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author | Tathagata Das <tathagata.das1565@gmail.com> | 2014-01-09 19:31:36 -0800 |
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committer | Tathagata Das <tathagata.das1565@gmail.com> | 2014-01-09 19:31:36 -0800 |
commit | 38d75e18fa48bd230e2ff9478e87274fb0cceb9f (patch) | |
tree | 5b6d15445a3376995068e9537482ca65078fc341 | |
parent | 4a5558ca9921ce89b3996e9ead13b07123fc7a2d (diff) | |
parent | 4b074fac054848ebd3397a3cce0a3e7871d3860c (diff) | |
download | spark-38d75e18fa48bd230e2ff9478e87274fb0cceb9f.tar.gz spark-38d75e18fa48bd230e2ff9478e87274fb0cceb9f.tar.bz2 spark-38d75e18fa48bd230e2ff9478e87274fb0cceb9f.zip |
Merge remote-tracking branch 'apache/master' into driver-test
55 files changed, 282 insertions, 186 deletions
diff --git a/conf/log4j.properties.template b/conf/log4j.properties.template index d72dbadc39..17d1978dde 100644 --- a/conf/log4j.properties.template +++ b/conf/log4j.properties.template @@ -1,6 +1,7 @@ # Set everything to be logged to the console log4j.rootCategory=INFO, console log4j.appender.console=org.apache.log4j.ConsoleAppender +log4j.appender.console.target=System.err log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n diff --git a/core/src/main/resources/org/apache/spark/log4j-defaults.properties b/core/src/main/resources/org/apache/spark/log4j-defaults.properties index d72dbadc39..17d1978dde 100644 --- a/core/src/main/resources/org/apache/spark/log4j-defaults.properties +++ b/core/src/main/resources/org/apache/spark/log4j-defaults.properties @@ -1,6 +1,7 @@ # Set everything to be logged to the console log4j.rootCategory=INFO, console log4j.appender.console=org.apache.log4j.ConsoleAppender +log4j.appender.console.target=System.err log4j.appender.console.layout=org.apache.log4j.PatternLayout log4j.appender.console.layout.ConversionPattern=%d{yy/MM/dd HH:mm:ss} %p %c{1}: %m%n diff --git a/core/src/main/scala/org/apache/spark/Partitioner.scala b/core/src/main/scala/org/apache/spark/Partitioner.scala index 31b0773bfe..9b043f06dd 100644 --- a/core/src/main/scala/org/apache/spark/Partitioner.scala +++ b/core/src/main/scala/org/apache/spark/Partitioner.scala @@ -61,7 +61,8 @@ object Partitioner { } /** - * A [[org.apache.spark.Partitioner]] that implements hash-based partitioning using Java's `Object.hashCode`. + * A [[org.apache.spark.Partitioner]] that implements hash-based partitioning using + * Java's `Object.hashCode`. * * Java arrays have hashCodes that are based on the arrays' identities rather than their contents, * so attempting to partition an RDD[Array[_]] or RDD[(Array[_], _)] using a HashPartitioner will @@ -84,8 +85,8 @@ class HashPartitioner(partitions: Int) extends Partitioner { } /** - * A [[org.apache.spark.Partitioner]] that partitions sortable records by range into roughly equal ranges. - * Determines the ranges by sampling the RDD passed in. + * A [[org.apache.spark.Partitioner]] that partitions sortable records by range into roughly + * equal ranges. The ranges are determined by sampling the content of the RDD passed in. */ class RangePartitioner[K <% Ordered[K]: ClassTag, V]( partitions: Int, diff --git a/core/src/main/scala/org/apache/spark/SparkContext.scala b/core/src/main/scala/org/apache/spark/SparkContext.scala index 101a7e6db9..d83c88985a 100644 --- a/core/src/main/scala/org/apache/spark/SparkContext.scala +++ b/core/src/main/scala/org/apache/spark/SparkContext.scala @@ -31,9 +31,9 @@ import scala.reflect.{ClassTag, classTag} import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.io.{ArrayWritable, BooleanWritable, BytesWritable, DoubleWritable, -FloatWritable, IntWritable, LongWritable, NullWritable, Text, Writable} + FloatWritable, IntWritable, LongWritable, NullWritable, Text, Writable} import org.apache.hadoop.mapred.{FileInputFormat, InputFormat, JobConf, SequenceFileInputFormat, -TextInputFormat} + TextInputFormat} import org.apache.hadoop.mapreduce.{InputFormat => NewInputFormat, Job => NewHadoopJob} import org.apache.hadoop.mapreduce.lib.input.{FileInputFormat => NewFileInputFormat} import org.apache.mesos.MesosNativeLibrary @@ -49,7 +49,7 @@ import org.apache.spark.scheduler.local.LocalBackend import org.apache.spark.storage.{BlockManagerSource, RDDInfo, StorageStatus, StorageUtils} import org.apache.spark.ui.SparkUI import org.apache.spark.util.{Utils, TimeStampedHashMap, MetadataCleaner, MetadataCleanerType, -ClosureCleaner} + ClosureCleaner} /** * Main entry point for Spark functionality. A SparkContext represents the connection to a Spark @@ -116,7 +116,7 @@ class SparkContext( throw new SparkException("An application must be set in your configuration") } - if (conf.get("spark.logConf", "false").toBoolean) { + if (conf.getBoolean("spark.logConf", false)) { logInfo("Spark configuration:\n" + conf.toDebugString) } @@ -244,6 +244,10 @@ class SparkContext( localProperties.set(new Properties()) } + /** + * Set a local property that affects jobs submitted from this thread, such as the + * Spark fair scheduler pool. + */ def setLocalProperty(key: String, value: String) { if (localProperties.get() == null) { localProperties.set(new Properties()) @@ -255,6 +259,10 @@ class SparkContext( } } + /** + * Get a local property set in this thread, or null if it is missing. See + * [[org.apache.spark.SparkContext.setLocalProperty]]. + */ def getLocalProperty(key: String): String = Option(localProperties.get).map(_.getProperty(key)).getOrElse(null) @@ -265,7 +273,7 @@ class SparkContext( } /** - * Assigns a group id to all the jobs started by this thread until the group id is set to a + * Assigns a group ID to all the jobs started by this thread until the group ID is set to a * different value or cleared. * * Often, a unit of execution in an application consists of multiple Spark actions or jobs. @@ -288,7 +296,7 @@ class SparkContext( setLocalProperty(SparkContext.SPARK_JOB_GROUP_ID, groupId) } - /** Clear the job group id and its description. */ + /** Clear the current thread's job group ID and its description. */ def clearJobGroup() { setLocalProperty(SparkContext.SPARK_JOB_DESCRIPTION, null) setLocalProperty(SparkContext.SPARK_JOB_GROUP_ID, null) @@ -517,15 +525,15 @@ class SparkContext( // Methods for creating shared variables /** - * Create an [[org.apache.spark.Accumulator]] variable of a given type, which tasks can "add" values - * to using the `+=` method. Only the driver can access the accumulator's `value`. + * Create an [[org.apache.spark.Accumulator]] variable of a given type, which tasks can "add" + * values to using the `+=` method. Only the driver can access the accumulator's `value`. */ def accumulator[T](initialValue: T)(implicit param: AccumulatorParam[T]) = new Accumulator(initialValue, param) /** - * Create an [[org.apache.spark.Accumulable]] shared variable, to which tasks can add values with `+=`. - * Only the driver can access the accumuable's `value`. + * Create an [[org.apache.spark.Accumulable]] shared variable, to which tasks can add values + * with `+=`. Only the driver can access the accumuable's `value`. * @tparam T accumulator type * @tparam R type that can be added to the accumulator */ @@ -538,14 +546,16 @@ class SparkContext( * Growable and TraversableOnce are the standard APIs that guarantee += and ++=, implemented by * standard mutable collections. So you can use this with mutable Map, Set, etc. */ - def accumulableCollection[R <% Growable[T] with TraversableOnce[T] with Serializable, T](initialValue: R) = { + def accumulableCollection[R <% Growable[T] with TraversableOnce[T] with Serializable, T] + (initialValue: R) = { val param = new GrowableAccumulableParam[R,T] new Accumulable(initialValue, param) } /** - * Broadcast a read-only variable to the cluster, returning a [[org.apache.spark.broadcast.Broadcast]] object for - * reading it in distributed functions. The variable will be sent to each cluster only once. + * Broadcast a read-only variable to the cluster, returning a + * [[org.apache.spark.broadcast.Broadcast]] object for reading it in distributed functions. + * The variable will be sent to each cluster only once. */ def broadcast[T](value: T) = env.broadcastManager.newBroadcast[T](value, isLocal) @@ -1010,7 +1020,8 @@ object SparkContext { implicit def stringToText(s: String) = new Text(s) - private implicit def arrayToArrayWritable[T <% Writable: ClassTag](arr: Traversable[T]): ArrayWritable = { + private implicit def arrayToArrayWritable[T <% Writable: ClassTag](arr: Traversable[T]) + : ArrayWritable = { def anyToWritable[U <% Writable](u: U): Writable = u new ArrayWritable(classTag[T].runtimeClass.asInstanceOf[Class[Writable]], @@ -1033,7 +1044,9 @@ object SparkContext { implicit def booleanWritableConverter() = simpleWritableConverter[Boolean, BooleanWritable](_.get) - implicit def bytesWritableConverter() = simpleWritableConverter[Array[Byte], BytesWritable](_.getBytes) + implicit def bytesWritableConverter() = { + simpleWritableConverter[Array[Byte], BytesWritable](_.getBytes) + } implicit def stringWritableConverter() = simpleWritableConverter[String, Text](_.toString) @@ -1049,7 +1062,8 @@ object SparkContext { if (uri != null) { val uriStr = uri.toString if (uriStr.startsWith("jar:file:")) { - // URI will be of the form "jar:file:/path/foo.jar!/package/cls.class", so pull out the /path/foo.jar + // URI will be of the form "jar:file:/path/foo.jar!/package/cls.class", + // so pull out the /path/foo.jar List(uriStr.substring("jar:file:".length, uriStr.indexOf('!'))) } else { Nil @@ -1203,7 +1217,7 @@ object SparkContext { case mesosUrl @ MESOS_REGEX(_) => MesosNativeLibrary.load() val scheduler = new TaskSchedulerImpl(sc) - val coarseGrained = sc.conf.get("spark.mesos.coarse", "false").toBoolean + val coarseGrained = sc.conf.getBoolean("spark.mesos.coarse", false) val url = mesosUrl.stripPrefix("mesos://") // strip scheme from raw Mesos URLs val backend = if (coarseGrained) { new CoarseMesosSchedulerBackend(scheduler, sc, url, appName) diff --git a/core/src/main/scala/org/apache/spark/SparkEnv.scala b/core/src/main/scala/org/apache/spark/SparkEnv.scala index 2e36ccb9a0..e093e2f162 100644 --- a/core/src/main/scala/org/apache/spark/SparkEnv.scala +++ b/core/src/main/scala/org/apache/spark/SparkEnv.scala @@ -162,7 +162,7 @@ object SparkEnv extends Logging { actorSystem.actorOf(Props(newActor), name = name) } else { val driverHost: String = conf.get("spark.driver.host", "localhost") - val driverPort: Int = conf.get("spark.driver.port", "7077").toInt + val driverPort: Int = conf.getInt("spark.driver.port", 7077) Utils.checkHost(driverHost, "Expected hostname") val url = s"akka.tcp://spark@$driverHost:$driverPort/user/$name" val timeout = AkkaUtils.lookupTimeout(conf) diff --git a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala index 103a1c2051..618d95015f 100644 --- a/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala +++ b/core/src/main/scala/org/apache/spark/SparkHadoopWriter.scala @@ -127,10 +127,6 @@ class SparkHadoopWriter(@transient jobConf: JobConf) cmtr.commitJob(getJobContext()) } - def cleanup() { - getOutputCommitter().cleanupJob(getJobContext()) - } - // ********* Private Functions ********* private def getOutputFormat(): OutputFormat[AnyRef,AnyRef] = { diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala index da30cf619a..b0dedc6f4e 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaDoubleRDD.scala @@ -207,13 +207,13 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[Double, Jav * e.g. for the array * [1,10,20,50] the buckets are [1,10) [10,20) [20,50] * e.g 1<=x<10 , 10<=x<20, 20<=x<50 - * And on the input of 1 and 50 we would have a histogram of 1,0,0 - * + * And on the input of 1 and 50 we would have a histogram of 1,0,0 + * * Note: if your histogram is evenly spaced (e.g. [0, 10, 20, 30]) this can be switched * from an O(log n) inseration to O(1) per element. (where n = # buckets) if you set evenBuckets * to true. * buckets must be sorted and not contain any duplicates. - * buckets array must be at least two elements + * buckets array must be at least two elements * All NaN entries are treated the same. If you have a NaN bucket it must be * the maximum value of the last position and all NaN entries will be counted * in that bucket. @@ -225,6 +225,12 @@ class JavaDoubleRDD(val srdd: RDD[scala.Double]) extends JavaRDDLike[Double, Jav def histogram(buckets: Array[Double], evenBuckets: Boolean): Array[Long] = { srdd.histogram(buckets.map(_.toDouble), evenBuckets) } + + /** Assign a name to this RDD */ + def setName(name: String): JavaDoubleRDD = { + srdd.setName(name) + this + } } object JavaDoubleRDD { diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala index 55c87450ac..0fb7e195b3 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaPairRDD.scala @@ -647,6 +647,12 @@ class JavaPairRDD[K, V](val rdd: RDD[(K, V)])(implicit val kClassTag: ClassTag[K def countApproxDistinctByKey(relativeSD: Double, numPartitions: Int): JavaRDD[(K, Long)] = { rdd.countApproxDistinctByKey(relativeSD, numPartitions) } + + /** Assign a name to this RDD */ + def setName(name: String): JavaPairRDD[K, V] = { + rdd.setName(name) + this + } } object JavaPairRDD { diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala index 037cd1c774..7d48ce01cf 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDD.scala @@ -127,6 +127,12 @@ JavaRDDLike[T, JavaRDD[T]] { wrapRDD(rdd.subtract(other, p)) override def toString = rdd.toString + + /** Assign a name to this RDD */ + def setName(name: String): JavaRDD[T] = { + rdd.setName(name) + this + } } object JavaRDD { diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala index 924d8af060..ebbbbd8806 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaRDDLike.scala @@ -245,6 +245,11 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { } /** + * Return an array that contains all of the elements in this RDD. + */ + def toArray(): JList[T] = collect() + + /** * Return an array that contains all of the elements in a specific partition of this RDD. */ def collectPartitions(partitionIds: Array[Int]): Array[JList[T]] = { @@ -455,4 +460,5 @@ trait JavaRDDLike[T, This <: JavaRDDLike[T, This]] extends Serializable { */ def countApproxDistinct(relativeSD: Double = 0.05): Long = rdd.countApproxDistinct(relativeSD) + def name(): String = rdd.name } diff --git a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala index e93b10fd7e..7a6f044965 100644 --- a/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala +++ b/core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala @@ -425,6 +425,51 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork def clearCallSite() { sc.clearCallSite() } + + /** + * Set a local property that affects jobs submitted from this thread, such as the + * Spark fair scheduler pool. + */ + def setLocalProperty(key: String, value: String): Unit = sc.setLocalProperty(key, value) + + /** + * Get a local property set in this thread, or null if it is missing. See + * [[org.apache.spark.api.java.JavaSparkContext.setLocalProperty]]. + */ + def getLocalProperty(key: String): String = sc.getLocalProperty(key) + + /** + * Assigns a group ID to all the jobs started by this thread until the group ID is set to a + * different value or cleared. + * + * Often, a unit of execution in an application consists of multiple Spark actions or jobs. + * Application programmers can use this method to group all those jobs together and give a + * group description. Once set, the Spark web UI will associate such jobs with this group. + * + * The application can also use [[org.apache.spark.api.java.JavaSparkContext.cancelJobGroup]] + * to cancel all running jobs in this group. For example, + * {{{ + * // In the main thread: + * sc.setJobGroup("some_job_to_cancel", "some job description"); + * rdd.map(...).count(); + * + * // In a separate thread: + * sc.cancelJobGroup("some_job_to_cancel"); + * }}} + */ + def setJobGroup(groupId: String, description: String): Unit = sc.setJobGroup(groupId, description) + + /** Clear the current thread's job group ID and its description. */ + def clearJobGroup(): Unit = sc.clearJobGroup() + + /** + * Cancel active jobs for the specified group. See + * [[org.apache.spark.api.java.JavaSparkContext.setJobGroup]] for more information. + */ + def cancelJobGroup(groupId: String): Unit = sc.cancelJobGroup(groupId) + + /** Cancel all jobs that have been scheduled or are running. */ + def cancelAllJobs(): Unit = sc.cancelAllJobs() } object JavaSparkContext { @@ -436,5 +481,12 @@ object JavaSparkContext { * Find the JAR from which a given class was loaded, to make it easy for users to pass * their JARs to SparkContext. */ - def jarOfClass(cls: Class[_]) = SparkContext.jarOfClass(cls).toArray + def jarOfClass(cls: Class[_]): Array[String] = SparkContext.jarOfClass(cls).toArray + + /** + * Find the JAR that contains the class of a particular object, to make it easy for users + * to pass their JARs to SparkContext. In most cases you can call jarOfObject(this) in + * your driver program. + */ + def jarOfObject(obj: AnyRef): Array[String] = SparkContext.jarOfObject(obj).toArray } diff --git a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala index 32cc70e8c9..40c519b5bd 100644 --- a/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala +++ b/core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala @@ -41,7 +41,7 @@ private[spark] class PythonRDD[T: ClassTag]( accumulator: Accumulator[JList[Array[Byte]]]) extends RDD[Array[Byte]](parent) { - val bufferSize = conf.get("spark.buffer.size", "65536").toInt + val bufferSize = conf.getInt("spark.buffer.size", 65536) override def getPartitions = parent.partitions @@ -250,7 +250,7 @@ private class PythonAccumulatorParam(@transient serverHost: String, serverPort: Utils.checkHost(serverHost, "Expected hostname") - val bufferSize = SparkEnv.get.conf.get("spark.buffer.size", "65536").toInt + val bufferSize = SparkEnv.get.conf.getInt("spark.buffer.size", 65536) override def zero(value: JList[Array[Byte]]): JList[Array[Byte]] = new JArrayList diff --git a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala index db596d5fcc..0eacda3d7d 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/HttpBroadcast.scala @@ -92,8 +92,8 @@ private object HttpBroadcast extends Logging { def initialize(isDriver: Boolean, conf: SparkConf) { synchronized { if (!initialized) { - bufferSize = conf.get("spark.buffer.size", "65536").toInt - compress = conf.get("spark.broadcast.compress", "true").toBoolean + bufferSize = conf.getInt("spark.buffer.size", 65536) + compress = conf.getBoolean("spark.broadcast.compress", true) if (isDriver) { createServer(conf) conf.set("spark.httpBroadcast.uri", serverUri) diff --git a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala index 9530938278..fdf92eca4f 100644 --- a/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala +++ b/core/src/main/scala/org/apache/spark/broadcast/TorrentBroadcast.scala @@ -180,7 +180,7 @@ extends Logging { initialized = false } - lazy val BLOCK_SIZE = conf.get("spark.broadcast.blockSize", "4096").toInt * 1024 + lazy val BLOCK_SIZE = conf.getInt("spark.broadcast.blockSize", 4096) * 1024 def blockifyObject[T](obj: T): TorrentInfo = { val byteArray = Utils.serialize[T](obj) diff --git a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala index cd3f3ebefc..d9ea96afcf 100644 --- a/core/src/main/scala/org/apache/spark/deploy/master/Master.scala +++ b/core/src/main/scala/org/apache/spark/deploy/master/Master.scala @@ -46,9 +46,9 @@ private[spark] class Master(host: String, port: Int, webUiPort: Int) extends Act val conf = new SparkConf val DATE_FORMAT = new SimpleDateFormat("yyyyMMddHHmmss") // For application IDs - val WORKER_TIMEOUT = conf.get("spark.worker.timeout", "60").toLong * 1000 - val RETAINED_APPLICATIONS = conf.get("spark.deploy.retainedApplications", "200").toInt - val REAPER_ITERATIONS = conf.get("spark.dead.worker.persistence", "15").toInt + val WORKER_TIMEOUT = conf.getLong("spark.worker.timeout", 60) * 1000 + val RETAINED_APPLICATIONS = conf.getInt("spark.deploy.retainedApplications", 200) + val REAPER_ITERATIONS = conf.getInt("spark.dead.worker.persistence", 15) val RECOVERY_DIR = conf.get("spark.deploy.recoveryDirectory", "") val RECOVERY_MODE = conf.get("spark.deploy.recoveryMode", "NONE") 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 273bacded6..5182dcbb2a 100644 --- a/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala +++ b/core/src/main/scala/org/apache/spark/deploy/worker/Worker.scala @@ -59,7 +59,7 @@ private[spark] class Worker( val DATE_FORMAT = new SimpleDateFormat("yyyyMMddHHmmss") // For worker and executor IDs // Send a heartbeat every (heartbeat timeout) / 4 milliseconds - val HEARTBEAT_MILLIS = conf.get("spark.worker.timeout", "60").toLong * 1000 / 4 + val HEARTBEAT_MILLIS = conf.getLong("spark.worker.timeout", 60) * 1000 / 4 val REGISTRATION_TIMEOUT = 20.seconds val REGISTRATION_RETRIES = 3 diff --git a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala index a1e98845f6..5980177320 100644 --- a/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala +++ b/core/src/main/scala/org/apache/spark/io/CompressionCodec.scala @@ -71,7 +71,7 @@ class LZFCompressionCodec(conf: SparkConf) extends CompressionCodec { class SnappyCompressionCodec(conf: SparkConf) extends CompressionCodec { override def compressedOutputStream(s: OutputStream): OutputStream = { - val blockSize = conf.get("spark.io.compression.snappy.block.size", "32768").toInt + val blockSize = conf.getInt("spark.io.compression.snappy.block.size", 32768) new SnappyOutputStream(s, blockSize) } diff --git a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala index 46c40d0a2a..e6e01783c8 100644 --- a/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala +++ b/core/src/main/scala/org/apache/spark/network/ConnectionManager.scala @@ -54,22 +54,22 @@ private[spark] class ConnectionManager(port: Int, conf: SparkConf) extends Loggi private val selector = SelectorProvider.provider.openSelector() private val handleMessageExecutor = new ThreadPoolExecutor( - conf.get("spark.core.connection.handler.threads.min", "20").toInt, - conf.get("spark.core.connection.handler.threads.max", "60").toInt, - conf.get("spark.core.connection.handler.threads.keepalive", "60").toInt, TimeUnit.SECONDS, + conf.getInt("spark.core.connection.handler.threads.min", 20), + conf.getInt("spark.core.connection.handler.threads.max", 60), + conf.getInt("spark.core.connection.handler.threads.keepalive", 60), TimeUnit.SECONDS, new LinkedBlockingDeque[Runnable]()) private val handleReadWriteExecutor = new ThreadPoolExecutor( - conf.get("spark.core.connection.io.threads.min", "4").toInt, - conf.get("spark.core.connection.io.threads.max", "32").toInt, - conf.get("spark.core.connection.io.threads.keepalive", "60").toInt, TimeUnit.SECONDS, + conf.getInt("spark.core.connection.io.threads.min", 4), + conf.getInt("spark.core.connection.io.threads.max", 32), + conf.getInt("spark.core.connection.io.threads.keepalive", 60), TimeUnit.SECONDS, new LinkedBlockingDeque[Runnable]()) // Use a different, yet smaller, thread pool - infrequently used with very short lived tasks : which should be executed asap private val handleConnectExecutor = new ThreadPoolExecutor( - conf.get("spark.core.connection.connect.threads.min", "1").toInt, - conf.get("spark.core.connection.connect.threads.max", "8").toInt, - conf.get("spark.core.connection.connect.threads.keepalive", "60").toInt, TimeUnit.SECONDS, + conf.getInt("spark.core.connection.connect.threads.min", 1), + conf.getInt("spark.core.connection.connect.threads.max", 8), + conf.getInt("spark.core.connection.connect.threads.keepalive", 60), TimeUnit.SECONDS, new LinkedBlockingDeque[Runnable]()) private val serverChannel = ServerSocketChannel.open() diff --git a/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala b/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala index b729eb11c5..d87157e12c 100644 --- a/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala +++ b/core/src/main/scala/org/apache/spark/network/netty/ShuffleCopier.scala @@ -36,7 +36,7 @@ private[spark] class ShuffleCopier(conf: SparkConf) extends Logging { resultCollectCallback: (BlockId, Long, ByteBuf) => Unit) { val handler = new ShuffleCopier.ShuffleClientHandler(resultCollectCallback) - val connectTimeout = conf.get("spark.shuffle.netty.connect.timeout", "60000").toInt + val connectTimeout = conf.getInt("spark.shuffle.netty.connect.timeout", 60000) val fc = new FileClient(handler, connectTimeout) try { diff --git a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala index 6d4f46125f..83109d1a6f 100644 --- a/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/CheckpointRDD.scala @@ -97,7 +97,7 @@ private[spark] object CheckpointRDD extends Logging { throw new IOException("Checkpoint failed: temporary path " + tempOutputPath + " already exists") } - val bufferSize = env.conf.get("spark.buffer.size", "65536").toInt + val bufferSize = env.conf.getInt("spark.buffer.size", 65536) val fileOutputStream = if (blockSize < 0) { fs.create(tempOutputPath, false, bufferSize) @@ -131,7 +131,7 @@ private[spark] object CheckpointRDD extends Logging { ): Iterator[T] = { val env = SparkEnv.get val fs = path.getFileSystem(broadcastedConf.value.value) - val bufferSize = env.conf.get("spark.buffer.size", "65536").toInt + val bufferSize = env.conf.getInt("spark.buffer.size", 65536) val fileInputStream = fs.open(path, bufferSize) val serializer = env.serializer.newInstance() val deserializeStream = serializer.deserializeStream(fileInputStream) diff --git a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala index 2662d48c84..73d15b9082 100644 --- a/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/NewHadoopRDD.scala @@ -76,7 +76,7 @@ class NewHadoopRDD[K, V]( val split = theSplit.asInstanceOf[NewHadoopPartition] logInfo("Input split: " + split.serializableHadoopSplit) val conf = confBroadcast.value.value - val attemptId = newTaskAttemptID(jobtrackerId, id, true, split.index, 0) + val attemptId = newTaskAttemptID(jobtrackerId, id, isMap = true, split.index, 0) val hadoopAttemptContext = newTaskAttemptContext(conf, attemptId) val format = inputFormatClass.newInstance if (format.isInstanceOf[Configurable]) { diff --git a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala index 04a8d05988..c118ddfc01 100644 --- a/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala +++ b/core/src/main/scala/org/apache/spark/rdd/PairRDDFunctions.scala @@ -18,35 +18,34 @@ package org.apache.spark.rdd import java.nio.ByteBuffer -import java.util.Date import java.text.SimpleDateFormat +import java.util.Date import java.util.{HashMap => JHashMap} -import scala.collection.{mutable, Map} +import scala.collection.Map +import scala.collection.mutable import scala.collection.mutable.ArrayBuffer import scala.collection.JavaConversions._ import scala.reflect.{ClassTag, classTag} -import org.apache.hadoop.mapred._ -import org.apache.hadoop.io.compress.CompressionCodec import org.apache.hadoop.conf.Configuration import org.apache.hadoop.fs.Path import org.apache.hadoop.io.SequenceFile.CompressionType -import org.apache.hadoop.mapred.FileOutputFormat -import org.apache.hadoop.mapred.OutputFormat +import org.apache.hadoop.io.compress.CompressionCodec +import org.apache.hadoop.mapred.{FileOutputCommitter, FileOutputFormat, JobConf, OutputFormat} import org.apache.hadoop.mapreduce.{OutputFormat => NewOutputFormat} -import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat} -import org.apache.hadoop.mapreduce.SparkHadoopMapReduceUtil import org.apache.hadoop.mapreduce.{Job => NewAPIHadoopJob} import org.apache.hadoop.mapreduce.{RecordWriter => NewRecordWriter} +import org.apache.hadoop.mapreduce.lib.output.{FileOutputFormat => NewFileOutputFormat} import com.clearspring.analytics.stream.cardinality.HyperLogLog +// SparkHadoopWriter and SparkHadoopMapReduceUtil are actually source files defined in Spark. +import org.apache.hadoop.mapred.SparkHadoopWriter +import org.apache.hadoop.mapreduce.SparkHadoopMapReduceUtil import org.apache.spark._ import org.apache.spark.SparkContext._ import org.apache.spark.partial.{BoundedDouble, PartialResult} -import org.apache.spark.Aggregator -import org.apache.spark.Partitioner import org.apache.spark.Partitioner.defaultPartitioner import org.apache.spark.util.SerializableHyperLogLog @@ -120,9 +119,9 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) } /** - * Merge the values for each key using an associative function and a neutral "zero value" which may - * be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for - * list concatenation, 0 for addition, or 1 for multiplication.). + * Merge the values for each key using an associative function and a neutral "zero value" which + * may be added to the result an arbitrary number of times, and must not change the result + * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ def foldByKey(zeroValue: V, partitioner: Partitioner)(func: (V, V) => V): RDD[(K, V)] = { // Serialize the zero value to a byte array so that we can get a new clone of it on each key @@ -138,18 +137,18 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) } /** - * Merge the values for each key using an associative function and a neutral "zero value" which may - * be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for - * list concatenation, 0 for addition, or 1 for multiplication.). + * Merge the values for each key using an associative function and a neutral "zero value" which + * may be added to the result an arbitrary number of times, and must not change the result + * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ def foldByKey(zeroValue: V, numPartitions: Int)(func: (V, V) => V): RDD[(K, V)] = { foldByKey(zeroValue, new HashPartitioner(numPartitions))(func) } /** - * Merge the values for each key using an associative function and a neutral "zero value" which may - * be added to the result an arbitrary number of times, and must not change the result (e.g., Nil for - * list concatenation, 0 for addition, or 1 for multiplication.). + * Merge the values for each key using an associative function and a neutral "zero value" which + * may be added to the result an arbitrary number of times, and must not change the result + * (e.g., Nil for list concatenation, 0 for addition, or 1 for multiplication.). */ def foldByKey(zeroValue: V)(func: (V, V) => V): RDD[(K, V)] = { foldByKey(zeroValue, defaultPartitioner(self))(func) @@ -226,7 +225,7 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) } /** - * Return approximate number of distinct values for each key in this RDD. + * Return approximate number of distinct values for each key in this RDD. * The accuracy of approximation can be controlled through the relative standard deviation * (relativeSD) parameter, which also controls the amount of memory used. Lower values result in * more accurate counts but increase the memory footprint and vise versa. HashPartitions the @@ -579,7 +578,8 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) */ def saveAsHadoopFile[F <: OutputFormat[K, V]]( path: String, codec: Class[_ <: CompressionCodec]) (implicit fm: ClassTag[F]) { - saveAsHadoopFile(path, getKeyClass, getValueClass, fm.runtimeClass.asInstanceOf[Class[F]], codec) + val runtimeClass = fm.runtimeClass + saveAsHadoopFile(path, getKeyClass, getValueClass, runtimeClass.asInstanceOf[Class[F]], codec) } /** @@ -599,7 +599,8 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) keyClass: Class[_], valueClass: Class[_], outputFormatClass: Class[_ <: NewOutputFormat[_, _]], - conf: Configuration = self.context.hadoopConfiguration) { + conf: Configuration = self.context.hadoopConfiguration) + { val job = new NewAPIHadoopJob(conf) job.setOutputKeyClass(keyClass) job.setOutputValueClass(valueClass) @@ -613,7 +614,8 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) // around by taking a mod. We expect that no task will be attempted 2 billion times. val attemptNumber = (context.attemptId % Int.MaxValue).toInt /* "reduce task" <split #> <attempt # = spark task #> */ - val attemptId = newTaskAttemptID(jobtrackerID, stageId, false, context.partitionId, attemptNumber) + val attemptId = newTaskAttemptID(jobtrackerID, stageId, isMap = false, context.partitionId, + attemptNumber) val hadoopContext = newTaskAttemptContext(wrappedConf.value, attemptId) val format = outputFormatClass.newInstance val committer = format.getOutputCommitter(hadoopContext) @@ -632,13 +634,12 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) * however we're only going to use this local OutputCommitter for * setupJob/commitJob, so we just use a dummy "map" task. */ - val jobAttemptId = newTaskAttemptID(jobtrackerID, stageId, true, 0, 0) + val jobAttemptId = newTaskAttemptID(jobtrackerID, stageId, isMap = true, 0, 0) val jobTaskContext = newTaskAttemptContext(wrappedConf.value, jobAttemptId) val jobCommitter = jobFormat.getOutputCommitter(jobTaskContext) jobCommitter.setupJob(jobTaskContext) val count = self.context.runJob(self, writeShard _).sum jobCommitter.commitJob(jobTaskContext) - jobCommitter.cleanupJob(jobTaskContext) } /** @@ -668,7 +669,9 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) codec: Option[Class[_ <: CompressionCodec]] = None) { conf.setOutputKeyClass(keyClass) conf.setOutputValueClass(valueClass) - // conf.setOutputFormat(outputFormatClass) // Doesn't work in Scala 2.9 due to what may be a generics bug + // Doesn't work in Scala 2.9 due to what may be a generics bug + // TODO: Should we uncomment this for Scala 2.10? + // conf.setOutputFormat(outputFormatClass) conf.set("mapred.output.format.class", outputFormatClass.getName) for (c <- codec) { conf.setCompressMapOutput(true) @@ -702,7 +705,8 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) throw new SparkException("Output value class not set") } - logInfo("Saving as hadoop file of type (" + keyClass.getSimpleName+ ", " + valueClass.getSimpleName+ ")") + logDebug("Saving as hadoop file of type (" + keyClass.getSimpleName + ", " + + valueClass.getSimpleName+ ")") val writer = new SparkHadoopWriter(conf) writer.preSetup() @@ -728,7 +732,6 @@ class PairRDDFunctions[K: ClassTag, V: ClassTag](self: RDD[(K, V)]) self.context.runJob(self, writeToFile _) writer.commitJob() - writer.cleanup() } /** diff --git a/core/src/main/scala/org/apache/spark/rdd/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala index 3f41b66279..f9dc12eee3 100644 --- a/core/src/main/scala/org/apache/spark/rdd/RDD.scala +++ b/core/src/main/scala/org/apache/spark/rdd/RDD.scala @@ -23,7 +23,6 @@ import scala.collection.Map import scala.collection.JavaConversions.mapAsScalaMap import scala.collection.mutable.ArrayBuffer -import scala.collection.mutable.HashMap import scala.reflect.{classTag, ClassTag} import org.apache.hadoop.io.BytesWritable @@ -52,11 +51,13 @@ import org.apache.spark._ * partitioned collection of elements that can be operated on in parallel. This class contains the * basic operations available on all RDDs, such as `map`, `filter`, and `persist`. In addition, * [[org.apache.spark.rdd.PairRDDFunctions]] contains operations available only on RDDs of key-value - * pairs, such as `groupByKey` and `join`; [[org.apache.spark.rdd.DoubleRDDFunctions]] contains - * operations available only on RDDs of Doubles; and [[org.apache.spark.rdd.SequenceFileRDDFunctions]] - * contains operations available on RDDs that can be saved as SequenceFiles. These operations are - * automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] through implicit - * conversions when you `import org.apache.spark.SparkContext._`. + * pairs, such as `groupByKey` and `join`; + * [[org.apache.spark.rdd.DoubleRDDFunctions]] contains operations available only on RDDs of + * Doubles; and + * [[org.apache.spark.rdd.SequenceFileRDDFunctions]] contains operations available on RDDs that + * can be saved as SequenceFiles. + * These operations are automatically available on any RDD of the right type (e.g. RDD[(Int, Int)] + * through implicit conversions when you `import org.apache.spark.SparkContext._`. * * Internally, each RDD is characterized by five main properties: * @@ -235,12 +236,9 @@ abstract class RDD[T: ClassTag]( /** * Compute an RDD partition or read it from a checkpoint if the RDD is checkpointing. */ - private[spark] def computeOrReadCheckpoint(split: Partition, context: TaskContext): Iterator[T] = { - if (isCheckpointed) { - firstParent[T].iterator(split, context) - } else { - compute(split, context) - } + private[spark] def computeOrReadCheckpoint(split: Partition, context: TaskContext): Iterator[T] = + { + if (isCheckpointed) firstParent[T].iterator(split, context) else compute(split, context) } // Transformations (return a new RDD) @@ -268,6 +266,9 @@ abstract class RDD[T: ClassTag]( def distinct(numPartitions: Int): RDD[T] = map(x => (x, null)).reduceByKey((x, y) => x, numPartitions).map(_._1) + /** + * Return a new RDD containing the distinct elements in this RDD. + */ def distinct(): RDD[T] = distinct(partitions.size) /** @@ -280,7 +281,7 @@ abstract class RDD[T: ClassTag]( * which can avoid performing a shuffle. */ def repartition(numPartitions: Int): RDD[T] = { - coalesce(numPartitions, true) + coalesce(numPartitions, shuffle = true) } /** @@ -646,7 +647,8 @@ abstract class RDD[T: ClassTag]( } /** - * Reduces the elements of this RDD using the specified commutative and associative binary operator. + * Reduces the elements of this RDD using the specified commutative and + * associative binary operator. */ def reduce(f: (T, T) => T): T = { val cleanF = sc.clean(f) @@ -953,7 +955,7 @@ abstract class RDD[T: ClassTag]( private var storageLevel: StorageLevel = StorageLevel.NONE /** Record user function generating this RDD. */ - @transient private[spark] val origin = sc.getCallSite + @transient private[spark] val origin = sc.getCallSite() private[spark] def elementClassTag: ClassTag[T] = classTag[T] diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala index e22b1e53e8..c52d6175d2 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala @@ -31,7 +31,7 @@ import org.apache.spark.util.Utils private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedulerImpl) extends Logging { - private val THREADS = sparkEnv.conf.get("spark.resultGetter.threads", "4").toInt + private val THREADS = sparkEnv.conf.getInt("spark.resultGetter.threads", 4) private val getTaskResultExecutor = Utils.newDaemonFixedThreadPool( THREADS, "Result resolver thread") diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala index 0c8ed62759..d4f74d3e18 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala @@ -51,15 +51,15 @@ private[spark] class TaskSchedulerImpl( isLocal: Boolean = false) extends TaskScheduler with Logging { - def this(sc: SparkContext) = this(sc, sc.conf.get("spark.task.maxFailures", "4").toInt) + def this(sc: SparkContext) = this(sc, sc.conf.getInt("spark.task.maxFailures", 4)) val conf = sc.conf // How often to check for speculative tasks - val SPECULATION_INTERVAL = conf.get("spark.speculation.interval", "100").toLong + val SPECULATION_INTERVAL = conf.getLong("spark.speculation.interval", 100) // Threshold above which we warn user initial TaskSet may be starved - val STARVATION_TIMEOUT = conf.get("spark.starvation.timeout", "15000").toLong + val STARVATION_TIMEOUT = conf.getLong("spark.starvation.timeout", 15000) // TaskSetManagers are not thread safe, so any access to one should be synchronized // on this class. @@ -125,7 +125,7 @@ private[spark] class TaskSchedulerImpl( override def start() { backend.start() - if (!isLocal && conf.get("spark.speculation", "false").toBoolean) { + if (!isLocal && conf.getBoolean("spark.speculation", false)) { logInfo("Starting speculative execution thread") import sc.env.actorSystem.dispatcher sc.env.actorSystem.scheduler.schedule(SPECULATION_INTERVAL milliseconds, diff --git a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala index 6dd1469d8f..a10e5397ad 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala @@ -57,11 +57,11 @@ private[spark] class TaskSetManager( val conf = sched.sc.conf // CPUs to request per task - val CPUS_PER_TASK = conf.get("spark.task.cpus", "1").toInt + val CPUS_PER_TASK = conf.getInt("spark.task.cpus", 1) // Quantile of tasks at which to start speculation - val SPECULATION_QUANTILE = conf.get("spark.speculation.quantile", "0.75").toDouble - val SPECULATION_MULTIPLIER = conf.get("spark.speculation.multiplier", "1.5").toDouble + val SPECULATION_QUANTILE = conf.getDouble("spark.speculation.quantile", 0.75) + val SPECULATION_MULTIPLIER = conf.getDouble("spark.speculation.multiplier", 1.5) // Serializer for closures and tasks. val env = SparkEnv.get @@ -116,7 +116,7 @@ private[spark] class TaskSetManager( // How frequently to reprint duplicate exceptions in full, in milliseconds val EXCEPTION_PRINT_INTERVAL = - conf.get("spark.logging.exceptionPrintInterval", "10000").toLong + conf.getLong("spark.logging.exceptionPrintInterval", 10000) // Map of recent exceptions (identified by string representation and top stack frame) to // duplicate count (how many times the same exception has appeared) and time the full exception diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala index 2f5bcafe40..8d596a76c2 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala @@ -63,7 +63,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A context.system.eventStream.subscribe(self, classOf[RemotingLifecycleEvent]) // Periodically revive offers to allow delay scheduling to work - val reviveInterval = conf.get("spark.scheduler.revive.interval", "1000").toLong + val reviveInterval = conf.getLong("spark.scheduler.revive.interval", 1000) import context.dispatcher context.system.scheduler.schedule(0.millis, reviveInterval.millis, self, ReviveOffers) } @@ -209,8 +209,7 @@ class CoarseGrainedSchedulerBackend(scheduler: TaskSchedulerImpl, actorSystem: A } override def defaultParallelism(): Int = { - conf.getOption("spark.default.parallelism").map(_.toInt).getOrElse( - math.max(totalCoreCount.get(), 2)) + conf.getInt("spark.default.parallelism", math.max(totalCoreCount.get(), 2)) } // Called by subclasses when notified of a lost worker diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala index b44d1e43c8..d99c76117c 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/SimrSchedulerBackend.scala @@ -33,7 +33,7 @@ private[spark] class SimrSchedulerBackend( val tmpPath = new Path(driverFilePath + "_tmp") val filePath = new Path(driverFilePath) - val maxCores = conf.get("spark.simr.executor.cores", "1").toInt + val maxCores = conf.getInt("spark.simr.executor.cores", 1) override def start() { super.start() diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala index d46fceba89..e16d60c54c 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/CoarseMesosSchedulerBackend.scala @@ -77,7 +77,7 @@ private[spark] class CoarseMesosSchedulerBackend( "Spark home is not set; set it through the spark.home system " + "property, the SPARK_HOME environment variable or the SparkContext constructor")) - val extraCoresPerSlave = conf.get("spark.mesos.extra.cores", "0").toInt + val extraCoresPerSlave = conf.getInt("spark.mesos.extra.cores", 0) var nextMesosTaskId = 0 diff --git a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala index ae8d527352..b428c82a48 100644 --- a/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala +++ b/core/src/main/scala/org/apache/spark/scheduler/cluster/mesos/MesosSchedulerBackend.scala @@ -340,5 +340,5 @@ private[spark] class MesosSchedulerBackend( } // TODO: query Mesos for number of cores - override def defaultParallelism() = sc.conf.get("spark.default.parallelism", "8").toInt + override def defaultParallelism() = sc.conf.getInt("spark.default.parallelism", 8) } diff --git a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala index a24a3b04b8..c14cd47556 100644 --- a/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala +++ b/core/src/main/scala/org/apache/spark/serializer/KryoSerializer.scala @@ -36,7 +36,7 @@ import org.apache.spark.storage.{GetBlock, GotBlock, PutBlock} */ class KryoSerializer(conf: SparkConf) extends org.apache.spark.serializer.Serializer with Logging { private val bufferSize = { - conf.get("spark.kryoserializer.buffer.mb", "2").toInt * 1024 * 1024 + conf.getInt("spark.kryoserializer.buffer.mb", 2) * 1024 * 1024 } def newKryoOutput() = new KryoOutput(bufferSize) @@ -48,7 +48,7 @@ class KryoSerializer(conf: SparkConf) extends org.apache.spark.serializer.Serial // Allow disabling Kryo reference tracking if user knows their object graphs don't have loops. // Do this before we invoke the user registrator so the user registrator can override this. - kryo.setReferences(conf.get("spark.kryo.referenceTracking", "true").toBoolean) + kryo.setReferences(conf.getBoolean("spark.kryo.referenceTracking", true)) for (cls <- KryoSerializer.toRegister) kryo.register(cls) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala index 47478631a1..4fa2ab96d9 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockFetcherIterator.scala @@ -327,7 +327,7 @@ object BlockFetcherIterator { fetchRequestsSync.put(request) } - copiers = startCopiers(conf.get("spark.shuffle.copier.threads", "6").toInt) + copiers = startCopiers(conf.getInt("spark.shuffle.copier.threads", 6)) logInfo("Started " + fetchRequestsSync.size + " remote gets in " + Utils.getUsedTimeMs(startTime)) diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala index 6d2cda97b0..c56e2ca2df 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManager.scala @@ -58,8 +58,8 @@ private[spark] class BlockManager( // If we use Netty for shuffle, start a new Netty-based shuffle sender service. private val nettyPort: Int = { - val useNetty = conf.get("spark.shuffle.use.netty", "false").toBoolean - val nettyPortConfig = conf.get("spark.shuffle.sender.port", "0").toInt + val useNetty = conf.getBoolean("spark.shuffle.use.netty", false) + val nettyPortConfig = conf.getInt("spark.shuffle.sender.port", 0) if (useNetty) diskBlockManager.startShuffleBlockSender(nettyPortConfig) else 0 } @@ -72,14 +72,14 @@ private[spark] class BlockManager( // Max megabytes of data to keep in flight per reducer (to avoid over-allocating memory // for receiving shuffle outputs) val maxBytesInFlight = - conf.get("spark.reducer.maxMbInFlight", "48").toLong * 1024 * 1024 + conf.getLong("spark.reducer.maxMbInFlight", 48) * 1024 * 1024 // Whether to compress broadcast variables that are stored - val compressBroadcast = conf.get("spark.broadcast.compress", "true").toBoolean + val compressBroadcast = conf.getBoolean("spark.broadcast.compress", true) // Whether to compress shuffle output that are stored - val compressShuffle = conf.get("spark.shuffle.compress", "true").toBoolean + val compressShuffle = conf.getBoolean("spark.shuffle.compress", true) // Whether to compress RDD partitions that are stored serialized - val compressRdds = conf.get("spark.rdd.compress", "false").toBoolean + val compressRdds = conf.getBoolean("spark.rdd.compress", false) val heartBeatFrequency = BlockManager.getHeartBeatFrequency(conf) @@ -443,7 +443,7 @@ private[spark] class BlockManager( : BlockFetcherIterator = { val iter = - if (conf.get("spark.shuffle.use.netty", "false").toBoolean) { + if (conf.getBoolean("spark.shuffle.use.netty", false)) { new BlockFetcherIterator.NettyBlockFetcherIterator(this, blocksByAddress, serializer) } else { new BlockFetcherIterator.BasicBlockFetcherIterator(this, blocksByAddress, serializer) @@ -469,7 +469,7 @@ private[spark] class BlockManager( def getDiskWriter(blockId: BlockId, file: File, serializer: Serializer, bufferSize: Int) : BlockObjectWriter = { val compressStream: OutputStream => OutputStream = wrapForCompression(blockId, _) - val syncWrites = conf.get("spark.shuffle.sync", "false").toBoolean + val syncWrites = conf.getBoolean("spark.shuffle.sync", false) new DiskBlockObjectWriter(blockId, file, serializer, bufferSize, compressStream, syncWrites) } @@ -864,15 +864,15 @@ private[spark] object BlockManager extends Logging { val ID_GENERATOR = new IdGenerator def getMaxMemory(conf: SparkConf): Long = { - val memoryFraction = conf.get("spark.storage.memoryFraction", "0.66").toDouble + val memoryFraction = conf.getDouble("spark.storage.memoryFraction", 0.66) (Runtime.getRuntime.maxMemory * memoryFraction).toLong } def getHeartBeatFrequency(conf: SparkConf): Long = - conf.get("spark.storage.blockManagerTimeoutIntervalMs", "60000").toLong / 4 + conf.getLong("spark.storage.blockManagerTimeoutIntervalMs", 60000) / 4 def getDisableHeartBeatsForTesting(conf: SparkConf): Boolean = - conf.get("spark.test.disableBlockManagerHeartBeat", "false").toBoolean + conf.getBoolean("spark.test.disableBlockManagerHeartBeat", false) /** * Attempt to clean up a ByteBuffer if it is memory-mapped. This uses an *unsafe* Sun API that diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala index 51a29ed8ef..c54e4f2664 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala @@ -30,8 +30,8 @@ import org.apache.spark.util.AkkaUtils private[spark] class BlockManagerMaster(var driverActor : ActorRef, conf: SparkConf) extends Logging { - val AKKA_RETRY_ATTEMPTS: Int = conf.get("spark.akka.num.retries", "3").toInt - val AKKA_RETRY_INTERVAL_MS: Int = conf.get("spark.akka.retry.wait", "3000").toInt + val AKKA_RETRY_ATTEMPTS: Int = conf.getInt("spark.akka.num.retries", 3) + val AKKA_RETRY_INTERVAL_MS: Int = conf.getInt("spark.akka.retry.wait", 3000) val DRIVER_AKKA_ACTOR_NAME = "BlockManagerMaster" diff --git a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala index 58452d9657..2c1a4e2f5d 100644 --- a/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala +++ b/core/src/main/scala/org/apache/spark/storage/BlockManagerMasterActor.scala @@ -348,14 +348,19 @@ object BlockManagerMasterActor { if (storageLevel.isValid) { // isValid means it is either stored in-memory or on-disk. - _blocks.put(blockId, BlockStatus(storageLevel, memSize, diskSize)) + // But the memSize here indicates the data size in or dropped from memory, + // and the diskSize here indicates the data size in or dropped to disk. + // They can be both larger than 0, when a block is dropped from memory to disk. + // Therefore, a safe way to set BlockStatus is to set its info in accurate modes. if (storageLevel.useMemory) { + _blocks.put(blockId, BlockStatus(storageLevel, memSize, 0)) _remainingMem -= memSize logInfo("Added %s in memory on %s (size: %s, free: %s)".format( blockId, blockManagerId.hostPort, Utils.bytesToString(memSize), Utils.bytesToString(_remainingMem))) } if (storageLevel.useDisk) { + _blocks.put(blockId, BlockStatus(storageLevel, 0, diskSize)) logInfo("Added %s on disk on %s (size: %s)".format( blockId, blockManagerId.hostPort, Utils.bytesToString(diskSize))) } diff --git a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala index 55dcb3742c..edc1133172 100644 --- a/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/DiskBlockManager.scala @@ -38,7 +38,7 @@ private[spark] class DiskBlockManager(shuffleManager: ShuffleBlockManager, rootD extends PathResolver with Logging { private val MAX_DIR_CREATION_ATTEMPTS: Int = 10 - private val subDirsPerLocalDir = shuffleManager.conf.get("spark.diskStore.subDirectories", "64").toInt + private val subDirsPerLocalDir = shuffleManager.conf.getInt("spark.diskStore.subDirectories", 64) // Create one local directory for each path mentioned in spark.local.dir; then, inside this // directory, create multiple subdirectories that we will hash files into, in order to avoid diff --git a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala index 39dc7bb19a..e2b24298a5 100644 --- a/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala +++ b/core/src/main/scala/org/apache/spark/storage/ShuffleBlockManager.scala @@ -64,9 +64,9 @@ class ShuffleBlockManager(blockManager: BlockManager) { // Turning off shuffle file consolidation causes all shuffle Blocks to get their own file. // TODO: Remove this once the shuffle file consolidation feature is stable. val consolidateShuffleFiles = - conf.get("spark.shuffle.consolidateFiles", "false").toBoolean + conf.getBoolean("spark.shuffle.consolidateFiles", false) - private val bufferSize = conf.get("spark.shuffle.file.buffer.kb", "100").toInt * 1024 + private val bufferSize = conf.getInt("spark.shuffle.file.buffer.kb", 100) * 1024 /** * Contains all the state related to a particular shuffle. This includes a pool of unused diff --git a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala index b7b87250b9..bcd2824450 100644 --- a/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala +++ b/core/src/main/scala/org/apache/spark/ui/jobs/JobProgressListener.scala @@ -33,7 +33,7 @@ import org.apache.spark.scheduler._ */ private[spark] class JobProgressListener(val sc: SparkContext) extends SparkListener { // How many stages to remember - val RETAINED_STAGES = sc.conf.get("spark.ui.retained_stages", "1000").toInt + val RETAINED_STAGES = sc.conf.getInt("spark.ui.retainedStages", 1000) val DEFAULT_POOL_NAME = "default" val stageIdToPool = new HashMap[Int, String]() diff --git a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala index 3f009a8998..761d378c7f 100644 --- a/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala +++ b/core/src/main/scala/org/apache/spark/util/AkkaUtils.scala @@ -44,13 +44,13 @@ private[spark] object AkkaUtils { def createActorSystem(name: String, host: String, port: Int, indestructible: Boolean = false, conf: SparkConf): (ActorSystem, Int) = { - val akkaThreads = conf.get("spark.akka.threads", "4").toInt - val akkaBatchSize = conf.get("spark.akka.batchSize", "15").toInt + val akkaThreads = conf.getInt("spark.akka.threads", 4) + val akkaBatchSize = conf.getInt("spark.akka.batchSize", 15) - val akkaTimeout = conf.get("spark.akka.timeout", "100").toInt + val akkaTimeout = conf.getInt("spark.akka.timeout", 100) - val akkaFrameSize = conf.get("spark.akka.frameSize", "10").toInt - val akkaLogLifecycleEvents = conf.get("spark.akka.logLifecycleEvents", "false").toBoolean + val akkaFrameSize = conf.getInt("spark.akka.frameSize", 10) + val akkaLogLifecycleEvents = conf.getBoolean("spark.akka.logLifecycleEvents", false) val lifecycleEvents = if (akkaLogLifecycleEvents) "on" else "off" if (!akkaLogLifecycleEvents) { // As a workaround for Akka issue #3787, we coerce the "EndpointWriter" log to be silent. @@ -58,12 +58,12 @@ private[spark] object AkkaUtils { Option(Logger.getLogger("akka.remote.EndpointWriter")).map(l => l.setLevel(Level.FATAL)) } - val logAkkaConfig = if (conf.get("spark.akka.logAkkaConfig", "false").toBoolean) "on" else "off" + val logAkkaConfig = if (conf.getBoolean("spark.akka.logAkkaConfig", false)) "on" else "off" - val akkaHeartBeatPauses = conf.get("spark.akka.heartbeat.pauses", "600").toInt + val akkaHeartBeatPauses = conf.getInt("spark.akka.heartbeat.pauses", 600) val akkaFailureDetector = - conf.get("spark.akka.failure-detector.threshold", "300.0").toDouble - val akkaHeartBeatInterval = conf.get("spark.akka.heartbeat.interval", "1000").toInt + conf.getDouble("spark.akka.failure-detector.threshold", 300.0) + val akkaHeartBeatInterval = conf.getInt("spark.akka.heartbeat.interval", 1000) val akkaConf = ConfigFactory.parseMap(conf.getAkkaConf.toMap[String, String]).withFallback( ConfigFactory.parseString( @@ -103,7 +103,7 @@ private[spark] object AkkaUtils { /** Returns the default Spark timeout to use for Akka ask operations. */ def askTimeout(conf: SparkConf): FiniteDuration = { - Duration.create(conf.get("spark.akka.askTimeout", "30").toLong, "seconds") + Duration.create(conf.getLong("spark.akka.askTimeout", 30), "seconds") } /** Returns the default Spark timeout to use for Akka remote actor lookup. */ diff --git a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala index aa7f52cafb..3d1e90a352 100644 --- a/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala +++ b/core/src/main/scala/org/apache/spark/util/MetadataCleaner.scala @@ -74,7 +74,7 @@ object MetadataCleanerType extends Enumeration { // initialization of StreamingContext. It's okay for users trying to configure stuff themselves. object MetadataCleaner { def getDelaySeconds(conf: SparkConf) = { - conf.get("spark.cleaner.ttl", "3500").toInt + conf.getInt("spark.cleaner.ttl", 3500) } def getDelaySeconds(conf: SparkConf, cleanerType: MetadataCleanerType.MetadataCleanerType): Int = diff --git a/core/src/main/scala/org/apache/spark/util/XORShiftRandom.scala b/core/src/main/scala/org/apache/spark/util/XORShiftRandom.scala index e9907e6c85..08b31ac64f 100644 --- a/core/src/main/scala/org/apache/spark/util/XORShiftRandom.scala +++ b/core/src/main/scala/org/apache/spark/util/XORShiftRandom.scala @@ -91,4 +91,4 @@ private[spark] object XORShiftRandom { } -}
\ No newline at end of file +} diff --git a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala index 1eec6726f4..c9f6cc5d07 100644 --- a/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala +++ b/core/src/test/scala/org/apache/spark/scheduler/TaskSetManagerSuite.scala @@ -83,7 +83,7 @@ class TaskSetManagerSuite extends FunSuite with LocalSparkContext with Logging { private val conf = new SparkConf - val LOCALITY_WAIT = conf.get("spark.locality.wait", "3000").toLong + val LOCALITY_WAIT = conf.getLong("spark.locality.wait", 3000) val MAX_TASK_FAILURES = 4 test("TaskSet with no preferences") { diff --git a/core/src/test/scala/org/apache/spark/util/XORShiftRandomSuite.scala b/core/src/test/scala/org/apache/spark/util/XORShiftRandomSuite.scala index b78367b6ca..f1d7b61b31 100644 --- a/core/src/test/scala/org/apache/spark/util/XORShiftRandomSuite.scala +++ b/core/src/test/scala/org/apache/spark/util/XORShiftRandomSuite.scala @@ -73,4 +73,4 @@ class XORShiftRandomSuite extends FunSuite with ShouldMatchers { } -}
\ No newline at end of file +} diff --git a/docs/configuration.md b/docs/configuration.md index 1d6c3d1633..6717757781 100644 --- a/docs/configuration.md +++ b/docs/configuration.md @@ -130,7 +130,7 @@ Apart from these, the following properties are also available, and may be useful </td> </tr> <tr> - <td>spark.ui.retained_stages</td> + <td>spark.ui.retainedStages</td> <td>1000</td> <td> How many stages the Spark UI remembers before garbage collecting. diff --git a/docs/running-on-yarn.md b/docs/running-on-yarn.md index 717071d72c..b206270107 100644 --- a/docs/running-on-yarn.md +++ b/docs/running-on-yarn.md @@ -114,6 +114,8 @@ For example: SPARK_YARN_APP_JAR=examples/target/scala-{{site.SCALA_VERSION}}/spark-examples-assembly-{{site.SPARK_VERSION}}.jar \ MASTER=yarn-client ./bin/spark-shell +You can also send extra files to yarn cluster for worker to use by exporting SPARK_YARN_DIST_FILES=file1,file2... etc. + # Building Spark for Hadoop/YARN 2.2.x See [Building Spark with Maven](building-with-maven.html) for instructions on how to build Spark using the Maven process. diff --git a/make-distribution.sh b/make-distribution.sh index 1a3a5d0209..e6b5956d1e 100755 --- a/make-distribution.sh +++ b/make-distribution.sh @@ -44,7 +44,7 @@ DISTDIR="$FWDIR/dist" # Get version from SBT export TERM=dumb # Prevents color codes in SBT output -VERSIONSTRING=$FWDIR/sbt/sbt "show version" +VERSIONSTRING=$($FWDIR/sbt/sbt "show version") if [ $? == -1 ] ;then echo -e "You need sbt installed and available on your path." @@ -145,6 +145,11 @@ <enabled>false</enabled> </snapshots> </repository> + <repository> + <id>cloudera-repo</id> + <name>Cloudera Repository</name> + <url>https://repository.cloudera.com/artifactory/cloudera-repos</url> + </repository> </repositories> <dependencyManagement> @@ -25,37 +25,26 @@ URL1=http://typesafe.artifactoryonline.com/typesafe/ivy-releases/org.scala-sbt/s URL2=http://repo.typesafe.com/typesafe/ivy-releases/org.scala-sbt/sbt-launch/${SBT_VERSION}/sbt-launch.jar JAR=sbt/sbt-launch-${SBT_VERSION}.jar -printf "Checking for system sbt [" -if hash sbt 2>/dev/null; then - printf "FOUND]\n" - # Use System SBT - sbt "$@" -else - printf "NOT FOUND]\n" - # Download sbt or use already downloaded - if [ ! -d .sbtlib ]; then - mkdir .sbtlib - fi - if [ ! -f ${JAR} ]; then - # Download - printf "Attempting to fetch sbt\n" - if hash curl 2>/dev/null; then - curl --progress-bar ${URL1} > ${JAR} || curl --progress-bar ${URL2} > ${JAR} - elif hash wget 2>/dev/null; then - wget --progress=bar ${URL1} -O ${JAR} || wget --progress=bar ${URL2} -O ${JAR} - else - printf "You do not have curl or wget installed, please install sbt manually from http://www.scala-sbt.org/\n" - exit -1 - fi - fi - if [ ! -f ${JAR} ]; then - # We failed to download - printf "Our attempt to download sbt locally to ${JAR} failed. Please install sbt manually from http://www.scala-sbt.org/\n" +# Download sbt launch jar if it hasn't been downloaded yet +if [ ! -f ${JAR} ]; then + # Download + printf "Attempting to fetch sbt\n" + if hash curl 2>/dev/null; then + curl --progress-bar ${URL1} > ${JAR} || curl --progress-bar ${URL2} > ${JAR} + elif hash wget 2>/dev/null; then + wget --progress=bar ${URL1} -O ${JAR} || wget --progress=bar ${URL2} -O ${JAR} + else + printf "You do not have curl or wget installed, please install sbt manually from http://www.scala-sbt.org/\n" exit -1 fi - printf "Launching sbt from ${JAR}\n" - java \ - -Xmx1200m -XX:MaxPermSize=350m -XX:ReservedCodeCacheSize=256m \ - -jar ${JAR} \ - "$@" fi +if [ ! -f ${JAR} ]; then + # We failed to download + printf "Our attempt to download sbt locally to ${JAR} failed. Please install sbt manually from http://www.scala-sbt.org/\n" + exit -1 +fi +printf "Launching sbt from ${JAR}\n" +java \ + -Xmx1200m -XX:MaxPermSize=350m -XX:ReservedCodeCacheSize=256m \ + -jar ${JAR} \ + "$@" diff --git a/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala b/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala index 27d474c0a0..d41f726f83 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/dstream/NetworkInputDStream.scala @@ -175,7 +175,7 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging private class NetworkReceiverActor extends Actor { logInfo("Attempting to register with tracker") val ip = env.conf.get("spark.driver.host", "localhost") - val port = env.conf.get("spark.driver.port", "7077").toInt + val port = env.conf.getInt("spark.driver.port", 7077) val url = "akka.tcp://spark@%s:%s/user/NetworkInputTracker".format(ip, port) val tracker = env.actorSystem.actorSelection(url) val timeout = 5.seconds @@ -212,7 +212,7 @@ abstract class NetworkReceiver[T: ClassTag]() extends Serializable with Logging case class Block(id: BlockId, buffer: ArrayBuffer[T], metadata: Any = null) val clock = new SystemClock() - val blockInterval = env.conf.get("spark.streaming.blockInterval", "200").toLong + val blockInterval = env.conf.getLong("spark.streaming.blockInterval", 200) val blockIntervalTimer = new RecurringTimer(clock, blockInterval, updateCurrentBuffer) val blockStorageLevel = storageLevel val blocksForPushing = new ArrayBlockingQueue[Block](1000) diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala index 7341bfbc99..c8ee93bf5b 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/Job.scala @@ -38,4 +38,4 @@ class Job(val time: Time, func: () => _) { } override def toString = id -}
\ No newline at end of file +} diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala index 917b4c57f6..2fa6853ae0 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 @@ -110,7 +110,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging { // or if the property is defined set it to that time if (clock.isInstanceOf[ManualClock]) { val lastTime = ssc.initialCheckpoint.checkpointTime.milliseconds - val jumpTime = ssc.sc.conf.get("spark.streaming.manualClock.jump", "0").toLong + val jumpTime = ssc.sc.conf.getLong("spark.streaming.manualClock.jump", 0) clock.asInstanceOf[ManualClock].setTime(lastTime + jumpTime) } diff --git a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala index 9304fc1a93..30c070c274 100644 --- a/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala +++ b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala @@ -31,7 +31,7 @@ private[streaming] class JobScheduler(val ssc: StreamingContext) extends Logging { val jobSets = new ConcurrentHashMap[Time, JobSet] - val numConcurrentJobs = ssc.conf.get("spark.streaming.concurrentJobs", "1").toInt + val numConcurrentJobs = ssc.conf.getInt("spark.streaming.concurrentJobs", 1) val executor = Executors.newFixedThreadPool(numConcurrentJobs) val generator = new JobGenerator(this) val listenerBus = new StreamingListenerBus() diff --git a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 6abb4d5017..23781ea35c 100644 --- a/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/alpha/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -102,7 +102,7 @@ class Client(args: ClientArguments, conf: Configuration, sparkConf: SparkConf) (System.getenv("SPARK_JAR") == null) -> "Error: You must set SPARK_JAR environment variable!", (args.userJar == null) -> "Error: You must specify a user jar!", (args.userClass == null) -> "Error: You must specify a user class!", - (args.numWorkers <= 0) -> "Error: You must specify atleast 1 worker!", + (args.numWorkers <= 0) -> "Error: You must specify at least 1 worker!", (args.amMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: AM memory size must be " + "greater than: " + YarnAllocationHandler.MEMORY_OVERHEAD), (args.workerMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: Worker memory size " + diff --git a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala index 324ef4616f..4b1b5da048 100644 --- a/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala +++ b/yarn/common/src/main/scala/org/apache/spark/scheduler/cluster/YarnClientSchedulerBackend.scala @@ -39,6 +39,7 @@ private[spark] class YarnClientSchedulerBackend( val defaultWorkerNumber = "1" val userJar = System.getenv("SPARK_YARN_APP_JAR") + val distFiles = System.getenv("SPARK_YARN_DIST_FILES") var workerCores = System.getenv("SPARK_WORKER_CORES") var workerMemory = System.getenv("SPARK_WORKER_MEMORY") var workerNumber = System.getenv("SPARK_WORKER_INSTANCES") @@ -64,7 +65,8 @@ private[spark] class YarnClientSchedulerBackend( "--worker-memory", workerMemory, "--worker-cores", workerCores, "--num-workers", workerNumber, - "--master-class", "org.apache.spark.deploy.yarn.WorkerLauncher" + "--master-class", "org.apache.spark.deploy.yarn.WorkerLauncher", + "--files", distFiles ) val args = new ClientArguments(argsArray, conf) diff --git a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala index 440ad5cde5..be323d7783 100644 --- a/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala +++ b/yarn/stable/src/main/scala/org/apache/spark/deploy/yarn/Client.scala @@ -122,7 +122,7 @@ class Client(args: ClientArguments, conf: Configuration, sparkConf: SparkConf) (System.getenv("SPARK_JAR") == null) -> "Error: You must set SPARK_JAR environment variable!", (args.userJar == null) -> "Error: You must specify a user jar!", (args.userClass == null) -> "Error: You must specify a user class!", - (args.numWorkers <= 0) -> "Error: You must specify atleast 1 worker!", + (args.numWorkers <= 0) -> "Error: You must specify at least 1 worker!", (args.amMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: AM memory size must be" + "greater than: " + YarnAllocationHandler.MEMORY_OVERHEAD), (args.workerMemory <= YarnAllocationHandler.MEMORY_OVERHEAD) -> ("Error: Worker memory size" + |