Merge branch 'master' of github.com:apache/spark into dag-viz-skipped

core/src/main/scala/org/apache/spark/executor/CoarseGrainedExecutorBackend.scala

@@ -33,7 +33,7 @@ import org.apache.spark.deploy.worker.WorkerWatcher
 import org.apache.spark.scheduler.TaskDescription
 import org.apache.spark.scheduler.cluster.CoarseGrainedClusterMessages._
 import org.apache.spark.serializer.SerializerInstance
-import org.apache.spark.util.{SignalLogger, Utils}
+import org.apache.spark.util.{ThreadUtils, SignalLogger, Utils}
 
 private[spark] class CoarseGrainedExecutorBackend(
     override val rpcEnv: RpcEnv,
@@ -55,18 +55,19 @@ private[spark] class CoarseGrainedExecutorBackend(
   private[this] val ser: SerializerInstance = env.closureSerializer.newInstance()
 
   override def onStart() {
-    import scala.concurrent.ExecutionContext.Implicits.global
     logInfo("Connecting to driver: " + driverUrl)
     rpcEnv.asyncSetupEndpointRefByURI(driverUrl).flatMap { ref =>
+      // This is a very fast action so we can use "ThreadUtils.sameThread"
       driver = Some(ref)
       ref.ask[RegisteredExecutor.type](
         RegisterExecutor(executorId, self, hostPort, cores, extractLogUrls))
-    } onComplete {
+    }(ThreadUtils.sameThread).onComplete {
+      // This is a very fast action so we can use "ThreadUtils.sameThread"
       case Success(msg) => Utils.tryLogNonFatalError {
         Option(self).foreach(_.send(msg)) // msg must be RegisteredExecutor
       }
       case Failure(e) => logError(s"Cannot register with driver: $driverUrl", e)
-    }
+    }(ThreadUtils.sameThread)
   }
 
   def extractLogUrls: Map[String, String] = {

core/src/main/scala/org/apache/spark/io/CompressionCodec.scala

@@ -17,7 +17,7 @@
 
 package org.apache.spark.io
 
-import java.io.{InputStream, OutputStream}
+import java.io.{IOException, InputStream, OutputStream}
 
 import com.ning.compress.lzf.{LZFInputStream, LZFOutputStream}
 import net.jpountz.lz4.{LZ4BlockInputStream, LZ4BlockOutputStream}
@@ -154,8 +154,53 @@ class SnappyCompressionCodec(conf: SparkConf) extends CompressionCodec {
 
   override def compressedOutputStream(s: OutputStream): OutputStream = {
     val blockSize = conf.getSizeAsBytes("spark.io.compression.snappy.blockSize", "32k").toInt
-    new SnappyOutputStream(s, blockSize)
+    new SnappyOutputStreamWrapper(new SnappyOutputStream(s, blockSize))
   }
 
   override def compressedInputStream(s: InputStream): InputStream = new SnappyInputStream(s)
 }
+
+/**
+ * Wrapper over [[SnappyOutputStream]] which guards against write-after-close and double-close
+ * issues. See SPARK-7660 for more details. This wrapping can be removed if we upgrade to a version
+ * of snappy-java that contains the fix for https://github.com/xerial/snappy-java/issues/107.
+ */
+private final class SnappyOutputStreamWrapper(os: SnappyOutputStream) extends OutputStream {
+
+  private[this] var closed: Boolean = false
+
+  override def write(b: Int): Unit = {
+    if (closed) {
+      throw new IOException("Stream is closed")
+    }
+    os.write(b)
+  }
+
+  override def write(b: Array[Byte]): Unit = {
+    if (closed) {
+      throw new IOException("Stream is closed")
+    }
+    os.write(b)
+  }
+
+  override def write(b: Array[Byte], off: Int, len: Int): Unit = {
+    if (closed) {
+      throw new IOException("Stream is closed")
+    }
+    os.write(b, off, len)
+  }
+
+  override def flush(): Unit = {
+    if (closed) {
+      throw new IOException("Stream is closed")
+    }
+    os.flush()
+  }
+
+  override def close(): Unit = {
+    if (!closed) {
+      closed = true
+      os.close()
+    }
+  }
+}

core/src/main/scala/org/apache/spark/rdd/AsyncRDDActions.scala

@@ -19,8 +19,10 @@ package org.apache.spark.rdd
 
 import java.util.concurrent.atomic.AtomicLong
 
+import org.apache.spark.util.ThreadUtils
+
 import scala.collection.mutable.ArrayBuffer
-import scala.concurrent.ExecutionContext.Implicits.global
+import scala.concurrent.ExecutionContext
 import scala.reflect.ClassTag
 
 import org.apache.spark.{ComplexFutureAction, FutureAction, Logging}
@@ -66,6 +68,8 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi
     val f = new ComplexFutureAction[Seq[T]]
 
     f.run {
+      // This is a blocking action so we should use "AsyncRDDActions.futureExecutionContext" which
+      // is a cached thread pool.
       val results = new ArrayBuffer[T](num)
       val totalParts = self.partitions.length
       var partsScanned = 0
@@ -101,7 +105,7 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi
         partsScanned += numPartsToTry
       }
       results.toSeq
-    }
+    }(AsyncRDDActions.futureExecutionContext)
 
     f
   }
@@ -123,3 +127,8 @@ class AsyncRDDActions[T: ClassTag](self: RDD[T]) extends Serializable with Loggi
       (index, data) => Unit, Unit)
   }
 }
+
+private object AsyncRDDActions {
+  val futureExecutionContext = ExecutionContext.fromExecutorService(
+    ThreadUtils.newDaemonCachedThreadPool("AsyncRDDActions-future", 128))
+}

core/src/main/scala/org/apache/spark/scheduler/TaskResult.scala

@@ -40,6 +40,9 @@ class DirectTaskResult[T](var valueBytes: ByteBuffer, var accumUpdates: Map[Long
     var metrics: TaskMetrics)
   extends TaskResult[T] with Externalizable {
 
+  private var valueObjectDeserialized = false
+  private var valueObject: T = _
+
   def this() = this(null.asInstanceOf[ByteBuffer], null, null)
 
   override def writeExternal(out: ObjectOutput): Unit = Utils.tryOrIOException {
@@ -72,10 +75,26 @@ class DirectTaskResult[T](var valueBytes: ByteBuffer, var accumUpdates: Map[Long
       }
     }
     metrics = in.readObject().asInstanceOf[TaskMetrics]
+    valueObjectDeserialized = false
   }
 
+  /**
+   * When `value()` is called at the first time, it needs to deserialize `valueObject` from
+   * `valueBytes`. It may cost dozens of seconds for a large instance. So when calling `value` at
+   * the first time, the caller should avoid to block other threads.
+   *
+   * After the first time, `value()` is trivial and just returns the deserialized `valueObject`.
+   */
   def value(): T = {
-    val resultSer = SparkEnv.get.serializer.newInstance()
-    resultSer.deserialize(valueBytes)
+    if (valueObjectDeserialized) {
+      valueObject
+    } else {
+      // This should not run when holding a lock because it may cost dozens of seconds for a large
+      // value.
+      val resultSer = SparkEnv.get.serializer.newInstance()
+      valueObject = resultSer.deserialize(valueBytes)
+      valueObjectDeserialized = true
+      valueObject
+    }
   }
 }

core/src/main/scala/org/apache/spark/scheduler/TaskResultGetter.scala

@@ -54,6 +54,10 @@ private[spark] class TaskResultGetter(sparkEnv: SparkEnv, scheduler: TaskSchedul
               if (!taskSetManager.canFetchMoreResults(serializedData.limit())) {
                 return
               }
+              // deserialize "value" without holding any lock so that it won't block other threads.
+              // We should call it here, so that when it's called again in
+              // "TaskSetManager.handleSuccessfulTask", it does not need to deserialize the value.
+              directResult.value()
               (directResult, serializedData.limit())
             case IndirectTaskResult(blockId, size) =>
               if (!taskSetManager.canFetchMoreResults(size)) {

core/src/main/scala/org/apache/spark/scheduler/TaskSetManager.scala

@@ -620,6 +620,12 @@ private[spark] class TaskSetManager(
     val index = info.index
     info.markSuccessful()
     removeRunningTask(tid)
+    // This method is called by "TaskSchedulerImpl.handleSuccessfulTask" which holds the
+    // "TaskSchedulerImpl" lock until exiting. To avoid the SPARK-7655 issue, we should not
+    // "deserialize" the value when holding a lock to avoid blocking other threads. So we call
+    // "result.value()" in "TaskResultGetter.enqueueSuccessfulTask" before reaching here.
+    // Note: "result.value()" only deserializes the value when it's called at the first time, so
+    // here "result.value()" just returns the value and won't block other threads.
     sched.dagScheduler.taskEnded(
       tasks(index), Success, result.value(), result.accumUpdates, info, result.metrics)
     if (!successful(index)) {

core/src/main/scala/org/apache/spark/storage/BlockManager.scala

@@ -21,8 +21,7 @@ import java.io.{BufferedOutputStream, ByteArrayOutputStream, File, InputStream,
 import java.nio.{ByteBuffer, MappedByteBuffer}
 
 import scala.collection.mutable.{ArrayBuffer, HashMap}
-import scala.concurrent.{Await, Future}
-import scala.concurrent.ExecutionContext.Implicits.global
+import scala.concurrent.{ExecutionContext, Await, Future}
 import scala.concurrent.duration._
 import scala.util.Random
 
@@ -77,6 +76,9 @@ private[spark] class BlockManager(
 
   private val blockInfo = new TimeStampedHashMap[BlockId, BlockInfo]
 
+  private val futureExecutionContext = ExecutionContext.fromExecutorService(
+    ThreadUtils.newDaemonCachedThreadPool("block-manager-future", 128))
+
   // Actual storage of where blocks are kept
   private var externalBlockStoreInitialized = false
   private[spark] val memoryStore = new MemoryStore(this, maxMemory)
@@ -266,11 +268,13 @@ private[spark] class BlockManager(
     asyncReregisterLock.synchronized {
       if (asyncReregisterTask == null) {
         asyncReregisterTask = Future[Unit] {
+          // This is a blocking action and should run in futureExecutionContext which is a cached
+          // thread pool
           reregister()
           asyncReregisterLock.synchronized {
             asyncReregisterTask = null
           }
-        }
+        }(futureExecutionContext)
       }
     }
   }
@@ -744,7 +748,11 @@ private[spark] class BlockManager(
       case b: ByteBufferValues if putLevel.replication > 1 =>
         // Duplicate doesn't copy the bytes, but just creates a wrapper
         val bufferView = b.buffer.duplicate()
-        Future { replicate(blockId, bufferView, putLevel) }
+        Future {
+          // This is a blocking action and should run in futureExecutionContext which is a cached
+          // thread pool
+          replicate(blockId, bufferView, putLevel)
+        }(futureExecutionContext)
       case _ => null
     }
 
@@ -1218,6 +1226,7 @@ private[spark] class BlockManager(
     }
     metadataCleaner.cancel()
     broadcastCleaner.cancel()
+    futureExecutionContext.shutdownNow()
     logInfo("BlockManager stopped")
   }
 }

core/src/main/scala/org/apache/spark/storage/BlockManagerMaster.scala

@@ -17,13 +17,14 @@
 
 package org.apache.spark.storage
 
+import scala.collection.Iterable
+import scala.collection.generic.CanBuildFrom
 import scala.concurrent.{Await, Future}
-import scala.concurrent.ExecutionContext.Implicits.global
 
 import org.apache.spark.rpc.RpcEndpointRef
 import org.apache.spark.{Logging, SparkConf, SparkException}
 import org.apache.spark.storage.BlockManagerMessages._
-import org.apache.spark.util.RpcUtils
+import org.apache.spark.util.{ThreadUtils, RpcUtils}
 
 private[spark]
 class BlockManagerMaster(
@@ -102,8 +103,8 @@ class BlockManagerMaster(
     val future = driverEndpoint.askWithRetry[Future[Seq[Int]]](RemoveRdd(rddId))
     future.onFailure {
       case e: Exception =>
-        logWarning(s"Failed to remove RDD $rddId - ${e.getMessage}}")
-    }
+        logWarning(s"Failed to remove RDD $rddId - ${e.getMessage}}", e)
+    }(ThreadUtils.sameThread)
     if (blocking) {
       Await.result(future, timeout)
     }
@@ -114,8 +115,8 @@ class BlockManagerMaster(
     val future = driverEndpoint.askWithRetry[Future[Seq[Boolean]]](RemoveShuffle(shuffleId))
     future.onFailure {
       case e: Exception =>
-        logWarning(s"Failed to remove shuffle $shuffleId - ${e.getMessage}}")
-    }
+        logWarning(s"Failed to remove shuffle $shuffleId - ${e.getMessage}}", e)
+    }(ThreadUtils.sameThread)
     if (blocking) {
       Await.result(future, timeout)
     }
@@ -128,8 +129,8 @@ class BlockManagerMaster(
     future.onFailure {
       case e: Exception =>
         logWarning(s"Failed to remove broadcast $broadcastId" +
-          s" with removeFromMaster = $removeFromMaster - ${e.getMessage}}")
-    }
+          s" with removeFromMaster = $removeFromMaster - ${e.getMessage}}", e)
+    }(ThreadUtils.sameThread)
     if (blocking) {
       Await.result(future, timeout)
     }
@@ -169,11 +170,17 @@ class BlockManagerMaster(
     val response = driverEndpoint.
       askWithRetry[Map[BlockManagerId, Future[Option[BlockStatus]]]](msg)
     val (blockManagerIds, futures) = response.unzip
-    val result = Await.result(Future.sequence(futures), timeout)
-    if (result == null) {
+    implicit val sameThread = ThreadUtils.sameThread
+    val cbf =
+      implicitly[
+        CanBuildFrom[Iterable[Future[Option[BlockStatus]]],
+        Option[BlockStatus],
+        Iterable[Option[BlockStatus]]]]
+    val blockStatus = Await.result(
+      Future.sequence[Option[BlockStatus], Iterable](futures)(cbf, ThreadUtils.sameThread), timeout)
+    if (blockStatus == null) {
       throw new SparkException("BlockManager returned null for BlockStatus query: " + blockId)
     }
-    val blockStatus = result.asInstanceOf[Iterable[Option[BlockStatus]]]
     blockManagerIds.zip(blockStatus).flatMap { case (blockManagerId, status) =>
       status.map { s => (blockManagerId, s) }
     }.toMap

core/src/test/java/org/apache/spark/shuffle/unsafe/UnsafeShuffleWriterSuite.java

@@ -35,7 +35,6 @@
 import org.mockito.MockitoAnnotations;
 import org.mockito.invocation.InvocationOnMock;
 import org.mockito.stubbing.Answer;
-import org.xerial.snappy.buffer.CachedBufferAllocator;
 import static org.hamcrest.MatcherAssert.assertThat;
 import static org.hamcrest.Matchers.greaterThan;
 import static org.hamcrest.Matchers.lessThan;
@@ -97,13 +96,6 @@ public OutputStream apply(OutputStream stream) {
   @After
   public void tearDown() {
     Utils.deleteRecursively(tempDir);
-    // This call is a workaround for SPARK-7660, a snappy-java bug which is exposed by this test
-    // suite. Clearing the cached buffer allocator's pool of reusable buffers masks this bug,
-    // preventing a test failure in JavaAPISuite that would otherwise occur. The underlying bug
-    // needs to be fixed, but in the meantime this workaround avoids spurious Jenkins failures.
-    synchronized (CachedBufferAllocator.class) {
-      CachedBufferAllocator.queueTable.clear();
-    }
     final long leakedMemory = taskMemoryManager.cleanUpAllAllocatedMemory();
     if (leakedMemory != 0) {
       fail("Test leaked " + leakedMemory + " bytes of managed memory");

ec2/spark_ec2.py

@@ -48,7 +48,7 @@
     from urllib.request import urlopen, Request
     from urllib.error import HTTPError
 
-SPARK_EC2_VERSION = "1.2.1"
+SPARK_EC2_VERSION = "1.3.1"
 SPARK_EC2_DIR = os.path.dirname(os.path.realpath(__file__))
 
 VALID_SPARK_VERSIONS = set([
@@ -65,6 +65,8 @@
     "1.1.1",
     "1.2.0",
     "1.2.1",
+    "1.3.0",
+    "1.3.1",
 ])
 
 SPARK_TACHYON_MAP = {
@@ -75,6 +77,8 @@
     "1.1.1": "0.5.0",
     "1.2.0": "0.5.0",
     "1.2.1": "0.5.0",
+    "1.3.0": "0.5.0",
+    "1.3.1": "0.5.0",
 }
 
 DEFAULT_SPARK_VERSION = SPARK_EC2_VERSION

examples/src/main/java/org/apache/spark/examples/sql/JavaSparkSQL.java

@@ -94,7 +94,7 @@ public String call(Row row) {
 
     System.out.println("=== Data source: Parquet File ===");
     // DataFrames can be saved as parquet files, maintaining the schema information.
-    schemaPeople.saveAsParquetFile("people.parquet");
+    schemaPeople.write().parquet("people.parquet");
 
     // Read in the parquet file created above.
     // Parquet files are self-describing so the schema is preserved.
@@ -151,7 +151,7 @@ public String call(Row row) {
     List<String> jsonData = Arrays.asList(
           "{\"name\":\"Yin\",\"address\":{\"city\":\"Columbus\",\"state\":\"Ohio\"}}");
     JavaRDD<String> anotherPeopleRDD = ctx.parallelize(jsonData);
-    DataFrame peopleFromJsonRDD = sqlContext.jsonRDD(anotherPeopleRDD.rdd());
+    DataFrame peopleFromJsonRDD = sqlContext.read().json(anotherPeopleRDD.rdd());
 
     // Take a look at the schema of this new DataFrame.
     peopleFromJsonRDD.printSchema();