Merge pull request apache#7 from apache/master

Update

assembly/pom.xml

@@ -88,6 +88,20 @@
 
   <build>
     <plugins>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-deploy-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-install-plugin</artifactId>
+        <configuration>
+          <skip>true</skip>
+        </configuration>
+      </plugin>
       <!-- Use the shade plugin to create a big JAR with all the dependencies -->
       <plugin>
         <groupId>org.apache.maven.plugins</groupId>

bin/compute-classpath.sh

@@ -43,6 +43,7 @@ if [ -n "$SPARK_PREPEND_CLASSES" ]; then
   echo "NOTE: SPARK_PREPEND_CLASSES is set, placing locally compiled Spark"\
     "classes ahead of assembly." >&2
   CLASSPATH="$CLASSPATH:$FWDIR/core/target/scala-$SCALA_VERSION/classes"
+  CLASSPATH="$CLASSPATH:$FWDIR/core/target/jars/*"
   CLASSPATH="$CLASSPATH:$FWDIR/repl/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/mllib/target/scala-$SCALA_VERSION/classes"
   CLASSPATH="$CLASSPATH:$FWDIR/bagel/target/scala-$SCALA_VERSION/classes"

bin/spark-class

@@ -105,7 +105,7 @@ else
     exit 1
   fi
 fi
-JAVA_VERSION=$("$RUNNER" -version 2>&1 | sed 's/java version "\(.*\)\.\(.*\)\..*"/\1\2/; 1q')
+JAVA_VERSION=$("$RUNNER" -version 2>&1 | sed 's/.* version "\(.*\)\.\(.*\)\..*"/\1\2/; 1q')
 
 # Set JAVA_OPTS to be able to load native libraries and to set heap size
 if [ "$JAVA_VERSION" -ge 18 ]; then

core/pom.xml

@@ -351,6 +351,33 @@
           </execution>
         </executions>
       </plugin>
+      <!--
+        Copy guava to the build directory. This is needed to make the SPARK_PREPEND_CLASSES
+        option work in compute-classpath.sh, since it would put the non-shaded Spark classes in
+        the runtime classpath.
+      -->
+      <plugin>
+        <groupId>org.apache.maven.plugins</groupId>
+        <artifactId>maven-dependency-plugin</artifactId>
+        <executions>
+          <execution>
+            <id>copy-dependencies</id>
+            <phase>package</phase>
+            <goals>
+              <goal>copy-dependencies</goal>
+            </goals>
+            <configuration>
+              <outputDirectory>${project.build.directory}</outputDirectory>
+              <overWriteReleases>false</overWriteReleases>
+              <overWriteSnapshots>false</overWriteSnapshots>
+              <overWriteIfNewer>true</overWriteIfNewer>
+              <useSubDirectoryPerType>true</useSubDirectoryPerType>
+              <includeArtifactIds>guava</includeArtifactIds>
+              <silent>true</silent>
+            </configuration>
+          </execution>
+        </executions>
+      </plugin>
     </plugins>
 
     <resources>

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

@@ -1072,11 +1072,8 @@ class SparkContext(config: SparkConf) extends Logging {
     val callSite = getCallSite
     val cleanedFunc = clean(func)
     logInfo("Starting job: " + callSite.shortForm)
-    val start = System.nanoTime
     dagScheduler.runJob(rdd, cleanedFunc, partitions, callSite, allowLocal,
       resultHandler, localProperties.get)
-    logInfo(
-      "Job finished: " + callSite.shortForm + ", took " + (System.nanoTime - start) / 1e9 + " s")
     rdd.doCheckpoint()
   }
 

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

@@ -108,6 +108,14 @@ class SparkEnv (
       pythonWorkers.get(key).foreach(_.stopWorker(worker))
     }
   }
+
+  private[spark]
+  def releasePythonWorker(pythonExec: String, envVars: Map[String, String], worker: Socket) {
+    synchronized {
+      val key = (pythonExec, envVars)
+      pythonWorkers.get(key).foreach(_.releaseWorker(worker))
+    }
+  }
 }
 
 object SparkEnv extends Logging {

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

@@ -21,7 +21,7 @@ import scala.collection.mutable.ArrayBuffer
 
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.executor.TaskMetrics
-import org.apache.spark.util.TaskCompletionListener
+import org.apache.spark.util.{TaskCompletionListenerException, TaskCompletionListener}
 
 
 /**
@@ -41,7 +41,7 @@ class TaskContext(
     val attemptId: Long,
     val runningLocally: Boolean = false,
     private[spark] val taskMetrics: TaskMetrics = TaskMetrics.empty)
-  extends Serializable {
+  extends Serializable with Logging {
 
   @deprecated("use partitionId", "0.8.1")
   def splitId = partitionId
@@ -103,8 +103,20 @@ class TaskContext(
   /** Marks the task as completed and triggers the listeners. */
   private[spark] def markTaskCompleted(): Unit = {
     completed = true
+    val errorMsgs = new ArrayBuffer[String](2)
     // Process complete callbacks in the reverse order of registration
-    onCompleteCallbacks.reverse.foreach { _.onTaskCompletion(this) }
+    onCompleteCallbacks.reverse.foreach { listener =>
+      try {
+        listener.onTaskCompletion(this)
+      } catch {
+        case e: Throwable =>
+          errorMsgs += e.getMessage
+          logError("Error in TaskCompletionListener", e)
+      }
+    }
+    if (errorMsgs.nonEmpty) {
+      throw new TaskCompletionListenerException(errorMsgs)
+    }
   }
 
   /** Marks the task for interruption, i.e. cancellation. */

core/src/main/scala/org/apache/spark/api/java/JavaSparkContext.scala

@@ -17,6 +17,7 @@
 
 package org.apache.spark.api.java
 
+import java.io.Closeable
 import java.util
 import java.util.{Map => JMap}
 
@@ -40,7 +41,9 @@ import org.apache.spark.rdd.{EmptyRDD, HadoopRDD, NewHadoopRDD, RDD}
  * 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 {
+class JavaSparkContext(val sc: SparkContext)
+  extends JavaSparkContextVarargsWorkaround with Closeable {
+
   /**
    * Create a JavaSparkContext that loads settings from system properties (for instance, when
    * launching with ./bin/spark-submit).
@@ -534,6 +537,8 @@ class JavaSparkContext(val sc: SparkContext) extends JavaSparkContextVarargsWork
     sc.stop()
   }
 
+  override def close(): Unit = stop()
+
   /**
    * Get Spark's home location from either a value set through the constructor,
    * or the spark.home Java property, or the SPARK_HOME environment variable

core/src/main/scala/org/apache/spark/api/python/PythonRDD.scala

@@ -23,6 +23,7 @@ import java.nio.charset.Charset
 import java.util.{List => JList, ArrayList => JArrayList, Map => JMap, Collections}
 
 import scala.collection.JavaConversions._
+import scala.collection.mutable
 import scala.language.existentials
 import scala.reflect.ClassTag
 import scala.util.{Try, Success, Failure}
@@ -52,6 +53,7 @@ private[spark] class PythonRDD(
   extends RDD[Array[Byte]](parent) {
 
   val bufferSize = conf.getInt("spark.buffer.size", 65536)
+  val reuse_worker = conf.getBoolean("spark.python.worker.reuse", true)
 
   override def getPartitions = parent.partitions
 
@@ -63,19 +65,26 @@ private[spark] class PythonRDD(
     val localdir = env.blockManager.diskBlockManager.localDirs.map(
       f => f.getPath()).mkString(",")
     envVars += ("SPARK_LOCAL_DIRS" -> localdir) // it's also used in monitor thread
+    if (reuse_worker) {
+      envVars += ("SPARK_REUSE_WORKER" -> "1")
+    }
     val worker: Socket = env.createPythonWorker(pythonExec, envVars.toMap)
 
     // Start a thread to feed the process input from our parent's iterator
     val writerThread = new WriterThread(env, worker, split, context)
 
+    var complete_cleanly = false
     context.addTaskCompletionListener { context =>
       writerThread.shutdownOnTaskCompletion()
-
-      // Cleanup the worker socket. This will also cause the Python worker to exit.
-      try {
-        worker.close()
-      } catch {
-        case e: Exception => logWarning("Failed to close worker socket", e)
+      if (reuse_worker && complete_cleanly) {
+        env.releasePythonWorker(pythonExec, envVars.toMap, worker)
+      } else {
+        try {
+          worker.close()
+        } catch {
+          case e: Exception =>
+            logWarning("Failed to close worker socket", e)
+        }
       }
     }
 
@@ -115,6 +124,10 @@ private[spark] class PythonRDD(
               val total = finishTime - startTime
               logInfo("Times: total = %s, boot = %s, init = %s, finish = %s".format(total, boot,
                 init, finish))
+              val memoryBytesSpilled = stream.readLong()
+              val diskBytesSpilled = stream.readLong()
+              context.taskMetrics.memoryBytesSpilled += memoryBytesSpilled
+              context.taskMetrics.diskBytesSpilled += diskBytesSpilled
               read()
             case SpecialLengths.PYTHON_EXCEPTION_THROWN =>
               // Signals that an exception has been thrown in python
@@ -133,6 +146,7 @@ private[spark] class PythonRDD(
                 stream.readFully(update)
                 accumulator += Collections.singletonList(update)
               }
+               complete_cleanly = true
               null
           }
         } catch {
@@ -195,29 +209,45 @@ private[spark] class PythonRDD(
           PythonRDD.writeUTF(include, dataOut)
         }
         // Broadcast variables
-        dataOut.writeInt(broadcastVars.length)
+        val oldBids = PythonRDD.getWorkerBroadcasts(worker)
+        val newBids = broadcastVars.map(_.id).toSet
+        // number of different broadcasts
+        val cnt = oldBids.diff(newBids).size + newBids.diff(oldBids).size
+        dataOut.writeInt(cnt)
+        for (bid <- oldBids) {
+          if (!newBids.contains(bid)) {
+            // remove the broadcast from worker
+            dataOut.writeLong(- bid - 1)  // bid >= 0
+            oldBids.remove(bid)
+          }
+        }
         for (broadcast <- broadcastVars) {
-          dataOut.writeLong(broadcast.id)
-          dataOut.writeInt(broadcast.value.length)
-          dataOut.write(broadcast.value)
+          if (!oldBids.contains(broadcast.id)) {
+            // send new broadcast
+            dataOut.writeLong(broadcast.id)
+            dataOut.writeInt(broadcast.value.length)
+            dataOut.write(broadcast.value)
+            oldBids.add(broadcast.id)
+          }
         }
         dataOut.flush()
         // Serialized command:
         dataOut.writeInt(command.length)
         dataOut.write(command)
         // Data values
         PythonRDD.writeIteratorToStream(parent.iterator(split, context), dataOut)
+        dataOut.writeInt(SpecialLengths.END_OF_DATA_SECTION)
         dataOut.flush()
       } catch {
         case e: Exception if context.isCompleted || context.isInterrupted =>
           logDebug("Exception thrown after task completion (likely due to cleanup)", e)
+          worker.shutdownOutput()
 
         case e: Exception =>
           // We must avoid throwing exceptions here, because the thread uncaught exception handler
           // will kill the whole executor (see org.apache.spark.executor.Executor).
           _exception = e
-      } finally {
-        Try(worker.shutdownOutput()) // kill Python worker process
+          worker.shutdownOutput()
       }
     }
   }
@@ -278,6 +308,14 @@ private object SpecialLengths {
 private[spark] object PythonRDD extends Logging {
   val UTF8 = Charset.forName("UTF-8")
 
+  // remember the broadcasts sent to each worker
+  private val workerBroadcasts = new mutable.WeakHashMap[Socket, mutable.Set[Long]]()
+  private def getWorkerBroadcasts(worker: Socket) = {
+    synchronized {
+      workerBroadcasts.getOrElseUpdate(worker, new mutable.HashSet[Long]())
+    }
+  }
+
   /**
    * Adapter for calling SparkContext#runJob from Python.
    *