worker.drivers.values) { + if (driver.desc.supervise) { + logInfo(s"Re-launching ${driver.id}") + relaunchDriver(driver) + } else { + logInfo(s"Not re-launching ${driver.id} because it was not supervised") + removeDriver(driver.id, DriverState.ERROR, None) + } + } persistenceEngine.removeWorker(worker) } + def relaunchDriver(driver: DriverInfo) { + driver.worker = None + driver.state = DriverState.RELAUNCHING + waitingDrivers += driver + schedule() + } + def createApplication(desc: ApplicationDescription, driver: ActorRef): ApplicationInfo = { val now = System.currentTimeMillis() val date = new Date(now) - new ApplicationInfo(now, newApplicationId(date), desc, date, driver, desc.appUiUrl) + new ApplicationInfo( + now, newApplicationId(date), desc, date, driver, desc.appUiUrl, defaultCores) } def registerApplication(app: ApplicationInfo): Unit = { @@ -501,6 +642,41 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act } } } + + def newDriverId(submitDate: Date): String = { + val appId = "driver-%s-%04d".format(DATE_FORMAT.format(submitDate), nextDriverNumber) + nextDriverNumber += 1 + appId + } + + def createDriver(desc: DriverDescription): DriverInfo = { + val now = System.currentTimeMillis() + val date = new Date(now) + new DriverInfo(now, newDriverId(date), desc, date) + } + + def launchDriver(worker: WorkerInfo, driver: DriverInfo) { + logInfo("Launching driver " + driver.id + " on worker " + worker.id) + worker.addDriver(driver) + driver.worker = Some(worker) + worker.actor ! LaunchDriver(driver.id, driver.desc) + driver.state = DriverState.RUNNING + } + + def removeDriver(driverId: String, finalState: DriverState, exception: Option[Exception]) { + drivers.find(d => d.id == driverId) match { + case Some(driver) => + logInfo(s"Removing driver: $driverId") + drivers -= driver + completedDrivers += driver + persistenceEngine.removeDriver(driver) + driver.state = finalState + driver.exception = exception + driver.worker.foreach(w => w.removeDriver(driver)) + case None => + logWarning(s"Asked to remove unknown driver: $driverId") + } + } } private[spark] object Master { diff --git a/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala index 94b986caf2..e3640ea4f7 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/PersistenceEngine.scala @@ -35,11 +35,15 @@ private[spark] trait PersistenceEngine { def removeWorker(worker: WorkerInfo) + def addDriver(driver: DriverInfo) + + def removeDriver(driver: DriverInfo) + /** * Returns the persisted data sorted by their respective ids (which implies that they're * sorted by time of creation). */ - def readPersistedData(): (Seq[ApplicationInfo], Seq[WorkerInfo]) + def readPersistedData(): (Seq[ApplicationInfo], Seq[DriverInfo], Seq[WorkerInfo]) def close() {} } @@ -49,5 +53,8 @@ private[spark] class BlackHolePersistenceEngine extends PersistenceEngine { override def removeApplication(app: ApplicationInfo) {} override def addWorker(worker: WorkerInfo) {} override def removeWorker(worker: WorkerInfo) {} - override def readPersistedData() = (Nil, Nil) + override def addDriver(driver: DriverInfo) {} + override def removeDriver(driver: DriverInfo) {} + + override def readPersistedData() = (Nil, Nil, Nil) } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala b/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala index e05f587b58..c5fa9cf7d7 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/WorkerInfo.scala @@ -17,8 +17,10 @@ package org.apache.spark.deploy.master -import akka.actor.ActorRef import scala.collection.mutable + +import akka.actor.ActorRef + import org.apache.spark.util.Utils private[spark] class WorkerInfo( @@ -35,7 +37,8 @@ private[spark] class WorkerInfo( Utils.checkHost(host, "Expected hostname") assert (port > 0) - @transient var executors: mutable.HashMap[String, ExecutorInfo] = _ // fullId => info + @transient var executors: mutable.HashMap[String, ExecutorInfo] = _ // executorId => info + @transient var drivers: mutable.HashMap[String, DriverInfo] = _ // driverId => info @transient var state: WorkerState.Value = _ @transient var coresUsed: Int = _ @transient var memoryUsed: Int = _ @@ -54,6 +57,7 @@ private[spark] class WorkerInfo( private def init() { executors = new mutable.HashMap + drivers = new mutable.HashMap state = WorkerState.ALIVE coresUsed = 0 memoryUsed = 0 @@ -83,6 +87,18 @@ private[spark] class WorkerInfo( executors.values.exists(_.application == app) } + def addDriver(driver: DriverInfo) { + drivers(driver.id) = driver + memoryUsed += driver.desc.mem + coresUsed += driver.desc.cores + } + + def removeDriver(driver: DriverInfo) { + drivers -= driver.id + memoryUsed -= driver.desc.mem + coresUsed -= driver.desc.cores + } + def webUiAddress : String = { "http://" + this.publicAddress + ":" + this.webUiPort } diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala index 52000d4f9c..f24f49ea8a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperPersistenceEngine.scala @@ -49,6 +49,14 @@ class ZooKeeperPersistenceEngine(serialization: Serialization, conf: SparkConf) zk.delete(WORKING_DIR + "/app_" + app.id) } + override def addDriver(driver: DriverInfo) { + serializeIntoFile(WORKING_DIR + "/driver_" + driver.id, driver) + } + + override def removeDriver(driver: DriverInfo) { + zk.delete(WORKING_DIR + "/driver_" + driver.id) + } + override def addWorker(worker: WorkerInfo) { serializeIntoFile(WORKING_DIR + "/worker_" + worker.id, worker) } @@ -61,13 +69,15 @@ class ZooKeeperPersistenceEngine(serialization: Serialization, conf: SparkConf) zk.close() } - override def readPersistedData(): (Seq[ApplicationInfo], Seq[WorkerInfo]) = { + override def readPersistedData(): (Seq[ApplicationInfo], Seq[DriverInfo], Seq[WorkerInfo]) = { val sortedFiles = zk.getChildren(WORKING_DIR).toList.sorted val appFiles = sortedFiles.filter(_.startsWith("app_")) val apps = appFiles.map(deserializeFromFile[ApplicationInfo]) + val driverFiles = sortedFiles.filter(_.startsWith("driver_")) + val drivers = driverFiles.map(deserializeFromFile[DriverInfo]) val workerFiles = sortedFiles.filter(_.startsWith("worker_")) val workers = workerFiles.map(deserializeFromFile[WorkerInfo]) - (apps, workers) + (apps, drivers, workers) } private def serializeIntoFile(path: String, value: AnyRef) { diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/IndexPage.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/IndexPage.scala index 4ef762892c..a9af8df552 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/IndexPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/IndexPage.scala @@ -18,6 +18,7 @@ package org.apache.spark.deploy.master.ui import scala.concurrent.Await +import scala.concurrent.duration._ import scala.xml.Node import akka.pattern.ask @@ -26,7 +27,7 @@ import net.liftweb.json.JsonAST.JValue import org.apache.spark.deploy.{DeployWebUI, JsonProtocol} import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, RequestMasterState} -import org.apache.spark.deploy.master.{ApplicationInfo, WorkerInfo} +import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, WorkerInfo} import org.apache.spark.ui.UIUtils import org.apache.spark.util.Utils @@ -56,6 +57,16 @@ private[spark] class IndexPage(parent: MasterWebUI) { val completedApps = state.completedApps.sortBy(_.endTime).reverse val completedAppsTable = UIUtils.listingTable(appHeaders, appRow, completedApps) + val driverHeaders = Seq("ID", "Submitted Time", "Worker", "State", "Cores", "Memory", "Main Class") + val activeDrivers = state.activeDrivers.sortBy(_.startTime).reverse + val activeDriversTable = UIUtils.listingTable(driverHeaders, driverRow, activeDrivers) + val completedDrivers = state.completedDrivers.sortBy(_.startTime).reverse + val completedDriversTable = UIUtils.listingTable(driverHeaders, driverRow, completedDrivers) + + // For now we only show driver information if the user has submitted drivers to the cluster. + // This is until we integrate the notion of drivers and applications in the UI. + def hasDrivers = activeDrivers.length > 0 || completedDrivers.length > 0 + val content = <div class="row-fluid"> <div class="span12"> @@ -70,6 +81,9 @@ private[spark] class IndexPage(parent: MasterWebUI) { <li><strong>Applications:</strong> {state.activeApps.size} Running, {state.completedApps.size} Completed </li> + <li><strong>Drivers:</strong> + {state.activeDrivers.size} Running, + {state.completedDrivers.size} Completed </li> </ul> </div> </div> @@ -84,17 +98,39 @@ private[spark] class IndexPage(parent: MasterWebUI) { <div class="row-fluid"> <div class="span12"> <h4> Running Applications </h4> - {activeAppsTable} </div> </div> + <div> + {if (hasDrivers) + <div class="row-fluid"> + <div class="span12"> + <h4> Running Drivers </h4> + {activeDriversTable} + </div> + </div> + } + </div> + <div class="row-fluid"> <div class="span12"> <h4> Completed Applications </h4> {completedAppsTable} </div> + </div> + + <div> + {if (hasDrivers) + <div class="row-fluid"> + <div class="span12"> + <h4> Completed Drivers </h4> + {completedDriversTable} + </div> + </div> + } </div>; + UIUtils.basicSparkPage(content, "Spark Master at " + state.uri) } @@ -134,4 +170,20 @@ private[spark] class IndexPage(parent: MasterWebUI) { <td>{DeployWebUI.formatDuration(app.duration)}</td> </tr> } + + def driverRow(driver: DriverInfo): Seq[Node] = { + <tr> + <td>{driver.id} </td> + <td>{driver.submitDate}</td> + <td>{driver.worker.map(w => <a href={w.webUiAddress}>{w.id.toString}</a>).getOrElse("None")}</td> + <td>{driver.state}</td> + <td sorttable_customkey={driver.desc.cores.toString}> + {driver.desc.cores} + </td> + <td sorttable_customkey={driver.desc.mem.toString}> + {Utils.megabytesToString(driver.desc.mem.toLong)} + </td> + <td>{driver.desc.command.arguments(1)}</td> + </tr> + } } diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala new file mode 100644 index 0000000000..7507bf8ad0 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/worker/CommandUtils.scala @@ -0,0 +1,63 @@ +package org.apache.spark.deploy.worker + +import java.io.{File, FileOutputStream, IOException, InputStream} +import java.lang.System._ + +import org.apache.spark.Logging +import org.apache.spark.deploy.Command +import org.apache.spark.util.Utils + +/** + ** Utilities for running commands with the spark classpath. + */ +object CommandUtils extends Logging { + private[spark] def buildCommandSeq(command: Command, memory: Int, sparkHome: String): Seq[String] = { + val runner = getEnv("JAVA_HOME", command).map(_ + "/bin/java").getOrElse("java") + + // SPARK-698: do not call the run.cmd script, as process.destroy() + // fails to kill a process tree on Windows + Seq(runner) ++ buildJavaOpts(command, memory, sparkHome) ++ Seq(command.mainClass) ++ + command.arguments + } + + private def getEnv(key: String, command: Command): Option[String] = + command.environment.get(key).orElse(Option(System.getenv(key))) + + /** + * Attention: this must always be aligned with the environment variables in the run scripts and + * the way the JAVA_OPTS are assembled there. + */ + def buildJavaOpts(command: Command, memory: Int, sparkHome: String): Seq[String] = { + val libraryOpts = getEnv("SPARK_LIBRARY_PATH", command) + .map(p => List("-Djava.library.path=" + p)) + .getOrElse(Nil) + val workerLocalOpts = Option(getenv("SPARK_JAVA_OPTS")).map(Utils.splitCommandString).getOrElse(Nil) + val userOpts = getEnv("SPARK_JAVA_OPTS", command).map(Utils.splitCommandString).getOrElse(Nil) + val memoryOpts = Seq(s"-Xms${memory}M", s"-Xmx${memory}M") + + // Figure out our classpath with the external compute-classpath script + val ext = if (System.getProperty("os.name").startsWith("Windows")) ".cmd" else ".sh" + val classPath = Utils.executeAndGetOutput( + Seq(sparkHome + "/bin/compute-classpath" + ext), + extraEnvironment=command.environment) + + Seq("-cp", classPath) ++ libraryOpts ++ workerLocalOpts ++ userOpts ++ memoryOpts + } + + /** Spawn a thread that will redirect a given stream to a file */ + def redirectStream(in: InputStream, file: File) { + val out = new FileOutputStream(file, true) + // TODO: It would be nice to add a shutdown hook here that explains why the output is + // terminating. Otherwise if the worker dies the executor logs will silently stop. + new Thread("redirect output to " + file) { + override def run() { + try { + Utils.copyStream(in, out, true) + } catch { + case e: IOException => + logInfo("Redirection to " + file + " closed: " + e.getMessage) + } + } + }.start() + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala new file mode 100644 index 0000000000..b4df1a0dd4 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverRunner.scala @@ -0,0 +1,234 @@ +/* + * 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.deploy.worker + +import java.io._ + +import scala.collection.JavaConversions._ +import scala.collection.mutable.Map + +import akka.actor.ActorRef +import com.google.common.base.Charsets +import com.google.common.io.Files +import org.apache.hadoop.conf.Configuration +import org.apache.hadoop.fs.{FileUtil, Path} + +import org.apache.spark.Logging +import org.apache.spark.deploy.{Command, DriverDescription} +import org.apache.spark.deploy.DeployMessages.DriverStateChanged +import org.apache.spark.deploy.master.DriverState +import org.apache.spark.deploy.master.DriverState.DriverState + +/** + * Manages the execution of one driver, including automatically restarting the driver on failure. + */ +private[spark] class DriverRunner( + val driverId: String, + val workDir: File, + val sparkHome: File, + val driverDesc: DriverDescription, + val worker: ActorRef, + val workerUrl: String) + extends Logging { + + @volatile var process: Option[Process] = None + @volatile var killed = false + + // Populated once finished + var finalState: Option[DriverState] = None + var finalException: Option[Exception] = None + var finalExitCode: Option[Int] = None + + // Decoupled for testing + private[deploy] def setClock(_clock: Clock) = clock = _clock + private[deploy] def setSleeper(_sleeper: Sleeper) = sleeper = _sleeper + private var clock = new Clock { + def currentTimeMillis(): Long = System.currentTimeMillis() + } + private var sleeper = new Sleeper { + def sleep(seconds: Int): Unit = (0 until seconds).takeWhile(f => {Thread.sleep(1000); !killed}) + } + + /** Starts a thread to run and manage the driver. */ + def start() = { + new Thread("DriverRunner for " + driverId) { + override def run() { + try { + val driverDir = createWorkingDirectory() + val localJarFilename = downloadUserJar(driverDir) + + // Make sure user application jar is on the classpath + // TODO: If we add ability to submit multiple jars they should also be added here + val env = Map(driverDesc.command.environment.toSeq: _*) + env("SPARK_CLASSPATH") = env.getOrElse("SPARK_CLASSPATH", "") + s":$localJarFilename" + val newCommand = Command(driverDesc.command.mainClass, + driverDesc.command.arguments.map(substituteVariables), env) + val command = CommandUtils.buildCommandSeq(newCommand, driverDesc.mem, + sparkHome.getAbsolutePath) + launchDriver(command, env, driverDir, driverDesc.supervise) + } + catch { + case e: Exception => finalException = Some(e) + } + + val state = + if (killed) { DriverState.KILLED } + else if (finalException.isDefined) { DriverState.ERROR } + else { + finalExitCode match { + case Some(0) => DriverState.FINISHED + case _ => DriverState.FAILED + } + } + + finalState = Some(state) + + worker ! DriverStateChanged(driverId, state, finalException) + } + }.start() + } + + /** Terminate this driver (or prevent it from ever starting if not yet started) */ + def kill() { + synchronized { + process.foreach(p => p.destroy()) + killed = true + } + } + + /** Replace variables in a command argument passed to us */ + private def substituteVariables(argument: String): String = argument match { + case "{{WORKER_URL}}" => workerUrl + case other => other + } + + /** + * Creates the working directory for this driver. + * Will throw an exception if there are errors preparing the directory. + */ + private def createWorkingDirectory(): File = { + val driverDir = new File(workDir, driverId) + if (!driverDir.exists() && !driverDir.mkdirs()) { + throw new IOException("Failed to create directory " + driverDir) + } + driverDir + } + + /** + * Download the user jar into the supplied directory and return its local path. + * Will throw an exception if there are errors downloading the jar. + */ + private def downloadUserJar(driverDir: File): String = { + + val jarPath = new Path(driverDesc.jarUrl) + + val emptyConf = new Configuration() + val jarFileSystem = jarPath.getFileSystem(emptyConf) + + val destPath = new File(driverDir.getAbsolutePath, jarPath.getName) + val jarFileName = jarPath.getName + val localJarFile = new File(driverDir, jarFileName) + val localJarFilename = localJarFile.getAbsolutePath + + if (!localJarFile.exists()) { // May already exist if running multiple workers on one node + logInfo(s"Copying user jar $jarPath to $destPath") + FileUtil.copy(jarFileSystem, jarPath, destPath, false, emptyConf) + } + + if (!localJarFile.exists()) { // Verify copy succeeded + throw new Exception(s"Did not see expected jar $jarFileName in $driverDir") + } + + localJarFilename + } + + private def launchDriver(command: Seq[String], envVars: Map[String, String], baseDir: File, + supervise: Boolean) { + val builder = new ProcessBuilder(command: _*).directory(baseDir) + envVars.map{ case(k,v) => builder.environment().put(k, v) } + + def initialize(process: Process) = { + // Redirect stdout and stderr to files + val stdout = new File(baseDir, "stdout") + CommandUtils.redirectStream(process.getInputStream, stdout) + + val stderr = new File(baseDir, "stderr") + val header = "Launch Command: %s\n%s\n\n".format( + command.mkString("\"", "\" \"", "\""), "=" * 40) + Files.append(header, stderr, Charsets.UTF_8) + CommandUtils.redirectStream(process.getErrorStream, stderr) + } + runCommandWithRetry(ProcessBuilderLike(builder), initialize, supervise) + } + + private[deploy] def runCommandWithRetry(command: ProcessBuilderLike, initialize: Process => Unit, + supervise: Boolean) { + // Time to wait between submission retries. + var waitSeconds = 1 + // A run of this many seconds resets the exponential back-off. + val successfulRunDuration = 5 + + var keepTrying = !killed + + while (keepTrying) { + logInfo("Launch Command: " + command.command.mkString("\"", "\" \"", "\"")) + + synchronized { + if (killed) { return } + process = Some(command.start()) + initialize(process.get) + } + + val processStart = clock.currentTimeMillis() + val exitCode = process.get.waitFor() + if (clock.currentTimeMillis() - processStart > successfulRunDuration * 1000) { + waitSeconds = 1 + } + + if (supervise && exitCode != 0 && !killed) { + logInfo(s"Command exited with status $exitCode, re-launching after $waitSeconds s.") + sleeper.sleep(waitSeconds) + waitSeconds = waitSeconds * 2 // exponential back-off + } + + keepTrying = supervise && exitCode != 0 && !killed + finalExitCode = Some(exitCode) + } + } +} + +private[deploy] trait Clock { + def currentTimeMillis(): Long +} + +private[deploy] trait Sleeper { + def sleep(seconds: Int) +} + +// Needed because ProcessBuilder is a final class and cannot be mocked +private[deploy] trait ProcessBuilderLike { + def start(): Process + def command: Seq[String] +} + +private[deploy] object ProcessBuilderLike { + def apply(processBuilder: ProcessBuilder) = new ProcessBuilderLike { + def start() = processBuilder.start() + def command = processBuilder.command() + } +} diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala new file mode 100644 index 0000000000..1640d5fee0 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/worker/DriverWrapper.scala @@ -0,0 +1,31 @@ +package org.apache.spark.deploy.worker + +import akka.actor._ + +import org.apache.spark.SparkConf +import org.apache.spark.util.{AkkaUtils, Utils} + +/** + * Utility object for launching driver programs such that they share fate with the Worker process. + */ +object DriverWrapper { + def main(args: Array[String]) { + args.toList match { + case workerUrl :: mainClass :: extraArgs => + val (actorSystem, _) = AkkaUtils.createActorSystem("Driver", + Utils.localHostName(), 0, false, new SparkConf()) + actorSystem.actorOf(Props(classOf[WorkerWatcher], workerUrl), name = "workerWatcher") + + // Delegate to supplied main class + val clazz = Class.forName(args(1)) + val mainMethod = clazz.getMethod("main", classOf[Array[String]]) + mainMethod.invoke(null, extraArgs.toArray[String]) + + actorSystem.shutdown() + + case _ => + System.err.println("Usage: DriverWrapper <workerUrl> <driverMainClass> [options]") + System.exit(-1) + } + } +}
\ No newline at end of file diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala index fff9cb60c7..18885d7ca6 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ExecutorRunner.scala @@ -18,17 +18,15 @@ package org.apache.spark.deploy.worker import java.io._ -import java.lang.System.getenv import akka.actor.ActorRef import com.google.common.base.Charsets import com.google.common.io.Files -import org.apache.spark.{Logging} -import org.apache.spark.deploy.{ExecutorState, ApplicationDescription} +import org.apache.spark.Logging +import org.apache.spark.deploy.{ExecutorState, ApplicationDescription, Command} import org.apache.spark.deploy.DeployMessages.ExecutorStateChanged -import org.apache.spark.util.Utils /** * Manages the execution of one executor process. @@ -44,16 +42,17 @@ private[spark] class ExecutorRunner( val host: String, val sparkHome: File, val workDir: File, + val workerUrl: String, var state: ExecutorState.Value) extends Logging { val fullId = appId + "/" + execId var workerThread: Thread = null var process: Process = null - var shutdownHook: Thread = null - private def getAppEnv(key: String): Option[String] = - appDesc.command.environment.get(key).orElse(Option(getenv(key))) + // NOTE: This is now redundant with the automated shut-down enforced by the Executor. It might + // make sense to remove this in the future. + var shutdownHook: Thread = null def start() { workerThread = new Thread("ExecutorRunner for " + fullId) { @@ -92,55 +91,17 @@ private[spark] class ExecutorRunner( /** Replace variables such as {{EXECUTOR_ID}} and {{CORES}} in a command argument passed to us */ def substituteVariables(argument: String): String = argument match { + case "{{WORKER_URL}}" => workerUrl case "{{EXECUTOR_ID}}" => execId.toString case "{{HOSTNAME}}" => host case "{{CORES}}" => cores.toString case other => other } - def buildCommandSeq(): Seq[String] = { - val command = appDesc.command - val runner = getAppEnv("JAVA_HOME").map(_ + "/bin/java").getOrElse("java") - // SPARK-698: do not call the run.cmd script, as process.destroy() - // fails to kill a process tree on Windows - Seq(runner) ++ buildJavaOpts() ++ Seq(command.mainClass) ++ - (command.arguments ++ Seq(appId)).map(substituteVariables) - } - - /** - * Attention: this must always be aligned with the environment variables in the run scripts and - * the way the JAVA_OPTS are assembled there. - */ - def buildJavaOpts(): Seq[String] = { - val libraryOpts = getAppEnv("SPARK_LIBRARY_PATH") - .map(p => List("-Djava.library.path=" + p)) - .getOrElse(Nil) - val workerLocalOpts = Option(getenv("SPARK_JAVA_OPTS")).map(Utils.splitCommandString).getOrElse(Nil) - val userOpts = getAppEnv("SPARK_JAVA_OPTS").map(Utils.splitCommandString).getOrElse(Nil) - val memoryOpts = Seq("-Xms" + memory + "M", "-Xmx" + memory + "M") - - // Figure out our classpath with the external compute-classpath script - val ext = if (System.getProperty("os.name").startsWith("Windows")) ".cmd" else ".sh" - val classPath = Utils.executeAndGetOutput( - Seq(sparkHome + "/bin/compute-classpath" + ext), - extraEnvironment=appDesc.command.environment) - - Seq("-cp", classPath) ++ libraryOpts ++ workerLocalOpts ++ userOpts ++ memoryOpts - } - - /** Spawn a thread that will redirect a given stream to a file */ - def redirectStream(in: InputStream, file: File) { - val out = new FileOutputStream(file, true) - new Thread("redirect output to " + file) { - override def run() { - try { - Utils.copyStream(in, out, true) - } catch { - case e: IOException => - logInfo("Redirection to " + file + " closed: " + e.getMessage) - } - } - }.start() + def getCommandSeq = { + val command = Command(appDesc.command.mainClass, + appDesc.command.arguments.map(substituteVariables) ++ Seq(appId), appDesc.command.environment) + CommandUtils.buildCommandSeq(command, memory, sparkHome.getAbsolutePath) } /** @@ -155,7 +116,7 @@ private[spark] class ExecutorRunner( } // Launch the process - val command = buildCommandSeq() + val command = getCommandSeq logInfo("Launch command: " + command.mkString("\"", "\" \"", "\"")) val builder = new ProcessBuilder(command: _*).directory(executorDir) val env = builder.environment() @@ -172,11 +133,11 @@ private[spark] class ExecutorRunner( // Redirect its stdout and stderr to files val stdout = new File(executorDir, "stdout") - redirectStream(process.getInputStream, stdout) + CommandUtils.redirectStream(process.getInputStream, stdout) val stderr = new File(executorDir, "stderr") Files.write(header, stderr, Charsets.UTF_8) - redirectStream(process.getErrorStream, stderr) + CommandUtils.redirectStream(process.getErrorStream, stderr) // Wait for it to exit; this is actually a bad thing if it happens, because we expect to run // long-lived processes only. However, in the future, we might restart the executor a few diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala index fcaf4e92b1..5182dcbb2a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala @@ -26,10 +26,12 @@ import scala.concurrent.duration._ import akka.actor._ import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent} + import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.deploy.{ExecutorDescription, ExecutorState} import org.apache.spark.deploy.DeployMessages._ -import org.apache.spark.deploy.master.Master +import org.apache.spark.deploy.master.{DriverState, Master} +import org.apache.spark.deploy.master.DriverState.DriverState import org.apache.spark.deploy.worker.ui.WorkerWebUI import org.apache.spark.metrics.MetricsSystem import org.apache.spark.util.{AkkaUtils, Utils} @@ -44,6 +46,8 @@ private[spark] class Worker( cores: Int, memory: Int, masterUrls: Array[String], + actorSystemName: String, + actorName: String, workDirPath: String = null, val conf: SparkConf) extends Actor with Logging { @@ -55,7 +59,7 @@ private[spark] class Worker( val DATE_FORMAT = new SimpleDateFormat("yyyyMMddHHmmss") // For worker and executor IDs // Send a heartbeat every (heartbeat timeout) / 4 milliseconds - val HEARTBEAT_MILLIS = conf.get("spark.worker.timeout", "60").toLong * 1000 / 4 + val HEARTBEAT_MILLIS = conf.getLong("spark.worker.timeout", 60) * 1000 / 4 val REGISTRATION_TIMEOUT = 20.seconds val REGISTRATION_RETRIES = 3 @@ -68,6 +72,7 @@ private[spark] class Worker( var masterAddress: Address = null var activeMasterUrl: String = "" var activeMasterWebUiUrl : String = "" + val akkaUrl = "akka.tcp://%s@%s:%s/user/%s".format(actorSystemName, host, port, actorName) @volatile var registered = false @volatile var connected = false val workerId = generateWorkerId() @@ -75,6 +80,9 @@ private[spark] class Worker( var workDir: File = null val executors = new HashMap[String, ExecutorRunner] val finishedExecutors = new HashMap[String, ExecutorRunner] + val drivers = new HashMap[String, DriverRunner] + val finishedDrivers = new HashMap[String, DriverRunner] + val publicAddress = { val envVar = System.getenv("SPARK_PUBLIC_DNS") if (envVar != null) envVar else host @@ -185,7 +193,10 @@ private[spark] class Worker( val execs = executors.values. map(e => new ExecutorDescription(e.appId, e.execId, e.cores, e.state)) - sender ! WorkerSchedulerStateResponse(workerId, execs.toList) + sender ! WorkerSchedulerStateResponse(workerId, execs.toList, drivers.keys.toSeq) + + case Heartbeat => + logInfo(s"Received heartbeat from driver ${sender.path}") case RegisterWorkerFailed(message) => if (!registered) { @@ -199,7 +210,7 @@ private[spark] class Worker( } else { logInfo("Asked to launch executor %s/%d for %s".format(appId, execId, appDesc.name)) val manager = new ExecutorRunner(appId, execId, appDesc, cores_, memory_, - self, workerId, host, new File(execSparkHome_), workDir, ExecutorState.RUNNING) + self, workerId, host, new File(execSparkHome_), workDir, akkaUrl, ExecutorState.RUNNING) executors(appId + "/" + execId) = manager manager.start() coresUsed += cores_ @@ -219,8 +230,8 @@ private[spark] class Worker( logInfo("Executor " + fullId + " finished with state " + state + message.map(" message " + _).getOrElse("") + exitStatus.map(" exitStatus " + _).getOrElse("")) - finishedExecutors(fullId) = executor executors -= fullId + finishedExecutors(fullId) = executor coresUsed -= executor.cores memoryUsed -= executor.memory } @@ -239,13 +250,52 @@ private[spark] class Worker( } } + case LaunchDriver(driverId, driverDesc) => { + logInfo(s"Asked to launch driver $driverId") + val driver = new DriverRunner(driverId, workDir, sparkHome, driverDesc, self, akkaUrl) + drivers(driverId) = driver + driver.start() + + coresUsed += driverDesc.cores + memoryUsed += driverDesc.mem + } + + case KillDriver(driverId) => { + logInfo(s"Asked to kill driver $driverId") + drivers.get(driverId) match { + case Some(runner) => + runner.kill() + case None => + logError(s"Asked to kill unknown driver $driverId") + } + } + + case DriverStateChanged(driverId, state, exception) => { + state match { + case DriverState.ERROR => + logWarning(s"Driver $driverId failed with unrecoverable exception: ${exception.get}") + case DriverState.FINISHED => + logInfo(s"Driver $driverId exited successfully") + case DriverState.KILLED => + logInfo(s"Driver $driverId was killed by user") + } + masterLock.synchronized { + master ! DriverStateChanged(driverId, state, exception) + } + val driver = drivers.remove(driverId).get + finishedDrivers(driverId) = driver + memoryUsed -= driver.driverDesc.mem + coresUsed -= driver.driverDesc.cores + } + case x: DisassociatedEvent if x.remoteAddress == masterAddress => logInfo(s"$x Disassociated !") masterDisconnected() case RequestWorkerState => { sender ! WorkerStateResponse(host, port, workerId, executors.values.toList, - finishedExecutors.values.toList, activeMasterUrl, cores, memory, + finishedExecutors.values.toList, drivers.values.toList, + finishedDrivers.values.toList, activeMasterUrl, cores, memory, coresUsed, memoryUsed, activeMasterWebUiUrl) } } @@ -282,10 +332,11 @@ private[spark] object Worker { // The LocalSparkCluster runs multiple local sparkWorkerX actor systems val conf = new SparkConf val systemName = "sparkWorker" + workerNumber.map(_.toString).getOrElse("") + val actorName = "Worker" val (actorSystem, boundPort) = AkkaUtils.createActorSystem(systemName, host, port, conf = conf) actorSystem.actorOf(Props(classOf[Worker], host, boundPort, webUiPort, cores, memory, - masterUrls, workDir, conf), name = "Worker") + masterUrls, systemName, actorName, workDir, conf), name = actorName) (actorSystem, boundPort) } diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala new file mode 100644 index 0000000000..0e0d0cd626 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/deploy/worker/WorkerWatcher.scala @@ -0,0 +1,55 @@ +package org.apache.spark.deploy.worker + +import akka.actor.{Actor, Address, AddressFromURIString} +import akka.remote.{AssociatedEvent, AssociationErrorEvent, AssociationEvent, DisassociatedEvent, RemotingLifecycleEvent} + +import org.apache.spark.Logging +import org.apache.spark.deploy.DeployMessages.SendHeartbeat + +/** + * Actor which connects to a worker process and terminates the JVM if the connection is severed. + * Provides fate sharing between a worker and its associated child processes. + */ +private[spark] class WorkerWatcher(workerUrl: String) extends Actor + with Logging { + override def preStart() { + context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) + + logInfo(s"Connecting to worker $workerUrl") + val worker = context.actorSelection(workerUrl) + worker ! SendHeartbeat // need to send a message here to initiate connection + } + + // Used to avoid shutting down JVM during tests + private[deploy] var isShutDown = false + private[deploy] def setTesting(testing: Boolean) = isTesting = testing + private var isTesting = false + + // Lets us filter events only from the worker's actor system + private val expectedHostPort = AddressFromURIString(workerUrl).hostPort + private def isWorker(address: Address) = address.hostPort == expectedHostPort + + def exitNonZero() = if (isTesting) isShutDown = true else System.exit(-1) + + override def receive = { + case AssociatedEvent(localAddress, remoteAddress, inbound) if isWorker(remoteAddress) => + logInfo(s"Successfully connected to $workerUrl") + + case AssociationErrorEvent(cause, localAddress, remoteAddress, inbound) + if isWorker(remoteAddress) => + // These logs may not be seen if the worker (and associated pipe) has died + logError(s"Could not initialize connection to worker $workerUrl. Exiting.") + logError(s"Error was: $cause") + exitNonZero() + + case DisassociatedEvent(localAddress, remoteAddress, inbound) if isWorker(remoteAddress) => + // This log message will never be seen + logError(s"Lost connection to worker actor $workerUrl. Exiting.") + exitNonZero() + + case e: AssociationEvent => + // pass through association events relating to other remote actor systems + + case e => logWarning(s"Received unexpected actor system event: $e") + } +}
\ No newline at end of file diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/IndexPage.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/IndexPage.scala index 0d59048313..925c6fb183 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/IndexPage.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/IndexPage.scala @@ -17,24 +17,20 @@ package org.apache.spark.deploy.worker.ui -import javax.servlet.http.HttpServletRequest - -import scala.xml.Node - -import scala.concurrent.duration._ import scala.concurrent.Await +import scala.xml.Node import akka.pattern.ask - +import javax.servlet.http.HttpServletRequest import net.liftweb.json.JsonAST.JValue import org.apache.spark.deploy.JsonProtocol import org.apache.spark.deploy.DeployMessages.{RequestWorkerState, WorkerStateResponse} -import org.apache.spark.deploy.worker.ExecutorRunner +import org.apache.spark.deploy.master.DriverState +import org.apache.spark.deploy.worker.{DriverRunner, ExecutorRunner} import org.apache.spark.ui.UIUtils import org.apache.spark.util.Utils - private[spark] class IndexPage(parent: WorkerWebUI) { val workerActor = parent.worker.self val worker = parent.worker @@ -56,6 +52,16 @@ private[spark] class IndexPage(parent: WorkerWebUI) { val finishedExecutorTable = UIUtils.listingTable(executorHeaders, executorRow, workerState.finishedExecutors) + val driverHeaders = Seq("DriverID", "Main Class", "State", "Cores", "Memory", "Logs", "Notes") + val runningDrivers = workerState.drivers.sortBy(_.driverId).reverse + val runningDriverTable = UIUtils.listingTable(driverHeaders, driverRow, runningDrivers) + val finishedDrivers = workerState.finishedDrivers.sortBy(_.driverId).reverse + def finishedDriverTable = UIUtils.listingTable(driverHeaders, driverRow, finishedDrivers) + + // For now we only show driver information if the user has submitted drivers to the cluster. + // This is until we integrate the notion of drivers and applications in the UI. + def hasDrivers = runningDrivers.length > 0 || finishedDrivers.length > 0 + val content = <div class="row-fluid"> <!-- Worker Details --> <div class="span12"> @@ -79,11 +85,33 @@ private[spark] class IndexPage(parent: WorkerWebUI) { </div> </div> + <div> + {if (hasDrivers) + <div class="row-fluid"> <!-- Running Drivers --> + <div class="span12"> + <h4> Running Drivers {workerState.drivers.size} </h4> + {runningDriverTable} + </div> + </div> + } + </div> + <div class="row-fluid"> <!-- Finished Executors --> <div class="span12"> <h4> Finished Executors </h4> {finishedExecutorTable} </div> + </div> + + <div> + {if (hasDrivers) + <div class="row-fluid"> <!-- Finished Drivers --> + <div class="span12"> + <h4> Finished Drivers </h4> + {finishedDriverTable} + </div> + </div> + } </div>; UIUtils.basicSparkPage(content, "Spark Worker at %s:%s".format( @@ -111,6 +139,27 @@ private[spark] class IndexPage(parent: WorkerWebUI) { .format(executor.appId, executor.execId)}>stderr</a> </td> </tr> + } + def driverRow(driver: DriverRunner): Seq[Node] = { + <tr> + <td>{driver.driverId}</td> + <td>{driver.driverDesc.command.arguments(1)}</td> + <td>{driver.finalState.getOrElse(DriverState.RUNNING)}</td> + <td sorttable_customkey={driver.driverDesc.cores.toString}> + {driver.driverDesc.cores.toString} + </td> + <td sorttable_customkey={driver.driverDesc.mem.toString}> + {Utils.megabytesToString(driver.driverDesc.mem)} + </td> + <td> + <a href={s"logPage?driverId=${driver.driverId}&logType=stdout"}>stdout</a> + <a href={s"logPage?driverId=${driver.driverId}&logType=stderr"}>stderr</a> + </td> + <td> + {driver.finalException.getOrElse("")} + </td> + </tr> + } } diff --git a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala index c382034c99..8daa47b2b2 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/ui/WorkerWebUI.scala @@ -69,30 +69,48 @@ class WorkerWebUI(val worker: Worker, val workDir: File, requestedPort: Option[I def log(request: HttpServletRequest): String = { val defaultBytes = 100 * 1024 - val appId = request.getParameter("appId") - val executorId = request.getParameter("executorId") + + val appId = Option(request.getParameter("appId")) + val executorId = Option(request.getParameter("executorId")) + val driverId = Option(request.getParameter("driverId")) val logType = request.getParameter("logType") val offset = Option(request.getParameter("offset")).map(_.toLong) val byteLength = Option(request.getParameter("byteLength")).map(_.toInt).getOrElse(defaultBytes) - val path = "%s/%s/%s/%s".format(workDir.getPath, appId, executorId, logType) + + val path = (appId, executorId, driverId) match { + case (Some(a), Some(e), None) => + s"${workDir.getPath}/$appId/$executorId/$logType" + case (None, None, Some(d)) => + s"${workDir.getPath}/$driverId/$logType" + case _ => + throw new Exception("Request must specify either application or driver identifiers") + } val (startByte, endByte) = getByteRange(path, offset, byteLength) val file = new File(path) val logLength = file.length - val pre = "==== Bytes %s-%s of %s of %s/%s/%s ====\n" - .format(startByte, endByte, logLength, appId, executorId, logType) + val pre = s"==== Bytes $startByte-$endByte of $logLength of $path ====\n" pre + Utils.offsetBytes(path, startByte, endByte) } def logPage(request: HttpServletRequest): Seq[scala.xml.Node] = { val defaultBytes = 100 * 1024 - val appId = request.getParameter("appId") - val executorId = request.getParameter("executorId") + val appId = Option(request.getParameter("appId")) + val executorId = Option(request.getParameter("executorId")) + val driverId = Option(request.getParameter("driverId")) val logType = request.getParameter("logType") val offset = Option(request.getParameter("offset")).map(_.toLong) val byteLength = Option(request.getParameter("byteLength")).map(_.toInt).getOrElse(defaultBytes) - val path = "%s/%s/%s/%s".format(workDir.getPath, appId, executorId, logType) + + val (path, params) = (appId, executorId, driverId) match { + case (Some(a), Some(e), None) => + (s"${workDir.getPath}/$a/$e/$logType", s"appId=$a&executorId=$e") + case (None, None, Some(d)) => + (s"${workDir.getPath}/$d/$logType", s"driverId=$d") + case _ => + throw new Exception("Request must specify either application or driver identifiers") + } val (startByte, endByte) = getByteRange(path, offset, byteLength) val file = new File(path) @@ -106,9 +124,8 @@ class WorkerWebUI(val worker: Worker, val workDir: File, requestedPort: Option[I val backButton = if (startByte > 0) { - <a href={"?appId=%s&executorId=%s&logType=%s&offset=%s&byteLength=%s" - .format(appId, executorId, logType, math.max(startByte-byteLength, 0), - byteLength)}> + <a href={"?%s&logType=%s&offset=%s&byteLength=%s" + .format(params, logType, math.max(startByte-byteLength, 0), byteLength)}> <button type="button" class="btn btn-default"> Previous {Utils.bytesToString(math.min(byteLength, startByte))} </button> @@ -122,8 +139,8 @@ class WorkerWebUI(val worker: Worker, val workDir: File, requestedPort: Option[I val nextButton = if (endByte < logLength) { - <a href={"?appId=%s&executorId=%s&logType=%s&offset=%s&byteLength=%s". - format(appId, executorId, logType, endByte, byteLength)}> + <a href={"?%s&logType=%s&offset=%s&byteLength=%s". + format(params, logType, endByte, byteLength)}> <button type="button" class="btn btn-default"> Next {Utils.bytesToString(math.min(byteLength, logLength-endByte))} </button> diff --git a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala index 53a2b94a52..f9e43e0e94 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -24,8 +24,9 @@ import akka.remote._ import org.apache.spark.{SparkConf, SparkContext, Logging} import org.apache.spark.TaskState.TaskState +import org.apache.spark.deploy.worker.WorkerWatcher import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ -import org.apache.spark.util.{Utils, AkkaUtils} +import org.apache.spark.util.{AkkaUtils, Utils} private[spark] class CoarseGrainedExecutorBackend( driverUrl: String, @@ -91,7 +92,8 @@ private[spark] class CoarseGrainedExecutorBackend( } private[spark] object CoarseGrainedExecutorBackend { - def run(driverUrl: String, executorId: String, hostname: String, cores: Int) { + def run(driverUrl: String, executorId: String, hostname: String, cores: Int, + workerUrl: Option[String]) { // Debug code Utils.checkHost(hostname) @@ -105,17 +107,24 @@ private[spark] object CoarseGrainedExecutorBackend { actorSystem.actorOf( Props(classOf[CoarseGrainedExecutorBackend], driverUrl, executorId, sparkHostPort, cores), name = "Executor") + workerUrl.foreach{ url => + actorSystem.actorOf(Props(classOf[WorkerWatcher], url), name = "WorkerWatcher") + } actorSystem.awaitTermination() } def main(args: Array[String]) { - if (args.length < 4) { - //the reason we allow the last appid argument is to make it easy to kill rogue executors - System.err.println( - "Usage: CoarseGrainedExecutorBackend <driverUrl> <executorId> <hostname> <cores> " + - "[<appid>]") - System.exit(1) + args.length match { + case x if x < 4 => + System.err.println( + // Worker url is used in spark standalone mode to enforce fate-sharing with worker + "Usage: CoarseGrainedExecutorBackend <driverUrl> <executorId> <hostname> " + + "<cores> [<workerUrl>]") + System.exit(1) + case 4 => + run(args(0), args(1), args(2), args(3).toInt, None) + case x if x > 4 => + run(args(0), args(1), args(2), args(3).toInt, Some(args(4))) } - run(args(0), args(1), args(2), args(3).toInt) } } diff --git a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala index a1e98845f6..5980177320 100644 --- a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala +++ b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala @@ -71,7 +71,7 @@ class LZFCompressionCodec(conf: SparkConf) extends CompressionCodec { class SnappyCompressionCodec(conf: SparkConf) extends CompressionCodec { override def compressedOutputStream(s: OutputStream): OutputStream = { - val blockSize = conf.get("spark.io.compression.snappy.block.size", "32768").toInt + val blockSize = conf.getInt("spark.io.compression.snappy.block.size", 32768) new SnappyOutputStream(s, blockSize) } diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala index 46c40d0a2a..e6e01783c8 100644 --- a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala @@ -54,22 +54,22 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf) extends Loggi private val selector = SelectorProvider.provider.openSelector() private val handleMessageExecutor = new ThreadPoolExecutor( - conf.get("spark.core.connection.handler.threads.min", "20").toInt, - conf.get("spark.core.connection.handler.threads.max", "60").toInt, - conf.get("spark.core.connection.handler.threads.keepalive", "60").toInt, TimeUnit.SECONDS, + conf.getInt("spark.core.connection.handler.threads.min", 20), + conf.getInt("spark.core.connection.handler.threads.max", 60), + conf.getInt("spark.core.connection.handler.threads.keepalive", 60), TimeUnit.SECONDS, new LinkedBlockingDeque[Runnable]()) private val handleReadWriteExecutor = new ThreadPoolExecutor( - conf.get("spark.core.connection.io.threads.min", "4").toInt, - conf.get("spark.core.connection.io.threads.max", "32").toInt, - conf.get("spark.core.connection.io.threads.keepalive", "60").toInt, TimeUnit.SECONDS, + conf.getInt("spark.core.connection.io.threads.min", 4), + conf.getInt("spark.core.connection.io.threads.max", 32), + conf.getInt("spark.core.connection.io.threads.keepalive", 60), TimeUnit.SECONDS, new LinkedBlockingDeque[Runnable]()) // Use a different, yet smaller, thread pool - infrequently used with very short lived tasks : which should be executed asap private val handleConnectExecutor = new ThreadPoolExecutor( - conf.get("spark.core.connection.connect.threads.min", "1").toInt, - conf.get("spark.core.connection.connect.threads.max", "8").toInt, - conf.get("spark.core.connection.connect.threads.keepalive", "60").toInt, TimeUnit.SECONDS, + conf.getInt("spark.core.connection.connect.threads.min", 1), + conf.getInt("spark.core.connection.connect.threads.max", 8), + conf.getInt("spark.core.connection.connect.threads.keepalive", 60), TimeUnit.SECONDS, new LinkedBlockingDeque[Runnable]()) private val serverChannel = ServerSocketChannel.open() diff --git a/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala b/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala index b729eb11c5..d87157e12c 100644 --- a/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala +++ b/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala @@ -36,7 +36,7 @@ private[spark] class ShuffleCopier(conf: SparkConf) extends Logging { resultCollectCallback: (BlockId, Long, ByteBuf) => Unit) { val handler = new ShuffleCopier.ShuffleClientHandler(resultCollectCallback) - val connectTimeout = conf.get("spark.shuffle.netty.connect.timeout", "60000").toInt + val connectTimeout = conf.getInt("spark.shuffle.netty.connect.timeout", 60000) val fc = new FileClient(handler, connectTimeout) try { diff --git a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala index 6d4f46125f..83109d1a6f 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala @@ -97,7 +97,7 @@ private[spark] object CheckpointRDD extends Logging { throw new IOException("Checkpoint failed: temporary path " + tempOutputPath + " already exists") } - val bufferSize = env.conf.get("spark.buffer.size", "65536").toInt + val bufferSize = env.conf.getInt("spark.buffer.size", 65536) val fileOutputStream = if (blockSize < 0) { fs.create(tempOutputPath, false, bufferSize) @@ -131,7 +131,7 @@ private[spark] object CheckpointRDD extends Logging { ): Iterator[T] = { val env = SparkEnv.get val fs = path.getFileSystem(broadcastedConf.value.value) - val bufferSize = env.conf.get("spark.buffer.size", "65536").toInt + val bufferSize = env.conf.getInt("spark.buffer.size", 65536) val fileInputStream = fs.open(path, bufferSize) val serializer = env.serializer.newInstance() val deserializeStream = serializer.deserializeStream(fileInputStream) diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala index 2662d48c84..73d15b9082 100644 --- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala @@ -76,7 +76,7 @@ class NewHadoopRDD[K, V]( val split = theSplit.asInstanceOf[NewHadoopPartition] logInfo("Input split: " + split.serializableHadoopSplit) val conf = confBroadcast.value.value - val attemptId = newTaskAttemptID(jobtrackerId, id, true, split.index, 0) + val attemptId = newTaskAttemptID(jobtrackerId, id, isMap = true, split.index, 0) val hadoopAttemptContext = newTaskAttemptContext(conf, attemptId) val format = inputFormatClass.newInstance if (format.isInstanceOf[Configurable]) { diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index 04a8d05988..c118ddfc01 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -18,35 +18,34 @@ package org.apache.spark.rdd import java.nio.ByteBuffer -import java.util.Date import java.text.SimpleDateFormat +import java.util.Date import java.util.{HashMap => JHashMap} -import scala.collection.{mutable, Map} +import scala.collection.Map +import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.collection.JavaConversions._ import scala.reflect.{ClassTag, classTag} -import org.apache.hadoop.mapred._ -import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.io.SequenceFile.CompressionType -import org.apache.hadoop.mapred.FileOutputFormat -import org.apache.hadoop.mapred.OutputFormat +import org.apache.hadoop.io.compress.CompressionCodec +import org.apache.hadoop.mapred.{FileOutputCommitter, FileOutputFormat, JobConf, OutputFormat} import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat} -import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat} -import org.apache.hadoop.mapreduce.SparkHadoopMapReduceUtil import org.apache.hadoop.mapreduce.{Job => NewAPIHadoopJob} import org.apache.hadoop.mapreduce.{RecordWriter => NewRecordWriter} +import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat} import com.clearspring.analytics.stream.cardinality.HyperLogLog +// SparkHadoopWriter and SparkHadoopMapReduceUtil are actually source files defined in Spark. +import org.apache.hadoop.mapred.SparkHadoopWriter +import org.apache.hadoop.mapreduce.SparkHadoopMapReduceUtil import org.apache.spark._ import org.apache.spark.SparkContext._ import org.apache.spark.partial.{BoundedDouble, PartialResult} -import org.apache.spark.Aggregator -import org.apache.spark.Partitioner import org.apache.spark.Partitioner.defaultPartitioner import org.apache.spark.util.SerializableHyperLogLog @@ -120,9 +119,9 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) } /** - * Merge the values for each key using an associative function and a neutral "zero value" which may - * be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for - * list concatenation, 0 for addition, or 1 for multiplication.). + * Merge the values for each key using an associative function and a neutral "zero value" which + * may be added to the result an arbitrary number of times, and must not change the result + * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ def foldByKey(zeroValue: V, partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V)] = { // Serialize the zero value to a byte array so that we can get a new clone of it on each key @@ -138,18 +137,18 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) } /** - * Merge the values for each key using an associative function and a neutral "zero value" which may - * be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for - * list concatenation, 0 for addition, or 1 for multiplication.). + * Merge the values for each key using an associative function and a neutral "zero value" which + * may be added to the result an arbitrary number of times, and must not change the result + * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ def foldByKey(zeroValue: V, numPartitions: Int)(func: (V, V) => V): RDD[(K, V)] = { foldByKey(zeroValue, new HashPartitioner(numPartitions))(func) } /** - * Merge the values for each key using an associative function and a neutral "zero value" which may - * be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for - * list concatenation, 0 for addition, or 1 for multiplication.). + * Merge the values for each key using an associative function and a neutral "zero value" which + * may be added to the result an arbitrary number of times, and must not change the result + * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ def foldByKey(zeroValue: V)(func: (V, V) => V): RDD[(K, V)] = { foldByKey(zeroValue, defaultPartitioner(self))(func) @@ -226,7 +225,7 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) } /** - * Return approximate number of distinct values for each key in this RDD. + * Return approximate number of distinct values for each key in this RDD. * The accuracy of approximation can be controlled through the relative standard deviation * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in * more accurate counts but increase the memory footprint and vise versa. HashPartitions the @@ -579,7 +578,8 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) */ def saveAsHadoopFile[F <: OutputFormat[K, V]]( path: String, codec: Class[_ <: CompressionCodec]) (implicit fm: ClassTag[F]) { - saveAsHadoopFile(path, getKeyClass, getValueClass, fm.runtimeClass.asInstanceOf[Class[F]], codec) + val runtimeClass = fm.runtimeClass + saveAsHadoopFile(path, getKeyClass, getValueClass, runtimeClass.asInstanceOf[Class[F]], codec) } /** @@ -599,7 +599,8 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: NewOutputFormat[_, _]], - conf: Configuration = self.context.hadoopConfiguration) { + conf: Configuration = self.context.hadoopConfiguration) + { val job = new NewAPIHadoopJob(conf) job.setOutputKeyClass(keyClass) job.setOutputValueClass(valueClass) @@ -613,7 +614,8 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) // around by taking a mod. We expect that no task will be attempted 2 billion times. val attemptNumber = (context.attemptId % Int.MaxValue).toInt /* "reduce task" <split #> <attempt # = spark task #> */ - val attemptId = newTaskAttemptID(jobtrackerID, stageId, false, context.partitionId, attemptNumber) + val attemptId = newTaskAttemptID(jobtrackerID, stageId, isMap = false, context.partitionId, + attemptNumber) val hadoopContext = newTaskAttemptContext(wrappedConf.value, attemptId) val format = outputFormatClass.newInstance val committer = format.getOutputCommitter(hadoopContext) @@ -632,13 +634,12 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) * however we're only going to use this local OutputCommitter for * setupJob/commitJob, so we just use a dummy "map" task. */ - val jobAttemptId = newTaskAttemptID(jobtrackerID, stageId, true, 0, 0) + val jobAttemptId = newTaskAttemptID(jobtrackerID, stageId, isMap = true, 0, 0) val jobTaskContext = newTaskAttemptContext(wrappedConf.value, jobAttemptId) val jobCommitter = jobFormat.getOutputCommitter(jobTaskContext) jobCommitter.setupJob(jobTaskContext) val count = self.context.runJob(self, writeShard _).sum jobCommitter.commitJob(jobTaskContext) - jobCommitter.cleanupJob(jobTaskContext) } /** @@ -668,7 +669,9 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) codec: Option[Class[_ <: CompressionCodec]] = None) { conf.setOutputKeyClass(keyClass) conf.setOutputValueClass(valueClass) - // conf.setOutputFormat(outputFormatClass) // Doesn't work in Scala 2.9 due to what may be a generics bug + // Doesn't work in Scala 2.9 due to what may be a generics bug + // TODO: Should we uncomment this for Scala 2.10? + // conf.setOutputFormat(outputFormatClass) conf.set("mapred.output.format.class", outputFormatClass.getName) for (c <- codec) { conf.setCompressMapOutput(true) @@ -702,7 +705,8 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) throw new SparkException("Output value class not set") } - logInfo("Saving as hadoop file of type (" + keyClass.getSimpleName+ ", " + valueClass.getSimpleName+ ")") + logDebug("Saving as hadoop file of type (" + keyClass.getSimpleName + ", " + + valueClass.getSimpleName+ ")") val writer = new SparkHadoopWriter(conf) writer.preSetup() @@ -728,7 +732,6 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) self.context.runJob(self, writeToFile _) writer.commitJob() - writer.cleanup() } /** diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index 3f41b66279..f9dc12eee3 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -23,7 +23,6 @@ import scala.collection.Map import scala.collection.JavaConversions.mapAsScalaMap import scala.collection.mutable.ArrayBuffer -import scala.collection.mutable.HashMap import scala.reflect.{classTag, ClassTag} import org.apache.hadoop.io.BytesWritable @@ -52,11 +51,13 @@ import org.apache.spark._ * partitioned collection of elements that can be operated on in parallel. This class contains the * basic operations available on all RDDs, such as `map`, `filter`, and `persist`. In addition, * [[org.apache.spark.rdd.PairRDDFunctions]] contains operations available only on RDDs of key-value - * pairs, such as `groupByKey` and `join`; [[org.apache.spark.rdd.DoubleRDDFunctions]] contains - * operations available only on RDDs of Doubles; and [[org.apache.spark.rdd.SequenceFileRDDFunctions]] - * contains operations available on RDDs that can be saved as SequenceFiles. These operations are - * automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit - * conversions when you `import org.apache.spark.SparkContext._`. + * pairs, such as `groupByKey` and `join`; + * [[org.apache.spark.rdd.DoubleRDDFunctions]] contains operations available only on RDDs of + * Doubles; and + * [[org.apache.spark.rdd.SequenceFileRDDFunctions]] contains operations available on RDDs that + * can be saved as SequenceFiles. + * These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] + * through implicit conversions when you `import org.apache.spark.SparkContext._`. * * Internally, each RDD is characterized by five main properties: * @@ -235,12 +236,9 @@ abstract class RDD[T: ClassTag]( /** * Compute an RDD partition or read it from a checkpoint if the RDD is checkpointing. */ - private[spark] def computeOrReadCheckpoint(split: Partition, context: TaskContext): Iterator[T] = { - if (isCheckpointed) { - firstParent[T].iterator(split, context) - } else { - compute(split, context) - } + private[spark] def computeOrReadCheckpoint(split: Partition, context: TaskContext): Iterator[T] = + { + if (isCheckpointed) firstParent[T].iterator(split, context) else compute(split, context) } // Transformations (return a new RDD) @@ -268,6 +266,9 @@ abstract class RDD[T: ClassTag]( def distinct(numPartitions: Int): RDD[T] = map(x => (x, null)).reduceByKey((x, y) => x, numPartitions).map(_._1) + /** + * Return a new RDD containing the distinct elements in this RDD. + */ def distinct(): RDD[T] = distinct(partitions.size) /** @@ -280,7 +281,7 @@ abstract class RDD[T: ClassTag]( * which can avoid performing a shuffle. */ def repartition(numPartitions: Int): RDD[T] = { - coalesce(numPartitions, true) + coalesce(numPartitions, shuffle = true) } /** @@ -646,7 +647,8 @@ abstract class RDD[T: ClassTag]( } /** - * Reduces the elements of this RDD using the specified commutative and associative binary operator. + * Reduces the elements of this RDD using the specified commutative and + * associative binary operator. */ def reduce(f: (T, T) => T): T = { val cleanF = sc.clean(f) @@ -953,7 +955,7 @@ abstract class RDD[T: ClassTag]( private var storageLevel: StorageLevel = StorageLevel.NONE /** Record user function generating this RDD. */ - @transient private[spark] val origin = sc.getCallSite + @transient private[spark] val origin = sc.getCallSite() private[spark] def elementClassTag: ClassTag[T] = classTag[T] diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala index e22b1e53e8..c52d6175d2 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala @@ -31,7 +31,7 @@ import org.apache.spark.util.Utils private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedulerImpl) extends Logging { - private val THREADS = sparkEnv.conf.get("spark.resultGetter.threads", "4").toInt + private val THREADS = sparkEnv.conf.getInt("spark.resultGetter.threads", 4) private val getTaskResultExecutor = Utils.newDaemonFixedThreadPool( THREADS, "Result resolver thread") diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala index 0c8ed62759..d4f74d3e18 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -51,15 +51,15 @@ private[spark] class TaskSchedulerImpl( isLocal: Boolean = false) extends TaskScheduler with Logging { - def this(sc: SparkContext) = this(sc, sc.conf.get("spark.task.maxFailures", "4").toInt) + def this(sc: SparkContext) = this(sc, sc.conf.getInt("spark.task.maxFailures", 4)) val conf = sc.conf // How often to check for speculative tasks - val SPECULATION_INTERVAL = conf.get("spark.speculation.interval", "100").toLong + val SPECULATION_INTERVAL = conf.getLong("spark.speculation.interval", 100) // Threshold above which we warn user initial TaskSet may be starved - val STARVATION_TIMEOUT = conf.get("spark.starvation.timeout", "15000").toLong + val STARVATION_TIMEOUT = conf.getLong("spark.starvation.timeout", 15000) // TaskSetManagers are not thread safe, so any access to one should be synchronized // on this class. @@ -125,7 +125,7 @@ private[spark] class TaskSchedulerImpl( override def start() { backend.start() - if (!isLocal && conf.get("spark.speculation", "false").toBoolean) { + if (!isLocal && conf.getBoolean("spark.speculation", false)) { logInfo("Starting speculative execution thread") import sc.env.actorSystem.dispatcher sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds, diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala index 6dd1469d8f..a10e5397ad 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala @@ -57,11 +57,11 @@ private[spark] class TaskSetManager( val conf = sched.sc.conf // CPUs to request per task - val CPUS_PER_TASK = conf.get("spark.task.cpus", "1").toInt + val CPUS_PER_TASK = conf.getInt("spark.task.cpus", 1) // Quantile of tasks at which to start speculation - val SPECULATION_QUANTILE = conf.get("spark.speculation.quantile", "0.75").toDouble - val SPECULATION_MULTIPLIER = conf.get("spark.speculation.multiplier", "1.5").toDouble + val SPECULATION_QUANTILE = conf.getDouble("spark.speculation.quantile", 0.75) + val SPECULATION_MULTIPLIER = conf.getDouble("spark.speculation.multiplier", 1.5) // Serializer for closures and tasks. val env = SparkEnv.get @@ -116,7 +116,7 @@ private[spark] class TaskSetManager( // How frequently to reprint duplicate exceptions in full, in milliseconds val EXCEPTION_PRINT_INTERVAL = - conf.get("spark.logging.exceptionPrintInterval", "10000").toLong + conf.getLong("spark.logging.exceptionPrintInterval", 10000) // Map of recent exceptions (identified by string representation and top stack frame) to // duplicate count (how many times the same exception has appeared) and time the full exception diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index 2f5bcafe40..8d596a76c2 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -63,7 +63,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) // Periodically revive offers to allow delay scheduling to work - val reviveInterval = conf.get("spark.scheduler.revive.interval", "1000").toLong + val reviveInterval = conf.getLong("spark.scheduler.revive.interval", 1000) import context.dispatcher context.system.scheduler.schedule(0.millis, reviveInterval.millis, self, ReviveOffers) } @@ -209,8 +209,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A } override def defaultParallelism(): Int = { - conf.getOption("spark.default.parallelism").map(_.toInt).getOrElse( - math.max(totalCoreCount.get(), 2)) + conf.getInt("spark.default.parallelism", math.max(totalCoreCount.get(), 2)) } // Called by subclasses when notified of a lost worker diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala index b44d1e43c8..d99c76117c 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala @@ -33,7 +33,7 @@ private[spark] class SimrSchedulerBackend( val tmpPath = new Path(driverFilePath + "_tmp") val filePath = new Path(driverFilePath) - val maxCores = conf.get("spark.simr.executor.cores", "1").toInt + val maxCores = conf.getInt("spark.simr.executor.cores", 1) override def start() { super.start() diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala index 9858717d13..faa6e1ebe8 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SparkDeploySchedulerBackend.scala @@ -20,7 +20,7 @@ package org.apache.spark.scheduler.cluster import scala.collection.mutable.HashMap import org.apache.spark.{Logging, SparkContext} -import org.apache.spark.deploy.client.{Client, ClientListener} +import org.apache.spark.deploy.client.{AppClient, AppClientListener} import org.apache.spark.deploy.{Command, ApplicationDescription} import org.apache.spark.scheduler.{ExecutorExited, ExecutorLossReason, SlaveLost, TaskSchedulerImpl} import org.apache.spark.util.Utils @@ -31,14 +31,14 @@ private[spark] class SparkDeploySchedulerBackend( masters: Array[String], appName: String) extends CoarseGrainedSchedulerBackend(scheduler, sc.env.actorSystem) - with ClientListener + with AppClientListener with Logging { - var client: Client = null + var client: AppClient = null var stopping = false var shutdownCallback : (SparkDeploySchedulerBackend) => Unit = _ - val maxCores = conf.get("spark.cores.max", Int.MaxValue.toString).toInt + val maxCores = conf.getOption("spark.cores.max").map(_.toInt) override def start() { super.start() @@ -47,14 +47,14 @@ private[spark] class SparkDeploySchedulerBackend( val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( conf.get("spark.driver.host"), conf.get("spark.driver.port"), CoarseGrainedSchedulerBackend.ACTOR_NAME) - val args = Seq(driverUrl, "{{EXECUTOR_ID}}", "{{HOSTNAME}}", "{{CORES}}") + val args = Seq(driverUrl, "{{EXECUTOR_ID}}", "{{HOSTNAME}}", "{{CORES}}", "{{WORKER_URL}}") val command = Command( "org.apache.spark.executor.CoarseGrainedExecutorBackend", args, sc.executorEnvs) val sparkHome = sc.getSparkHome().getOrElse(null) val appDesc = new ApplicationDescription(appName, maxCores, sc.executorMemory, command, sparkHome, "http://" + sc.ui.appUIAddress) - client = new Client(sc.env.actorSystem, masters, appDesc, this, conf) + client = new AppClient(sc.env.actorSystem, masters, appDesc, this, conf) client.start() } diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala index d46fceba89..e16d60c54c 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala @@ -77,7 +77,7 @@ private[spark] class CoarseMesosSchedulerBackend( "Spark home is not set; set it through the spark.home system " + "property, the SPARK_HOME environment variable or the SparkContext constructor")) - val extraCoresPerSlave = conf.get("spark.mesos.extra.cores", "0").toInt + val extraCoresPerSlave = conf.getInt("spark.mesos.extra.cores", 0) var nextMesosTaskId = 0 diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala index ae8d527352..b428c82a48 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala @@ -340,5 +340,5 @@ private[spark] class MesosSchedulerBackend( } // TODO: query Mesos for number of cores - override def defaultParallelism() = sc.conf.get("spark.default.parallelism", "8").toInt + override def defaultParallelism() = sc.conf.getInt("spark.default.parallelism", 8) } diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala index a24a3b04b8..c14cd47556 100644 --- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala @@ -36,7 +36,7 @@ import org.apache.spark.storage.{GetBlock, GotBlock, PutBlock} */ class KryoSerializer(conf: SparkConf) extends org.apache.spark.serializer.Serializer with Logging { private val bufferSize = { - conf.get("spark.kryoserializer.buffer.mb", "2").toInt * 1024 * 1024 + conf.getInt("spark.kryoserializer.buffer.mb", 2) * 1024 * 1024 } def newKryoOutput() = new KryoOutput(bufferSize) @@ -48,7 +48,7 @@ class KryoSerializer(conf: SparkConf) extends org.apache.spark.serializer.Serial // Allow disabling Kryo reference tracking if user knows their object graphs don't have loops. // Do this before we invoke the user registrator so the user registrator can override this. - kryo.setReferences(conf.get("spark.kryo.referenceTracking", "true").toBoolean) + kryo.setReferences(conf.getBoolean("spark.kryo.referenceTracking", true)) for (cls <- KryoSerializer.toRegister) kryo.register(cls) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala index 47478631a1..4fa2ab96d9 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala @@ -327,7 +327,7 @@ object BlockFetcherIterator { fetchRequestsSync.put(request) } - copiers = startCopiers(conf.get("spark.shuffle.copier.threads", "6").toInt) + copiers = startCopiers(conf.getInt("spark.shuffle.copier.threads", 6)) logInfo("Started " + fetchRequestsSync.size + " remote gets in " + Utils.getUsedTimeMs(startTime)) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index 6d2cda97b0..c56e2ca2df 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -58,8 +58,8 @@ private[spark] class BlockManager( // If we use Netty for shuffle, start a new Netty-based shuffle sender service. private val nettyPort: Int = { - val useNetty = conf.get("spark.shuffle.use.netty", "false").toBoolean - val nettyPortConfig = conf.get("spark.shuffle.sender.port", "0").toInt + val useNetty = conf.getBoolean("spark.shuffle.use.netty", false) + val nettyPortConfig = conf.getInt("spark.shuffle.sender.port", 0) if (useNetty) diskBlockManager.startShuffleBlockSender(nettyPortConfig) else 0 } @@ -72,14 +72,14 @@ private[spark] class BlockManager( // Max megabytes of data to keep in flight per reducer (to avoid over-allocating memory // for receiving shuffle outputs) val maxBytesInFlight = - conf.get("spark.reducer.maxMbInFlight", "48").toLong * 1024 * 1024 + conf.getLong("spark.reducer.maxMbInFlight", 48) * 1024 * 1024 // Whether to compress broadcast variables that are stored - val compressBroadcast = conf.get("spark.broadcast.compress", "true").toBoolean + val compressBroadcast = conf.getBoolean("spark.broadcast.compress", true) // Whether to compress shuffle output that are stored - val compressShuffle = conf.get("spark.shuffle.compress", "true").toBoolean + val compressShuffle = conf.getBoolean("spark.shuffle.compress", true) // Whether to compress RDD partitions that are stored serialized - val compressRdds = conf.get("spark.rdd.compress", "false").toBoolean + val compressRdds = conf.getBoolean("spark.rdd.compress", false) val heartBeatFrequency = BlockManager.getHeartBeatFrequency(conf) @@ -443,7 +443,7 @@ private[spark] class BlockManager( : BlockFetcherIterator = { val iter = - if (conf.get("spark.shuffle.use.netty", "false").toBoolean) { + if (conf.getBoolean("spark.shuffle.use.netty", false)) { new BlockFetcherIterator.NettyBlockFetcherIterator(this, blocksByAddress, serializer) } else { new BlockFetcherIterator.BasicBlockFetcherIterator(this, blocksByAddress, serializer) @@ -469,7 +469,7 @@ private[spark] class BlockManager( def getDiskWriter(blockId: BlockId, file: File, serializer: Serializer, bufferSize: Int) : BlockObjectWriter = { val compressStream: OutputStream => OutputStream = wrapForCompression(blockId, _) - val syncWrites = conf.get("spark.shuffle.sync", "false").toBoolean + val syncWrites = conf.getBoolean("spark.shuffle.sync", false) new DiskBlockObjectWriter(blockId, file, serializer, bufferSize, compressStream, syncWrites) } @@ -864,15 +864,15 @@ private[spark] object BlockManager extends Logging { val ID_GENERATOR = new IdGenerator def getMaxMemory(conf: SparkConf): Long = { - val memoryFraction = conf.get("spark.storage.memoryFraction", "0.66").toDouble + val memoryFraction = conf.getDouble("spark.storage.memoryFraction", 0.66) (Runtime.getRuntime.maxMemory * memoryFraction).toLong } def getHeartBeatFrequency(conf: SparkConf): Long = - conf.get("spark.storage.blockManagerTimeoutIntervalMs", "60000").toLong / 4 + conf.getLong("spark.storage.blockManagerTimeoutIntervalMs", 60000) / 4 def getDisableHeartBeatsForTesting(conf: SparkConf): Boolean = - conf.get("spark.test.disableBlockManagerHeartBeat", "false").toBoolean + conf.getBoolean("spark.test.disableBlockManagerHeartBeat", false) /** * Attempt to clean up a ByteBuffer if it is memory-mapped. This uses an *unsafe* Sun API that diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala index b5afe8cd23..c54e4f2664 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala @@ -28,11 +28,10 @@ import org.apache.spark.storage.BlockManagerMessages._ import org.apache.spark.util.AkkaUtils private[spark] -class BlockManagerMaster(var driverActor : Either[ActorRef, ActorSelection], - conf: SparkConf) extends Logging { +class BlockManagerMaster(var driverActor : ActorRef, conf: SparkConf) extends Logging { - val AKKA_RETRY_ATTEMPTS: Int = conf.get("spark.akka.num.retries", "3").toInt - val AKKA_RETRY_INTERVAL_MS: Int = conf.get("spark.akka.retry.wait", "3000").toInt + val AKKA_RETRY_ATTEMPTS: Int = conf.getInt("spark.akka.num.retries", 3) + val AKKA_RETRY_INTERVAL_MS: Int = conf.getInt("spark.akka.retry.wait", 3000) val DRIVER_AKKA_ACTOR_NAME = "BlockManagerMaster" @@ -159,10 +158,7 @@ class BlockManagerMaster(var driverActor : Either[ActorRef, ActorSelection], while (attempts < AKKA_RETRY_ATTEMPTS) { attempts += 1 try { - val future = driverActor match { - case Left(a: ActorRef) => a.ask(message)(timeout) - case Right(b: ActorSelection) => b.ask(message)(timeout) - } + val future = driverActor.ask(message)(timeout) val result = Await.result(future, timeout) if (result == null) { throw new SparkException("BlockManagerMaster returned null") diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala index 58452d9657..2c1a4e2f5d 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala @@ -348,14 +348,19 @@ object BlockManagerMasterActor { if (storageLevel.isValid) { // isValid means it is either stored in-memory or on-disk. - _blocks.put(blockId, BlockStatus(storageLevel, memSize, diskSize)) + // But the memSize here indicates the data size in or dropped from memory, + // and the diskSize here indicates the data size in or dropped to disk. + // They can be both larger than 0, when a block is dropped from memory to disk. + // Therefore, a safe way to set BlockStatus is to set its info in accurate modes. if (storageLevel.useMemory) { + _blocks.put(blockId, BlockStatus(storageLevel, memSize, 0)) _remainingMem -= memSize logInfo("Added %s in memory on %s (size: %s, free: %s)".format( blockId, blockManagerId.hostPort, Utils.bytesToString(memSize), Utils.bytesToString(_remainingMem))) } if (storageLevel.useDisk) { + _blocks.put(blockId, BlockStatus(storageLevel, 0, diskSize)) logInfo("Added %s on disk on %s (size: %s)".format( blockId, blockManagerId.hostPort, Utils.bytesToString(diskSize))) } diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala index 55dcb3742c..edc1133172 100644 --- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala @@ -38,7 +38,7 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD extends PathResolver with Logging { private val MAX_DIR_CREATION_ATTEMPTS: Int = 10 - private val subDirsPerLocalDir = shuffleManager.conf.get("spark.diskStore.subDirectories", "64").toInt + private val subDirsPerLocalDir = shuffleManager.conf.getInt("spark.diskStore.subDirectories", 64) // Create one local directory for each path mentioned in spark.local.dir; then, inside this // directory, create multiple subdirectories that we will hash files into, in order to avoid diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala index 39dc7bb19a..e2b24298a5 100644 --- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala @@ -64,9 +64,9 @@ class ShuffleBlockManager(blockManager: BlockManager) { // Turning off shuffle file consolidation causes all shuffle Blocks to get their own file. // TODO: Remove this once the shuffle file consolidation feature is stable. val consolidateShuffleFiles = - conf.get("spark.shuffle.consolidateFiles", "false").toBoolean + conf.getBoolean("spark.shuffle.consolidateFiles", false) - private val bufferSize = conf.get("spark.shuffle.file.buffer.kb", "100").toInt * 1024 + private val bufferSize = conf.getInt("spark.shuffle.file.buffer.kb", 100) * 1024 /** * Contains all the state related to a particular shuffle. This includes a pool of unused diff --git a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala index dca98c6c05..729ba2c550 100644 --- a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala +++ b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala @@ -95,7 +95,7 @@ private[spark] object ThreadingTest { val conf = new SparkConf() val serializer = new KryoSerializer(conf) val blockManagerMaster = new BlockManagerMaster( - Left(actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf)))), conf) + actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf))), conf) val blockManager = new BlockManager( "<driver>", actorSystem, blockManagerMaster, serializer, 1024 * 1024, conf) val producers = (1 to numProducers).map(i => new ProducerThread(blockManager, i)) diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala index b7b87250b9..bcd2824450 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala @@ -33,7 +33,7 @@ import org.apache.spark.scheduler._ */ private[spark] class JobProgressListener(val sc: SparkContext) extends SparkListener { // How many stages to remember - val RETAINED_STAGES = sc.conf.get("spark.ui.retained_stages", "1000").toInt + val RETAINED_STAGES = sc.conf.getInt("spark.ui.retainedStages", 1000) val DEFAULT_POOL_NAME = "default" val stageIdToPool = new HashMap[Int, String]() diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala index 2ee37815de..761d378c7f 100644 --- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala +++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala @@ -44,13 +44,13 @@ private[spark] object AkkaUtils { def createActorSystem(name: String, host: String, port: Int, indestructible: Boolean = false, conf: SparkConf): (ActorSystem, Int) = { - val akkaThreads = conf.get("spark.akka.threads", "4").toInt - val akkaBatchSize = conf.get("spark.akka.batchSize", "15").toInt + val akkaThreads = conf.getInt("spark.akka.threads", 4) + val akkaBatchSize = conf.getInt("spark.akka.batchSize", 15) - val akkaTimeout = conf.get("spark.akka.timeout", "100").toInt + val akkaTimeout = conf.getInt("spark.akka.timeout", 100) - val akkaFrameSize = conf.get("spark.akka.frameSize", "10").toInt - val akkaLogLifecycleEvents = conf.get("spark.akka.logLifecycleEvents", "false").toBoolean + val akkaFrameSize = conf.getInt("spark.akka.frameSize", 10) + val akkaLogLifecycleEvents = conf.getBoolean("spark.akka.logLifecycleEvents", false) val lifecycleEvents = if (akkaLogLifecycleEvents) "on" else "off" if (!akkaLogLifecycleEvents) { // As a workaround for Akka issue #3787, we coerce the "EndpointWriter" log to be silent. @@ -58,12 +58,12 @@ private[spark] object AkkaUtils { Option(Logger.getLogger("akka.remote.EndpointWriter")).map(l => l.setLevel(Level.FATAL)) } - val logAkkaConfig = if (conf.get("spark.akka.logAkkaConfig", "false").toBoolean) "on" else "off" + val logAkkaConfig = if (conf.getBoolean("spark.akka.logAkkaConfig", false)) "on" else "off" - val akkaHeartBeatPauses = conf.get("spark.akka.heartbeat.pauses", "600").toInt + val akkaHeartBeatPauses = conf.getInt("spark.akka.heartbeat.pauses", 600) val akkaFailureDetector = - conf.get("spark.akka.failure-detector.threshold", "300.0").toDouble - val akkaHeartBeatInterval = conf.get("spark.akka.heartbeat.interval", "1000").toInt + conf.getDouble("spark.akka.failure-detector.threshold", 300.0) + val akkaHeartBeatInterval = conf.getInt("spark.akka.heartbeat.interval", 1000) val akkaConf = ConfigFactory.parseMap(conf.getAkkaConf.toMap[String, String]).withFallback( ConfigFactory.parseString( @@ -103,6 +103,11 @@ private[spark] object AkkaUtils { /** Returns the default Spark timeout to use for Akka ask operations. */ def askTimeout(conf: SparkConf): FiniteDuration = { - Duration.create(conf.get("spark.akka.askTimeout", "30").toLong, "seconds") + Duration.create(conf.getLong("spark.akka.askTimeout", 30), "seconds") + } + + /** Returns the default Spark timeout to use for Akka remote actor lookup. */ + def lookupTimeout(conf: SparkConf): FiniteDuration = { + Duration.create(conf.get("spark.akka.lookupTimeout", "30").toLong, "seconds") } } diff --git a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala index aa7f52cafb..3d1e90a352 100644 --- a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala +++ b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala @@ -74,7 +74,7 @@ object MetadataCleanerType extends Enumeration { // initialization of StreamingContext. It's okay for users trying to configure stuff themselves. object MetadataCleaner { def getDelaySeconds(conf: SparkConf) = { - conf.get("spark.cleaner.ttl", "3500").toInt + conf.getInt("spark.cleaner.ttl", 3500) } def getDelaySeconds(conf: SparkConf, cleanerType: MetadataCleanerType.MetadataCleanerType): Int = diff --git a/core/src/main/scala/org/apache/spark/util/XORShiftRandom.scala b/core/src/main/scala/org/apache/spark/util/XORShiftRandom.scala index e9907e6c85..08b31ac64f 100644 --- a/core/src/main/scala/org/apache/spark/util/XORShiftRandom.scala +++ b/core/src/main/scala/org/apache/spark/util/XORShiftRandom.scala @@ -91,4 +91,4 @@ private[spark] object XORShiftRandom { } -}
\ No newline at end of file +} diff --git a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala index 10b8b441fd..afc1beff98 100644 --- a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala +++ b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala @@ -23,6 +23,7 @@ import akka.actor._ import org.apache.spark.scheduler.MapStatus import org.apache.spark.storage.BlockManagerId import org.apache.spark.util.AkkaUtils +import scala.concurrent.Await class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { private val conf = new SparkConf @@ -49,14 +50,14 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { test("master start and stop") { val actorSystem = ActorSystem("test") val tracker = new MapOutputTrackerMaster(conf) - tracker.trackerActor = Left(actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker)))) + tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker))) tracker.stop() } test("master register and fetch") { val actorSystem = ActorSystem("test") val tracker = new MapOutputTrackerMaster(conf) - tracker.trackerActor = Left(actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker)))) + tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker))) tracker.registerShuffle(10, 2) val compressedSize1000 = MapOutputTracker.compressSize(1000L) val compressedSize10000 = MapOutputTracker.compressSize(10000L) @@ -75,7 +76,7 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { test("master register and unregister and fetch") { val actorSystem = ActorSystem("test") val tracker = new MapOutputTrackerMaster(conf) - tracker.trackerActor = Left(actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker)))) + tracker.trackerActor = actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker))) tracker.registerShuffle(10, 2) val compressedSize1000 = MapOutputTracker.compressSize(1000L) val compressedSize10000 = MapOutputTracker.compressSize(10000L) @@ -101,13 +102,15 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { System.setProperty("spark.hostPort", hostname + ":" + boundPort) val masterTracker = new MapOutputTrackerMaster(conf) - masterTracker.trackerActor = Left(actorSystem.actorOf( - Props(new MapOutputTrackerMasterActor(masterTracker)), "MapOutputTracker")) + masterTracker.trackerActor = actorSystem.actorOf( + Props(new MapOutputTrackerMasterActor(masterTracker)), "MapOutputTracker") val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0, conf = conf) val slaveTracker = new MapOutputTracker(conf) - slaveTracker.trackerActor = Right(slaveSystem.actorSelection( - "akka.tcp://spark@localhost:" + boundPort + "/user/MapOutputTracker")) + val selection = slaveSystem.actorSelection( + s"akka.tcp://spark@localhost:$boundPort/user/MapOutputTracker") + val timeout = AkkaUtils.lookupTimeout(conf) + slaveTracker.trackerActor = Await.result(selection.resolveOne(timeout), timeout) masterTracker.registerShuffle(10, 1) masterTracker.incrementEpoch() diff --git a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala index 0b38e239f9..d05bbd6ff7 100644 --- a/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala +++ b/core/src/test/scala/org/apache/spark/deploy/JsonProtocolSuite.scala @@ -25,8 +25,8 @@ import net.liftweb.json.JsonAST.JValue import org.scalatest.FunSuite import org.apache.spark.deploy.DeployMessages.{MasterStateResponse, WorkerStateResponse} -import org.apache.spark.deploy.master.{ApplicationInfo, RecoveryState, WorkerInfo} -import org.apache.spark.deploy.worker.ExecutorRunner +import org.apache.spark.deploy.master.{ApplicationInfo, DriverInfo, RecoveryState, WorkerInfo} +import org.apache.spark.deploy.worker.{ExecutorRunner, DriverRunner} class JsonProtocolSuite extends FunSuite { test("writeApplicationInfo") { @@ -50,11 +50,13 @@ class JsonProtocolSuite extends FunSuite { } test("writeMasterState") { - val workers = Array[WorkerInfo](createWorkerInfo(), createWorkerInfo()) - val activeApps = Array[ApplicationInfo](createAppInfo()) + val workers = Array(createWorkerInfo(), createWorkerInfo()) + val activeApps = Array(createAppInfo()) val completedApps = Array[ApplicationInfo]() + val activeDrivers = Array(createDriverInfo()) + val completedDrivers = Array(createDriverInfo()) val stateResponse = new MasterStateResponse("host", 8080, workers, activeApps, completedApps, - RecoveryState.ALIVE) + activeDrivers, completedDrivers, RecoveryState.ALIVE) val output = JsonProtocol.writeMasterState(stateResponse) assertValidJson(output) } @@ -62,25 +64,44 @@ class JsonProtocolSuite extends FunSuite { test("writeWorkerState") { val executors = List[ExecutorRunner]() val finishedExecutors = List[ExecutorRunner](createExecutorRunner(), createExecutorRunner()) + val drivers = List(createDriverRunner()) + val finishedDrivers = List(createDriverRunner(), createDriverRunner()) val stateResponse = new WorkerStateResponse("host", 8080, "workerId", executors, - finishedExecutors, "masterUrl", 4, 1234, 4, 1234, "masterWebUiUrl") + finishedExecutors, drivers, finishedDrivers, "masterUrl", 4, 1234, 4, 1234, "masterWebUiUrl") val output = JsonProtocol.writeWorkerState(stateResponse) assertValidJson(output) } - def createAppDesc() : ApplicationDescription = { + def createAppDesc(): ApplicationDescription = { val cmd = new Command("mainClass", List("arg1", "arg2"), Map()) - new ApplicationDescription("name", 4, 1234, cmd, "sparkHome", "appUiUrl") + new ApplicationDescription("name", Some(4), 1234, cmd, "sparkHome", "appUiUrl") } + def createAppInfo() : ApplicationInfo = { - new ApplicationInfo(3, "id", createAppDesc(), new Date(123456789), null, "appUriStr") + new ApplicationInfo( + 3, "id", createAppDesc(), new Date(123456789), null, "appUriStr", Int.MaxValue) } - def createWorkerInfo() : WorkerInfo = { + + def createDriverCommand() = new Command( + "org.apache.spark.FakeClass", Seq("some arg --and-some options -g foo"), + Map(("K1", "V1"), ("K2", "V2")) + ) + + def createDriverDesc() = new DriverDescription("hdfs://some-dir/some.jar", 100, 3, + false, createDriverCommand()) + + def createDriverInfo(): DriverInfo = new DriverInfo(3, "driver-3", createDriverDesc(), new Date()) + + def createWorkerInfo(): WorkerInfo = { new WorkerInfo("id", "host", 8080, 4, 1234, null, 80, "publicAddress") } - def createExecutorRunner() : ExecutorRunner = { + def createExecutorRunner(): ExecutorRunner = { new ExecutorRunner("appId", 123, createAppDesc(), 4, 1234, null, "workerId", "host", - new File("sparkHome"), new File("workDir"), ExecutorState.RUNNING) + new File("sparkHome"), new File("workDir"), "akka://worker", ExecutorState.RUNNING) + } + def createDriverRunner(): DriverRunner = { + new DriverRunner("driverId", new File("workDir"), new File("sparkHome"), createDriverDesc(), + null, "akka://worker") } def assertValidJson(json: JValue) { diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala new file mode 100644 index 0000000000..45dbcaffae --- /dev/null +++ b/core/src/test/scala/org/apache/spark/deploy/worker/DriverRunnerTest.scala @@ -0,0 +1,131 @@ +package org.apache.spark.deploy.worker + +import java.io.File + +import scala.collection.JavaConversions._ + +import org.mockito.Mockito._ +import org.mockito.Matchers._ +import org.scalatest.FunSuite + +import org.apache.spark.deploy.{Command, DriverDescription} +import org.mockito.stubbing.Answer +import org.mockito.invocation.InvocationOnMock + +class DriverRunnerTest extends FunSuite { + private def createDriverRunner() = { + val command = new Command("mainClass", Seq(), Map()) + val driverDescription = new DriverDescription("jarUrl", 512, 1, true, command) + new DriverRunner("driverId", new File("workDir"), new File("sparkHome"), driverDescription, + null, "akka://1.2.3.4/worker/") + } + + private def createProcessBuilderAndProcess(): (ProcessBuilderLike, Process) = { + val processBuilder = mock(classOf[ProcessBuilderLike]) + when(processBuilder.command).thenReturn(Seq("mocked", "command")) + val process = mock(classOf[Process]) + when(processBuilder.start()).thenReturn(process) + (processBuilder, process) + } + + test("Process succeeds instantly") { + val runner = createDriverRunner() + + val sleeper = mock(classOf[Sleeper]) + runner.setSleeper(sleeper) + + val (processBuilder, process) = createProcessBuilderAndProcess() + // One failure then a successful run + when(process.waitFor()).thenReturn(0) + runner.runCommandWithRetry(processBuilder, p => (), supervise = true) + + verify(process, times(1)).waitFor() + verify(sleeper, times(0)).sleep(anyInt()) + } + + test("Process failing several times and then succeeding") { + val runner = createDriverRunner() + + val sleeper = mock(classOf[Sleeper]) + runner.setSleeper(sleeper) + + val (processBuilder, process) = createProcessBuilderAndProcess() + // fail, fail, fail, success + when(process.waitFor()).thenReturn(-1).thenReturn(-1).thenReturn(-1).thenReturn(0) + runner.runCommandWithRetry(processBuilder, p => (), supervise = true) + + verify(process, times(4)).waitFor() + verify(sleeper, times(3)).sleep(anyInt()) + verify(sleeper, times(1)).sleep(1) + verify(sleeper, times(1)).sleep(2) + verify(sleeper, times(1)).sleep(4) + } + + test("Process doesn't restart if not supervised") { + val runner = createDriverRunner() + + val sleeper = mock(classOf[Sleeper]) + runner.setSleeper(sleeper) + + val (processBuilder, process) = createProcessBuilderAndProcess() + when(process.waitFor()).thenReturn(-1) + + runner.runCommandWithRetry(processBuilder, p => (), supervise = false) + + verify(process, times(1)).waitFor() + verify(sleeper, times(0)).sleep(anyInt()) + } + + test("Process doesn't restart if killed") { + val runner = createDriverRunner() + + val sleeper = mock(classOf[Sleeper]) + runner.setSleeper(sleeper) + + val (processBuilder, process) = createProcessBuilderAndProcess() + when(process.waitFor()).thenAnswer(new Answer[Int] { + def answer(invocation: InvocationOnMock): Int = { + runner.kill() + -1 + } + }) + + runner.runCommandWithRetry(processBuilder, p => (), supervise = true) + + verify(process, times(1)).waitFor() + verify(sleeper, times(0)).sleep(anyInt()) + } + + test("Reset of backoff counter") { + val runner = createDriverRunner() + + val sleeper = mock(classOf[Sleeper]) + runner.setSleeper(sleeper) + + val clock = mock(classOf[Clock]) + runner.setClock(clock) + + val (processBuilder, process) = createProcessBuilderAndProcess() + + when(process.waitFor()) + .thenReturn(-1) // fail 1 + .thenReturn(-1) // fail 2 + .thenReturn(-1) // fail 3 + .thenReturn(-1) // fail 4 + .thenReturn(0) // success + when(clock.currentTimeMillis()) + .thenReturn(0).thenReturn(1000) // fail 1 (short) + .thenReturn(1000).thenReturn(2000) // fail 2 (short) + .thenReturn(2000).thenReturn(10000) // fail 3 (long) + .thenReturn(10000).thenReturn(11000) // fail 4 (short) + .thenReturn(11000).thenReturn(21000) // success (long) + + runner.runCommandWithRetry(processBuilder, p => (), supervise = true) + + verify(sleeper, times(4)).sleep(anyInt()) + // Expected sequence of sleeps is 1,2,1,2 + verify(sleeper, times(2)).sleep(1) + verify(sleeper, times(2)).sleep(2) + } + +} diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala index 7e5aaa3f98..a79ee690d3 100644 --- a/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala +++ b/core/src/test/scala/org/apache/spark/deploy/worker/ExecutorRunnerTest.scala @@ -27,12 +27,12 @@ class ExecutorRunnerTest extends FunSuite { test("command includes appId") { def f(s:String) = new File(s) val sparkHome = sys.env.get("SPARK_HOME").orElse(sys.props.get("spark.home")).get - val appDesc = new ApplicationDescription("app name", 8, 500, Command("foo", Seq(),Map()), + val appDesc = new ApplicationDescription("app name", Some(8), 500, Command("foo", Seq(),Map()), sparkHome, "appUiUrl") val appId = "12345-worker321-9876" val er = new ExecutorRunner(appId, 1, appDesc, 8, 500, null, "blah", "worker321", f(sparkHome), - f("ooga"), ExecutorState.RUNNING) + f("ooga"), "blah", ExecutorState.RUNNING) - assert(er.buildCommandSeq().last === appId) + assert(er.getCommandSeq.last === appId) } } diff --git a/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala new file mode 100644 index 0000000000..94d88d307a --- /dev/null +++ b/core/src/test/scala/org/apache/spark/deploy/worker/WorkerWatcherSuite.scala @@ -0,0 +1,32 @@ +package org.apache.spark.deploy.worker + + +import akka.testkit.TestActorRef +import org.scalatest.FunSuite +import akka.remote.DisassociatedEvent +import akka.actor.{ActorSystem, AddressFromURIString, Props} + +class WorkerWatcherSuite extends FunSuite { + test("WorkerWatcher shuts down on valid disassociation") { + val actorSystem = ActorSystem("test") + val targetWorkerUrl = "akka://1.2.3.4/user/Worker" + val targetWorkerAddress = AddressFromURIString(targetWorkerUrl) + val actorRef = TestActorRef[WorkerWatcher](Props(classOf[WorkerWatcher], targetWorkerUrl))(actorSystem) + val workerWatcher = actorRef.underlyingActor + workerWatcher.setTesting(testing = true) + actorRef.underlyingActor.receive(new DisassociatedEvent(null, targetWorkerAddress, false)) + assert(actorRef.underlyingActor.isShutDown) + } + + test("WorkerWatcher stays alive on invalid disassociation") { + val actorSystem = ActorSystem("test") + val targetWorkerUrl = "akka://1.2.3.4/user/Worker" + val otherAkkaURL = "akka://4.3.2.1/user/OtherActor" + val otherAkkaAddress = AddressFromURIString(otherAkkaURL) + val actorRef = TestActorRef[WorkerWatcher](Props(classOf[WorkerWatcher], targetWorkerUrl))(actorSystem) + val workerWatcher = actorRef.underlyingActor + workerWatcher.setTesting(testing = true) + actorRef.underlyingActor.receive(new DisassociatedEvent(null, otherAkkaAddress, false)) + assert(!actorRef.underlyingActor.isShutDown) + } +}
\ No newline at end of file diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala index 1eec6726f4..c9f6cc5d07 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala @@ -83,7 +83,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { private val conf = new SparkConf - val LOCALITY_WAIT = conf.get("spark.locality.wait", "3000").toLong + val LOCALITY_WAIT = conf.getLong("spark.locality.wait", 3000) val MAX_TASK_FAILURES = 4 test("TaskSet with no preferences") { diff --git a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala index a0fc3445be..f60ce270c7 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -40,8 +40,6 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT 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 conf.set("spark.kryoserializer.buffer.mb", "1") @@ -58,10 +56,10 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT conf.set("spark.hostPort", "localhost:" + boundPort) master = new BlockManagerMaster( - Left(actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf)))), conf) + actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf))), conf) // Set the arch to 64-bit and compressedOops to true to get a deterministic test-case - System.setProperty("os.arch", "amd64") + oldArch = System.setProperty("os.arch", "amd64") conf.set("os.arch", "amd64") conf.set("spark.test.useCompressedOops", "true") conf.set("spark.storage.disableBlockManagerHeartBeat", "true") @@ -94,11 +92,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT System.clearProperty("os.arch") } - if (oldOops != null) { - conf.set("spark.test.useCompressedOops", oldOops) - } else { - System.clearProperty("spark.test.useCompressedOops") - } + System.clearProperty("spark.test.useCompressedOops") } test("StorageLevel object caching") { diff --git a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala index af4b31d53c..829f389460 100644 --- a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala @@ -23,9 +23,7 @@ import scala.collection.mutable import com.google.common.io.Files import org.apache.spark.SparkConf -import org.scalatest.{BeforeAndAfterAll, BeforeAndAfterEach, FunSuite} -import scala.util.Try -import akka.actor.{Props, ActorSelection, ActorSystem} +import org.scalatest.{BeforeAndAfterEach, FunSuite} class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach { private val testConf = new SparkConf(false) diff --git a/core/src/test/scala/org/apache/spark/util/XORShiftRandomSuite.scala b/core/src/test/scala/org/apache/spark/util/XORShiftRandomSuite.scala index b78367b6ca..f1d7b61b31 100644 --- a/core/src/test/scala/org/apache/spark/util/XORShiftRandomSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/XORShiftRandomSuite.scala @@ -73,4 +73,4 @@ class XORShiftRandomSuite extends FunSuite with ShouldMatchers { } -}
\ No newline at end of file +} diff --git a/docs/configuration.md b/docs/configuration.md index 1d36ecb9c1..6717757781 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -77,13 +77,14 @@ there are at least five properties that you will commonly want to control: </tr> <tr> <td>spark.cores.max</td> - <td>(infinite)</td> + <td>(not set)</td> <td> When running on a <a href="spark-standalone.html">standalone deploy cluster</a> or a <a href="running-on-mesos.html#mesos-run-modes">Mesos cluster in "coarse-grained" sharing mode</a>, the maximum amount of CPU cores to request for the application from - across the cluster (not from each machine). The default will use all available cores - offered by the cluster manager. + across the cluster (not from each machine). If not set, the default will be + <code>spark.deploy.defaultCores</code> on Spark's standalone cluster manager, or + infinite (all available cores) on Mesos. </td> </tr> </table> @@ -129,7 +130,7 @@ Apart from these, the following properties are also available, and may be useful </td> </tr> <tr> - <td>spark.ui.retained_stages</td> + <td>spark.ui.retainedStages</td> <td>1000</td> <td> How many stages the Spark UI remembers before garbage collecting. @@ -404,12 +405,36 @@ Apart from these, the following properties are also available, and may be useful </td> </tr> <tr> - <td>spark.log-conf</td> + <td>spark.logConf</td> <td>false</td> <td> Log the supplied SparkConf as INFO at start of spark context. </td> </tr> +<tr> + <td>spark.deploy.spreadOut</td> + <td>true</td> + <td> + Whether the standalone cluster manager should spread applications out across nodes or try + to consolidate them onto as few nodes as possible. Spreading out is usually better for + data locality in HDFS, but consolidating is more efficient for compute-intensive workloads. <br/> + <b>Note:</b> this setting needs to be configured in the standalone cluster master, not in individual + applications; you can set it through <code>SPARK_JAVA_OPTS</code> in <code>spark-env.sh</code>. + </td> +</tr> +<tr> + <td>spark.deploy.defaultCores</td> + <td>(infinite)</td> + <td> + Default number of cores to give to applications in Spark's standalone mode if they don't + set <code>spark.cores.max</code>. If not set, applications always get all available + cores unless they configure <code>spark.cores.max</code> themselves. + Set this lower on a shared cluster to prevent users from grabbing + the whole cluster by default. <br/> + <b>Note:</b> this setting needs to be configured in the standalone cluster master, not in individual + applications; you can set it through <code>SPARK_JAVA_OPTS</code> in <code>spark-env.sh</code>. + </td> +</tr> </table> ## Viewing Spark Properties diff --git a/docs/css/bootstrap.min.css b/docs/css/bootstrap.min.css index 119bb5dba7..30ea440c00 100644 --- a/docs/css/bootstrap.min.css +++ b/docs/css/bootstrap.min.css @@ -6,4 +6,4 @@ * http://www.apache.org/licenses/LICENSE-2.0 * * Designed and built with all the love in the world @twitter by @mdo and @fat. - 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b/docs/job-scheduling.md index 5951155fe3..df2faa5e41 100644 --- a/docs/job-scheduling.md +++ b/docs/job-scheduling.md @@ -32,9 +32,8 @@ Resource allocation can be configured as follows, based on the cluster type: * **Standalone mode:** By default, applications submitted to the standalone mode cluster will run in FIFO (first-in-first-out) order, and each application will try to use all available nodes. You can limit - the number of nodes an application uses by setting the `spark.cores.max` configuration property in it. This - will allow multiple users/applications to run concurrently. For example, you might launch a long-running - server that uses 10 cores, and allow users to launch shells that use 20 cores each. + the number of nodes an application uses by setting the `spark.cores.max` configuration property in it, + or change the default for applications that don't set this setting through `spark.deploy.defaultCores`. Finally, in addition to controlling cores, each application's `spark.executor.memory` setting controls its memory use. * **Mesos:** To use static partitioning on Mesos, set the `spark.mesos.coarse` configuration property to `true`, diff --git a/docs/mllib-guide.md b/docs/mllib-guide.md index 95537ef185..45ee166688 100644 --- a/docs/mllib-guide.md +++ b/docs/mllib-guide.md @@ -3,6 +3,9 @@ layout: global title: Machine Learning Library (MLlib) --- +* Table of contents +{:toc} + MLlib is a Spark implementation of some common machine learning (ML) functionality, as well associated tests and data generators. MLlib currently supports four common types of machine learning problem settings, @@ -39,57 +42,6 @@ underlying gradient descent primitive (described parameter (*regParam*) along with various parameters associated with gradient descent (*stepSize*, *numIterations*, *miniBatchFraction*). -The following code snippet illustrates how to load a sample dataset, execute a -training algorithm on this training data using a static method in the algorithm -object, and make predictions with the resulting model to compute the training -error. - -{% highlight scala %} -import org.apache.spark.SparkContext -import org.apache.spark.mllib.classification.SVMWithSGD -import org.apache.spark.mllib.regression.LabeledPoint - -// Load and parse the data file -val data = sc.textFile("mllib/data/sample_svm_data.txt") -val parsedData = data.map { line => - val parts = line.split(' ') - LabeledPoint(parts(0).toDouble, parts.tail.map(x => x.toDouble).toArray) -} - -// Run training algorithm -val numIterations = 20 -val model = SVMWithSGD.train(parsedData, numIterations) - -// Evaluate model on training examples and compute training error -val labelAndPreds = parsedData.map { point => - val prediction = model.predict(point.features) - (point.label, prediction) -} -val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / parsedData.count -println("trainError = " + trainErr) -{% endhighlight %} - -The `SVMWithSGD.train()` method by default performs L2 regularization with the -regularization parameter set to 1.0. If we want to configure this algorithm, we -can customize `SVMWithSGD` further by creating a new object directly and -calling setter methods. All other MLlib algorithms support customization in -this way as well. For example, the following code produces an L1 regularized -variant of SVMs with regularization parameter set to 0.1, and runs the training -algorithm for 200 iterations. - -{% highlight scala %} -import org.apache.spark.mllib.optimization.L1Updater - -val svmAlg = new SVMWithSGD() -svmAlg.optimizer.setNumIterations(200) - .setRegParam(0.1) - .setUpdater(new L1Updater) -val modelL1 = svmAlg.run(parsedData) -{% endhighlight %} - -Both of the code snippets above can be executed in `bin/spark-shell` to generate a -classifier for the provided dataset. - Available algorithms for binary classification: * [SVMWithSGD](api/mllib/index.html#org.apache.spark.mllib.classification.SVMWithSGD) @@ -121,14 +73,14 @@ of entities with one another based on some notion of similarity. Clustering is often used for exploratory analysis and/or as a component of a hierarchical supervised learning pipeline (in which distinct classifiers or regression models are trained for each cluster). MLlib supports -[k-means](http://en.wikipedia.org/wiki/K-means_clustering) clustering, arguably -the most commonly used clustering approach that clusters the data points into -*k* clusters. The MLlib implementation includes a parallelized +[k-means](http://en.wikipedia.org/wiki/K-means_clustering) clustering, one of +the most commonly used clustering algorithms that clusters the data points into +predfined number of clusters. The MLlib implementation includes a parallelized variant of the [k-means++](http://en.wikipedia.org/wiki/K-means%2B%2B) method called [kmeans||](http://theory.stanford.edu/~sergei/papers/vldb12-kmpar.pdf). The implementation in MLlib has the following parameters: -* *k* is the number of clusters. +* *k* is the number of desired clusters. * *maxIterations* is the maximum number of iterations to run. * *initializationMode* specifies either random initialization or initialization via k-means\|\|. @@ -169,7 +121,7 @@ the entries in the user-item matrix as *explicit* preferences given by the user It is common in many real-world use cases to only have access to *implicit feedback* (e.g. views, clicks, purchases, likes, shares etc.). The approach used in MLlib to deal with such data is taken from -[Collaborative Filtering for Implicit Feedback Datasets](http://research.yahoo.com/pub/2433). +[Collaborative Filtering for Implicit Feedback Datasets](http://www2.research.att.com/~yifanhu/PUB/cf.pdf). Essentially instead of trying to model the matrix of ratings directly, this approach treats the data as a combination of binary preferences and *confidence values*. The ratings are then related to the level of confidence in observed user preferences, rather than explicit ratings given to items. @@ -210,3 +162,269 @@ at each iteration. Available algorithms for gradient descent: * [GradientDescent](api/mllib/index.html#org.apache.spark.mllib.optimization.GradientDescent) + +# Using MLLib in Scala + +Following code snippets can be executed in `spark-shell`. + +## Binary Classification + +The following code snippet illustrates how to load a sample dataset, execute a +training algorithm on this training data using a static method in the algorithm +object, and make predictions with the resulting model to compute the training +error. + +{% highlight scala %} +import org.apache.spark.SparkContext +import org.apache.spark.mllib.classification.SVMWithSGD +import org.apache.spark.mllib.regression.LabeledPoint + +// Load and parse the data file +val data = sc.textFile("mllib/data/sample_svm_data.txt") +val parsedData = data.map { line => + val parts = line.split(' ') + LabeledPoint(parts(0).toDouble, parts.tail.map(x => x.toDouble).toArray) +} + +// Run training algorithm to build the model +val numIterations = 20 +val model = SVMWithSGD.train(parsedData, numIterations) + +// Evaluate model on training examples and compute training error +val labelAndPreds = parsedData.map { point => + val prediction = model.predict(point.features) + (point.label, prediction) +} +val trainErr = labelAndPreds.filter(r => r._1 != r._2).count.toDouble / parsedData.count +println("Training Error = " + trainErr) +{% endhighlight %} + + +The `SVMWithSGD.train()` method by default performs L2 regularization with the +regularization parameter set to 1.0. If we want to configure this algorithm, we +can customize `SVMWithSGD` further by creating a new object directly and +calling setter methods. All other MLlib algorithms support customization in +this way as well. For example, the following code produces an L1 regularized +variant of SVMs with regularization parameter set to 0.1, and runs the training +algorithm for 200 iterations. + +{% highlight scala %} +import org.apache.spark.mllib.optimization.L1Updater + +val svmAlg = new SVMWithSGD() +svmAlg.optimizer.setNumIterations(200) + .setRegParam(0.1) + .setUpdater(new L1Updater) +val modelL1 = svmAlg.run(parsedData) +{% endhighlight %} + +## Linear Regression +The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint. The +example then uses LinearRegressionWithSGD to build a simple linear model to predict label values. We +compute the Mean Squared Error at the end to evaluate +[goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit) + +{% highlight scala %} +import org.apache.spark.mllib.regression.LinearRegressionWithSGD +import org.apache.spark.mllib.regression.LabeledPoint + +// Load and parse the data +val data = sc.textFile("mllib/data/ridge-data/lpsa.data") +val parsedData = data.map { line => + val parts = line.split(',') + LabeledPoint(parts(0).toDouble, parts(1).split(' ').map(x => x.toDouble).toArray) +} + +// Building the model +val numIterations = 20 +val model = LinearRegressionWithSGD.train(parsedData, numIterations) + +// Evaluate model on training examples and compute training error +val valuesAndPreds = parsedData.map { point => + val prediction = model.predict(point.features) + (point.label, prediction) +} +val MSE = valuesAndPreds.map{ case(v, p) => math.pow((v - p), 2)}.reduce(_ + _)/valuesAndPreds.count +println("training Mean Squared Error = " + MSE) +{% endhighlight %} + + +Similarly you can use RidgeRegressionWithSGD and LassoWithSGD and compare training +[Mean Squared Errors](http://en.wikipedia.org/wiki/Mean_squared_error). + +## Clustering +In the following example after loading and parsing data, we use the KMeans object to cluster the data +into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within +Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In fact the +optimal *k* is usually one where there is an "elbow" in the WSSSE graph. + +{% highlight scala %} +import org.apache.spark.mllib.clustering.KMeans + +// Load and parse the data +val data = sc.textFile("kmeans_data.txt") +val parsedData = data.map( _.split(' ').map(_.toDouble)) + +// Cluster the data into two classes using KMeans +val numIterations = 20 +val numClusters = 2 +val clusters = KMeans.train(parsedData, numClusters, numIterations) + +// Evaluate clustering by computing Within Set Sum of Squared Errors +val WSSSE = clusters.computeCost(parsedData) +println("Within Set Sum of Squared Errors = " + WSSSE) +{% endhighlight %} + + +## Collaborative Filtering +In the following example we load rating data. Each row consists of a user, a product and a rating. +We use the default ALS.train() method which assumes ratings are explicit. We evaluate the recommendation +model by measuring the Mean Squared Error of rating prediction. + +{% highlight scala %} +import org.apache.spark.mllib.recommendation.ALS +import org.apache.spark.mllib.recommendation.Rating + +// Load and parse the data +val data = sc.textFile("mllib/data/als/test.data") +val ratings = data.map(_.split(',') match { + case Array(user, item, rate) => Rating(user.toInt, item.toInt, rate.toDouble) +}) + +// Build the recommendation model using ALS +val numIterations = 20 +val model = ALS.train(ratings, 1, 20, 0.01) + +// Evaluate the model on rating data +val usersProducts = ratings.map{ case Rating(user, product, rate) => (user, product)} +val predictions = model.predict(usersProducts).map{ + case Rating(user, product, rate) => ((user, product), rate) +} +val ratesAndPreds = ratings.map{ + case Rating(user, product, rate) => ((user, product), rate) +}.join(predictions) +val MSE = ratesAndPreds.map{ + case ((user, product), (r1, r2)) => math.pow((r1- r2), 2) +}.reduce(_ + _)/ratesAndPreds.count +println("Mean Squared Error = " + MSE) +{% endhighlight %} + +If the rating matrix is derived from other source of information (i.e., it is inferred from +other signals), you can use the trainImplicit method to get better results. + +{% highlight scala %} +val model = ALS.trainImplicit(ratings, 1, 20, 0.01) +{% endhighlight %} + +# Using MLLib in Python +Following examples can be tested in the PySpark shell. + +## Binary Classification +The following example shows how to load a sample dataset, build Logistic Regression model, +and make predictions with the resulting model to compute the training error. + +{% highlight python %} +from pyspark.mllib.classification import LogisticRegressionWithSGD +from numpy import array + +# Load and parse the data +data = sc.textFile("mllib/data/sample_svm_data.txt") +parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')])) +model = LogisticRegressionWithSGD.train(sc, parsedData) + +# Build the model +labelsAndPreds = parsedData.map(lambda point: (int(point.item(0)), + model.predict(point.take(range(1, point.size))))) + +# Evaluating the model on training data +trainErr = labelsAndPreds.filter(lambda (v, p): v != p).count() / float(parsedData.count()) +print("Training Error = " + str(trainErr)) +{% endhighlight %} + +## Linear Regression +The following example demonstrate how to load training data, parse it as an RDD of LabeledPoint. The +example then uses LinearRegressionWithSGD to build a simple linear model to predict label values. We +compute the Mean Squared Error at the end to evaluate +[goodness of fit](http://en.wikipedia.org/wiki/Goodness_of_fit) + +{% highlight python %} +from pyspark.mllib.regression import LinearRegressionWithSGD +from numpy import array + +# Load and parse the data +data = sc.textFile("mllib/data/ridge-data/lpsa.data") +parsedData = data.map(lambda line: array([float(x) for x in line.replace(',', ' ').split(' ')])) + +# Build the model +model = LinearRegressionWithSGD.train(sc, parsedData) + +# Evaluate the model on training data +valuesAndPreds = parsedData.map(lambda point: (point.item(0), + model.predict(point.take(range(1, point.size))))) +MSE = valuesAndPreds.map(lambda (v, p): (v - p)**2).reduce(lambda x, y: x + y)/valuesAndPreds.count() +print("Mean Squared Error = " + str(MSE)) +{% endhighlight %} + + +## Clustering +In the following example after loading and parsing data, we use the KMeans object to cluster the data +into two clusters. The number of desired clusters is passed to the algorithm. We then compute Within +Set Sum of Squared Error (WSSSE). You can reduce this error measure by increasing *k*. In fact the +optimal *k* is usually one where there is an "elbow" in the WSSSE graph. + +{% highlight python %} +from pyspark.mllib.clustering import KMeans +from numpy import array +from math import sqrt + +# Load and parse the data +data = sc.textFile("kmeans_data.txt") +parsedData = data.map(lambda line: array([float(x) for x in line.split(' ')])) + +# Build the model (cluster the data) +clusters = KMeans.train(sc, parsedData, 2, maxIterations=10, + runs=30, initialization_mode="random") + +# Evaluate clustering by computing Within Set Sum of Squared Errors +def error(point): + center = clusters.centers[clusters.predict(point)] + return sqrt(sum([x**2 for x in (point - center)])) + +WSSSE = parsedData.map(lambda point: error(point)).reduce(lambda x, y: x + y) +print("Within Set Sum of Squared Error = " + str(WSSSE)) +{% endhighlight %} + +Similarly you can use RidgeRegressionWithSGD and LassoWithSGD and compare training Mean Squared +Errors. + +## Collaborative Filtering +In the following example we load rating data. Each row consists of a user, a product and a rating. +We use the default ALS.train() method which assumes ratings are explicit. We evaluate the +recommendation by measuring the Mean Squared Error of rating prediction. + +{% highlight python %} +from pyspark.mllib.recommendation import ALS +from numpy import array + +# Load and parse the data +data = sc.textFile("mllib/data/als/test.data") +ratings = data.map(lambda line: array([float(x) for x in line.split(',')])) + +# Build the recommendation model using Alternating Least Squares +model = ALS.train(sc, ratings, 1, 20) + +# Evaluate the model on training data +testdata = ratings.map(lambda p: (int(p[0]), int(p[1]))) +predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2])) +ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions) +MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).reduce(lambda x, y: x + y)/ratesAndPreds.count() +print("Mean Squared Error = " + str(MSE)) +{% endhighlight %} + +If the rating matrix is derived from other source of information (i.e., it is inferred from other +signals), you can use the trainImplicit method to get better results. + +{% highlight python %} +# Build the recommendation model using Alternating Least Squares based on implicit ratings +model = ALS.trainImplicit(sc, ratings, 1, 20) +{% endhighlight %} diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index 717071d72c..b206270107 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -114,6 +114,8 @@ For example: SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \ MASTER=yarn-client ./bin/spark-shell +You can also send extra files to yarn cluster for worker to use by exporting SPARK_YARN_DIST_FILES=file1,file2... etc. + # Building Spark for Hadoop/YARN 2.2.x See [Building Spark with Maven](building-with-maven.html) for instructions on how to build Spark using the Maven process. diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md index c851833a18..2a186261b7 100644 --- a/docs/spark-standalone.md +++ b/docs/spark-standalone.md @@ -10,11 +10,7 @@ In addition to running on the Mesos or YARN cluster managers, Spark also provide # Installing Spark Standalone to a Cluster -The easiest way to deploy Spark is by running the `./make-distribution.sh` script to create a binary distribution. -This distribution can be deployed to any machine with the Java runtime installed; there is no need to install Scala. - -The recommended procedure is to deploy and start the master on one node first, get the master spark URL, -then modify `conf/spark-env.sh` in the `dist/` directory before deploying to all the other nodes. +To install Spark Standlone mode, you simply place a compiled version of Spark on each node on the cluster. You can obtain pre-built versions of Spark with each release or [build it yourself](index.html#building). # Starting a Cluster Manually @@ -150,6 +146,38 @@ automatically set MASTER from the `SPARK_MASTER_IP` and `SPARK_MASTER_PORT` vari You can also pass an option `-c <numCores>` to control the number of cores that spark-shell uses on the cluster. +# Launching Applications Inside the Cluster + +You may also run your application entirely inside of the cluster by submitting your application driver using the submission client. The syntax for submitting applications is as follows: + + + ./spark-class org.apache.spark.deploy.Client launch + [client-options] \ + <cluster-url> <application-jar-url> <main-class> \ + [application-options] + + cluster-url: The URL of the master node. + application-jar-url: Path to a bundled jar including your application and all dependencies. Currently, the URL must be globally visible inside of your cluster, for instance, an `hdfs://` path or a `file://` path that is present on all nodes. + main-class: The entry point for your application. + + Client Options: + --memory <count> (amount of memory, in MB, allocated for your driver program) + --cores <count> (number of cores allocated for your driver program) + --supervise (whether to automatically restart your driver on application or node failure) + --verbose (prints increased logging output) + +Keep in mind that your driver program will be executed on a remote worker machine. You can control the execution environment in the following ways: + + * _Environment variables_: These will be captured from the environment in which you launch the client and applied when launching the driver program. + * _Java options_: You can add java options by setting `SPARK_JAVA_OPTS` in the environment in which you launch the submission client. + * _Dependencies_: You'll still need to call `sc.addJar` inside of your program to make your bundled application jar visible on all worker nodes. + +Once you submit a driver program, it will appear in the cluster management UI at port 8080 and +be assigned an identifier. If you'd like to prematurely terminate the program, you can do so using +the same client: + + ./spark-class org.apache.spark.deploy.client.DriverClient kill <driverId> + # Resource Scheduling The standalone cluster mode currently only supports a simple FIFO scheduler across applications. @@ -167,6 +195,16 @@ val conf = new SparkConf() val sc = new SparkContext(conf) {% endhighlight %} +In addition, you can configure `spark.deploy.defaultCores` on the cluster master process to change the +default for applications that don't set `spark.cores.max` to something less than infinite. +Do this by adding the following to `conf/spark-env.sh`: + +{% highlight bash %} +export SPARK_JAVA_OPTS="-Dspark.deploy.defaultCores=<value>" +{% endhighlight %} + +This is useful on shared clusters where users might not have configured a maximum number of cores +individually. # Monitoring and Logging diff --git a/examples/pom.xml b/examples/pom.xml index 7e41bef252..cb4f7ee33b 100644 --- a/examples/pom.xml +++ b/examples/pom.xml @@ -72,6 +72,31 @@ <scope>provided</scope> </dependency> <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-twitter_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-kafka_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-flume_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-zeromq_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-mqtt_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> <groupId>org.apache.hbase</groupId> <artifactId>hbase</artifactId> <version>0.94.6</version> @@ -87,21 +112,6 @@ </exclusions> </dependency> <dependency> - <groupId>com.sksamuel.kafka</groupId> - <artifactId>kafka_${scala.binary.version}</artifactId> - <version>0.8.0-beta1</version> - <exclusions> - <exclusion> - <groupId>com.sun.jmx</groupId> - <artifactId>jmxri</artifactId> - </exclusion> - <exclusion> - <groupId>com.sun.jdmk</groupId> - <artifactId>jmxtools</artifactId> - </exclusion> - </exclusions> - </dependency> - <dependency> <groupId>org.eclipse.jetty</groupId> <artifactId>jetty-server</artifactId> </dependency> @@ -174,7 +184,7 @@ <artifactId>maven-shade-plugin</artifactId> <configuration> <shadedArtifactAttached>false</shadedArtifactAttached> - <outputFile>${project.build.directory}/scala-${scala.version}/${project.artifactId}-assembly-${project.version}.jar</outputFile> + <outputFile>${project.build.directory}/scala-${scala.binary.version}/${project.artifactId}-assembly-${project.version}.jar</outputFile> <artifactSet> <includes> <include>*:*</include> diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java index fd683ce0d3..b11cfa667e 100644 --- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java +++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaFlumeEventCount.java @@ -20,7 +20,8 @@ package org.apache.spark.streaming.examples; import org.apache.spark.api.java.function.Function; import org.apache.spark.streaming.*; import org.apache.spark.streaming.api.java.*; -import org.apache.spark.streaming.dstream.SparkFlumeEvent; +import org.apache.spark.streaming.flume.FlumeUtils; +import org.apache.spark.streaming.flume.SparkFlumeEvent; /** * Produces a count of events received from Flume. @@ -52,11 +53,10 @@ public final class JavaFlumeEventCount { Duration batchInterval = new Duration(2000); - JavaStreamingContext sc = new JavaStreamingContext(master, "FlumeEventCount", batchInterval, + JavaStreamingContext ssc = new JavaStreamingContext(master, "FlumeEventCount", batchInterval, System.getenv("SPARK_HOME"), JavaStreamingContext.jarOfClass(JavaFlumeEventCount.class)); - - JavaDStream<SparkFlumeEvent> flumeStream = sc.flumeStream("localhost", port); + JavaDStream<SparkFlumeEvent> flumeStream = FlumeUtils.createStream(ssc, "localhost", port); flumeStream.count(); @@ -67,6 +67,6 @@ public final class JavaFlumeEventCount { } }).print(); - sc.start(); + ssc.start(); } } diff --git a/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java b/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java index d8b4f4dddd..16b8a948e6 100644 --- a/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java +++ b/examples/src/main/java/org/apache/spark/streaming/examples/JavaKafkaWordCount.java @@ -30,6 +30,7 @@ import org.apache.spark.streaming.Duration; import org.apache.spark.streaming.api.java.JavaDStream; import org.apache.spark.streaming.api.java.JavaPairDStream; import org.apache.spark.streaming.api.java.JavaStreamingContext; +import org.apache.spark.streaming.kafka.KafkaUtils; import scala.Tuple2; /** @@ -59,7 +60,7 @@ public final class JavaKafkaWordCount { } // Create the context with a 1 second batch size - JavaStreamingContext ssc = new JavaStreamingContext(args[0], "KafkaWordCount", + JavaStreamingContext jssc = new JavaStreamingContext(args[0], "KafkaWordCount", new Duration(2000), System.getenv("SPARK_HOME"), JavaStreamingContext.jarOfClass(JavaKafkaWordCount.class)); @@ -70,7 +71,7 @@ public final class JavaKafkaWordCount { topicMap.put(topic, numThreads); } - JavaPairDStream<String, String> messages = ssc.kafkaStream(args[1], args[2], topicMap); + JavaPairDStream<String, String> messages = KafkaUtils.createStream(jssc, args[1], args[2], topicMap); JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() { @Override @@ -100,6 +101,6 @@ public final class JavaKafkaWordCount { }); wordCounts.print(); - ssc.start(); + jssc.start(); } } diff --git a/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala b/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala new file mode 100644 index 0000000000..65251e9319 --- /dev/null +++ b/examples/src/main/scala/org/apache/spark/examples/DriverSubmissionTest.scala @@ -0,0 +1,46 @@ +/* + * 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.examples + +import scala.collection.JavaConversions._ + +/** Prints out environmental information, sleeps, and then exits. Made to + * test driver submission in the standalone scheduler. */ +object DriverSubmissionTest { + def main(args: Array[String]) { + if (args.size < 1) { + println("Usage: DriverSubmissionTest <seconds-to-sleep>") + System.exit(0) + } + val numSecondsToSleep = args(0).toInt + + val env = System.getenv() + val properties = System.getProperties() + + println("Environment variables containing SPARK_TEST:") + env.filter{case (k, v) => k.contains("SPARK_TEST")}.foreach(println) + + println("System properties containing spark.test:") + properties.filter{case (k, v) => k.toString.contains("spark.test")}.foreach(println) + + for (i <- 1 until numSecondsToSleep) { + println(s"Alive for $i out of $numSecondsToSleep seconds") + Thread.sleep(1000) + } + } +} diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala index 5ef1928294..ae3709b3d9 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/FlumeEventCount.scala @@ -20,6 +20,7 @@ package org.apache.spark.streaming.examples import org.apache.spark.util.IntParam import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming._ +import org.apache.spark.streaming.flume._ /** * Produces a count of events received from Flume. @@ -51,7 +52,7 @@ object FlumeEventCount { System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass)) // Create a flume stream - val stream = ssc.flumeStream(host,port,StorageLevel.MEMORY_ONLY) + val stream = FlumeUtils.createStream(ssc, host,port,StorageLevel.MEMORY_ONLY_SER_2) // Print out the count of events received from this server in each batch stream.count().map(cnt => "Received " + cnt + " flume events." ).print() diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala index 197461655e..31a94bd224 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/KafkaWordCount.scala @@ -24,6 +24,7 @@ import kafka.producer._ import org.apache.spark.streaming._ import org.apache.spark.streaming.StreamingContext._ import org.apache.spark.streaming.util.RawTextHelper._ +import org.apache.spark.streaming.kafka._ /** * Consumes messages from one or more topics in Kafka and does wordcount. @@ -52,7 +53,7 @@ object KafkaWordCount { ssc.checkpoint("checkpoint") val topicpMap = topics.split(",").map((_,numThreads.toInt)).toMap - val lines = ssc.kafkaStream(zkQuorum, group, topicpMap).map(_._2) + val lines = KafkaUtils.createStream(ssc, zkQuorum, group, topicpMap).map(_._2) val words = lines.flatMap(_.split(" ")) val wordCounts = words.map(x => (x, 1l)) .reduceByKeyAndWindow(add _, subtract _, Minutes(10), Seconds(2), 2) diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala index 2d02ef77c0..325290b66f 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/MQTTWordCount.scala @@ -17,11 +17,6 @@ package org.apache.spark.streaming.examples -import org.apache.spark.streaming.{ Seconds, StreamingContext } -import org.apache.spark.streaming.StreamingContext._ -import org.apache.spark.streaming.dstream.MQTTReceiver -import org.apache.spark.storage.StorageLevel - import org.eclipse.paho.client.mqttv3.MqttClient import org.eclipse.paho.client.mqttv3.MqttClientPersistence import org.eclipse.paho.client.mqttv3.persist.MqttDefaultFilePersistence @@ -29,6 +24,11 @@ import org.eclipse.paho.client.mqttv3.MqttException import org.eclipse.paho.client.mqttv3.MqttMessage import org.eclipse.paho.client.mqttv3.MqttTopic +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.{Seconds, StreamingContext} +import org.apache.spark.streaming.StreamingContext._ +import org.apache.spark.streaming.mqtt._ + /** * A simple Mqtt publisher for demonstration purposes, repeatedly publishes * Space separated String Message "hello mqtt demo for spark streaming" @@ -97,7 +97,7 @@ object MQTTWordCount { val ssc = new StreamingContext(master, "MqttWordCount", Seconds(2), System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass)) - val lines = ssc.mqttStream(brokerUrl, topic, StorageLevel.MEMORY_ONLY) + val lines = MQTTUtils.createStream(ssc, brokerUrl, topic, StorageLevel.MEMORY_ONLY_SER_2) val words = lines.flatMap(x => x.toString.split(" ")) val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _) diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala index 9d21d3178f..3ccdc908e2 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdCMS.scala @@ -23,6 +23,8 @@ import com.twitter.algebird._ import org.apache.spark.streaming.StreamingContext._ import org.apache.spark.SparkContext._ +import org.apache.spark.streaming.twitter._ + /** * Illustrates the use of the Count-Min Sketch, from Twitter's Algebird library, to compute * windowed and global Top-K estimates of user IDs occurring in a Twitter stream. @@ -33,7 +35,7 @@ import org.apache.spark.SparkContext._ * <p> * <p> * <a href="http://highlyscalable.wordpress.com/2012/05/01/probabilistic-structures-web-analytics-data-mining/"> - * This blog post</a> has a good overview of the Count-Min Sketch (CMS). The CMS is a datastructure + * This blog post</a> has a good overview of the Count-Min Sketch (CMS). The CMS is a data structure * for approximate frequency estimation in data streams (e.g. Top-K elements, frequency of any given element, etc), * that uses space sub-linear in the number of elements in the stream. Once elements are added to the CMS, the * estimated count of an element can be computed, as well as "heavy-hitters" that occur more than a threshold @@ -61,7 +63,7 @@ object TwitterAlgebirdCMS { val ssc = new StreamingContext(master, "TwitterAlgebirdCMS", Seconds(10), System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass)) - val stream = ssc.twitterStream(None, filters, StorageLevel.MEMORY_ONLY_SER) + val stream = TwitterUtils.createStream(ssc, None, filters, StorageLevel.MEMORY_ONLY_SER_2) val users = stream.map(status => status.getUser.getId) diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala index 5111e6f62a..c7e83e76b0 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterAlgebirdHLL.scala @@ -21,7 +21,7 @@ import org.apache.spark.streaming.{Seconds, StreamingContext} import org.apache.spark.storage.StorageLevel import com.twitter.algebird.HyperLogLog._ import com.twitter.algebird.HyperLogLogMonoid -import org.apache.spark.streaming.dstream.TwitterInputDStream +import org.apache.spark.streaming.twitter._ /** * Illustrates the use of the HyperLogLog algorithm, from Twitter's Algebird library, to compute @@ -50,7 +50,7 @@ object TwitterAlgebirdHLL { val ssc = new StreamingContext(master, "TwitterAlgebirdHLL", Seconds(5), System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass)) - val stream = ssc.twitterStream(None, filters, StorageLevel.MEMORY_ONLY_SER) + val stream = TwitterUtils.createStream(ssc, None, filters, StorageLevel.MEMORY_ONLY_SER) val users = stream.map(status => status.getUser.getId) diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala index 7a3df687b7..e2b0418d55 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/TwitterPopularTags.scala @@ -20,6 +20,7 @@ package org.apache.spark.streaming.examples import org.apache.spark.streaming.{Seconds, StreamingContext} import StreamingContext._ import org.apache.spark.SparkContext._ +import org.apache.spark.streaming.twitter._ /** * Calculates popular hashtags (topics) over sliding 10 and 60 second windows from a Twitter @@ -39,7 +40,7 @@ object TwitterPopularTags { val ssc = new StreamingContext(master, "TwitterPopularTags", Seconds(2), System.getenv("SPARK_HOME"), StreamingContext.jarOfClass(this.getClass)) - val stream = ssc.twitterStream(None, filters) + val stream = TwitterUtils.createStream(ssc, None, filters) val hashTags = stream.flatMap(status => status.getText.split(" ").filter(_.startsWith("#"))) diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala index beda73a71b..03902ec353 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/ZeroMQWordCount.scala @@ -20,11 +20,13 @@ package org.apache.spark.streaming.examples import akka.actor.ActorSystem import akka.actor.actorRef2Scala import akka.zeromq._ -import org.apache.spark.streaming.{ Seconds, StreamingContext } -import org.apache.spark.streaming.StreamingContext._ import akka.zeromq.Subscribe import akka.util.ByteString +import org.apache.spark.streaming.{Seconds, StreamingContext} +import org.apache.spark.streaming.StreamingContext._ +import org.apache.spark.streaming.zeromq._ + /** * A simple publisher for demonstration purposes, repeatedly publishes random Messages * every one second. @@ -83,11 +85,10 @@ object ZeroMQWordCount { def bytesToStringIterator(x: Seq[ByteString]) = (x.map(_.utf8String)).iterator //For this stream, a zeroMQ publisher should be running. - val lines = ssc.zeroMQStream(url, Subscribe(topic), bytesToStringIterator) + val lines = ZeroMQUtils.createStream(ssc, url, Subscribe(topic), bytesToStringIterator _) val words = lines.flatMap(_.split(" ")) val wordCounts = words.map(x => (x, 1)).reduceByKey(_ + _) wordCounts.print() ssc.start() } - } diff --git a/external/flume/pom.xml b/external/flume/pom.xml new file mode 100644 index 0000000000..443910a03a --- /dev/null +++ b/external/flume/pom.xml @@ -0,0 +1,93 @@ +<?xml version="1.0" encoding="UTF-8"?> +<!-- + ~ 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. + --> + +<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> + <modelVersion>4.0.0</modelVersion> + <parent> + <groupId>org.apache.spark</groupId> + <artifactId>spark-parent</artifactId> + <version>0.9.0-incubating-SNAPSHOT</version> + <relativePath>../../pom.xml</relativePath> + </parent> + + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-flume_2.10</artifactId> + <packaging>jar</packaging> + <name>Spark Project External Flume</name> + <url>http://spark.incubator.apache.org/</url> + + <dependencies> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming_${scala.binary.version}</artifactId> + <version>${project.version}</version> + <type>test-jar</type> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.apache.flume</groupId> + <artifactId>flume-ng-sdk</artifactId> + <version>1.2.0</version> + <exclusions> + <exclusion> + <groupId>org.jboss.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> + <exclusion> + <groupId>org.xerial.snappy</groupId> + <artifactId>*</artifactId> + </exclusion> + </exclusions> + </dependency> + <dependency> + <groupId>org.scalatest</groupId> + <artifactId>scalatest_${scala.binary.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.scalacheck</groupId> + <artifactId>scalacheck_${scala.binary.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>com.novocode</groupId> + <artifactId>junit-interface</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.slf4j</groupId> + <artifactId>slf4j-log4j12</artifactId> + <scope>test</scope> + </dependency> + </dependencies> + <build> + <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory> + <plugins> + <plugin> + <groupId>org.scalatest</groupId> + <artifactId>scalatest-maven-plugin</artifactId> + </plugin> + </plugins> + </build> +</project> diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlumeInputDStream.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala index 60d79175f1..ce3ef47cfe 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/FlumeInputDStream.scala +++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeInputDStream.scala @@ -15,7 +15,7 @@ * limitations under the License. */ -package org.apache.spark.streaming.dstream +package org.apache.spark.streaming.flume import java.net.InetSocketAddress import java.io.{ObjectInput, ObjectOutput, Externalizable} @@ -30,9 +30,10 @@ import org.apache.flume.source.avro.Status import org.apache.avro.ipc.specific.SpecificResponder import org.apache.avro.ipc.NettyServer -import org.apache.spark.streaming.StreamingContext import org.apache.spark.util.Utils import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.StreamingContext +import org.apache.spark.streaming.dstream._ private[streaming] class FlumeInputDStream[T: ClassTag]( diff --git a/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala new file mode 100644 index 0000000000..834b775d4f --- /dev/null +++ b/external/flume/src/main/scala/org/apache/spark/streaming/flume/FlumeUtils.scala @@ -0,0 +1,70 @@ +/* + * 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.streaming.flume + +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.{StreamingContext, DStream} +import org.apache.spark.streaming.api.java.{JavaStreamingContext, JavaDStream} + +object FlumeUtils { + /** + * Create a input stream from a Flume source. + * @param ssc StreamingContext object + * @param hostname Hostname of the slave machine to which the flume data will be sent + * @param port Port of the slave machine to which the flume data will be sent + * @param storageLevel Storage level to use for storing the received objects + */ + def createStream ( + ssc: StreamingContext, + hostname: String, + port: Int, + storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2 + ): DStream[SparkFlumeEvent] = { + val inputStream = new FlumeInputDStream[SparkFlumeEvent](ssc, hostname, port, storageLevel) + ssc.registerInputStream(inputStream) + inputStream + } + + /** + * Creates a input stream from a Flume source. + * @param hostname Hostname of the slave machine to which the flume data will be sent + * @param port Port of the slave machine to which the flume data will be sent + */ + def createStream( + jssc: JavaStreamingContext, + hostname: String, + port: Int + ): JavaDStream[SparkFlumeEvent] = { + createStream(jssc.ssc, hostname, port) + } + + /** + * Creates a input stream from a Flume source. + * @param hostname Hostname of the slave machine to which the flume data will be sent + * @param port Port of the slave machine to which the flume data will be sent + * @param storageLevel Storage level to use for storing the received objects + */ + def createStream( + jssc: JavaStreamingContext, + hostname: String, + port: Int, + storageLevel: StorageLevel + ): JavaDStream[SparkFlumeEvent] = { + createStream(jssc.ssc, hostname, port, storageLevel) + } +} diff --git a/external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumeStreamSuite.java b/external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumeStreamSuite.java new file mode 100644 index 0000000000..733389b98d --- /dev/null +++ b/external/flume/src/test/java/org/apache/spark/streaming/flume/JavaFlumeStreamSuite.java @@ -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.streaming.flume; + +import org.apache.spark.storage.StorageLevel; +import org.apache.spark.streaming.LocalJavaStreamingContext; +import org.apache.spark.streaming.api.java.JavaDStream; + +import org.junit.Test; + +public class JavaFlumeStreamSuite extends LocalJavaStreamingContext { + @Test + public void testFlumeStream() { + // tests the API, does not actually test data receiving + JavaDStream<SparkFlumeEvent> test1 = FlumeUtils.createStream(ssc, "localhost", 12345); + JavaDStream<SparkFlumeEvent> test2 = FlumeUtils.createStream(ssc, "localhost", 12345, + StorageLevel.MEMORY_AND_DISK_SER_2()); + } +} diff --git a/external/flume/src/test/resources/log4j.properties b/external/flume/src/test/resources/log4j.properties new file mode 100644 index 0000000000..063529a9cb --- /dev/null +++ b/external/flume/src/test/resources/log4j.properties @@ -0,0 +1,29 @@ +# +# 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. +# + +# Set everything to be logged to the file streaming/target/unit-tests.log +log4j.rootCategory=INFO, file +# log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file.append=false +log4j.appender.file.file=streaming/target/unit-tests.log +log4j.appender.file.layout=org.apache.log4j.PatternLayout +log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n + +# Ignore messages below warning level from Jetty, because it's a bit verbose +log4j.logger.org.eclipse.jetty=WARN + diff --git a/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala new file mode 100644 index 0000000000..2e8e9fac45 --- /dev/null +++ b/external/flume/src/test/scala/org/apache/spark/streaming/flume/FlumeStreamSuite.scala @@ -0,0 +1,86 @@ +/* + * 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.streaming.flume + +import scala.collection.JavaConversions._ +import scala.collection.mutable.{ArrayBuffer, SynchronizedBuffer} + +import java.net.InetSocketAddress +import java.nio.ByteBuffer +import java.nio.charset.Charset + +import org.apache.avro.ipc.NettyTransceiver +import org.apache.avro.ipc.specific.SpecificRequestor +import org.apache.flume.source.avro.{AvroFlumeEvent, AvroSourceProtocol} + +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.{TestOutputStream, StreamingContext, TestSuiteBase} +import org.apache.spark.streaming.util.ManualClock + +class FlumeStreamSuite extends TestSuiteBase { + + val testPort = 9999 + + test("flume input stream") { + // Set up the streaming context and input streams + val ssc = new StreamingContext(conf, batchDuration) + val flumeStream = FlumeUtils.createStream(ssc, "localhost", testPort, StorageLevel.MEMORY_AND_DISK) + val outputBuffer = new ArrayBuffer[Seq[SparkFlumeEvent]] + with SynchronizedBuffer[Seq[SparkFlumeEvent]] + val outputStream = new TestOutputStream(flumeStream, outputBuffer) + ssc.registerOutputStream(outputStream) + ssc.start() + + val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] + val input = Seq(1, 2, 3, 4, 5) + Thread.sleep(1000) + val transceiver = new NettyTransceiver(new InetSocketAddress("localhost", testPort)) + val client = SpecificRequestor.getClient( + classOf[AvroSourceProtocol], transceiver) + + for (i <- 0 until input.size) { + val event = new AvroFlumeEvent + event.setBody(ByteBuffer.wrap(input(i).toString.getBytes())) + event.setHeaders(Map[CharSequence, CharSequence]("test" -> "header")) + client.append(event) + Thread.sleep(500) + clock.addToTime(batchDuration.milliseconds) + } + + val startTime = System.currentTimeMillis() + while (outputBuffer.size < input.size && System.currentTimeMillis() - startTime < maxWaitTimeMillis) { + logInfo("output.size = " + outputBuffer.size + ", input.size = " + input.size) + Thread.sleep(100) + } + Thread.sleep(1000) + val timeTaken = System.currentTimeMillis() - startTime + assert(timeTaken < maxWaitTimeMillis, "Operation timed out after " + timeTaken + " ms") + logInfo("Stopping context") + ssc.stop() + + val decoder = Charset.forName("UTF-8").newDecoder() + + assert(outputBuffer.size === input.length) + for (i <- 0 until outputBuffer.size) { + assert(outputBuffer(i).size === 1) + val str = decoder.decode(outputBuffer(i).head.event.getBody) + assert(str.toString === input(i).toString) + assert(outputBuffer(i).head.event.getHeaders.get("test") === "header") + } + } +} diff --git a/external/kafka/pom.xml b/external/kafka/pom.xml new file mode 100644 index 0000000000..f782e0e126 --- /dev/null +++ b/external/kafka/pom.xml @@ -0,0 +1,97 @@ +<?xml version="1.0" encoding="UTF-8"?> +<!-- + ~ 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. + --> + +<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> + <modelVersion>4.0.0</modelVersion> + <parent> + <groupId>org.apache.spark</groupId> + <artifactId>spark-parent</artifactId> + <version>0.9.0-incubating-SNAPSHOT</version> + <relativePath>../../pom.xml</relativePath> + </parent> + + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-kafka_2.10</artifactId> + <packaging>jar</packaging> + <name>Spark Project External Kafka</name> + <url>http://spark.incubator.apache.org/</url> + + <dependencies> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming_${scala.binary.version}</artifactId> + <version>${project.version}</version> + <type>test-jar</type> + <scope>test</scope> + </dependency> + <dependency> + <groupId>com.sksamuel.kafka</groupId> + <artifactId>kafka_${scala.binary.version}</artifactId> + <version>0.8.0-beta1</version> + <exclusions> + <exclusion> + <groupId>com.sun.jmx</groupId> + <artifactId>jmxri</artifactId> + </exclusion> + <exclusion> + <groupId>com.sun.jdmk</groupId> + <artifactId>jmxtools</artifactId> + </exclusion> + <exclusion> + <groupId>net.sf.jopt-simple</groupId> + <artifactId>jopt-simple</artifactId> + </exclusion> + </exclusions> + </dependency> + <dependency> + <groupId>org.scalatest</groupId> + <artifactId>scalatest_${scala.binary.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.scalacheck</groupId> + <artifactId>scalacheck_${scala.binary.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>com.novocode</groupId> + <artifactId>junit-interface</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.slf4j</groupId> + <artifactId>slf4j-log4j12</artifactId> + <scope>test</scope> + </dependency> + </dependencies> + <build> + <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory> + <plugins> + <plugin> + <groupId>org.scalatest</groupId> + <artifactId>scalatest-maven-plugin</artifactId> + </plugin> + </plugins> + </build> +</project> diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/KafkaInputDStream.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala index 526f5564c7..a2cd49c573 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/KafkaInputDStream.scala +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaInputDStream.scala @@ -15,11 +15,10 @@ * limitations under the License. */ -package org.apache.spark.streaming.dstream +package org.apache.spark.streaming.kafka -import org.apache.spark.Logging -import org.apache.spark.storage.StorageLevel -import org.apache.spark.streaming.StreamingContext +import scala.collection.Map +import scala.reflect.ClassTag import java.util.Properties import java.util.concurrent.Executors @@ -30,8 +29,10 @@ import kafka.utils.VerifiableProperties import kafka.utils.ZKStringSerializer import org.I0Itec.zkclient._ -import scala.collection.Map -import scala.reflect.ClassTag +import org.apache.spark.Logging +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.StreamingContext +import org.apache.spark.streaming.dstream._ /** * Input stream that pulls messages from a Kafka Broker. diff --git a/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala new file mode 100644 index 0000000000..c2d851f943 --- /dev/null +++ b/external/kafka/src/main/scala/org/apache/spark/streaming/kafka/KafkaUtils.scala @@ -0,0 +1,153 @@ +/* + * 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.streaming.kafka + +import scala.reflect.ClassTag +import scala.collection.JavaConversions._ + +import java.lang.{Integer => JInt} +import java.util.{Map => JMap} + +import kafka.serializer.{Decoder, StringDecoder} + +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.{StreamingContext, DStream} +import org.apache.spark.streaming.api.java.{JavaStreamingContext, JavaPairDStream} + + +object KafkaUtils { + /** + * Create an input stream that pulls messages from a Kafka Broker. + * @param ssc StreamingContext object + * @param zkQuorum Zookeeper quorum (hostname:port,hostname:port,..) + * @param groupId The group id for this consumer + * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed + * in its own thread + * @param storageLevel Storage level to use for storing the received objects + * (default: StorageLevel.MEMORY_AND_DISK_SER_2) + */ + def createStream( + ssc: StreamingContext, + zkQuorum: String, + groupId: String, + topics: Map[String, Int], + storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2 + ): DStream[(String, String)] = { + val kafkaParams = Map[String, String]( + "zookeeper.connect" -> zkQuorum, "group.id" -> groupId, + "zookeeper.connection.timeout.ms" -> "10000") + createStream[String, String, StringDecoder, StringDecoder]( + ssc, kafkaParams, topics, storageLevel) + } + + /** + * Create an input stream that pulls messages from a Kafka Broker. + * @param ssc StreamingContext object + * @param kafkaParams Map of kafka configuration parameters, + * see http://kafka.apache.org/08/configuration.html + * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed + * in its own thread. + * @param storageLevel Storage level to use for storing the received objects + */ + def createStream[K: ClassTag, V: ClassTag, U <: Decoder[_]: Manifest, T <: Decoder[_]: Manifest]( + ssc: StreamingContext, + kafkaParams: Map[String, String], + topics: Map[String, Int], + storageLevel: StorageLevel + ): DStream[(K, V)] = { + val inputStream = new KafkaInputDStream[K, V, U, T](ssc, kafkaParams, topics, storageLevel) + ssc.registerInputStream(inputStream) + inputStream + } + + /** + * Create an input stream that pulls messages form a Kafka Broker. + * @param jssc JavaStreamingContext object + * @param zkQuorum Zookeeper quorum (hostname:port,hostname:port,..) + * @param groupId The group id for this consumer + * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed + * in its own thread + */ + def createStream( + jssc: JavaStreamingContext, + zkQuorum: String, + groupId: String, + topics: JMap[String, JInt] + ): JavaPairDStream[String, String] = { + implicit val cmt: ClassTag[String] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[String]] + createStream(jssc.ssc, zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*)) + } + + /** + * Create an input stream that pulls messages form a Kafka Broker. + * @param jssc JavaStreamingContext object + * @param zkQuorum Zookeeper quorum (hostname:port,hostname:port,..). + * @param groupId The group id for this consumer. + * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed + * in its own thread. + * @param storageLevel RDD storage level. + * + */ + def createStream( + jssc: JavaStreamingContext, + zkQuorum: String, + groupId: String, + topics: JMap[String, JInt], + storageLevel: StorageLevel + ): JavaPairDStream[String, String] = { + implicit val cmt: ClassTag[String] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[String]] + createStream(jssc.ssc, zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*), storageLevel) + } + + /** + * Create an input stream that pulls messages form a Kafka Broker. + * @param jssc JavaStreamingContext object + * @param keyTypeClass Key type of RDD + * @param valueTypeClass value type of RDD + * @param keyDecoderClass Type of kafka key decoder + * @param valueDecoderClass Type of kafka value decoder + * @param kafkaParams Map of kafka configuration parameters, + * see http://kafka.apache.org/08/configuration.html + * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed + * in its own thread + * @param storageLevel RDD storage level. Defaults to MEMORY_AND_DISK_2. + */ + def createStream[K, V, U <: Decoder[_], T <: Decoder[_]]( + jssc: JavaStreamingContext, + keyTypeClass: Class[K], + valueTypeClass: Class[V], + keyDecoderClass: Class[U], + valueDecoderClass: Class[T], + kafkaParams: JMap[String, String], + topics: JMap[String, JInt], + storageLevel: StorageLevel + ): JavaPairDStream[K, V] = { + implicit val keyCmt: ClassTag[K] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]] + implicit val valueCmt: ClassTag[V] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]] + + implicit val keyCmd: Manifest[U] = implicitly[Manifest[AnyRef]].asInstanceOf[Manifest[U]] + implicit val valueCmd: Manifest[T] = implicitly[Manifest[AnyRef]].asInstanceOf[Manifest[T]] + + createStream[K, V, U, T]( + jssc.ssc, kafkaParams.toMap, Map(topics.mapValues(_.intValue()).toSeq: _*), storageLevel) + } +} diff --git a/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java new file mode 100644 index 0000000000..7b4999447e --- /dev/null +++ b/external/kafka/src/test/java/org/apache/spark/streaming/kafka/JavaKafkaStreamSuite.java @@ -0,0 +1,45 @@ +/* + * 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.streaming.kafka; + +import java.util.HashMap; +import org.junit.Test; +import com.google.common.collect.Maps; +import kafka.serializer.StringDecoder; +import org.apache.spark.storage.StorageLevel; +import org.apache.spark.streaming.LocalJavaStreamingContext; +import org.apache.spark.streaming.api.java.JavaPairDStream; + +public class JavaKafkaStreamSuite extends LocalJavaStreamingContext { + @Test + public void testKafkaStream() { + HashMap<String, Integer> topics = Maps.newHashMap(); + + // tests the API, does not actually test data receiving + JavaPairDStream<String, String> test1 = KafkaUtils.createStream(ssc, "localhost:12345", "group", topics); + JavaPairDStream<String, String> test2 = KafkaUtils.createStream(ssc, "localhost:12345", "group", topics, + StorageLevel.MEMORY_AND_DISK_SER_2()); + + HashMap<String, String> kafkaParams = Maps.newHashMap(); + kafkaParams.put("zookeeper.connect", "localhost:12345"); + kafkaParams.put("group.id","consumer-group"); + JavaPairDStream<String, String> test3 = KafkaUtils.createStream(ssc, + String.class, String.class, StringDecoder.class, StringDecoder.class, + kafkaParams, topics, StorageLevel.MEMORY_AND_DISK_SER_2()); + } +} diff --git a/external/kafka/src/test/resources/log4j.properties b/external/kafka/src/test/resources/log4j.properties new file mode 100644 index 0000000000..063529a9cb --- /dev/null +++ b/external/kafka/src/test/resources/log4j.properties @@ -0,0 +1,29 @@ +# +# 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. +# + +# Set everything to be logged to the file streaming/target/unit-tests.log +log4j.rootCategory=INFO, file +# log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file.append=false +log4j.appender.file.file=streaming/target/unit-tests.log +log4j.appender.file.layout=org.apache.log4j.PatternLayout +log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n + +# Ignore messages below warning level from Jetty, because it's a bit verbose +log4j.logger.org.eclipse.jetty=WARN + diff --git a/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.scala new file mode 100644 index 0000000000..9c81f23c19 --- /dev/null +++ b/external/kafka/src/test/scala/org/apache/spark/streaming/kafka/KafkaStreamSuite.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.streaming.kafka + +import kafka.serializer.StringDecoder +import org.apache.spark.streaming.{StreamingContext, TestSuiteBase} +import org.apache.spark.storage.StorageLevel + +class KafkaStreamSuite extends TestSuiteBase { + + test("kafka input stream") { + val ssc = new StreamingContext(master, framework, batchDuration) + val topics = Map("my-topic" -> 1) + + // tests the API, does not actually test data receiving + val test1 = KafkaUtils.createStream(ssc, "localhost:1234", "group", topics) + val test2 = KafkaUtils.createStream(ssc, "localhost:12345", "group", topics, StorageLevel.MEMORY_AND_DISK_SER_2) + val kafkaParams = Map("zookeeper.connect"->"localhost:12345","group.id"->"consumer-group") + val test3 = KafkaUtils.createStream[String, String, StringDecoder, StringDecoder]( + ssc, kafkaParams, topics, StorageLevel.MEMORY_AND_DISK_SER_2) + + // TODO: Actually test receiving data + } +} diff --git a/external/mqtt/pom.xml b/external/mqtt/pom.xml new file mode 100644 index 0000000000..31b4fa87de --- /dev/null +++ b/external/mqtt/pom.xml @@ -0,0 +1,108 @@ +<?xml version="1.0" encoding="UTF-8"?> +<!-- + ~ 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. + --> + +<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> + <modelVersion>4.0.0</modelVersion> + <parent> + <groupId>org.apache.spark</groupId> + <artifactId>spark-parent</artifactId> + <version>0.9.0-incubating-SNAPSHOT</version> + <relativePath>../../pom.xml</relativePath> + </parent> + + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-mqtt_2.10</artifactId> + <packaging>jar</packaging> + <name>Spark Project External MQTT</name> + <url>http://spark.incubator.apache.org/</url> + + <repositories> + <repository> + <id>mqtt-repo</id> + <name>MQTT Repository</name> + <url>https://repo.eclipse.org/content/repositories/paho-releases</url> + <releases> + <enabled>true</enabled> + </releases> + <snapshots> + <enabled>false</enabled> + </snapshots> + </repository> + </repositories> + + <dependencies> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming_${scala.binary.version}</artifactId> + <version>${project.version}</version> + <type>test-jar</type> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.eclipse.paho</groupId> + <artifactId>mqtt-client</artifactId> + <version>0.4.0</version> + </dependency> + <dependency> + <groupId>${akka.group}</groupId> + <artifactId>akka-zeromq_${scala.binary.version}</artifactId> + <version>${akka.version}</version> + <exclusions> + <exclusion> + <groupId>org.jboss.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> + </exclusions> + </dependency> + <dependency> + <groupId>org.scalatest</groupId> + <artifactId>scalatest_${scala.binary.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.scalacheck</groupId> + <artifactId>scalacheck_${scala.binary.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>com.novocode</groupId> + <artifactId>junit-interface</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.slf4j</groupId> + <artifactId>slf4j-log4j12</artifactId> + <scope>test</scope> + </dependency> + </dependencies> + <build> + <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory> + <plugins> + <plugin> + <groupId>org.scalatest</groupId> + <artifactId>scalatest-maven-plugin</artifactId> + </plugin> + </plugins> + </build> +</project> diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MQTTInputDStream.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala index ef4a737568..c8987a3ee0 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/MQTTInputDStream.scala +++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTInputDStream.scala @@ -15,11 +15,12 @@ * limitations under the License. */ -package org.apache.spark.streaming.dstream +package org.apache.spark.streaming.mqtt -import org.apache.spark.Logging -import org.apache.spark.storage.StorageLevel -import org.apache.spark.streaming.{ Time, DStreamCheckpointData, StreamingContext } +import scala.collection.Map +import scala.collection.mutable.HashMap +import scala.collection.JavaConversions._ +import scala.reflect.ClassTag import java.util.Properties import java.util.concurrent.Executors @@ -34,10 +35,10 @@ import org.eclipse.paho.client.mqttv3.MqttException import org.eclipse.paho.client.mqttv3.MqttMessage import org.eclipse.paho.client.mqttv3.MqttTopic -import scala.collection.Map -import scala.collection.mutable.HashMap -import scala.collection.JavaConversions._ -import scala.reflect.ClassTag +import org.apache.spark.Logging +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.StreamingContext +import org.apache.spark.streaming.dstream._ /** * Input stream that subscribe messages from a Mqtt Broker. @@ -49,15 +50,14 @@ import scala.reflect.ClassTag private[streaming] class MQTTInputDStream[T: ClassTag]( - @transient ssc_ : StreamingContext, - brokerUrl: String, - topic: String, - storageLevel: StorageLevel + @transient ssc_ : StreamingContext, + brokerUrl: String, + topic: String, + storageLevel: StorageLevel ) extends NetworkInputDStream[T](ssc_) with Logging { def getReceiver(): NetworkReceiver[T] = { - new MQTTReceiver(brokerUrl, topic, storageLevel) - .asInstanceOf[NetworkReceiver[T]] + new MQTTReceiver(brokerUrl, topic, storageLevel).asInstanceOf[NetworkReceiver[T]] } } diff --git a/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala new file mode 100644 index 0000000000..0e6c25dbee --- /dev/null +++ b/external/mqtt/src/main/scala/org/apache/spark/streaming/mqtt/MQTTUtils.scala @@ -0,0 +1,75 @@ +/* + * 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.streaming.mqtt + +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.{StreamingContext, DStream} +import org.apache.spark.streaming.api.java.{JavaStreamingContext, JavaDStream} +import scala.reflect.ClassTag + +object MQTTUtils { + /** + * Create an input stream that receives messages pushed by a MQTT publisher. + * @param ssc StreamingContext object + * @param brokerUrl Url of remote MQTT publisher + * @param topic Topic name to subscribe to + * @param storageLevel RDD storage level. Defaults to StorageLevel.MEMORY_AND_DISK_SER_2. + */ + def createStream( + ssc: StreamingContext, + brokerUrl: String, + topic: String, + storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2 + ): DStream[String] = { + val inputStream = new MQTTInputDStream[String](ssc, brokerUrl, topic, storageLevel) + ssc.registerInputStream(inputStream) + inputStream + } + + /** + * Create an input stream that receives messages pushed by a MQTT publisher. + * @param jssc JavaStreamingContext object + * @param brokerUrl Url of remote MQTT publisher + * @param topic Topic name to subscribe to + */ + def createStream( + jssc: JavaStreamingContext, + brokerUrl: String, + topic: String + ): JavaDStream[String] = { + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[String]] + createStream(jssc.ssc, brokerUrl, topic) + } + + /** + * Create an input stream that receives messages pushed by a MQTT publisher. + * @param jssc JavaStreamingContext object + * @param brokerUrl Url of remote MQTT publisher + * @param topic Topic name to subscribe to + * @param storageLevel RDD storage level. + */ + def createStream( + jssc: JavaStreamingContext, + brokerUrl: String, + topic: String, + storageLevel: StorageLevel + ): JavaDStream[String] = { + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[String]] + createStream(jssc.ssc, brokerUrl, topic, storageLevel) + } +} diff --git a/external/mqtt/src/test/java/org/apache/spark/streaming/mqtt/JavaMQTTStreamSuite.java b/external/mqtt/src/test/java/org/apache/spark/streaming/mqtt/JavaMQTTStreamSuite.java new file mode 100644 index 0000000000..44743aaecf --- /dev/null +++ b/external/mqtt/src/test/java/org/apache/spark/streaming/mqtt/JavaMQTTStreamSuite.java @@ -0,0 +1,37 @@ +/* + * 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.streaming.mqtt; + +import org.apache.spark.storage.StorageLevel; +import org.apache.spark.streaming.api.java.JavaDStream; +import org.junit.Test; + +import org.apache.spark.streaming.LocalJavaStreamingContext; + +public class JavaMQTTStreamSuite extends LocalJavaStreamingContext { + @Test + public void testMQTTStream() { + String brokerUrl = "abc"; + String topic = "def"; + + // tests the API, does not actually test data receiving + JavaDStream<String> test1 = MQTTUtils.createStream(ssc, brokerUrl, topic); + JavaDStream<String> test2 = MQTTUtils.createStream(ssc, brokerUrl, topic, + StorageLevel.MEMORY_AND_DISK_SER_2()); + } +} diff --git a/external/mqtt/src/test/resources/log4j.properties b/external/mqtt/src/test/resources/log4j.properties new file mode 100644 index 0000000000..063529a9cb --- /dev/null +++ b/external/mqtt/src/test/resources/log4j.properties @@ -0,0 +1,29 @@ +# +# 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. +# + +# Set everything to be logged to the file streaming/target/unit-tests.log +log4j.rootCategory=INFO, file +# log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file.append=false +log4j.appender.file.file=streaming/target/unit-tests.log +log4j.appender.file.layout=org.apache.log4j.PatternLayout +log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n + +# Ignore messages below warning level from Jetty, because it's a bit verbose +log4j.logger.org.eclipse.jetty=WARN + diff --git a/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala new file mode 100644 index 0000000000..fcc159e85a --- /dev/null +++ b/external/mqtt/src/test/scala/org/apache/spark/streaming/mqtt/MQTTStreamSuite.scala @@ -0,0 +1,36 @@ +/* + * 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.streaming.mqtt + +import org.apache.spark.streaming.{StreamingContext, TestSuiteBase} +import org.apache.spark.storage.StorageLevel + +class MQTTStreamSuite extends TestSuiteBase { + + test("MQTT input stream") { + val ssc = new StreamingContext(master, framework, batchDuration) + val brokerUrl = "abc" + val topic = "def" + + // tests the API, does not actually test data receiving + val test1 = MQTTUtils.createStream(ssc, brokerUrl, topic) + val test2 = MQTTUtils.createStream(ssc, brokerUrl, topic, StorageLevel.MEMORY_AND_DISK_SER_2) + + // TODO: Actually test receiving data + } +} diff --git a/external/twitter/pom.xml b/external/twitter/pom.xml new file mode 100644 index 0000000000..216e6c1d8f --- /dev/null +++ b/external/twitter/pom.xml @@ -0,0 +1,89 @@ +<?xml version="1.0" encoding="UTF-8"?> +<!-- + ~ 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. + --> + +<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> + <modelVersion>4.0.0</modelVersion> + <parent> + <groupId>org.apache.spark</groupId> + <artifactId>spark-parent</artifactId> + <version>0.9.0-incubating-SNAPSHOT</version> + <relativePath>../../pom.xml</relativePath> + </parent> + + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-twitter_2.10</artifactId> + <packaging>jar</packaging> + <name>Spark Project External Twitter</name> + <url>http://spark.incubator.apache.org/</url> + + <dependencies> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming_${scala.binary.version}</artifactId> + <version>${project.version}</version> + <type>test-jar</type> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.twitter4j</groupId> + <artifactId>twitter4j-stream</artifactId> + <version>3.0.3</version> + <exclusions> + <exclusion> + <groupId>org.jboss.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> + </exclusions> + </dependency> + <dependency> + <groupId>org.scalatest</groupId> + <artifactId>scalatest_${scala.binary.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.scalacheck</groupId> + <artifactId>scalacheck_${scala.binary.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>com.novocode</groupId> + <artifactId>junit-interface</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.slf4j</groupId> + <artifactId>slf4j-log4j12</artifactId> + <scope>test</scope> + </dependency> + </dependencies> + <build> + <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory> + <plugins> + <plugin> + <groupId>org.scalatest</groupId> + <artifactId>scalatest-maven-plugin</artifactId> + </plugin> + </plugins> + </build> +</project> diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TwitterInputDStream.scala b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala index 387e15b0e6..5cc721d7f9 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/TwitterInputDStream.scala +++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterInputDStream.scala @@ -15,18 +15,19 @@ * limitations under the License. */ -package org.apache.spark.streaming.dstream +package org.apache.spark.streaming.twitter -import org.apache.spark._ -import org.apache.spark.streaming._ -import storage.StorageLevel +import java.util.prefs.Preferences import twitter4j._ import twitter4j.auth.Authorization -import java.util.prefs.Preferences import twitter4j.conf.ConfigurationBuilder import twitter4j.conf.PropertyConfiguration import twitter4j.auth.OAuthAuthorization import twitter4j.auth.AccessToken +import org.apache.spark._ +import org.apache.spark.streaming._ +import org.apache.spark.streaming.dstream._ +import org.apache.spark.storage.StorageLevel /* A stream of Twitter statuses, potentially filtered by one or more keywords. * diff --git a/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterUtils.scala b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterUtils.scala new file mode 100644 index 0000000000..5e506ffabc --- /dev/null +++ b/external/twitter/src/main/scala/org/apache/spark/streaming/twitter/TwitterUtils.scala @@ -0,0 +1,126 @@ +/* + * 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.streaming.twitter + +import twitter4j.Status +import twitter4j.auth.Authorization +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.{StreamingContext, DStream} +import org.apache.spark.streaming.api.java.{JavaDStream, JavaStreamingContext} + +object TwitterUtils { + /** + * Create a input stream that returns tweets received from Twitter. + * @param ssc StreamingContext object + * @param twitterAuth Twitter4J authentication, or None to use Twitter4J's default OAuth + * authorization; this uses the system properties twitter4j.oauth.consumerKey, + * twitter4j.oauth.consumerSecret, twitter4j.oauth.accessToken and + * twitter4j.oauth.accessTokenSecret + * @param filters Set of filter strings to get only those tweets that match them + * @param storageLevel Storage level to use for storing the received objects + */ + def createStream( + ssc: StreamingContext, + twitterAuth: Option[Authorization], + filters: Seq[String] = Nil, + storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2 + ): DStream[Status] = { + val inputStream = new TwitterInputDStream(ssc, twitterAuth, filters, storageLevel) + ssc.registerInputStream(inputStream) + inputStream + } + + /** + * Create a input stream that returns tweets received from Twitter using Twitter4J's default + * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, + * twitter4j.oauth.consumerSecret, twitter4j.oauth.accessToken and + * twitter4j.oauth.accessTokenSecret. + * @param jssc JavaStreamingContext object + */ + def createStream(jssc: JavaStreamingContext): JavaDStream[Status] = { + createStream(jssc.ssc, None) + } + + /** + * Create a input stream that returns tweets received from Twitter using Twitter4J's default + * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, + * twitter4j.oauth.consumerSecret, twitter4j.oauth.accessToken and + * twitter4j.oauth.accessTokenSecret. + * @param jssc JavaStreamingContext object + * @param filters Set of filter strings to get only those tweets that match them + */ + def createStream(jssc: JavaStreamingContext, filters: Array[String]): JavaDStream[Status] = { + createStream(jssc.ssc, None, filters) + } + + /** + * Create a input stream that returns tweets received from Twitter using Twitter4J's default + * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, + * twitter4j.oauth.consumerSecret, twitter4j.oauth.accessToken and + * twitter4j.oauth.accessTokenSecret. + * @param jssc JavaStreamingContext object + * @param filters Set of filter strings to get only those tweets that match them + * @param storageLevel Storage level to use for storing the received objects + */ + def createStream( + jssc: JavaStreamingContext, + filters: Array[String], + storageLevel: StorageLevel + ): JavaDStream[Status] = { + createStream(jssc.ssc, None, filters, storageLevel) + } + + /** + * Create a input stream that returns tweets received from Twitter. + * @param jssc JavaStreamingContext object + * @param twitterAuth Twitter4J Authorization + */ + def createStream(jssc: JavaStreamingContext, twitterAuth: Authorization): JavaDStream[Status] = { + createStream(jssc.ssc, Some(twitterAuth)) + } + + /** + * Create a input stream that returns tweets received from Twitter. + * @param jssc JavaStreamingContext object + * @param twitterAuth Twitter4J Authorization + * @param filters Set of filter strings to get only those tweets that match them + */ + def createStream( + jssc: JavaStreamingContext, + twitterAuth: Authorization, + filters: Array[String] + ): JavaDStream[Status] = { + createStream(jssc.ssc, Some(twitterAuth), filters) + } + + /** + * Create a input stream that returns tweets received from Twitter. + * @param jssc JavaStreamingContext object + * @param twitterAuth Twitter4J Authorization object + * @param filters Set of filter strings to get only those tweets that match them + * @param storageLevel Storage level to use for storing the received objects + */ + def createStream( + jssc: JavaStreamingContext, + twitterAuth: Authorization, + filters: Array[String], + storageLevel: StorageLevel + ): JavaDStream[Status] = { + createStream(jssc.ssc, Some(twitterAuth), filters, storageLevel) + } +} diff --git a/external/twitter/src/test/java/org/apache/spark/streaming/twitter/JavaTwitterStreamSuite.java b/external/twitter/src/test/java/org/apache/spark/streaming/twitter/JavaTwitterStreamSuite.java new file mode 100644 index 0000000000..e46b4e5c75 --- /dev/null +++ b/external/twitter/src/test/java/org/apache/spark/streaming/twitter/JavaTwitterStreamSuite.java @@ -0,0 +1,46 @@ +/* + * 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.streaming.twitter; + +import java.util.Arrays; + +import org.junit.Test; +import twitter4j.Status; +import twitter4j.auth.Authorization; +import twitter4j.auth.NullAuthorization; +import org.apache.spark.storage.StorageLevel; +import org.apache.spark.streaming.LocalJavaStreamingContext; +import org.apache.spark.streaming.api.java.JavaDStream; + +public class JavaTwitterStreamSuite extends LocalJavaStreamingContext { + @Test + public void testTwitterStream() { + String[] filters = (String[])Arrays.<String>asList("filter1", "filter2").toArray(); + Authorization auth = NullAuthorization.getInstance(); + + // tests the API, does not actually test data receiving + JavaDStream<Status> test1 = TwitterUtils.createStream(ssc); + JavaDStream<Status> test2 = TwitterUtils.createStream(ssc, filters); + JavaDStream<Status> test3 = TwitterUtils.createStream( + ssc, filters, StorageLevel.MEMORY_AND_DISK_SER_2()); + JavaDStream<Status> test4 = TwitterUtils.createStream(ssc, auth); + JavaDStream<Status> test5 = TwitterUtils.createStream(ssc, auth, filters); + JavaDStream<Status> test6 = TwitterUtils.createStream(ssc, + auth, filters, StorageLevel.MEMORY_AND_DISK_SER_2()); + } +} diff --git a/external/twitter/src/test/resources/log4j.properties b/external/twitter/src/test/resources/log4j.properties new file mode 100644 index 0000000000..063529a9cb --- /dev/null +++ b/external/twitter/src/test/resources/log4j.properties @@ -0,0 +1,29 @@ +# +# 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. +# + +# Set everything to be logged to the file streaming/target/unit-tests.log +log4j.rootCategory=INFO, file +# log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file.append=false +log4j.appender.file.file=streaming/target/unit-tests.log +log4j.appender.file.layout=org.apache.log4j.PatternLayout +log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n + +# Ignore messages below warning level from Jetty, because it's a bit verbose +log4j.logger.org.eclipse.jetty=WARN + diff --git a/external/twitter/src/test/scala/org/apache/spark/streaming/twitter/TwitterStreamSuite.scala b/external/twitter/src/test/scala/org/apache/spark/streaming/twitter/TwitterStreamSuite.scala new file mode 100644 index 0000000000..a0a8fe617b --- /dev/null +++ b/external/twitter/src/test/scala/org/apache/spark/streaming/twitter/TwitterStreamSuite.scala @@ -0,0 +1,43 @@ +/* + * 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.streaming.twitter + +import org.apache.spark.streaming.{StreamingContext, TestSuiteBase} +import org.apache.spark.storage.StorageLevel +import twitter4j.auth.{NullAuthorization, Authorization} + +class TwitterStreamSuite extends TestSuiteBase { + + test("kafka input stream") { + val ssc = new StreamingContext(master, framework, batchDuration) + val filters = Seq("filter1", "filter2") + val authorization: Authorization = NullAuthorization.getInstance() + + // tests the API, does not actually test data receiving + val test1 = TwitterUtils.createStream(ssc, None) + val test2 = TwitterUtils.createStream(ssc, None, filters) + val test3 = TwitterUtils.createStream(ssc, None, filters, StorageLevel.MEMORY_AND_DISK_SER_2) + val test4 = TwitterUtils.createStream(ssc, Some(authorization)) + val test5 = TwitterUtils.createStream(ssc, Some(authorization), filters) + val test6 = TwitterUtils.createStream(ssc, Some(authorization), filters, + StorageLevel.MEMORY_AND_DISK_SER_2) + + // Note that actually testing the data receiving is hard as authentication keys are + // necessary for accessing Twitter live stream + } +} diff --git a/external/zeromq/pom.xml b/external/zeromq/pom.xml new file mode 100644 index 0000000000..c240d59574 --- /dev/null +++ b/external/zeromq/pom.xml @@ -0,0 +1,89 @@ +<?xml version="1.0" encoding="UTF-8"?> +<!-- + ~ 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. + --> + +<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"> + <modelVersion>4.0.0</modelVersion> + <parent> + <groupId>org.apache.spark</groupId> + <artifactId>spark-parent</artifactId> + <version>0.9.0-incubating-SNAPSHOT</version> + <relativePath>../../pom.xml</relativePath> + </parent> + + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming-zeromq_2.10</artifactId> + <packaging>jar</packaging> + <name>Spark Project External ZeroMQ</name> + <url>http://spark.incubator.apache.org/</url> + + <dependencies> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming_${scala.binary.version}</artifactId> + <version>${project.version}</version> + </dependency> + <dependency> + <groupId>org.apache.spark</groupId> + <artifactId>spark-streaming_${scala.binary.version}</artifactId> + <version>${project.version}</version> + <type>test-jar</type> + <scope>test</scope> + </dependency> + <dependency> + <groupId>${akka.group}</groupId> + <artifactId>akka-zeromq_${scala.binary.version}</artifactId> + <version>${akka.version}</version> + <exclusions> + <exclusion> + <groupId>org.jboss.netty</groupId> + <artifactId>netty</artifactId> + </exclusion> + </exclusions> + </dependency> + <dependency> + <groupId>org.scalatest</groupId> + <artifactId>scalatest_${scala.binary.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.scalacheck</groupId> + <artifactId>scalacheck_${scala.binary.version}</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>com.novocode</groupId> + <artifactId>junit-interface</artifactId> + <scope>test</scope> + </dependency> + <dependency> + <groupId>org.slf4j</groupId> + <artifactId>slf4j-log4j12</artifactId> + <scope>test</scope> + </dependency> + </dependencies> + <build> + <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory> + <testOutputDirectory>target/scala-${scala.binary.version}/test-classes</testOutputDirectory> + <plugins> + <plugin> + <groupId>org.scalatest</groupId> + <artifactId>scalatest-maven-plugin</artifactId> + </plugin> + </plugins> + </build> +</project> diff --git a/streaming/src/main/scala/org/apache/spark/streaming/receivers/ZeroMQReceiver.scala b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQReceiver.scala index f164d516b0..769761e3b8 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/receivers/ZeroMQReceiver.scala +++ b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQReceiver.scala @@ -15,7 +15,7 @@ * limitations under the License. */ -package org.apache.spark.streaming.receivers +package org.apache.spark.streaming.zeromq import scala.reflect.ClassTag @@ -24,6 +24,7 @@ import akka.util.ByteString import akka.zeromq._ import org.apache.spark.Logging +import org.apache.spark.streaming.receivers._ /** * A receiver to subscribe to ZeroMQ stream. diff --git a/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQUtils.scala b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQUtils.scala new file mode 100644 index 0000000000..546d9df3b5 --- /dev/null +++ b/external/zeromq/src/main/scala/org/apache/spark/streaming/zeromq/ZeroMQUtils.scala @@ -0,0 +1,126 @@ +/* + * 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.streaming.zeromq + +import scala.reflect.ClassTag +import scala.collection.JavaConversions._ +import akka.actor.{Props, SupervisorStrategy} +import akka.util.ByteString +import akka.zeromq.Subscribe +import org.apache.spark.api.java.function.{Function => JFunction} +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.receivers.ReceiverSupervisorStrategy +import org.apache.spark.streaming.{StreamingContext, DStream} +import org.apache.spark.streaming.api.java.{JavaStreamingContext, JavaDStream} + +object ZeroMQUtils { + /** + * Create an input stream that receives messages pushed by a zeromq publisher. + * @param ssc StreamingContext object + * @param publisherUrl Url of remote zeromq publisher + * @param subscribe Topic to subscribe to + * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic + * and each frame has sequence of byte thus it needs the converter + * (which might be deserializer of bytes) to translate from sequence + * of sequence of bytes, where sequence refer to a frame + * and sub sequence refer to its payload. + * @param storageLevel RDD storage level. Defaults to StorageLevel.MEMORY_AND_DISK_SER_2. + */ + def createStream[T: ClassTag]( + ssc: StreamingContext, + publisherUrl: String, + subscribe: Subscribe, + bytesToObjects: Seq[ByteString] ⇒ Iterator[T], + storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2, + supervisorStrategy: SupervisorStrategy = ReceiverSupervisorStrategy.defaultStrategy + ): DStream[T] = { + ssc.actorStream(Props(new ZeroMQReceiver(publisherUrl, subscribe, bytesToObjects)), + "ZeroMQReceiver", storageLevel, supervisorStrategy) + } + + /** + * Create an input stream that receives messages pushed by a zeromq publisher. + * @param jssc JavaStreamingContext object + * @param publisherUrl Url of remote ZeroMQ publisher + * @param subscribe Topic to subscribe to + * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic and each frame has sequence + * of byte thus it needs the converter(which might be deserializer of bytes) + * to translate from sequence of sequence of bytes, where sequence refer to a frame + * and sub sequence refer to its payload. + * @param storageLevel Storage level to use for storing the received objects + */ + def createStream[T]( + jssc: JavaStreamingContext, + publisherUrl: String, + subscribe: Subscribe, + bytesToObjects: JFunction[Array[Array[Byte]], java.lang.Iterable[T]], + storageLevel: StorageLevel, + supervisorStrategy: SupervisorStrategy + ): JavaDStream[T] = { + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] + val fn = (x: Seq[ByteString]) => bytesToObjects.apply(x.map(_.toArray).toArray).toIterator + createStream[T](jssc.ssc, publisherUrl, subscribe, fn, storageLevel, supervisorStrategy) + } + + /** + * Create an input stream that receives messages pushed by a zeromq publisher. + * @param jssc JavaStreamingContext object + * @param publisherUrl Url of remote zeromq publisher + * @param subscribe Topic to subscribe to + * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic and each frame has sequence + * of byte thus it needs the converter(which might be deserializer of bytes) + * to translate from sequence of sequence of bytes, where sequence refer to a frame + * and sub sequence refer to its payload. + * @param storageLevel RDD storage level. + */ + def createStream[T]( + jssc: JavaStreamingContext, + publisherUrl: String, + subscribe: Subscribe, + bytesToObjects: JFunction[Array[Array[Byte]], java.lang.Iterable[T]], + storageLevel: StorageLevel + ): JavaDStream[T] = { + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] + val fn = (x: Seq[ByteString]) => bytesToObjects.apply(x.map(_.toArray).toArray).toIterator + createStream[T](jssc.ssc, publisherUrl, subscribe, fn, storageLevel) + } + + /** + * Create an input stream that receives messages pushed by a zeromq publisher. + * @param jssc JavaStreamingContext object + * @param publisherUrl Url of remote zeromq publisher + * @param subscribe Topic to subscribe to + * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic and each frame has sequence + * of byte thus it needs the converter(which might be deserializer of bytes) + * to translate from sequence of sequence of bytes, where sequence refer to a frame + * and sub sequence refer to its payload. + */ + def createStream[T]( + jssc: JavaStreamingContext, + publisherUrl: String, + subscribe: Subscribe, + bytesToObjects: JFunction[Array[Array[Byte]], java.lang.Iterable[T]] + ): JavaDStream[T] = { + implicit val cm: ClassTag[T] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] + val fn = (x: Seq[ByteString]) => bytesToObjects.apply(x.map(_.toArray).toArray).toIterator + createStream[T](jssc.ssc, publisherUrl, subscribe, fn) + } +} diff --git a/external/zeromq/src/test/java/org/apache/spark/streaming/zeromq/JavaZeroMQStreamSuite.java b/external/zeromq/src/test/java/org/apache/spark/streaming/zeromq/JavaZeroMQStreamSuite.java new file mode 100644 index 0000000000..d2361e14b8 --- /dev/null +++ b/external/zeromq/src/test/java/org/apache/spark/streaming/zeromq/JavaZeroMQStreamSuite.java @@ -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.streaming.zeromq; + +import org.junit.Test; +import akka.actor.SupervisorStrategy; +import akka.util.ByteString; +import akka.zeromq.Subscribe; +import org.apache.spark.api.java.function.Function; +import org.apache.spark.storage.StorageLevel; +import org.apache.spark.streaming.LocalJavaStreamingContext; +import org.apache.spark.streaming.api.java.JavaDStream; + +public class JavaZeroMQStreamSuite extends LocalJavaStreamingContext { + + @Test // tests the API, does not actually test data receiving + public void testZeroMQStream() { + String publishUrl = "abc"; + Subscribe subscribe = new Subscribe((ByteString)null); + Function<byte[][], Iterable<String>> bytesToObjects = new Function<byte[][], Iterable<String>>() { + @Override + public Iterable<String> call(byte[][] bytes) throws Exception { + return null; + } + }; + + JavaDStream<String> test1 = ZeroMQUtils.<String>createStream( + ssc, publishUrl, subscribe, bytesToObjects); + JavaDStream<String> test2 = ZeroMQUtils.<String>createStream( + ssc, publishUrl, subscribe, bytesToObjects, StorageLevel.MEMORY_AND_DISK_SER_2()); + JavaDStream<String> test3 = ZeroMQUtils.<String>createStream( + ssc,publishUrl, subscribe, bytesToObjects, StorageLevel.MEMORY_AND_DISK_SER_2(), + SupervisorStrategy.defaultStrategy()); + } +} diff --git a/external/zeromq/src/test/resources/log4j.properties b/external/zeromq/src/test/resources/log4j.properties new file mode 100644 index 0000000000..063529a9cb --- /dev/null +++ b/external/zeromq/src/test/resources/log4j.properties @@ -0,0 +1,29 @@ +# +# 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. +# + +# Set everything to be logged to the file streaming/target/unit-tests.log +log4j.rootCategory=INFO, file +# log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file=org.apache.log4j.FileAppender +log4j.appender.file.append=false +log4j.appender.file.file=streaming/target/unit-tests.log +log4j.appender.file.layout=org.apache.log4j.PatternLayout +log4j.appender.file.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss.SSS} %p %c{1}: %m%n + +# Ignore messages below warning level from Jetty, because it's a bit verbose +log4j.logger.org.eclipse.jetty=WARN + diff --git a/external/zeromq/src/test/scala/org/apache/spark/streaming/zeromq/ZeroMQStreamSuite.scala b/external/zeromq/src/test/scala/org/apache/spark/streaming/zeromq/ZeroMQStreamSuite.scala new file mode 100644 index 0000000000..4193b8a02f --- /dev/null +++ b/external/zeromq/src/test/scala/org/apache/spark/streaming/zeromq/ZeroMQStreamSuite.scala @@ -0,0 +1,44 @@ +/* + * 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.streaming.zeromq + +import akka.actor.SupervisorStrategy +import akka.util.ByteString +import akka.zeromq.Subscribe + +import org.apache.spark.storage.StorageLevel +import org.apache.spark.streaming.{StreamingContext, TestSuiteBase} + +class ZeroMQStreamSuite extends TestSuiteBase { + + test("zeromq input stream") { + val ssc = new StreamingContext(master, framework, batchDuration) + val publishUrl = "abc" + val subscribe = new Subscribe(null.asInstanceOf[ByteString]) + val bytesToObjects = (bytes: Seq[ByteString]) => null.asInstanceOf[Iterator[String]] + + // tests the API, does not actually test data receiving + val test1 = ZeroMQUtils.createStream(ssc, publishUrl, subscribe, bytesToObjects) + val test2 = ZeroMQUtils.createStream( + ssc, publishUrl, subscribe, bytesToObjects, StorageLevel.MEMORY_AND_DISK_SER_2) + val test3 = ZeroMQUtils.createStream(ssc, publishUrl, subscribe, bytesToObjects, + StorageLevel.MEMORY_AND_DISK_SER_2, SupervisorStrategy.defaultStrategy) + + // TODO: Actually test data receiving + } +} diff --git a/make-distribution.sh b/make-distribution.sh index 1a3a5d0209..e6b5956d1e 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -44,7 +44,7 @@ DISTDIR="$FWDIR/dist" # Get version from SBT export TERM=dumb # Prevents color codes in SBT output -VERSIONSTRING=$FWDIR/sbt/sbt "show version" +VERSIONSTRING=$($FWDIR/sbt/sbt "show version") if [ $? == -1 ] ;then echo -e "You need sbt installed and available on your path." diff --git a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala index 8247c1ebc5..2d8623392e 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/api/python/PythonMLLibAPI.scala @@ -197,6 +197,7 @@ class PythonMLLibAPI extends Serializable { return ret } + /** Unpack a Rating object from an array of bytes */ private def unpackRating(ratingBytes: Array[Byte]): Rating = { val bb = ByteBuffer.wrap(ratingBytes) bb.order(ByteOrder.nativeOrder()) @@ -206,6 +207,35 @@ class PythonMLLibAPI extends Serializable { return new Rating(user, product, rating) } + /** Unpack a tuple of Ints from an array of bytes */ + private[spark] def unpackTuple(tupleBytes: Array[Byte]): (Int, Int) = { + val bb = ByteBuffer.wrap(tupleBytes) + bb.order(ByteOrder.nativeOrder()) + val v1 = bb.getInt() + val v2 = bb.getInt() + (v1, v2) + } + + /** + * Serialize a Rating object into an array of bytes. + * It can be deserialized using RatingDeserializer(). + * + * @param rate + * @return + */ + private[spark] def serializeRating(rate: Rating): Array[Byte] = { + val len = 3 + val bytes = new Array[Byte](4 + 8 * len) + val bb = ByteBuffer.wrap(bytes) + bb.order(ByteOrder.nativeOrder()) + bb.putInt(len) + val db = bb.asDoubleBuffer() + db.put(rate.user.toDouble) + db.put(rate.product.toDouble) + db.put(rate.rating) + bytes + } + /** * Java stub for Python mllib ALS.train(). This stub returns a handle * to the Java object instead of the content of the Java object. Extra care diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala index af43d89c70..443fc5de5b 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/MatrixFactorizationModel.scala @@ -19,8 +19,11 @@ package org.apache.spark.mllib.recommendation import org.apache.spark.rdd.RDD import org.apache.spark.SparkContext._ +import org.apache.spark.mllib.api.python.PythonMLLibAPI import org.jblas._ +import org.apache.spark.api.java.JavaRDD + /** * Model representing the result of matrix factorization. @@ -44,6 +47,39 @@ class MatrixFactorizationModel( userVector.dot(productVector) } - // TODO: Figure out what good bulk prediction methods would look like. + /** + * Predict the rating of many users for many products. + * The output RDD has an element per each element in the input RDD (including all duplicates) + * unless a user or product is missing in the training set. + * + * @param usersProducts RDD of (user, product) pairs. + * @return RDD of Ratings. + */ + def predict(usersProducts: RDD[(Int, Int)]): RDD[Rating] = { + val users = userFeatures.join(usersProducts).map{ + case (user, (uFeatures, product)) => (product, (user, uFeatures)) + } + users.join(productFeatures).map { + case (product, ((user, uFeatures), pFeatures)) => + val userVector = new DoubleMatrix(uFeatures) + val productVector = new DoubleMatrix(pFeatures) + Rating(user, product, userVector.dot(productVector)) + } + } + + /** + * Predict the rating of many users for many products. + * This is a Java stub for python predictAll() + * + * @param usersProductsJRDD A JavaRDD with serialized tuples (user, product) + * @return JavaRDD of serialized Rating objects. + */ + def predict(usersProductsJRDD: JavaRDD[Array[Byte]]): JavaRDD[Array[Byte]] = { + val pythonAPI = new PythonMLLibAPI() + val usersProducts = usersProductsJRDD.rdd.map(xBytes => pythonAPI.unpackTuple(xBytes)) + predict(usersProducts).map(rate => pythonAPI.serializeRating(rate)) + } + + // TODO: Figure out what other good bulk prediction methods would look like. // Probably want a way to get the top users for a product or vice-versa. } diff --git a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala index fafc5ec5f2..e683a90f57 100644 --- a/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala +++ b/mllib/src/test/scala/org/apache/spark/mllib/recommendation/ALSSuite.scala @@ -90,18 +90,34 @@ class ALSSuite extends FunSuite with BeforeAndAfterAll { testALS(50, 100, 1, 15, 0.7, 0.3) } + test("rank-1 matrices bulk") { + testALS(50, 100, 1, 15, 0.7, 0.3, false, true) + } + test("rank-2 matrices") { testALS(100, 200, 2, 15, 0.7, 0.3) } + test("rank-2 matrices bulk") { + testALS(100, 200, 2, 15, 0.7, 0.3, false, true) + } + test("rank-1 matrices implicit") { testALS(80, 160, 1, 15, 0.7, 0.4, true) } + test("rank-1 matrices implicit bulk") { + testALS(80, 160, 1, 15, 0.7, 0.4, true, true) + } + test("rank-2 matrices implicit") { testALS(100, 200, 2, 15, 0.7, 0.4, true) } + test("rank-2 matrices implicit bulk") { + testALS(100, 200, 2, 15, 0.7, 0.4, true, true) + } + /** * Test if we can correctly factorize R = U * P where U and P are of known rank. * @@ -111,9 +127,12 @@ class ALSSuite extends FunSuite with BeforeAndAfterAll { * @param iterations number of iterations to run * @param samplingRate what fraction of the user-product pairs are known * @param matchThreshold max difference allowed to consider a predicted rating correct + * @param implicitPrefs flag to test implicit feedback + * @param bulkPredict flag to test bulk prediciton */ def testALS(users: Int, products: Int, features: Int, iterations: Int, - samplingRate: Double, matchThreshold: Double, implicitPrefs: Boolean = false) + samplingRate: Double, matchThreshold: Double, implicitPrefs: Boolean = false, + bulkPredict: Boolean = false) { val (sampledRatings, trueRatings, truePrefs) = ALSSuite.generateRatings(users, products, features, samplingRate, implicitPrefs) @@ -130,7 +149,17 @@ class ALSSuite extends FunSuite with BeforeAndAfterAll { for ((p, vec) <- model.productFeatures.collect(); i <- 0 until features) { predictedP.put(p, i, vec(i)) } - val predictedRatings = predictedU.mmul(predictedP.transpose) + val predictedRatings = bulkPredict match { + case false => predictedU.mmul(predictedP.transpose) + case true => + val allRatings = new DoubleMatrix(users, products) + val usersProducts = for (u <- 0 until users; p <- 0 until products) yield (u, p) + val userProductsRDD = sc.parallelize(usersProducts) + model.predict(userProductsRDD).collect().foreach { elem => + allRatings.put(elem.user, elem.product, elem.rating) + } + allRatings + } if (!implicitPrefs) { for (u <- 0 until users; p <- 0 until products) { @@ -87,12 +87,17 @@ <modules> <module>core</module> <module>bagel</module> - <module>examples</module> <module>mllib</module> <module>tools</module> <module>streaming</module> <module>repl</module> <module>assembly</module> + <module>external/twitter</module> + <module>external/kafka</module> + <module>external/flume</module> + <module>external/zeromq</module> + <module>external/mqtt</module> + <module>examples</module> </modules> <properties> @@ -141,15 +146,9 @@ </snapshots> </repository> <repository> - <id>mqtt-repo</id> - <name>MQTT Repository</name> - <url>https://repo.eclipse.org/content/repositories/paho-releases</url> - <releases> - <enabled>true</enabled> - </releases> - <snapshots> - <enabled>false</enabled> - </snapshots> + <id>cloudera-repo</id> + <name>Cloudera Repository</name> + <url>https://repository.cloudera.com/artifactory/cloudera-repos</url> </repository> </repositories> @@ -260,7 +259,7 @@ </dependency> <dependency> <groupId>${akka.group}</groupId> - <artifactId>akka-zeromq_${scala.binary.version}</artifactId> + <artifactId>akka-testkit_${scala.binary.version}</artifactId> <version>${akka.version}</version> <exclusions> <exclusion> @@ -358,6 +357,12 @@ <scope>test</scope> </dependency> <dependency> + <groupId>org.mockito</groupId> + <artifactId>mockito-all</artifactId> + <scope>test</scope> + <version>1.8.5</version> + </dependency> + <dependency> <groupId>commons-io</groupId> <artifactId>commons-io</artifactId> <version>2.4</version> diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 051e5105f3..f794918d22 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -50,9 +50,6 @@ object SparkBuild extends Build { lazy val repl = Project("repl", file("repl"), settings = replSettings) .dependsOn(core, bagel, mllib) - lazy val examples = Project("examples", file("examples"), settings = examplesSettings) - .dependsOn(core, mllib, bagel, streaming) - lazy val tools = Project("tools", file("tools"), settings = toolsSettings) dependsOn(core) dependsOn(streaming) lazy val bagel = Project("bagel", file("bagel"), settings = bagelSettings) dependsOn(core) @@ -93,10 +90,31 @@ object SparkBuild extends Build { lazy val maybeYarn = if (isYarnEnabled) Seq[ClasspathDependency](if (isNewHadoop) yarn else yarnAlpha) else Seq[ClasspathDependency]() lazy val maybeYarnRef = if (isYarnEnabled) Seq[ProjectReference](if (isNewHadoop) yarn else yarnAlpha) else Seq[ProjectReference]() + lazy val externalTwitter = Project("external-twitter", file("external/twitter"), settings = twitterSettings) + .dependsOn(streaming % "compile->compile;test->test") + + lazy val externalKafka = Project("external-kafka", file("external/kafka"), settings = kafkaSettings) + .dependsOn(streaming % "compile->compile;test->test") + + lazy val externalFlume = Project("external-flume", file("external/flume"), settings = flumeSettings) + .dependsOn(streaming % "compile->compile;test->test") + + lazy val externalZeromq = Project("external-zeromq", file("external/zeromq"), settings = zeromqSettings) + .dependsOn(streaming % "compile->compile;test->test") + + lazy val externalMqtt = Project("external-mqtt", file("external/mqtt"), settings = mqttSettings) + .dependsOn(streaming % "compile->compile;test->test") + + lazy val allExternal = Seq[ClasspathDependency](externalTwitter, externalKafka, externalFlume, externalZeromq, externalMqtt) + lazy val allExternalRefs = Seq[ProjectReference](externalTwitter, externalKafka, externalFlume, externalZeromq, externalMqtt) + + lazy val examples = Project("examples", file("examples"), settings = examplesSettings) + .dependsOn(core, mllib, bagel, streaming, externalTwitter) dependsOn(allExternal: _*) + // Everything except assembly, tools and examples belong to packageProjects lazy val packageProjects = Seq[ProjectReference](core, repl, bagel, streaming, mllib) ++ maybeYarnRef - lazy val allProjects = packageProjects ++ Seq[ProjectReference](examples, tools, assemblyProj) + lazy val allProjects = packageProjects ++ allExternalRefs ++ Seq[ProjectReference](examples, tools, assemblyProj) def sharedSettings = Defaults.defaultSettings ++ Seq( organization := "org.apache.spark", @@ -125,7 +143,7 @@ object SparkBuild extends Build { // also check the local Maven repository ~/.m2 resolvers ++= Seq(Resolver.file("Local Maven Repo", file(Path.userHome + "/.m2/repository"))), - // For Sonatype publishing + // For Sonatype publishing resolvers ++= Seq("sonatype-snapshots" at "https://oss.sonatype.org/content/repositories/snapshots", "sonatype-staging" at "https://oss.sonatype.org/service/local/staging/deploy/maven2/"), @@ -167,7 +185,7 @@ object SparkBuild extends Build { </issueManagement> ), -/* + /* publishTo <<= version { (v: String) => val nexus = "https://oss.sonatype.org/" if (v.trim.endsWith("SNAPSHOT")) @@ -176,8 +194,7 @@ object SparkBuild extends Build { Some("sonatype-staging" at nexus + "service/local/staging/deploy/maven2") }, -*/ - + */ libraryDependencies ++= Seq( "io.netty" % "netty-all" % "4.0.13.Final", @@ -233,6 +250,7 @@ object SparkBuild extends Build { "org.ow2.asm" % "asm" % "4.0", "org.spark-project.akka" %% "akka-remote" % "2.2.3-shaded-protobuf" excludeAll(excludeNetty), "org.spark-project.akka" %% "akka-slf4j" % "2.2.3-shaded-protobuf" excludeAll(excludeNetty), + "org.spark-project.akka" %% "akka-testkit" % "2.2.3-shaded-protobuf" % "test", "net.liftweb" %% "lift-json" % "2.5.1" excludeAll(excludeNetty), "it.unimi.dsi" % "fastutil" % "6.4.4", "colt" % "colt" % "1.2.0", @@ -266,7 +284,6 @@ object SparkBuild extends Build { libraryDependencies <+= scalaVersion(v => "org.scala-lang" % "scala-reflect" % v ) ) - def examplesSettings = sharedSettings ++ Seq( name := "spark-examples", libraryDependencies ++= Seq( @@ -303,22 +320,8 @@ object SparkBuild extends Build { def streamingSettings = sharedSettings ++ Seq( name := "spark-streaming", - resolvers ++= Seq( - "Eclipse Repository" at "https://repo.eclipse.org/content/repositories/paho-releases/", - "Apache repo" at "https://repository.apache.org/content/repositories/releases" - ), - libraryDependencies ++= Seq( - "org.apache.flume" % "flume-ng-sdk" % "1.2.0" % "compile" excludeAll(excludeNetty, excludeSnappy), - "com.sksamuel.kafka" %% "kafka" % "0.8.0-beta1" - exclude("com.sun.jdmk", "jmxtools") - exclude("com.sun.jmx", "jmxri") - exclude("net.sf.jopt-simple", "jopt-simple") - excludeAll(excludeNetty), - "org.eclipse.paho" % "mqtt-client" % "0.4.0", - "com.github.sgroschupf" % "zkclient" % "0.1" excludeAll(excludeNetty), - "org.twitter4j" % "twitter4j-stream" % "3.0.3" excludeAll(excludeNetty), - "org.spark-project.akka" %% "akka-zeromq" % "2.2.3-shaded-protobuf" excludeAll(excludeNetty) + "commons-io" % "commons-io" % "2.4" ) ) @@ -352,8 +355,8 @@ object SparkBuild extends Build { def yarnEnabledSettings = Seq( libraryDependencies ++= Seq( // Exclude rule required for all ? - "org.apache.hadoop" % "hadoop-client" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib), - "org.apache.hadoop" % "hadoop-yarn-api" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib), + "org.apache.hadoop" % "hadoop-client" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib), + "org.apache.hadoop" % "hadoop-yarn-api" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib), "org.apache.hadoop" % "hadoop-yarn-common" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib), "org.apache.hadoop" % "hadoop-yarn-client" % hadoopVersion excludeAll(excludeJackson, excludeNetty, excludeAsm, excludeCglib) ) @@ -378,4 +381,43 @@ object SparkBuild extends Build { case _ => MergeStrategy.first } ) + + def twitterSettings() = sharedSettings ++ Seq( + name := "spark-streaming-twitter", + libraryDependencies ++= Seq( + "org.twitter4j" % "twitter4j-stream" % "3.0.3" excludeAll(excludeNetty) + ) + ) + + def kafkaSettings() = sharedSettings ++ Seq( + name := "spark-streaming-kafka", + libraryDependencies ++= Seq( + "com.github.sgroschupf" % "zkclient" % "0.1" excludeAll(excludeNetty), + "com.sksamuel.kafka" %% "kafka" % "0.8.0-beta1" + exclude("com.sun.jdmk", "jmxtools") + exclude("com.sun.jmx", "jmxri") + exclude("net.sf.jopt-simple", "jopt-simple") + excludeAll(excludeNetty) + ) + ) + + def flumeSettings() = sharedSettings ++ Seq( + name := "spark-streaming-flume", + libraryDependencies ++= Seq( + "org.apache.flume" % "flume-ng-sdk" % "1.2.0" % "compile" excludeAll(excludeNetty, excludeSnappy) + ) + ) + + def zeromqSettings() = sharedSettings ++ Seq( + name := "spark-streaming-zeromq", + libraryDependencies ++= Seq( + "org.spark-project.akka" %% "akka-zeromq" % "2.2.3-shaded-protobuf" excludeAll(excludeNetty) + ) + ) + + def mqttSettings() = streamingSettings ++ Seq( + name := "spark-streaming-mqtt", + resolvers ++= Seq("Eclipse Repo" at "https://repo.eclipse.org/content/repositories/paho-releases/"), + libraryDependencies ++= Seq("org.eclipse.paho" % "mqtt-client" % "0.4.0") + ) } diff --git a/python/pyspark/mllib/_common.py b/python/pyspark/mllib/_common.py index e74ba0fabc..769d88dfb9 100644 --- a/python/pyspark/mllib/_common.py +++ b/python/pyspark/mllib/_common.py @@ -18,6 +18,9 @@ from numpy import ndarray, copyto, float64, int64, int32, ones, array_equal, array, dot, shape from pyspark import SparkContext +from pyspark.serializers import Serializer +import struct + # Double vector format: # # [8-byte 1] [8-byte length] [length*8 bytes of data] @@ -213,6 +216,28 @@ def _serialize_rating(r): intpart[0], intpart[1], doublepart[0] = r return ba +class RatingDeserializer(Serializer): + def loads(self, stream): + length = struct.unpack("!i", stream.read(4))[0] + ba = stream.read(length) + res = ndarray(shape=(3, ), buffer=ba, dtype="float64", offset=4) + return int(res[0]), int(res[1]), res[2] + + def load_stream(self, stream): + while True: + try: + yield self.loads(stream) + except struct.error: + return + except EOFError: + return + +def _serialize_tuple(t): + ba = bytearray(8) + intpart = ndarray(shape=[2], buffer=ba, dtype=int32) + intpart[0], intpart[1] = t + return ba + def _test(): import doctest globs = globals().copy() diff --git a/python/pyspark/mllib/recommendation.py b/python/pyspark/mllib/recommendation.py index 14d06cba21..0eeb5bb66b 100644 --- a/python/pyspark/mllib/recommendation.py +++ b/python/pyspark/mllib/recommendation.py @@ -20,7 +20,9 @@ from pyspark.mllib._common import \ _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \ _serialize_double_matrix, _deserialize_double_matrix, \ _serialize_double_vector, _deserialize_double_vector, \ - _get_initial_weights, _serialize_rating, _regression_train_wrapper + _get_initial_weights, _serialize_rating, _regression_train_wrapper, \ + _serialize_tuple, RatingDeserializer +from pyspark.rdd import RDD class MatrixFactorizationModel(object): """A matrix factorisation model trained by regularized alternating @@ -33,6 +35,9 @@ class MatrixFactorizationModel(object): >>> model = ALS.trainImplicit(sc, ratings, 1) >>> model.predict(2,2) is not None True + >>> testset = sc.parallelize([(1, 2), (1, 1)]) + >>> model.predictAll(testset).count == 2 + True """ def __init__(self, sc, java_model): @@ -45,6 +50,11 @@ class MatrixFactorizationModel(object): def predict(self, user, product): return self._java_model.predict(user, product) + def predictAll(self, usersProducts): + usersProductsJRDD = _get_unmangled_rdd(usersProducts, _serialize_tuple) + return RDD(self._java_model.predict(usersProductsJRDD._jrdd), + self._context, RatingDeserializer()) + class ALS(object): @classmethod def train(cls, sc, ratings, rank, iterations=5, lambda_=0.01, blocks=-1): diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkCommandLine.scala b/repl/src/main/scala/org/apache/spark/repl/SparkCommandLine.scala new file mode 100644 index 0000000000..05816941b5 --- /dev/null +++ b/repl/src/main/scala/org/apache/spark/repl/SparkCommandLine.scala @@ -0,0 +1,37 @@ +/* + * 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.repl + +import scala.tools.nsc.{Settings, CompilerCommand} +import scala.Predef._ + +/** + * Command class enabling Spark-specific command line options (provided by + * <i>org.apache.spark.repl.SparkRunnerSettings</i>). + */ +class SparkCommandLine(args: List[String], override val settings: Settings) + extends CompilerCommand(args, settings) { + + def this(args: List[String], error: String => Unit) { + this(args, new SparkRunnerSettings(error)) + } + + def this(args: List[String]) { + this(args, str => Console.println("Error: " + str)) + } +} diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala index 7e54421b52..87d94d51be 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala @@ -835,7 +835,7 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter, // runs :load `file` on any files passed via -i def loadFiles(settings: Settings) = settings match { - case settings: GenericRunnerSettings => + case settings: SparkRunnerSettings => for (filename <- settings.loadfiles.value) { val cmd = ":load " + filename command(cmd) @@ -902,7 +902,6 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter, addThunk(printWelcome()) addThunk(initializeSpark()) - loadFiles(settings) // it is broken on startup; go ahead and exit if (intp.reporter.hasErrors) return false @@ -922,6 +921,8 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter, } // printWelcome() + loadFiles(settings) + try loop() catch AbstractOrMissingHandler() finally closeInterpreter() @@ -957,7 +958,7 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter, /** process command-line arguments and do as they request */ def process(args: Array[String]): Boolean = { - val command = new CommandLine(args.toList, echo) + val command = new SparkCommandLine(args.toList, msg => echo(msg)) def neededHelp(): String = (if (command.settings.help.value) command.usageMsg + "\n" else "") + (if (command.settings.Xhelp.value) command.xusageMsg + "\n" else "") diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkRunnerSettings.scala b/repl/src/main/scala/org/apache/spark/repl/SparkRunnerSettings.scala new file mode 100644 index 0000000000..7fd5fbb424 --- /dev/null +++ b/repl/src/main/scala/org/apache/spark/repl/SparkRunnerSettings.scala @@ -0,0 +1,32 @@ +/* + * 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.repl + +import scala.tools.nsc.Settings + +/** + * <i>scala.tools.nsc.Settings</i> implementation adding Spark-specific REPL + * command line options. + */ +class SparkRunnerSettings(error: String => Unit) extends Settings(error){ + + val loadfiles = MultiStringSetting( + "-i", + "file", + "load a file (assumes the code is given interactively)") +} @@ -1,4 +1,22 @@ #!/bin/bash + +# +# 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. +# + # This script launches sbt for this project. If present it uses the system # version of sbt. If there is no system version of sbt it attempts to download # sbt locally. @@ -7,37 +25,26 @@ URL1=http://typesafe.artifactoryonline.com/typesafe/ivy-releases/org.scala-sbt/s URL2=http://repo.typesafe.com/typesafe/ivy-releases/org.scala-sbt/sbt-launch/${SBT_VERSION}/sbt-launch.jar JAR=sbt/sbt-launch-${SBT_VERSION}.jar -printf "Checking for system sbt [" -if hash sbt 2>/dev/null; then - printf "FOUND]\n" - # Use System SBT - sbt "$@" -else - printf "NOT FOUND]\n" - # Download sbt or use already downloaded - if [ ! -d .sbtlib ]; then - mkdir .sbtlib - fi - if [ ! -f ${JAR} ]; then - # Download - printf "Attempting to fetch sbt\n" - if hash curl 2>/dev/null; then - curl --progress-bar ${URL1} > ${JAR} || curl --progress-bar ${URL2} > ${JAR} - elif hash wget 2>/dev/null; then - wget --progress=bar ${URL1} -O ${JAR} || wget --progress=bar ${URL2} -O ${JAR} - else - printf "You do not have curl or wget installed, please install sbt manually from http://www.scala-sbt.org/\n" - exit -1 - fi - fi - if [ ! -f ${JAR} ]; then - # We failed to download - printf "Our attempt to download sbt locally to ${JAR} failed. Please install sbt manually from http://www.scala-sbt.org/\n" +# Download sbt launch jar if it hasn't been downloaded yet +if [ ! -f ${JAR} ]; then + # Download + printf "Attempting to fetch sbt\n" + if hash curl 2>/dev/null; then + curl --progress-bar ${URL1} > ${JAR} || curl --progress-bar ${URL2} > ${JAR} + elif hash wget 2>/dev/null; then + wget --progress=bar ${URL1} -O ${JAR} || wget --progress=bar ${URL2} -O ${JAR} + else + printf "You do not have curl or wget installed, please install sbt manually from http://www.scala-sbt.org/\n" exit -1 fi - printf "Launching sbt from ${JAR}\n" - java \ - -Xmx1200m -XX:MaxPermSize=350m -XX:ReservedCodeCacheSize=256m \ - -jar ${JAR} \ - "$@" fi +if [ ! -f ${JAR} ]; then + # We failed to download + printf "Our attempt to download sbt locally to ${JAR} failed. Please install sbt manually from http://www.scala-sbt.org/\n" + exit -1 +fi +printf "Launching sbt from ${JAR}\n" +java \ + -Xmx1200m -XX:MaxPermSize=350m -XX:ReservedCodeCacheSize=256m \ + -jar ${JAR} \ + "$@" diff --git a/streaming/pom.xml b/streaming/pom.xml index e3b6fee9b2..459756912d 100644 --- a/streaming/pom.xml +++ b/streaming/pom.xml @@ -61,59 +61,10 @@ <version>1.9.11</version> </dependency> <dependency> - <groupId>com.sksamuel.kafka</groupId> - <artifactId>kafka_${scala.binary.version}</artifactId> - <version>0.8.0-beta1</version> - <exclusions> - <exclusion> - <groupId>com.sun.jmx</groupId> - <artifactId>jmxri</artifactId> - </exclusion> - <exclusion> - <groupId>com.sun.jdmk</groupId> - <artifactId>jmxtools</artifactId> - </exclusion> - <exclusion> - <groupId>net.sf.jopt-simple</groupId> - <artifactId>jopt-simple</artifactId> - </exclusion> - </exclusions> - </dependency> - <dependency> - <groupId>org.apache.flume</groupId> - <artifactId>flume-ng-sdk</artifactId> - <version>1.2.0</version> - <exclusions> - <exclusion> - <groupId>org.jboss.netty</groupId> - <artifactId>netty</artifactId> - </exclusion> - <exclusion> - <groupId>org.xerial.snappy</groupId> - <artifactId>*</artifactId> - </exclusion> - </exclusions> - </dependency> - <dependency> - <groupId>org.twitter4j</groupId> - <artifactId>twitter4j-stream</artifactId> - <version>3.0.3</version> - <exclusions> - <exclusion> - <groupId>org.jboss.netty</groupId> - <artifactId>netty</artifactId> - </exclusion> - </exclusions> - </dependency> - <dependency> <groupId>org.scala-lang</groupId> <artifactId>scala-library</artifactId> </dependency> <dependency> - <groupId>${akka.group}</groupId> - <artifactId>akka-zeromq_${scala.binary.version}</artifactId> - </dependency> - <dependency> <groupId>org.scalatest</groupId> <artifactId>scalatest_${scala.binary.version}</artifactId> <scope>test</scope> @@ -137,11 +88,6 @@ <groupId>commons-io</groupId> <artifactId>commons-io</artifactId> </dependency> - <dependency> - <groupId>org.eclipse.paho</groupId> - <artifactId>mqtt-client</artifactId> - <version>0.4.0</version> - </dependency> </dependencies> <build> <outputDirectory>target/scala-${scala.binary.version}/classes</outputDirectory> @@ -151,6 +97,35 @@ <groupId>org.scalatest</groupId> <artifactId>scalatest-maven-plugin</artifactId> </plugin> + + <!-- + This plugin forces the generation of jar containing streaming test classes, + so that the tests classes of external modules can use them. The two execution profiles + are necessary - first one for 'mvn package', second one for 'mvn compile'. Ideally, + 'mvn compile' should not compile test classes and therefore should not need this. + However, an open Maven bug (http://jira.codehaus.org/browse/MNG-3559) + causes the compilation to fail if streaming test-jar is not generated. Hence, the + second execution profile for 'mvn compile'. + --> + <plugin> + <groupId>org.apache.maven.plugins</groupId> + <artifactId>maven-jar-plugin</artifactId> + <version>2.2</version> + <executions> + <execution> + <goals> + <goal>test-jar</goal> + </goals> + </execution> + <execution> + <id>test-jar-on-compile</id> + <phase>compile</phase> + <goals> + <goal>test-jar</goal> + </goals> + </execution> + </executions> + </plugin> </plugins> </build> </project> diff --git a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala index b3a7cf08b9..693cb7fc30 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala @@ -17,23 +17,6 @@ package org.apache.spark.streaming -import akka.actor.Props -import akka.actor.SupervisorStrategy -import akka.zeromq.Subscribe -import akka.util.ByteString - -import org.apache.spark.streaming.dstream._ -import org.apache.spark.streaming.scheduler.StreamingListener - -import org.apache.spark._ -import org.apache.spark.rdd.RDD -import org.apache.spark.streaming.receivers.ReceiverSupervisorStrategy -import org.apache.spark.streaming.receivers.ZeroMQReceiver -import org.apache.spark.storage.StorageLevel -import org.apache.spark.util.MetadataCleaner -import org.apache.spark.streaming.receivers.ActorReceiver -import org.apache.spark.streaming.scheduler.{JobScheduler, NetworkInputTracker} - import scala.collection.mutable.Queue import scala.collection.Map import scala.reflect.ClassTag @@ -41,14 +24,21 @@ import scala.reflect.ClassTag import java.io.InputStream import java.util.concurrent.atomic.AtomicInteger +import akka.actor.Props +import akka.actor.SupervisorStrategy import org.apache.hadoop.io.LongWritable import org.apache.hadoop.io.Text import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} import org.apache.hadoop.mapreduce.lib.input.TextInputFormat import org.apache.hadoop.fs.Path -import twitter4j.Status -import twitter4j.auth.Authorization +import org.apache.spark._ +import org.apache.spark.rdd.RDD +import org.apache.spark.storage.StorageLevel +import org.apache.spark.util.MetadataCleaner +import org.apache.spark.streaming.dstream._ +import org.apache.spark.streaming.receivers._ +import org.apache.spark.streaming.scheduler._ /** * A StreamingContext is the main entry point for Spark Streaming functionality. Besides the basic @@ -238,74 +228,6 @@ class StreamingContext private ( } /** - * Create an input stream that receives messages pushed by a zeromq publisher. - * @param publisherUrl Url of remote zeromq publisher - * @param subscribe topic to subscribe to - * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic - * and each frame has sequence of byte thus it needs the converter - * (which might be deserializer of bytes) to translate from sequence - * of sequence of bytes, where sequence refer to a frame - * and sub sequence refer to its payload. - * @param storageLevel RDD storage level. Defaults to memory-only. - */ - def zeroMQStream[T: ClassTag]( - publisherUrl:String, - subscribe: Subscribe, - bytesToObjects: Seq[ByteString] ⇒ Iterator[T], - storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2, - supervisorStrategy: SupervisorStrategy = ReceiverSupervisorStrategy.defaultStrategy - ): DStream[T] = { - actorStream(Props(new ZeroMQReceiver(publisherUrl, subscribe, bytesToObjects)), - "ZeroMQReceiver", storageLevel, supervisorStrategy) - } - - /** - * Create an input stream that pulls messages from a Kafka Broker. - * @param zkQuorum Zookeper quorum (hostname:port,hostname:port,..). - * @param groupId The group id for this consumer. - * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed - * in its own thread. - * @param storageLevel Storage level to use for storing the received objects - * (default: StorageLevel.MEMORY_AND_DISK_SER_2) - */ - def kafkaStream( - zkQuorum: String, - groupId: String, - topics: Map[String, Int], - storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2 - ): DStream[(String, String)] = { - val kafkaParams = Map[String, String]( - "zookeeper.connect" -> zkQuorum, "group.id" -> groupId, - "zookeeper.connection.timeout.ms" -> "10000") - kafkaStream[String, String, kafka.serializer.StringDecoder, kafka.serializer.StringDecoder]( - kafkaParams, - topics, - storageLevel) - } - - /** - * Create an input stream that pulls messages from a Kafka Broker. - * @param kafkaParams Map of kafka configuration paramaters. - * See: http://kafka.apache.org/configuration.html - * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed - * in its own thread. - * @param storageLevel Storage level to use for storing the received objects - */ - def kafkaStream[ - K: ClassTag, - V: ClassTag, - U <: kafka.serializer.Decoder[_]: Manifest, - T <: kafka.serializer.Decoder[_]: Manifest]( - kafkaParams: Map[String, String], - topics: Map[String, Int], - storageLevel: StorageLevel - ): DStream[(K, V)] = { - val inputStream = new KafkaInputDStream[K, V, U, T](this, kafkaParams, topics, storageLevel) - registerInputStream(inputStream) - inputStream - } - - /** * Create a input stream from TCP source hostname:port. Data is received using * a TCP socket and the receive bytes is interpreted as UTF8 encoded `\n` delimited * lines. @@ -344,22 +266,6 @@ class StreamingContext private ( } /** - * Create a input stream from a Flume source. - * @param hostname Hostname of the slave machine to which the flume data will be sent - * @param port Port of the slave machine to which the flume data will be sent - * @param storageLevel Storage level to use for storing the received objects - */ - def flumeStream ( - hostname: String, - port: Int, - storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2 - ): DStream[SparkFlumeEvent] = { - val inputStream = new FlumeInputDStream[SparkFlumeEvent](this, hostname, port, storageLevel) - registerInputStream(inputStream) - inputStream - } - - /** * Create a input stream from network source hostname:port, where data is received * as serialized blocks (serialized using the Spark's serializer) that can be directly * pushed into the block manager without deserializing them. This is the most efficient @@ -434,24 +340,6 @@ class StreamingContext private ( } /** - * Create a input stream that returns tweets received from Twitter. - * @param twitterAuth Twitter4J authentication, or None to use Twitter4J's default OAuth - * authorization; this uses the system properties twitter4j.oauth.consumerKey, - * .consumerSecret, .accessToken and .accessTokenSecret. - * @param filters Set of filter strings to get only those tweets that match them - * @param storageLevel Storage level to use for storing the received objects - */ - def twitterStream( - twitterAuth: Option[Authorization] = None, - filters: Seq[String] = Nil, - storageLevel: StorageLevel = StorageLevel.MEMORY_AND_DISK_SER_2 - ): DStream[Status] = { - val inputStream = new TwitterInputDStream(this, twitterAuth, filters, storageLevel) - registerInputStream(inputStream) - inputStream - } - - /** * Create an input stream from a queue of RDDs. In each batch, * it will process either one or all of the RDDs returned by the queue. * @param queue Queue of RDDs @@ -484,21 +372,6 @@ class StreamingContext private ( inputStream } -/** - * Create an input stream that receives messages pushed by a mqtt publisher. - * @param brokerUrl Url of remote mqtt publisher - * @param topic topic name to subscribe to - * @param storageLevel RDD storage level. Defaults to memory-only. - */ - - def mqttStream( - brokerUrl: String, - topic: String, - storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_SER_2): DStream[String] = { - val inputStream = new MQTTInputDStream[String](this, brokerUrl, topic, storageLevel) - registerInputStream(inputStream) - inputStream - } /** * Create a unified DStream from multiple DStreams of the same type and same slide duration. */ diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala index 7dec4b3ad7..7068f32517 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala @@ -17,19 +17,16 @@ package org.apache.spark.streaming.api.java -import java.io.InputStream -import java.lang.{Integer => JInt} -import java.util.{List => JList, Map => JMap} import scala.collection.JavaConversions._ import scala.reflect.ClassTag +import java.io.InputStream +import java.lang.{Integer => JInt} +import java.util.{List => JList, Map => JMap} + import akka.actor.{Props, SupervisorStrategy} -import akka.util.ByteString -import akka.zeromq.Subscribe import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} -import twitter4j.Status -import twitter4j.auth.Authorization import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.api.java.{JavaPairRDD, JavaRDD, JavaSparkContext} @@ -37,7 +34,6 @@ import org.apache.spark.api.java.function.{Function => JFunction, Function2 => J import org.apache.spark.rdd.RDD import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming._ -import org.apache.spark.streaming.dstream._ import org.apache.spark.streaming.scheduler.StreamingListener /** @@ -141,81 +137,6 @@ class JavaStreamingContext(val ssc: StreamingContext) { val sc: JavaSparkContext = new JavaSparkContext(ssc.sc) /** - * Create an input stream that pulls messages form a Kafka Broker. - * @param zkQuorum Zookeper quorum (hostname:port,hostname:port,..). - * @param groupId The group id for this consumer. - * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed - * in its own thread. - */ - def kafkaStream( - zkQuorum: String, - groupId: String, - topics: JMap[String, JInt]) - : JavaPairDStream[String, String] = { - implicit val cmt: ClassTag[String] = - implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[String]] - ssc.kafkaStream(zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*), - StorageLevel.MEMORY_ONLY_SER_2) - - } - - /** - * Create an input stream that pulls messages form a Kafka Broker. - * @param zkQuorum Zookeper quorum (hostname:port,hostname:port,..). - * @param groupId The group id for this consumer. - * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed - * in its own thread. - * @param storageLevel RDD storage level. Defaults to memory-only - * - */ - def kafkaStream( - zkQuorum: String, - groupId: String, - topics: JMap[String, JInt], - storageLevel: StorageLevel) - : JavaPairDStream[String, String] = { - implicit val cmt: ClassTag[String] = - implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[String]] - ssc.kafkaStream(zkQuorum, groupId, Map(topics.mapValues(_.intValue()).toSeq: _*), - storageLevel) - } - - /** - * Create an input stream that pulls messages form a Kafka Broker. - * @param keyTypeClass Key type of RDD - * @param valueTypeClass value type of RDD - * @param keyDecoderClass Type of kafka key decoder - * @param valueDecoderClass Type of kafka value decoder - * @param kafkaParams Map of kafka configuration paramaters. - * See: http://kafka.apache.org/configuration.html - * @param topics Map of (topic_name -> numPartitions) to consume. Each partition is consumed - * in its own thread. - * @param storageLevel RDD storage level. Defaults to memory-only - */ - def kafkaStream[K, V, U <: kafka.serializer.Decoder[_], T <: kafka.serializer.Decoder[_]]( - keyTypeClass: Class[K], - valueTypeClass: Class[V], - keyDecoderClass: Class[U], - valueDecoderClass: Class[T], - kafkaParams: JMap[String, String], - topics: JMap[String, JInt], - storageLevel: StorageLevel) - : JavaPairDStream[K, V] = { - implicit val keyCmt: ClassTag[K] = - implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[K]] - implicit val valueCmt: ClassTag[V] = - implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[V]] - - implicit val keyCmd: Manifest[U] = implicitly[Manifest[AnyRef]].asInstanceOf[Manifest[U]] - implicit val valueCmd: Manifest[T] = implicitly[Manifest[AnyRef]].asInstanceOf[Manifest[T]] - - ssc.kafkaStream[K, V, U, T]( - kafkaParams.toMap, - Map(topics.mapValues(_.intValue()).toSeq: _*), - storageLevel) - } - - /** * Create a input stream from network source hostname:port. Data is received using * a TCP socket and the receive bytes is interpreted as UTF8 encoded \n delimited * lines. @@ -329,98 +250,6 @@ class JavaStreamingContext(val ssc: StreamingContext) { } /** - * Creates a input stream from a Flume source. - * @param hostname Hostname of the slave machine to which the flume data will be sent - * @param port Port of the slave machine to which the flume data will be sent - * @param storageLevel Storage level to use for storing the received objects - */ - def flumeStream(hostname: String, port: Int, storageLevel: StorageLevel): - JavaDStream[SparkFlumeEvent] = { - ssc.flumeStream(hostname, port, storageLevel) - } - - - /** - * Create a input stream from a Flume source. - * @param hostname Hostname of the slave machine to which the flume data will be sent - * @param port Port of the slave machine to which the flume data will be sent - */ - def flumeStream(hostname: String, port: Int): JavaDStream[SparkFlumeEvent] = { - ssc.flumeStream(hostname, port) - } - - /** - * Create a input stream that returns tweets received from Twitter. - * @param twitterAuth Twitter4J Authorization object - * @param filters Set of filter strings to get only those tweets that match them - * @param storageLevel Storage level to use for storing the received objects - */ - def twitterStream( - twitterAuth: Authorization, - filters: Array[String], - storageLevel: StorageLevel - ): JavaDStream[Status] = { - ssc.twitterStream(Some(twitterAuth), filters, storageLevel) - } - - /** - * Create a input stream that returns tweets received from Twitter using Twitter4J's default - * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, - * .consumerSecret, .accessToken and .accessTokenSecret to be set. - * @param filters Set of filter strings to get only those tweets that match them - * @param storageLevel Storage level to use for storing the received objects - */ - def twitterStream( - filters: Array[String], - storageLevel: StorageLevel - ): JavaDStream[Status] = { - ssc.twitterStream(None, filters, storageLevel) - } - - /** - * Create a input stream that returns tweets received from Twitter. - * @param twitterAuth Twitter4J Authorization - * @param filters Set of filter strings to get only those tweets that match them - */ - def twitterStream( - twitterAuth: Authorization, - filters: Array[String] - ): JavaDStream[Status] = { - ssc.twitterStream(Some(twitterAuth), filters) - } - - /** - * Create a input stream that returns tweets received from Twitter using Twitter4J's default - * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, - * .consumerSecret, .accessToken and .accessTokenSecret to be set. - * @param filters Set of filter strings to get only those tweets that match them - */ - def twitterStream( - filters: Array[String] - ): JavaDStream[Status] = { - ssc.twitterStream(None, filters) - } - - /** - * Create a input stream that returns tweets received from Twitter. - * @param twitterAuth Twitter4J Authorization - */ - def twitterStream( - twitterAuth: Authorization - ): JavaDStream[Status] = { - ssc.twitterStream(Some(twitterAuth)) - } - - /** - * Create a input stream that returns tweets received from Twitter using Twitter4J's default - * OAuth authentication; this requires the system properties twitter4j.oauth.consumerKey, - * .consumerSecret, .accessToken and .accessTokenSecret to be set. - */ - def twitterStream(): JavaDStream[Status] = { - ssc.twitterStream() - } - - /** * Create an input stream with any arbitrary user implemented actor receiver. * @param props Props object defining creation of the actor * @param name Name of the actor @@ -483,70 +312,6 @@ class JavaStreamingContext(val ssc: StreamingContext) { } /** - * Create an input stream that receives messages pushed by a zeromq publisher. - * @param publisherUrl Url of remote zeromq publisher - * @param subscribe topic to subscribe to - * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic and each frame has sequence - * of byte thus it needs the converter(which might be deserializer of bytes) - * to translate from sequence of sequence of bytes, where sequence refer to a frame - * and sub sequence refer to its payload. - * @param storageLevel Storage level to use for storing the received objects - */ - def zeroMQStream[T]( - publisherUrl:String, - subscribe: Subscribe, - bytesToObjects: Seq[ByteString] ⇒ Iterator[T], - storageLevel: StorageLevel, - supervisorStrategy: SupervisorStrategy - ): JavaDStream[T] = { - implicit val cm: ClassTag[T] = - implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] - ssc.zeroMQStream[T](publisherUrl, subscribe, bytesToObjects, storageLevel, supervisorStrategy) - } - - /** - * Create an input stream that receives messages pushed by a zeromq publisher. - * @param publisherUrl Url of remote zeromq publisher - * @param subscribe topic to subscribe to - * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic and each frame has sequence - * of byte thus it needs the converter(which might be deserializer of bytes) - * to translate from sequence of sequence of bytes, where sequence refer to a frame - * and sub sequence refer to its payload. - * @param storageLevel RDD storage level. Defaults to memory-only. - */ - def zeroMQStream[T]( - publisherUrl:String, - subscribe: Subscribe, - bytesToObjects: JFunction[Array[Array[Byte]], java.lang.Iterable[T]], - storageLevel: StorageLevel - ): JavaDStream[T] = { - implicit val cm: ClassTag[T] = - implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] - def fn(x: Seq[ByteString]) = bytesToObjects.apply(x.map(_.toArray).toArray).toIterator - ssc.zeroMQStream[T](publisherUrl, subscribe, fn, storageLevel) - } - - /** - * Create an input stream that receives messages pushed by a zeromq publisher. - * @param publisherUrl Url of remote zeromq publisher - * @param subscribe topic to subscribe to - * @param bytesToObjects A zeroMQ stream publishes sequence of frames for each topic and each frame has sequence - * of byte thus it needs the converter(which might be deserializer of bytes) - * to translate from sequence of sequence of bytes, where sequence refer to a frame - * and sub sequence refer to its payload. - */ - def zeroMQStream[T]( - publisherUrl:String, - subscribe: Subscribe, - bytesToObjects: JFunction[Array[Array[Byte]], java.lang.Iterable[T]] - ): JavaDStream[T] = { - implicit val cm: ClassTag[T] = - implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] - def fn(x: Seq[ByteString]) = bytesToObjects.apply(x.map(_.toArray).toArray).toIterator - ssc.zeroMQStream[T](publisherUrl, subscribe, fn) - } - - /** * Registers an output stream that will be computed every interval */ def registerOutputStream(outputStream: JavaDStreamLike[_, _, _]) { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala index 27d474c0a0..d41f726f83 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala @@ -175,7 +175,7 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging private class NetworkReceiverActor extends Actor { logInfo("Attempting to register with tracker") val ip = env.conf.get("spark.driver.host", "localhost") - val port = env.conf.get("spark.driver.port", "7077").toInt + val port = env.conf.getInt("spark.driver.port", 7077) val url = "akka.tcp://spark@%s:%s/user/NetworkInputTracker".format(ip, port) val tracker = env.actorSystem.actorSelection(url) val timeout = 5.seconds @@ -212,7 +212,7 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging case class Block(id: BlockId, buffer: ArrayBuffer[T], metadata: Any = null) val clock = new SystemClock() - val blockInterval = env.conf.get("spark.streaming.blockInterval", "200").toLong + val blockInterval = env.conf.getLong("spark.streaming.blockInterval", 200) val blockIntervalTimer = new RecurringTimer(clock, blockInterval, updateCurrentBuffer) val blockStorageLevel = storageLevel val blocksForPushing = new ArrayBlockingQueue[Block](1000) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala index 7341bfbc99..c8ee93bf5b 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala @@ -38,4 +38,4 @@ class Job(val time: Time, func: () => _) { } override def toString = id -}
\ No newline at end of file +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala index 5f8be93a98..3c624e8199 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala @@ -104,7 +104,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { // or if the property is defined set it to that time if (clock.isInstanceOf[ManualClock]) { val lastTime = ssc.initialCheckpoint.checkpointTime.milliseconds - val jumpTime = ssc.sc.conf.get("spark.streaming.manualClock.jump", "0").toLong + val jumpTime = ssc.sc.conf.getLong("spark.streaming.manualClock.jump", 0) clock.asInstanceOf[ManualClock].setTime(lastTime + jumpTime) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala index 9304fc1a93..30c070c274 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala @@ -31,7 +31,7 @@ private[streaming] class JobScheduler(val ssc: StreamingContext) extends Logging { val jobSets = new ConcurrentHashMap[Time, JobSet] - val numConcurrentJobs = ssc.conf.get("spark.streaming.concurrentJobs", "1").toInt + val numConcurrentJobs = ssc.conf.getInt("spark.streaming.concurrentJobs", 1) val executor = Executors.newFixedThreadPool(numConcurrentJobs) val generator = new JobGenerator(this) val listenerBus = new StreamingListenerBus() diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala index abff55d77c..75f7244643 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/NetworkInputTracker.scala @@ -160,7 +160,9 @@ class NetworkInputTracker( } // Run the dummy Spark job to ensure that all slaves have registered. // This avoids all the receivers to be scheduled on the same node. - ssc.sparkContext.makeRDD(1 to 50, 50).map(x => (x, 1)).reduceByKey(_ + _, 20).collect() + if (!ssc.sparkContext.isLocal) { + ssc.sparkContext.makeRDD(1 to 50, 50).map(x => (x, 1)).reduceByKey(_ + _, 20).collect() + } // Distribute the receivers and start them ssc.sparkContext.runJob(tempRDD, startReceiver) 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 d53d433693..0d2145da9a 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java @@ -17,24 +17,19 @@ package org.apache.spark.streaming; -import com.google.common.base.Optional; -import com.google.common.collect.Lists; -import com.google.common.collect.Maps; -import com.google.common.io.Files; - -import kafka.serializer.StringDecoder; +import scala.Tuple2; -import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat; -import org.apache.spark.SparkConf; -import org.apache.spark.streaming.api.java.JavaDStreamLike; import org.junit.After; import org.junit.Assert; -import org.junit.Before; import org.junit.Test; +import java.io.*; +import java.util.*; -import scala.Tuple2; -import twitter4j.Status; +import com.google.common.base.Optional; +import com.google.common.collect.Lists; +import com.google.common.io.Files; +import org.apache.spark.SparkConf; import org.apache.spark.HashPartitioner; import org.apache.spark.api.java.JavaPairRDD; import org.apache.spark.api.java.JavaRDD; @@ -44,42 +39,11 @@ import org.apache.spark.storage.StorageLevel; import org.apache.spark.streaming.api.java.JavaDStream; import org.apache.spark.streaming.api.java.JavaPairDStream; import org.apache.spark.streaming.api.java.JavaStreamingContext; -import org.apache.spark.streaming.dstream.SparkFlumeEvent; -import org.apache.spark.streaming.JavaTestUtils; -import org.apache.spark.streaming.JavaCheckpointTestUtils; - -import java.io.*; -import java.util.*; - -import akka.actor.Props; -import akka.zeromq.Subscribe; - // 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 JavaStreamingContext ssc; - - @Before - public void setUp() { - SparkConf conf = new SparkConf() - .setMaster("local[2]") - .setAppName("test") - .set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock"); - ssc = new JavaStreamingContext(conf, new Duration(1000)); - ssc.checkpoint("checkpoint"); - } - - @After - public void tearDown() { - ssc.stop(); - ssc = null; - - // To avoid Akka rebinding to the same port, since it doesn't unbind immediately on shutdown - System.clearProperty("spark.driver.port"); - } - +public class JavaAPISuite extends LocalJavaStreamingContext implements Serializable { @Test public void testCount() { List<List<Integer>> inputData = Arrays.asList( @@ -1601,26 +1565,6 @@ public class JavaAPISuite implements Serializable { // Java arguments and assign it to a JavaDStream without producing type errors. Testing of the // InputStream functionality is deferred to the existing Scala tests. @Test - public void testKafkaStream() { - HashMap<String, Integer> topics = Maps.newHashMap(); - JavaPairDStream<String, String> test1 = ssc.kafkaStream("localhost:12345", "group", topics); - JavaPairDStream<String, String> test2 = ssc.kafkaStream("localhost:12345", "group", topics, - StorageLevel.MEMORY_AND_DISK()); - - HashMap<String, String> kafkaParams = Maps.newHashMap(); - kafkaParams.put("zookeeper.connect","localhost:12345"); - kafkaParams.put("group.id","consumer-group"); - JavaPairDStream<String, String> test3 = ssc.kafkaStream( - String.class, - String.class, - StringDecoder.class, - StringDecoder.class, - kafkaParams, - topics, - StorageLevel.MEMORY_AND_DISK()); - } - - @Test public void testSocketTextStream() { JavaDStream<String> test = ssc.socketTextStream("localhost", 12345); } @@ -1658,36 +1602,4 @@ public class JavaAPISuite implements Serializable { public void testRawSocketStream() { JavaDStream<String> test = ssc.rawSocketStream("localhost", 12345); } - - @Test - public void testFlumeStream() { - JavaDStream<SparkFlumeEvent> test = ssc.flumeStream("localhost", 12345, StorageLevel.MEMORY_ONLY()); - } - - @Test - public void testFileStream() { - JavaPairDStream<String, String> foo = - ssc.<String, String, SequenceFileInputFormat<String,String>>fileStream("/tmp/foo"); - } - - @Test - public void testTwitterStream() { - String[] filters = new String[] { "good", "bad", "ugly" }; - JavaDStream<Status> test = ssc.twitterStream(filters, StorageLevel.MEMORY_ONLY()); - } - - @Test - public void testActorStream() { - JavaDStream<String> test = ssc.actorStream((Props)null, "TestActor", StorageLevel.MEMORY_ONLY()); - } - - @Test - public void testZeroMQStream() { - JavaDStream<String> test = ssc.zeroMQStream("url", (Subscribe) null, new Function<byte[][], Iterable<String>>() { - @Override - public Iterable<String> call(byte[][] b) throws Exception { - return null; - } - }); - } } diff --git a/streaming/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java b/streaming/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java new file mode 100644 index 0000000000..34bee56885 --- /dev/null +++ b/streaming/src/test/java/org/apache/spark/streaming/LocalJavaStreamingContext.java @@ -0,0 +1,46 @@ +/* + * 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.streaming; + +import org.apache.spark.streaming.api.java.JavaStreamingContext; +import org.junit.After; +import org.junit.Before; + +public abstract class LocalJavaStreamingContext { + + protected transient JavaStreamingContext ssc; + + @Before + public void setUp() { + System.clearProperty("spark.driver.port"); + System.clearProperty("spark.hostPort"); + System.setProperty("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock"); + ssc = new JavaStreamingContext("local[2]", "test", new Duration(1000)); + ssc.checkpoint("checkpoint"); + } + + @After + public void tearDown() { + ssc.stop(); + ssc = null; + + // 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"); + } +} 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 5185954521..a8e053278a 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala @@ -23,7 +23,7 @@ import akka.actor.IOManager import akka.actor.Props import akka.util.ByteString -import org.apache.spark.streaming.dstream.{FileInputDStream, NetworkReceiver, SparkFlumeEvent} +import org.apache.spark.streaming.dstream.{NetworkReceiver} import java.net.{InetSocketAddress, SocketException, Socket, ServerSocket} import java.io.{File, BufferedWriter, OutputStreamWriter} import java.util.concurrent.{Executors, TimeUnit, ArrayBlockingQueue} @@ -31,18 +31,11 @@ import collection.mutable.{SynchronizedBuffer, ArrayBuffer} import util.ManualClock import org.apache.spark.storage.StorageLevel import org.apache.spark.streaming.receivers.Receiver -import org.apache.spark.{SparkContext, Logging} +import org.apache.spark.Logging import scala.util.Random import org.apache.commons.io.FileUtils import org.scalatest.BeforeAndAfter -import org.apache.flume.source.avro.AvroSourceProtocol -import org.apache.flume.source.avro.AvroFlumeEvent -import org.apache.flume.source.avro.Status -import org.apache.avro.ipc.{specific, NettyTransceiver} -import org.apache.avro.ipc.specific.SpecificRequestor -import java.nio.ByteBuffer import collection.JavaConversions._ -import java.nio.charset.Charset import com.google.common.io.Files import java.util.concurrent.atomic.AtomicInteger @@ -56,7 +49,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { testServer.start() // Set up the streaming context and input streams - val ssc = new StreamingContext(new SparkContext(conf), batchDuration) + val ssc = new StreamingContext(conf, batchDuration) val networkStream = ssc.socketTextStream("localhost", testServer.port, StorageLevel.MEMORY_AND_DISK) val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] val outputStream = new TestOutputStream(networkStream, outputBuffer) @@ -99,62 +92,13 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { } - test("flume input stream") { - // Set up the streaming context and input streams - val ssc = new StreamingContext(new SparkContext(conf), batchDuration) - val flumeStream = ssc.flumeStream("localhost", testPort, StorageLevel.MEMORY_AND_DISK) - val outputBuffer = new ArrayBuffer[Seq[SparkFlumeEvent]] - with SynchronizedBuffer[Seq[SparkFlumeEvent]] - val outputStream = new TestOutputStream(flumeStream, outputBuffer) - ssc.registerOutputStream(outputStream) - ssc.start() - - val clock = ssc.scheduler.clock.asInstanceOf[ManualClock] - val input = Seq(1, 2, 3, 4, 5) - Thread.sleep(1000) - val transceiver = new NettyTransceiver(new InetSocketAddress("localhost", testPort)) - val client = SpecificRequestor.getClient( - classOf[AvroSourceProtocol], transceiver) - - for (i <- 0 until input.size) { - val event = new AvroFlumeEvent - event.setBody(ByteBuffer.wrap(input(i).toString.getBytes())) - event.setHeaders(Map[CharSequence, CharSequence]("test" -> "header")) - client.append(event) - Thread.sleep(500) - clock.addToTime(batchDuration.milliseconds) - } - - val startTime = System.currentTimeMillis() - while (outputBuffer.size < input.size && System.currentTimeMillis() - startTime < maxWaitTimeMillis) { - logInfo("output.size = " + outputBuffer.size + ", input.size = " + input.size) - Thread.sleep(100) - } - Thread.sleep(1000) - val timeTaken = System.currentTimeMillis() - startTime - assert(timeTaken < maxWaitTimeMillis, "Operation timed out after " + timeTaken + " ms") - logInfo("Stopping context") - ssc.stop() - - val decoder = Charset.forName("UTF-8").newDecoder() - - assert(outputBuffer.size === input.length) - for (i <- 0 until outputBuffer.size) { - assert(outputBuffer(i).size === 1) - val str = decoder.decode(outputBuffer(i).head.event.getBody) - assert(str.toString === input(i).toString) - assert(outputBuffer(i).head.event.getHeaders.get("test") === "header") - } - } - - test("file input stream") { // Disable manual clock as FileInputDStream does not work with manual clock conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.SystemClock") // Set up the streaming context and input streams val testDir = Files.createTempDir() - val ssc = new StreamingContext(new SparkContext(conf), batchDuration) + val ssc = new StreamingContext(conf, batchDuration) val fileStream = ssc.textFileStream(testDir.toString) val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] def output = outputBuffer.flatMap(x => x) @@ -206,7 +150,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { testServer.start() // Set up the streaming context and input streams - val ssc = new StreamingContext(new SparkContext(conf), batchDuration) + val ssc = new StreamingContext(conf, batchDuration) val networkStream = ssc.actorStream[String](Props(new TestActor(port)), "TestActor", StorageLevel.MEMORY_AND_DISK) //Had to pass the local value of port to prevent from closing over entire scope val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] @@ -249,20 +193,6 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { } } - test("kafka input stream") { - val ssc = new StreamingContext(new SparkContext(conf), batchDuration) - val topics = Map("my-topic" -> 1) - val test1 = ssc.kafkaStream("localhost:12345", "group", topics) - val test2 = ssc.kafkaStream("localhost:12345", "group", topics, StorageLevel.MEMORY_AND_DISK) - - // Test specifying decoder - val kafkaParams = Map("zookeeper.connect"->"localhost:12345","group.id"->"consumer-group") - val test3 = ssc.kafkaStream[ - String, - String, - kafka.serializer.StringDecoder, - kafka.serializer.StringDecoder](kafkaParams, topics, StorageLevel.MEMORY_AND_DISK) - } test("multi-thread receiver") { // set up the test receiver @@ -273,7 +203,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { MultiThreadTestReceiver.haveAllThreadsFinished = false // set up the network stream using the test receiver - val ssc = new StreamingContext(new SparkContext(conf), batchDuration) + val ssc = new StreamingContext(conf, batchDuration) val networkStream = ssc.networkStream[Int](testReceiver) val countStream = networkStream.count val outputBuffer = new ArrayBuffer[Seq[Long]] with SynchronizedBuffer[Seq[Long]] diff --git a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala index 33464bc3a1..b20d02f996 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala @@ -142,16 +142,12 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { // Default before function for any streaming test suite. Override this // if you want to add your stuff to "before" (i.e., don't call before { } ) def beforeFunction() { - //if (useManualClock) { - // System.setProperty( - // "spark.streaming.clock", - // "org.apache.spark.streaming.util.ManualClock" - // ) - //} else { - // System.clearProperty("spark.streaming.clock") - //} if (useManualClock) { + logInfo("Using manual clock") conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") + } else { + logInfo("Using real clock") + conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.SystemClock") } } @@ -175,9 +171,8 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { operation: DStream[U] => DStream[V], numPartitions: Int = numInputPartitions ): StreamingContext = { - val sc = new SparkContext(conf) // Create StreamingContext - val ssc = new StreamingContext(sc, batchDuration) + val ssc = new StreamingContext(conf, batchDuration) if (checkpointDir != null) { ssc.checkpoint(checkpointDir) } @@ -201,9 +196,8 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { input2: Seq[Seq[V]], operation: (DStream[U], DStream[V]) => DStream[W] ): StreamingContext = { - val sc = new SparkContext(conf) // Create StreamingContext - val ssc = new StreamingContext(sc, batchDuration) + val ssc = new StreamingContext(conf, batchDuration) if (checkpointDir != null) { ssc.checkpoint(checkpointDir) } @@ -279,7 +273,7 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { val startTime = System.currentTimeMillis() while (output.size < numExpectedOutput && System.currentTimeMillis() - startTime < maxWaitTimeMillis) { logInfo("output.size = " + output.size + ", numExpectedOutput = " + numExpectedOutput) - Thread.sleep(100) + Thread.sleep(10) } val timeTaken = System.currentTimeMillis() - startTime diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 6abb4d5017..23781ea35c 100644 --- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -102,7 +102,7 @@ class Client(args: ClientArguments, conf: Configuration, sparkConf: SparkConf) (System.getenv("SPARK_JAR") == null) -> "Error: You must set SPARK_JAR environment variable!", (args.userJar == null) -> "Error: You must specify a user jar!", (args.userClass == null) -> "Error: You must specify a user class!", - (args.numWorkers <= 0) -> "Error: You must specify atleast 1 worker!", + (args.numWorkers <= 0) -> "Error: You must specify at least 1 worker!", (args.amMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: AM memory size must be " + "greater than: " + YarnAllocationHandler.MEMORY_OVERHEAD), (args.workerMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: Worker memory size " + diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala index 324ef4616f..4b1b5da048 100644 --- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala +++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -39,6 +39,7 @@ private[spark] class YarnClientSchedulerBackend( val defaultWorkerNumber = "1" val userJar = System.getenv("SPARK_YARN_APP_JAR") + val distFiles = System.getenv("SPARK_YARN_DIST_FILES") var workerCores = System.getenv("SPARK_WORKER_CORES") var workerMemory = System.getenv("SPARK_WORKER_MEMORY") var workerNumber = System.getenv("SPARK_WORKER_INSTANCES") @@ -64,7 +65,8 @@ private[spark] class YarnClientSchedulerBackend( "--worker-memory", workerMemory, "--worker-cores", workerCores, "--num-workers", workerNumber, - "--master-class", "org.apache.spark.deploy.yarn.WorkerLauncher" + "--master-class", "org.apache.spark.deploy.yarn.WorkerLauncher", + "--files", distFiles ) val args = new ClientArguments(argsArray, conf) diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 440ad5cde5..be323d7783 100644 --- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -122,7 +122,7 @@ class Client(args: ClientArguments, conf: Configuration, sparkConf: SparkConf) (System.getenv("SPARK_JAR") == null) -> "Error: You must set SPARK_JAR environment variable!", (args.userJar == null) -> "Error: You must specify a user jar!", (args.userClass == null) -> "Error: You must specify a user class!", - (args.numWorkers <= 0) -> "Error: You must specify atleast 1 worker!", + (args.numWorkers <= 0) -> "Error: You must specify at least 1 worker!", (args.amMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: AM memory size must be" + "greater than: " + YarnAllocationHandler.MEMORY_OVERHEAD), (args.workerMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: Worker memory size" + |