Merge branch 'master' of github.com:apache/spark into rest

Conflicts:
	core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala

README.md

@@ -26,7 +26,7 @@ To build Spark and its example programs, run:
 
 (You do not need to do this if you downloaded a pre-built package.)
 More detailed documentation is available from the project site, at
-["Building Spark with Maven"](http://spark.apache.org/docs/latest/building-spark.html).
+["Building Spark"](http://spark.apache.org/docs/latest/building-spark.html).
 
 ## Interactive Scala Shell
 

bin/compute-classpath.sh

@@ -50,8 +50,8 @@ fi
 if [ -n "$SPARK_PREPEND_CLASSES" ]; then
   echo "NOTE: SPARK_PREPEND_CLASSES is set, placing locally compiled Spark"\
     "classes ahead of assembly." >&2
+  # Spark classes
   CLASSPATH="$CLASSPATH:$FWDIR/core/target/scala-$SPARK_SCALA_VERSION/classes"
-  CLASSPATH="$CLASSPATH:$FWDIR/core/target/jars/*"
   CLASSPATH="$CLASSPATH:$FWDIR/repl/target/scala-$SPARK_SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/mllib/target/scala-$SPARK_SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/bagel/target/scala-$SPARK_SCALA_VERSION/classes"
@@ -63,6 +63,8 @@ if [ -n "$SPARK_PREPEND_CLASSES" ]; then
   CLASSPATH="$CLASSPATH:$FWDIR/sql/hive/target/scala-$SPARK_SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/sql/hive-thriftserver/target/scala-$SPARK_SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/yarn/stable/target/scala-$SPARK_SCALA_VERSION/classes"
+  # Jars for shaded deps in their original form (copied here during build)
+  CLASSPATH="$CLASSPATH:$FWDIR/core/target/jars/*"
 fi
 
 # Use spark-assembly jar from either RELEASE or assembly directory

core/pom.xml

@@ -94,22 +94,35 @@
       <groupId>org.apache.curator</groupId>
       <artifactId>curator-recipes</artifactId>
     </dependency>
+
+    <!-- Jetty dependencies promoted to compile here so they are shaded
+         and inlined into spark-core jar -->
     <dependency>
       <groupId>org.eclipse.jetty</groupId>
       <artifactId>jetty-plus</artifactId>
+      <scope>compile</scope>
     </dependency>
     <dependency>
       <groupId>org.eclipse.jetty</groupId>
       <artifactId>jetty-security</artifactId>
+      <scope>compile</scope>
     </dependency>
     <dependency>
       <groupId>org.eclipse.jetty</groupId>
       <artifactId>jetty-util</artifactId>
+      <scope>compile</scope>
     </dependency>
     <dependency>
       <groupId>org.eclipse.jetty</groupId>
       <artifactId>jetty-server</artifactId>
+      <scope>compile</scope>
     </dependency>
+    <dependency>
+      <groupId>org.eclipse.jetty</groupId>
+      <artifactId>jetty-http</artifactId>
+      <scope>compile</scope>
+    </dependency>
+
     <dependency>
       <groupId>org.apache.commons</groupId>
       <artifactId>commons-lang3</artifactId>
@@ -356,19 +369,24 @@
         <groupId>org.apache.maven.plugins</groupId>
         <artifactId>maven-dependency-plugin</artifactId>
         <executions>
+          <!-- When using SPARK_PREPEND_CLASSES Spark classes compiled locally don't use
+               shaded deps. So here we store jars in their original form which are added
+               when the classpath is computed. -->
           <execution>
             <id>copy-dependencies</id>
             <phase>package</phase>
             <goals>
               <goal>copy-dependencies</goal>
             </goals>
-            <configuration>
+           <configuration>
               <outputDirectory>${project.build.directory}</outputDirectory>
               <overWriteReleases>false</overWriteReleases>
               <overWriteSnapshots>false</overWriteSnapshots>
               <overWriteIfNewer>true</overWriteIfNewer>
               <useSubDirectoryPerType>true</useSubDirectoryPerType>
-              <includeArtifactIds>guava</includeArtifactIds>
+              <includeArtifactIds>
+                guava,jetty-io,jetty-http,jetty-plus,jetty-util,jetty-server
+              </includeArtifactIds>
               <silent>true</silent>
             </configuration>
           </execution>

core/src/main/scala/org/apache/spark/ExecutorAllocationManager.scala

@@ -49,6 +49,7 @@ import org.apache.spark.scheduler._
  *   spark.dynamicAllocation.enabled - Whether this feature is enabled
  *   spark.dynamicAllocation.minExecutors - Lower bound on the number of executors
  *   spark.dynamicAllocation.maxExecutors - Upper bound on the number of executors
+ *   spark.dynamicAllocation.initialExecutors - Number of executors to start with
  *
  *   spark.dynamicAllocation.schedulerBacklogTimeout (M) -
  *     If there are backlogged tasks for this duration, add new executors
@@ -70,9 +71,10 @@ private[spark] class ExecutorAllocationManager(
 
   import ExecutorAllocationManager._
 
-  // Lower and upper bounds on the number of executors. These are required.
-  private val minNumExecutors = conf.getInt("spark.dynamicAllocation.minExecutors", -1)
-  private val maxNumExecutors = conf.getInt("spark.dynamicAllocation.maxExecutors", -1)
+  // Lower and upper bounds on the number of executors.
+  private val minNumExecutors = conf.getInt("spark.dynamicAllocation.minExecutors", 0)
+  private val maxNumExecutors = conf.getInt("spark.dynamicAllocation.maxExecutors",
+    Integer.MAX_VALUE)
 
   // How long there must be backlogged tasks for before an addition is triggered
   private val schedulerBacklogTimeout = conf.getLong(
@@ -132,10 +134,10 @@ private[spark] class ExecutorAllocationManager(
    */
   private def validateSettings(): Unit = {
     if (minNumExecutors < 0 || maxNumExecutors < 0) {
-      throw new SparkException("spark.dynamicAllocation.{min/max}Executors must be set!")
+      throw new SparkException("spark.dynamicAllocation.{min/max}Executors must be positive!")
     }
-    if (minNumExecutors == 0 || maxNumExecutors == 0) {
-      throw new SparkException("spark.dynamicAllocation.{min/max}Executors cannot be 0!")
+    if (maxNumExecutors == 0) {
+      throw new SparkException("spark.dynamicAllocation.maxExecutors cannot be 0!")
     }
     if (minNumExecutors > maxNumExecutors) {
       throw new SparkException(s"spark.dynamicAllocation.minExecutors ($minNumExecutors) must " +

core/src/main/scala/org/apache/spark/deploy/PythonRunner.scala

@@ -26,7 +26,7 @@ import org.apache.spark.api.python.PythonUtils
 import org.apache.spark.util.{RedirectThread, Utils}
 
 /**
- * A main class used by spark-submit to launch Python applications. It executes python as a
+ * A main class used to launch Python applications. It executes python as a
  * subprocess and then has it connect back to the JVM to access system properties, etc.
  */
 object PythonRunner {

core/src/main/scala/org/apache/spark/deploy/SparkHadoopUtil.scala

@@ -141,7 +141,7 @@ class SparkHadoopUtil extends Logging {
       val baselineBytesRead = f()
       Some(() => f() - baselineBytesRead)
     } catch {
-      case e: NoSuchMethodException => {
+      case e @ (_: NoSuchMethodException | _: ClassNotFoundException) => {
         logDebug("Couldn't find method for retrieving thread-level FileSystem input data", e)
         None
       }
@@ -163,7 +163,7 @@ class SparkHadoopUtil extends Logging {
       val baselineBytesWritten = f()
       Some(() => f() - baselineBytesWritten)
     } catch {
-      case e: NoSuchMethodException => {
+      case e @ (_: NoSuchMethodException | _: ClassNotFoundException) => {
         logDebug("Couldn't find method for retrieving thread-level FileSystem output data", e)
         None
       }

core/src/main/scala/org/apache/spark/deploy/SparkSubmit.scala

@@ -23,6 +23,8 @@ import java.net.URL
 
 import scala.collection.mutable.{ArrayBuffer, HashMap, Map}
 
+import org.apache.hadoop.fs.Path
+
 import org.apache.spark.deploy.rest._
 import org.apache.spark.executor.ExecutorURLClassLoader
 import org.apache.spark.util.Utils
@@ -203,21 +205,38 @@ object SparkSubmit {
       }
     }
 
+    val isYarnCluster = clusterManager == YARN && deployMode == CLUSTER
+
+    // Require all python files to be local, so we can add them to the PYTHONPATH
+    // In YARN cluster mode, python files are distributed as regular files, which can be non-local
+    if (args.isPython && !isYarnCluster) {
+      if (Utils.nonLocalPaths(args.primaryResource).nonEmpty) {
+        printErrorAndExit(s"Only local python files are supported: $args.primaryResource")
+      }
+      val nonLocalPyFiles = Utils.nonLocalPaths(args.pyFiles).mkString(",")
+      if (nonLocalPyFiles.nonEmpty) {
+        printErrorAndExit(s"Only local additional python files are supported: $nonLocalPyFiles")
+      }
+    }
+
     // The following modes are not supported or applicable
     (clusterManager, deployMode) match {
       case (MESOS, CLUSTER) =>
         printErrorAndExit("Cluster deploy mode is currently not supported for Mesos clusters.")
-      case (_, CLUSTER) if args.isPython =>
-        printErrorAndExit("Cluster deploy mode is currently not supported for python applications.")
+      case (STANDALONE, CLUSTER) if args.isPython =>
+        printErrorAndExit("Cluster deploy mode is currently not supported for python " +
+          "applications on standalone clusters.")
       case (_, CLUSTER) if isShell(args.primaryResource) =>
         printErrorAndExit("Cluster deploy mode is not applicable to Spark shells.")
       case (_, CLUSTER) if isSqlShell(args.mainClass) =>
         printErrorAndExit("Cluster deploy mode is not applicable to Spark SQL shell.")
+      case (_, CLUSTER) if isThriftServer(args.mainClass) =>
+        printErrorAndExit("Cluster deploy mode is not applicable to Spark Thrift server.")
       case _ =>
     }
 
     // If we're running a python app, set the main class to our specific python runner
-    if (args.isPython) {
+    if (args.isPython && deployMode == CLIENT) {
       if (args.primaryResource == PYSPARK_SHELL) {
         args.mainClass = "py4j.GatewayServer"
         args.childArgs = ArrayBuffer("--die-on-broken-pipe", "0")
@@ -234,6 +253,13 @@ object SparkSubmit {
       }
     }
 
+    // In yarn-cluster mode for a python app, add primary resource and pyFiles to files
+    // that can be distributed with the job
+    if (args.isPython && isYarnCluster) {
+      args.files = mergeFileLists(args.files, args.primaryResource)
+      args.files = mergeFileLists(args.files, args.pyFiles)
+    }
+
     // Special flag to avoid deprecation warnings at the client
     sysProps("SPARK_SUBMIT") = "true"
 
@@ -311,7 +337,6 @@ object SparkSubmit {
     // Add the application jar automatically so the user doesn't have to call sc.addJar
     // For YARN cluster mode, the jar is already distributed on each node as "app.jar"
     // For python files, the primary resource is already distributed as a regular file
-    val isYarnCluster = clusterManager == YARN && deployMode == CLUSTER
     if (!isYarnCluster && !args.isPython) {
       var jars = sysProps.get("spark.jars").map(x => x.split(",").toSeq).getOrElse(Seq.empty)
       if (isUserJar(args.primaryResource)) {
@@ -337,10 +362,22 @@ object SparkSubmit {
     // In yarn-cluster mode, use yarn.Client as a wrapper around the user class
     if (isYarnCluster) {
       childMainClass = "org.apache.spark.deploy.yarn.Client"
-      if (args.primaryResource != SPARK_INTERNAL) {
-        childArgs += ("--jar", args.primaryResource)
+      if (args.isPython) {
+        val mainPyFile = new Path(args.primaryResource).getName
+        childArgs += ("--primary-py-file", mainPyFile)
+        if (args.pyFiles != null) {
+          // These files will be distributed to each machine's working directory, so strip the
+          // path prefix
+          val pyFilesNames = args.pyFiles.split(",").map(p => (new Path(p)).getName).mkString(",")
+          childArgs += ("--py-files", pyFilesNames)
+        }
+        childArgs += ("--class", "org.apache.spark.deploy.PythonRunner")
+      } else {
+        if (args.primaryResource != SPARK_INTERNAL) {
+          childArgs += ("--jar", args.primaryResource)
+        }
+        childArgs += ("--class", args.mainClass)
       }
-      childArgs += ("--class", args.mainClass)
       if (args.childArgs != null) {
         args.childArgs.foreach { arg => childArgs += ("--arg", arg) }
       }
@@ -504,6 +541,13 @@ object SparkSubmit {
     mainClass == "org.apache.spark.sql.hive.thriftserver.SparkSQLCLIDriver"
   }
 
+  /**
+   * Return whether the given main class represents a thrift server.
+   */
+  private[spark] def isThriftServer(mainClass: String): Boolean = {
+    mainClass == "org.apache.spark.sql.hive.thriftserver.HiveThriftServer2"
+  }
+
   /**
    * Return whether the given primary resource requires running python.
    */

core/src/main/scala/org/apache/spark/deploy/SparkSubmitArguments.scala

@@ -207,18 +207,6 @@ private[spark] class SparkSubmitArguments(args: Seq[String], env: Map[String, St
       SparkSubmit.printErrorAndExit("--py-files given but primary resource is not a Python script")
     }
 
-    // Require all python files to be local, so we can add them to the PYTHONPATH
-    if (isPython) {
-      if (Utils.nonLocalPaths(primaryResource).nonEmpty) {
-        SparkSubmit.printErrorAndExit(s"Only local python files are supported: $primaryResource")
-      }
-      val nonLocalPyFiles = Utils.nonLocalPaths(pyFiles).mkString(",")
-      if (nonLocalPyFiles.nonEmpty) {
-        SparkSubmit.printErrorAndExit(
-          s"Only local additional python files are supported: $nonLocalPyFiles")
-      }
-    }
-
     if (master.startsWith("yarn")) {
       val hasHadoopEnv = env.contains("HADOOP_CONF_DIR") || env.contains("YARN_CONF_DIR")
       if (!hasHadoopEnv && !Utils.isTesting) {