diff options
149 files changed, 2291 insertions, 1221 deletions
diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala index 6e922a612a..5f73d234aa 100644 --- a/core/src/main/scala/org/apache/spark/Accumulators.scala +++ b/core/src/main/scala/org/apache/spark/Accumulators.scala @@ -41,7 +41,7 @@ class Accumulable[R, T] ( @transient initialValue: R, param: AccumulableParam[R, T]) extends Serializable { - + val id = Accumulators.newId @transient private var value_ = initialValue // Current value on master val zero = param.zero(initialValue) // Zero value to be passed to workers @@ -113,7 +113,7 @@ class Accumulable[R, T] ( def setValue(newValue: R) { this.value = newValue } - + // Called by Java when deserializing an object private def readObject(in: ObjectInputStream) { in.defaultReadObject() @@ -177,7 +177,7 @@ class GrowableAccumulableParam[R <% Growable[T] with TraversableOnce[T] with Ser def zero(initialValue: R): R = { // We need to clone initialValue, but it's hard to specify that R should also be Cloneable. // Instead we'll serialize it to a buffer and load it back. - val ser = new JavaSerializer().newInstance() + val ser = new JavaSerializer(new SparkConf(false)).newInstance() val copy = ser.deserialize[R](ser.serialize(initialValue)) copy.clear() // In case it contained stuff copy @@ -215,7 +215,7 @@ private object Accumulators { val originals = Map[Long, Accumulable[_, _]]() val localAccums = Map[Thread, Map[Long, Accumulable[_, _]]]() var lastId: Long = 0 - + def newId: Long = synchronized { lastId += 1 return lastId diff --git a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala index ccffcc356c..cdae167aef 100644 --- a/core/src/main/scala/org/apache/spark/MapOutputTracker.scala +++ b/core/src/main/scala/org/apache/spark/MapOutputTracker.scala @@ -50,9 +50,9 @@ private[spark] class MapOutputTrackerMasterActor(tracker: MapOutputTrackerMaster } } -private[spark] class MapOutputTracker extends Logging { +private[spark] class MapOutputTracker(conf: SparkConf) extends Logging { - private val timeout = AkkaUtils.askTimeout + private val timeout = AkkaUtils.askTimeout(conf) // Set to the MapOutputTrackerActor living on the driver var trackerActor: Either[ActorRef, ActorSelection] = _ @@ -65,7 +65,7 @@ private[spark] class MapOutputTracker extends Logging { protected val epochLock = new java.lang.Object private val metadataCleaner = - new MetadataCleaner(MetadataCleanerType.MAP_OUTPUT_TRACKER, this.cleanup) + new MetadataCleaner(MetadataCleanerType.MAP_OUTPUT_TRACKER, this.cleanup, conf) // Send a message to the trackerActor and get its result within a default timeout, or // throw a SparkException if this fails. @@ -129,7 +129,7 @@ private[spark] class MapOutputTracker extends Logging { if (fetchedStatuses == null) { // We won the race to fetch the output locs; do so logInfo("Doing the fetch; tracker actor = " + trackerActor) - val hostPort = Utils.localHostPort() + val hostPort = Utils.localHostPort(conf) // This try-finally prevents hangs due to timeouts: try { val fetchedBytes = @@ -192,7 +192,8 @@ private[spark] class MapOutputTracker extends Logging { } } -private[spark] class MapOutputTrackerMaster extends MapOutputTracker { +private[spark] class MapOutputTrackerMaster(conf: SparkConf) + extends MapOutputTracker(conf) { // Cache a serialized version of the output statuses for each shuffle to send them out faster private var cacheEpoch = epoch diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala index bcec41c439..31b0773bfe 100644 --- a/core/src/main/scala/org/apache/spark/Partitioner.scala +++ b/core/src/main/scala/org/apache/spark/Partitioner.scala @@ -52,7 +52,7 @@ object Partitioner { for (r <- bySize if r.partitioner != None) { return r.partitioner.get } - if (System.getProperty("spark.default.parallelism") != null) { + if (rdd.context.conf.contains("spark.default.parallelism")) { return new HashPartitioner(rdd.context.defaultParallelism) } else { return new HashPartitioner(bySize.head.partitions.size) @@ -90,7 +90,7 @@ class HashPartitioner(partitions: Int) extends Partitioner { class RangePartitioner[K <% Ordered[K]: ClassTag, V]( partitions: Int, @transient rdd: RDD[_ <: Product2[K,V]], - private val ascending: Boolean = true) + private val ascending: Boolean = true) extends Partitioner { // An array of upper bounds for the first (partitions - 1) partitions diff --git a/core/src/main/scala/org/apache/spark/SparkConf.scala b/core/src/main/scala/org/apache/spark/SparkConf.scala new file mode 100644 index 0000000000..98343e9532 --- /dev/null +++ b/core/src/main/scala/org/apache/spark/SparkConf.scala @@ -0,0 +1,189 @@ +package org.apache.spark + +import scala.collection.JavaConverters._ +import scala.collection.mutable.HashMap + +import com.typesafe.config.ConfigFactory + +/** + * Configuration for a Spark application. Used to set various Spark parameters as key-value pairs. + * + * Most of the time, you would create a SparkConf object with `new SparkConf()`, which will load + * values from both the `spark.*` Java system properties and any `spark.conf` on your application's + * classpath (if it has one). In this case, system properties take priority over `spark.conf`, and + * any parameters you set directly on the `SparkConf` object take priority over both of those. + * + * For unit tests, you can also call `new SparkConf(false)` to skip loading external settings and + * get the same configuration no matter what is on the classpath. + * + * All setter methods in this class support chaining. For example, you can write + * `new SparkConf().setMaster("local").setAppName("My app")`. + * + * Note that once a SparkConf object is passed to Spark, it is cloned and can no longer be modified + * by the user. Spark does not support modifying the configuration at runtime. + * + * @param loadDefaults whether to load values from the system properties and classpath + */ +class SparkConf(loadDefaults: Boolean) extends Serializable with Cloneable { + + /** Create a SparkConf that loads defaults from system properties and the classpath */ + def this() = this(true) + + private val settings = new HashMap[String, String]() + + if (loadDefaults) { + ConfigFactory.invalidateCaches() + val typesafeConfig = ConfigFactory.systemProperties() + .withFallback(ConfigFactory.parseResources("spark.conf")) + for (e <- typesafeConfig.entrySet().asScala if e.getKey.startsWith("spark.")) { + settings(e.getKey) = e.getValue.unwrapped.toString + } + } + + /** Set a configuration variable. */ + def set(key: String, value: String): SparkConf = { + if (key == null) { + throw new NullPointerException("null key") + } + if (value == null) { + throw new NullPointerException("null value") + } + settings(key) = value + this + } + + /** + * The master URL to connect to, such as "local" to run locally with one thread, "local[4]" to + * run locally with 4 cores, or "spark://master:7077" to run on a Spark standalone cluster. + */ + def setMaster(master: String): SparkConf = { + set("spark.master", master) + } + + /** Set a name for your application. Shown in the Spark web UI. */ + def setAppName(name: String): SparkConf = { + set("spark.app.name", name) + } + + /** Set JAR files to distribute to the cluster. */ + def setJars(jars: Seq[String]): SparkConf = { + set("spark.jars", jars.mkString(",")) + } + + /** Set JAR files to distribute to the cluster. (Java-friendly version.) */ + def setJars(jars: Array[String]): SparkConf = { + setJars(jars.toSeq) + } + + /** + * Set an environment variable to be used when launching executors for this application. + * These variables are stored as properties of the form spark.executorEnv.VAR_NAME + * (for example spark.executorEnv.PATH) but this method makes them easier to set. + */ + def setExecutorEnv(variable: String, value: String): SparkConf = { + set("spark.executorEnv." + variable, value) + } + + /** + * Set multiple environment variables to be used when launching executors. + * These variables are stored as properties of the form spark.executorEnv.VAR_NAME + * (for example spark.executorEnv.PATH) but this method makes them easier to set. + */ + def setExecutorEnv(variables: Seq[(String, String)]): SparkConf = { + for ((k, v) <- variables) { + setExecutorEnv(k, v) + } + this + } + + /** + * Set multiple environment variables to be used when launching executors. + * (Java-friendly version.) + */ + def setExecutorEnv(variables: Array[(String, String)]): SparkConf = { + setExecutorEnv(variables.toSeq) + } + + /** + * Set the location where Spark is installed on worker nodes. + */ + def setSparkHome(home: String): SparkConf = { + set("spark.home", home) + } + + /** Set multiple parameters together */ + def setAll(settings: Traversable[(String, String)]) = { + this.settings ++= settings + this + } + + /** Set a parameter if it isn't already configured */ + def setIfMissing(key: String, value: String): SparkConf = { + if (!settings.contains(key)) { + settings(key) = value + } + this + } + + /** Remove a parameter from the configuration */ + def remove(key: String): SparkConf = { + settings.remove(key) + this + } + + /** Get a parameter; throws a NoSuchElementException if it's not set */ + def get(key: String): String = { + settings.getOrElse(key, throw new NoSuchElementException(key)) + } + + /** Get a parameter, falling back to a default if not set */ + def get(key: String, defaultValue: String): String = { + settings.getOrElse(key, defaultValue) + } + + /** Get a parameter as an Option */ + def getOption(key: String): Option[String] = { + settings.get(key) + } + + /** Get all parameters as a list of pairs */ + def getAll: Array[(String, String)] = settings.clone().toArray + + /** Get a parameter as an integer, falling back to a default if not set */ + def getInt(key: String, defaultValue: Int): Int = { + getOption(key).map(_.toInt).getOrElse(defaultValue) + } + + /** Get a parameter as a long, falling back to a default if not set */ + def getLong(key: String, defaultValue: Long): Long = { + getOption(key).map(_.toLong).getOrElse(defaultValue) + } + + /** Get a parameter as a double, falling back to a default if not set */ + def getDouble(key: String, defaultValue: Double): Double = { + getOption(key).map(_.toDouble).getOrElse(defaultValue) + } + + /** Get all executor environment variables set on this SparkConf */ + def getExecutorEnv: Seq[(String, String)] = { + val prefix = "spark.executorEnv." + getAll.filter{case (k, v) => k.startsWith(prefix)} + .map{case (k, v) => (k.substring(prefix.length), v)} + } + + /** Does the configuration contain a given parameter? */ + def contains(key: String): Boolean = settings.contains(key) + + /** Copy this object */ + override def clone: SparkConf = { + new SparkConf(false).setAll(settings) + } + + /** + * Return a string listing all keys and values, one per line. This is useful to print the + * configuration out for debugging. + */ + def toDebugString: String = { + settings.toArray.sorted.map{case (k, v) => k + "=" + v}.mkString("\n") + } +} diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index d3903ce9e3..4d6a97e255 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -22,33 +22,20 @@ import java.net.URI import java.util.{UUID, Properties} import java.util.concurrent.atomic.AtomicInteger -import scala.collection.Map +import scala.collection.{Map, Set} import scala.collection.generic.Growable -import scala.collection.mutable.ArrayBuffer -import scala.collection.mutable.HashMap + +import scala.collection.mutable.{ArrayBuffer, HashMap} import scala.reflect.{ClassTag, classTag} import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path -import org.apache.hadoop.io.ArrayWritable -import org.apache.hadoop.io.BooleanWritable -import org.apache.hadoop.io.BytesWritable -import org.apache.hadoop.io.DoubleWritable -import org.apache.hadoop.io.FloatWritable -import org.apache.hadoop.io.IntWritable -import org.apache.hadoop.io.LongWritable -import org.apache.hadoop.io.NullWritable -import org.apache.hadoop.io.Text -import org.apache.hadoop.io.Writable -import org.apache.hadoop.mapred.FileInputFormat -import org.apache.hadoop.mapred.InputFormat -import org.apache.hadoop.mapred.JobConf -import org.apache.hadoop.mapred.SequenceFileInputFormat -import org.apache.hadoop.mapred.TextInputFormat -import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} -import org.apache.hadoop.mapreduce.{Job => NewHadoopJob} +import org.apache.hadoop.io.{ArrayWritable, BooleanWritable, BytesWritable, DoubleWritable, +FloatWritable, IntWritable, LongWritable, NullWritable, Text, Writable} +import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf, SequenceFileInputFormat, +TextInputFormat} +import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat, Job => NewHadoopJob} import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat} - import org.apache.mesos.MesosNativeLibrary import org.apache.spark.deploy.{LocalSparkCluster, SparkHadoopUtil} @@ -61,50 +48,97 @@ import org.apache.spark.scheduler.cluster.mesos.{CoarseMesosSchedulerBackend, Me 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.{ClosureCleaner, MetadataCleaner, MetadataCleanerType, - TimeStampedHashMap, Utils} +import org.apache.spark.util.{Utils, TimeStampedHashMap, MetadataCleaner, MetadataCleanerType, +ClosureCleaner} /** * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark * cluster, and can be used to create RDDs, accumulators and broadcast variables on that cluster. * - * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). - * @param appName A name for your application, to display on the cluster web UI. - * @param sparkHome Location where Spark is installed on cluster nodes. - * @param jars Collection of JARs to send to the cluster. These can be paths on the local file - * system or HDFS, HTTP, HTTPS, or FTP URLs. - * @param environment Environment variables to set on worker nodes. + * @param config a Spark Config object describing the application configuration. Any settings in + * this config overrides the default configs as well as system properties. + * @param preferredNodeLocationData used in YARN mode to select nodes to launch containers on. Can + * be generated using [[org.apache.spark.scheduler.InputFormatInfo.computePreferredLocations]] + * from a list of input files or InputFormats for the application. */ class SparkContext( - val master: String, - val appName: String, - val sparkHome: String = null, - val jars: Seq[String] = Nil, - val environment: Map[String, String] = Map(), + config: SparkConf, // This is used only by YARN for now, but should be relevant to other cluster types (Mesos, etc) - // too. This is typically generated from InputFormatInfo.computePreferredLocations .. host, set - // of data-local splits on host - val preferredNodeLocationData: scala.collection.Map[String, scala.collection.Set[SplitInfo]] = - scala.collection.immutable.Map()) + // too. This is typically generated from InputFormatInfo.computePreferredLocations. It contains + // a map from hostname to a list of input format splits on the host. + val preferredNodeLocationData: Map[String, Set[SplitInfo]] = Map()) extends Logging { - // Set Spark driver host and port system properties - if (System.getProperty("spark.driver.host") == null) { - System.setProperty("spark.driver.host", Utils.localHostName()) + /** + * Alternative constructor that allows setting common Spark properties directly + * + * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). + * @param appName A name for your application, to display on the cluster web UI + * @param conf a [[org.apache.spark.SparkConf]] object specifying other Spark parameters + */ + def this(master: String, appName: String, conf: SparkConf) = + this(SparkContext.updatedConf(conf, master, appName)) + + /** + * Alternative constructor that allows setting common Spark properties directly + * + * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). + * @param appName A name for your application, to display on the cluster web UI. + * @param sparkHome Location where Spark is installed on cluster nodes. + * @param jars Collection of JARs to send to the cluster. These can be paths on the local file + * system or HDFS, HTTP, HTTPS, or FTP URLs. + * @param environment Environment variables to set on worker nodes. + */ + def this( + master: String, + appName: String, + sparkHome: String = null, + jars: Seq[String] = Nil, + environment: Map[String, String] = Map(), + preferredNodeLocationData: Map[String, Set[SplitInfo]] = Map()) = + { + this(SparkContext.updatedConf(new SparkConf(), master, appName, sparkHome, jars, environment), + preferredNodeLocationData) + } + + private[spark] val conf = config.clone() + + /** + * Return a copy of this SparkContext's configuration. The configuration ''cannot'' be + * changed at runtime. + */ + def getConf: SparkConf = conf.clone() + + if (!conf.contains("spark.master")) { + throw new SparkException("A master URL must be set in your configuration") + } + if (!conf.contains("spark.app.name")) { + throw new SparkException("An application must be set in your configuration") } - if (System.getProperty("spark.driver.port") == null) { - System.setProperty("spark.driver.port", "0") + + // Set Spark driver host and port system properties + conf.setIfMissing("spark.driver.host", Utils.localHostName()) + conf.setIfMissing("spark.driver.port", "0") + + val jars: Seq[String] = if (conf.contains("spark.jars")) { + conf.get("spark.jars").split(",").filter(_.size != 0) + } else { + null } + val master = conf.get("spark.master") + val appName = conf.get("spark.app.name") + val isLocal = (master == "local" || master.startsWith("local[")) // Create the Spark execution environment (cache, map output tracker, etc) - private[spark] val env = SparkEnv.createFromSystemProperties( + private[spark] val env = SparkEnv.create( + conf, "<driver>", - System.getProperty("spark.driver.host"), - System.getProperty("spark.driver.port").toInt, - true, - isLocal) + conf.get("spark.driver.host"), + conf.get("spark.driver.port").toInt, + isDriver = true, + isLocal = isLocal) SparkEnv.set(env) // Used to store a URL for each static file/jar together with the file's local timestamp @@ -113,7 +147,8 @@ class SparkContext( // Keeps track of all persisted RDDs private[spark] val persistentRdds = new TimeStampedHashMap[Int, RDD[_]] - private[spark] val metadataCleaner = new MetadataCleaner(MetadataCleanerType.SPARK_CONTEXT, this.cleanup) + private[spark] val metadataCleaner = + new MetadataCleaner(MetadataCleanerType.SPARK_CONTEXT, this.cleanup, conf) // Initialize the Spark UI private[spark] val ui = new SparkUI(this) @@ -123,23 +158,24 @@ class SparkContext( // Add each JAR given through the constructor if (jars != null) { - jars.foreach { addJar(_) } + jars.foreach(addJar) } + private[spark] val executorMemory = conf.getOption("spark.executor.memory") + .orElse(Option(System.getenv("SPARK_MEM"))) + .map(Utils.memoryStringToMb) + .getOrElse(512) + // Environment variables to pass to our executors private[spark] val executorEnvs = HashMap[String, String]() // Note: SPARK_MEM is included for Mesos, but overwritten for standalone mode in ExecutorRunner - for (key <- Seq("SPARK_CLASSPATH", "SPARK_LIBRARY_PATH", "SPARK_JAVA_OPTS", "SPARK_TESTING")) { - val value = System.getenv(key) - if (value != null) { - executorEnvs(key) = value - } + for (key <- Seq("SPARK_CLASSPATH", "SPARK_LIBRARY_PATH", "SPARK_JAVA_OPTS", "SPARK_TESTING"); + value <- Option(System.getenv(key))) { + executorEnvs(key) = value } // Since memory can be set with a system property too, use that - executorEnvs("SPARK_MEM") = SparkContext.executorMemoryRequested + "m" - if (environment != null) { - executorEnvs ++= environment - } + executorEnvs("SPARK_MEM") = executorMemory + "m" + executorEnvs ++= conf.getExecutorEnv // Set SPARK_USER for user who is running SparkContext. val sparkUser = Option { @@ -161,24 +197,24 @@ class SparkContext( /** A default Hadoop Configuration for the Hadoop code (e.g. file systems) that we reuse. */ val hadoopConfiguration = { val env = SparkEnv.get - val conf = SparkHadoopUtil.get.newConfiguration() + val hadoopConf = SparkHadoopUtil.get.newConfiguration() // Explicitly check for S3 environment variables if (System.getenv("AWS_ACCESS_KEY_ID") != null && System.getenv("AWS_SECRET_ACCESS_KEY") != null) { - conf.set("fs.s3.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID")) - conf.set("fs.s3n.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID")) - conf.set("fs.s3.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY")) - conf.set("fs.s3n.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY")) + hadoopConf.set("fs.s3.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID")) + hadoopConf.set("fs.s3n.awsAccessKeyId", System.getenv("AWS_ACCESS_KEY_ID")) + hadoopConf.set("fs.s3.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY")) + hadoopConf.set("fs.s3n.awsSecretAccessKey", System.getenv("AWS_SECRET_ACCESS_KEY")) } // Copy any "spark.hadoop.foo=bar" system properties into conf as "foo=bar" - Utils.getSystemProperties.foreach { case (key, value) => + conf.getAll.foreach { case (key, value) => if (key.startsWith("spark.hadoop.")) { - conf.set(key.substring("spark.hadoop.".length), value) + hadoopConf.set(key.substring("spark.hadoop.".length), value) } } - val bufferSize = System.getProperty("spark.buffer.size", "65536") - conf.set("io.file.buffer.size", bufferSize) - conf + val bufferSize = conf.get("spark.buffer.size", "65536") + hadoopConf.set("io.file.buffer.size", bufferSize) + hadoopConf } private[spark] var checkpointDir: Option[String] = None @@ -188,7 +224,7 @@ class SparkContext( override protected def childValue(parent: Properties): Properties = new Properties(parent) } - private[spark] def getLocalProperties(): Properties = localProperties.get() + private[spark] def getLocalProperties: Properties = localProperties.get() private[spark] def setLocalProperties(props: Properties) { localProperties.set(props) @@ -519,7 +555,7 @@ class SparkContext( addedFiles(key) = System.currentTimeMillis // Fetch the file locally in case a job is executed using DAGScheduler.runLocally(). - Utils.fetchFile(path, new File(SparkFiles.getRootDirectory)) + Utils.fetchFile(path, new File(SparkFiles.getRootDirectory), conf) logInfo("Added file " + path + " at " + key + " with timestamp " + addedFiles(key)) } @@ -689,15 +725,27 @@ class SparkContext( * (in that order of preference). If neither of these is set, return None. */ private[spark] def getSparkHome(): Option[String] = { - if (sparkHome != null) { - Some(sparkHome) - } else if (System.getProperty("spark.home") != null) { - Some(System.getProperty("spark.home")) - } else if (System.getenv("SPARK_HOME") != null) { - Some(System.getenv("SPARK_HOME")) - } else { - None - } + conf.getOption("spark.home").orElse(Option(System.getenv("SPARK_HOME"))) + } + + /** + * Support function for API backtraces. + */ + def setCallSite(site: String) { + setLocalProperty("externalCallSite", site) + } + + /** + * Support function for API backtraces. + */ + def clearCallSite() { + setLocalProperty("externalCallSite", null) + } + + private[spark] def getCallSite(): String = { + val callSite = getLocalProperty("externalCallSite") + if (callSite == null) return Utils.formatSparkCallSite + callSite } /** @@ -712,7 +760,7 @@ class SparkContext( partitions: Seq[Int], allowLocal: Boolean, resultHandler: (Int, U) => Unit) { - val callSite = Utils.formatSparkCallSite + val callSite = getCallSite val cleanedFunc = clean(func) logInfo("Starting job: " + callSite) val start = System.nanoTime @@ -796,7 +844,7 @@ class SparkContext( func: (TaskContext, Iterator[T]) => U, evaluator: ApproximateEvaluator[U, R], timeout: Long): PartialResult[R] = { - val callSite = Utils.formatSparkCallSite + val callSite = getCallSite logInfo("Starting job: " + callSite) val start = System.nanoTime val result = dagScheduler.runApproximateJob(rdd, func, evaluator, callSite, timeout, @@ -816,7 +864,7 @@ class SparkContext( resultFunc: => R): SimpleFutureAction[R] = { val cleanF = clean(processPartition) - val callSite = Utils.formatSparkCallSite + val callSite = getCallSite val waiter = dagScheduler.submitJob( rdd, (context: TaskContext, iter: Iterator[T]) => cleanF(iter), @@ -984,7 +1032,7 @@ object SparkContext { /** * Find the JAR from which a given class was loaded, to make it easy for users to pass - * their JARs to SparkContext + * their JARs to SparkContext. */ def jarOfClass(cls: Class[_]): Seq[String] = { val uri = cls.getResource("/" + cls.getName.replace('.', '/') + ".class") @@ -1001,21 +1049,44 @@ object SparkContext { } } - /** Find the JAR that contains the class of a particular object */ + /** + * 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): Seq[String] = jarOfClass(obj.getClass) - /** Get the amount of memory per executor requested through system properties or SPARK_MEM */ - private[spark] val executorMemoryRequested = { - // TODO: Might need to add some extra memory for the non-heap parts of the JVM - Option(System.getProperty("spark.executor.memory")) - .orElse(Option(System.getenv("SPARK_MEM"))) - .map(Utils.memoryStringToMb) - .getOrElse(512) + /** + * Creates a modified version of a SparkConf with the parameters that can be passed separately + * to SparkContext, to make it easier to write SparkContext's constructors. This ignores + * parameters that are passed as the default value of null, instead of throwing an exception + * like SparkConf would. + */ + private def updatedConf( + conf: SparkConf, + master: String, + appName: String, + sparkHome: String = null, + jars: Seq[String] = Nil, + environment: Map[String, String] = Map()): SparkConf = + { + val res = conf.clone() + res.setMaster(master) + res.setAppName(appName) + if (sparkHome != null) { + res.setSparkHome(sparkHome) + } + if (!jars.isEmpty) { + res.setJars(jars) + } + res.setExecutorEnv(environment.toSeq) + res } - // Creates a task scheduler based on a given master URL. Extracted for testing. - private - def createTaskScheduler(sc: SparkContext, master: String, appName: String): TaskScheduler = { + /** Creates a task scheduler based on a given master URL. Extracted for testing. */ + private def createTaskScheduler(sc: SparkContext, master: String, appName: String) + : TaskScheduler = + { // Regular expression used for local[N] master format val LOCAL_N_REGEX = """local\[([0-9]+)\]""".r // Regular expression for local[N, maxRetries], used in tests with failing tasks @@ -1061,10 +1132,10 @@ object SparkContext { case LOCAL_CLUSTER_REGEX(numSlaves, coresPerSlave, memoryPerSlave) => // Check to make sure memory requested <= memoryPerSlave. Otherwise Spark will just hang. val memoryPerSlaveInt = memoryPerSlave.toInt - if (SparkContext.executorMemoryRequested > memoryPerSlaveInt) { + if (sc.executorMemory > memoryPerSlaveInt) { throw new SparkException( "Asked to launch cluster with %d MB RAM / worker but requested %d MB/worker".format( - memoryPerSlaveInt, SparkContext.executorMemoryRequested)) + memoryPerSlaveInt, sc.executorMemory)) } val scheduler = new TaskSchedulerImpl(sc) @@ -1122,7 +1193,7 @@ object SparkContext { case mesosUrl @ MESOS_REGEX(_) => MesosNativeLibrary.load() val scheduler = new TaskSchedulerImpl(sc) - val coarseGrained = System.getProperty("spark.mesos.coarse", "false").toBoolean + val coarseGrained = sc.conf.get("spark.mesos.coarse", "false").toBoolean 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 826f5c2d8c..634a94f0a7 100644 --- a/core/src/main/scala/org/apache/spark/SparkEnv.scala +++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala @@ -40,7 +40,7 @@ import com.google.common.collect.MapMaker * objects needs to have the right SparkEnv set. You can get the current environment with * SparkEnv.get (e.g. after creating a SparkContext) and set it with SparkEnv.set. */ -class SparkEnv ( +class SparkEnv private[spark] ( val executorId: String, val actorSystem: ActorSystem, val serializerManager: SerializerManager, @@ -54,7 +54,8 @@ class SparkEnv ( val connectionManager: ConnectionManager, val httpFileServer: HttpFileServer, val sparkFilesDir: String, - val metricsSystem: MetricsSystem) { + val metricsSystem: MetricsSystem, + val conf: SparkConf) { private val pythonWorkers = mutable.HashMap[(String, Map[String, String]), PythonWorkerFactory]() @@ -62,7 +63,7 @@ class SparkEnv ( // (e.g., HadoopFileRDD uses this to cache JobConfs and InputFormats). private[spark] val hadoopJobMetadata = new MapMaker().softValues().makeMap[String, Any]() - def stop() { + private[spark] def stop() { pythonWorkers.foreach { case(key, worker) => worker.stop() } httpFileServer.stop() mapOutputTracker.stop() @@ -78,6 +79,7 @@ class SparkEnv ( //actorSystem.awaitTermination() } + private[spark] def createPythonWorker(pythonExec: String, envVars: Map[String, String]): java.net.Socket = { synchronized { val key = (pythonExec, envVars) @@ -106,33 +108,35 @@ object SparkEnv extends Logging { /** * Returns the ThreadLocal SparkEnv. */ - def getThreadLocal : SparkEnv = { + def getThreadLocal: SparkEnv = { env.get() } - def createFromSystemProperties( + private[spark] def create( + conf: SparkConf, executorId: String, hostname: String, port: Int, isDriver: Boolean, isLocal: Boolean): SparkEnv = { - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, port) + val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, port, + conf = conf) // Bit of a hack: If this is the driver and our port was 0 (meaning bind to any free port), // figure out which port number Akka actually bound to and set spark.driver.port to it. if (isDriver && port == 0) { - System.setProperty("spark.driver.port", boundPort.toString) + conf.set("spark.driver.port", boundPort.toString) } // set only if unset until now. - if (System.getProperty("spark.hostPort", null) == null) { + if (!conf.contains("spark.hostPort")) { if (!isDriver){ // unexpected Utils.logErrorWithStack("Unexpected NOT to have spark.hostPort set") } Utils.checkHost(hostname) - System.setProperty("spark.hostPort", hostname + ":" + boundPort) + conf.set("spark.hostPort", hostname + ":" + boundPort) } val classLoader = Thread.currentThread.getContextClassLoader @@ -140,25 +144,26 @@ object SparkEnv extends Logging { // Create an instance of the class named by the given Java system property, or by // defaultClassName if the property is not set, and return it as a T def instantiateClass[T](propertyName: String, defaultClassName: String): T = { - val name = System.getProperty(propertyName, defaultClassName) + val name = conf.get(propertyName, defaultClassName) Class.forName(name, true, classLoader).newInstance().asInstanceOf[T] } val serializerManager = new SerializerManager val serializer = serializerManager.setDefault( - System.getProperty("spark.serializer", "org.apache.spark.serializer.JavaSerializer")) + conf.get("spark.serializer", "org.apache.spark.serializer.JavaSerializer"), conf) val closureSerializer = serializerManager.get( - System.getProperty("spark.closure.serializer", "org.apache.spark.serializer.JavaSerializer")) + conf.get("spark.closure.serializer", "org.apache.spark.serializer.JavaSerializer"), + conf) def registerOrLookup(name: String, newActor: => Actor): Either[ActorRef, ActorSelection] = { if (isDriver) { logInfo("Registering " + name) Left(actorSystem.actorOf(Props(newActor), name = name)) } else { - val driverHost: String = System.getProperty("spark.driver.host", "localhost") - val driverPort: Int = System.getProperty("spark.driver.port", "7077").toInt + val driverHost: String = conf.get("spark.driver.host", "localhost") + val driverPort: Int = conf.get("spark.driver.port", "7077").toInt Utils.checkHost(driverHost, "Expected hostname") val url = "akka.tcp://spark@%s:%s/user/%s".format(driverHost, driverPort, name) logInfo("Connecting to " + name + ": " + url) @@ -168,21 +173,21 @@ object SparkEnv extends Logging { val blockManagerMaster = new BlockManagerMaster(registerOrLookup( "BlockManagerMaster", - new BlockManagerMasterActor(isLocal))) - val blockManager = new BlockManager(executorId, actorSystem, blockManagerMaster, serializer) + new BlockManagerMasterActor(isLocal, conf)), conf) + val blockManager = new BlockManager(executorId, actorSystem, blockManagerMaster, serializer, conf) val connectionManager = blockManager.connectionManager - val broadcastManager = new BroadcastManager(isDriver) + val broadcastManager = new BroadcastManager(isDriver, conf) val cacheManager = new CacheManager(blockManager) // Have to assign trackerActor after initialization as MapOutputTrackerActor // requires the MapOutputTracker itself val mapOutputTracker = if (isDriver) { - new MapOutputTrackerMaster() + new MapOutputTrackerMaster(conf) } else { - new MapOutputTracker() + new MapOutputTracker(conf) } mapOutputTracker.trackerActor = registerOrLookup( "MapOutputTracker", @@ -193,12 +198,12 @@ object SparkEnv extends Logging { val httpFileServer = new HttpFileServer() httpFileServer.initialize() - System.setProperty("spark.fileserver.uri", httpFileServer.serverUri) + conf.set("spark.fileserver.uri", httpFileServer.serverUri) val metricsSystem = if (isDriver) { - MetricsSystem.createMetricsSystem("driver") + MetricsSystem.createMetricsSystem("driver", conf) } else { - MetricsSystem.createMetricsSystem("executor") + MetricsSystem.createMetricsSystem("executor", conf) } metricsSystem.start() @@ -212,7 +217,7 @@ object SparkEnv extends Logging { } // Warn about deprecated spark.cache.class property - if (System.getProperty("spark.cache.class") != null) { + if (conf.contains("spark.cache.class")) { logWarning("The spark.cache.class property is no longer being used! Specify storage " + "levels using the RDD.persist() method instead.") } @@ -231,6 +236,7 @@ object SparkEnv extends Logging { connectionManager, httpFileServer, sparkFilesDir, - metricsSystem) + metricsSystem, + conf) } } 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 50f2021d01..5be5317f40 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 @@ -29,17 +29,22 @@ import org.apache.hadoop.mapred.JobConf import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat} import com.google.common.base.Optional -import org.apache.spark.{Accumulable, AccumulableParam, Accumulator, AccumulatorParam, SparkContext} +import org.apache.spark._ import org.apache.spark.SparkContext.IntAccumulatorParam import org.apache.spark.SparkContext.DoubleAccumulatorParam import org.apache.spark.broadcast.Broadcast import org.apache.spark.rdd.RDD +import scala.Tuple2 /** * A Java-friendly version of [[org.apache.spark.SparkContext]] that returns [[org.apache.spark.api.java.JavaRDD]]s and * works with Java collections instead of Scala ones. */ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWorkaround { + /** + * @param conf a [[org.apache.spark.SparkConf]] object specifying Spark parameters + */ + def this(conf: SparkConf) = this(new SparkContext(conf)) /** * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). @@ -50,6 +55,14 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork /** * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). * @param appName A name for your application, to display on the cluster web UI + * @param conf a [[org.apache.spark.SparkConf]] object specifying other Spark parameters + */ + def this(master: String, appName: String, conf: SparkConf) = + this(conf.setMaster(master).setAppName(appName)) + + /** + * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). + * @param appName A name for your application, to display on the cluster web UI * @param sparkHome The SPARK_HOME directory on the slave nodes * @param jarFile JAR file to send to the cluster. This can be a path on the local file system * or an HDFS, HTTP, HTTPS, or FTP URL. @@ -392,6 +405,26 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[T]] new JavaRDD(sc.checkpointFile(path)) } + + /** + * Return a copy of this JavaSparkContext's configuration. The configuration ''cannot'' be + * changed at runtime. + */ + def getConf: SparkConf = sc.getConf + + /** + * Pass-through to SparkContext.setCallSite. For API support only. + */ + def setCallSite(site: String) { + sc.setCallSite(site) + } + + /** + * Pass-through to SparkContext.setCallSite. For API support only. + */ + def clearCallSite() { + sc.clearCallSite() + } } object JavaSparkContext { 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 ca42c76928..32cc70e8c9 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 = System.getProperty("spark.buffer.size", "65536").toInt + val bufferSize = conf.get("spark.buffer.size", "65536").toInt override def getPartitions = parent.partitions @@ -250,7 +250,7 @@ private class PythonAccumulatorParam(@transient serverHost: String, serverPort: Utils.checkHost(serverHost, "Expected hostname") - val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt + val bufferSize = SparkEnv.get.conf.get("spark.buffer.size", "65536").toInt override def zero(value: JList[Array[Byte]]): JList[Array[Byte]] = new JArrayList diff --git a/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala index 43c18294c5..0fc478a419 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/Broadcast.scala @@ -31,8 +31,8 @@ abstract class Broadcast[T](private[spark] val id: Long) extends Serializable { override def toString = "Broadcast(" + id + ")" } -private[spark] -class BroadcastManager(val _isDriver: Boolean) extends Logging with Serializable { +private[spark] +class BroadcastManager(val _isDriver: Boolean, conf: SparkConf) extends Logging with Serializable { private var initialized = false private var broadcastFactory: BroadcastFactory = null @@ -43,14 +43,14 @@ class BroadcastManager(val _isDriver: Boolean) extends Logging with Serializable private def initialize() { synchronized { if (!initialized) { - val broadcastFactoryClass = System.getProperty( + val broadcastFactoryClass = conf.get( "spark.broadcast.factory", "org.apache.spark.broadcast.HttpBroadcastFactory") broadcastFactory = Class.forName(broadcastFactoryClass).newInstance.asInstanceOf[BroadcastFactory] // Initialize appropriate BroadcastFactory and BroadcastObject - broadcastFactory.initialize(isDriver) + broadcastFactory.initialize(isDriver, conf) initialized = true } diff --git a/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala b/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala index 68bff75b90..fb161ce69d 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/BroadcastFactory.scala @@ -17,6 +17,8 @@ package org.apache.spark.broadcast +import org.apache.spark.SparkConf + /** * An interface for all the broadcast implementations in Spark (to allow * multiple broadcast implementations). SparkContext uses a user-specified @@ -24,7 +26,7 @@ package org.apache.spark.broadcast * entire Spark job. */ private[spark] trait BroadcastFactory { - def initialize(isDriver: Boolean): Unit + def initialize(isDriver: Boolean, conf: SparkConf): Unit def newBroadcast[T](value: T, isLocal: Boolean, id: Long): Broadcast[T] def stop(): Unit } 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 47db720416..db596d5fcc 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala @@ -24,14 +24,14 @@ import java.util.concurrent.TimeUnit import it.unimi.dsi.fastutil.io.FastBufferedInputStream import it.unimi.dsi.fastutil.io.FastBufferedOutputStream -import org.apache.spark.{HttpServer, Logging, SparkEnv} +import org.apache.spark.{SparkConf, HttpServer, Logging, SparkEnv} import org.apache.spark.io.CompressionCodec import org.apache.spark.storage.{BroadcastBlockId, StorageLevel} import org.apache.spark.util.{MetadataCleaner, MetadataCleanerType, TimeStampedHashSet, Utils} private[spark] class HttpBroadcast[T](@transient var value_ : T, isLocal: Boolean, id: Long) extends Broadcast[T](id) with Logging with Serializable { - + def value = value_ def blockId = BroadcastBlockId(id) @@ -40,7 +40,7 @@ private[spark] class HttpBroadcast[T](@transient var value_ : T, isLocal: Boolea SparkEnv.get.blockManager.putSingle(blockId, value_, StorageLevel.MEMORY_AND_DISK, false) } - if (!isLocal) { + if (!isLocal) { HttpBroadcast.write(id, value_) } @@ -64,7 +64,7 @@ private[spark] class HttpBroadcast[T](@transient var value_ : T, isLocal: Boolea } private[spark] class HttpBroadcastFactory extends BroadcastFactory { - def initialize(isDriver: Boolean) { HttpBroadcast.initialize(isDriver) } + def initialize(isDriver: Boolean, conf: SparkConf) { HttpBroadcast.initialize(isDriver, conf) } def newBroadcast[T](value_ : T, isLocal: Boolean, id: Long) = new HttpBroadcast[T](value_, isLocal, id) @@ -81,44 +81,51 @@ private object HttpBroadcast extends Logging { private var serverUri: String = null private var server: HttpServer = null + // TODO: This shouldn't be a global variable so that multiple SparkContexts can coexist private val files = new TimeStampedHashSet[String] - private val cleaner = new MetadataCleaner(MetadataCleanerType.HTTP_BROADCAST, cleanup) + private var cleaner: MetadataCleaner = null - private val httpReadTimeout = TimeUnit.MILLISECONDS.convert(5,TimeUnit.MINUTES).toInt + private val httpReadTimeout = TimeUnit.MILLISECONDS.convert(5, TimeUnit.MINUTES).toInt - private lazy val compressionCodec = CompressionCodec.createCodec() + private var compressionCodec: CompressionCodec = null - def initialize(isDriver: Boolean) { + def initialize(isDriver: Boolean, conf: SparkConf) { synchronized { if (!initialized) { - bufferSize = System.getProperty("spark.buffer.size", "65536").toInt - compress = System.getProperty("spark.broadcast.compress", "true").toBoolean + bufferSize = conf.get("spark.buffer.size", "65536").toInt + compress = conf.get("spark.broadcast.compress", "true").toBoolean if (isDriver) { - createServer() + createServer(conf) + conf.set("spark.httpBroadcast.uri", serverUri) } - serverUri = System.getProperty("spark.httpBroadcast.uri") + serverUri = conf.get("spark.httpBroadcast.uri") + cleaner = new MetadataCleaner(MetadataCleanerType.HTTP_BROADCAST, cleanup, conf) + compressionCodec = CompressionCodec.createCodec(conf) initialized = true } } } - + def stop() { synchronized { if (server != null) { server.stop() server = null } + if (cleaner != null) { + cleaner.cancel() + cleaner = null + } + compressionCodec = null initialized = false - cleaner.cancel() } } - private def createServer() { - broadcastDir = Utils.createTempDir(Utils.getLocalDir) + private def createServer(conf: SparkConf) { + broadcastDir = Utils.createTempDir(Utils.getLocalDir(conf)) server = new HttpServer(broadcastDir) server.start() serverUri = server.uri - System.setProperty("spark.httpBroadcast.uri", serverUri) logInfo("Broadcast server started at " + serverUri) } @@ -143,7 +150,7 @@ private object HttpBroadcast extends Logging { val in = { val httpConnection = new URL(url).openConnection() httpConnection.setReadTimeout(httpReadTimeout) - val inputStream = httpConnection.getInputStream() + val inputStream = httpConnection.getInputStream if (compress) { compressionCodec.compressedInputStream(inputStream) } else { 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 073a0a5029..9530938278 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala @@ -83,13 +83,13 @@ extends Broadcast[T](id) with Logging with Serializable { case None => val start = System.nanoTime logInfo("Started reading broadcast variable " + id) - + // Initialize @transient variables that will receive garbage values from the master. resetWorkerVariables() if (receiveBroadcast(id)) { value_ = TorrentBroadcast.unBlockifyObject[T](arrayOfBlocks, totalBytes, totalBlocks) - + // Store the merged copy in cache so that the next worker doesn't need to rebuild it. // This creates a tradeoff between memory usage and latency. // Storing copy doubles the memory footprint; not storing doubles deserialization cost. @@ -122,14 +122,14 @@ extends Broadcast[T](id) with Logging with Serializable { while (attemptId > 0 && totalBlocks == -1) { TorrentBroadcast.synchronized { SparkEnv.get.blockManager.getSingle(metaId) match { - case Some(x) => + case Some(x) => val tInfo = x.asInstanceOf[TorrentInfo] totalBlocks = tInfo.totalBlocks totalBytes = tInfo.totalBytes arrayOfBlocks = new Array[TorrentBlock](totalBlocks) hasBlocks = 0 - - case None => + + case None => Thread.sleep(500) } } @@ -145,13 +145,13 @@ extends Broadcast[T](id) with Logging with Serializable { val pieceId = BroadcastHelperBlockId(broadcastId, "piece" + pid) TorrentBroadcast.synchronized { SparkEnv.get.blockManager.getSingle(pieceId) match { - case Some(x) => + case Some(x) => arrayOfBlocks(pid) = x.asInstanceOf[TorrentBlock] hasBlocks += 1 SparkEnv.get.blockManager.putSingle( pieceId, arrayOfBlocks(pid), StorageLevel.MEMORY_AND_DISK, true) - - case None => + + case None => throw new SparkException("Failed to get " + pieceId + " of " + broadcastId) } } @@ -166,21 +166,22 @@ private object TorrentBroadcast extends Logging { private var initialized = false - - def initialize(_isDriver: Boolean) { + private var conf: SparkConf = null + def initialize(_isDriver: Boolean, conf: SparkConf) { + TorrentBroadcast.conf = conf //TODO: we might have to fix it in tests synchronized { if (!initialized) { initialized = true } } } - + def stop() { initialized = false } - val BLOCK_SIZE = System.getProperty("spark.broadcast.blockSize", "4096").toInt * 1024 - + lazy val BLOCK_SIZE = conf.get("spark.broadcast.blockSize", "4096").toInt * 1024 + def blockifyObject[T](obj: T): TorrentInfo = { val byteArray = Utils.serialize[T](obj) val bais = new ByteArrayInputStream(byteArray) @@ -209,7 +210,7 @@ extends Logging { } def unBlockifyObject[T](arrayOfBlocks: Array[TorrentBlock], - totalBytes: Int, + totalBytes: Int, totalBlocks: Int): T = { var retByteArray = new Array[Byte](totalBytes) for (i <- 0 until totalBlocks) { @@ -222,23 +223,23 @@ extends Logging { } private[spark] case class TorrentBlock( - blockID: Int, - byteArray: Array[Byte]) + blockID: Int, + byteArray: Array[Byte]) extends Serializable private[spark] case class TorrentInfo( @transient arrayOfBlocks : Array[TorrentBlock], - totalBlocks: Int, - totalBytes: Int) + totalBlocks: Int, + totalBytes: Int) extends Serializable { - - @transient var hasBlocks = 0 + + @transient var hasBlocks = 0 } private[spark] class TorrentBroadcastFactory extends BroadcastFactory { - - def initialize(isDriver: Boolean) { TorrentBroadcast.initialize(isDriver) } + + def initialize(isDriver: Boolean, conf: SparkConf) { TorrentBroadcast.initialize(isDriver, conf) } def newBroadcast[T](value_ : T, isLocal: Boolean, id: Long) = new TorrentBroadcast[T](value_, isLocal, id) diff --git a/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala b/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala index 0aa8852649..4dfb19ed8a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala +++ b/core/src/main/scala/org/apache/spark/deploy/FaultToleranceTest.scala @@ -190,7 +190,7 @@ private[spark] object FaultToleranceTest extends App with Logging { /** Creates a SparkContext, which constructs a Client to interact with our cluster. */ def createClient() = { if (sc != null) { sc.stop() } - // Counter-hack: Because of a hack in SparkEnv#createFromSystemProperties() that changes this + // Counter-hack: Because of a hack in SparkEnv#create() that changes this // property, we need to reset it. System.setProperty("spark.driver.port", "0") sc = new SparkContext(getMasterUrls(masters), "fault-tolerance", containerSparkHome) @@ -417,4 +417,4 @@ private[spark] object Docker extends Logging { "docker ps -l -q".!(ProcessLogger(line => id = line)) new DockerId(id) } -}
\ No newline at end of file +} diff --git a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala index 59d12a3e6f..ffc0cb0903 100644 --- a/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala +++ b/core/src/main/scala/org/apache/spark/deploy/LocalSparkCluster.scala @@ -22,7 +22,7 @@ import akka.actor.ActorSystem import org.apache.spark.deploy.worker.Worker import org.apache.spark.deploy.master.Master import org.apache.spark.util.Utils -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, Logging} import scala.collection.mutable.ArrayBuffer @@ -43,7 +43,8 @@ class LocalSparkCluster(numWorkers: Int, coresPerWorker: Int, memoryPerWorker: I logInfo("Starting a local Spark cluster with " + numWorkers + " workers.") /* Start the Master */ - val (masterSystem, masterPort, _) = Master.startSystemAndActor(localHostname, 0, 0) + val conf = new SparkConf(false) + val (masterSystem, masterPort, _) = Master.startSystemAndActor(localHostname, 0, 0, conf) masterActorSystems += masterSystem val masterUrl = "spark://" + localHostname + ":" + masterPort val masters = Array(masterUrl) @@ -55,7 +56,7 @@ class LocalSparkCluster(numWorkers: Int, coresPerWorker: Int, memoryPerWorker: I workerActorSystems += workerSystem } - return masters + masters } def stop() { diff --git a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala index fc1537f796..27dc42bf7e 100644 --- a/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala +++ b/core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala @@ -34,10 +34,10 @@ class SparkHadoopUtil { UserGroupInformation.setConfiguration(conf) def runAsUser(user: String)(func: () => Unit) { - // if we are already running as the user intended there is no reason to do the doAs. It + // if we are already running as the user intended there is no reason to do the doAs. It // will actually break secure HDFS access as it doesn't fill in the credentials. Also if - // the user is UNKNOWN then we shouldn't be creating a remote unknown user - // (this is actually the path spark on yarn takes) since SPARK_USER is initialized only + // the user is UNKNOWN then we shouldn't be creating a remote unknown user + // (this is actually the path spark on yarn takes) since SPARK_USER is initialized only // in SparkContext. val currentUser = Option(System.getProperty("user.name")). getOrElse(SparkContext.SPARK_UNKNOWN_USER) @@ -67,11 +67,15 @@ class SparkHadoopUtil { } object SparkHadoopUtil { + private val hadoop = { - val yarnMode = java.lang.Boolean.valueOf(System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE"))) + val yarnMode = java.lang.Boolean.valueOf( + System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE"))) if (yarnMode) { try { - Class.forName("org.apache.spark.deploy.yarn.YarnSparkHadoopUtil").newInstance.asInstanceOf[SparkHadoopUtil] + Class.forName("org.apache.spark.deploy.yarn.YarnSparkHadoopUtil") + .newInstance() + .asInstanceOf[SparkHadoopUtil] } catch { case th: Throwable => throw new SparkException("Unable to load YARN support", th) } diff --git a/core/src/main/scala/org/apache/spark/deploy/client/Client.scala b/core/src/main/scala/org/apache/spark/deploy/client/Client.scala index 953755e40d..9bbd635ab9 100644 --- a/core/src/main/scala/org/apache/spark/deploy/client/Client.scala +++ b/core/src/main/scala/org/apache/spark/deploy/client/Client.scala @@ -19,20 +19,18 @@ package org.apache.spark.deploy.client import java.util.concurrent.TimeoutException -import scala.concurrent.duration._ import scala.concurrent.Await +import scala.concurrent.duration._ import akka.actor._ import akka.pattern.ask -import akka.remote.{RemotingLifecycleEvent, DisassociatedEvent} - -import org.apache.spark.{SparkException, Logging} +import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent} +import org.apache.spark.{Logging, SparkConf, SparkException} import org.apache.spark.deploy.{ApplicationDescription, ExecutorState} import org.apache.spark.deploy.DeployMessages._ 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. @@ -43,7 +41,8 @@ private[spark] class Client( actorSystem: ActorSystem, masterUrls: Array[String], appDescription: ApplicationDescription, - listener: ClientListener) + listener: ClientListener, + conf: SparkConf) extends Logging { val REGISTRATION_TIMEOUT = 20.seconds @@ -178,7 +177,7 @@ private[spark] class Client( def stop() { if (actor != null) { try { - val timeout = AkkaUtils.askTimeout + val timeout = AkkaUtils.askTimeout(conf) val future = actor.ask(StopClient)(timeout) Await.result(future, timeout) } catch { 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 5b62d3ba6c..ef649fd80c 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 @@ -18,7 +18,7 @@ package org.apache.spark.deploy.client import org.apache.spark.util.{Utils, AkkaUtils} -import org.apache.spark.{Logging} +import org.apache.spark.{SparkConf, SparkContext, Logging} import org.apache.spark.deploy.{Command, ApplicationDescription} private[spark] object TestClient { @@ -45,11 +45,13 @@ private[spark] object TestClient { def main(args: Array[String]) { val url = args(0) - val (actorSystem, port) = AkkaUtils.createActorSystem("spark", Utils.localIpAddress, 0) + 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()), "dummy-spark-home", "ignored") + "TestClient", 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) + val client = new Client(actorSystem, Array(url), desc, listener, new SparkConf) client.start() actorSystem.awaitTermination() } 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 eebd0794b8..7b696cfcca 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 @@ -29,7 +29,7 @@ import akka.pattern.ask import akka.remote.{DisassociatedEvent, RemotingLifecycleEvent} import akka.serialization.SerializationExtension -import org.apache.spark.{Logging, SparkException} +import org.apache.spark.{SparkConf, SparkContext, Logging, SparkException} import org.apache.spark.deploy.{ApplicationDescription, ExecutorState} import org.apache.spark.deploy.DeployMessages._ import org.apache.spark.deploy.master.MasterMessages._ @@ -38,14 +38,16 @@ import org.apache.spark.metrics.MetricsSystem import org.apache.spark.util.{AkkaUtils, Utils} private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Actor with Logging { - import context.dispatcher + import context.dispatcher // to use Akka's scheduler.schedule() + + val conf = new SparkConf val DATE_FORMAT = new SimpleDateFormat("yyyyMMddHHmmss") // For application IDs - val WORKER_TIMEOUT = System.getProperty("spark.worker.timeout", "60").toLong * 1000 - val RETAINED_APPLICATIONS = System.getProperty("spark.deploy.retainedApplications", "200").toInt - val REAPER_ITERATIONS = System.getProperty("spark.dead.worker.persistence", "15").toInt - val RECOVERY_DIR = System.getProperty("spark.deploy.recoveryDirectory", "") - val RECOVERY_MODE = System.getProperty("spark.deploy.recoveryMode", "NONE") + 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 RECOVERY_DIR = conf.get("spark.deploy.recoveryDirectory", "") + val RECOVERY_MODE = conf.get("spark.deploy.recoveryMode", "NONE") var nextAppNumber = 0 val workers = new HashSet[WorkerInfo] @@ -63,8 +65,8 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act Utils.checkHost(host, "Expected hostname") - val masterMetricsSystem = MetricsSystem.createMetricsSystem("master") - val applicationMetricsSystem = MetricsSystem.createMetricsSystem("applications") + val masterMetricsSystem = MetricsSystem.createMetricsSystem("master", conf) + val applicationMetricsSystem = MetricsSystem.createMetricsSystem("applications", conf) val masterSource = new MasterSource(this) val webUi = new MasterWebUI(this, webUiPort) @@ -86,7 +88,7 @@ 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 = System.getProperty("spark.deploy.spreadOut", "true").toBoolean + val spreadOutApps = conf.get("spark.deploy.spreadOut", "true").toBoolean override def preStart() { logInfo("Starting Spark master at " + masterUrl) @@ -103,7 +105,7 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act persistenceEngine = RECOVERY_MODE match { case "ZOOKEEPER" => logInfo("Persisting recovery state to ZooKeeper") - new ZooKeeperPersistenceEngine(SerializationExtension(context.system)) + new ZooKeeperPersistenceEngine(SerializationExtension(context.system), conf) case "FILESYSTEM" => logInfo("Persisting recovery state to directory: " + RECOVERY_DIR) new FileSystemPersistenceEngine(RECOVERY_DIR, SerializationExtension(context.system)) @@ -113,7 +115,7 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act leaderElectionAgent = RECOVERY_MODE match { case "ZOOKEEPER" => - context.actorOf(Props(classOf[ZooKeeperLeaderElectionAgent], self, masterUrl)) + context.actorOf(Props(classOf[ZooKeeperLeaderElectionAgent], self, masterUrl, conf)) case _ => context.actorOf(Props(classOf[MonarchyLeaderAgent], self)) } @@ -495,7 +497,7 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act removeWorker(worker) } else { if (worker.lastHeartbeat < currentTime - ((REAPER_ITERATIONS + 1) * WORKER_TIMEOUT)) - workers -= worker // we've seen this DEAD worker in the UI, etc. for long enough; cull it + workers -= worker // we've seen this DEAD worker in the UI, etc. for long enough; cull it } } } @@ -507,8 +509,9 @@ private[spark] object Master { val sparkUrlRegex = "spark://([^:]+):([0-9]+)".r def main(argStrings: Array[String]) { - val args = new MasterArguments(argStrings) - val (actorSystem, _, _) = startSystemAndActor(args.host, args.port, args.webUiPort) + val conf = new SparkConf + val args = new MasterArguments(argStrings, conf) + val (actorSystem, _, _) = startSystemAndActor(args.host, args.port, args.webUiPort, conf) actorSystem.awaitTermination() } @@ -522,10 +525,12 @@ private[spark] object Master { } } - def startSystemAndActor(host: String, port: Int, webUiPort: Int): (ActorSystem, Int, Int) = { - val (actorSystem, boundPort) = AkkaUtils.createActorSystem(systemName, host, port) + def startSystemAndActor(host: String, port: Int, webUiPort: Int, conf: SparkConf) + : (ActorSystem, Int, Int) = + { + val (actorSystem, boundPort) = AkkaUtils.createActorSystem(systemName, host, port, conf = conf) val actor = actorSystem.actorOf(Props(classOf[Master], host, boundPort, webUiPort), actorName) - val timeout = AkkaUtils.askTimeout + val timeout = AkkaUtils.askTimeout(conf) val respFuture = actor.ask(RequestWebUIPort)(timeout) val resp = Await.result(respFuture, timeout).asInstanceOf[WebUIPortResponse] (actorSystem, boundPort, resp.webUIBoundPort) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala b/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala index 9d89b455fb..e7f3224091 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/MasterArguments.scala @@ -18,16 +18,17 @@ package org.apache.spark.deploy.master import org.apache.spark.util.{Utils, IntParam} +import org.apache.spark.SparkConf /** * Command-line parser for the master. */ -private[spark] class MasterArguments(args: Array[String]) { +private[spark] class MasterArguments(args: Array[String], conf: SparkConf) { var host = Utils.localHostName() var port = 7077 var webUiPort = 8080 - - // Check for settings in environment variables + + // Check for settings in environment variables if (System.getenv("SPARK_MASTER_HOST") != null) { host = System.getenv("SPARK_MASTER_HOST") } @@ -37,8 +38,8 @@ private[spark] class MasterArguments(args: Array[String]) { if (System.getenv("SPARK_MASTER_WEBUI_PORT") != null) { webUiPort = System.getenv("SPARK_MASTER_WEBUI_PORT").toInt } - if (System.getProperty("master.ui.port") != null) { - webUiPort = System.getProperty("master.ui.port").toInt + if (conf.contains("master.ui.port")) { + webUiPort = conf.get("master.ui.port").toInt } parse(args.toList) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/SparkZooKeeperSession.scala b/core/src/main/scala/org/apache/spark/deploy/master/SparkZooKeeperSession.scala index 6cc7fd2ff4..999090ad74 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/SparkZooKeeperSession.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/SparkZooKeeperSession.scala @@ -23,7 +23,7 @@ import org.apache.zookeeper._ import org.apache.zookeeper.Watcher.Event.KeeperState import org.apache.zookeeper.data.Stat -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, Logging} /** * Provides a Scala-side interface to the standard ZooKeeper client, with the addition of retry @@ -35,8 +35,9 @@ import org.apache.spark.Logging * Additionally, all commands sent to ZooKeeper will be retried until they either fail too many * times or a semantic exception is thrown (e.g., "node already exists"). */ -private[spark] class SparkZooKeeperSession(zkWatcher: SparkZooKeeperWatcher) extends Logging { - val ZK_URL = System.getProperty("spark.deploy.zookeeper.url", "") +private[spark] class SparkZooKeeperSession(zkWatcher: SparkZooKeeperWatcher, + conf: SparkConf) extends Logging { + val ZK_URL = conf.get("spark.deploy.zookeeper.url", "") val ZK_ACL = ZooDefs.Ids.OPEN_ACL_UNSAFE val ZK_TIMEOUT_MILLIS = 30000 diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala index 7d535b08de..77c23fb9fb 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ZooKeeperLeaderElectionAgent.scala @@ -21,16 +21,17 @@ import akka.actor.ActorRef import org.apache.zookeeper._ import org.apache.zookeeper.Watcher.Event.EventType -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, Logging} import org.apache.spark.deploy.master.MasterMessages._ -private[spark] class ZooKeeperLeaderElectionAgent(val masterActor: ActorRef, masterUrl: String) +private[spark] class ZooKeeperLeaderElectionAgent(val masterActor: ActorRef, + masterUrl: String, conf: SparkConf) extends LeaderElectionAgent with SparkZooKeeperWatcher with Logging { - val WORKING_DIR = System.getProperty("spark.deploy.zookeeper.dir", "/spark") + "/leader_election" + val WORKING_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") + "/leader_election" private val watcher = new ZooKeeperWatcher() - private val zk = new SparkZooKeeperSession(this) + private val zk = new SparkZooKeeperSession(this, conf) private var status = LeadershipStatus.NOT_LEADER private var myLeaderFile: String = _ private var leaderUrl: String = _ 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 825344b3bb..52000d4f9c 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 @@ -17,19 +17,19 @@ package org.apache.spark.deploy.master -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, Logging} import org.apache.zookeeper._ import akka.serialization.Serialization -class ZooKeeperPersistenceEngine(serialization: Serialization) +class ZooKeeperPersistenceEngine(serialization: Serialization, conf: SparkConf) extends PersistenceEngine with SparkZooKeeperWatcher with Logging { - val WORKING_DIR = System.getProperty("spark.deploy.zookeeper.dir", "/spark") + "/master_status" + val WORKING_DIR = conf.get("spark.deploy.zookeeper.dir", "/spark") + "/master_status" - val zk = new SparkZooKeeperSession(this) + val zk = new SparkZooKeeperSession(this, conf) zk.connect() diff --git a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala index 9ab594b682..ead35662fc 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/ui/MasterWebUI.scala @@ -31,7 +31,7 @@ import org.apache.spark.util.{AkkaUtils, Utils} */ private[spark] class MasterWebUI(val master: Master, requestedPort: Int) extends Logging { - val timeout = AkkaUtils.askTimeout + val timeout = AkkaUtils.askTimeout(master.conf) val host = Utils.localHostName() val port = requestedPort 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 87531b6719..fcaf4e92b1 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 @@ -25,23 +25,14 @@ import scala.collection.mutable.HashMap import scala.concurrent.duration._ import akka.actor._ -import akka.remote.{ DisassociatedEvent, RemotingLifecycleEvent} - -import org.apache.spark.{SparkException, Logging} +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.worker.ui.WorkerWebUI import org.apache.spark.metrics.MetricsSystem -import org.apache.spark.util.{Utils, AkkaUtils} -import org.apache.spark.deploy.DeployMessages.WorkerStateResponse -import org.apache.spark.deploy.DeployMessages.RegisterWorkerFailed -import org.apache.spark.deploy.DeployMessages.KillExecutor -import org.apache.spark.deploy.DeployMessages.ExecutorStateChanged -import org.apache.spark.deploy.DeployMessages.Heartbeat -import org.apache.spark.deploy.DeployMessages.RegisteredWorker -import org.apache.spark.deploy.DeployMessages.LaunchExecutor -import org.apache.spark.deploy.DeployMessages.RegisterWorker +import org.apache.spark.util.{AkkaUtils, Utils} /** * @param masterUrls Each url should look like spark://host:port. @@ -53,7 +44,8 @@ private[spark] class Worker( cores: Int, memory: Int, masterUrls: Array[String], - workDirPath: String = null) + workDirPath: String = null, + val conf: SparkConf) extends Actor with Logging { import context.dispatcher @@ -63,7 +55,7 @@ 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 = System.getProperty("spark.worker.timeout", "60").toLong * 1000 / 4 + val HEARTBEAT_MILLIS = conf.get("spark.worker.timeout", "60").toLong * 1000 / 4 val REGISTRATION_TIMEOUT = 20.seconds val REGISTRATION_RETRIES = 3 @@ -92,7 +84,7 @@ private[spark] class Worker( var coresUsed = 0 var memoryUsed = 0 - val metricsSystem = MetricsSystem.createMetricsSystem("worker") + val metricsSystem = MetricsSystem.createMetricsSystem("worker", conf) val workerSource = new WorkerSource(this) def coresFree: Int = cores - coresUsed @@ -275,6 +267,7 @@ private[spark] class Worker( } private[spark] object Worker { + def main(argStrings: Array[String]) { val args = new WorkerArguments(argStrings) val (actorSystem, _) = startSystemAndActor(args.host, args.port, args.webUiPort, args.cores, @@ -283,13 +276,16 @@ private[spark] object Worker { } def startSystemAndActor(host: String, port: Int, webUiPort: Int, cores: Int, memory: Int, - masterUrls: Array[String], workDir: String, workerNumber: Option[Int] = None) - : (ActorSystem, Int) = { + masterUrls: Array[String], workDir: String, workerNumber: Option[Int] = None) + : (ActorSystem, Int) = + { // The LocalSparkCluster runs multiple local sparkWorkerX actor systems + val conf = new SparkConf val systemName = "sparkWorker" + workerNumber.map(_.toString).getOrElse("") - val (actorSystem, boundPort) = AkkaUtils.createActorSystem(systemName, host, port) + val (actorSystem, boundPort) = AkkaUtils.createActorSystem(systemName, host, port, + conf = conf) actorSystem.actorOf(Props(classOf[Worker], host, boundPort, webUiPort, cores, memory, - masterUrls, workDir), name = "Worker") + masterUrls, workDir, conf), name = "Worker") (actorSystem, boundPort) } 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 19aa800a95..c382034c99 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 @@ -22,7 +22,7 @@ import java.io.File import javax.servlet.http.HttpServletRequest import org.eclipse.jetty.server.{Handler, Server} -import org.apache.spark.Logging +import org.apache.spark.{Logging, SparkConf} import org.apache.spark.deploy.worker.Worker import org.apache.spark.ui.{JettyUtils, UIUtils} import org.apache.spark.ui.JettyUtils._ @@ -34,10 +34,10 @@ import org.apache.spark.util.{AkkaUtils, Utils} private[spark] class WorkerWebUI(val worker: Worker, val workDir: File, requestedPort: Option[Int] = None) extends Logging { - val timeout = AkkaUtils.askTimeout + val timeout = AkkaUtils.askTimeout(worker.conf) val host = Utils.localHostName() val port = requestedPort.getOrElse( - System.getProperty("worker.ui.port", WorkerWebUI.DEFAULT_PORT).toInt) + worker.conf.get("worker.ui.port", WorkerWebUI.DEFAULT_PORT).toInt) var server: Option[Server] = None var boundPort: Option[Int] = None 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 debbdd4c44..53a2b94a52 100644 --- a/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala +++ b/core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala @@ -22,7 +22,7 @@ import java.nio.ByteBuffer import akka.actor._ import akka.remote._ -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, SparkContext, Logging} import org.apache.spark.TaskState.TaskState import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._ import org.apache.spark.util.{Utils, AkkaUtils} @@ -98,10 +98,10 @@ private[spark] object CoarseGrainedExecutorBackend { // Create a new ActorSystem to run the backend, because we can't create a SparkEnv / Executor // before getting started with all our system properties, etc val (actorSystem, boundPort) = AkkaUtils.createActorSystem("sparkExecutor", hostname, 0, - indestructible = true) + indestructible = true, conf = new SparkConf) // set it val sparkHostPort = hostname + ":" + boundPort - System.setProperty("spark.hostPort", sparkHostPort) +// conf.set("spark.hostPort", sparkHostPort) actorSystem.actorOf( Props(classOf[CoarseGrainedExecutorBackend], driverUrl, executorId, sparkHostPort, cores), name = "Executor") diff --git a/core/src/main/scala/org/apache/spark/executor/Executor.scala b/core/src/main/scala/org/apache/spark/executor/Executor.scala index 782be9a429..e51d274d33 100644 --- a/core/src/main/scala/org/apache/spark/executor/Executor.scala +++ b/core/src/main/scala/org/apache/spark/executor/Executor.scala @@ -56,16 +56,17 @@ private[spark] class Executor( // Make sure the local hostname we report matches the cluster scheduler's name for this host Utils.setCustomHostname(slaveHostname) - // Set spark.* system properties from executor arg - for ((key, value) <- properties) { - System.setProperty(key, value) - } + // Set spark.* properties from executor arg + val conf = new SparkConf(false) + conf.setAll(properties) // If we are in yarn mode, systems can have different disk layouts so we must set it // to what Yarn on this system said was available. This will be used later when SparkEnv // created. - if (java.lang.Boolean.valueOf(System.getenv("SPARK_YARN_MODE"))) { - System.setProperty("spark.local.dir", getYarnLocalDirs()) + if (java.lang.Boolean.valueOf( + System.getProperty("SPARK_YARN_MODE", System.getenv("SPARK_YARN_MODE")))) + { + conf.set("spark.local.dir", getYarnLocalDirs()) } // Create our ClassLoader and set it on this thread @@ -106,7 +107,7 @@ private[spark] class Executor( // Initialize Spark environment (using system properties read above) private val env = { if (!isLocal) { - val _env = SparkEnv.createFromSystemProperties(executorId, slaveHostname, 0, + val _env = SparkEnv.create(conf, executorId, slaveHostname, 0, isDriver = false, isLocal = false) SparkEnv.set(_env) _env.metricsSystem.registerSource(executorSource) @@ -140,11 +141,6 @@ private[spark] class Executor( val tr = runningTasks.get(taskId) if (tr != null) { tr.kill() - // We remove the task also in the finally block in TaskRunner.run. - // The reason we need to remove it here is because killTask might be called before the task - // is even launched, and never reaching that finally block. ConcurrentHashMap's remove is - // idempotent. - runningTasks.remove(taskId) } } @@ -166,6 +162,8 @@ private[spark] class Executor( class TaskRunner(execBackend: ExecutorBackend, taskId: Long, serializedTask: ByteBuffer) extends Runnable { + object TaskKilledException extends Exception + @volatile private var killed = false @volatile private var task: Task[Any] = _ @@ -199,9 +197,11 @@ private[spark] class Executor( // If this task has been killed before we deserialized it, let's quit now. Otherwise, // continue executing the task. if (killed) { - logInfo("Executor killed task " + taskId) - execBackend.statusUpdate(taskId, TaskState.KILLED, ser.serialize(TaskKilled)) - return + // Throw an exception rather than returning, because returning within a try{} block + // causes a NonLocalReturnControl exception to be thrown. The NonLocalReturnControl + // exception will be caught by the catch block, leading to an incorrect ExceptionFailure + // for the task. + throw TaskKilledException } attemptedTask = Some(task) @@ -215,9 +215,7 @@ private[spark] class Executor( // If the task has been killed, let's fail it. if (task.killed) { - logInfo("Executor killed task " + taskId) - execBackend.statusUpdate(taskId, TaskState.KILLED, ser.serialize(TaskKilled)) - return + throw TaskKilledException } val resultSer = SparkEnv.get.serializer.newInstance() @@ -259,6 +257,11 @@ private[spark] class Executor( execBackend.statusUpdate(taskId, TaskState.FAILED, ser.serialize(reason)) } + case TaskKilledException => { + logInfo("Executor killed task " + taskId) + execBackend.statusUpdate(taskId, TaskState.KILLED, ser.serialize(TaskKilled)) + } + case t: Throwable => { val serviceTime = (System.currentTimeMillis() - taskStart).toInt val metrics = attemptedTask.flatMap(t => t.metrics) @@ -301,7 +304,7 @@ private[spark] class Executor( * new classes defined by the REPL as the user types code */ private def addReplClassLoaderIfNeeded(parent: ClassLoader): ClassLoader = { - val classUri = System.getProperty("spark.repl.class.uri") + val classUri = conf.get("spark.repl.class.uri", null) if (classUri != null) { logInfo("Using REPL class URI: " + classUri) try { @@ -329,12 +332,12 @@ private[spark] class Executor( // Fetch missing dependencies for ((name, timestamp) <- newFiles if currentFiles.getOrElse(name, -1L) < timestamp) { logInfo("Fetching " + name + " with timestamp " + timestamp) - Utils.fetchFile(name, new File(SparkFiles.getRootDirectory)) + Utils.fetchFile(name, new File(SparkFiles.getRootDirectory), conf) currentFiles(name) = timestamp } for ((name, timestamp) <- newJars if currentJars.getOrElse(name, -1L) < timestamp) { logInfo("Fetching " + name + " with timestamp " + timestamp) - Utils.fetchFile(name, new File(SparkFiles.getRootDirectory)) + Utils.fetchFile(name, new File(SparkFiles.getRootDirectory), conf) currentJars(name) = timestamp // Add it to our class loader val localName = name.split("/").last 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 570a979b56..a1e98845f6 100644 --- a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala +++ b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala @@ -22,6 +22,7 @@ import java.io.{InputStream, OutputStream} import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream} import org.xerial.snappy.{SnappyInputStream, SnappyOutputStream} +import org.apache.spark.{SparkEnv, SparkConf} /** @@ -37,15 +38,15 @@ trait CompressionCodec { private[spark] object CompressionCodec { - - def createCodec(): CompressionCodec = { - createCodec(System.getProperty( + def createCodec(conf: SparkConf): CompressionCodec = { + createCodec(conf, conf.get( "spark.io.compression.codec", classOf[LZFCompressionCodec].getName)) } - def createCodec(codecName: String): CompressionCodec = { - Class.forName(codecName, true, Thread.currentThread.getContextClassLoader) - .newInstance().asInstanceOf[CompressionCodec] + def createCodec(conf: SparkConf, codecName: String): CompressionCodec = { + val ctor = Class.forName(codecName, true, Thread.currentThread.getContextClassLoader) + .getConstructor(classOf[SparkConf]) + ctor.newInstance(conf).asInstanceOf[CompressionCodec] } } @@ -53,7 +54,7 @@ private[spark] object CompressionCodec { /** * LZF implementation of [[org.apache.spark.io.CompressionCodec]]. */ -class LZFCompressionCodec extends CompressionCodec { +class LZFCompressionCodec(conf: SparkConf) extends CompressionCodec { override def compressedOutputStream(s: OutputStream): OutputStream = { new LZFOutputStream(s).setFinishBlockOnFlush(true) @@ -67,10 +68,10 @@ class LZFCompressionCodec extends CompressionCodec { * Snappy implementation of [[org.apache.spark.io.CompressionCodec]]. * Block size can be configured by spark.io.compression.snappy.block.size. */ -class SnappyCompressionCodec extends CompressionCodec { +class SnappyCompressionCodec(conf: SparkConf) extends CompressionCodec { override def compressedOutputStream(s: OutputStream): OutputStream = { - val blockSize = System.getProperty("spark.io.compression.snappy.block.size", "32768").toInt + val blockSize = conf.get("spark.io.compression.snappy.block.size", "32768").toInt new SnappyOutputStream(s, blockSize) } diff --git a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala index 8e038ce98c..9930537b34 100644 --- a/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala +++ b/core/src/main/scala/org/apache/spark/metrics/MetricsSystem.scala @@ -24,7 +24,7 @@ import java.util.concurrent.TimeUnit import scala.collection.mutable -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, Logging} import org.apache.spark.metrics.sink.{MetricsServlet, Sink} import org.apache.spark.metrics.source.Source @@ -62,9 +62,10 @@ import org.apache.spark.metrics.source.Source * * [options] is the specific property of this source or sink. */ -private[spark] class MetricsSystem private (val instance: String) extends Logging { +private[spark] class MetricsSystem private (val instance: String, + conf: SparkConf) extends Logging { - val confFile = System.getProperty("spark.metrics.conf") + val confFile = conf.get("spark.metrics.conf", null) val metricsConfig = new MetricsConfig(Option(confFile)) val sinks = new mutable.ArrayBuffer[Sink] @@ -158,5 +159,6 @@ private[spark] object MetricsSystem { } } - def createMetricsSystem(instance: String): MetricsSystem = new MetricsSystem(instance) + def createMetricsSystem(instance: String, conf: SparkConf): MetricsSystem = + new MetricsSystem(instance, conf) } 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 703bc6a9ca..46c40d0a2a 100644 --- a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala @@ -37,7 +37,7 @@ import scala.concurrent.duration._ import org.apache.spark.util.Utils -private[spark] class ConnectionManager(port: Int) extends Logging { +private[spark] class ConnectionManager(port: Int, conf: SparkConf) extends Logging { class MessageStatus( val message: Message, @@ -54,22 +54,22 @@ private[spark] class ConnectionManager(port: Int) extends Logging { private val selector = SelectorProvider.provider.openSelector() private val handleMessageExecutor = new ThreadPoolExecutor( - System.getProperty("spark.core.connection.handler.threads.min","20").toInt, - System.getProperty("spark.core.connection.handler.threads.max","60").toInt, - System.getProperty("spark.core.connection.handler.threads.keepalive","60").toInt, TimeUnit.SECONDS, + 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, new LinkedBlockingDeque[Runnable]()) private val handleReadWriteExecutor = new ThreadPoolExecutor( - System.getProperty("spark.core.connection.io.threads.min","4").toInt, - System.getProperty("spark.core.connection.io.threads.max","32").toInt, - System.getProperty("spark.core.connection.io.threads.keepalive","60").toInt, TimeUnit.SECONDS, + 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, 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( - System.getProperty("spark.core.connection.connect.threads.min","1").toInt, - System.getProperty("spark.core.connection.connect.threads.max","8").toInt, - System.getProperty("spark.core.connection.connect.threads.keepalive","60").toInt, TimeUnit.SECONDS, + 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, new LinkedBlockingDeque[Runnable]()) private val serverChannel = ServerSocketChannel.open() @@ -594,7 +594,7 @@ private[spark] class ConnectionManager(port: Int) extends Logging { private[spark] object ConnectionManager { def main(args: Array[String]) { - val manager = new ConnectionManager(9999) + val manager = new ConnectionManager(9999, new SparkConf) manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => { println("Received [" + msg + "] from [" + id + "]") None diff --git a/core/src/main/scala/org/apache/spark/network/ReceiverTest.scala b/core/src/main/scala/org/apache/spark/network/ReceiverTest.scala index 781715108b..1c9d6030d6 100644 --- a/core/src/main/scala/org/apache/spark/network/ReceiverTest.scala +++ b/core/src/main/scala/org/apache/spark/network/ReceiverTest.scala @@ -19,19 +19,19 @@ package org.apache.spark.network import java.nio.ByteBuffer import java.net.InetAddress +import org.apache.spark.SparkConf private[spark] object ReceiverTest { - def main(args: Array[String]) { - val manager = new ConnectionManager(9999) + val manager = new ConnectionManager(9999, new SparkConf) println("Started connection manager with id = " + manager.id) - - manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => { + + manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => { /*println("Received [" + msg + "] from [" + id + "] at " + System.currentTimeMillis)*/ - val buffer = ByteBuffer.wrap("response".getBytes()) + val buffer = ByteBuffer.wrap("response".getBytes) Some(Message.createBufferMessage(buffer, msg.id)) }) - Thread.currentThread.join() + Thread.currentThread.join() } } diff --git a/core/src/main/scala/org/apache/spark/network/SenderTest.scala b/core/src/main/scala/org/apache/spark/network/SenderTest.scala index 777574980f..dcbd183c88 100644 --- a/core/src/main/scala/org/apache/spark/network/SenderTest.scala +++ b/core/src/main/scala/org/apache/spark/network/SenderTest.scala @@ -19,29 +19,29 @@ package org.apache.spark.network import java.nio.ByteBuffer import java.net.InetAddress +import org.apache.spark.SparkConf private[spark] object SenderTest { - def main(args: Array[String]) { - + if (args.length < 2) { println("Usage: SenderTest <target host> <target port>") System.exit(1) } - + val targetHost = args(0) val targetPort = args(1).toInt val targetConnectionManagerId = new ConnectionManagerId(targetHost, targetPort) - val manager = new ConnectionManager(0) + val manager = new ConnectionManager(0, new SparkConf) println("Started connection manager with id = " + manager.id) - manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => { + manager.onReceiveMessage((msg: Message, id: ConnectionManagerId) => { println("Received [" + msg + "] from [" + id + "]") None }) - - val size = 100 * 1024 * 1024 + + val size = 100 * 1024 * 1024 val buffer = ByteBuffer.allocate(size).put(Array.tabulate[Byte](size)(x => x.toByte)) buffer.flip @@ -50,7 +50,7 @@ private[spark] object SenderTest { val count = 100 (0 until count).foreach(i => { val dataMessage = Message.createBufferMessage(buffer.duplicate) - val startTime = System.currentTimeMillis + val startTime = System.currentTimeMillis /*println("Started timer at " + startTime)*/ val responseStr = manager.sendMessageReliablySync(targetConnectionManagerId, dataMessage) match { case Some(response) => 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 b1e1576dad..b729eb11c5 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 @@ -23,20 +23,20 @@ import io.netty.buffer.ByteBuf import io.netty.channel.ChannelHandlerContext import io.netty.util.CharsetUtil -import org.apache.spark.Logging +import org.apache.spark.{SparkContext, SparkConf, Logging} import org.apache.spark.network.ConnectionManagerId import scala.collection.JavaConverters._ import org.apache.spark.storage.BlockId -private[spark] class ShuffleCopier extends Logging { +private[spark] class ShuffleCopier(conf: SparkConf) extends Logging { def getBlock(host: String, port: Int, blockId: BlockId, resultCollectCallback: (BlockId, Long, ByteBuf) => Unit) { val handler = new ShuffleCopier.ShuffleClientHandler(resultCollectCallback) - val connectTimeout = System.getProperty("spark.shuffle.netty.connect.timeout", "60000").toInt + val connectTimeout = conf.get("spark.shuffle.netty.connect.timeout", "60000").toInt val fc = new FileClient(handler, connectTimeout) try { @@ -104,10 +104,10 @@ private[spark] object ShuffleCopier extends Logging { val threads = if (args.length > 3) args(3).toInt else 10 val copiers = Executors.newFixedThreadPool(80) - val tasks = (for (i <- Range(0, threads)) yield { + val tasks = (for (i <- Range(0, threads)) yield { Executors.callable(new Runnable() { def run() { - val copier = new ShuffleCopier() + val copier = new ShuffleCopier(new SparkConf) copier.getBlock(host, port, blockId, echoResultCollectCallBack) } }) 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 293a7d1f68..6d4f46125f 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala @@ -76,7 +76,6 @@ class CheckpointRDD[T: ClassTag](sc: SparkContext, val checkpointPath: String) } private[spark] object CheckpointRDD extends Logging { - def splitIdToFile(splitId: Int): String = { "part-%05d".format(splitId) } @@ -98,7 +97,7 @@ private[spark] object CheckpointRDD extends Logging { throw new IOException("Checkpoint failed: temporary path " + tempOutputPath + " already exists") } - val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt + val bufferSize = env.conf.get("spark.buffer.size", "65536").toInt val fileOutputStream = if (blockSize < 0) { fs.create(tempOutputPath, false, bufferSize) @@ -132,7 +131,7 @@ private[spark] object CheckpointRDD extends Logging { ): Iterator[T] = { val env = SparkEnv.get val fs = path.getFileSystem(broadcastedConf.value.value) - val bufferSize = System.getProperty("spark.buffer.size", "65536").toInt + val bufferSize = env.conf.get("spark.buffer.size", "65536").toInt 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/CoGroupedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala index 911a002884..4ba4696fef 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CoGroupedRDD.scala @@ -114,7 +114,7 @@ class CoGroupedRDD[K](@transient var rdds: Seq[RDD[_ <: Product2[K, _]]], part: map.changeValue(k, update) } - val ser = SparkEnv.get.serializerManager.get(serializerClass) + val ser = SparkEnv.get.serializerManager.get(serializerClass, SparkEnv.get.conf) for ((dep, depNum) <- split.deps.zipWithIndex) dep match { case NarrowCoGroupSplitDep(rdd, _, itsSplit) => { // Read them from the parent diff --git a/core/src/main/scala/org/apache/spark/rdd/PartitionerAwareUnionRDD.scala b/core/src/main/scala/org/apache/spark/rdd/PartitionerAwareUnionRDD.scala new file mode 100644 index 0000000000..4c625d062e --- /dev/null +++ b/core/src/main/scala/org/apache/spark/rdd/PartitionerAwareUnionRDD.scala @@ -0,0 +1,110 @@ +/* + * Licensed to the Apache Software Foundation (ASF) under one or more + * contributor license agreements. See the NOTICE file distributed with + * this work for additional information regarding copyright ownership. + * The ASF licenses this file to You under the Apache License, Version 2.0 + * (the "License"); you may not use this file except in compliance with + * the License. You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package org.apache.spark.rdd + +import scala.reflect.ClassTag +import java.io.{ObjectOutputStream, IOException} +import org.apache.spark.{TaskContext, OneToOneDependency, SparkContext, Partition} + + +/** + * Class representing partitions of PartitionerAwareUnionRDD, which maintains the list of corresponding partitions + * of parent RDDs. + */ +private[spark] +class PartitionerAwareUnionRDDPartition( + @transient val rdds: Seq[RDD[_]], + val idx: Int + ) extends Partition { + var parents = rdds.map(_.partitions(idx)).toArray + + override val index = idx + override def hashCode(): Int = idx + + @throws(classOf[IOException]) + private def writeObject(oos: ObjectOutputStream) { + // Update the reference to parent partition at the time of task serialization + parents = rdds.map(_.partitions(index)).toArray + oos.defaultWriteObject() + } +} + +/** + * Class representing an RDD that can take multiple RDDs partitioned by the same partitioner and + * unify them into a single RDD while preserving the partitioner. So m RDDs with p partitions each + * will be unified to a single RDD with p partitions and the same partitioner. The preferred + * location for each partition of the unified RDD will be the most common preferred location + * of the corresponding partitions of the parent RDDs. For example, location of partition 0 + * of the unified RDD will be where most of partition 0 of the parent RDDs are located. + */ +private[spark] +class PartitionerAwareUnionRDD[T: ClassTag]( + sc: SparkContext, + var rdds: Seq[RDD[T]] + ) extends RDD[T](sc, rdds.map(x => new OneToOneDependency(x))) { + require(rdds.length > 0) + require(rdds.flatMap(_.partitioner).toSet.size == 1, + "Parent RDDs have different partitioners: " + rdds.flatMap(_.partitioner)) + + override val partitioner = rdds.head.partitioner + + override def getPartitions: Array[Partition] = { + val numPartitions = partitioner.get.numPartitions + (0 until numPartitions).map(index => { + new PartitionerAwareUnionRDDPartition(rdds, index) + }).toArray + } + + // Get the location where most of the partitions of parent RDDs are located + override def getPreferredLocations(s: Partition): Seq[String] = { + logDebug("Finding preferred location for " + this + ", partition " + s.index) + val parentPartitions = s.asInstanceOf[PartitionerAwareUnionRDDPartition].parents + val locations = rdds.zip(parentPartitions).flatMap { + case (rdd, part) => { + val parentLocations = currPrefLocs(rdd, part) + logDebug("Location of " + rdd + " partition " + part.index + " = " + parentLocations) + parentLocations + } + } + val location = if (locations.isEmpty) { + None + } else { + // Find the location that maximum number of parent partitions prefer + Some(locations.groupBy(x => x).maxBy(_._2.length)._1) + } + logDebug("Selected location for " + this + ", partition " + s.index + " = " + location) + location.toSeq + } + + override def compute(s: Partition, context: TaskContext): Iterator[T] = { + val parentPartitions = s.asInstanceOf[PartitionerAwareUnionRDDPartition].parents + rdds.zip(parentPartitions).iterator.flatMap { + case (rdd, p) => rdd.iterator(p, context) + } + } + + override def clearDependencies() { + super.clearDependencies() + rdds = null + } + + // Get the *current* preferred locations from the DAGScheduler (as opposed to the static ones) + private def currPrefLocs(rdd: RDD[_], part: Partition): Seq[String] = { + rdd.context.getPreferredLocs(rdd, part.index).map(tl => tl.host) + } +} 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 4960e6e82f..3f41b66279 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -82,6 +82,7 @@ abstract class RDD[T: ClassTag]( def this(@transient oneParent: RDD[_]) = this(oneParent.context , List(new OneToOneDependency(oneParent))) + private[spark] def conf = sc.conf // ======================================================================= // Methods that should be implemented by subclasses of RDD // ======================================================================= @@ -952,7 +953,7 @@ abstract class RDD[T: ClassTag]( private var storageLevel: StorageLevel = StorageLevel.NONE /** Record user function generating this RDD. */ - @transient private[spark] val origin = Utils.formatSparkCallSite + @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/rdd/RDDCheckpointData.scala b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala index 642dabaad5..bc688110f4 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDDCheckpointData.scala @@ -40,7 +40,7 @@ private[spark] object CheckpointState extends Enumeration { * manages the post-checkpoint state by providing the updated partitions, iterator and preferred locations * of the checkpointed RDD. */ -private[spark] class RDDCheckpointData[T: ClassTag](rdd: RDD[T]) +private[spark] class RDDCheckpointData[T: ClassTag](@transient rdd: RDD[T]) extends Logging with Serializable { import CheckpointState._ diff --git a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala index 3682c84598..0ccb309d0d 100644 --- a/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/ShuffledRDD.scala @@ -59,7 +59,7 @@ class ShuffledRDD[K, V, P <: Product2[K, V] : ClassTag]( override def compute(split: Partition, context: TaskContext): Iterator[P] = { val shuffledId = dependencies.head.asInstanceOf[ShuffleDependency[K, V]].shuffleId SparkEnv.get.shuffleFetcher.fetch[P](shuffledId, split.index, context, - SparkEnv.get.serializerManager.get(serializerClass)) + SparkEnv.get.serializerManager.get(serializerClass, SparkEnv.get.conf)) } override def clearDependencies() { diff --git a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala index aab30b1bb4..4f90c7d3d6 100644 --- a/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/SubtractedRDD.scala @@ -93,7 +93,7 @@ private[spark] class SubtractedRDD[K: ClassTag, V: ClassTag, W: ClassTag]( override def compute(p: Partition, context: TaskContext): Iterator[(K, V)] = { val partition = p.asInstanceOf[CoGroupPartition] - val serializer = SparkEnv.get.serializerManager.get(serializerClass) + val serializer = SparkEnv.get.serializerManager.get(serializerClass, SparkEnv.get.conf) val map = new JHashMap[K, ArrayBuffer[V]] def getSeq(k: K): ArrayBuffer[V] = { val seq = map.get(k) diff --git a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala index 7603eb292f..043e01dbfb 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/DAGScheduler.scala @@ -159,7 +159,8 @@ class DAGScheduler( val activeJobs = new HashSet[ActiveJob] val resultStageToJob = new HashMap[Stage, ActiveJob] - val metadataCleaner = new MetadataCleaner(MetadataCleanerType.DAG_SCHEDULER, this.cleanup) + val metadataCleaner = new MetadataCleaner( + MetadataCleanerType.DAG_SCHEDULER, this.cleanup, env.conf) /** * Starts the event processing actor. The actor has two responsibilities: diff --git a/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala b/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala index 1791ee660d..90eb8a747f 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/InputFormatInfo.scala @@ -32,7 +32,7 @@ import scala.collection.JavaConversions._ /** * Parses and holds information about inputFormat (and files) specified as a parameter. */ -class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Class[_], +class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Class[_], val path: String) extends Logging { var mapreduceInputFormat: Boolean = false @@ -40,7 +40,7 @@ class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Cl validate() - override def toString(): String = { + override def toString: String = { "InputFormatInfo " + super.toString + " .. inputFormatClazz " + inputFormatClazz + ", path : " + path } @@ -125,7 +125,7 @@ class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Cl } private def findPreferredLocations(): Set[SplitInfo] = { - logDebug("mapreduceInputFormat : " + mapreduceInputFormat + ", mapredInputFormat : " + mapredInputFormat + + logDebug("mapreduceInputFormat : " + mapreduceInputFormat + ", mapredInputFormat : " + mapredInputFormat + ", inputFormatClazz : " + inputFormatClazz) if (mapreduceInputFormat) { return prefLocsFromMapreduceInputFormat() @@ -143,14 +143,14 @@ class InputFormatInfo(val configuration: Configuration, val inputFormatClazz: Cl object InputFormatInfo { /** Computes the preferred locations based on input(s) and returned a location to block map. - Typical use of this method for allocation would follow some algo like this - (which is what we currently do in YARN branch) : + Typical use of this method for allocation would follow some algo like this: + a) For each host, count number of splits hosted on that host. b) Decrement the currently allocated containers on that host. c) Compute rack info for each host and update rack -> count map based on (b). d) Allocate nodes based on (c) - e) On the allocation result, ensure that we dont allocate "too many" jobs on a single node - (even if data locality on that is very high) : this is to prevent fragility of job if a single + e) On the allocation result, ensure that we dont allocate "too many" jobs on a single node + (even if data locality on that is very high) : this is to prevent fragility of job if a single (or small set of) hosts go down. go to (a) until required nodes are allocated. diff --git a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala index 310ec62ca8..28f3ba53b8 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ResultTask.scala @@ -32,7 +32,9 @@ private[spark] object ResultTask { // expensive on the master node if it needs to launch thousands of tasks. val serializedInfoCache = new TimeStampedHashMap[Int, Array[Byte]] - val metadataCleaner = new MetadataCleaner(MetadataCleanerType.RESULT_TASK, serializedInfoCache.clearOldValues) + // TODO: This object shouldn't have global variables + val metadataCleaner = new MetadataCleaner( + MetadataCleanerType.RESULT_TASK, serializedInfoCache.clearOldValues, new SparkConf) def serializeInfo(stageId: Int, rdd: RDD[_], func: (TaskContext, Iterator[_]) => _): Array[Byte] = { synchronized { diff --git a/core/src/main/scala/org/apache/spark/scheduler/SchedulableBuilder.scala b/core/src/main/scala/org/apache/spark/scheduler/SchedulableBuilder.scala index 356fe56bf3..3cf995ea74 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SchedulableBuilder.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SchedulableBuilder.scala @@ -20,7 +20,7 @@ package org.apache.spark.scheduler import java.io.{FileInputStream, InputStream} import java.util.{NoSuchElementException, Properties} -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, Logging} import scala.xml.XML @@ -49,10 +49,10 @@ private[spark] class FIFOSchedulableBuilder(val rootPool: Pool) } } -private[spark] class FairSchedulableBuilder(val rootPool: Pool) +private[spark] class FairSchedulableBuilder(val rootPool: Pool, conf: SparkConf) extends SchedulableBuilder with Logging { - val schedulerAllocFile = Option(System.getProperty("spark.scheduler.allocation.file")) + val schedulerAllocFile = conf.getOption("spark.scheduler.allocation.file") val DEFAULT_SCHEDULER_FILE = "fairscheduler.xml" val FAIR_SCHEDULER_PROPERTIES = "spark.scheduler.pool" val DEFAULT_POOL_NAME = "default" diff --git a/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala index 89aa098664..02bdbba825 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/SchedulerBackend.scala @@ -31,7 +31,4 @@ private[spark] trait SchedulerBackend { def defaultParallelism(): Int def killTask(taskId: Long, executorId: String): Unit = throw new UnsupportedOperationException - - // Memory used by each executor (in megabytes) - protected val executorMemory: Int = SparkContext.executorMemoryRequested } diff --git a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala index 0f2deb4bcb..a37ead5632 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/ShuffleMapTask.scala @@ -37,7 +37,9 @@ private[spark] object ShuffleMapTask { // expensive on the master node if it needs to launch thousands of tasks. val serializedInfoCache = new TimeStampedHashMap[Int, Array[Byte]] - val metadataCleaner = new MetadataCleaner(MetadataCleanerType.SHUFFLE_MAP_TASK, serializedInfoCache.clearOldValues) + // TODO: This object shouldn't have global variables + val metadataCleaner = new MetadataCleaner( + MetadataCleanerType.SHUFFLE_MAP_TASK, serializedInfoCache.clearOldValues, new SparkConf) def serializeInfo(stageId: Int, rdd: RDD[_], dep: ShuffleDependency[_,_]): Array[Byte] = { synchronized { @@ -152,7 +154,7 @@ private[spark] class ShuffleMapTask( try { // Obtain all the block writers for shuffle blocks. - val ser = SparkEnv.get.serializerManager.get(dep.serializerClass) + val ser = SparkEnv.get.serializerManager.get(dep.serializerClass, SparkEnv.get.conf) shuffle = shuffleBlockManager.forMapTask(dep.shuffleId, partitionId, numOutputSplits, ser) // Write the map output to its associated buckets. 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 89102720fa..e22b1e53e8 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala @@ -30,7 +30,8 @@ import org.apache.spark.util.Utils */ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedulerImpl) extends Logging { - private val THREADS = System.getProperty("spark.resultGetter.threads", "4").toInt + + private val THREADS = sparkEnv.conf.get("spark.resultGetter.threads", "4").toInt 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 1b0f82fa24..0c8ed62759 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -35,7 +35,7 @@ import org.apache.spark.scheduler.SchedulingMode.SchedulingMode * It can also work with a local setup by using a LocalBackend and setting isLocal to true. * It handles common logic, like determining a scheduling order across jobs, waking up to launch * speculative tasks, etc. - * + * * Clients should first call initialize() and start(), then submit task sets through the * runTasks method. * @@ -47,15 +47,19 @@ import org.apache.spark.scheduler.SchedulingMode.SchedulingMode */ private[spark] class TaskSchedulerImpl( val sc: SparkContext, - val maxTaskFailures : Int = System.getProperty("spark.task.maxFailures", "4").toInt, + val maxTaskFailures: Int, isLocal: Boolean = false) - extends TaskScheduler with Logging { + extends TaskScheduler with Logging +{ + def this(sc: SparkContext) = this(sc, sc.conf.get("spark.task.maxFailures", "4").toInt) + + val conf = sc.conf // How often to check for speculative tasks - val SPECULATION_INTERVAL = System.getProperty("spark.speculation.interval", "100").toLong + val SPECULATION_INTERVAL = conf.get("spark.speculation.interval", "100").toLong // Threshold above which we warn user initial TaskSet may be starved - val STARVATION_TIMEOUT = System.getProperty("spark.starvation.timeout", "15000").toLong + val STARVATION_TIMEOUT = conf.get("spark.starvation.timeout", "15000").toLong // TaskSetManagers are not thread safe, so any access to one should be synchronized // on this class. @@ -92,7 +96,7 @@ private[spark] class TaskSchedulerImpl( var rootPool: Pool = null // default scheduler is FIFO val schedulingMode: SchedulingMode = SchedulingMode.withName( - System.getProperty("spark.scheduler.mode", "FIFO")) + conf.get("spark.scheduler.mode", "FIFO")) // This is a var so that we can reset it for testing purposes. private[spark] var taskResultGetter = new TaskResultGetter(sc.env, this) @@ -110,7 +114,7 @@ private[spark] class TaskSchedulerImpl( case SchedulingMode.FIFO => new FIFOSchedulableBuilder(rootPool) case SchedulingMode.FAIR => - new FairSchedulableBuilder(rootPool) + new FairSchedulableBuilder(rootPool, conf) } } schedulableBuilder.buildPools() @@ -121,7 +125,7 @@ private[spark] class TaskSchedulerImpl( override def start() { backend.start() - if (!isLocal && System.getProperty("spark.speculation", "false").toBoolean) { + if (!isLocal && conf.get("spark.speculation", "false").toBoolean) { logInfo("Starting speculative execution thread") import sc.env.actorSystem.dispatcher sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds, @@ -281,7 +285,8 @@ private[spark] class TaskSchedulerImpl( } } case None => - logInfo("Ignoring update from TID " + tid + " because its task set is gone") + logInfo("Ignoring update with state %s from TID %s because its task set is gone" + .format(state, tid)) } } catch { case e: Exception => logError("Exception in statusUpdate", e) @@ -324,7 +329,7 @@ private[spark] class TaskSchedulerImpl( // Have each task set throw a SparkException with the error for ((taskSetId, manager) <- activeTaskSets) { try { - manager.error(message) + manager.abort(message) } catch { case e: Exception => logError("Exception in error callback", e) } 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 7929051791..6dd1469d8f 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala @@ -54,12 +54,14 @@ private[spark] class TaskSetManager( clock: Clock = SystemClock) extends Schedulable with Logging { + val conf = sched.sc.conf + // CPUs to request per task - val CPUS_PER_TASK = System.getProperty("spark.task.cpus", "1").toInt + val CPUS_PER_TASK = conf.get("spark.task.cpus", "1").toInt // Quantile of tasks at which to start speculation - val SPECULATION_QUANTILE = System.getProperty("spark.speculation.quantile", "0.75").toDouble - val SPECULATION_MULTIPLIER = System.getProperty("spark.speculation.multiplier", "1.5").toDouble + val SPECULATION_QUANTILE = conf.get("spark.speculation.quantile", "0.75").toDouble + val SPECULATION_MULTIPLIER = conf.get("spark.speculation.multiplier", "1.5").toDouble // Serializer for closures and tasks. val env = SparkEnv.get @@ -114,7 +116,7 @@ private[spark] class TaskSetManager( // How frequently to reprint duplicate exceptions in full, in milliseconds val EXCEPTION_PRINT_INTERVAL = - System.getProperty("spark.logging.exceptionPrintInterval", "10000").toLong + conf.get("spark.logging.exceptionPrintInterval", "10000").toLong // 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 @@ -546,11 +548,6 @@ private[spark] class TaskSetManager( } } - def error(message: String) { - // Save the error message - abort("Error: " + message) - } - def abort(message: String) { // TODO: Kill running tasks if we were not terminated due to a Mesos error sched.dagScheduler.taskSetFailed(taskSet, message) @@ -676,14 +673,14 @@ private[spark] class TaskSetManager( } private def getLocalityWait(level: TaskLocality.TaskLocality): Long = { - val defaultWait = System.getProperty("spark.locality.wait", "3000") + val defaultWait = conf.get("spark.locality.wait", "3000") level match { case TaskLocality.PROCESS_LOCAL => - System.getProperty("spark.locality.wait.process", defaultWait).toLong + conf.get("spark.locality.wait.process", defaultWait).toLong case TaskLocality.NODE_LOCAL => - System.getProperty("spark.locality.wait.node", defaultWait).toLong + conf.get("spark.locality.wait.node", defaultWait).toLong case TaskLocality.RACK_LOCAL => - System.getProperty("spark.locality.wait.rack", defaultWait).toLong + conf.get("spark.locality.wait.rack", defaultWait).toLong case TaskLocality.ANY => 0L } 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 5c534a6f43..2f5bcafe40 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 @@ -48,8 +48,8 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A { // Use an atomic variable to track total number of cores in the cluster for simplicity and speed var totalCoreCount = new AtomicInteger(0) - - private val timeout = AkkaUtils.askTimeout + val conf = scheduler.sc.conf + private val timeout = AkkaUtils.askTimeout(conf) class DriverActor(sparkProperties: Seq[(String, String)]) extends Actor { private val executorActor = new HashMap[String, ActorRef] @@ -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 = System.getProperty("spark.scheduler.revive.interval", "1000").toLong + val reviveInterval = conf.get("spark.scheduler.revive.interval", "1000").toLong import context.dispatcher context.system.scheduler.schedule(0.millis, reviveInterval.millis, self, ReviveOffers) } @@ -119,7 +119,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A removeExecutor(executorId, reason) sender ! true - case DisassociatedEvent(_, address, _) => + case DisassociatedEvent(_, address, _) => addressToExecutorId.get(address).foreach(removeExecutor(_, "remote Akka client disassociated")) } @@ -164,14 +164,12 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A override def start() { val properties = new ArrayBuffer[(String, String)] - val iterator = System.getProperties.entrySet.iterator - while (iterator.hasNext) { - val entry = iterator.next - val (key, value) = (entry.getKey.toString, entry.getValue.toString) + for ((key, value) <- scheduler.sc.conf.getAll) { if (key.startsWith("spark.") && !key.equals("spark.hostPort")) { properties += ((key, value)) } } + //TODO (prashant) send conf instead of properties driverActor = actorSystem.actorOf( Props(new DriverActor(properties)), name = CoarseGrainedSchedulerBackend.ACTOR_NAME) } @@ -210,8 +208,10 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A driverActor ! KillTask(taskId, executorId) } - override def defaultParallelism() = Option(System.getProperty("spark.default.parallelism")) - .map(_.toInt).getOrElse(math.max(totalCoreCount.get(), 2)) + override def defaultParallelism(): Int = { + conf.getOption("spark.default.parallelism").map(_.toInt).getOrElse( + math.max(totalCoreCount.get(), 2)) + } // Called by subclasses when notified of a lost worker def removeExecutor(executorId: String, reason: String) { 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 ec3e68e970..b44d1e43c8 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 @@ -33,13 +33,13 @@ private[spark] class SimrSchedulerBackend( val tmpPath = new Path(driverFilePath + "_tmp") val filePath = new Path(driverFilePath) - val maxCores = System.getProperty("spark.simr.executor.cores", "1").toInt + val maxCores = conf.get("spark.simr.executor.cores", "1").toInt override def start() { super.start() val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( - System.getProperty("spark.driver.host"), System.getProperty("spark.driver.port"), + sc.conf.get("spark.driver.host"), sc.conf.get("spark.driver.port"), CoarseGrainedSchedulerBackend.ACTOR_NAME) val conf = new Configuration() 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 404ce7a452..9858717d13 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 @@ -38,23 +38,23 @@ private[spark] class SparkDeploySchedulerBackend( var stopping = false var shutdownCallback : (SparkDeploySchedulerBackend) => Unit = _ - val maxCores = System.getProperty("spark.cores.max", Int.MaxValue.toString).toInt + val maxCores = conf.get("spark.cores.max", Int.MaxValue.toString).toInt override def start() { super.start() // The endpoint for executors to talk to us val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( - System.getProperty("spark.driver.host"), System.getProperty("spark.driver.port"), + conf.get("spark.driver.host"), conf.get("spark.driver.port"), CoarseGrainedSchedulerBackend.ACTOR_NAME) val args = Seq(driverUrl, "{{EXECUTOR_ID}}", "{{HOSTNAME}}", "{{CORES}}") val command = Command( "org.apache.spark.executor.CoarseGrainedExecutorBackend", args, sc.executorEnvs) val sparkHome = sc.getSparkHome().getOrElse(null) - val appDesc = new ApplicationDescription(appName, maxCores, executorMemory, command, sparkHome, + val appDesc = new ApplicationDescription(appName, maxCores, sc.executorMemory, command, sparkHome, "http://" + sc.ui.appUIAddress) - client = new Client(sc.env.actorSystem, masters, appDesc, this) + client = new Client(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 39573fc8c9..08811520cf 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 @@ -62,7 +62,7 @@ private[spark] class CoarseMesosSchedulerBackend( var driver: SchedulerDriver = null // Maximum number of cores to acquire (TODO: we'll need more flexible controls here) - val maxCores = System.getProperty("spark.cores.max", Int.MaxValue.toString).toInt + val maxCores = conf.get("spark.cores.max", Int.MaxValue.toString).toInt // Cores we have acquired with each Mesos task ID val coresByTaskId = new HashMap[Int, Int] @@ -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 = System.getProperty("spark.mesos.extra.cores", "0").toInt + val extraCoresPerSlave = conf.get("spark.mesos.extra.cores", "0").toInt var nextMesosTaskId = 0 @@ -122,10 +122,10 @@ private[spark] class CoarseMesosSchedulerBackend( val command = CommandInfo.newBuilder() .setEnvironment(environment) val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( - System.getProperty("spark.driver.host"), - System.getProperty("spark.driver.port"), + conf.get("spark.driver.host"), + conf.get("spark.driver.port"), CoarseGrainedSchedulerBackend.ACTOR_NAME) - val uri = System.getProperty("spark.executor.uri") + val uri = conf.get("spark.executor.uri", null) if (uri == null) { val runScript = new File(sparkHome, "spark-class").getCanonicalPath command.setValue( @@ -177,7 +177,7 @@ private[spark] class CoarseMesosSchedulerBackend( val slaveId = offer.getSlaveId.toString val mem = getResource(offer.getResourcesList, "mem") val cpus = getResource(offer.getResourcesList, "cpus").toInt - if (totalCoresAcquired < maxCores && mem >= executorMemory && cpus >= 1 && + if (totalCoresAcquired < maxCores && mem >= sc.executorMemory && cpus >= 1 && failuresBySlaveId.getOrElse(slaveId, 0) < MAX_SLAVE_FAILURES && !slaveIdsWithExecutors.contains(slaveId)) { // Launch an executor on the slave @@ -193,7 +193,7 @@ private[spark] class CoarseMesosSchedulerBackend( .setCommand(createCommand(offer, cpusToUse + extraCoresPerSlave)) .setName("Task " + taskId) .addResources(createResource("cpus", cpusToUse)) - .addResources(createResource("mem", executorMemory)) + .addResources(createResource("mem", sc.executorMemory)) .build() d.launchTasks(offer.getId, Collections.singletonList(task), filters) } else { 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 6aa788c460..bb278fb155 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 @@ -100,7 +100,7 @@ private[spark] class MesosSchedulerBackend( } val command = CommandInfo.newBuilder() .setEnvironment(environment) - val uri = System.getProperty("spark.executor.uri") + val uri = sc.conf.get("spark.executor.uri", null) if (uri == null) { command.setValue(new File(sparkHome, "spark-executor").getCanonicalPath) } else { @@ -113,7 +113,7 @@ private[spark] class MesosSchedulerBackend( val memory = Resource.newBuilder() .setName("mem") .setType(Value.Type.SCALAR) - .setScalar(Value.Scalar.newBuilder().setValue(executorMemory).build()) + .setScalar(Value.Scalar.newBuilder().setValue(sc.executorMemory).build()) .build() ExecutorInfo.newBuilder() .setExecutorId(ExecutorID.newBuilder().setValue(execId).build()) @@ -198,7 +198,7 @@ private[spark] class MesosSchedulerBackend( def enoughMemory(o: Offer) = { val mem = getResource(o.getResourcesList, "mem") val slaveId = o.getSlaveId.getValue - mem >= executorMemory || slaveIdsWithExecutors.contains(slaveId) + mem >= sc.executorMemory || slaveIdsWithExecutors.contains(slaveId) } for ((offer, index) <- offers.zipWithIndex if enoughMemory(offer)) { @@ -340,5 +340,5 @@ private[spark] class MesosSchedulerBackend( } // TODO: query Mesos for number of cores - override def defaultParallelism() = System.getProperty("spark.default.parallelism", "8").toInt + override def defaultParallelism() = sc.conf.get("spark.default.parallelism", "8").toInt } diff --git a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala index 4edc6a0d3f..897d47a9ad 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/local/LocalBackend.scala @@ -47,7 +47,8 @@ private[spark] class LocalActor( private val localExecutorId = "localhost" private val localExecutorHostname = "localhost" - val executor = new Executor(localExecutorId, localExecutorHostname, Seq.empty, isLocal = true) + val executor = new Executor( + localExecutorId, localExecutorHostname, scheduler.conf.getAll, isLocal = true) def receive = { case ReviveOffers => diff --git a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala index 4de81617b1..5d3d43623d 100644 --- a/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/JavaSerializer.scala @@ -21,6 +21,7 @@ import java.io._ import java.nio.ByteBuffer import org.apache.spark.util.ByteBufferInputStream +import org.apache.spark.SparkConf private[spark] class JavaSerializationStream(out: OutputStream) extends SerializationStream { val objOut = new ObjectOutputStream(out) @@ -77,6 +78,6 @@ private[spark] class JavaSerializerInstance extends SerializerInstance { /** * A Spark serializer that uses Java's built-in serialization. */ -class JavaSerializer extends Serializer { +class JavaSerializer(conf: SparkConf) extends Serializer { def newInstance(): SerializerInstance = new JavaSerializerInstance } 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 e748c2275d..a24a3b04b8 100644 --- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala @@ -25,18 +25,18 @@ import com.esotericsoftware.kryo.{KryoException, Kryo} import com.esotericsoftware.kryo.io.{Input => KryoInput, Output => KryoOutput} import com.twitter.chill.{EmptyScalaKryoInstantiator, AllScalaRegistrar} -import org.apache.spark.{SerializableWritable, Logging} +import org.apache.spark._ import org.apache.spark.broadcast.HttpBroadcast import org.apache.spark.scheduler.MapStatus import org.apache.spark.storage._ +import org.apache.spark.storage.{GetBlock, GotBlock, PutBlock} /** * A Spark serializer that uses the [[https://code.google.com/p/kryo/ Kryo serialization library]]. */ -class KryoSerializer extends org.apache.spark.serializer.Serializer with Logging { - +class KryoSerializer(conf: SparkConf) extends org.apache.spark.serializer.Serializer with Logging { private val bufferSize = { - System.getProperty("spark.kryoserializer.buffer.mb", "2").toInt * 1024 * 1024 + conf.get("spark.kryoserializer.buffer.mb", "2").toInt * 1024 * 1024 } def newKryoOutput() = new KryoOutput(bufferSize) @@ -48,7 +48,7 @@ class KryoSerializer extends org.apache.spark.serializer.Serializer with Logging // 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(System.getProperty("spark.kryo.referenceTracking", "true").toBoolean) + kryo.setReferences(conf.get("spark.kryo.referenceTracking", "true").toBoolean) for (cls <- KryoSerializer.toRegister) kryo.register(cls) @@ -58,13 +58,13 @@ class KryoSerializer extends org.apache.spark.serializer.Serializer with Logging // Allow the user to register their own classes by setting spark.kryo.registrator try { - Option(System.getProperty("spark.kryo.registrator")).foreach { regCls => + for (regCls <- conf.getOption("spark.kryo.registrator")) { logDebug("Running user registrator: " + regCls) val reg = Class.forName(regCls, true, classLoader).newInstance().asInstanceOf[KryoRegistrator] reg.registerClasses(kryo) } } catch { - case _: Exception => println("Failed to register spark.kryo.registrator") + case e: Exception => logError("Failed to run spark.kryo.registrator", e) } // Register Chill's classes; we do this after our ranges and the user's own classes to let diff --git a/core/src/main/scala/org/apache/spark/serializer/SerializerManager.scala b/core/src/main/scala/org/apache/spark/serializer/SerializerManager.scala index 2955986fec..22465272f3 100644 --- a/core/src/main/scala/org/apache/spark/serializer/SerializerManager.scala +++ b/core/src/main/scala/org/apache/spark/serializer/SerializerManager.scala @@ -18,6 +18,7 @@ package org.apache.spark.serializer import java.util.concurrent.ConcurrentHashMap +import org.apache.spark.SparkConf /** @@ -32,12 +33,12 @@ private[spark] class SerializerManager { def default = _default - def setDefault(clsName: String): Serializer = { - _default = get(clsName) + def setDefault(clsName: String, conf: SparkConf): Serializer = { + _default = get(clsName, conf) _default } - def get(clsName: String): Serializer = { + def get(clsName: String, conf: SparkConf): Serializer = { if (clsName == null) { default } else { @@ -51,8 +52,9 @@ private[spark] class SerializerManager { serializer = serializers.get(clsName) if (serializer == null) { val clsLoader = Thread.currentThread.getContextClassLoader - serializer = - Class.forName(clsName, true, clsLoader).newInstance().asInstanceOf[Serializer] + val cls = Class.forName(clsName, true, clsLoader) + val constructor = cls.getConstructor(classOf[SparkConf]) + serializer = constructor.newInstance(conf).asInstanceOf[Serializer] serializers.put(clsName, serializer) } serializer 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 e51c5b30a3..47478631a1 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala @@ -312,7 +312,7 @@ object BlockFetcherIterator { logDebug("Sending request for %d blocks (%s) from %s".format( req.blocks.size, Utils.bytesToString(req.size), req.address.host)) val cmId = new ConnectionManagerId(req.address.host, req.address.nettyPort) - val cpier = new ShuffleCopier + val cpier = new ShuffleCopier(blockManager.conf) cpier.getBlocks(cmId, req.blocks, putResult) logDebug("Sent request for remote blocks " + req.blocks + " from " + req.address.host ) } @@ -327,7 +327,7 @@ object BlockFetcherIterator { fetchRequestsSync.put(request) } - copiers = startCopiers(System.getProperty("spark.shuffle.copier.threads", "6").toInt) + copiers = startCopiers(conf.get("spark.shuffle.copier.threads", "6").toInt) 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 19a025a329..6d2cda97b0 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -30,7 +30,7 @@ import scala.concurrent.duration._ import it.unimi.dsi.fastutil.io.{FastBufferedOutputStream, FastByteArrayOutputStream} -import org.apache.spark.{Logging, SparkEnv, SparkException} +import org.apache.spark.{SparkConf, Logging, SparkEnv, SparkException} import org.apache.spark.io.CompressionCodec import org.apache.spark.network._ import org.apache.spark.serializer.Serializer @@ -43,12 +43,13 @@ private[spark] class BlockManager( actorSystem: ActorSystem, val master: BlockManagerMaster, val defaultSerializer: Serializer, - maxMemory: Long) + maxMemory: Long, + val conf: SparkConf) extends Logging { val shuffleBlockManager = new ShuffleBlockManager(this) val diskBlockManager = new DiskBlockManager(shuffleBlockManager, - System.getProperty("spark.local.dir", System.getProperty("java.io.tmpdir"))) + conf.get("spark.local.dir", System.getProperty("java.io.tmpdir"))) private val blockInfo = new TimeStampedHashMap[BlockId, BlockInfo] @@ -57,12 +58,12 @@ private[spark] class BlockManager( // If we use Netty for shuffle, start a new Netty-based shuffle sender service. private val nettyPort: Int = { - val useNetty = System.getProperty("spark.shuffle.use.netty", "false").toBoolean - val nettyPortConfig = System.getProperty("spark.shuffle.sender.port", "0").toInt + val useNetty = conf.get("spark.shuffle.use.netty", "false").toBoolean + val nettyPortConfig = conf.get("spark.shuffle.sender.port", "0").toInt if (useNetty) diskBlockManager.startShuffleBlockSender(nettyPortConfig) else 0 } - val connectionManager = new ConnectionManager(0) + val connectionManager = new ConnectionManager(0, conf) implicit val futureExecContext = connectionManager.futureExecContext val blockManagerId = BlockManagerId( @@ -71,18 +72,18 @@ 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 = - System.getProperty("spark.reducer.maxMbInFlight", "48").toLong * 1024 * 1024 + conf.get("spark.reducer.maxMbInFlight", "48").toLong * 1024 * 1024 // Whether to compress broadcast variables that are stored - val compressBroadcast = System.getProperty("spark.broadcast.compress", "true").toBoolean + val compressBroadcast = conf.get("spark.broadcast.compress", "true").toBoolean // Whether to compress shuffle output that are stored - val compressShuffle = System.getProperty("spark.shuffle.compress", "true").toBoolean + val compressShuffle = conf.get("spark.shuffle.compress", "true").toBoolean // Whether to compress RDD partitions that are stored serialized - val compressRdds = System.getProperty("spark.rdd.compress", "false").toBoolean + val compressRdds = conf.get("spark.rdd.compress", "false").toBoolean - val heartBeatFrequency = BlockManager.getHeartBeatFrequencyFromSystemProperties + val heartBeatFrequency = BlockManager.getHeartBeatFrequency(conf) - val hostPort = Utils.localHostPort() + val hostPort = Utils.localHostPort(conf) val slaveActor = actorSystem.actorOf(Props(new BlockManagerSlaveActor(this)), name = "BlockManagerActor" + BlockManager.ID_GENERATOR.next) @@ -100,8 +101,11 @@ private[spark] class BlockManager( var heartBeatTask: Cancellable = null - private val metadataCleaner = new MetadataCleaner(MetadataCleanerType.BLOCK_MANAGER, this.dropOldNonBroadcastBlocks) - private val broadcastCleaner = new MetadataCleaner(MetadataCleanerType.BROADCAST_VARS, this.dropOldBroadcastBlocks) + private val metadataCleaner = new MetadataCleaner( + MetadataCleanerType.BLOCK_MANAGER, this.dropOldNonBroadcastBlocks, conf) + private val broadcastCleaner = new MetadataCleaner( + MetadataCleanerType.BROADCAST_VARS, this.dropOldBroadcastBlocks, conf) + initialize() // The compression codec to use. Note that the "lazy" val is necessary because we want to delay @@ -109,14 +113,14 @@ private[spark] class BlockManager( // program could be using a user-defined codec in a third party jar, which is loaded in // Executor.updateDependencies. When the BlockManager is initialized, user level jars hasn't been // loaded yet. - private lazy val compressionCodec: CompressionCodec = CompressionCodec.createCodec() + private lazy val compressionCodec: CompressionCodec = CompressionCodec.createCodec(conf) /** * Construct a BlockManager with a memory limit set based on system properties. */ def this(execId: String, actorSystem: ActorSystem, master: BlockManagerMaster, - serializer: Serializer) = { - this(execId, actorSystem, master, serializer, BlockManager.getMaxMemoryFromSystemProperties) + serializer: Serializer, conf: SparkConf) = { + this(execId, actorSystem, master, serializer, BlockManager.getMaxMemory(conf), conf) } /** @@ -126,7 +130,7 @@ private[spark] class BlockManager( private def initialize() { master.registerBlockManager(blockManagerId, maxMemory, slaveActor) BlockManagerWorker.startBlockManagerWorker(this) - if (!BlockManager.getDisableHeartBeatsForTesting) { + if (!BlockManager.getDisableHeartBeatsForTesting(conf)) { heartBeatTask = actorSystem.scheduler.schedule(0.seconds, heartBeatFrequency.milliseconds) { heartBeat() } @@ -439,7 +443,7 @@ private[spark] class BlockManager( : BlockFetcherIterator = { val iter = - if (System.getProperty("spark.shuffle.use.netty", "false").toBoolean) { + if (conf.get("spark.shuffle.use.netty", "false").toBoolean) { new BlockFetcherIterator.NettyBlockFetcherIterator(this, blocksByAddress, serializer) } else { new BlockFetcherIterator.BasicBlockFetcherIterator(this, blocksByAddress, serializer) @@ -465,7 +469,8 @@ private[spark] class BlockManager( def getDiskWriter(blockId: BlockId, file: File, serializer: Serializer, bufferSize: Int) : BlockObjectWriter = { val compressStream: OutputStream => OutputStream = wrapForCompression(blockId, _) - new DiskBlockObjectWriter(blockId, file, serializer, bufferSize, compressStream) + val syncWrites = conf.get("spark.shuffle.sync", "false").toBoolean + new DiskBlockObjectWriter(blockId, file, serializer, bufferSize, compressStream, syncWrites) } /** @@ -856,19 +861,18 @@ private[spark] class BlockManager( private[spark] object BlockManager extends Logging { - val ID_GENERATOR = new IdGenerator - def getMaxMemoryFromSystemProperties: Long = { - val memoryFraction = System.getProperty("spark.storage.memoryFraction", "0.66").toDouble + def getMaxMemory(conf: SparkConf): Long = { + val memoryFraction = conf.get("spark.storage.memoryFraction", "0.66").toDouble (Runtime.getRuntime.maxMemory * memoryFraction).toLong } - def getHeartBeatFrequencyFromSystemProperties: Long = - System.getProperty("spark.storage.blockManagerTimeoutIntervalMs", "60000").toLong / 4 + def getHeartBeatFrequency(conf: SparkConf): Long = + conf.get("spark.storage.blockManagerTimeoutIntervalMs", "60000").toLong / 4 - def getDisableHeartBeatsForTesting: Boolean = - System.getProperty("spark.test.disableBlockManagerHeartBeat", "false").toBoolean + def getDisableHeartBeatsForTesting(conf: SparkConf): Boolean = + conf.get("spark.test.disableBlockManagerHeartBeat", "false").toBoolean /** * 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 e1d68ef592..b5afe8cd23 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala @@ -23,19 +23,20 @@ import scala.concurrent.ExecutionContext.Implicits.global import akka.actor._ import akka.pattern.ask -import org.apache.spark.{Logging, SparkException} +import org.apache.spark.{SparkConf, Logging, SparkException} import org.apache.spark.storage.BlockManagerMessages._ import org.apache.spark.util.AkkaUtils private[spark] -class BlockManagerMaster(var driverActor : Either[ActorRef, ActorSelection]) extends Logging { +class BlockManagerMaster(var driverActor : Either[ActorRef, ActorSelection], + conf: SparkConf) extends Logging { - val AKKA_RETRY_ATTEMPTS: Int = System.getProperty("spark.akka.num.retries", "3").toInt - val AKKA_RETRY_INTERVAL_MS: Int = System.getProperty("spark.akka.retry.wait", "3000").toInt + 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 DRIVER_AKKA_ACTOR_NAME = "BlockManagerMaster" - val timeout = AkkaUtils.askTimeout + val timeout = AkkaUtils.askTimeout(conf) /** Remove a dead executor from the driver actor. This is only called on the driver side. */ def removeExecutor(execId: String) { 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 e0eb02ce81..58452d9657 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala @@ -27,7 +27,7 @@ import scala.concurrent.duration._ import akka.actor.{Actor, ActorRef, Cancellable} import akka.pattern.ask -import org.apache.spark.{Logging, SparkException} +import org.apache.spark.{SparkConf, Logging, SparkException} import org.apache.spark.storage.BlockManagerMessages._ import org.apache.spark.util.{AkkaUtils, Utils} @@ -36,7 +36,7 @@ import org.apache.spark.util.{AkkaUtils, Utils} * all slaves' block managers. */ private[spark] -class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { +class BlockManagerMasterActor(val isLocal: Boolean, conf: SparkConf) extends Actor with Logging { // Mapping from block manager id to the block manager's information. private val blockManagerInfo = @@ -48,18 +48,18 @@ class BlockManagerMasterActor(val isLocal: Boolean) extends Actor with Logging { // Mapping from block id to the set of block managers that have the block. private val blockLocations = new JHashMap[BlockId, mutable.HashSet[BlockManagerId]] - private val akkaTimeout = AkkaUtils.askTimeout + private val akkaTimeout = AkkaUtils.askTimeout(conf) - val slaveTimeout = System.getProperty("spark.storage.blockManagerSlaveTimeoutMs", - "" + (BlockManager.getHeartBeatFrequencyFromSystemProperties * 3)).toLong + val slaveTimeout = conf.get("spark.storage.blockManagerSlaveTimeoutMs", + "" + (BlockManager.getHeartBeatFrequency(conf) * 3)).toLong - val checkTimeoutInterval = System.getProperty("spark.storage.blockManagerTimeoutIntervalMs", + val checkTimeoutInterval = conf.get("spark.storage.blockManagerTimeoutIntervalMs", "60000").toLong var timeoutCheckingTask: Cancellable = null override def preStart() { - if (!BlockManager.getDisableHeartBeatsForTesting) { + if (!BlockManager.getDisableHeartBeatsForTesting(conf)) { import context.dispatcher timeoutCheckingTask = context.system.scheduler.schedule( 0.seconds, checkTimeoutInterval.milliseconds, self, ExpireDeadHosts) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala index b4451fc7b8..61e63c60d5 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockObjectWriter.scala @@ -74,7 +74,8 @@ class DiskBlockObjectWriter( file: File, serializer: Serializer, bufferSize: Int, - compressStream: OutputStream => OutputStream) + compressStream: OutputStream => OutputStream, + syncWrites: Boolean) extends BlockObjectWriter(blockId) with Logging { @@ -97,8 +98,6 @@ class DiskBlockObjectWriter( override def flush() = out.flush() } - private val syncWrites = System.getProperty("spark.shuffle.sync", "false").toBoolean - /** The file channel, used for repositioning / truncating the file. */ private var channel: FileChannel = null private var bs: OutputStream = null 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 fcd2e97982..55dcb3742c 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 = System.getProperty("spark.diskStore.subDirectories", "64").toInt + private val subDirsPerLocalDir = shuffleManager.conf.get("spark.diskStore.subDirectories", "64").toInt // 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 212ef6506f..39dc7bb19a 100644 --- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala @@ -59,12 +59,14 @@ private[spark] trait ShuffleWriterGroup { */ private[spark] class ShuffleBlockManager(blockManager: BlockManager) { + def conf = blockManager.conf + // 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 = - System.getProperty("spark.shuffle.consolidateFiles", "false").toBoolean + conf.get("spark.shuffle.consolidateFiles", "false").toBoolean - private val bufferSize = System.getProperty("spark.shuffle.file.buffer.kb", "100").toInt * 1024 + private val bufferSize = conf.get("spark.shuffle.file.buffer.kb", "100").toInt * 1024 /** * Contains all the state related to a particular shuffle. This includes a pool of unused @@ -85,8 +87,8 @@ class ShuffleBlockManager(blockManager: BlockManager) { type ShuffleId = Int private val shuffleStates = new TimeStampedHashMap[ShuffleId, ShuffleState] - private - val metadataCleaner = new MetadataCleaner(MetadataCleanerType.SHUFFLE_BLOCK_MANAGER, this.cleanup) + private val metadataCleaner = + new MetadataCleaner(MetadataCleanerType.SHUFFLE_BLOCK_MANAGER, this.cleanup, conf) def forMapTask(shuffleId: Int, mapId: Int, numBuckets: Int, serializer: Serializer) = { new ShuffleWriterGroup { diff --git a/core/src/main/scala/org/apache/spark/storage/StoragePerfTester.scala b/core/src/main/scala/org/apache/spark/storage/StoragePerfTester.scala index d52b3d8284..40734aab49 100644 --- a/core/src/main/scala/org/apache/spark/storage/StoragePerfTester.scala +++ b/core/src/main/scala/org/apache/spark/storage/StoragePerfTester.scala @@ -56,7 +56,7 @@ object StoragePerfTester { def writeOutputBytes(mapId: Int, total: AtomicLong) = { val shuffle = blockManager.shuffleBlockManager.forMapTask(1, mapId, numOutputSplits, - new KryoSerializer()) + new KryoSerializer(sc.conf)) val writers = shuffle.writers for (i <- 1 to recordsPerMap) { writers(i % numOutputSplits).write(writeData) 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 a8db37ded1..dca98c6c05 100644 --- a/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala +++ b/core/src/main/scala/org/apache/spark/storage/ThreadingTest.scala @@ -22,6 +22,7 @@ import akka.actor._ import java.util.concurrent.ArrayBlockingQueue import util.Random import org.apache.spark.serializer.KryoSerializer +import org.apache.spark.{SparkConf, SparkContext} /** * This class tests the BlockManager and MemoryStore for thread safety and @@ -91,11 +92,12 @@ private[spark] object ThreadingTest { def main(args: Array[String]) { System.setProperty("spark.kryoserializer.buffer.mb", "1") val actorSystem = ActorSystem("test") - val serializer = new KryoSerializer + val conf = new SparkConf() + val serializer = new KryoSerializer(conf) val blockManagerMaster = new BlockManagerMaster( - Left(actorSystem.actorOf(Props(new BlockManagerMasterActor(true))))) + Left(actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf)))), conf) val blockManager = new BlockManager( - "<driver>", actorSystem, blockManagerMaster, serializer, 1024 * 1024) + "<driver>", actorSystem, blockManagerMaster, serializer, 1024 * 1024, conf) val producers = (1 to numProducers).map(i => new ProducerThread(blockManager, i)) val consumers = producers.map(p => new ConsumerThread(blockManager, p.queue)) producers.foreach(_.start) diff --git a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala index f1d86c0221..50dfdbdf5a 100644 --- a/core/src/main/scala/org/apache/spark/ui/SparkUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/SparkUI.scala @@ -1,4 +1,4 @@ -/* +/* * 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. @@ -32,7 +32,7 @@ import org.apache.spark.util.Utils /** Top level user interface for Spark */ private[spark] class SparkUI(sc: SparkContext) extends Logging { val host = Option(System.getenv("SPARK_PUBLIC_DNS")).getOrElse(Utils.localHostName()) - val port = Option(System.getProperty("spark.ui.port")).getOrElse(SparkUI.DEFAULT_PORT).toInt + val port = sc.conf.get("spark.ui.port", SparkUI.DEFAULT_PORT).toInt var boundPort: Option[Int] = None var server: Option[Server] = None diff --git a/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala b/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala index fcd1b518d0..58d47a201d 100644 --- a/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala +++ b/core/src/main/scala/org/apache/spark/ui/UIWorkloadGenerator.scala @@ -19,7 +19,7 @@ package org.apache.spark.ui import scala.util.Random -import org.apache.spark.SparkContext +import org.apache.spark.{SparkConf, SparkContext} import org.apache.spark.SparkContext._ import org.apache.spark.scheduler.SchedulingMode @@ -30,6 +30,7 @@ import org.apache.spark.scheduler.SchedulingMode * Usage: ./run spark.ui.UIWorkloadGenerator [master] */ private[spark] object UIWorkloadGenerator { + val NUM_PARTITIONS = 100 val INTER_JOB_WAIT_MS = 5000 @@ -38,14 +39,14 @@ private[spark] object UIWorkloadGenerator { println("usage: ./spark-class org.apache.spark.ui.UIWorkloadGenerator [master] [FIFO|FAIR]") System.exit(1) } - val master = args(0) - val schedulingMode = SchedulingMode.withName(args(1)) - val appName = "Spark UI Tester" + val conf = new SparkConf().setMaster(args(0)).setAppName("Spark UI tester") + + val schedulingMode = SchedulingMode.withName(args(1)) if (schedulingMode == SchedulingMode.FAIR) { - System.setProperty("spark.scheduler.mode", "FAIR") + conf.set("spark.scheduler.mode", "FAIR") } - val sc = new SparkContext(master, appName) + val sc = new SparkContext(conf) def setProperties(s: String) = { if(schedulingMode == SchedulingMode.FAIR) { @@ -55,11 +56,11 @@ private[spark] object UIWorkloadGenerator { } val baseData = sc.makeRDD(1 to NUM_PARTITIONS * 10, NUM_PARTITIONS) - def nextFloat() = (new Random()).nextFloat() + def nextFloat() = new Random().nextFloat() val jobs = Seq[(String, () => Long)]( ("Count", baseData.count), - ("Cache and Count", baseData.map(x => x).cache.count), + ("Cache and Count", baseData.map(x => x).cache().count), ("Single Shuffle", baseData.map(x => (x % 10, x)).reduceByKey(_ + _).count), ("Entirely failed phase", baseData.map(x => throw new Exception).count), ("Partially failed phase", { diff --git a/core/src/main/scala/org/apache/spark/ui/env/EnvironmentUI.scala b/core/src/main/scala/org/apache/spark/ui/env/EnvironmentUI.scala index c5bf2acc9e..88f41be8d3 100644 --- a/core/src/main/scala/org/apache/spark/ui/env/EnvironmentUI.scala +++ b/core/src/main/scala/org/apache/spark/ui/env/EnvironmentUI.scala @@ -48,12 +48,15 @@ private[spark] class EnvironmentUI(sc: SparkContext) { def jvmTable = UIUtils.listingTable(Seq("Name", "Value"), jvmRow, jvmInformation, fixedWidth = true) - val properties = System.getProperties.iterator.toSeq - val classPathProperty = properties.find { case (k, v) => - k.contains("java.class.path") + val sparkProperties = sc.conf.getAll.sorted + + val systemProperties = System.getProperties.iterator.toSeq + val classPathProperty = systemProperties.find { case (k, v) => + k == "java.class.path" }.getOrElse(("", "")) - val sparkProperties = properties.filter(_._1.startsWith("spark")).sorted - val otherProperties = properties.diff(sparkProperties :+ classPathProperty).sorted + val otherProperties = systemProperties.filter { case (k, v) => + k != "java.class.path" && !k.startsWith("spark.") + }.sorted val propertyHeaders = Seq("Name", "Value") def propertyRow(kv: (String, String)) = <tr><td>{kv._1}</td><td>{kv._2}</td></tr> @@ -63,7 +66,7 @@ private[spark] class EnvironmentUI(sc: SparkContext) { UIUtils.listingTable(propertyHeaders, propertyRow, otherProperties, fixedWidth = true) val classPathEntries = classPathProperty._2 - .split(System.getProperty("path.separator", ":")) + .split(sc.conf.get("path.separator", ":")) .filterNot(e => e.isEmpty) .map(e => (e, "System Classpath")) val addedJars = sc.addedJars.iterator.toSeq.map{case (path, time) => (path, "Added By User")} 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 058bc2a2e5..b7b87250b9 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 = System.getProperty("spark.ui.retained_stages", "1000").toInt + val RETAINED_STAGES = sc.conf.get("spark.ui.retained_stages", "1000").toInt val DEFAULT_POOL_NAME = "default" val stageIdToPool = new HashMap[Int, String]() @@ -106,7 +106,7 @@ private[spark] class JobProgressListener(val sc: SparkContext) extends SparkList val stages = poolToActiveStages.getOrElseUpdate(poolName, new HashSet[StageInfo]()) stages += stage } - + override def onTaskStart(taskStart: SparkListenerTaskStart) = synchronized { val sid = taskStart.task.stageId val tasksActive = stageIdToTasksActive.getOrElseUpdate(sid, new HashSet[TaskInfo]()) 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 1c8b51b8bc..362cea5e3e 100644 --- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala +++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala @@ -21,6 +21,7 @@ import scala.concurrent.duration.{Duration, FiniteDuration} import akka.actor.{ActorSystem, ExtendedActorSystem, IndestructibleActorSystem} import com.typesafe.config.ConfigFactory +import org.apache.spark.SparkConf /** * Various utility classes for working with Akka. @@ -37,22 +38,22 @@ private[spark] object AkkaUtils { * If indestructible is set to true, the Actor System will continue running in the event * of a fatal exception. This is used by [[org.apache.spark.executor.Executor]]. */ - def createActorSystem(name: String, host: String, port: Int, indestructible: Boolean = false) - : (ActorSystem, Int) = { + def createActorSystem(name: String, host: String, port: Int, indestructible: Boolean = false, + conf: SparkConf): (ActorSystem, Int) = { - val akkaThreads = System.getProperty("spark.akka.threads", "4").toInt - val akkaBatchSize = System.getProperty("spark.akka.batchSize", "15").toInt + val akkaThreads = conf.get("spark.akka.threads", "4").toInt + val akkaBatchSize = conf.get("spark.akka.batchSize", "15").toInt - val akkaTimeout = System.getProperty("spark.akka.timeout", "100").toInt + val akkaTimeout = conf.get("spark.akka.timeout", "100").toInt - val akkaFrameSize = System.getProperty("spark.akka.frameSize", "10").toInt + val akkaFrameSize = conf.get("spark.akka.frameSize", "10").toInt val lifecycleEvents = - if (System.getProperty("spark.akka.logLifecycleEvents", "false").toBoolean) "on" else "off" + if (conf.get("spark.akka.logLifecycleEvents", "false").toBoolean) "on" else "off" - val akkaHeartBeatPauses = System.getProperty("spark.akka.heartbeat.pauses", "600").toInt + val akkaHeartBeatPauses = conf.get("spark.akka.heartbeat.pauses", "600").toInt val akkaFailureDetector = - System.getProperty("spark.akka.failure-detector.threshold", "300.0").toDouble - val akkaHeartBeatInterval = System.getProperty("spark.akka.heartbeat.interval", "1000").toInt + conf.get("spark.akka.failure-detector.threshold", "300.0").toDouble + val akkaHeartBeatInterval = conf.get("spark.akka.heartbeat.interval", "1000").toInt val akkaConf = ConfigFactory.parseString( s""" @@ -87,7 +88,7 @@ private[spark] object AkkaUtils { } /** Returns the default Spark timeout to use for Akka ask operations. */ - def askTimeout: FiniteDuration = { - Duration.create(System.getProperty("spark.akka.askTimeout", "30").toLong, "seconds") + def askTimeout(conf: SparkConf): FiniteDuration = { + Duration.create(conf.get("spark.akka.askTimeout", "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 fe56960cbf..aa7f52cafb 100644 --- a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala +++ b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala @@ -18,16 +18,21 @@ package org.apache.spark.util import java.util.{TimerTask, Timer} -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, SparkContext, Logging} /** * Runs a timer task to periodically clean up metadata (e.g. old files or hashtable entries) */ -class MetadataCleaner(cleanerType: MetadataCleanerType.MetadataCleanerType, cleanupFunc: (Long) => Unit) extends Logging { +class MetadataCleaner( + cleanerType: MetadataCleanerType.MetadataCleanerType, + cleanupFunc: (Long) => Unit, + conf: SparkConf) + extends Logging +{ val name = cleanerType.toString - private val delaySeconds = MetadataCleaner.getDelaySeconds(cleanerType) + private val delaySeconds = MetadataCleaner.getDelaySeconds(conf, cleanerType) private val periodSeconds = math.max(10, delaySeconds / 10) private val timer = new Timer(name + " cleanup timer", true) @@ -65,22 +70,28 @@ object MetadataCleanerType extends Enumeration { def systemProperty(which: MetadataCleanerType.MetadataCleanerType) = "spark.cleaner.ttl." + which.toString } +// TODO: This mutates a Conf to set properties right now, which is kind of ugly when used in the +// 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 + } - // using only sys props for now : so that workers can also get to it while preserving earlier behavior. - def getDelaySeconds = System.getProperty("spark.cleaner.ttl", "-1").toInt - - def getDelaySeconds(cleanerType: MetadataCleanerType.MetadataCleanerType): Int = { - System.getProperty(MetadataCleanerType.systemProperty(cleanerType), getDelaySeconds.toString).toInt + def getDelaySeconds(conf: SparkConf, cleanerType: MetadataCleanerType.MetadataCleanerType): Int = + { + conf.get(MetadataCleanerType.systemProperty(cleanerType), getDelaySeconds(conf).toString) + .toInt } - def setDelaySeconds(cleanerType: MetadataCleanerType.MetadataCleanerType, delay: Int) { - System.setProperty(MetadataCleanerType.systemProperty(cleanerType), delay.toString) + def setDelaySeconds(conf: SparkConf, cleanerType: MetadataCleanerType.MetadataCleanerType, + delay: Int) + { + conf.set(MetadataCleanerType.systemProperty(cleanerType), delay.toString) } - def setDelaySeconds(delay: Int, resetAll: Boolean = true) { + def setDelaySeconds(conf: SparkConf, delay: Int, resetAll: Boolean = true) { // override for all ? - System.setProperty("spark.cleaner.ttl", delay.toString) + conf.set("spark.cleaner.ttl", delay.toString) if (resetAll) { for (cleanerType <- MetadataCleanerType.values) { System.clearProperty(MetadataCleanerType.systemProperty(cleanerType)) diff --git a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala index a25b37a2a9..bddb3bb735 100644 --- a/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala +++ b/core/src/main/scala/org/apache/spark/util/SizeEstimator.scala @@ -30,10 +30,10 @@ import java.lang.management.ManagementFactory import scala.collection.mutable.ArrayBuffer import it.unimi.dsi.fastutil.ints.IntOpenHashSet -import org.apache.spark.Logging +import org.apache.spark.{SparkEnv, SparkConf, SparkContext, Logging} /** - * Estimates the sizes of Java objects (number of bytes of memory they occupy), for use in + * Estimates the sizes of Java objects (number of bytes of memory they occupy), for use in * memory-aware caches. * * Based on the following JavaWorld article: @@ -89,9 +89,11 @@ private[spark] object SizeEstimator extends Logging { classInfos.put(classOf[Object], new ClassInfo(objectSize, Nil)) } - private def getIsCompressedOops : Boolean = { + private def getIsCompressedOops: Boolean = { + // This is only used by tests to override the detection of compressed oops. The test + // actually uses a system property instead of a SparkConf, so we'll stick with that. if (System.getProperty("spark.test.useCompressedOops") != null) { - return System.getProperty("spark.test.useCompressedOops").toBoolean + return System.getProperty("spark.test.useCompressedOops").toBoolean } try { @@ -103,7 +105,7 @@ private[spark] object SizeEstimator extends Logging { val getVMMethod = hotSpotMBeanClass.getDeclaredMethod("getVMOption", Class.forName("java.lang.String")) - val bean = ManagementFactory.newPlatformMXBeanProxy(server, + val bean = ManagementFactory.newPlatformMXBeanProxy(server, hotSpotMBeanName, hotSpotMBeanClass) // TODO: We could use reflection on the VMOption returned ? return getVMMethod.invoke(bean, "UseCompressedOops").toString.contains("true") @@ -251,7 +253,7 @@ private[spark] object SizeEstimator extends Logging { if (info != null) { return info } - + val parent = getClassInfo(cls.getSuperclass) var shellSize = parent.shellSize var pointerFields = parent.pointerFields diff --git a/core/src/main/scala/org/apache/spark/util/Utils.scala b/core/src/main/scala/org/apache/spark/util/Utils.scala index 3f7858d2de..5f1253100b 100644 --- a/core/src/main/scala/org/apache/spark/util/Utils.scala +++ b/core/src/main/scala/org/apache/spark/util/Utils.scala @@ -36,14 +36,13 @@ import org.apache.hadoop.fs.{Path, FileSystem, FileUtil} import org.apache.spark.serializer.{DeserializationStream, SerializationStream, SerializerInstance} import org.apache.spark.deploy.SparkHadoopUtil import java.nio.ByteBuffer -import org.apache.spark.{SparkException, Logging} +import org.apache.spark.{SparkConf, SparkContext, SparkException, Logging} /** * Various utility methods used by Spark. */ private[spark] object Utils extends Logging { - /** Serialize an object using Java serialization */ def serialize[T](o: T): Array[Byte] = { val bos = new ByteArrayOutputStream() @@ -239,9 +238,9 @@ private[spark] object Utils extends Logging { * Throws SparkException if the target file already exists and has different contents than * the requested file. */ - def fetchFile(url: String, targetDir: File) { + def fetchFile(url: String, targetDir: File, conf: SparkConf) { val filename = url.split("/").last - val tempDir = getLocalDir + val tempDir = getLocalDir(conf) val tempFile = File.createTempFile("fetchFileTemp", null, new File(tempDir)) val targetFile = new File(targetDir, filename) val uri = new URI(url) @@ -311,8 +310,8 @@ private[spark] object Utils extends Logging { * return a single directory, even though the spark.local.dir property might be a list of * multiple paths. */ - def getLocalDir: String = { - System.getProperty("spark.local.dir", System.getProperty("java.io.tmpdir")).split(',')(0) + def getLocalDir(conf: SparkConf): String = { + conf.get("spark.local.dir", System.getProperty("java.io.tmpdir")).split(',')(0) } /** @@ -397,13 +396,12 @@ private[spark] object Utils extends Logging { InetAddress.getByName(address).getHostName } - def localHostPort(): String = { - val retval = System.getProperty("spark.hostPort", null) + def localHostPort(conf: SparkConf): String = { + val retval = conf.get("spark.hostPort", null) if (retval == null) { logErrorWithStack("spark.hostPort not set but invoking localHostPort") return localHostName() } - retval } @@ -415,9 +413,12 @@ private[spark] object Utils extends Logging { assert(hostPort.indexOf(':') != -1, message) } - // Used by DEBUG code : remove when all testing done def logErrorWithStack(msg: String) { - try { throw new Exception } catch { case ex: Exception => { logError(msg, ex) } } + try { + throw new Exception + } catch { + case ex: Exception => logError(msg, ex) + } } // Typically, this will be of order of number of nodes in cluster @@ -837,7 +838,7 @@ private[spark] object Utils extends Logging { } } - /** + /** * Timing method based on iterations that permit JVM JIT optimization. * @param numIters number of iterations * @param f function to be executed diff --git a/core/src/test/resources/spark.conf b/core/src/test/resources/spark.conf new file mode 100644 index 0000000000..aa4e751235 --- /dev/null +++ b/core/src/test/resources/spark.conf @@ -0,0 +1,8 @@ +# A simple spark.conf file used only in our unit tests + +spark.test.intTestProperty = 1 + +spark.test { + stringTestProperty = "hi" + listTestProperty = ["a", "b"] +} diff --git a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala index 70bfb81661..ec13b329b2 100644 --- a/core/src/test/scala/org/apache/spark/CheckpointSuite.scala +++ b/core/src/test/scala/org/apache/spark/CheckpointSuite.scala @@ -55,15 +55,15 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { } test("RDDs with one-to-one dependencies") { - testCheckpointing(_.map(x => x.toString)) - testCheckpointing(_.flatMap(x => 1 to x)) - testCheckpointing(_.filter(_ % 2 == 0)) - testCheckpointing(_.sample(false, 0.5, 0)) - testCheckpointing(_.glom()) - testCheckpointing(_.mapPartitions(_.map(_.toString))) - testCheckpointing(_.map(x => (x % 2, 1)).reduceByKey(_ + _).mapValues(_.toString)) - testCheckpointing(_.map(x => (x % 2, 1)).reduceByKey(_ + _).flatMapValues(x => 1 to x)) - testCheckpointing(_.pipe(Seq("cat"))) + testRDD(_.map(x => x.toString)) + testRDD(_.flatMap(x => 1 to x)) + testRDD(_.filter(_ % 2 == 0)) + testRDD(_.sample(false, 0.5, 0)) + testRDD(_.glom()) + testRDD(_.mapPartitions(_.map(_.toString))) + testRDD(_.map(x => (x % 2, 1)).reduceByKey(_ + _).mapValues(_.toString)) + testRDD(_.map(x => (x % 2, 1)).reduceByKey(_ + _).flatMapValues(x => 1 to x)) + testRDD(_.pipe(Seq("cat"))) } test("ParallelCollection") { @@ -95,7 +95,7 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { } test("ShuffledRDD") { - testCheckpointing(rdd => { + testRDD(rdd => { // Creating ShuffledRDD directly as PairRDDFunctions.combineByKey produces a MapPartitionedRDD new ShuffledRDD[Int, Int, (Int, Int)](rdd.map(x => (x % 2, 1)), partitioner) }) @@ -103,25 +103,17 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { test("UnionRDD") { def otherRDD = sc.makeRDD(1 to 10, 1) - - // Test whether the size of UnionRDDPartitions reduce in size after parent RDD is checkpointed. - // Current implementation of UnionRDD has transient reference to parent RDDs, - // so only the partitions will reduce in serialized size, not the RDD. - testCheckpointing(_.union(otherRDD), false, true) - testParentCheckpointing(_.union(otherRDD), false, true) + testRDD(_.union(otherRDD)) + testRDDPartitions(_.union(otherRDD)) } test("CartesianRDD") { def otherRDD = sc.makeRDD(1 to 10, 1) - testCheckpointing(new CartesianRDD(sc, _, otherRDD)) - - // Test whether size of CoalescedRDD reduce in size after parent RDD is checkpointed - // Current implementation of CoalescedRDDPartition has transient reference to parent RDD, - // so only the RDD will reduce in serialized size, not the partitions. - testParentCheckpointing(new CartesianRDD(sc, _, otherRDD), true, false) + testRDD(new CartesianRDD(sc, _, otherRDD)) + testRDDPartitions(new CartesianRDD(sc, _, otherRDD)) // Test that the CartesianRDD updates parent partitions (CartesianRDD.s1/s2) after - // the parent RDD has been checkpointed and parent partitions have been changed to HadoopPartitions. + // the parent RDD has been checkpointed and parent partitions have been changed. // Note that this test is very specific to the current implementation of CartesianRDD. val ones = sc.makeRDD(1 to 100, 10).map(x => x) ones.checkpoint() // checkpoint that MappedRDD @@ -132,23 +124,20 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { val splitAfterCheckpoint = serializeDeserialize(cartesian.partitions.head.asInstanceOf[CartesianPartition]) assert( - (splitAfterCheckpoint.s1 != splitBeforeCheckpoint.s1) && - (splitAfterCheckpoint.s2 != splitBeforeCheckpoint.s2), - "CartesianRDD.parents not updated after parent RDD checkpointed" + (splitAfterCheckpoint.s1.getClass != splitBeforeCheckpoint.s1.getClass) && + (splitAfterCheckpoint.s2.getClass != splitBeforeCheckpoint.s2.getClass), + "CartesianRDD.s1 and CartesianRDD.s2 not updated after parent RDD is checkpointed" ) } test("CoalescedRDD") { - testCheckpointing(_.coalesce(2)) + testRDD(_.coalesce(2)) + testRDDPartitions(_.coalesce(2)) - // Test whether size of CoalescedRDD reduce in size after parent RDD is checkpointed - // Current implementation of CoalescedRDDPartition has transient reference to parent RDD, - // so only the RDD will reduce in serialized size, not the partitions. - testParentCheckpointing(_.coalesce(2), true, false) - - // Test that the CoalescedRDDPartition updates parent partitions (CoalescedRDDPartition.parents) after - // the parent RDD has been checkpointed and parent partitions have been changed to HadoopPartitions. - // Note that this test is very specific to the current implementation of CoalescedRDDPartitions + // Test that the CoalescedRDDPartition updates parent partitions (CoalescedRDDPartition.parents) + // after the parent RDD has been checkpointed and parent partitions have been changed. + // Note that this test is very specific to the current implementation of + // CoalescedRDDPartitions. val ones = sc.makeRDD(1 to 100, 10).map(x => x) ones.checkpoint() // checkpoint that MappedRDD val coalesced = new CoalescedRDD(ones, 2) @@ -158,33 +147,78 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { val splitAfterCheckpoint = serializeDeserialize(coalesced.partitions.head.asInstanceOf[CoalescedRDDPartition]) assert( - splitAfterCheckpoint.parents.head != splitBeforeCheckpoint.parents.head, - "CoalescedRDDPartition.parents not updated after parent RDD checkpointed" + splitAfterCheckpoint.parents.head.getClass != splitBeforeCheckpoint.parents.head.getClass, + "CoalescedRDDPartition.parents not updated after parent RDD is checkpointed" ) } test("CoGroupedRDD") { - val longLineageRDD1 = generateLongLineageRDDForCoGroupedRDD() - testCheckpointing(rdd => { + val longLineageRDD1 = generateFatPairRDD() + testRDD(rdd => { CheckpointSuite.cogroup(longLineageRDD1, rdd.map(x => (x % 2, 1)), partitioner) - }, false, true) + }) - val longLineageRDD2 = generateLongLineageRDDForCoGroupedRDD() - testParentCheckpointing(rdd => { + val longLineageRDD2 = generateFatPairRDD() + testRDDPartitions(rdd => { CheckpointSuite.cogroup( longLineageRDD2, sc.makeRDD(1 to 2, 2).map(x => (x % 2, 1)), partitioner) - }, false, true) + }) } test("ZippedRDD") { - testCheckpointing( - rdd => new ZippedRDD(sc, rdd, rdd.map(x => x)), true, false) - - // Test whether size of ZippedRDD reduce in size after parent RDD is checkpointed - // Current implementation of ZippedRDDPartitions has transient references to parent RDDs, - // so only the RDD will reduce in serialized size, not the partitions. - testParentCheckpointing( - rdd => new ZippedRDD(sc, rdd, rdd.map(x => x)), true, false) + testRDD(rdd => new ZippedRDD(sc, rdd, rdd.map(x => x))) + testRDDPartitions(rdd => new ZippedRDD(sc, rdd, rdd.map(x => x))) + + // Test that the ZippedPartition updates parent partitions + // after the parent RDD has been checkpointed and parent partitions have been changed. + // Note that this test is very specific to the current implementation of ZippedRDD. + val rdd = generateFatRDD() + val zippedRDD = new ZippedRDD(sc, rdd, rdd.map(x => x)) + zippedRDD.rdd1.checkpoint() + zippedRDD.rdd2.checkpoint() + val partitionBeforeCheckpoint = + serializeDeserialize(zippedRDD.partitions.head.asInstanceOf[ZippedPartition[_, _]]) + zippedRDD.count() + val partitionAfterCheckpoint = + serializeDeserialize(zippedRDD.partitions.head.asInstanceOf[ZippedPartition[_, _]]) + assert( + partitionAfterCheckpoint.partition1.getClass != partitionBeforeCheckpoint.partition1.getClass && + partitionAfterCheckpoint.partition2.getClass != partitionBeforeCheckpoint.partition2.getClass, + "ZippedRDD.partition1 and ZippedRDD.partition2 not updated after parent RDD is checkpointed" + ) + } + + test("PartitionerAwareUnionRDD") { + testRDD(rdd => { + new PartitionerAwareUnionRDD[(Int, Int)](sc, Array( + generateFatPairRDD(), + rdd.map(x => (x % 2, 1)).reduceByKey(partitioner, _ + _) + )) + }) + + testRDDPartitions(rdd => { + new PartitionerAwareUnionRDD[(Int, Int)](sc, Array( + generateFatPairRDD(), + rdd.map(x => (x % 2, 1)).reduceByKey(partitioner, _ + _) + )) + }) + + // Test that the PartitionerAwareUnionRDD updates parent partitions + // (PartitionerAwareUnionRDD.parents) after the parent RDD has been checkpointed and parent + // partitions have been changed. Note that this test is very specific to the current + // implementation of PartitionerAwareUnionRDD. + val pairRDD = generateFatPairRDD() + pairRDD.checkpoint() + val unionRDD = new PartitionerAwareUnionRDD(sc, Array(pairRDD)) + val partitionBeforeCheckpoint = serializeDeserialize( + unionRDD.partitions.head.asInstanceOf[PartitionerAwareUnionRDDPartition]) + pairRDD.count() + val partitionAfterCheckpoint = serializeDeserialize( + unionRDD.partitions.head.asInstanceOf[PartitionerAwareUnionRDDPartition]) + assert( + partitionBeforeCheckpoint.parents.head.getClass != partitionAfterCheckpoint.parents.head.getClass, + "PartitionerAwareUnionRDDPartition.parents not updated after parent RDD is checkpointed" + ) } test("CheckpointRDD with zero partitions") { @@ -198,29 +232,32 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { } /** - * Test checkpointing of the final RDD generated by the given operation. By default, - * this method tests whether the size of serialized RDD has reduced after checkpointing or not. - * It can also test whether the size of serialized RDD partitions has reduced after checkpointing or - * not, but this is not done by default as usually the partitions do not refer to any RDD and - * therefore never store the lineage. + * Test checkpointing of the RDD generated by the given operation. It tests whether the + * serialized size of the RDD is reduce after checkpointing or not. This function should be called + * on all RDDs that have a parent RDD (i.e., do not call on ParallelCollection, BlockRDD, etc.). */ - def testCheckpointing[U: ClassTag]( - op: (RDD[Int]) => RDD[U], - testRDDSize: Boolean = true, - testRDDPartitionSize: Boolean = false - ) { + def testRDD[U: ClassTag](op: (RDD[Int]) => RDD[U]) { // Generate the final RDD using given RDD operation - val baseRDD = generateLongLineageRDD() + val baseRDD = generateFatRDD() val operatedRDD = op(baseRDD) val parentRDD = operatedRDD.dependencies.headOption.orNull val rddType = operatedRDD.getClass.getSimpleName val numPartitions = operatedRDD.partitions.length + // Force initialization of all the data structures in RDDs + // Without this, serializing the RDD will give a wrong estimate of the size of the RDD + initializeRdd(operatedRDD) + + val partitionsBeforeCheckpoint = operatedRDD.partitions + // Find serialized sizes before and after the checkpoint - val (rddSizeBeforeCheckpoint, splitSizeBeforeCheckpoint) = getSerializedSizes(operatedRDD) + logInfo("RDD after checkpoint: " + operatedRDD + "\n" + operatedRDD.toDebugString) + val (rddSizeBeforeCheckpoint, partitionSizeBeforeCheckpoint) = getSerializedSizes(operatedRDD) operatedRDD.checkpoint() val result = operatedRDD.collect() - val (rddSizeAfterCheckpoint, splitSizeAfterCheckpoint) = getSerializedSizes(operatedRDD) + operatedRDD.collect() // force re-initialization of post-checkpoint lazy variables + val (rddSizeAfterCheckpoint, partitionSizeAfterCheckpoint) = getSerializedSizes(operatedRDD) + logInfo("RDD after checkpoint: " + operatedRDD + "\n" + operatedRDD.toDebugString) // Test whether the checkpoint file has been created assert(sc.checkpointFile[U](operatedRDD.getCheckpointFile.get).collect() === result) @@ -228,6 +265,9 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { // Test whether dependencies have been changed from its earlier parent RDD assert(operatedRDD.dependencies.head.rdd != parentRDD) + // Test whether the partitions have been changed from its earlier partitions + assert(operatedRDD.partitions.toList != partitionsBeforeCheckpoint.toList) + // Test whether the partitions have been changed to the new Hadoop partitions assert(operatedRDD.partitions.toList === operatedRDD.checkpointData.get.getPartitions.toList) @@ -237,122 +277,72 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { // Test whether the data in the checkpointed RDD is same as original assert(operatedRDD.collect() === result) - // Test whether serialized size of the RDD has reduced. If the RDD - // does not have any dependency to another RDD (e.g., ParallelCollection, - // ShuffleRDD with ShuffleDependency), it may not reduce in size after checkpointing. - if (testRDDSize) { - logInfo("Size of " + rddType + - "[" + rddSizeBeforeCheckpoint + " --> " + rddSizeAfterCheckpoint + "]") - assert( - rddSizeAfterCheckpoint < rddSizeBeforeCheckpoint, - "Size of " + rddType + " did not reduce after checkpointing " + - "[" + rddSizeBeforeCheckpoint + " --> " + rddSizeAfterCheckpoint + "]" - ) - } + // Test whether serialized size of the RDD has reduced. + logInfo("Size of " + rddType + + " [" + rddSizeBeforeCheckpoint + " --> " + rddSizeAfterCheckpoint + "]") + assert( + rddSizeAfterCheckpoint < rddSizeBeforeCheckpoint, + "Size of " + rddType + " did not reduce after checkpointing " + + " [" + rddSizeBeforeCheckpoint + " --> " + rddSizeAfterCheckpoint + "]" + ) - // Test whether serialized size of the partitions has reduced. If the partitions - // do not have any non-transient reference to another RDD or another RDD's partitions, it - // does not refer to a lineage and therefore may not reduce in size after checkpointing. - // However, if the original partitions before checkpointing do refer to a parent RDD, the partitions - // must be forgotten after checkpointing (to remove all reference to parent RDDs) and - // replaced with the HadooPartitions of the checkpointed RDD. - if (testRDDPartitionSize) { - logInfo("Size of " + rddType + " partitions " - + "[" + splitSizeBeforeCheckpoint + " --> " + splitSizeAfterCheckpoint + "]") - assert( - splitSizeAfterCheckpoint < splitSizeBeforeCheckpoint, - "Size of " + rddType + " partitions did not reduce after checkpointing " + - "[" + splitSizeBeforeCheckpoint + " --> " + splitSizeAfterCheckpoint + "]" - ) - } } /** * Test whether checkpointing of the parent of the generated RDD also * truncates the lineage or not. Some RDDs like CoGroupedRDD hold on to its parent * RDDs partitions. So even if the parent RDD is checkpointed and its partitions changed, - * this RDD will remember the partitions and therefore potentially the whole lineage. + * the generated RDD will remember the partitions and therefore potentially the whole lineage. + * This function should be called only those RDD whose partitions refer to parent RDD's + * partitions (i.e., do not call it on simple RDD like MappedRDD). + * */ - def testParentCheckpointing[U: ClassTag]( - op: (RDD[Int]) => RDD[U], - testRDDSize: Boolean, - testRDDPartitionSize: Boolean - ) { + def testRDDPartitions[U: ClassTag](op: (RDD[Int]) => RDD[U]) { // Generate the final RDD using given RDD operation - val baseRDD = generateLongLineageRDD() + val baseRDD = generateFatRDD() val operatedRDD = op(baseRDD) - val parentRDD = operatedRDD.dependencies.head.rdd + val parentRDDs = operatedRDD.dependencies.map(_.rdd) val rddType = operatedRDD.getClass.getSimpleName - val parentRDDType = parentRDD.getClass.getSimpleName - // Get the partitions and dependencies of the parent in case they're lazily computed - parentRDD.dependencies - parentRDD.partitions + // Force initialization of all the data structures in RDDs + // Without this, serializing the RDD will give a wrong estimate of the size of the RDD + initializeRdd(operatedRDD) // Find serialized sizes before and after the checkpoint - val (rddSizeBeforeCheckpoint, splitSizeBeforeCheckpoint) = getSerializedSizes(operatedRDD) - parentRDD.checkpoint() // checkpoint the parent RDD, not the generated one - val result = operatedRDD.collect() - val (rddSizeAfterCheckpoint, splitSizeAfterCheckpoint) = getSerializedSizes(operatedRDD) + logInfo("RDD after checkpoint: " + operatedRDD + "\n" + operatedRDD.toDebugString) + val (rddSizeBeforeCheckpoint, partitionSizeBeforeCheckpoint) = getSerializedSizes(operatedRDD) + parentRDDs.foreach(_.checkpoint()) // checkpoint the parent RDD, not the generated one + val result = operatedRDD.collect() // force checkpointing + operatedRDD.collect() // force re-initialization of post-checkpoint lazy variables + val (rddSizeAfterCheckpoint, partitionSizeAfterCheckpoint) = getSerializedSizes(operatedRDD) + logInfo("RDD after checkpoint: " + operatedRDD + "\n" + operatedRDD.toDebugString) // Test whether the data in the checkpointed RDD is same as original assert(operatedRDD.collect() === result) - // Test whether serialized size of the RDD has reduced because of its parent being - // checkpointed. If this RDD or its parent RDD do not have any dependency - // to another RDD (e.g., ParallelCollection, ShuffleRDD with ShuffleDependency), it may - // not reduce in size after checkpointing. - if (testRDDSize) { - assert( - rddSizeAfterCheckpoint < rddSizeBeforeCheckpoint, - "Size of " + rddType + " did not reduce after checkpointing parent " + parentRDDType + - "[" + rddSizeBeforeCheckpoint + " --> " + rddSizeAfterCheckpoint + "]" - ) - } - - // Test whether serialized size of the partitions has reduced because of its parent being - // checkpointed. If the partitions do not have any non-transient reference to another RDD - // or another RDD's partitions, it does not refer to a lineage and therefore may not reduce - // in size after checkpointing. However, if the partitions do refer to the *partitions* of a parent - // RDD, then these partitions must update reference to the parent RDD partitions as the parent RDD's - // partitions must have changed after checkpointing. - if (testRDDPartitionSize) { - assert( - splitSizeAfterCheckpoint < splitSizeBeforeCheckpoint, - "Size of " + rddType + " partitions did not reduce after checkpointing parent " + parentRDDType + - "[" + splitSizeBeforeCheckpoint + " --> " + splitSizeAfterCheckpoint + "]" - ) - } - + // Test whether serialized size of the partitions has reduced + logInfo("Size of partitions of " + rddType + + " [" + partitionSizeBeforeCheckpoint + " --> " + partitionSizeAfterCheckpoint + "]") + assert( + partitionSizeAfterCheckpoint < partitionSizeBeforeCheckpoint, + "Size of " + rddType + " partitions did not reduce after checkpointing parent RDDs" + + " [" + partitionSizeBeforeCheckpoint + " --> " + partitionSizeAfterCheckpoint + "]" + ) } /** - * Generate an RDD with a long lineage of one-to-one dependencies. + * Generate an RDD such that both the RDD and its partitions have large size. */ - def generateLongLineageRDD(): RDD[Int] = { - var rdd = sc.makeRDD(1 to 100, 4) - for (i <- 1 to 50) { - rdd = rdd.map(x => x + 1) - } - rdd + def generateFatRDD(): RDD[Int] = { + new FatRDD(sc.makeRDD(1 to 100, 4)).map(x => x) } /** - * Generate an RDD with a long lineage specifically for CoGroupedRDD. - * A CoGroupedRDD can have a long lineage only one of its parents have a long lineage - * and narrow dependency with this RDD. This method generate such an RDD by a sequence - * of cogroups and mapValues which creates a long lineage of narrow dependencies. + * Generate an pair RDD (with partitioner) such that both the RDD and its partitions + * have large size. */ - def generateLongLineageRDDForCoGroupedRDD() = { - val add = (x: (Seq[Int], Seq[Int])) => (x._1 ++ x._2).reduce(_ + _) - - def ones: RDD[(Int, Int)] = sc.makeRDD(1 to 2, 2).map(x => (x % 2, 1)).reduceByKey(partitioner, _ + _) - - var cogrouped: RDD[(Int, (Seq[Int], Seq[Int]))] = ones.cogroup(ones) - for(i <- 1 to 10) { - cogrouped = cogrouped.mapValues(add).cogroup(ones) - } - cogrouped.mapValues(add) + def generateFatPairRDD() = { + new FatPairRDD(sc.makeRDD(1 to 100, 4), partitioner).mapValues(x => x) } /** @@ -360,8 +350,26 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { * upon checkpointing. Ignores the checkpointData field, which may grow when we checkpoint. */ def getSerializedSizes(rdd: RDD[_]): (Int, Int) = { - (Utils.serialize(rdd).length - Utils.serialize(rdd.checkpointData).length, - Utils.serialize(rdd.partitions).length) + val rddSize = Utils.serialize(rdd).size + val rddCpDataSize = Utils.serialize(rdd.checkpointData).size + val rddPartitionSize = Utils.serialize(rdd.partitions).size + val rddDependenciesSize = Utils.serialize(rdd.dependencies).size + + // Print detailed size, helps in debugging + logInfo("Serialized sizes of " + rdd + + ": RDD = " + rddSize + + ", RDD checkpoint data = " + rddCpDataSize + + ", RDD partitions = " + rddPartitionSize + + ", RDD dependencies = " + rddDependenciesSize + ) + // this makes sure that serializing the RDD's checkpoint data does not + // serialize the whole RDD as well + assert( + rddSize > rddCpDataSize, + "RDD's checkpoint data (" + rddCpDataSize + ") is equal or larger than the " + + "whole RDD with checkpoint data (" + rddSize + ")" + ) + (rddSize - rddCpDataSize, rddPartitionSize) } /** @@ -373,8 +381,49 @@ class CheckpointSuite extends FunSuite with LocalSparkContext with Logging { val bytes = Utils.serialize(obj) Utils.deserialize[T](bytes) } + + /** + * Recursively force the initialization of the all members of an RDD and it parents. + */ + def initializeRdd(rdd: RDD[_]) { + rdd.partitions // forces the + rdd.dependencies.map(_.rdd).foreach(initializeRdd(_)) + } } +/** RDD partition that has large serialized size. */ +class FatPartition(val partition: Partition) extends Partition { + val bigData = new Array[Byte](10000) + def index: Int = partition.index +} + +/** RDD that has large serialized size. */ +class FatRDD(parent: RDD[Int]) extends RDD[Int](parent) { + val bigData = new Array[Byte](100000) + + protected def getPartitions: Array[Partition] = { + parent.partitions.map(p => new FatPartition(p)) + } + + def compute(split: Partition, context: TaskContext): Iterator[Int] = { + parent.compute(split.asInstanceOf[FatPartition].partition, context) + } +} + +/** Pair RDD that has large serialized size. */ +class FatPairRDD(parent: RDD[Int], _partitioner: Partitioner) extends RDD[(Int, Int)](parent) { + val bigData = new Array[Byte](100000) + + protected def getPartitions: Array[Partition] = { + parent.partitions.map(p => new FatPartition(p)) + } + + @transient override val partitioner = Some(_partitioner) + + def compute(split: Partition, context: TaskContext): Iterator[(Int, Int)] = { + parent.compute(split.asInstanceOf[FatPartition].partition, context).map(x => (x, x)) + } +} object CheckpointSuite { // This is a custom cogroup function that does not use mapValues like diff --git a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala index 271dc905bc..10b8b441fd 100644 --- a/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala +++ b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala @@ -25,7 +25,7 @@ import org.apache.spark.storage.BlockManagerId import org.apache.spark.util.AkkaUtils class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { - + private val conf = new SparkConf test("compressSize") { assert(MapOutputTracker.compressSize(0L) === 0) assert(MapOutputTracker.compressSize(1L) === 1) @@ -48,14 +48,14 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { test("master start and stop") { val actorSystem = ActorSystem("test") - val tracker = new MapOutputTrackerMaster() + val tracker = new MapOutputTrackerMaster(conf) tracker.trackerActor = Left(actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker)))) tracker.stop() } test("master register and fetch") { val actorSystem = ActorSystem("test") - val tracker = new MapOutputTrackerMaster() + val tracker = new MapOutputTrackerMaster(conf) tracker.trackerActor = Left(actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker)))) tracker.registerShuffle(10, 2) val compressedSize1000 = MapOutputTracker.compressSize(1000L) @@ -74,7 +74,7 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { test("master register and unregister and fetch") { val actorSystem = ActorSystem("test") - val tracker = new MapOutputTrackerMaster() + val tracker = new MapOutputTrackerMaster(conf) tracker.trackerActor = Left(actorSystem.actorOf(Props(new MapOutputTrackerMasterActor(tracker)))) tracker.registerShuffle(10, 2) val compressedSize1000 = MapOutputTracker.compressSize(1000L) @@ -96,16 +96,16 @@ class MapOutputTrackerSuite extends FunSuite with LocalSparkContext { test("remote fetch") { val hostname = "localhost" - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0) + val (actorSystem, boundPort) = AkkaUtils.createActorSystem("spark", hostname, 0, conf = conf) System.setProperty("spark.driver.port", boundPort.toString) // Will be cleared by LocalSparkContext System.setProperty("spark.hostPort", hostname + ":" + boundPort) - val masterTracker = new MapOutputTrackerMaster() + val masterTracker = new MapOutputTrackerMaster(conf) masterTracker.trackerActor = Left(actorSystem.actorOf( Props(new MapOutputTrackerMasterActor(masterTracker)), "MapOutputTracker")) - val (slaveSystem, _) = AkkaUtils.createActorSystem("spark-slave", hostname, 0) - val slaveTracker = new 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")) diff --git a/core/src/test/scala/org/apache/spark/SharedSparkContext.scala b/core/src/test/scala/org/apache/spark/SharedSparkContext.scala index 288aa14eeb..c650ef4ed5 100644 --- a/core/src/test/scala/org/apache/spark/SharedSparkContext.scala +++ b/core/src/test/scala/org/apache/spark/SharedSparkContext.scala @@ -27,8 +27,10 @@ trait SharedSparkContext extends BeforeAndAfterAll { self: Suite => def sc: SparkContext = _sc + var conf = new SparkConf(false) + override def beforeAll() { - _sc = new SparkContext("local", "test") + _sc = new SparkContext("local", "test", conf) super.beforeAll() } diff --git a/core/src/test/scala/org/apache/spark/SparkConfSuite.scala b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala new file mode 100644 index 0000000000..ef5936dd2f --- /dev/null +++ b/core/src/test/scala/org/apache/spark/SparkConfSuite.scala @@ -0,0 +1,110 @@ +package org.apache.spark + +import org.scalatest.FunSuite + +class SparkConfSuite extends FunSuite with LocalSparkContext { + // This test uses the spark.conf in core/src/test/resources, which has a few test properties + test("loading from spark.conf") { + val conf = new SparkConf() + assert(conf.get("spark.test.intTestProperty") === "1") + assert(conf.get("spark.test.stringTestProperty") === "hi") + // NOTE: we don't use list properties yet, but when we do, we'll have to deal with this syntax + assert(conf.get("spark.test.listTestProperty") === "[a, b]") + } + + // This test uses the spark.conf in core/src/test/resources, which has a few test properties + test("system properties override spark.conf") { + try { + System.setProperty("spark.test.intTestProperty", "2") + val conf = new SparkConf() + assert(conf.get("spark.test.intTestProperty") === "2") + assert(conf.get("spark.test.stringTestProperty") === "hi") + } finally { + System.clearProperty("spark.test.intTestProperty") + } + } + + test("initializing without loading defaults") { + try { + System.setProperty("spark.test.intTestProperty", "2") + val conf = new SparkConf(false) + assert(!conf.contains("spark.test.intTestProperty")) + assert(!conf.contains("spark.test.stringTestProperty")) + } finally { + System.clearProperty("spark.test.intTestProperty") + } + } + + test("named set methods") { + val conf = new SparkConf(false) + + conf.setMaster("local[3]") + conf.setAppName("My app") + conf.setSparkHome("/path") + conf.setJars(Seq("a.jar", "b.jar")) + conf.setExecutorEnv("VAR1", "value1") + conf.setExecutorEnv(Seq(("VAR2", "value2"), ("VAR3", "value3"))) + + assert(conf.get("spark.master") === "local[3]") + assert(conf.get("spark.app.name") === "My app") + assert(conf.get("spark.home") === "/path") + assert(conf.get("spark.jars") === "a.jar,b.jar") + assert(conf.get("spark.executorEnv.VAR1") === "value1") + assert(conf.get("spark.executorEnv.VAR2") === "value2") + assert(conf.get("spark.executorEnv.VAR3") === "value3") + + // Test the Java-friendly versions of these too + conf.setJars(Array("c.jar", "d.jar")) + conf.setExecutorEnv(Array(("VAR4", "value4"), ("VAR5", "value5"))) + assert(conf.get("spark.jars") === "c.jar,d.jar") + assert(conf.get("spark.executorEnv.VAR4") === "value4") + assert(conf.get("spark.executorEnv.VAR5") === "value5") + } + + test("basic get and set") { + val conf = new SparkConf(false) + assert(conf.getAll.toSet === Set()) + conf.set("k1", "v1") + conf.setAll(Seq(("k2", "v2"), ("k3", "v3"))) + assert(conf.getAll.toSet === Set(("k1", "v1"), ("k2", "v2"), ("k3", "v3"))) + conf.set("k1", "v4") + conf.setAll(Seq(("k2", "v5"), ("k3", "v6"))) + assert(conf.getAll.toSet === Set(("k1", "v4"), ("k2", "v5"), ("k3", "v6"))) + assert(conf.contains("k1"), "conf did not contain k1") + assert(!conf.contains("k4"), "conf contained k4") + assert(conf.get("k1") === "v4") + intercept[Exception] { conf.get("k4") } + assert(conf.get("k4", "not found") === "not found") + assert(conf.getOption("k1") === Some("v4")) + assert(conf.getOption("k4") === None) + } + + test("creating SparkContext without master and app name") { + val conf = new SparkConf(false) + intercept[SparkException] { sc = new SparkContext(conf) } + } + + test("creating SparkContext without master") { + val conf = new SparkConf(false).setAppName("My app") + intercept[SparkException] { sc = new SparkContext(conf) } + } + + test("creating SparkContext without app name") { + val conf = new SparkConf(false).setMaster("local") + intercept[SparkException] { sc = new SparkContext(conf) } + } + + test("creating SparkContext with both master and app name") { + val conf = new SparkConf(false).setMaster("local").setAppName("My app") + sc = new SparkContext(conf) + assert(sc.master === "local") + assert(sc.appName === "My app") + } + + test("SparkContext property overriding") { + val conf = new SparkConf(false).setMaster("local").setAppName("My app") + sc = new SparkContext("local[2]", "My other app", conf) + assert(sc.master === "local[2]") + assert(sc.appName === "My other app") + } +} diff --git a/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala b/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala index ab81bfbe55..8d7546085f 100644 --- a/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala +++ b/core/src/test/scala/org/apache/spark/io/CompressionCodecSuite.scala @@ -20,9 +20,11 @@ package org.apache.spark.io import java.io.{ByteArrayInputStream, ByteArrayOutputStream} import org.scalatest.FunSuite +import org.apache.spark.SparkConf class CompressionCodecSuite extends FunSuite { + val conf = new SparkConf(false) def testCodec(codec: CompressionCodec) { // Write 1000 integers to the output stream, compressed. @@ -43,19 +45,19 @@ class CompressionCodecSuite extends FunSuite { } test("default compression codec") { - val codec = CompressionCodec.createCodec() + val codec = CompressionCodec.createCodec(conf) assert(codec.getClass === classOf[LZFCompressionCodec]) testCodec(codec) } test("lzf compression codec") { - val codec = CompressionCodec.createCodec(classOf[LZFCompressionCodec].getName) + val codec = CompressionCodec.createCodec(conf, classOf[LZFCompressionCodec].getName) assert(codec.getClass === classOf[LZFCompressionCodec]) testCodec(codec) } test("snappy compression codec") { - val codec = CompressionCodec.createCodec(classOf[SnappyCompressionCodec].getName) + val codec = CompressionCodec.createCodec(conf, classOf[SnappyCompressionCodec].getName) assert(codec.getClass === classOf[SnappyCompressionCodec]) testCodec(codec) } diff --git a/core/src/test/scala/org/apache/spark/metrics/MetricsSystemSuite.scala b/core/src/test/scala/org/apache/spark/metrics/MetricsSystemSuite.scala index 7181333adf..71a2c6c498 100644 --- a/core/src/test/scala/org/apache/spark/metrics/MetricsSystemSuite.scala +++ b/core/src/test/scala/org/apache/spark/metrics/MetricsSystemSuite.scala @@ -19,17 +19,19 @@ package org.apache.spark.metrics import org.scalatest.{BeforeAndAfter, FunSuite} import org.apache.spark.deploy.master.MasterSource +import org.apache.spark.SparkConf class MetricsSystemSuite extends FunSuite with BeforeAndAfter { var filePath: String = _ + var conf: SparkConf = null before { filePath = getClass.getClassLoader.getResource("test_metrics_system.properties").getFile() - System.setProperty("spark.metrics.conf", filePath) + conf = new SparkConf(false).set("spark.metrics.conf", filePath) } test("MetricsSystem with default config") { - val metricsSystem = MetricsSystem.createMetricsSystem("default") + val metricsSystem = MetricsSystem.createMetricsSystem("default", conf) val sources = metricsSystem.sources val sinks = metricsSystem.sinks @@ -39,7 +41,7 @@ class MetricsSystemSuite extends FunSuite with BeforeAndAfter { } test("MetricsSystem with sources add") { - val metricsSystem = MetricsSystem.createMetricsSystem("test") + val metricsSystem = MetricsSystem.createMetricsSystem("test", conf) val sources = metricsSystem.sources val sinks = metricsSystem.sinks diff --git a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala index 1383359f85..559ea051d3 100644 --- a/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala +++ b/core/src/test/scala/org/apache/spark/rdd/RDDSuite.scala @@ -84,6 +84,33 @@ class RDDSuite extends FunSuite with SharedSparkContext { assert(sc.union(Seq(nums, nums)).collect().toList === List(1, 2, 3, 4, 1, 2, 3, 4)) } + test("partitioner aware union") { + import SparkContext._ + def makeRDDWithPartitioner(seq: Seq[Int]) = { + sc.makeRDD(seq, 1) + .map(x => (x, null)) + .partitionBy(new HashPartitioner(2)) + .mapPartitions(_.map(_._1), true) + } + + val nums1 = makeRDDWithPartitioner(1 to 4) + val nums2 = makeRDDWithPartitioner(5 to 8) + assert(nums1.partitioner == nums2.partitioner) + assert(new PartitionerAwareUnionRDD(sc, Seq(nums1)).collect().toSet === Set(1, 2, 3, 4)) + + val union = new PartitionerAwareUnionRDD(sc, Seq(nums1, nums2)) + assert(union.collect().toSet === Set(1, 2, 3, 4, 5, 6, 7, 8)) + val nums1Parts = nums1.collectPartitions() + val nums2Parts = nums2.collectPartitions() + val unionParts = union.collectPartitions() + assert(nums1Parts.length === 2) + assert(nums2Parts.length === 2) + assert(unionParts.length === 2) + assert((nums1Parts(0) ++ nums2Parts(0)).toList === unionParts(0).toList) + assert((nums1Parts(1) ++ nums2Parts(1)).toList === unionParts(1).toList) + assert(union.partitioner === nums1.partitioner) + } + test("aggregate") { val pairs = sc.makeRDD(Array(("a", 1), ("b", 2), ("a", 2), ("c", 5), ("a", 3))) type StringMap = HashMap[String, Int] diff --git a/core/src/test/scala/org/apache/spark/scheduler/ClusterSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ClusterSchedulerSuite.scala index 702edb862f..7bf2020fe3 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/ClusterSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/ClusterSchedulerSuite.scala @@ -167,7 +167,7 @@ class ClusterSchedulerSuite extends FunSuite with LocalSparkContext with Logging val xmlPath = getClass.getClassLoader.getResource("fairscheduler.xml").getFile() System.setProperty("spark.scheduler.allocation.file", xmlPath) val rootPool = new Pool("", SchedulingMode.FAIR, 0, 0) - val schedulableBuilder = new FairSchedulableBuilder(rootPool) + val schedulableBuilder = new FairSchedulableBuilder(rootPool, sc.conf) schedulableBuilder.buildPools() assert(rootPool.getSchedulableByName("default") != null) diff --git a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala index 706d84a58b..2aa259daf3 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/DAGSchedulerSuite.scala @@ -17,21 +17,14 @@ package org.apache.spark.scheduler -import scala.collection.mutable.{Map, HashMap} - -import org.scalatest.FunSuite -import org.scalatest.BeforeAndAfter - -import org.apache.spark.LocalSparkContext -import org.apache.spark.MapOutputTrackerMaster -import org.apache.spark.SparkContext -import org.apache.spark.Partition -import org.apache.spark.TaskContext -import org.apache.spark.{Dependency, ShuffleDependency, OneToOneDependency} -import org.apache.spark.{FetchFailed, Success, TaskEndReason} +import scala.Tuple2 +import scala.collection.mutable.{HashMap, Map} + +import org.apache.spark._ import org.apache.spark.rdd.RDD import org.apache.spark.scheduler.SchedulingMode.SchedulingMode import org.apache.spark.storage.{BlockId, BlockManagerId, BlockManagerMaster} +import org.scalatest.{BeforeAndAfter, FunSuite} /** * Tests for DAGScheduler. These tests directly call the event processing functions in DAGScheduler @@ -46,7 +39,7 @@ import org.apache.spark.storage.{BlockId, BlockManagerId, BlockManagerMaster} * and capturing the resulting TaskSets from the mock TaskScheduler. */ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkContext { - + val conf = new SparkConf /** Set of TaskSets the DAGScheduler has requested executed. */ val taskSets = scala.collection.mutable.Buffer[TaskSet]() val taskScheduler = new TaskScheduler() { @@ -74,7 +67,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont */ val cacheLocations = new HashMap[(Int, Int), Seq[BlockManagerId]] // stub out BlockManagerMaster.getLocations to use our cacheLocations - val blockManagerMaster = new BlockManagerMaster(null) { + val blockManagerMaster = new BlockManagerMaster(null, conf) { override def getLocations(blockIds: Array[BlockId]): Seq[Seq[BlockManagerId]] = { blockIds.map { _.asRDDId.map(id => (id.rddId -> id.splitIndex)).flatMap(key => cacheLocations.get(key)). @@ -99,7 +92,7 @@ class DAGSchedulerSuite extends FunSuite with BeforeAndAfter with LocalSparkCont taskSets.clear() cacheLocations.clear() results.clear() - mapOutputTracker = new MapOutputTrackerMaster() + mapOutputTracker = new MapOutputTrackerMaster(conf) scheduler = new DAGScheduler(taskScheduler, mapOutputTracker, blockManagerMaster, sc.env) { override def runLocally(job: ActiveJob) { // don't bother with the thread while unit testing diff --git a/core/src/test/scala/org/apache/spark/scheduler/JobLoggerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/JobLoggerSuite.scala index d0bd20fc83..5cc48ee00a 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/JobLoggerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/JobLoggerSuite.scala @@ -95,7 +95,7 @@ class JobLoggerSuite extends FunSuite with LocalSparkContext with ShouldMatchers assert(sc.dagScheduler.listenerBus.waitUntilEmpty(WAIT_TIMEOUT_MILLIS)) - val user = System.getProperty("user.name", SparkContext.SPARK_UNKNOWN_USER) + val user = System.getProperty("user.name", SparkContext.SPARK_UNKNOWN_USER) joblogger.getLogDir should be ("/tmp/spark-%s".format(user)) joblogger.getJobIDtoPrintWriter.size should be (1) diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskResultGetterSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskResultGetterSuite.scala index ca97f7d2a5..4b52d9651e 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/TaskResultGetterSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskResultGetterSuite.scala @@ -21,7 +21,7 @@ import java.nio.ByteBuffer import org.scalatest.{BeforeAndAfter, BeforeAndAfterAll, FunSuite} -import org.apache.spark.{LocalSparkContext, SparkContext, SparkEnv} +import org.apache.spark.{LocalSparkContext, SparkConf, SparkContext, SparkEnv} import org.apache.spark.storage.TaskResultBlockId /** @@ -43,13 +43,13 @@ class ResultDeletingTaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedule case IndirectTaskResult(blockId) => sparkEnv.blockManager.master.removeBlock(blockId) case directResult: DirectTaskResult[_] => - taskSetManager.abort("Internal error: expect only indirect results") + taskSetManager.abort("Internal error: expect only indirect results") } serializedData.rewind() removedResult = true } super.enqueueSuccessfulTask(taskSetManager, tid, serializedData) - } + } } /** 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 3dcb01ae5e..1eec6726f4 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala @@ -81,7 +81,9 @@ class FakeClusterScheduler(sc: SparkContext, liveExecutors: (String, String)* /* class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { import TaskLocality.{ANY, PROCESS_LOCAL, NODE_LOCAL, RACK_LOCAL} - val LOCALITY_WAIT = System.getProperty("spark.locality.wait", "3000").toLong + private val conf = new SparkConf + + val LOCALITY_WAIT = conf.get("spark.locality.wait", "3000").toLong val MAX_TASK_FAILURES = 4 test("TaskSet with no preferences") { diff --git a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala index 636e3ab913..3898583275 100644 --- a/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala +++ b/core/src/test/scala/org/apache/spark/serializer/KryoSerializerSuite.scala @@ -22,12 +22,15 @@ import scala.collection.mutable import com.esotericsoftware.kryo.Kryo import org.scalatest.FunSuite -import org.apache.spark.SharedSparkContext +import org.apache.spark.{SparkConf, SharedSparkContext} import org.apache.spark.serializer.KryoTest._ class KryoSerializerSuite extends FunSuite with SharedSparkContext { + conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") + conf.set("spark.kryo.registrator", classOf[MyRegistrator].getName) + test("basic types") { - val ser = (new KryoSerializer).newInstance() + val ser = new KryoSerializer(conf).newInstance() def check[T](t: T) { assert(ser.deserialize[T](ser.serialize(t)) === t) } @@ -57,7 +60,7 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { } test("pairs") { - val ser = (new KryoSerializer).newInstance() + val ser = new KryoSerializer(conf).newInstance() def check[T](t: T) { assert(ser.deserialize[T](ser.serialize(t)) === t) } @@ -81,7 +84,7 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { } test("Scala data structures") { - val ser = (new KryoSerializer).newInstance() + val ser = new KryoSerializer(conf).newInstance() def check[T](t: T) { assert(ser.deserialize[T](ser.serialize(t)) === t) } @@ -104,7 +107,7 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { } test("ranges") { - val ser = (new KryoSerializer).newInstance() + val ser = new KryoSerializer(conf).newInstance() def check[T](t: T) { assert(ser.deserialize[T](ser.serialize(t)) === t) // Check that very long ranges don't get written one element at a time @@ -125,9 +128,7 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { } test("custom registrator") { - System.setProperty("spark.kryo.registrator", classOf[MyRegistrator].getName) - - val ser = (new KryoSerializer).newInstance() + val ser = new KryoSerializer(conf).newInstance() def check[T](t: T) { assert(ser.deserialize[T](ser.serialize(t)) === t) } @@ -190,18 +191,6 @@ class KryoSerializerSuite extends FunSuite with SharedSparkContext { .fold(new ClassWithoutNoArgConstructor(10))((t1, t2) => new ClassWithoutNoArgConstructor(t1.x + t2.x)).x assert(10 + control.sum === result) } - - override def beforeAll() { - System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer") - System.setProperty("spark.kryo.registrator", classOf[MyRegistrator].getName) - super.beforeAll() - } - - override def afterAll() { - super.afterAll() - System.clearProperty("spark.kryo.registrator") - System.clearProperty("spark.serializer") - } } object KryoTest { 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 5b4d63b954..a0fc3445be 100644 --- a/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/BlockManagerSuite.scala @@ -31,8 +31,10 @@ import org.scalatest.time.SpanSugar._ import org.apache.spark.util.{SizeEstimator, Utils, AkkaUtils, ByteBufferInputStream} import org.apache.spark.serializer.{JavaSerializer, KryoSerializer} +import org.apache.spark.{SparkConf, SparkContext} class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodTester { + private val conf = new SparkConf(false) var store: BlockManager = null var store2: BlockManager = null var actorSystem: ActorSystem = null @@ -42,30 +44,31 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT var oldHeartBeat: String = null // Reuse a serializer across tests to avoid creating a new thread-local buffer on each test - System.setProperty("spark.kryoserializer.buffer.mb", "1") - val serializer = new KryoSerializer + conf.set("spark.kryoserializer.buffer.mb", "1") + val serializer = new KryoSerializer(conf) // Implicitly convert strings to BlockIds for test clarity. implicit def StringToBlockId(value: String): BlockId = new TestBlockId(value) def rdd(rddId: Int, splitId: Int) = RDDBlockId(rddId, splitId) before { - val (actorSystem, boundPort) = AkkaUtils.createActorSystem("test", "localhost", 0) + val (actorSystem, boundPort) = AkkaUtils.createActorSystem("test", "localhost", 0, conf = conf) this.actorSystem = actorSystem - System.setProperty("spark.driver.port", boundPort.toString) - System.setProperty("spark.hostPort", "localhost:" + boundPort) + conf.set("spark.driver.port", boundPort.toString) + conf.set("spark.hostPort", "localhost:" + boundPort) master = new BlockManagerMaster( - Left(actorSystem.actorOf(Props(new BlockManagerMasterActor(true))))) + Left(actorSystem.actorOf(Props(new BlockManagerMasterActor(true, conf)))), conf) // Set the arch to 64-bit and compressedOops to true to get a deterministic test-case - oldArch = System.setProperty("os.arch", "amd64") - oldOops = System.setProperty("spark.test.useCompressedOops", "true") - oldHeartBeat = System.setProperty("spark.storage.disableBlockManagerHeartBeat", "true") + System.setProperty("os.arch", "amd64") + conf.set("os.arch", "amd64") + conf.set("spark.test.useCompressedOops", "true") + conf.set("spark.storage.disableBlockManagerHeartBeat", "true") val initialize = PrivateMethod[Unit]('initialize) SizeEstimator invokePrivate initialize() // Set some value ... - System.setProperty("spark.hostPort", Utils.localHostName() + ":" + 1111) + conf.set("spark.hostPort", Utils.localHostName() + ":" + 1111) } after { @@ -86,13 +89,13 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT master = null if (oldArch != null) { - System.setProperty("os.arch", oldArch) + conf.set("os.arch", oldArch) } else { System.clearProperty("os.arch") } if (oldOops != null) { - System.setProperty("spark.test.useCompressedOops", oldOops) + conf.set("spark.test.useCompressedOops", oldOops) } else { System.clearProperty("spark.test.useCompressedOops") } @@ -133,7 +136,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("master + 1 manager interaction") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -163,8 +166,8 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("master + 2 managers interaction") { - store = new BlockManager("exec1", actorSystem, master, serializer, 2000) - store2 = new BlockManager("exec2", actorSystem, master, new KryoSerializer, 2000) + store = new BlockManager("exec1", actorSystem, master, serializer, 2000, conf) + store2 = new BlockManager("exec2", actorSystem, master, new KryoSerializer(conf), 2000, conf) val peers = master.getPeers(store.blockManagerId, 1) assert(peers.size === 1, "master did not return the other manager as a peer") @@ -179,7 +182,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("removing block") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -227,7 +230,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("removing rdd") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -261,7 +264,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT test("reregistration on heart beat") { val heartBeat = PrivateMethod[Unit]('heartBeat) - store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf) val a1 = new Array[Byte](400) store.putSingle("a1", a1, StorageLevel.MEMORY_ONLY) @@ -277,7 +280,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("reregistration on block update") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) @@ -296,7 +299,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT test("reregistration doesn't dead lock") { val heartBeat = PrivateMethod[Unit]('heartBeat) - store = new BlockManager("<driver>", actorSystem, master, serializer, 2000) + store = new BlockManager("<driver>", actorSystem, master, serializer, 2000, conf) val a1 = new Array[Byte](400) val a2 = List(new Array[Byte](400)) @@ -333,7 +336,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("in-memory LRU storage") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -352,7 +355,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("in-memory LRU storage with serialization") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -371,7 +374,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("in-memory LRU for partitions of same RDD") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -390,7 +393,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("in-memory LRU for partitions of multiple RDDs") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) store.putSingle(rdd(0, 1), new Array[Byte](400), StorageLevel.MEMORY_ONLY) store.putSingle(rdd(0, 2), new Array[Byte](400), StorageLevel.MEMORY_ONLY) store.putSingle(rdd(1, 1), new Array[Byte](400), StorageLevel.MEMORY_ONLY) @@ -413,7 +416,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("on-disk storage") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -426,7 +429,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("disk and memory storage") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -441,7 +444,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("disk and memory storage with getLocalBytes") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -456,7 +459,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("disk and memory storage with serialization") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -471,7 +474,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("disk and memory storage with serialization and getLocalBytes") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -486,7 +489,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("LRU with mixed storage levels") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val a1 = new Array[Byte](400) val a2 = new Array[Byte](400) val a3 = new Array[Byte](400) @@ -511,7 +514,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("in-memory LRU with streams") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val list1 = List(new Array[Byte](200), new Array[Byte](200)) val list2 = List(new Array[Byte](200), new Array[Byte](200)) val list3 = List(new Array[Byte](200), new Array[Byte](200)) @@ -535,7 +538,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("LRU with mixed storage levels and streams") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, serializer, 1200, conf) val list1 = List(new Array[Byte](200), new Array[Byte](200)) val list2 = List(new Array[Byte](200), new Array[Byte](200)) val list3 = List(new Array[Byte](200), new Array[Byte](200)) @@ -581,7 +584,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT } test("overly large block") { - store = new BlockManager("<driver>", actorSystem, master, serializer, 500) + store = new BlockManager("<driver>", actorSystem, master, serializer, 500, conf) store.putSingle("a1", new Array[Byte](1000), StorageLevel.MEMORY_ONLY) assert(store.getSingle("a1") === None, "a1 was in store") store.putSingle("a2", new Array[Byte](1000), StorageLevel.MEMORY_AND_DISK) @@ -591,53 +594,53 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT test("block compression") { try { - System.setProperty("spark.shuffle.compress", "true") - store = new BlockManager("exec1", actorSystem, master, serializer, 2000) + conf.set("spark.shuffle.compress", "true") + store = new BlockManager("exec1", actorSystem, master, serializer, 2000, conf) store.putSingle(ShuffleBlockId(0, 0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) assert(store.memoryStore.getSize(ShuffleBlockId(0, 0, 0)) <= 100, "shuffle_0_0_0 was not compressed") store.stop() store = null - System.setProperty("spark.shuffle.compress", "false") - store = new BlockManager("exec2", actorSystem, master, serializer, 2000) + conf.set("spark.shuffle.compress", "false") + store = new BlockManager("exec2", actorSystem, master, serializer, 2000, conf) store.putSingle(ShuffleBlockId(0, 0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) assert(store.memoryStore.getSize(ShuffleBlockId(0, 0, 0)) >= 1000, "shuffle_0_0_0 was compressed") store.stop() store = null - System.setProperty("spark.broadcast.compress", "true") - store = new BlockManager("exec3", actorSystem, master, serializer, 2000) + conf.set("spark.broadcast.compress", "true") + store = new BlockManager("exec3", actorSystem, master, serializer, 2000, conf) store.putSingle(BroadcastBlockId(0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) assert(store.memoryStore.getSize(BroadcastBlockId(0)) <= 100, "broadcast_0 was not compressed") store.stop() store = null - System.setProperty("spark.broadcast.compress", "false") - store = new BlockManager("exec4", actorSystem, master, serializer, 2000) + conf.set("spark.broadcast.compress", "false") + store = new BlockManager("exec4", actorSystem, master, serializer, 2000, conf) store.putSingle(BroadcastBlockId(0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) assert(store.memoryStore.getSize(BroadcastBlockId(0)) >= 1000, "broadcast_0 was compressed") store.stop() store = null - System.setProperty("spark.rdd.compress", "true") - store = new BlockManager("exec5", actorSystem, master, serializer, 2000) + conf.set("spark.rdd.compress", "true") + store = new BlockManager("exec5", actorSystem, master, serializer, 2000, conf) store.putSingle(rdd(0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) assert(store.memoryStore.getSize(rdd(0, 0)) <= 100, "rdd_0_0 was not compressed") store.stop() store = null - System.setProperty("spark.rdd.compress", "false") - store = new BlockManager("exec6", actorSystem, master, serializer, 2000) + conf.set("spark.rdd.compress", "false") + store = new BlockManager("exec6", actorSystem, master, serializer, 2000, conf) store.putSingle(rdd(0, 0), new Array[Byte](1000), StorageLevel.MEMORY_ONLY_SER) assert(store.memoryStore.getSize(rdd(0, 0)) >= 1000, "rdd_0_0 was compressed") store.stop() store = null // Check that any other block types are also kept uncompressed - store = new BlockManager("exec7", actorSystem, master, serializer, 2000) + store = new BlockManager("exec7", actorSystem, master, serializer, 2000, conf) store.putSingle("other_block", new Array[Byte](1000), StorageLevel.MEMORY_ONLY) assert(store.memoryStore.getSize("other_block") >= 1000, "other_block was compressed") store.stop() @@ -651,7 +654,7 @@ class BlockManagerSuite extends FunSuite with BeforeAndAfter with PrivateMethodT test("block store put failure") { // Use Java serializer so we can create an unserializable error. - store = new BlockManager("<driver>", actorSystem, master, new JavaSerializer, 1200) + store = new BlockManager("<driver>", actorSystem, master, new JavaSerializer(conf), 1200, conf) // The put should fail since a1 is not serializable. class UnserializableClass 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 070982e798..af4b31d53c 100644 --- a/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/storage/DiskBlockManagerSuite.scala @@ -17,15 +17,18 @@ package org.apache.spark.storage -import java.io.{FileWriter, File} +import java.io.{File, FileWriter} 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} -class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach with BeforeAndAfterAll { - +class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach { + private val testConf = new SparkConf(false) val rootDir0 = Files.createTempDir() rootDir0.deleteOnExit() val rootDir1 = Files.createTempDir() @@ -35,21 +38,16 @@ class DiskBlockManagerSuite extends FunSuite with BeforeAndAfterEach with Before // This suite focuses primarily on consolidation features, // so we coerce consolidation if not already enabled. - val consolidateProp = "spark.shuffle.consolidateFiles" - val oldConsolidate = Option(System.getProperty(consolidateProp)) - System.setProperty(consolidateProp, "true") + testConf.set("spark.shuffle.consolidateFiles", "true") val shuffleBlockManager = new ShuffleBlockManager(null) { + override def conf = testConf.clone var idToSegmentMap = mutable.Map[ShuffleBlockId, FileSegment]() override def getBlockLocation(id: ShuffleBlockId) = idToSegmentMap(id) } var diskBlockManager: DiskBlockManager = _ - override def afterAll() { - oldConsolidate.map(c => System.setProperty(consolidateProp, c)) - } - override def beforeEach() { diskBlockManager = new DiskBlockManager(shuffleBlockManager, rootDirs) shuffleBlockManager.idToSegmentMap.clear() diff --git a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala index 5aff26f9fc..11ebdc352b 100644 --- a/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/SizeEstimatorSuite.scala @@ -20,6 +20,7 @@ package org.apache.spark.util import org.scalatest.FunSuite import org.scalatest.BeforeAndAfterAll import org.scalatest.PrivateMethodTester +import org.apache.spark.SparkContext class DummyClass1 {} @@ -139,7 +140,6 @@ class SizeEstimatorSuite test("64-bit arch with no compressed oops") { val arch = System.setProperty("os.arch", "amd64") val oops = System.setProperty("spark.test.useCompressedOops", "false") - val initialize = PrivateMethod[Unit]('initialize) SizeEstimator invokePrivate initialize() diff --git a/docs/_config.yml b/docs/_config.yml index 02067f9750..11d18f0ac2 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -4,7 +4,7 @@ markdown: kramdown # These allow the documentation to be updated with nerw releases # of Spark, Scala, and Mesos. SPARK_VERSION: 0.9.0-incubating-SNAPSHOT -SPARK_VERSION_SHORT: 0.9.0-SNAPSHOT +SPARK_VERSION_SHORT: 0.9.0 SCALA_VERSION: 2.10 MESOS_VERSION: 0.13.0 SPARK_ISSUE_TRACKER_URL: https://spark-project.atlassian.net diff --git a/docs/configuration.md b/docs/configuration.md index 677d182e50..567aba07f0 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -3,26 +3,37 @@ layout: global title: Spark Configuration --- -Spark provides three main locations to configure the system: +Spark provides three locations to configure the system: -* [Java system properties](#system-properties), which control internal configuration parameters and can be set - either programmatically (by calling `System.setProperty` *before* creating a `SparkContext`) or through - JVM arguments. -* [Environment variables](#environment-variables) for configuring per-machine settings such as the IP address, - which can be set in the `conf/spark-env.sh` script. -* [Logging configuration](#configuring-logging), which is done through `log4j.properties`. +* [Spark properties](#spark-properties) control most application parameters and can be set by passing + a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) object to SparkContext, or through Java + system properties. +* [Environment variables](#environment-variables) can be used to set per-machine settings, such as + the IP address, through the `conf/spark-env.sh` script on each node. +* [Logging](#configuring-logging) can be configured through `log4j.properties`. -# System Properties +# Spark Properties -To set a system property for configuring Spark, you need to either pass it with a -D flag to the JVM (for example `java -Dspark.cores.max=5 MyProgram`) or call `System.setProperty` in your code *before* creating your Spark context, as follows: +Spark properties control most application settings and are configured separately for each application. +The preferred way to set them is by passing a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) +class to your SparkContext constructor. +Alternatively, Spark will also load them from Java system properties (for compatibility with old versions +of Spark) and from a [`spark.conf` file](#configuration-files) on your classpath. + +SparkConf lets you configure most of the common properties to initialize a cluster (e.g., master URL and +application name), as well as arbitrary key-value pairs through the `set()` method. For example, we could +initialize an application as follows: {% highlight scala %} -System.setProperty("spark.cores.max", "5") -val sc = new SparkContext(...) +val conf = new SparkConf() + .setMaster("local") + .setAppName("My application") + .set("spark.executor.memory", "1g") +val sc = new SparkContext(conf) {% endhighlight %} -Most of the configurable system properties control internal settings that have reasonable default values. However, +Most of the properties control internal settings that have reasonable default values. However, there are at least five properties that you will commonly want to control: <table class="table"> @@ -385,11 +396,40 @@ Apart from these, the following properties are also available, and may be useful </tr> </table> +## Viewing Spark Properties + +The application web UI at `http://<driver>:4040` lists Spark properties in the "Environment" tab. +This is a useful place to check to make sure that your properties have been set correctly. + +## Configuration Files + +You can also configure Spark properties through a `spark.conf` file on your Java classpath. +Because these properties are usually application-specific, we recommend putting this fine *only* on your +application's classpath, and not in a global Spark classpath. + +The `spark.conf` file uses Typesafe Config's [HOCON format](https://github.com/typesafehub/config#json-superset), +which is a superset of Java properties files and JSON. For example, the following is a simple config file: + +{% highlight awk %} +# Comments are allowed +spark.executor.memory = 512m +spark.serializer = org.apache.spark.serializer.KryoSerializer +{% endhighlight %} + +The format also allows hierarchical nesting, as follows: + +{% highlight awk %} +spark.akka { + threads = 8 + timeout = 200 +} +{% endhighlight %} + # Environment Variables -Certain Spark settings can also be configured through environment variables, which are read from the `conf/spark-env.sh` +Certain Spark settings can be configured through environment variables, which are read from the `conf/spark-env.sh` script in the directory where Spark is installed (or `conf/spark-env.cmd` on Windows). These variables are meant to be for machine-specific settings, such -as library search paths. While Java system properties can also be set here, for application settings, we recommend setting +as library search paths. While Spark properties can also be set there through `SPARK_JAVA_OPTS`, for per-application settings, we recommend setting these properties within the application instead of in `spark-env.sh` so that different applications can use different settings. @@ -406,7 +446,8 @@ The following variables can be set in `spark-env.sh`: Note that applications can also add dependencies for themselves through `SparkContext.addJar` -- we recommend doing that when possible. * `SPARK_JAVA_OPTS`, to add JVM options. This includes Java options like garbage collector settings and any system - properties that you'd like to pass with `-D` (e.g., `-Dspark.local.dir=/disk1,/disk2`). + properties that you'd like to pass with `-D`. One use case is to set some Spark properties differently on this + machine, e.g., `-Dspark.local.dir=/disk1,/disk2`. * Options for the Spark [standalone cluster scripts](spark-standalone.html#cluster-launch-scripts), such as number of cores to use on each machine and maximum memory. diff --git a/docs/css/bootstrap.min.css b/docs/css/bootstrap.min.css index ccb529eaba..119bb5dba7 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. - */article,aside,details,figcaption,figure,footer,header,hgroup,nav,section{display:block}audio,canvas,video{display:inline-block;*display:inline;*zoom:1}audio:not([controls]){display:none}html{font-size:100%;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}a:focus{outline:thin dotted #333;outline:5px auto 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b/docs/job-scheduling.md index dbcb9ae343..5951155fe3 100644 --- a/docs/job-scheduling.md +++ b/docs/job-scheduling.md @@ -32,12 +32,12 @@ 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` system property in it. This + 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. 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` system property to `true`, +* **Mesos:** To use static partitioning on Mesos, set the `spark.mesos.coarse` configuration property to `true`, and optionally set `spark.cores.max` to limit each application's resource share as in the standalone mode. You should also set `spark.executor.memory` to control the executor memory. * **YARN:** The `--num-workers` option to the Spark YARN client controls how many workers it will allocate @@ -78,11 +78,13 @@ of cluster resources. This means that short jobs submitted while a long job is r resources right away and still get good response times, without waiting for the long job to finish. This mode is best for multi-user settings. -To enable the fair scheduler, simply set the `spark.scheduler.mode` to `FAIR` before creating +To enable the fair scheduler, simply set the `spark.scheduler.mode` property to `FAIR` when configuring a SparkContext: {% highlight scala %} -System.setProperty("spark.scheduler.mode", "FAIR") +val conf = new SparkConf().setMaster(...).setAppName(...) +conf.set("spark.scheduler.mode", "FAIR") +val sc = new SparkContext(conf) {% endhighlight %} ## Fair Scheduler Pools @@ -98,8 +100,8 @@ adding the `spark.scheduler.pool` "local property" to the SparkContext in the th This is done as follows: {% highlight scala %} -// Assuming context is your SparkContext variable -context.setLocalProperty("spark.scheduler.pool", "pool1") +// Assuming sc is your SparkContext variable +sc.setLocalProperty("spark.scheduler.pool", "pool1") {% endhighlight %} After setting this local property, _all_ jobs submitted within this thread (by calls in this thread @@ -108,7 +110,7 @@ it easy to have a thread run multiple jobs on behalf of the same user. If you'd pool that a thread is associated with, simply call: {% highlight scala %} -context.setLocalProperty("spark.scheduler.pool", null) +sc.setLocalProperty("spark.scheduler.pool", null) {% endhighlight %} ## Default Behavior of Pools @@ -138,10 +140,11 @@ properties: of the cluster. By default, each pool's `minShare` is 0. The pool properties can be set by creating an XML file, similar to `conf/fairscheduler.xml.template`, -and setting the `spark.scheduler.allocation.file` property: +and setting a `spark.scheduler.allocation.file` property in your +[SparkConf](configuration.html#spark-properties). {% highlight scala %} -System.setProperty("spark.scheduler.allocation.file", "/path/to/file") +conf.set("spark.scheduler.allocation.file", "/path/to/file") {% endhighlight %} The format of the XML file is simply a `<pool>` element for each pool, with different elements diff --git a/docs/monitoring.md b/docs/monitoring.md index 5ed0474477..0d5eb7065e 100644 --- a/docs/monitoring.md +++ b/docs/monitoring.md @@ -32,7 +32,8 @@ Spark has a configurable metrics system based on the This allows users to report Spark metrics to a variety of sinks including HTTP, JMX, and CSV files. The metrics system is configured via a configuration file that Spark expects to be present at `$SPARK_HOME/conf/metrics.conf`. A custom file location can be specified via the -`spark.metrics.conf` Java system property. Spark's metrics are decoupled into different +`spark.metrics.conf` [configuration property](configuration.html#spark-properties). +Spark's metrics are decoupled into different _instances_ corresponding to Spark components. Within each instance, you can configure a set of sinks to which metrics are reported. The following instances are currently supported: diff --git a/docs/python-programming-guide.md b/docs/python-programming-guide.md index a33977ed82..45a6250eca 100644 --- a/docs/python-programming-guide.md +++ b/docs/python-programming-guide.md @@ -131,15 +131,16 @@ sc = SparkContext("local", "App Name", pyFiles=['MyFile.py', 'lib.zip', 'app.egg Files listed here will be added to the `PYTHONPATH` and shipped to remote worker machines. Code dependencies can be added to an existing SparkContext using its `addPyFile()` method. -You can set [system properties](configuration.html#system-properties) -using `SparkContext.setSystemProperty()` class method *before* -instantiating SparkContext. For example, to set the amount of memory -per executor process: +You can set [configuration properties](configuration.html#spark-properties) by passing a +[SparkConf](api/pyspark/pyspark.conf.SparkConf-class.html) object to SparkContext: {% highlight python %} -from pyspark import SparkContext -SparkContext.setSystemProperty('spark.executor.memory', '2g') -sc = SparkContext("local", "App Name") +from pyspark import SparkConf, SparkContext +conf = (SparkConf() + .setMaster("local") + .setAppName("My app") + .set("spark.executor.memory", "1g")) +sc = SparkContext(conf = conf) {% endhighlight %} # API Docs diff --git a/docs/quick-start.md b/docs/quick-start.md index 5c55def398..1882ea75c0 100644 --- a/docs/quick-start.md +++ b/docs/quick-start.md @@ -124,7 +124,7 @@ object SimpleApp { } {% endhighlight %} -This program simply counts the number of lines containing 'a' and the number containing 'b' in the Spark README. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the proogram. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the application, the directory where Spark is installed, and a name for the jar file containing the application's code. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes. +This program just counts the number of lines containing 'a' and the number containing 'b' in the Spark README. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. Unlike the earlier examples with the Spark shell, which initializes its own SparkContext, we initialize a SparkContext as part of the proogram. We pass the SparkContext constructor four arguments, the type of scheduler we want to use (in this case, a local scheduler), a name for the application, the directory where Spark is installed, and a name for the jar file containing the application's code. The final two arguments are needed in a distributed setting, where Spark is running across several nodes, so we include them for completeness. Spark will automatically ship the jar files you list to slave nodes. This file depends on the Spark API, so we'll also include an sbt configuration file, `simple.sbt` which explains that Spark is a dependency. This file also adds a repository that Spark depends on: @@ -193,7 +193,7 @@ public class SimpleApp { } {% endhighlight %} -This program simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. As with the Scala example, we initialize a SparkContext, though we use the special `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend `spark.api.java.function.Function`. The [Java programming guide](java-programming-guide.html) describes these differences in more detail. +This program just counts the number of lines containing 'a' and the number containing 'b' in a text file. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. As with the Scala example, we initialize a SparkContext, though we use the special `JavaSparkContext` class to get a Java-friendly one. We also create RDDs (represented by `JavaRDD`) and run transformations on them. Finally, we pass functions to Spark by creating classes that extend `spark.api.java.function.Function`. The [Java programming guide](java-programming-guide.html) describes these differences in more detail. To build the program, we also write a Maven `pom.xml` file that lists Spark as a dependency. Note that Spark artifacts are tagged with a Scala version. @@ -224,11 +224,11 @@ To build the program, we also write a Maven `pom.xml` file that lists Spark as a If you also wish to read data from Hadoop's HDFS, you will also need to add a dependency on `hadoop-client` for your version of HDFS: {% highlight xml %} - <dependency> - <groupId>org.apache.hadoop</groupId> - <artifactId>hadoop-client</artifactId> - <version>...</version> - </dependency> +<dependency> + <groupId>org.apache.hadoop</groupId> + <artifactId>hadoop-client</artifactId> + <version>...</version> +</dependency> {% endhighlight %} We lay out these files according to the canonical Maven directory structure: @@ -270,7 +270,7 @@ print "Lines with a: %i, lines with b: %i" % (numAs, numBs) {% endhighlight %} -This program simply counts the number of lines containing 'a' and the number containing 'b' in a system log file. +This program just counts the number of lines containing 'a' and the number containing 'b' in a text file. Note that you'll need to replace $YOUR_SPARK_HOME with the location where Spark is installed. As with the Scala and Java examples, we use a SparkContext to create RDDs. We can pass Python functions to Spark, which are automatically serialized along with any variables that they reference. @@ -301,14 +301,38 @@ assembly jar (or "uber" jar) containing your code and its dependencies. Both have assembly plugins. When creating assembly jars, list Spark itself as a `provided` dependency; it need not be bundled since it is already present on the slaves. Once you have an assembled jar, -add it to the SparkContext as shown here. It is also possible to submit -your dependent jars one-by-one when creating a SparkContext. +add it to the SparkContext as shown here. It is also possible to add +your dependent jars one-by-one using the `addJar` method of `SparkContext`. + +For Python, you can use the `pyFiles` argument of SparkContext +or its `addPyFile` method to add `.py`, `.zip` or `.egg` files to be distributed. ### Setting Configuration Options -Spark includes several configuration options which influence the behavior -of your application. These should be set as -[JVM system properties](configuration.html#system-properties) in your -program. The options will be captured and shipped to all slave nodes. +Spark includes several [configuration options](configuration.html#spark-properties) +that influence the behavior of your application. +These should be set by building a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) +object and passing it to the SparkContext constructor. +For example, in Java and Scala, you can do: + +{% highlight scala %} +import org.apache.spark.{SparkConf, SparkContext} +val conf = new SparkConf() + .setMaster("local") + .setAppName("My application") + .set("spark.executor.memory", "1g") +val sc = new SparkContext(conf) +{% endhighlight %} + +Or in Python: + +{% highlight scala %} +from pyspark import SparkConf, SparkContext +conf = SparkConf() +conf.setMaster("local") +conf.setAppName("My application") +conf.set("spark.executor.memory", "1g")) +sc = SparkContext(conf = conf) +{% endhighlight %} ### Accessing Hadoop Filesystems diff --git a/docs/running-on-mesos.md b/docs/running-on-mesos.md index 322ff585f1..68259f0cb8 100644 --- a/docs/running-on-mesos.md +++ b/docs/running-on-mesos.md @@ -15,15 +15,16 @@ Spark can run on clusters managed by [Apache Mesos](http://mesos.apache.org/). F * `export MESOS_NATIVE_LIBRARY=<path to libmesos.so>`. This path is usually `<prefix>/lib/libmesos.so` (where the prefix is `/usr/local` by default, see above). Also, on Mac OS X, the library is called `libmesos.dylib` instead of `libmesos.so`. * `export SPARK_EXECUTOR_URI=<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>`. * `export MASTER=mesos://HOST:PORT` where HOST:PORT is the host and port (default: 5050) of your Mesos master (or `zk://...` if using Mesos with ZooKeeper). -8. To run a Spark application against the cluster, when you create your `SparkContext`, pass the string `mesos://HOST:PORT` as the first parameter. In addition, you'll need to set the `spark.executor.uri` property. For example: +8. To run a Spark application against the cluster, when you create your `SparkContext`, pass the string `mesos://HOST:PORT` as the master URL. In addition, you'll need to set the `spark.executor.uri` property. For example: {% highlight scala %} -System.setProperty("spark.executor.uri", "<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>") -val sc = new SparkContext("mesos://HOST:5050", "App Name", ...) +val conf = new SparkConf() + .setMaster("mesos://HOST:5050") + .setAppName("My app") + .set("spark.executor.uri", "<path to spark-{{site.SPARK_VERSION}}.tar.gz uploaded above>") +val sc = new SparkContext(conf) {% endhighlight %} -If you want to run Spark on Amazon EC2, you can use the Spark [EC2 launch scripts](ec2-scripts.html), which provide an easy way to launch a cluster with Mesos, Spark, and HDFS pre-configured. This will get you a cluster in about five minutes without any configuration on your part. - # Mesos Run Modes Spark can run over Mesos in two modes: "fine-grained" and "coarse-grained". In fine-grained mode, which is the default, @@ -34,17 +35,15 @@ launch only *one* long-running Spark task on each Mesos machine, and dynamically it. The benefit is much lower startup overhead, but at the cost of reserving the Mesos resources for the complete duration of the application. -To run in coarse-grained mode, set the `spark.mesos.coarse` system property to true *before* creating your SparkContext: +To run in coarse-grained mode, set the `spark.mesos.coarse` property in your [SparkConf](configuration.html#spark-properties): {% highlight scala %} -System.setProperty("spark.mesos.coarse", "true") -val sc = new SparkContext("mesos://HOST:5050", "App Name", ...) +conf.set("spark.mesos.coarse", "true") {% endhighlight %} In addition, for coarse-grained mode, you can control the maximum number of resources Spark will acquire. By default, it will acquire *all* cores in the cluster (that get offered by Mesos), which only makes sense if you run just one -application at a time. You can cap the maximum number of cores using `System.setProperty("spark.cores.max", "10")` (for example). -Again, this must be done *before* initializing a SparkContext. +application at a time. You can cap the maximum number of cores using `conf.set("spark.cores.max", "10")` (for example). # Running Alongside Hadoop diff --git a/docs/scala-programming-guide.md b/docs/scala-programming-guide.md index 3e7075c382..fe1bca789f 100644 --- a/docs/scala-programming-guide.md +++ b/docs/scala-programming-guide.md @@ -49,6 +49,9 @@ This is done through the following constructor: new SparkContext(master, appName, [sparkHome], [jars]) {% endhighlight %} +or through `new SparkContext(conf)`, which takes a [SparkConf](api/core/index.html#org.apache.spark.SparkConf) +object for more advanced configuration. + The `master` parameter is a string specifying a [Spark or Mesos cluster URL](#master-urls) to connect to, or a special "local" string to run in local mode, as described below. `appName` is a name for your application, which will be shown in the cluster web UI. Finally, the last two parameters are needed to deploy your code to a cluster if running in distributed mode, as described later. In the Spark shell, a special interpreter-aware SparkContext is already created for you, in the variable called `sc`. Making your own SparkContext will not work. You can set which master the context connects to using the `MASTER` environment variable, and you can add JARs to the classpath with the `ADD_JARS` variable. For example, to run `spark-shell` on four cores, use @@ -94,7 +97,6 @@ If you want to run your application on a cluster, you will need to specify the t If you run `spark-shell` on a cluster, you can add JARs to it by specifying the `ADD_JARS` environment variable before you launch it. This variable should contain a comma-separated list of JARs. For example, `ADD_JARS=a.jar,b.jar ./spark-shell` will launch a shell with `a.jar` and `b.jar` on its classpath. In addition, any new classes you define in the shell will automatically be distributed. - # Resilient Distributed Datasets (RDDs) Spark revolves around the concept of a _resilient distributed dataset_ (RDD), which is a fault-tolerant collection of elements that can be operated on in parallel. There are currently two types of RDDs: *parallelized collections*, which take an existing Scala collection and run functions on it in parallel, and *Hadoop datasets*, which run functions on each record of a file in Hadoop distributed file system or any other storage system supported by Hadoop. Both types of RDDs can be operated on through the same methods. diff --git a/docs/spark-standalone.md b/docs/spark-standalone.md index b822265b5a..f7f0b78908 100644 --- a/docs/spark-standalone.md +++ b/docs/spark-standalone.md @@ -154,11 +154,18 @@ You can also pass an option `-c <numCores>` to control the number of cores that The standalone cluster mode currently only supports a simple FIFO scheduler across applications. However, to allow multiple concurrent users, you can control the maximum number of resources each -application will acquire. +application will use. By default, it will acquire *all* cores in the cluster, which only makes sense if you just run one -application at a time. You can cap the number of cores using -`System.setProperty("spark.cores.max", "10")` (for example). -This value must be set *before* initializing your SparkContext. +application at a time. You can cap the number of cores by setting `spark.cores.max` in your +[SparkConf](configuration.html#spark-properties). For example: + +{% highlight scala %} +val conf = new SparkConf() + .setMaster(...) + .setAppName(...) + .set("spark.cores.max", "10") +val sc = new SparkContext(conf) +{% endhighlight %} # Monitoring and Logging diff --git a/docs/streaming-programming-guide.md b/docs/streaming-programming-guide.md index 82f42e0b8d..6f97db6a3e 100644 --- a/docs/streaming-programming-guide.md +++ b/docs/streaming-programming-guide.md @@ -326,7 +326,7 @@ Getting the best performance of a Spark Streaming application on a cluster requi There are a number of optimizations that can be done in Spark to minimize the processing time of each batch. These have been discussed in detail in [Tuning Guide](tuning.html). This section highlights some of the most important ones. ### Level of Parallelism -Cluster resources maybe under-utilized if the number of parallel tasks used in any stage of the computation is not high enough. For example, for distributed reduce operations like `reduceByKey` and `reduceByKeyAndWindow`, the default number of parallel tasks is 8. You can pass the level of parallelism as an argument (see the [`PairDStreamFunctions`](api/streaming/index.html#org.apache.spark.PairDStreamFunctions) documentation), or set the system property `spark.default.parallelism` to change the default. +Cluster resources maybe under-utilized if the number of parallel tasks used in any stage of the computation is not high enough. For example, for distributed reduce operations like `reduceByKey` and `reduceByKeyAndWindow`, the default number of parallel tasks is 8. You can pass the level of parallelism as an argument (see the [`PairDStreamFunctions`](api/streaming/index.html#org.apache.spark.PairDStreamFunctions) documentation), or set the [config property](configuration.html#spark-properties) `spark.default.parallelism` to change the default. ### Data Serialization The overhead of data serialization can be significant, especially when sub-second batch sizes are to be achieved. There are two aspects to it. @@ -349,7 +349,7 @@ For a Spark Streaming application running on a cluster to be stable, the process A good approach to figure out the right batch size for your application is to test it with a conservative batch size (say, 5-10 seconds) and a low data rate. To verify whether the system is able to keep up with data rate, you can check the value of the end-to-end delay experienced by each processed batch (in the Spark master logs, find the line having the phrase "Total delay"). If the delay is maintained to be less than the batch size, then system is stable. Otherwise, if the delay is continuously increasing, it means that the system is unable to keep up and it therefore unstable. Once you have an idea of a stable configuration, you can try increasing the data rate and/or reducing the batch size. Note that momentary increase in the delay due to temporary data rate increases maybe fine as long as the delay reduces back to a low value (i.e., less than batch size). ## 24/7 Operation -By default, Spark does not forget any of the metadata (RDDs generated, stages processed, etc.). But for a Spark Streaming application to operate 24/7, it is necessary for Spark to do periodic cleanup of it metadata. This can be enabled by setting the Java system property `spark.cleaner.ttl` to the number of seconds you want any metadata to persist. For example, setting `spark.cleaner.ttl` to 600 would cause Spark periodically cleanup all metadata and persisted RDDs that are older than 10 minutes. Note, that this property needs to be set before the SparkContext is created. +By default, Spark does not forget any of the metadata (RDDs generated, stages processed, etc.). But for a Spark Streaming application to operate 24/7, it is necessary for Spark to do periodic cleanup of it metadata. This can be enabled by setting the [config property](configuration.html#spark-properties) `spark.cleaner.ttl` to the number of seconds you want any metadata to persist. For example, setting `spark.cleaner.ttl` to 600 would cause Spark periodically cleanup all metadata and persisted RDDs that are older than 10 minutes. Note, that this property needs to be set before the SparkContext is created. This value is closely tied with any window operation that is being used. Any window operation would require the input data to be persisted in memory for at least the duration of the window. Hence it is necessary to set the delay to at least the value of the largest window operation used in the Spark Streaming application. If this delay is set too low, the application will throw an exception saying so. diff --git a/docs/tuning.md b/docs/tuning.md index a4be188169..bbb870085c 100644 --- a/docs/tuning.md +++ b/docs/tuning.md @@ -38,14 +38,15 @@ in your operations) and performance. It provides two serialization libraries: `Serializable` types and requires you to *register* the classes you'll use in the program in advance for best performance. -You can switch to using Kryo by calling `System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer")` -*before* creating your SparkContext. This setting configures the serializer used for not only shuffling data between worker +You can switch to using Kryo by initializing your job with a [SparkConf](configuration.html#spark-properties) +and calling `conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer")`. +This setting configures the serializer used for not only shuffling data between worker nodes but also when serializing RDDs to disk. The only reason Kryo is not the default is because of the custom registration requirement, but we recommend trying it in any network-intensive application. Finally, to register your classes with Kryo, create a public class that extends [`org.apache.spark.serializer.KryoRegistrator`](api/core/index.html#org.apache.spark.serializer.KryoRegistrator) and set the -`spark.kryo.registrator` system property to point to it, as follows: +`spark.kryo.registrator` config property to point to it, as follows: {% highlight scala %} import com.esotericsoftware.kryo.Kryo @@ -58,17 +59,17 @@ class MyRegistrator extends KryoRegistrator { } } -// Make sure to set these properties *before* creating a SparkContext! -System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer") -System.setProperty("spark.kryo.registrator", "mypackage.MyRegistrator") -val sc = new SparkContext(...) +val conf = new SparkConf().setMaster(...).setAppName(...) +conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") +conf.set("spark.kryo.registrator", "mypackage.MyRegistrator") +val sc = new SparkContext(conf) {% endhighlight %} The [Kryo documentation](http://code.google.com/p/kryo/) describes more advanced registration options, such as adding custom serialization code. If your objects are large, you may also need to increase the `spark.kryoserializer.buffer.mb` -system property. The default is 2, but this value needs to be large enough to hold the *largest* +config property. The default is 2, but this value needs to be large enough to hold the *largest* object you will serialize. Finally, if you don't register your classes, Kryo will still work, but it will have to store the @@ -165,7 +166,7 @@ cache RDDs. This means that 33% of memory is available for any objects created d In case your tasks slow down and you find that your JVM is garbage-collecting frequently or running out of memory, lowering this value will help reduce the memory consumption. To change this to say 50%, you can call -`System.setProperty("spark.storage.memoryFraction", "0.5")`. Combined with the use of serialized caching, +`conf.set("spark.storage.memoryFraction", "0.5")` on your SparkConf. Combined with the use of serialized caching, using a smaller cache should be sufficient to mitigate most of the garbage collection problems. In case you are interested in further tuning the Java GC, continue reading below. @@ -219,7 +220,7 @@ enough. Spark automatically sets the number of "map" tasks to run on each file a distributed "reduce" operations, such as `groupByKey` and `reduceByKey`, it uses the largest parent RDD's number of partitions. You can pass the level of parallelism as a second argument (see the [`spark.PairRDDFunctions`](api/core/index.html#org.apache.spark.rdd.PairRDDFunctions) documentation), -or set the system property `spark.default.parallelism` to change the default. +or set the config property `spark.default.parallelism` to change the default. In general, we recommend 2-3 tasks per CPU core in your cluster. ## Memory Usage of Reduce Tasks diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala index 72b5c7b88e..4c0de46964 100644 --- a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala +++ b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRank.scala @@ -36,16 +36,18 @@ object WikipediaPageRank { System.err.println("Usage: WikipediaPageRank <inputFile> <threshold> <numPartitions> <host> <usePartitioner>") System.exit(-1) } - - System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer") - System.setProperty("spark.kryo.registrator", classOf[PRKryoRegistrator].getName) + val sparkConf = new SparkConf() + sparkConf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") + sparkConf.set("spark.kryo.registrator", classOf[PRKryoRegistrator].getName) val inputFile = args(0) val threshold = args(1).toDouble val numPartitions = args(2).toInt val host = args(3) val usePartitioner = args(4).toBoolean - val sc = new SparkContext(host, "WikipediaPageRank") + + sparkConf.setMaster(host).setAppName("WikipediaPageRank") + val sc = new SparkContext(sparkConf) // Parse the Wikipedia page data into a graph val input = sc.textFile(inputFile) diff --git a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala index ddf6855325..2cf273a702 100644 --- a/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala +++ b/examples/src/main/scala/org/apache/spark/examples/bagel/WikipediaPageRankStandalone.scala @@ -34,15 +34,19 @@ object WikipediaPageRankStandalone { System.err.println("Usage: WikipediaPageRankStandalone <inputFile> <threshold> <numIterations> <host> <usePartitioner>") System.exit(-1) } + val sparkConf = new SparkConf() + sparkConf.set("spark.serializer", "spark.bagel.examples.WPRSerializer") - System.setProperty("spark.serializer", "spark.bagel.examples.WPRSerializer") val inputFile = args(0) val threshold = args(1).toDouble val numIterations = args(2).toInt val host = args(3) val usePartitioner = args(4).toBoolean - val sc = new SparkContext(host, "WikipediaPageRankStandalone") + + sparkConf.setMaster(host).setAppName("WikipediaPageRankStandalone") + + val sc = new SparkContext(sparkConf) val input = sc.textFile(inputFile) val partitioner = new HashPartitioner(sc.defaultParallelism) diff --git a/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala b/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala index 50e3f9639c..2402409e6e 100644 --- a/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala +++ b/examples/src/main/scala/org/apache/spark/streaming/examples/ActorWordCount.scala @@ -26,6 +26,7 @@ import akka.actor.ActorRef import akka.actor.Props import akka.actor.actorRef2Scala +import org.apache.spark.SparkConf import org.apache.spark.streaming.Seconds import org.apache.spark.streaming.StreamingContext import org.apache.spark.streaming.StreamingContext.toPairDStreamFunctions @@ -116,7 +117,7 @@ object FeederActor { val Seq(host, port) = args.toSeq - val actorSystem = AkkaUtils.createActorSystem("test", host, port.toInt)._1 + val actorSystem = AkkaUtils.createActorSystem("test", host, port.toInt, conf = new SparkConf)._1 val feeder = actorSystem.actorOf(Props[FeederActor], "FeederActor") println("Feeder started as:" + feeder) diff --git a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala index 36853acab5..8b27ecf82c 100644 --- a/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala +++ b/mllib/src/main/scala/org/apache/spark/mllib/recommendation/ALS.scala @@ -578,14 +578,13 @@ object ALS { val implicitPrefs = if (args.length >= 7) args(6).toBoolean else false val alpha = if (args.length >= 8) args(7).toDouble else 1 val blocks = if (args.length == 9) args(8).toInt else -1 - - System.setProperty("spark.serializer", "org.apache.spark.serializer.KryoSerializer") - System.setProperty("spark.kryo.registrator", classOf[ALSRegistrator].getName) - System.setProperty("spark.kryo.referenceTracking", "false") - System.setProperty("spark.kryoserializer.buffer.mb", "8") - System.setProperty("spark.locality.wait", "10000") - val sc = new SparkContext(master, "ALS") + sc.conf.set("spark.serializer", "org.apache.spark.serializer.KryoSerializer") + sc.conf.set("spark.kryo.registrator", classOf[ALSRegistrator].getName) + sc.conf.set("spark.kryo.referenceTracking", "false") + sc.conf.set("spark.kryoserializer.buffer.mb", "8") + sc.conf.set("spark.locality.wait", "10000") + val ratings = sc.textFile(ratingsFile).map { line => val fields = line.split(',') Rating(fields(0).toInt, fields(1).toInt, fields(2).toDouble) diff --git a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index eeeca3ea8a..7c32e0ab9b 100644 --- a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -38,14 +38,14 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.ipc.YarnRPC import org.apache.hadoop.yarn.util.{ConverterUtils, Records} -import org.apache.spark.{SparkContext, Logging} +import org.apache.spark.{SparkConf, SparkContext, Logging} import org.apache.spark.util.Utils class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) extends Logging { def this(args: ApplicationMasterArguments) = this(args, new Configuration()) - + private var rpc: YarnRPC = YarnRPC.create(conf) private val yarnConf: YarnConfiguration = new YarnConfiguration(conf) private var appAttemptId: ApplicationAttemptId = _ @@ -60,15 +60,20 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e private var isLastAMRetry: Boolean = true private var amClient: AMRMClient[ContainerRequest] = _ + private val sparkConf = new SparkConf() // Default to numWorkers * 2, with minimum of 3 - private val maxNumWorkerFailures = System.getProperty("spark.yarn.max.worker.failures", - math.max(args.numWorkers * 2, 3).toString()).toInt + private val maxNumWorkerFailures = sparkConf.getInt("spark.yarn.max.worker.failures", + math.max(args.numWorkers * 2, 3)) def run() { // Setup the directories so things go to YARN approved directories rather // than user specified and /tmp. System.setProperty("spark.local.dir", getLocalDirs()) + // set the web ui port to be ephemeral for yarn so we don't conflict with + // other spark processes running on the same box + System.setProperty("spark.ui.port", "0") + // Use priority 30 as it's higher then HDFS. It's same priority as MapReduce is using. ShutdownHookManager.get().addShutdownHook(new AppMasterShutdownHook(this), 30) @@ -81,16 +86,14 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e // Workaround until hadoop moves to something which has // https://issues.apache.org/jira/browse/HADOOP-8406 - fixed in (2.0.2-alpha but no 0.23 line) // org.apache.hadoop.io.compress.CompressionCodecFactory.getCodecClasses(conf) - + ApplicationMaster.register(this) // Start the user's JAR userThread = startUserClass() - + // This a bit hacky, but we need to wait until the spark.driver.port property has // been set by the Thread executing the user class. - waitForSparkMaster() - waitForSparkContextInitialized() // Do this after Spark master is up and SparkContext is created so that we can register UI Url. @@ -99,7 +102,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e // Allocate all containers allocateWorkers() - // Wait for the user class to Finish + // Wait for the user class to Finish userThread.join() System.exit(0) @@ -119,7 +122,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e } localDirs } - + private def getApplicationAttemptId(): ApplicationAttemptId = { val envs = System.getenv() val containerIdString = envs.get(ApplicationConstants.Environment.CONTAINER_ID.name()) @@ -128,35 +131,11 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e logInfo("ApplicationAttemptId: " + appAttemptId) appAttemptId } - + private def registerApplicationMaster(): RegisterApplicationMasterResponse = { logInfo("Registering the ApplicationMaster") amClient.registerApplicationMaster(Utils.localHostName(), 0, uiAddress) } - - private def waitForSparkMaster() { - logInfo("Waiting for Spark driver to be reachable.") - var driverUp = false - var tries = 0 - val numTries = System.getProperty("spark.yarn.applicationMaster.waitTries", "10").toInt - while (!driverUp && tries < numTries) { - val driverHost = System.getProperty("spark.driver.host") - val driverPort = System.getProperty("spark.driver.port") - try { - val socket = new Socket(driverHost, driverPort.toInt) - socket.close() - logInfo("Driver now available: %s:%s".format(driverHost, driverPort)) - driverUp = true - } catch { - case e: Exception => { - logWarning("Failed to connect to driver at %s:%s, retrying ...". - format(driverHost, driverPort)) - Thread.sleep(100) - tries = tries + 1 - } - } - } - } private def startUserClass(): Thread = { logInfo("Starting the user JAR in a separate Thread") @@ -199,7 +178,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e ApplicationMaster.sparkContextRef.synchronized { var numTries = 0 val waitTime = 10000L - val maxNumTries = System.getProperty("spark.yarn.ApplicationMaster.waitTries", "10").toInt + val maxNumTries = sparkConf.getInt("spark.yarn.applicationMaster.waitTries", 10) while (ApplicationMaster.sparkContextRef.get() == null && numTries < maxNumTries) { logInfo("Waiting for Spark context initialization ... " + numTries) numTries = numTries + 1 @@ -214,8 +193,9 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e yarnConf, amClient, appAttemptId, - args, - sparkContext.preferredNodeLocationData) + args, + sparkContext.preferredNodeLocationData, + sparkContext.getConf) } else { logWarning("Unable to retrieve SparkContext inspite of waiting for %d, maxNumTries = %d". format(numTries * waitTime, maxNumTries)) @@ -223,7 +203,8 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e yarnConf, amClient, appAttemptId, - args) + args, + sparkContext.getConf) } } } finally { @@ -265,7 +246,8 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e // we want to be reasonably responsive without causing too many requests to RM. val schedulerInterval = - System.getProperty("spark.yarn.scheduler.heartbeat.interval-ms", "5000").toLong + sparkConf.getLong("spark.yarn.scheduler.heartbeat.interval-ms", 5000) + // must be <= timeoutInterval / 2. val interval = math.min(timeoutInterval / 2, schedulerInterval) @@ -314,11 +296,11 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e for (container <- containers) { logInfo("Launching shell command on a new container." + ", containerId=" + container.getId() - + ", containerNode=" + container.getNodeId().getHost() + + ", containerNode=" + container.getNodeId().getHost() + ":" + container.getNodeId().getPort() + ", containerNodeURI=" + container.getNodeHttpAddress() + ", containerState" + container.getState() - + ", containerResourceMemory" + + ", containerResourceMemory" + container.getResource().getMemory()) } } @@ -338,12 +320,12 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e } /** - * Clean up the staging directory. + * Clean up the staging directory. */ - private def cleanupStagingDir() { + private def cleanupStagingDir() { var stagingDirPath: Path = null try { - val preserveFiles = System.getProperty("spark.yarn.preserve.staging.files", "false").toBoolean + val preserveFiles = sparkConf.get("spark.yarn.preserve.staging.files", "false").toBoolean if (!preserveFiles) { stagingDirPath = new Path(System.getenv("SPARK_YARN_STAGING_DIR")) if (stagingDirPath == null) { @@ -359,7 +341,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e } } - // The shutdown hook that runs when a signal is received AND during normal close of the JVM. + // The shutdown hook that runs when a signal is received AND during normal close of the JVM. class AppMasterShutdownHook(appMaster: ApplicationMaster) extends Runnable { def run() { @@ -415,18 +397,18 @@ object ApplicationMaster { // Note that this will unfortunately not properly clean up the staging files because it gets // called too late, after the filesystem is already shutdown. if (modified) { - Runtime.getRuntime().addShutdownHook(new Thread with Logging { + Runtime.getRuntime().addShutdownHook(new Thread with Logging { // This is not only logs, but also ensures that log system is initialized for this instance // when we are actually 'run'-ing. logInfo("Adding shutdown hook for context " + sc) - override def run() { - logInfo("Invoking sc stop from shutdown hook") - sc.stop() + override def run() { + logInfo("Invoking sc stop from shutdown hook") + sc.stop() // Best case ... for (master <- applicationMasters) { master.finishApplicationMaster(FinalApplicationStatus.SUCCEEDED) } - } + } } ) } diff --git a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 9fdee29498..a75066888c 100644 --- a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -40,7 +40,7 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.ipc.YarnRPC import org.apache.hadoop.yarn.util.{Apps, Records} -import org.apache.spark.Logging +import org.apache.spark.{Logging, SparkConf} import org.apache.spark.util.Utils import org.apache.spark.deploy.SparkHadoopUtil @@ -57,6 +57,8 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl val credentials = UserGroupInformation.getCurrentUser().getCredentials() private val SPARK_STAGING: String = ".sparkStaging" private val distCacheMgr = new ClientDistributedCacheManager() + private val sparkConf = new SparkConf + // Staging directory is private! -> rwx-------- val STAGING_DIR_PERMISSION: FsPermission = FsPermission.createImmutable(0700: Short) @@ -150,7 +152,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl queueInfo.getChildQueues.size)) } - def verifyClusterResources(app: GetNewApplicationResponse) = { + def verifyClusterResources(app: GetNewApplicationResponse) = { val maxMem = app.getMaximumResourceCapability().getMemory() logInfo("Max mem capabililty of a single resource in this cluster " + maxMem) @@ -221,7 +223,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl FileUtil.copy(remoteFs, originalPath, fs, newPath, false, conf) fs.setReplication(newPath, replication) if (setPerms) fs.setPermission(newPath, new FsPermission(APP_FILE_PERMISSION)) - } + } // Resolve any symlinks in the URI path so using a "current" symlink to point to a specific // version shows the specific version in the distributed cache configuration val qualPath = fs.makeQualified(newPath) @@ -244,7 +246,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl } } val dst = new Path(fs.getHomeDirectory(), appStagingDir) - val replication = System.getProperty("spark.yarn.submit.file.replication", "3").toShort + val replication = sparkConf.getInt("spark.yarn.submit.file.replication", 3).toShort if (UserGroupInformation.isSecurityEnabled()) { val dstFs = dst.getFileSystem(conf) @@ -269,7 +271,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl } val setPermissions = if (destName.equals(Client.APP_JAR)) true else false val destPath = copyRemoteFile(dst, new Path(localURI), replication, setPermissions) - distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, destName, statCache) } } @@ -283,7 +285,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl val destPath = copyRemoteFile(dst, localPath, replication) // Only add the resource to the Spark ApplicationMaster. val appMasterOnly = true - distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, linkname, statCache, appMasterOnly) } } @@ -295,7 +297,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl val localPath = new Path(localURI) val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) val destPath = copyRemoteFile(dst, localPath, replication) - distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, linkname, statCache) } } @@ -307,7 +309,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl val localPath = new Path(localURI) val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) val destPath = copyRemoteFile(dst, localPath, replication) - distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.ARCHIVE, + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.ARCHIVE, linkname, statCache) } } @@ -317,7 +319,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl } def setupLaunchEnv( - localResources: HashMap[String, LocalResource], + localResources: HashMap[String, LocalResource], stagingDir: String): HashMap[String, String] = { logInfo("Setting up the launch environment") val log4jConfLocalRes = localResources.getOrElse(Client.LOG4J_PROP, null) @@ -406,11 +408,11 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl } val commands = List[String]( - javaCommand + + javaCommand + " -server " + JAVA_OPTS + " " + args.amClass + - " --class " + args.userClass + + " --class " + args.userClass + " --jar " + args.userJar + userArgsToString(args) + " --worker-memory " + args.workerMemory + @@ -436,8 +438,8 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl super.submitApplication(appContext) } - def monitorApplication(appId: ApplicationId): Boolean = { - val interval = System.getProperty("spark.yarn.report.interval", "1000").toLong + def monitorApplication(appId: ApplicationId): Boolean = { + val interval = sparkConf.getLong("spark.yarn.report.interval", 1000) while (true) { Thread.sleep(interval) @@ -460,7 +462,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl val state = report.getYarnApplicationState() val dsStatus = report.getFinalApplicationStatus() - if (state == YarnApplicationState.FINISHED || + if (state == YarnApplicationState.FINISHED || state == YarnApplicationState.FAILED || state == YarnApplicationState.KILLED) { return true @@ -497,25 +499,25 @@ object Client { Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$()) // If log4j present, ensure ours overrides all others if (addLog4j) { - Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + Path.SEPARATOR + LOG4J_PROP) } // Normally the users app.jar is last in case conflicts with spark jars - val userClasspathFirst = System.getProperty("spark.yarn.user.classpath.first", "false") + val userClasspathFirst = new SparkConf().get("spark.yarn.user.classpath.first", "false") .toBoolean if (userClasspathFirst) { - Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + Path.SEPARATOR + APP_JAR) } - Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + Path.SEPARATOR + SPARK_JAR) Client.populateHadoopClasspath(conf, env) if (!userClasspathFirst) { - Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + Path.SEPARATOR + APP_JAR) } - Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + Path.SEPARATOR + "*") } } diff --git a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala index 70be15d0a3..7aac2328da 100644 --- a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala +++ b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -19,6 +19,7 @@ package org.apache.spark.deploy.yarn import scala.collection.mutable.{ArrayBuffer, HashMap} +import org.apache.spark.SparkConf import org.apache.spark.scheduler.{InputFormatInfo, SplitInfo} import org.apache.spark.util.IntParam import org.apache.spark.util.MemoryParam @@ -35,7 +36,7 @@ class ClientArguments(val args: Array[String]) { var workerMemory = 1024 // MB var workerCores = 1 var numWorkers = 2 - var amQueue = System.getProperty("QUEUE", "default") + var amQueue = new SparkConf().get("QUEUE", "default") var amMemory: Int = 512 // MB var amClass: String = "org.apache.spark.deploy.yarn.ApplicationMaster" var appName: String = "Spark" diff --git a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerLauncher.scala b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerLauncher.scala index bc31bb2eb0..99b824e129 100644 --- a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerLauncher.scala +++ b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerLauncher.scala @@ -28,7 +28,7 @@ import org.apache.hadoop.yarn.util.{ConverterUtils, Records} import akka.actor._ import akka.remote._ import akka.actor.Terminated -import org.apache.spark.{SparkContext, Logging} +import org.apache.spark.{SparkConf, SparkContext, Logging} import org.apache.spark.util.{Utils, AkkaUtils} import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend import org.apache.spark.scheduler.SplitInfo @@ -47,8 +47,10 @@ class WorkerLauncher(args: ApplicationMasterArguments, conf: Configuration) exte private var driverClosed:Boolean = false private var amClient: AMRMClient[ContainerRequest] = _ + private val sparkConf = new SparkConf - val actorSystem : ActorSystem = AkkaUtils.createActorSystem("sparkYarnAM", Utils.localHostName, 0)._1 + val actorSystem : ActorSystem = AkkaUtils.createActorSystem("sparkYarnAM", Utils.localHostName, 0, + conf = sparkConf)._1 var actor: ActorRef = _ // This actor just working as a monitor to watch on Driver Actor. @@ -136,8 +138,8 @@ class WorkerLauncher(args: ApplicationMasterArguments, conf: Configuration) exte Thread.sleep(100) } } - System.setProperty("spark.driver.host", driverHost) - System.setProperty("spark.driver.port", driverPort.toString) + sparkConf.set("spark.driver.host", driverHost) + sparkConf.set("spark.driver.port", driverPort.toString) val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( driverHost, driverPort.toString, CoarseGrainedSchedulerBackend.ACTOR_NAME) @@ -157,7 +159,8 @@ class WorkerLauncher(args: ApplicationMasterArguments, conf: Configuration) exte amClient, appAttemptId, args, - preferredNodeLocationData) + preferredNodeLocationData, + sparkConf) logInfo("Allocating " + args.numWorkers + " workers.") // Wait until all containers have finished diff --git a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala index 784a3112de..85ab08ef34 100644 --- a/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala +++ b/new-yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala @@ -26,7 +26,7 @@ import scala.collection import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} -import org.apache.spark.Logging +import org.apache.spark.{Logging, SparkConf} import org.apache.spark.scheduler.{SplitInfo,TaskSchedulerImpl} import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend import org.apache.spark.util.Utils @@ -64,7 +64,8 @@ private[yarn] class YarnAllocationHandler( val workerMemory: Int, val workerCores: Int, val preferredHostToCount: Map[String, Int], - val preferredRackToCount: Map[String, Int]) + val preferredRackToCount: Map[String, Int], + val sparkConf: SparkConf) extends Logging { // These three are locked on allocatedHostToContainersMap. Complementary data structures // allocatedHostToContainersMap : containers which are running : host, Set<containerid> @@ -254,8 +255,8 @@ private[yarn] class YarnAllocationHandler( } else { val workerId = workerIdCounter.incrementAndGet().toString val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( - System.getProperty("spark.driver.host"), - System.getProperty("spark.driver.port"), + sparkConf.get("spark.driver.host"), + sparkConf.get("spark.driver.port"), CoarseGrainedSchedulerBackend.ACTOR_NAME) logInfo("Launching container %s for on host %s".format(containerId, workerHostname)) @@ -565,7 +566,8 @@ object YarnAllocationHandler { conf: Configuration, amClient: AMRMClient[ContainerRequest], appAttemptId: ApplicationAttemptId, - args: ApplicationMasterArguments + args: ApplicationMasterArguments, + sparkConf: SparkConf ): YarnAllocationHandler = { new YarnAllocationHandler( conf, @@ -575,7 +577,8 @@ object YarnAllocationHandler { args.workerMemory, args.workerCores, Map[String, Int](), - Map[String, Int]()) + Map[String, Int](), + sparkConf) } def newAllocator( @@ -584,7 +587,8 @@ object YarnAllocationHandler { appAttemptId: ApplicationAttemptId, args: ApplicationMasterArguments, map: collection.Map[String, - collection.Set[SplitInfo]] + collection.Set[SplitInfo]], + sparkConf: SparkConf ): YarnAllocationHandler = { val (hostToSplitCount, rackToSplitCount) = generateNodeToWeight(conf, map) new YarnAllocationHandler( @@ -595,7 +599,8 @@ object YarnAllocationHandler { args.workerMemory, args.workerCores, hostToSplitCount, - rackToSplitCount) + rackToSplitCount, + sparkConf) } def newAllocator( @@ -605,7 +610,8 @@ object YarnAllocationHandler { maxWorkers: Int, workerMemory: Int, workerCores: Int, - map: collection.Map[String, collection.Set[SplitInfo]] + map: collection.Map[String, collection.Set[SplitInfo]], + sparkConf: SparkConf ): YarnAllocationHandler = { val (hostToCount, rackToCount) = generateNodeToWeight(conf, map) new YarnAllocationHandler( @@ -616,7 +622,8 @@ object YarnAllocationHandler { workerMemory, workerCores, hostToCount, - rackToCount) + rackToCount, + sparkConf) } // A simple method to copy the split info map. diff --git a/new-yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/new-yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala index 350fc760a4..4b69f5078b 100644 --- a/new-yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala +++ b/new-yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -53,8 +53,8 @@ private[spark] class YarnClientSchedulerBackend( if (workerNumber == null) workerNumber = defaultWorkerNumber - val driverHost = System.getProperty("spark.driver.host") - val driverPort = System.getProperty("spark.driver.port") + val driverHost = conf.get("spark.driver.host") + val driverPort = conf.get("spark.driver.port") val hostport = driverHost + ":" + driverPort val argsArray = Array[String]( diff --git a/project/SparkBuild.scala b/project/SparkBuild.scala index 2d2d635084..b335b5a20a 100644 --- a/project/SparkBuild.scala +++ b/project/SparkBuild.scala @@ -248,6 +248,7 @@ object SparkBuild extends Build { "com.codahale.metrics" % "metrics-graphite" % "3.0.0", "com.twitter" %% "chill" % "0.3.1", "com.twitter" % "chill-java" % "0.3.1", + "com.typesafe" % "config" % "1.0.2", "com.clearspring.analytics" % "stream" % "2.5.1" ) ) diff --git a/python/epydoc.conf b/python/epydoc.conf index 0b42e729f8..95a6af0974 100644 --- a/python/epydoc.conf +++ b/python/epydoc.conf @@ -34,4 +34,4 @@ private: no exclude: pyspark.cloudpickle pyspark.worker pyspark.join pyspark.java_gateway pyspark.examples pyspark.shell pyspark.test - pyspark.rddsampler pyspark.daemon + pyspark.rddsampler pyspark.daemon pyspark.mllib._common diff --git a/python/pyspark/__init__.py b/python/pyspark/__init__.py index 1f35f6f939..2b2c3a061a 100644 --- a/python/pyspark/__init__.py +++ b/python/pyspark/__init__.py @@ -20,28 +20,34 @@ PySpark is the Python API for Spark. Public classes: - - L{SparkContext<pyspark.context.SparkContext>} - Main entry point for Spark functionality. - - L{RDD<pyspark.rdd.RDD>} - A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. - - L{Broadcast<pyspark.broadcast.Broadcast>} - A broadcast variable that gets reused across tasks. - - L{Accumulator<pyspark.accumulators.Accumulator>} - An "add-only" shared variable that tasks can only add values to. - - L{SparkFiles<pyspark.files.SparkFiles>} - Access files shipped with jobs. - - L{StorageLevel<pyspark.storagelevel.StorageLevel>} - Finer-grained cache persistence levels. + - L{SparkContext<pyspark.context.SparkContext>} + Main entry point for Spark functionality. + - L{RDD<pyspark.rdd.RDD>} + A Resilient Distributed Dataset (RDD), the basic abstraction in Spark. + - L{Broadcast<pyspark.broadcast.Broadcast>} + A broadcast variable that gets reused across tasks. + - L{Accumulator<pyspark.accumulators.Accumulator>} + An "add-only" shared variable that tasks can only add values to. + - L{SparkConf<pyspark.conf.SparkConf>} + For configuring Spark. + - L{SparkFiles<pyspark.files.SparkFiles>} + Access files shipped with jobs. + - L{StorageLevel<pyspark.storagelevel.StorageLevel>} + Finer-grained cache persistence levels. """ + + + import sys import os sys.path.insert(0, os.path.join(os.environ["SPARK_HOME"], "python/lib/py4j0.7.egg")) +from pyspark.conf import SparkConf from pyspark.context import SparkContext from pyspark.rdd import RDD from pyspark.files import SparkFiles from pyspark.storagelevel import StorageLevel -__all__ = ["SparkContext", "RDD", "SparkFiles", "StorageLevel"] +__all__ = ["SparkConf", "SparkContext", "RDD", "SparkFiles", "StorageLevel"] diff --git a/python/pyspark/broadcast.py b/python/pyspark/broadcast.py index dfdaba274f..43f40f8783 100644 --- a/python/pyspark/broadcast.py +++ b/python/pyspark/broadcast.py @@ -45,7 +45,18 @@ def _from_id(bid): class Broadcast(object): + """ + A broadcast variable created with + L{SparkContext.broadcast()<pyspark.context.SparkContext.broadcast>}. + Access its value through C{.value}. + """ + def __init__(self, bid, value, java_broadcast=None, pickle_registry=None): + """ + Should not be called directly by users -- use + L{SparkContext.broadcast()<pyspark.context.SparkContext.broadcast>} + instead. + """ self.value = value self.bid = bid self._jbroadcast = java_broadcast diff --git a/python/pyspark/conf.py b/python/pyspark/conf.py new file mode 100644 index 0000000000..d72aed6a30 --- /dev/null +++ b/python/pyspark/conf.py @@ -0,0 +1,171 @@ +# +# 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. +# + +""" +>>> from pyspark.conf import SparkConf +>>> from pyspark.context import SparkContext +>>> conf = SparkConf() +>>> conf.setMaster("local").setAppName("My app") +<pyspark.conf.SparkConf object at ...> +>>> conf.get("spark.master") +u'local' +>>> conf.get("spark.app.name") +u'My app' +>>> sc = SparkContext(conf=conf) +>>> sc.master +u'local' +>>> sc.appName +u'My app' +>>> sc.sparkHome == None +True + +>>> conf = SparkConf() +>>> conf.setSparkHome("/path") +<pyspark.conf.SparkConf object at ...> +>>> conf.get("spark.home") +u'/path' +>>> conf.setExecutorEnv("VAR1", "value1") +<pyspark.conf.SparkConf object at ...> +>>> conf.setExecutorEnv(pairs = [("VAR3", "value3"), ("VAR4", "value4")]) +<pyspark.conf.SparkConf object at ...> +>>> conf.get("spark.executorEnv.VAR1") +u'value1' +>>> print conf.toDebugString() +spark.executorEnv.VAR1=value1 +spark.executorEnv.VAR3=value3 +spark.executorEnv.VAR4=value4 +spark.home=/path +>>> sorted(conf.getAll(), key=lambda p: p[0]) +[(u'spark.executorEnv.VAR1', u'value1'), (u'spark.executorEnv.VAR3', u'value3'), (u'spark.executorEnv.VAR4', u'value4'), (u'spark.home', u'/path')] +""" + + +class SparkConf(object): + """ + Configuration for a Spark application. Used to set various Spark + parameters as key-value pairs. + + Most of the time, you would create a SparkConf object with + C{SparkConf()}, which will load values from C{spark.*} Java system + properties and any C{spark.conf} on your Spark classpath. In this + case, system properties take priority over C{spark.conf}, and any + parameters you set directly on the C{SparkConf} object take priority + over both of those. + + For unit tests, you can also call C{SparkConf(false)} to skip + loading external settings and get the same configuration no matter + what is on the classpath. + + All setter methods in this class support chaining. For example, + you can write C{conf.setMaster("local").setAppName("My app")}. + + Note that once a SparkConf object is passed to Spark, it is cloned + and can no longer be modified by the user. + """ + + def __init__(self, loadDefaults=True, _jvm=None): + """ + Create a new Spark configuration. + + @param loadDefaults: whether to load values from Java system + properties and classpath (True by default) + @param _jvm: internal parameter used to pass a handle to the + Java VM; does not need to be set by users + """ + from pyspark.context import SparkContext + SparkContext._ensure_initialized() + _jvm = _jvm or SparkContext._jvm + self._jconf = _jvm.SparkConf(loadDefaults) + + def set(self, key, value): + """Set a configuration property.""" + self._jconf.set(key, unicode(value)) + return self + + def setMaster(self, value): + """Set master URL to connect to.""" + self._jconf.setMaster(value) + return self + + def setAppName(self, value): + """Set application name.""" + self._jconf.setAppName(value) + return self + + def setSparkHome(self, value): + """Set path where Spark is installed on worker nodes.""" + self._jconf.setSparkHome(value) + return self + + def setExecutorEnv(self, key=None, value=None, pairs=None): + """Set an environment variable to be passed to executors.""" + if (key != None and pairs != None) or (key == None and pairs == None): + raise Exception("Either pass one key-value pair or a list of pairs") + elif key != None: + self._jconf.setExecutorEnv(key, value) + elif pairs != None: + for (k, v) in pairs: + self._jconf.setExecutorEnv(k, v) + return self + + def setAll(self, pairs): + """ + Set multiple parameters, passed as a list of key-value pairs. + + @param pairs: list of key-value pairs to set + """ + for (k, v) in pairs: + self._jconf.set(k, v) + return self + + def get(self, key, defaultValue=None): + """Get the configured value for some key, or return a default otherwise.""" + if defaultValue == None: # Py4J doesn't call the right get() if we pass None + if not self._jconf.contains(key): + return None + return self._jconf.get(key) + else: + return self._jconf.get(key, defaultValue) + + def getAll(self): + """Get all values as a list of key-value pairs.""" + pairs = [] + for elem in self._jconf.getAll(): + pairs.append((elem._1(), elem._2())) + return pairs + + def contains(self, key): + """Does this configuration contain a given key?""" + return self._jconf.contains(key) + + def toDebugString(self): + """ + Returns a printable version of the configuration, as a list of + key=value pairs, one per line. + """ + return self._jconf.toDebugString() + + +def _test(): + import doctest + (failure_count, test_count) = doctest.testmod(optionflags=doctest.ELLIPSIS) + if failure_count: + exit(-1) + + +if __name__ == "__main__": + _test() diff --git a/python/pyspark/context.py b/python/pyspark/context.py index 108f36576a..f955aad7a4 100644 --- a/python/pyspark/context.py +++ b/python/pyspark/context.py @@ -24,6 +24,7 @@ from tempfile import NamedTemporaryFile from pyspark import accumulators from pyspark.accumulators import Accumulator from pyspark.broadcast import Broadcast +from pyspark.conf import SparkConf from pyspark.files import SparkFiles from pyspark.java_gateway import launch_gateway from pyspark.serializers import PickleSerializer, BatchedSerializer, MUTF8Deserializer @@ -49,14 +50,15 @@ class SparkContext(object): _python_includes = None # zip and egg files that need to be added to PYTHONPATH - def __init__(self, master, jobName, sparkHome=None, pyFiles=None, - environment=None, batchSize=1024, serializer=PickleSerializer()): + def __init__(self, master=None, appName=None, sparkHome=None, pyFiles=None, + environment=None, batchSize=1024, serializer=PickleSerializer(), conf=None): """ - Create a new SparkContext. + Create a new SparkContext. At least the master and app name should be set, + either through the named parameters here or through C{conf}. @param master: Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). - @param jobName: A name for your job, to display on the cluster web UI + @param appName: A name for your job, to display on the cluster web UI. @param sparkHome: Location where Spark is installed on cluster nodes. @param pyFiles: Collection of .zip or .py files to send to the cluster and add to PYTHONPATH. These can be paths on the local file @@ -67,6 +69,7 @@ class SparkContext(object): Java object. Set 1 to disable batching or -1 to use an unlimited batch size. @param serializer: The serializer for RDDs. + @param conf: A L{SparkConf} object setting Spark properties. >>> from pyspark.context import SparkContext @@ -79,10 +82,8 @@ class SparkContext(object): """ SparkContext._ensure_initialized(self) - self.master = master - self.jobName = jobName - self.sparkHome = sparkHome or None # None becomes null in Py4J self.environment = environment or {} + self._conf = conf or SparkConf(_jvm=self._jvm) self._batchSize = batchSize # -1 represents an unlimited batch size self._unbatched_serializer = serializer if batchSize == 1: @@ -91,10 +92,35 @@ class SparkContext(object): self.serializer = BatchedSerializer(self._unbatched_serializer, batchSize) + # Set any parameters passed directly to us on the conf + if master: + self._conf.setMaster(master) + if appName: + self._conf.setAppName(appName) + if sparkHome: + self._conf.setSparkHome(sparkHome) + if environment: + for key, value in environment.iteritems(): + self._conf.setExecutorEnv(key, value) + + # Check that we have at least the required parameters + if not self._conf.contains("spark.master"): + raise Exception("A master URL must be set in your configuration") + if not self._conf.contains("spark.app.name"): + raise Exception("An application name must be set in your configuration") + + # Read back our properties from the conf in case we loaded some of them from + # the classpath or an external config file + self.master = self._conf.get("spark.master") + self.appName = self._conf.get("spark.app.name") + self.sparkHome = self._conf.get("spark.home", None) + for (k, v) in self._conf.getAll(): + if k.startswith("spark.executorEnv."): + varName = k[len("spark.executorEnv."):] + self.environment[varName] = v + # Create the Java SparkContext through Py4J - empty_string_array = self._gateway.new_array(self._jvm.String, 0) - self._jsc = self._jvm.JavaSparkContext(master, jobName, sparkHome, - empty_string_array) + self._jsc = self._jvm.JavaSparkContext(self._conf._jconf) # Create a single Accumulator in Java that we'll send all our updates through; # they will be passed back to us through a TCP server @@ -105,6 +131,7 @@ class SparkContext(object): self._jvm.PythonAccumulatorParam(host, port)) self.pythonExec = os.environ.get("PYSPARK_PYTHON", 'python') + # Broadcast's __reduce__ method stores Broadcast instances here. # This allows other code to determine which Broadcast instances have # been pickled, so it can determine which Java broadcast objects to @@ -121,7 +148,7 @@ class SparkContext(object): self.addPyFile(path) # Create a temporary directory inside spark.local.dir: - local_dir = self._jvm.org.apache.spark.util.Utils.getLocalDir() + local_dir = self._jvm.org.apache.spark.util.Utils.getLocalDir(self._jsc.sc().conf()) self._temp_dir = \ self._jvm.org.apache.spark.util.Utils.createTempDir(local_dir).getAbsolutePath() @@ -131,8 +158,7 @@ class SparkContext(object): if not SparkContext._gateway: SparkContext._gateway = launch_gateway() SparkContext._jvm = SparkContext._gateway.jvm - SparkContext._writeToFile = \ - SparkContext._jvm.PythonRDD.writeToFile + SparkContext._writeToFile = SparkContext._jvm.PythonRDD.writeToFile if instance: if SparkContext._active_spark_context and SparkContext._active_spark_context != instance: @@ -143,8 +169,8 @@ class SparkContext(object): @classmethod def setSystemProperty(cls, key, value): """ - Set a system property, such as spark.executor.memory. This must be - invoked before instantiating SparkContext. + Set a Java system property, such as spark.executor.memory. This must + must be invoked before instantiating SparkContext. """ SparkContext._ensure_initialized() SparkContext._jvm.java.lang.System.setProperty(key, value) @@ -243,7 +269,8 @@ class SparkContext(object): def broadcast(self, value): """ - Broadcast a read-only variable to the cluster, returning a C{Broadcast} + Broadcast a read-only variable to the cluster, returning a + L{Broadcast<pyspark.broadcast.Broadcast>} object for reading it in distributed functions. The variable will be sent to each cluster only once. """ diff --git a/python/pyspark/java_gateway.py b/python/pyspark/java_gateway.py index eb79135b9d..d8ca9fce00 100644 --- a/python/pyspark/java_gateway.py +++ b/python/pyspark/java_gateway.py @@ -60,6 +60,7 @@ def launch_gateway(): # Connect to the gateway gateway = JavaGateway(GatewayClient(port=port), auto_convert=False) # Import the classes used by PySpark + java_import(gateway.jvm, "org.apache.spark.SparkConf") java_import(gateway.jvm, "org.apache.spark.api.java.*") java_import(gateway.jvm, "org.apache.spark.api.python.*") java_import(gateway.jvm, "org.apache.spark.mllib.api.python.*") diff --git a/python/pyspark/rdd.py b/python/pyspark/rdd.py index f87923e6fa..6fb4a7b3be 100644 --- a/python/pyspark/rdd.py +++ b/python/pyspark/rdd.py @@ -23,6 +23,7 @@ import operator import os import sys import shlex +import traceback from subprocess import Popen, PIPE from tempfile import NamedTemporaryFile from threading import Thread @@ -39,6 +40,46 @@ from py4j.java_collections import ListConverter, MapConverter __all__ = ["RDD"] +def _extract_concise_traceback(): + tb = traceback.extract_stack() + if len(tb) == 0: + return "I'm lost!" + # HACK: This function is in a file called 'rdd.py' in the top level of + # everything PySpark. Just trim off the directory name and assume + # everything in that tree is PySpark guts. + file, line, module, what = tb[len(tb) - 1] + sparkpath = os.path.dirname(file) + first_spark_frame = len(tb) - 1 + for i in range(0, len(tb)): + file, line, fun, what = tb[i] + if file.startswith(sparkpath): + first_spark_frame = i + break + if first_spark_frame == 0: + file, line, fun, what = tb[0] + return "%s at %s:%d" % (fun, file, line) + sfile, sline, sfun, swhat = tb[first_spark_frame] + ufile, uline, ufun, uwhat = tb[first_spark_frame-1] + return "%s at %s:%d" % (sfun, ufile, uline) + +_spark_stack_depth = 0 + +class _JavaStackTrace(object): + def __init__(self, sc): + self._traceback = _extract_concise_traceback() + self._context = sc + + def __enter__(self): + global _spark_stack_depth + if _spark_stack_depth == 0: + self._context._jsc.setCallSite(self._traceback) + _spark_stack_depth += 1 + + def __exit__(self, type, value, tb): + global _spark_stack_depth + _spark_stack_depth -= 1 + if _spark_stack_depth == 0: + self._context._jsc.setCallSite(None) class RDD(object): """ @@ -401,7 +442,8 @@ class RDD(object): """ Return a list that contains all of the elements in this RDD. """ - bytesInJava = self._jrdd.collect().iterator() + with _JavaStackTrace(self.context) as st: + bytesInJava = self._jrdd.collect().iterator() return list(self._collect_iterator_through_file(bytesInJava)) def _collect_iterator_through_file(self, iterator): @@ -582,13 +624,14 @@ class RDD(object): # TODO(shivaram): Similar to the scala implementation, update the take # method to scan multiple splits based on an estimate of how many elements # we have per-split. - for partition in range(mapped._jrdd.splits().size()): - partitionsToTake = self.ctx._gateway.new_array(self.ctx._jvm.int, 1) - partitionsToTake[0] = partition - iterator = mapped._jrdd.collectPartitions(partitionsToTake)[0].iterator() - items.extend(mapped._collect_iterator_through_file(iterator)) - if len(items) >= num: - break + with _JavaStackTrace(self.context) as st: + for partition in range(mapped._jrdd.splits().size()): + partitionsToTake = self.ctx._gateway.new_array(self.ctx._jvm.int, 1) + partitionsToTake[0] = partition + iterator = mapped._jrdd.collectPartitions(partitionsToTake)[0].iterator() + items.extend(mapped._collect_iterator_through_file(iterator)) + if len(items) >= num: + break return items[:num] def first(self): @@ -765,9 +808,10 @@ class RDD(object): yield outputSerializer.dumps(items) keyed = PipelinedRDD(self, add_shuffle_key) keyed._bypass_serializer = True - pairRDD = self.ctx._jvm.PairwiseRDD(keyed._jrdd.rdd()).asJavaPairRDD() - partitioner = self.ctx._jvm.PythonPartitioner(numPartitions, - id(partitionFunc)) + with _JavaStackTrace(self.context) as st: + pairRDD = self.ctx._jvm.PairwiseRDD(keyed._jrdd.rdd()).asJavaPairRDD() + partitioner = self.ctx._jvm.PythonPartitioner(numPartitions, + id(partitionFunc)) jrdd = pairRDD.partitionBy(partitioner).values() rdd = RDD(jrdd, self.ctx, BatchedSerializer(outputSerializer)) # This is required so that id(partitionFunc) remains unique, even if diff --git a/python/run-tests b/python/run-tests index d4dad672d2..4b71fff7c1 100755 --- a/python/run-tests +++ b/python/run-tests @@ -29,12 +29,13 @@ FAILED=0 rm -f unit-tests.log function run_test() { - $FWDIR/pyspark $1 2>&1 | tee -a unit-tests.log + SPARK_TESTING=0 $FWDIR/pyspark $1 2>&1 | tee -a unit-tests.log FAILED=$((PIPESTATUS[0]||$FAILED)) } run_test "pyspark/rdd.py" run_test "pyspark/context.py" +run_test "pyspark/conf.py" run_test "-m doctest pyspark/broadcast.py" run_test "-m doctest pyspark/accumulators.py" run_test "-m doctest pyspark/serializers.py" 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 523fd1222d..f108c70f21 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkILoop.scala @@ -35,6 +35,7 @@ import java.lang.{Class => jClass} import scala.reflect.api.{Mirror, TypeCreator, Universe => ApiUniverse} import org.apache.spark.Logging +import org.apache.spark.SparkConf import org.apache.spark.SparkContext /** The Scala interactive shell. It provides a read-eval-print loop @@ -929,10 +930,7 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter, } def createSparkContext(): SparkContext = { - val uri = System.getenv("SPARK_EXECUTOR_URI") - if (uri != null) { - System.setProperty("spark.executor.uri", uri) - } + val execUri = System.getenv("SPARK_EXECUTOR_URI") val master = this.master match { case Some(m) => m case None => { @@ -941,7 +939,16 @@ class SparkILoop(in0: Option[BufferedReader], protected val out: JPrintWriter, } } val jars = SparkILoop.getAddedJars.map(new java.io.File(_).getAbsolutePath) - sparkContext = new SparkContext(master, "Spark shell", System.getenv("SPARK_HOME"), jars) + val conf = new SparkConf() + .setMaster(master) + .setAppName("Spark shell") + .setSparkHome(System.getenv("SPARK_HOME")) + .setJars(jars) + .set("spark.repl.class.uri", intp.classServer.uri) + if (execUri != null) { + conf.set("spark.executor.uri", execUri) + } + sparkContext = new SparkContext(conf) echo("Created spark context..") sparkContext } diff --git a/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala b/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala index e1455ef8a1..59fdb0b37a 100644 --- a/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala +++ b/repl/src/main/scala/org/apache/spark/repl/SparkIMain.scala @@ -34,10 +34,8 @@ import scala.tools.reflect.StdRuntimeTags._ import scala.util.control.ControlThrowable import util.stackTraceString -import org.apache.spark.HttpServer +import org.apache.spark.{HttpServer, SparkConf, Logging} import org.apache.spark.util.Utils -import org.apache.spark.SparkEnv -import org.apache.spark.Logging // /** directory to save .class files to */ // private class ReplVirtualDirectory(out: JPrintWriter) extends VirtualDirectory("((memory))", None) { @@ -91,7 +89,7 @@ import org.apache.spark.Logging /** Local directory to save .class files too */ val outputDir = { val tmp = System.getProperty("java.io.tmpdir") - val rootDir = System.getProperty("spark.repl.classdir", tmp) + val rootDir = new SparkConf().get("spark.repl.classdir", tmp) Utils.createTempDir(rootDir) } if (SPARK_DEBUG_REPL) { @@ -112,7 +110,6 @@ import org.apache.spark.Logging // Start the classServer and store its URI in a spark system property // (which will be passed to executors so that they can connect to it) classServer.start() - System.setProperty("spark.repl.class.uri", classServer.uri) if (SPARK_DEBUG_REPL) { echo("Class server started, URI = " + classServer.uri) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala index 4960a85b97..ca0115f90e 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/Checkpoint.scala @@ -24,7 +24,7 @@ import java.util.concurrent.RejectedExecutionException import org.apache.hadoop.fs.{FileSystem, Path} import org.apache.hadoop.conf.Configuration -import org.apache.spark.Logging +import org.apache.spark.{SparkConf, Logging} import org.apache.spark.io.CompressionCodec import org.apache.spark.util.MetadataCleaner import org.apache.spark.deploy.SparkHadoopUtil @@ -35,14 +35,14 @@ class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time) extends Logging with Serializable { val master = ssc.sc.master val framework = ssc.sc.appName - val sparkHome = ssc.sc.sparkHome + val sparkHome = ssc.sc.getSparkHome.getOrElse(null) val jars = ssc.sc.jars - val environment = ssc.sc.environment val graph = ssc.graph val checkpointDir = ssc.checkpointDir val checkpointDuration = ssc.checkpointDuration val pendingTimes = ssc.scheduler.getPendingTimes() - val delaySeconds = MetadataCleaner.getDelaySeconds + val delaySeconds = MetadataCleaner.getDelaySeconds(ssc.conf) + val sparkConf = ssc.conf def validate() { assert(master != null, "Checkpoint.master is null") @@ -58,11 +58,13 @@ class Checkpoint(@transient ssc: StreamingContext, val checkpointTime: Time) * Convenience class to handle the writing of graph checkpoint to file */ private[streaming] -class CheckpointWriter(checkpointDir: String, hadoopConf: Configuration) extends Logging { +class CheckpointWriter(conf: SparkConf, checkpointDir: String, hadoopConf: Configuration) + extends Logging +{ val file = new Path(checkpointDir, "graph") val MAX_ATTEMPTS = 3 val executor = Executors.newFixedThreadPool(1) - val compressionCodec = CompressionCodec.createCodec() + val compressionCodec = CompressionCodec.createCodec(conf) // The file to which we actually write - and then "move" to file val writeFile = new Path(file.getParent, file.getName + ".next") // The file to which existing checkpoint is backed up (i.e. "moved") @@ -151,12 +153,12 @@ class CheckpointWriter(checkpointDir: String, hadoopConf: Configuration) extends private[streaming] object CheckpointReader extends Logging { - def read(path: String): Checkpoint = { + def read(conf: SparkConf, path: String): Checkpoint = { val fs = new Path(path).getFileSystem(new Configuration()) val attempts = Seq(new Path(path, "graph"), new Path(path, "graph.bk"), new Path(path), new Path(path + ".bk")) - val compressionCodec = CompressionCodec.createCodec() + val compressionCodec = CompressionCodec.createCodec(conf) attempts.foreach(file => { if (fs.exists(file)) { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/DStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/DStream.scala index 8ebe09da0d..00671ba520 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/DStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/DStream.scala @@ -206,7 +206,7 @@ abstract class DStream[T: ClassTag] ( checkpointDuration + "). Please set it to higher than " + checkpointDuration + "." ) - val metadataCleanerDelay = MetadataCleaner.getDelaySeconds + val metadataCleanerDelay = MetadataCleaner.getDelaySeconds(ssc.conf) logInfo("metadataCleanupDelay = " + metadataCleanerDelay) assert( metadataCleanerDelay < 0 || rememberDuration.milliseconds < metadataCleanerDelay * 1000, diff --git a/streaming/src/main/scala/org/apache/spark/streaming/PairDStreamFunctions.scala b/streaming/src/main/scala/org/apache/spark/streaming/PairDStreamFunctions.scala index 80af96c060..56dbcbda23 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/PairDStreamFunctions.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/PairDStreamFunctions.scala @@ -108,8 +108,9 @@ extends Serializable { createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiner: (C, C) => C, - partitioner: Partitioner) : DStream[(K, C)] = { - new ShuffledDStream[K, V, C](self, createCombiner, mergeValue, mergeCombiner, partitioner) + partitioner: Partitioner, + mapSideCombine: Boolean = true): DStream[(K, C)] = { + new ShuffledDStream[K, V, C](self, createCombiner, mergeValue, mergeCombiner, partitioner, mapSideCombine) } /** @@ -173,7 +174,13 @@ extends Serializable { slideDuration: Duration, partitioner: Partitioner ): DStream[(K, Seq[V])] = { - self.window(windowDuration, slideDuration).groupByKey(partitioner) + val createCombiner = (v: Seq[V]) => new ArrayBuffer[V] ++= v + val mergeValue = (buf: ArrayBuffer[V], v: Seq[V]) => buf ++= v + val mergeCombiner = (buf1: ArrayBuffer[V], buf2: ArrayBuffer[V]) => buf1 ++= buf2 + self.groupByKey(partitioner) + .window(windowDuration, slideDuration) + .combineByKey[ArrayBuffer[V]](createCombiner, mergeValue, mergeCombiner, partitioner) + .asInstanceOf[DStream[(K, Seq[V])]] } /** 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 659da2665a..304986f187 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala @@ -63,18 +63,27 @@ class StreamingContext private ( /** * Create a StreamingContext using an existing SparkContext. - * @param sparkContext Existing SparkContext - * @param batchDuration The time interval at which streaming data will be divided into batches + * @param sparkContext existing SparkContext + * @param batchDuration the time interval at which streaming data will be divided into batches */ def this(sparkContext: SparkContext, batchDuration: Duration) = { this(sparkContext, null, batchDuration) } /** + * Create a StreamingContext by providing the configuration necessary for a new SparkContext. + * @param conf a [[org.apache.spark.SparkConf]] object specifying Spark parameters + * @param batchDuration the time interval at which streaming data will be divided into batches + */ + def this(conf: SparkConf, batchDuration: Duration) = { + this(StreamingContext.createNewSparkContext(conf), null, batchDuration) + } + + /** * Create a StreamingContext by providing the details necessary for creating a new SparkContext. - * @param master Cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). - * @param appName A name for your job, to display on the cluster web UI - * @param batchDuration The time interval at which streaming data will be divided into batches + * @param master cluster URL to connect to (e.g. mesos://host:port, spark://host:port, local[4]). + * @param appName a name for your job, to display on the cluster web UI + * @param batchDuration the time interval at which streaming data will be divided into batches */ def this( master: String, @@ -92,18 +101,20 @@ class StreamingContext private ( * @param path Path either to the directory that was specified as the checkpoint directory, or * to the checkpoint file 'graph' or 'graph.bk'. */ - def this(path: String) = this(null, CheckpointReader.read(path), null) + def this(path: String) = this(null, CheckpointReader.read(new SparkConf(), path), null) if (sc_ == null && cp_ == null) { throw new Exception("Spark Streaming cannot be initialized with " + "both SparkContext and checkpoint as null") } - if(cp_ != null && cp_.delaySeconds >= 0 && MetadataCleaner.getDelaySeconds < 0) { - MetadataCleaner.setDelaySeconds(cp_.delaySeconds) + private val conf_ = Option(sc_).map(_.conf).getOrElse(cp_.sparkConf) + + if(cp_ != null && cp_.delaySeconds >= 0 && MetadataCleaner.getDelaySeconds(conf_) < 0) { + MetadataCleaner.setDelaySeconds(conf_, cp_.delaySeconds) } - if (MetadataCleaner.getDelaySeconds < 0) { + if (MetadataCleaner.getDelaySeconds(conf_) < 0) { throw new SparkException("Spark Streaming cannot be used without setting spark.cleaner.ttl; " + "set this property before creating a SparkContext (use SPARK_JAVA_OPTS for the shell)") } @@ -112,12 +123,14 @@ class StreamingContext private ( protected[streaming] val sc: SparkContext = { if (isCheckpointPresent) { - new SparkContext(cp_.master, cp_.framework, cp_.sparkHome, cp_.jars, cp_.environment) + new SparkContext(cp_.sparkConf) } else { sc_ } } + protected[streaming] val conf = sc.conf + protected[streaming] val env = SparkEnv.get protected[streaming] val graph: DStreamGraph = { @@ -582,18 +595,30 @@ object StreamingContext { new PairDStreamFunctions[K, V](stream) } + protected[streaming] def createNewSparkContext(conf: SparkConf): SparkContext = { + // Set the default cleaner delay to an hour if not already set. + // This should be sufficient for even 1 second batch intervals. + val sc = new SparkContext(conf) + if (MetadataCleaner.getDelaySeconds(sc.conf) < 0) { + MetadataCleaner.setDelaySeconds(sc.conf, 3600) + } + sc + } + protected[streaming] def createNewSparkContext( master: String, appName: String, sparkHome: String, jars: Seq[String], - environment: Map[String, String]): SparkContext = { + environment: Map[String, String]): SparkContext = + { + val sc = new SparkContext(master, appName, sparkHome, jars, environment) // Set the default cleaner delay to an hour if not already set. - // This should be sufficient for even 1 second interval. - if (MetadataCleaner.getDelaySeconds < 0) { - MetadataCleaner.setDelaySeconds(3600) + // This should be sufficient for even 1 second batch intervals. + if (MetadataCleaner.getDelaySeconds(sc.conf) < 0) { + MetadataCleaner.setDelaySeconds(sc.conf, 3600) } - new SparkContext(master, appName, sparkHome, jars, environment) + sc } protected[streaming] def rddToFileName[T](prefix: String, suffix: String, time: Time): String = { diff --git a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala index dfd6e27c3e..6c3467d405 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaPairDStream.scala @@ -155,7 +155,7 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( /** * Combine elements of each key in DStream's RDDs using custom function. This is similar to the - * combineByKey for RDDs. Please refer to combineByKey in [[org.apache.spark.PairRDDFunctions]] for more + * combineByKey for RDDs. Please refer to combineByKey in [[org.apache.spark.rdd.PairRDDFunctions]] for more * information. */ def combineByKey[C](createCombiner: JFunction[V, C], @@ -169,6 +169,22 @@ class JavaPairDStream[K, V](val dstream: DStream[(K, V)])( } /** + * Combine elements of each key in DStream's RDDs using custom function. This is similar to the + * combineByKey for RDDs. Please refer to combineByKey in [[org.apache.spark.rdd.PairRDDFunctions]] for more + * information. + */ + def combineByKey[C](createCombiner: JFunction[V, C], + mergeValue: JFunction2[C, V, C], + mergeCombiners: JFunction2[C, C, C], + partitioner: Partitioner, + mapSideCombine: Boolean + ): JavaPairDStream[K, C] = { + implicit val cm: ClassTag[C] = + implicitly[ClassTag[AnyRef]].asInstanceOf[ClassTag[C]] + dstream.combineByKey(createCombiner, mergeValue, mergeCombiners, partitioner, mapSideCombine) + } + + /** * Return a new DStream by applying `groupByKey` over a sliding window. This is similar to * `DStream.groupByKey()` but applies it over a sliding window. The new DStream generates RDDs * with the same interval as this DStream. Hash partitioning is used to generate the RDDs with 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 aad0d931e7..b79173c6aa 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 @@ -39,6 +39,7 @@ import org.apache.spark.api.java.function.{Function => JFunction, Function2 => J import org.apache.spark.api.java.{JavaPairRDD, JavaSparkContext, JavaRDD} import org.apache.spark.streaming._ import org.apache.spark.streaming.dstream._ +import org.apache.spark.SparkConf import org.apache.spark.streaming.scheduler.StreamingListener /** @@ -124,6 +125,14 @@ class JavaStreamingContext(val ssc: StreamingContext) { this(new StreamingContext(sparkContext.sc, batchDuration)) /** + * Creates a StreamingContext using an existing SparkContext. + * @param conf A Spark application configuration + * @param batchDuration The time interval at which streaming data will be divided into batches + */ + def this(conf: SparkConf, batchDuration: Duration) = + this(new StreamingContext(conf, batchDuration)) + + /** * Re-creates a StreamingContext from a checkpoint file. * @param path Path either to the directory that was specified as the checkpoint directory, or * to the checkpoint file 'graph' or 'graph.bk'. 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 8c7f42306d..27d474c0a0 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 @@ -174,8 +174,8 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging /** A helper actor that communicates with the NetworkInputTracker */ private class NetworkReceiverActor extends Actor { logInfo("Attempting to register with tracker") - val ip = System.getProperty("spark.driver.host", "localhost") - val port = System.getProperty("spark.driver.port", "7077").toInt + val ip = env.conf.get("spark.driver.host", "localhost") + val port = env.conf.get("spark.driver.port", "7077").toInt 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 = System.getProperty("spark.streaming.blockInterval", "200").toLong + val blockInterval = env.conf.get("spark.streaming.blockInterval", "200").toLong 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/dstream/ShuffledDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala index e6e0022097..84e69f277b 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/ShuffledDStream.scala @@ -29,8 +29,9 @@ class ShuffledDStream[K: ClassTag, V: ClassTag, C: ClassTag]( createCombiner: V => C, mergeValue: (C, V) => C, mergeCombiner: (C, C) => C, - partitioner: Partitioner - ) extends DStream [(K,C)] (parent.ssc) { + partitioner: Partitioner, + mapSideCombine: Boolean = true + ) extends DStream[(K,C)] (parent.ssc) { override def dependencies = List(parent) @@ -38,8 +39,8 @@ class ShuffledDStream[K: ClassTag, V: ClassTag, C: ClassTag]( override def compute(validTime: Time): Option[RDD[(K,C)]] = { parent.getOrCompute(validTime) match { - case Some(rdd) => - Some(rdd.combineByKey[C](createCombiner, mergeValue, mergeCombiner, partitioner)) + case Some(rdd) => Some(rdd.combineByKey[C]( + createCombiner, mergeValue, mergeCombiner, partitioner, mapSideCombine)) case None => None } } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala index 73d959331a..89c43ff935 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/WindowedDStream.scala @@ -17,10 +17,10 @@ package org.apache.spark.streaming.dstream -import org.apache.spark.rdd.RDD -import org.apache.spark.rdd.UnionRDD +import org.apache.spark.rdd.{PartitionerAwareUnionRDD, RDD, UnionRDD} import org.apache.spark.storage.StorageLevel -import org.apache.spark.streaming.{Duration, Interval, Time, DStream} +import org.apache.spark.streaming._ +import org.apache.spark.streaming.Duration import scala.reflect.ClassTag @@ -51,6 +51,14 @@ class WindowedDStream[T: ClassTag]( override def compute(validTime: Time): Option[RDD[T]] = { val currentWindow = new Interval(validTime - windowDuration + parent.slideDuration, validTime) - Some(new UnionRDD(ssc.sc, parent.slice(currentWindow))) + val rddsInWindow = parent.slice(currentWindow) + val windowRDD = if (rddsInWindow.flatMap(_.partitioner).distinct.length == 1) { + logDebug("Using partition aware union for windowing at " + validTime) + new PartitionerAwareUnionRDD(ssc.sc, rddsInWindow) + } else { + logDebug("Using normal union for windowing at " + validTime) + new UnionRDD(ssc.sc,rddsInWindow) + } + Some(windowRDD) } } 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 2eb3b3989e..5f8be93a98 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 @@ -46,14 +46,14 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { } })) val clock = { - val clockClass = System.getProperty( + val clockClass = ssc.sc.conf.get( "spark.streaming.clock", "org.apache.spark.streaming.util.SystemClock") Class.forName(clockClass).newInstance().asInstanceOf[Clock] } val timer = new RecurringTimer(clock, ssc.graph.batchDuration.milliseconds, longTime => eventProcessorActor ! GenerateJobs(new Time(longTime))) lazy val checkpointWriter = if (ssc.checkpointDuration != null && ssc.checkpointDir != null) { - new CheckpointWriter(ssc.checkpointDir, ssc.sparkContext.hadoopConfiguration) + new CheckpointWriter(ssc.conf, ssc.checkpointDir, ssc.sparkContext.hadoopConfiguration) } else { null } @@ -65,7 +65,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { startFirstTime() } } - + def stop() { timer.stop() if (checkpointWriter != null) checkpointWriter.stop() @@ -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 = System.getProperty("spark.streaming.manualClock.jump", "0").toLong + val jumpTime = ssc.sc.conf.get("spark.streaming.manualClock.jump", "0").toLong 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 488cc2f401..9304fc1a93 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 @@ -25,13 +25,13 @@ import org.apache.spark.streaming._ /** * This class schedules jobs to be run on Spark. It uses the JobGenerator to generate - * the jobs and runs them using a thread pool. Number of threads + * the jobs and runs them using a thread pool. Number of threads */ private[streaming] class JobScheduler(val ssc: StreamingContext) extends Logging { val jobSets = new ConcurrentHashMap[Time, JobSet] - val numConcurrentJobs = System.getProperty("spark.streaming.concurrentJobs", "1").toInt + val numConcurrentJobs = ssc.conf.get("spark.streaming.concurrentJobs", "1").toInt 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/util/RawTextSender.scala b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala index fc8655a083..6585d494a6 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/util/RawTextSender.scala @@ -20,7 +20,7 @@ package org.apache.spark.streaming.util import java.nio.ByteBuffer import org.apache.spark.util.{RateLimitedOutputStream, IntParam} import java.net.ServerSocket -import org.apache.spark.{Logging} +import org.apache.spark.{SparkConf, Logging} import it.unimi.dsi.fastutil.io.FastByteArrayOutputStream import scala.io.Source import java.io.IOException @@ -42,7 +42,7 @@ object RawTextSender extends Logging { // Repeat the input data multiple times to fill in a buffer val lines = Source.fromFile(file).getLines().toArray val bufferStream = new FastByteArrayOutputStream(blockSize + 1000) - val ser = new KryoSerializer().newInstance() + val ser = new KryoSerializer(new SparkConf()).newInstance() val serStream = ser.serializeStream(bufferStream) var i = 0 while (bufferStream.position < blockSize) { 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 daeb99f5b7..d53d433693 100644 --- a/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java +++ b/streaming/src/test/java/org/apache/spark/streaming/JavaAPISuite.java @@ -25,6 +25,7 @@ import com.google.common.io.Files; import kafka.serializer.StringDecoder; 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; @@ -62,8 +63,11 @@ public class JavaAPISuite implements Serializable { @Before public void setUp() { - System.setProperty("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock"); - ssc = new JavaStreamingContext("local[2]", "test", new Duration(1000)); + 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"); } @@ -101,7 +105,7 @@ public class JavaAPISuite implements Serializable { Arrays.asList("hello", "world"), Arrays.asList("goodnight", "moon")); - List<List<Integer>> expected = Arrays.asList( + List<List<Integer>> expected = Arrays.asList( Arrays.asList(5,5), Arrays.asList(9,4)); diff --git a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala index b35ca00b53..ee6b433d1f 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/BasicOperationsSuite.scala @@ -23,9 +23,9 @@ import org.apache.spark.rdd.RDD import org.apache.spark.SparkContext._ import util.ManualClock +import org.apache.spark.{SparkContext, SparkConf} class BasicOperationsSuite extends TestSuiteBase { - test("map") { val input = Seq(1 to 4, 5 to 8, 9 to 12) testOperation( @@ -375,7 +375,11 @@ class BasicOperationsSuite extends TestSuiteBase { } test("slice") { - val ssc = new StreamingContext("local[2]", "BasicOperationSuite", Seconds(1)) + val conf2 = new SparkConf() + .setMaster("local[2]") + .setAppName("BasicOperationsSuite") + .set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") + val ssc = new StreamingContext(new SparkContext(conf2), Seconds(1)) val input = Seq(Seq(1), Seq(2), Seq(3), Seq(4)) val stream = new TestInputStream[Int](ssc, input, 2) ssc.registerInputStream(stream) diff --git a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala index 4e25c9566c..8dc80ac2ed 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/CheckpointSuite.scala @@ -57,7 +57,7 @@ class CheckpointSuite extends TestSuiteBase { assert(batchDuration === Milliseconds(500), "batchDuration for this test must be 1 second") - System.setProperty("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") + conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") val stateStreamCheckpointInterval = Seconds(1) val fs = FileSystem.getLocal(new Configuration()) @@ -132,8 +132,9 @@ class CheckpointSuite extends TestSuiteBase { assert(!stateStream.generatedRDDs.isEmpty, "No restored RDDs in state stream after recovery from second failure") - // Adjust manual clock time as if it is being restarted after a delay - System.setProperty("spark.streaming.manualClock.jump", (batchDuration.milliseconds * 7).toString) + // Adjust manual clock time as if it is being restarted after a delay; this is a hack because + // we modify the conf object, but it works for this one property + ssc.conf.set("spark.streaming.manualClock.jump", (batchDuration.milliseconds * 7).toString) ssc.start() advanceTimeWithRealDelay(ssc, 4) ssc.stop() @@ -196,10 +197,6 @@ class CheckpointSuite extends TestSuiteBase { // It also tests whether batches, whose processing was incomplete due to the // failure, are re-processed or not. test("recovery with file input stream") { - // Disable manual clock as FileInputDStream does not work with manual clock - val clockProperty = System.getProperty("spark.streaming.clock") - System.clearProperty("spark.streaming.clock") - // Set up the streaming context and input streams val testDir = Files.createTempDir() var ssc = new StreamingContext(master, framework, Seconds(1)) @@ -296,10 +293,6 @@ class CheckpointSuite extends TestSuiteBase { ) // To ensure that all the inputs were received correctly assert(expectedOutput.last === output.last) - - // Enable manual clock back again for other tests - if (clockProperty != null) - System.setProperty("spark.streaming.clock", clockProperty) } 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 5fa14ad7c4..5185954521 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/InputStreamsSuite.scala @@ -56,9 +56,9 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { testServer.start() // Set up the streaming context and input streams - val ssc = new StreamingContext(master, framework, batchDuration) + val ssc = new StreamingContext(new SparkContext(conf), batchDuration) val networkStream = ssc.socketTextStream("localhost", testServer.port, StorageLevel.MEMORY_AND_DISK) - val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String ]] + val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] val outputStream = new TestOutputStream(networkStream, outputBuffer) def output = outputBuffer.flatMap(x => x) ssc.registerOutputStream(outputStream) @@ -101,7 +101,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { test("flume input stream") { // Set up the streaming context and input streams - val ssc = new StreamingContext(master, framework, batchDuration) + 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]] @@ -150,11 +150,11 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { test("file input stream") { // Disable manual clock as FileInputDStream does not work with manual clock - System.clearProperty("spark.streaming.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(master, framework, batchDuration) + val ssc = new StreamingContext(new SparkContext(conf), batchDuration) val fileStream = ssc.textFileStream(testDir.toString) val outputBuffer = new ArrayBuffer[Seq[String]] with SynchronizedBuffer[Seq[String]] def output = outputBuffer.flatMap(x => x) @@ -195,7 +195,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { FileUtils.deleteDirectory(testDir) // Enable manual clock back again for other tests - System.setProperty("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") + conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") } @@ -206,7 +206,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { testServer.start() // Set up the streaming context and input streams - val ssc = new StreamingContext(master, framework, batchDuration) + val ssc = new StreamingContext(new SparkContext(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]] @@ -250,7 +250,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { } test("kafka input stream") { - val ssc = new StreamingContext(master, framework, batchDuration) + 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) @@ -273,7 +273,7 @@ class InputStreamsSuite extends TestSuiteBase with BeforeAndAfter { MultiThreadTestReceiver.haveAllThreadsFinished = false // set up the network stream using the test receiver - val ssc = new StreamingContext(master, framework, batchDuration) + val ssc = new StreamingContext(new SparkContext(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 e969e91d13..33464bc3a1 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/TestSuiteBase.scala @@ -28,7 +28,7 @@ import java.io.{ObjectInputStream, IOException} import org.scalatest.{BeforeAndAfter, FunSuite} -import org.apache.spark.Logging +import org.apache.spark.{SparkContext, SparkConf, Logging} import org.apache.spark.rdd.RDD /** @@ -133,20 +133,26 @@ trait TestSuiteBase extends FunSuite with BeforeAndAfter with Logging { // Whether to actually wait in real time before changing manual clock def actuallyWait = false + //// A SparkConf to use in tests. Can be modified before calling setupStreams to configure things. + val conf = new SparkConf() + .setMaster(master) + .setAppName(framework) + .set("spark.cleaner.ttl", "3600") + // 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) { - System.setProperty( - "spark.streaming.clock", - "org.apache.spark.streaming.util.ManualClock" - ) - } else { - System.clearProperty("spark.streaming.clock") + conf.set("spark.streaming.clock", "org.apache.spark.streaming.util.ManualClock") } - // 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") } // Default after function for any streaming test suite. Override this @@ -169,9 +175,9 @@ 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(master, framework, batchDuration) + val ssc = new StreamingContext(sc, batchDuration) if (checkpointDir != null) { ssc.checkpoint(checkpointDir) } @@ -195,9 +201,9 @@ 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(master, framework, batchDuration) + val ssc = new StreamingContext(sc, batchDuration) if (checkpointDir != null) { ssc.checkpoint(checkpointDir) } diff --git a/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala b/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala index 6b4aaefcdf..c39abfc21b 100644 --- a/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala +++ b/streaming/src/test/scala/org/apache/spark/streaming/WindowOperationsSuite.scala @@ -18,7 +18,6 @@ package org.apache.spark.streaming import org.apache.spark.streaming.StreamingContext._ -import collection.mutable.ArrayBuffer class WindowOperationsSuite extends TestSuiteBase { @@ -225,9 +224,7 @@ class WindowOperationsSuite extends TestSuiteBase { val slideDuration = Seconds(1) val numBatches = expectedOutput.size * (slideDuration / batchDuration).toInt val operation = (s: DStream[(String, Int)]) => { - s.groupByKeyAndWindow(windowDuration, slideDuration) - .map(x => (x._1, x._2.toSet)) - .persist() + s.groupByKeyAndWindow(windowDuration, slideDuration).map(x => (x._1, x._2.toSet)) } testOperation(input, operation, expectedOutput, numBatches, true) } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala index 240ed8b32a..7cf120d3eb 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ApplicationMaster.scala @@ -36,14 +36,13 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.ipc.YarnRPC import org.apache.hadoop.yarn.util.{ConverterUtils, Records} -import org.apache.spark.{SparkContext, Logging} +import org.apache.spark.{SparkConf, SparkContext, Logging} import org.apache.spark.util.Utils - class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) extends Logging { def this(args: ApplicationMasterArguments) = this(args, new Configuration()) - + private var rpc: YarnRPC = YarnRPC.create(conf) private var resourceManager: AMRMProtocol = _ private var appAttemptId: ApplicationAttemptId = _ @@ -57,18 +56,24 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e private val maxAppAttempts: Int = conf.getInt(YarnConfiguration.RM_AM_MAX_RETRIES, YarnConfiguration.DEFAULT_RM_AM_MAX_RETRIES) private var isLastAMRetry: Boolean = true - // default to numWorkers * 2, with minimum of 3 - private val maxNumWorkerFailures = System.getProperty("spark.yarn.max.worker.failures", - math.max(args.numWorkers * 2, 3).toString()).toInt + + private val sparkConf = new SparkConf() + // Default to numWorkers * 2, with minimum of 3 + private val maxNumWorkerFailures = sparkConf.getInt("spark.yarn.max.worker.failures", + math.max(args.numWorkers * 2, 3)) def run() { // Setup the directories so things go to yarn approved directories rather // then user specified and /tmp. System.setProperty("spark.local.dir", getLocalDirs()) + // set the web ui port to be ephemeral for yarn so we don't conflict with + // other spark processes running on the same box + System.setProperty("spark.ui.port", "0") + // Use priority 30 as its higher then HDFS. Its same priority as MapReduce is using. ShutdownHookManager.get().addShutdownHook(new AppMasterShutdownHook(this), 30) - + appAttemptId = getApplicationAttemptId() isLastAMRetry = appAttemptId.getAttemptId() >= maxAppAttempts resourceManager = registerWithResourceManager() @@ -92,24 +97,22 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e // } //} // org.apache.hadoop.io.compress.CompressionCodecFactory.getCodecClasses(conf) - + ApplicationMaster.register(this) // Start the user's JAR userThread = startUserClass() - + // This a bit hacky, but we need to wait until the spark.driver.port property has // been set by the Thread executing the user class. - waitForSparkMaster() - waitForSparkContextInitialized() // Do this after spark master is up and SparkContext is created so that we can register UI Url val appMasterResponse: RegisterApplicationMasterResponse = registerApplicationMaster() - + // Allocate all containers allocateWorkers() - - // Wait for the user class to Finish + + // Wait for the user class to Finish userThread.join() System.exit(0) @@ -129,7 +132,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e } localDirs } - + private def getApplicationAttemptId(): ApplicationAttemptId = { val envs = System.getenv() val containerIdString = envs.get(ApplicationConstants.AM_CONTAINER_ID_ENV) @@ -138,7 +141,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e logInfo("ApplicationAttemptId: " + appAttemptId) appAttemptId } - + private def registerWithResourceManager(): AMRMProtocol = { val rmAddress = NetUtils.createSocketAddr(yarnConf.get( YarnConfiguration.RM_SCHEDULER_ADDRESS, @@ -146,44 +149,20 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e logInfo("Connecting to ResourceManager at " + rmAddress) rpc.getProxy(classOf[AMRMProtocol], rmAddress, conf).asInstanceOf[AMRMProtocol] } - + private def registerApplicationMaster(): RegisterApplicationMasterResponse = { logInfo("Registering the ApplicationMaster") val appMasterRequest = Records.newRecord(classOf[RegisterApplicationMasterRequest]) .asInstanceOf[RegisterApplicationMasterRequest] appMasterRequest.setApplicationAttemptId(appAttemptId) // Setting this to master host,port - so that the ApplicationReport at client has some - // sensible info. + // sensible info. // Users can then monitor stderr/stdout on that node if required. appMasterRequest.setHost(Utils.localHostName()) appMasterRequest.setRpcPort(0) appMasterRequest.setTrackingUrl(uiAddress) resourceManager.registerApplicationMaster(appMasterRequest) } - - private def waitForSparkMaster() { - logInfo("Waiting for spark driver to be reachable.") - var driverUp = false - var tries = 0 - val numTries = System.getProperty("spark.yarn.applicationMaster.waitTries", "10").toInt - while(!driverUp && tries < numTries) { - val driverHost = System.getProperty("spark.driver.host") - val driverPort = System.getProperty("spark.driver.port") - try { - val socket = new Socket(driverHost, driverPort.toInt) - socket.close() - logInfo("Driver now available: %s:%s".format(driverHost, driverPort)) - driverUp = true - } catch { - case e: Exception => { - logWarning("Failed to connect to driver at %s:%s, retrying ...". - format(driverHost, driverPort)) - Thread.sleep(100) - tries = tries + 1 - } - } - } - } private def startUserClass(): Thread = { logInfo("Starting the user JAR in a separate Thread") @@ -226,7 +205,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e ApplicationMaster.sparkContextRef.synchronized { var count = 0 val waitTime = 10000L - val numTries = System.getProperty("spark.yarn.ApplicationMaster.waitTries", "10").toInt + val numTries = sparkConf.getInt("spark.yarn.ApplicationMaster.waitTries", 10) while (ApplicationMaster.sparkContextRef.get() == null && count < numTries) { logInfo("Waiting for spark context initialization ... " + count) count = count + 1 @@ -241,8 +220,9 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e yarnConf, resourceManager, appAttemptId, - args, - sparkContext.preferredNodeLocationData) + args, + sparkContext.preferredNodeLocationData, + sparkContext.getConf) } else { logWarning("Unable to retrieve sparkContext inspite of waiting for %d, numTries = %d". format(count * waitTime, numTries)) @@ -250,7 +230,8 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e yarnConf, resourceManager, appAttemptId, - args) + args, + sparkContext.getConf) } } } finally { @@ -294,7 +275,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e // we want to be reasonably responsive without causing too many requests to RM. val schedulerInterval = - System.getProperty("spark.yarn.scheduler.heartbeat.interval-ms", "5000").toLong + sparkConf.getLong("spark.yarn.scheduler.heartbeat.interval-ms", 5000) // must be <= timeoutInterval / 2. val interval = math.min(timeoutInterval / 2, schedulerInterval) @@ -342,11 +323,11 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e for (container <- containers) { logInfo("Launching shell command on a new container." + ", containerId=" + container.getId() - + ", containerNode=" + container.getNodeId().getHost() + + ", containerNode=" + container.getNodeId().getHost() + ":" + container.getNodeId().getPort() + ", containerNodeURI=" + container.getNodeHttpAddress() + ", containerState" + container.getState() - + ", containerResourceMemory" + + ", containerResourceMemory" + container.getResource().getMemory()) } } @@ -372,12 +353,12 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e } /** - * Clean up the staging directory. + * Clean up the staging directory. */ - private def cleanupStagingDir() { + private def cleanupStagingDir() { var stagingDirPath: Path = null try { - val preserveFiles = System.getProperty("spark.yarn.preserve.staging.files", "false").toBoolean + val preserveFiles = sparkConf.get("spark.yarn.preserve.staging.files", "false").toBoolean if (!preserveFiles) { stagingDirPath = new Path(System.getenv("SPARK_YARN_STAGING_DIR")) if (stagingDirPath == null) { @@ -393,7 +374,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e } } - // The shutdown hook that runs when a signal is received AND during normal close of the JVM. + // The shutdown hook that runs when a signal is received AND during normal close of the JVM. class AppMasterShutdownHook(appMaster: ApplicationMaster) extends Runnable { def run() { @@ -446,18 +427,18 @@ object ApplicationMaster { // Note that this will unfortunately not properly clean up the staging files because it gets // called too late, after the filesystem is already shutdown. if (modified) { - Runtime.getRuntime().addShutdownHook(new Thread with Logging { + Runtime.getRuntime().addShutdownHook(new Thread with Logging { // This is not only logs, but also ensures that log system is initialized for this instance // when we are actually 'run'-ing. logInfo("Adding shutdown hook for context " + sc) - override def run() { - logInfo("Invoking sc stop from shutdown hook") - sc.stop() + override def run() { + logInfo("Invoking sc stop from shutdown hook") + sc.stop() // Best case ... for (master <- applicationMasters) { master.finishApplicationMaster(FinalApplicationStatus.SUCCEEDED) } - } + } } ) } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 79dd038065..2bd047c97a 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -40,7 +40,7 @@ import org.apache.hadoop.yarn.conf.YarnConfiguration import org.apache.hadoop.yarn.ipc.YarnRPC import org.apache.hadoop.yarn.util.{Apps, Records} -import org.apache.spark.Logging +import org.apache.spark.{Logging, SparkConf} import org.apache.spark.util.Utils import org.apache.spark.deploy.SparkHadoopUtil @@ -54,12 +54,13 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl val credentials = UserGroupInformation.getCurrentUser().getCredentials() private val SPARK_STAGING: String = ".sparkStaging" private val distCacheMgr = new ClientDistributedCacheManager() + private val sparkConf = new SparkConf // Staging directory is private! -> rwx-------- val STAGING_DIR_PERMISSION: FsPermission = FsPermission.createImmutable(0700:Short) // App files are world-wide readable and owner writable -> rw-r--r-- - val APP_FILE_PERMISSION: FsPermission = FsPermission.createImmutable(0644:Short) + val APP_FILE_PERMISSION: FsPermission = FsPermission.createImmutable(0644:Short) // for client user who want to monitor app status by itself. def runApp() = { @@ -103,7 +104,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl "greater than: " + YarnAllocationHandler.MEMORY_OVERHEAD), (args.workerMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: Worker memory size " + "must be greater than: " + YarnAllocationHandler.MEMORY_OVERHEAD) - ).foreach { case(cond, errStr) => + ).foreach { case(cond, errStr) => if (cond) { logError(errStr) args.printUsageAndExit(1) @@ -130,7 +131,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl queueInfo.getChildQueues.size)) } - def verifyClusterResources(app: GetNewApplicationResponse) = { + def verifyClusterResources(app: GetNewApplicationResponse) = { val maxMem = app.getMaximumResourceCapability().getMemory() logInfo("Max mem capabililty of a single resource in this cluster " + maxMem) @@ -146,7 +147,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl } // We could add checks to make sure the entire cluster has enough resources but that involves - // getting all the node reports and computing ourselves + // getting all the node reports and computing ourselves } def createApplicationSubmissionContext(appId: ApplicationId): ApplicationSubmissionContext = { @@ -207,7 +208,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl FileUtil.copy(remoteFs, originalPath, fs, newPath, false, conf) fs.setReplication(newPath, replication) if (setPerms) fs.setPermission(newPath, new FsPermission(APP_FILE_PERMISSION)) - } + } // Resolve any symlinks in the URI path so using a "current" symlink to point to a specific // version shows the specific version in the distributed cache configuration val qualPath = fs.makeQualified(newPath) @@ -230,7 +231,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl } } val dst = new Path(fs.getHomeDirectory(), appStagingDir) - val replication = System.getProperty("spark.yarn.submit.file.replication", "3").toShort + val replication = sparkConf.getInt("spark.yarn.submit.file.replication", 3).toShort if (UserGroupInformation.isSecurityEnabled()) { val dstFs = dst.getFileSystem(conf) @@ -241,7 +242,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl val statCache: Map[URI, FileStatus] = HashMap[URI, FileStatus]() - Map(Client.SPARK_JAR -> System.getenv("SPARK_JAR"), Client.APP_JAR -> args.userJar, + Map(Client.SPARK_JAR -> System.getenv("SPARK_JAR"), Client.APP_JAR -> args.userJar, Client.LOG4J_PROP -> System.getenv("SPARK_LOG4J_CONF")) .foreach { case(destName, _localPath) => val localPath: String = if (_localPath != null) _localPath.trim() else "" @@ -253,7 +254,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl } val setPermissions = if (destName.equals(Client.APP_JAR)) true else false val destPath = copyRemoteFile(dst, new Path(localURI), replication, setPermissions) - distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, destName, statCache) } } @@ -265,7 +266,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl val localPath = new Path(localURI) val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) val destPath = copyRemoteFile(dst, localPath, replication) - distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, linkname, statCache, true) } } @@ -277,7 +278,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl val localPath = new Path(localURI) val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) val destPath = copyRemoteFile(dst, localPath, replication) - distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.FILE, linkname, statCache) } } @@ -289,7 +290,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl val localPath = new Path(localURI) val linkname = Option(localURI.getFragment()).getOrElse(localPath.getName()) val destPath = copyRemoteFile(dst, localPath, replication) - distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.ARCHIVE, + distCacheMgr.addResource(fs, conf, destPath, localResources, LocalResourceType.ARCHIVE, linkname, statCache) } } @@ -299,7 +300,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl } def setupLaunchEnv( - localResources: HashMap[String, LocalResource], + localResources: HashMap[String, LocalResource], stagingDir: String): HashMap[String, String] = { logInfo("Setting up the launch environment") val log4jConfLocalRes = localResources.getOrElse(Client.LOG4J_PROP, null) @@ -354,7 +355,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl // Add Xmx for am memory JAVA_OPTS += "-Xmx" + amMemory + "m " - JAVA_OPTS += " -Djava.io.tmpdir=" + + JAVA_OPTS += " -Djava.io.tmpdir=" + new Path(Environment.PWD.$(), YarnConfiguration.DEFAULT_CONTAINER_TEMP_DIR) + " " // Commenting it out for now - so that people can refer to the properties if required. Remove @@ -387,11 +388,11 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl javaCommand = Environment.JAVA_HOME.$() + "/bin/java" } - val commands = List[String](javaCommand + + val commands = List[String](javaCommand + " -server " + JAVA_OPTS + " " + args.amClass + - " --class " + args.userClass + + " --class " + args.userClass + " --jar " + args.userJar + userArgsToString(args) + " --worker-memory " + args.workerMemory + @@ -421,9 +422,11 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl super.submitApplication(appContext) } - def monitorApplication(appId: ApplicationId): Boolean = { + def monitorApplication(appId: ApplicationId): Boolean = { + val interval = sparkConf.getLong("spark.yarn.report.interval", 1000) + while (true) { - Thread.sleep(1000) + Thread.sleep(interval) val report = super.getApplicationReport(appId) logInfo("Application report from ASM: \n" + @@ -443,7 +446,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl val state = report.getYarnApplicationState() val dsStatus = report.getFinalApplicationStatus() - if (state == YarnApplicationState.FINISHED || + if (state == YarnApplicationState.FINISHED || state == YarnApplicationState.FAILED || state == YarnApplicationState.KILLED) { return true @@ -479,25 +482,24 @@ object Client { Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$()) // If log4j present, ensure ours overrides all others if (addLog4j) { - Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + Path.SEPARATOR + LOG4J_PROP) } // Normally the users app.jar is last in case conflicts with spark jars - val userClasspathFirst = System.getProperty("spark.yarn.user.classpath.first", "false") - .toBoolean + val userClasspathFirst = new SparkConf().get("spark.yarn.user.classpath.first", "false").toBoolean if (userClasspathFirst) { - Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + Path.SEPARATOR + APP_JAR) } - Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + Path.SEPARATOR + SPARK_JAR) Client.populateHadoopClasspath(conf, env) if (!userClasspathFirst) { - Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + Path.SEPARATOR + APP_JAR) } - Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + + Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$() + Path.SEPARATOR + "*") } } diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala index b3a7886d93..9075ca71e7 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/ClientArguments.scala @@ -17,6 +17,7 @@ package org.apache.spark.deploy.yarn +import org.apache.spark.SparkConf import org.apache.spark.util.MemoryParam import org.apache.spark.util.IntParam import collection.mutable.{ArrayBuffer, HashMap} @@ -33,7 +34,7 @@ class ClientArguments(val args: Array[String]) { var workerMemory = 1024 var workerCores = 1 var numWorkers = 2 - var amQueue = System.getProperty("QUEUE", "default") + var amQueue = new SparkConf().get("QUEUE", "default") var amMemory: Int = 512 var amClass: String = "org.apache.spark.deploy.yarn.ApplicationMaster" var appName: String = "Spark" diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerLauncher.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerLauncher.scala index 69038844bb..a8de89c670 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerLauncher.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/WorkerLauncher.scala @@ -29,7 +29,7 @@ import org.apache.hadoop.yarn.util.{ConverterUtils, Records} import akka.actor._ import akka.remote._ import akka.actor.Terminated -import org.apache.spark.{SparkContext, Logging} +import org.apache.spark.{SparkConf, SparkContext, Logging} import org.apache.spark.util.{Utils, AkkaUtils} import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend import org.apache.spark.scheduler.SplitInfo @@ -46,8 +46,10 @@ class WorkerLauncher(args: ApplicationMasterArguments, conf: Configuration) exte private var yarnAllocator: YarnAllocationHandler = null private var driverClosed:Boolean = false + private val sparkConf = new SparkConf - val actorSystem : ActorSystem = AkkaUtils.createActorSystem("sparkYarnAM", Utils.localHostName, 0)._1 + val actorSystem : ActorSystem = AkkaUtils.createActorSystem("sparkYarnAM", Utils.localHostName, 0, + conf = sparkConf)._1 var actor: ActorRef = null // This actor just working as a monitor to watch on Driver Actor. @@ -162,8 +164,8 @@ class WorkerLauncher(args: ApplicationMasterArguments, conf: Configuration) exte Thread.sleep(100) } } - System.setProperty("spark.driver.host", driverHost) - System.setProperty("spark.driver.port", driverPort.toString) + sparkConf.set("spark.driver.host", driverHost) + sparkConf.set("spark.driver.port", driverPort.toString) val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( driverHost, driverPort.toString, CoarseGrainedSchedulerBackend.ACTOR_NAME) @@ -175,9 +177,11 @@ class WorkerLauncher(args: ApplicationMasterArguments, conf: Configuration) exte private def allocateWorkers() { // Fixme: should get preferredNodeLocationData from SparkContext, just fake a empty one for now. - val preferredNodeLocationData: scala.collection.Map[String, scala.collection.Set[SplitInfo]] = scala.collection.immutable.Map() + val preferredNodeLocationData: scala.collection.Map[String, scala.collection.Set[SplitInfo]] = + scala.collection.immutable.Map() - yarnAllocator = YarnAllocationHandler.newAllocator(yarnConf, resourceManager, appAttemptId, args, preferredNodeLocationData) + yarnAllocator = YarnAllocationHandler.newAllocator(yarnConf, resourceManager, appAttemptId, + args, preferredNodeLocationData, sparkConf) logInfo("Allocating " + args.numWorkers + " workers.") // Wait until all containers have finished diff --git a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala index a01657c9fa..c8af653b3f 100644 --- a/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala +++ b/yarn/src/main/scala/org/apache/spark/deploy/yarn/YarnAllocationHandler.scala @@ -26,7 +26,7 @@ import scala.collection import scala.collection.JavaConversions._ import scala.collection.mutable.{ArrayBuffer, HashMap, HashSet} -import org.apache.spark.Logging +import org.apache.spark.{Logging, SparkConf} import org.apache.spark.scheduler.{SplitInfo,TaskSchedulerImpl} import org.apache.spark.scheduler.cluster.CoarseGrainedSchedulerBackend import org.apache.spark.util.Utils @@ -62,7 +62,8 @@ private[yarn] class YarnAllocationHandler( val workerMemory: Int, val workerCores: Int, val preferredHostToCount: Map[String, Int], - val preferredRackToCount: Map[String, Int]) + val preferredRackToCount: Map[String, Int], + val sparkConf: SparkConf) extends Logging { // These three are locked on allocatedHostToContainersMap. Complementary data structures // allocatedHostToContainersMap : containers which are running : host, Set<containerid> @@ -239,7 +240,7 @@ private[yarn] class YarnAllocationHandler( // (workerIdCounter) val workerId = workerIdCounter.incrementAndGet().toString val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format( - System.getProperty("spark.driver.host"), System.getProperty("spark.driver.port"), + sparkConf.get("spark.driver.host"), sparkConf.get("spark.driver.port"), CoarseGrainedSchedulerBackend.ACTOR_NAME) logInfo("launching container on " + containerId + " host " + workerHostname) @@ -552,7 +553,8 @@ object YarnAllocationHandler { conf: Configuration, resourceManager: AMRMProtocol, appAttemptId: ApplicationAttemptId, - args: ApplicationMasterArguments): YarnAllocationHandler = { + args: ApplicationMasterArguments, + sparkConf: SparkConf): YarnAllocationHandler = { new YarnAllocationHandler( conf, @@ -562,7 +564,8 @@ object YarnAllocationHandler { args.workerMemory, args.workerCores, Map[String, Int](), - Map[String, Int]()) + Map[String, Int](), + sparkConf) } def newAllocator( @@ -571,7 +574,8 @@ object YarnAllocationHandler { appAttemptId: ApplicationAttemptId, args: ApplicationMasterArguments, map: collection.Map[String, - collection.Set[SplitInfo]]): YarnAllocationHandler = { + collection.Set[SplitInfo]], + sparkConf: SparkConf): YarnAllocationHandler = { val (hostToCount, rackToCount) = generateNodeToWeight(conf, map) new YarnAllocationHandler( @@ -582,7 +586,8 @@ object YarnAllocationHandler { args.workerMemory, args.workerCores, hostToCount, - rackToCount) + rackToCount, + sparkConf) } def newAllocator( @@ -592,7 +597,8 @@ object YarnAllocationHandler { maxWorkers: Int, workerMemory: Int, workerCores: Int, - map: collection.Map[String, collection.Set[SplitInfo]]): YarnAllocationHandler = { + map: collection.Map[String, collection.Set[SplitInfo]], + sparkConf: SparkConf): YarnAllocationHandler = { val (hostToCount, rackToCount) = generateNodeToWeight(conf, map) @@ -604,7 +610,8 @@ object YarnAllocationHandler { workerMemory, workerCores, hostToCount, - rackToCount) + rackToCount, + sparkConf) } // A simple method to copy the split info map. diff --git a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala index 350fc760a4..4b69f5078b 100644 --- a/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala +++ b/yarn/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -53,8 +53,8 @@ private[spark] class YarnClientSchedulerBackend( if (workerNumber == null) workerNumber = defaultWorkerNumber - val driverHost = System.getProperty("spark.driver.host") - val driverPort = System.getProperty("spark.driver.port") + val driverHost = conf.get("spark.driver.host") + val driverPort = conf.get("spark.driver.port") val hostport = driverHost + ":" + driverPort val argsArray = Array[String]( |