diff --git a/core/src/main/scala/org/apache/spark/Accumulators.scala b/core/src/main/scala/org/apache/spark/Accumulators.scala
index 6e922a612a0799afd3d36d2cb7721cd73056add1..5f73d234aa0506447f23d59ca0e91b68c3ab5fb7 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 ccffcc356ca045952eca413201eb192550d0c31f..cdae167aef8295c55f03b4d275287c93ab364891 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 bcec41c439afb6c3634450049f4cfef164d67026..31b0773bfe06c6ca4cebc75e4a13430857940dd9 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 0000000000000000000000000000000000000000..98343e95323008df2f7d099e0cddb0d93b8150ef
--- /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 d3903ce9e336dedde50700d00776205de82184c9..84bd0f7ffdf6426bad73022c12f2a5331c1464a7 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)
}
- if (System.getProperty("spark.driver.port") == null) {
- System.setProperty("spark.driver.port", "0")
+
+ 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")
}
+ // 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 +725,7 @@ 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")))
}
/**
@@ -984,7 +1012,7 @@ 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 +1029,44 @@ 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 +1112,10 @@ 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 +1173,7 @@ 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 826f5c2d8c6bde5ec81905906931556e216da1b6..634a94f0a7f06633d6b352e2e26c483143a2e5fd 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 50f2021d0194da58de8fccdeb7a45df15a3be049..0680a065a3082d1df80083a924c1d93970105f94 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]).
@@ -47,6 +52,14 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
*/
def this(master: String, appName: String) = this(new SparkContext(master, 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 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
@@ -392,6 +405,12 @@ 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
}
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 ca42c769286e6532b3c8e74c09d4995656fdba1e..32cc70e8c9dda4d98e44b8d5165ad5375507e460 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 43c18294c552b69a77e404ca3511634b60a94612..0fc478a41967c720f23da4f2d8a44d3ed26abd87 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 68bff75b908c73fe11dea8a46d5ccff51258a6a3..fb161ce69d40b160a81d4ad1e389d029a16b923a 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 47db720416447700dc34fabcfc272dc921e73675..db596d5fcc05413debab51d1ca9dd8dca0321090 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 073a0a50297d068963bc3759a557921ce6ee47a4..95309382786a902fceedfeac5e712e8ca021b952 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 0aa8852649e058adf514224fd07ba4b78b7c92e8..4dfb19ed8adb6865c6a5912eada5f292d5b3d624 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 59d12a3e6f842904f17caee6a45d255db2f5a95a..ffc0cb09032fb6e324fa33e828f256a7672b1033 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 fc1537f7963c44b4adb8e2e72c8786b92e6aaf70..27dc42bf7e50e260e1f43c5c4fc85da5a3c38cd3 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 953755e40dbbf0edd10285c0c574c411a9885205..9bbd635ab9bb002ce3934ffffec1b156eacfa45f 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 5b62d3ba6c0e0ac8bb456ea8a8d39d69f3117165..ef649fd80ca5266627011cf256d9c6b3e6cac5e7 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 eebd0794b8585de457e6e9e2c4ce7269160f1a6e..7b696cfcca66e027e1b99306778d2edd7df3f030 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 9d89b455fb961e6ddd1d9bc0883b408b3516ab38..e7f3224091ca8b4475a041de30f53afadd00f5f3 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 6cc7fd2ff498f483dc5868bce4d67802b3da42fc..999090ad7464dd5becec6c7a5ee4d2e8cb784fca 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 7d535b08def1650fd7b5f1b4fc0a0489ee9db76e..77c23fb9fbeea5b23266e66fb627a964cdaae2b3 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 825344b3bb534ca0b6334e2599876d930079f8fd..52000d4f9c11caa42a0900f0271d44e9d8f1d95d 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 9ab594b682c43ce45b5262ab5bd40ffdd8b8d637..ead35662fc75ae0c35b0da33056dc74e486876d9 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 87531b6719e1b108ece3586e6c0ffe9d86fe6d2c..fcaf4e92b18585f1a714a54eb33b71e20d35f33a 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 19aa800a958b3eb0612042d4917ae71f8947af63..c382034c99e000adbf4b003e0a9fb06565b907bb 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 debbdd4c444cc0095be31a33859fe47326bd160e..53a2b94a52aa32f4917eeb8508475e7dd59ea56f 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 782be9a429e72d98f0da2b1931fa41a7f6d6cece..3c92c205ea978c58fd00b8ff86077bd2d9cbca2e 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)
@@ -301,7 +302,7 @@ 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 +330,12 @@ 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 570a979b56879effacd4d93ccdccc3f52f076e78..a1e98845f6a848b8ab0651aab5e3b0d484d32a02 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 8e038ce98cf463e44977c1af859c6dc44526747e..9930537b34db01ac0e619cdf482bbf4bd9ab7fdb 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 703bc6a9ca9fa4a539644c6350fefc775808478a..46c40d0a2a02959ffb50ea7541c7092ac0a9bab2 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 781715108be6361197eed6e946d3a6664c5161b7..1c9d6030d68d76f886ad212e34c864b2b244d5b2 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 777574980fbc63a82ebc51b60ab02fa7a37c5c6e..dcbd183c88d09b4aad12b85674207adf8fce60ac 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 b1e1576dadc1a88c9b6de8949b0d41255c2a4055..b729eb11c514268798ecd21a679b6abbb3202e90 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 293a7d1f6841552c1570ead689bcf5e22a65f6a3..6d4f46125f1a60bf872dfe27baad4cf1d07070ad 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 911a002884d65db61efb9ebc040ec6324e66ee3e..4ba4696fef52159c8fd6c7141e8cce1ac5fd3056 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/RDD.scala b/core/src/main/scala/org/apache/spark/rdd/RDD.scala
index 4960e6e82f3781ee14469e2c6c25a93b6ba471c5..6a7b0f8a86b6d43826b76d099d3b69c9dc1ca655 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
// =======================================================================
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 3682c84598e730a659f6fd1c639f53afb7403892..0ccb309d0d9d56a8fd5a7f762632ca218b488b3d 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 aab30b1bb49048ee90a513adece417e28ed64372..4f90c7d3d68ab0dbd55c335756e861a92ae16170 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 7603eb292f677f841f97498fd15ea39d21a16f87..043e01dbfbf28d930eb08ad6e03a12be5ab5a0f2 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 1791ee660db0212ca7afc4dbc99cb18639087009..90eb8a747f91c88436aa9f6ffd205eade6380f8e 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 310ec62ca8afe3a25264bb1d3368c06e86ea4f1e..28f3ba53b84253e3b5029c686d3ef9c7936eadf9 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 356fe56bf32c73c70f2f67ad2c5502957577fd80..3cf995ea74244c2440d101b5a247fa6896454bc3 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 89aa098664dae46e227ba833ff5264deae083660..02bdbba825781968fe672ca969e9919be7c3a319 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 0f2deb4bcbbb207b7757cd183896ceb3dc7b2ff9..a37ead563271a9b2171b1095eb7589151abe1777 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 89102720fa0fddb00e1dfdf2297ebe4a6ca1950a..e22b1e53e80482149fc19d7c65d5930a71a352d8 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 1b0f82fa24a199432435376fda60868ccb632bfe..d94b706854f3a5e8314fd254bb1998bbb66f6fec 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,
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 7929051791672ca1fedb91aba8795ef28c26bf77..67ad99a4d72ee3a5eb6d2d82661f1a421dec38e4 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
@@ -676,14 +678,14 @@ 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 5c534a6f436f87de32cb4511e807e3ee6aa2ae4f..2f5bcafe40394d24a28bfd66915bb98436652f2f 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 ec3e68e9709378fd4ce5f45c668f90250aebff31..b44d1e43c85c770b5f7d3d76c5d29d556be2e93d 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 404ce7a452c60ec552e483d471b2f9d633f26995..9858717d134a5ba5a357e940eed7ca3cfffe8970 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 39573fc8c9b5138c7d72154527bf3d4c0fea772f..08811520cfefc76b023d4cf6dd96c221bd0f0001 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 6aa788c4609cdfceb1fec08c412e38cd507cf0f6..bb278fb155c79de55e965eaace32dcf4a0407524 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 4edc6a0d3f2a06a17a0768e90b3a69bfd7d15348..897d47a9ad981951e756d58f46545d9c8f42ef4c 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 4de81617b1dd8bf38b617b86e1e67d1158729463..5d3d43623d9d2368b7527c12dcf165f012f0943c 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 e748c2275d589c6702576b72ac49038e86033c93..a24a3b04b87ccf57f365143781b98226b4c256a0 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 2955986feced55d1a2f3ec00f174b5b793560f8c..22465272f3722200d699195b44ac6007f5f15f77 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 e51c5b30a3daba19b3407c6a7ec1191f486747c6..47478631a11f0289282250fe9ba4d00622479236 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 19a025a32973aceada0d25abbf5a5dd4d390ec70..6d2cda97b04ebb5c9e08b2ee33c6450a2499a449 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 e1d68ef59296af77c6d6e1b09bbd2690ce6e7444..b5afe8cd23ca8e22abc5eb86714d2c4bd88bc08d 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 e0eb02ce815ca0e4c7da0ac1a8ef1fd2fe4ce0af..58452d96574c9b2d78dc148b14d91e0a2b50769e 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 b4451fc7b8e5666a9c93c8e89d27ebb08b224543..61e63c60d56e3b8e46bfa6fd1599863cfd0143be 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 fcd2e9798295596b48966c4b4b2529f4730a7ea3..55dcb3742c9677829a900779686142a4f3e6559d 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 212ef6506f6f5092307be9797a091c1bff7bbed3..39dc7bb19afeed7bd87a230f7a9b9ac23221675a 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 d52b3d8284d1510b1ba6d2b3c7adf00865a6e740..40734aab49f93107b512f1affe324baaefb08509 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 a8db37ded14331b2b2b45d3d7e8fb90be364a4d1..dca98c6c059883fa84c93041e8b477952d8bb59e 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 f1d86c0221fea897133068e17e8bf37e1048e5c2..50dfdbdf5ae9b5aac467c533da11b587c5f8f637 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 fcd1b518d099b366e2532f700282fd1c658aee1a..58d47a201dd31fab5fa7f0cb32a0065746f8f27d 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 c5bf2acc9ec95d1ee11ed85d7ff2cff68f5ac405..88f41be8d3dd2df802076b9c642ed6e8cf00a471 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 058bc2a2e53a0ce19200192c59861739b55a39d7..b7b87250b98ee86adf9adc3abe2b6cac733f9bf9 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 1c8b51b8bc1e15f3f4e79c457beb814639a04bf2..362cea5e3e462a1c27687ee55ee0c26358cd3e5a 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 fe56960cbf952c4d7ac47969cec31fcbd0ae66ed..aa7f52cafbf37f5f3e45a286652b8308fee82216 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 a25b37a2a96a9eb2eb567c2cfadad22e7e062582..bddb3bb7350bc43af2b958f15ddc5f523de3eb4a 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 3f7858d2dedc311cdbf70017d10d0c17cd173dbc..5f1253100b33888834b65f2d6f238412b65dded8 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 0000000000000000000000000000000000000000..aa4e7512354d30506a80c5674056732620d8ad34
--- /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/MapOutputTrackerSuite.scala b/core/src/test/scala/org/apache/spark/MapOutputTrackerSuite.scala
index 271dc905bc386d6dcc2313c759d934bdf6f5ab2e..10b8b441fde18c14adcf55ea3a95100a4e0356f7 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 288aa14eeb03b2bcc7be42bd751716260bb90a11..c650ef4ed58cb3b015accb3951c90f7bc88d5c41 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 0000000000000000000000000000000000000000..ef5936dd2f588101491b078fa9f83d104a0c75e3
--- /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 ab81bfbe559479ce688fa024944bd982f445730e..8d7546085f489767b313c7f96f83e74b3f0b9ffe 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 7181333adfe689461d7ac93d131c8dd5dd4f4872..71a2c6c498eef7244b8fab582dc533ec14bd9aa1 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/scheduler/ClusterSchedulerSuite.scala b/core/src/test/scala/org/apache/spark/scheduler/ClusterSchedulerSuite.scala
index 702edb862fea97e317d6d74a218692b5700d088b..7bf2020fe378eae92a89355d20005efc9c822ec9 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 706d84a58b5630a80ea2bf23ea01e197ffa3f43a..2aa259daf38b30b5e8670c70e5a4e95bc0cb6cf9 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 d0bd20fc83ea8cadb803ad06e51e707d068bf90a..5cc48ee00a8990bbe5a97ba7812e2a6c0af9cdfc 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 ca97f7d2a5be6fa979e2f7f656834ed0bef80ac7..4b52d9651ebe82cbc8ff640e09445d89e0bd4299 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 3dcb01ae5e44630d0daef407e883a3992430b63c..1eec6726f48bc0900c64c97b0bb9edaaa641705f 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 636e3ab91310321d251786fadae143b385f13b72..3898583275557ac0089b69b06b3056605d34e0cf 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 5b4d63b954cc850ff9b0bd8c2f08e6612edd2607..a0fc3445be42227d70027838998cbf6b11366928 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 070982e798f343b2bdbdeaf6c663b0e0b2575873..af4b31d53c26c6ba27b3e9bbbf1d54ca99c6ae5d 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 5aff26f9fcb29a55bcde8c92a09f4139624a2af7..11ebdc352bd74ae2f85bf300bdf9316d044406dc 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 02067f9750f489d43dfb16d33273cdcb9b86e0db..11d18f0ac20939968302e99d071171a2f58d542c 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 677d182e50b3d8d4a32bf8672784ce1689236bd0..567aba07f0e914820435c3da99183630dbdac6e6 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 ccb529eababd8ab79be308facbf9c7aa0cf67abf..119bb5dba7a81fc31d4985ad665b3264db17ac1b 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 -webkit-focus-ring-color;outline-offset:-2px}a:hover,a:active{outline:0}sub,sup{position:relative;font-size:75%;line-height:0;vertical-align:baseline}sup{top:-0.5em}sub{bottom:-0.25em}img{height:auto;max-width:100%;vertical-align:middle;border:0;-ms-interpolation-mode:bicubic}#map_canvas img{max-width:none}button,input,select,textarea{margin:0;font-size:100%;vertical-align:middle}button,input{*overflow:visible;line-height:normal}button::-moz-focus-inner,input::-moz-focus-inner{padding:0;border:0}button,input[type="button"],input[type="reset"],input[type="submit"]{cursor:pointer;-webkit-appearance:button}input[type="search"]{-webkit-box-sizing:content-box;-moz-box-sizing:content-box;box-sizing:content-box;-webkit-appearance:textfield}input[type="search"]::-webkit-search-decoration,input[type="search"]::-webkit-search-cancel-button{-webkit-appearance:none}textarea{overflow:auto;vertical-align:top}.clearfix{*zoom:1}.clearfix:before,.clearfix:after{display:table;line-height:0;content:""}.clearfix:after{clear:both}.hide-text{font:0/0 a;color:transparent;text-shadow:none;background-color:transparent;border:0}.input-block-level{display:block;width:100%;min-height:30px;-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}body{margin:0;font-family:"Helvetica Neue",Helvetica,Arial,sans-serif;font-size:14px;line-height:20px;color:#333;background-color:#fff}a{color:#08c;text-decoration:none}a:hover{color:#005580;text-decoration:underline}.img-rounded{-webkit-border-radius:6px;-moz-border-radius:6px;border-radius:6px}.img-polaroid{padding:4px;background-color:#fff;border:1px solid #ccc;border:1px solid rgba(0,0,0,0.2);-webkit-box-shadow:0 1px 3px rgba(0,0,0,0.1);-moz-box-shadow:0 1px 3px rgba(0,0,0,0.1);box-shadow:0 1px 3px 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diff --git a/docs/job-scheduling.md b/docs/job-scheduling.md
index dbcb9ae343357f648637eb3487819725beb304bd..5951155fe3c0e3cc76003a488ac0ecd6c470df12 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 5ed0474477302b2768bdd59e6a66afea867a126c..0d5eb7065e9f0a9ad7d12d5171b75310836346cb 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 55e39b1de17a06c4c4a9e025769c66b0977cffb8..96f93e24fe41dbab13dc049f8581334459cd5005 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 8f782db5b822b5a60a22c94be84b01cbb5bf9eeb..bdf127d40467b0f6034a091bb86dbb60bb41f619 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 322ff585f18786809faee52542604ad9b3903c78..68259f0cb8ad13a6bcc7e835871434e1982e6dd0 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 56d2a3a4a020282b935b9563dc43455367b6e6b8..1db255ca534577c64c49c3e9951bb22a36bdb0b9 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 b822265b5a0cc4479e3540752b745aadeae8cfbc..f7f0b789081d4005fcb140dda775032ed5286efa 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 82f42e0b8d16a74e2d3e9b4881a6e6bfdd75c93a..6f97db6a3e73395407c7b98e1697296311b32d97 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 a4be18816928e7fe08c24b76186af63131ed03cd..bbb870085c249ec4703b95640dda533ce153ce01 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 72b5c7b88e19bb8c8e8dd4b08c3d7e32c07b02e5..4c0de469645ab401a45fa2ef4f096da3cee58229 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 ddf6855325e732529fa13eb1781643dacde7e162..2cf273a702d24be266eb54f3a1ea59c381c39cc5 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 50e3f9639cd5b81d5fa20d94ab9f30ca2367102d..2402409e6ef87552dc3e8835584180cfa199f9f7 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 36853acab514dd631d31f9b881ff57654bd5a0be..8b27ecf82c06d7686873da088bbf3b3de84dffe0 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 eeeca3ea8a33e4f77562383dfef50a7507628894..91e35e2d34e2ef4d5db0b96a04139e0daa9971ee 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
@@ -45,7 +45,7 @@ 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 = _
@@ -61,13 +61,13 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e
private var amClient: AMRMClient[ContainerRequest] = _
// Default to numWorkers * 2, with minimum of 3
- private val maxNumWorkerFailures = System.getProperty("spark.yarn.max.worker.failures",
+ private val maxNumWorkerFailures = conf.getOrElse("spark.yarn.max.worker.failures",
math.max(args.numWorkers * 2, 3).toString()).toInt
def run() {
// Setup the directories so things go to YARN approved directories rather
// than user specified and /tmp.
- System.setProperty("spark.local.dir", getLocalDirs())
+ conf.set("spark.local.dir", getLocalDirs())
// Use priority 30 as it's higher then HDFS. It's same priority as MapReduce is using.
ShutdownHookManager.get().addShutdownHook(new AppMasterShutdownHook(this), 30)
@@ -81,12 +81,12 @@ 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()
@@ -99,7 +99,7 @@ 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 +119,7 @@ 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())
@@ -128,20 +128,20 @@ 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
+ val numTries = conf.getOrElse("spark.yarn.applicationMaster.waitTries", "10").toInt
while (!driverUp && tries < numTries) {
- 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")
try {
val socket = new Socket(driverHost, driverPort.toInt)
socket.close()
@@ -199,7 +199,7 @@ 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 = conf.getOrElse("spark.yarn.ApplicationMaster.waitTries", "10").toInt
while (ApplicationMaster.sparkContextRef.get() == null && numTries < maxNumTries) {
logInfo("Waiting for Spark context initialization ... " + numTries)
numTries = numTries + 1
@@ -214,7 +214,7 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e
yarnConf,
amClient,
appAttemptId,
- args,
+ args,
sparkContext.preferredNodeLocationData)
} else {
logWarning("Unable to retrieve SparkContext inspite of waiting for %d, maxNumTries = %d".
@@ -265,7 +265,7 @@ 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
+ conf.getOrElse("spark.yarn.scheduler.heartbeat.interval-ms", "5000").toLong
// must be <= timeoutInterval / 2.
val interval = math.min(timeoutInterval / 2, schedulerInterval)
@@ -314,11 +314,11 @@ 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 +338,12 @@ 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 = conf.getOrElse("spark.yarn.preserve.staging.files", "false").toBoolean
if (!preserveFiles) {
stagingDirPath = new Path(System.getenv("SPARK_YARN_STAGING_DIR"))
if (stagingDirPath == null) {
@@ -359,7 +359,7 @@ 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 +415,18 @@ 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 9fdee29498c71e740074eb35e87462df7daef2f8..1bba6a5ae4ac86ccb2b3986e5704b3bdc3825353 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
import org.apache.spark.util.Utils
import org.apache.spark.deploy.SparkHadoopUtil
@@ -150,7 +150,7 @@ 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 +221,7 @@ 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 +244,7 @@ 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 = conf.getOrElse("spark.yarn.submit.file.replication", "3").toShort
if (UserGroupInformation.isSecurityEnabled()) {
val dstFs = dst.getFileSystem(conf)
@@ -269,7 +269,7 @@ 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 +283,7 @@ 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 +295,7 @@ 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 +307,7 @@ 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 +317,7 @@ 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 +406,11 @@ 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 +436,8 @@ 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 = new SparkConf().getOrElse("spark.yarn.report.interval", "1000").toLong
while (true) {
Thread.sleep(interval)
@@ -460,7 +460,7 @@ 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 +497,25 @@ 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 = conf.getOrElse("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 70be15d0a3999021636d93ef13f1683b9e1945cb..1a9bb97b3ececa8606feb06afd6bbb2cd846f7af 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
@@ -35,7 +35,7 @@ class ClientArguments(val args: Array[String]) {
var workerMemory = 1024 // MB
var workerCores = 1
var numWorkers = 2
- var amQueue = System.getProperty("QUEUE", "default")
+ var amQueue = conf.getOrElse("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 bc31bb2eb09013651f23b2fa52ce017ea1f82bdc..f7d73f0d83f474894c1f9a1bb21cda4788e41cf3 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
@@ -136,8 +136,8 @@ class WorkerLauncher(args: ApplicationMasterArguments, conf: Configuration) exte
Thread.sleep(100)
}
}
- System.setProperty("spark.driver.host", driverHost)
- System.setProperty("spark.driver.port", driverPort.toString)
+ conf.set("spark.driver.host", driverHost)
+ conf.set("spark.driver.port", driverPort.toString)
val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format(
driverHost, driverPort.toString, CoarseGrainedSchedulerBackend.ACTOR_NAME)
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 784a3112de374644101f142fe96a347638c5108d..abc3447746f9e27d9db43cc7cd12e078f12b941c 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
@@ -254,8 +254,8 @@ 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"),
+ conf.get("spark.driver.host"),
+ conf.get("spark.driver.port"),
CoarseGrainedSchedulerBackend.ACTOR_NAME)
logInfo("Launching container %s for on host %s".format(containerId, workerHostname))
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 350fc760a428a5733eeeadf474b341f9a2341ccb..4b69f5078b0ab10818f1d6ecd5d9655cd00327c8 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 b3b5fc788f791628eee2e70a3efe6ed4e0e8f909..2eef2dfa5e72aceaeef01695d5d56eb236314f81 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 0b42e729f8dcc756c711584de2b2a4f071b480c5..95a6af09748065a3e1939e8662a818dcc9211d44 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 1f35f6f939d8e70c16ec3ebaff5d0b70df59c7bc..2b2c3a061a71d49b0bc5f9815be1bfa2d90cb191 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 dfdaba274f43a86f97c66f17fdb6553f1a24676a..43f40f8783bfd426cb94f031527cc11ee43059c7 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 0000000000000000000000000000000000000000..d72aed6a30ec1eeb75ec2b6ced790283b210a378
--- /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 108f36576a0fb39d2c4a503a2ceab8e02cd32187..f955aad7a4f12bc21c0aabc4fd2b9ffa58caf576 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 eb79135b9d0c542f3e77c2029b73c9930c9958d7..d8ca9fce0037b4bc33653e7dbf9513973edf546b 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/run-tests b/python/run-tests
index d4dad672d284299a0a1fc116b24f7833eefe1b7b..4b71fff7c12711c856abc95dd322561f6604ecb4 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 523fd1222dd71dff1770f1488c21ceb73617b9f6..f108c70f215f5d7655f105388c8ff515e83d6a42 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 e1455ef8a110a14317a03aab916254e41b4f8511..59fdb0b37a7663de7469767cb791322c0a31bbae 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 4960a85b972fec8edcd30ef0501da9382c4c9cb0..ca0115f90e49eb48b8dbaea9529ad63f1bafe01d 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 8ebe09da0dcb72ad16491894893776a2a4f5f080..00671ba5206f95382c021c220c081cea484656ab 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/StreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/StreamingContext.scala
index 659da2665ac6dfe4cd5676178b37cc8d64051686..304986f1879368a341498364d1f54c526cc2a1a8 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/JavaStreamingContext.scala b/streaming/src/main/scala/org/apache/spark/streaming/api/java/JavaStreamingContext.scala
index aad0d931e7bdec605cab7beaf9c18cd7dec4aee7..b79173c6aa9dbd03c68f61aaad3137f77aec0a03 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
/**
@@ -123,6 +124,14 @@ class JavaStreamingContext(val ssc: StreamingContext) {
def this(sparkContext: JavaSparkContext, batchDuration: Duration) =
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
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 8c7f42306d58a3e9b8644833388d2ca3cec7f6ce..27d474c0a0459a3aa556ba8e7bea3bb967396dbc 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/scheduler/JobGenerator.scala b/streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobGenerator.scala
index 2eb3b3989e2dba0834528dab31244631890256c1..5f8be93a9851846aa14063d8a0fe6678c5cf067a 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 488cc2f401d25c1073a21fc4cd6b95aa014841d0..9304fc1a9338d6b7336a607ccbd09346fcc6ce9b 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 fc8655a0839d0ece4e4cd079e8befc726eb6e3c6..6585d494a6f86e04503a0c4c85283aa3031e6462 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 daeb99f5b7bf32f24f0db3079768a15876791e23..d53d4336933d705fe74817c9907d8f6dbee98a80 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 b35ca00b53d71cbbfbcb513cdf31e77d87205ff2..ee6b433d1f1fa2d05a33991cad2d7cdf0b81a7c5 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 4e25c9566c0632ab5f2b4706aa2c5a319cafcd5a..8dc80ac2edfa060f8f572adfb705ad1ad63e7139 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 5fa14ad7c489a19d5b2ae3cc6726cbcb43ed005f..5185954521205b8c0c8a644d0c7faf40e4aa83bc 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 e969e91d13e9ad762a92e777b751e8d7abd12560..33464bc3a1c76bb6545e6f5156f173231e41be16 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 6b4aaefcdf90f02ffaa26e5ed2faa6c5e1f565b0..c92c34d49bd367da19f36cc0f16f64bb38970436 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 {
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 240ed8b32ae93b7099b7d83adb2fcbf600a444b8..dc9228180f60a1b1564a9ec1d13fa6fc28a6a26c 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
@@ -43,7 +43,7 @@ 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 = _
@@ -58,17 +58,17 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e
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",
+ private val maxNumWorkerFailures = conf.getOrElse("spark.yarn.max.worker.failures",
math.max(args.numWorkers * 2, 3).toString()).toInt
def run() {
// Setup the directories so things go to yarn approved directories rather
// then user specified and /tmp.
- System.setProperty("spark.local.dir", getLocalDirs())
+ conf.set("spark.local.dir", getLocalDirs())
// 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()
@@ -92,11 +92,11 @@ 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()
@@ -105,11 +105,11 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e
// 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 +129,7 @@ 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 +138,7 @@ 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,
@@ -146,29 +146,29 @@ 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
+ val numTries = conf.getOrElse("spark.yarn.applicationMaster.waitTries", "10").toInt
while(!driverUp && tries < numTries) {
- 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")
try {
val socket = new Socket(driverHost, driverPort.toInt)
socket.close()
@@ -226,7 +226,7 @@ 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 = conf.getOrElse("spark.yarn.ApplicationMaster.waitTries", "10").toInt
while (ApplicationMaster.sparkContextRef.get() == null && count < numTries) {
logInfo("Waiting for spark context initialization ... " + count)
count = count + 1
@@ -241,8 +241,8 @@ class ApplicationMaster(args: ApplicationMasterArguments, conf: Configuration) e
yarnConf,
resourceManager,
appAttemptId,
- args,
- sparkContext.preferredNodeLocationData)
+ args,
+ sparkContext.preferredNodeLocationData)
} else {
logWarning("Unable to retrieve sparkContext inspite of waiting for %d, numTries = %d".
format(count * waitTime, numTries))
@@ -294,7 +294,7 @@ 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
+ conf.getOrElse("spark.yarn.scheduler.heartbeat.interval-ms", "5000").toLong
// must be <= timeoutInterval / 2.
val interval = math.min(timeoutInterval / 2, schedulerInterval)
@@ -342,11 +342,11 @@ 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 +372,12 @@ 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 = conf.getOrElse("spark.yarn.preserve.staging.files", "false").toBoolean
if (!preserveFiles) {
stagingDirPath = new Path(System.getenv("SPARK_YARN_STAGING_DIR"))
if (stagingDirPath == null) {
@@ -393,7 +393,7 @@ 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 +446,18 @@ 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 79dd03806523bcea066fcafd05160fe732032d22..595a7ee8c3d83c329e07268a05675d4aac646812 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
import org.apache.spark.util.Utils
import org.apache.spark.deploy.SparkHadoopUtil
@@ -59,7 +59,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
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 +103,7 @@ 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 +130,7 @@ 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 +146,7 @@ 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 +207,7 @@ 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 +230,7 @@ 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 = conf.getOrElse("spark.yarn.submit.file.replication", "3").toShort
if (UserGroupInformation.isSecurityEnabled()) {
val dstFs = dst.getFileSystem(conf)
@@ -241,7 +241,7 @@ 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 +253,7 @@ 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 +265,7 @@ 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 +277,7 @@ 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 +289,7 @@ 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 +299,7 @@ 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 +354,7 @@ 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 +387,11 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
javaCommand = Environment.JAVA_HOME.$() + "/bin/java"
}
- val commands = List[String](javaCommand +
+ val commands = List[String](javaCommand +
" -server " +
JAVA_OPTS +
" " + args.amClass +
- " --class " + args.userClass +
+ " --class " + args.userClass +
" --jar " + args.userJar +
userArgsToString(args) +
" --worker-memory " + args.workerMemory +
@@ -421,9 +421,11 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
super.submitApplication(appContext)
}
- def monitorApplication(appId: ApplicationId): Boolean = {
+ def monitorApplication(appId: ApplicationId): Boolean = {
+ val interval = new SparkConf().getOrElse("spark.yarn.report.interval", "1000").toLong
+
while (true) {
- Thread.sleep(1000)
+ Thread.sleep(interval)
val report = super.getApplicationReport(appId)
logInfo("Application report from ASM: \n" +
@@ -443,7 +445,7 @@ class Client(conf: Configuration, args: ClientArguments) extends YarnClientImpl
val state = report.getYarnApplicationState()
val dsStatus = report.getFinalApplicationStatus()
- if (state == YarnApplicationState.FINISHED ||
+ if (state == YarnApplicationState.FINISHED ||
state == YarnApplicationState.FAILED ||
state == YarnApplicationState.KILLED) {
return true
@@ -479,25 +481,25 @@ object Client {
Apps.addToEnvironment(env, Environment.CLASSPATH.name, Environment.PWD.$())
// 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 = conf.getOrElse("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 b3a7886d9322688966a2722a33b3bd8f786d9949..e9e46a193b145deae0194228e51afd1128ee5056 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
@@ -33,7 +33,7 @@ class ClientArguments(val args: Array[String]) {
var workerMemory = 1024
var workerCores = 1
var numWorkers = 2
- var amQueue = System.getProperty("QUEUE", "default")
+ var amQueue = conf.getOrElse("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 69038844bbe4324d3f28cb1a902c99a7c147b087..c1e79cbe665a5835aa935960b777f3975da8f194 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
@@ -162,8 +162,8 @@ class WorkerLauncher(args: ApplicationMasterArguments, conf: Configuration) exte
Thread.sleep(100)
}
}
- System.setProperty("spark.driver.host", driverHost)
- System.setProperty("spark.driver.port", driverPort.toString)
+ conf.set("spark.driver.host", driverHost)
+ conf.set("spark.driver.port", driverPort.toString)
val driverUrl = "akka.tcp://spark@%s:%s/user/%s".format(
driverHost, driverPort.toString, CoarseGrainedSchedulerBackend.ACTOR_NAME)
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 a01657c9fa89cc9fcca08bf13f45363d42be76bd..5966a0f7577572e277277f56c41928b4ce93d59b 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
@@ -239,7 +239,7 @@ 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"),
+ conf.get("spark.driver.host"), conf.get("spark.driver.port"),
CoarseGrainedSchedulerBackend.ACTOR_NAME)
logInfo("launching container on " + containerId + " host " + workerHostname)
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 350fc760a428a5733eeeadf474b341f9a2341ccb..4b69f5078b0ab10818f1d6ecd5d9655cd00327c8 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](