Merge pull request #10 from apache/master

Update

Author: YanTangZhai

LICENSE

@@ -754,7 +754,7 @@ SUCH DAMAGE.
 
 
 ========================================================================
-For Timsort (core/src/main/java/org/apache/spark/util/collection/Sorter.java):
+For Timsort (core/src/main/java/org/apache/spark/util/collection/TimSort.java):
 ========================================================================
 Copyright (C) 2008 The Android Open Source Project
 
@@ -771,6 +771,25 @@ See the License for the specific language governing permissions and
 limitations under the License.
 
 
+========================================================================
+For LimitedInputStream
+  (network/common/src/main/java/org/apache/spark/network/util/LimitedInputStream.java):
+========================================================================
+Copyright (C) 2007 The Guava Authors
+
+Licensed 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.
+
+
 ========================================================================
 BSD-style licenses
 ========================================================================

README.md

@@ -13,7 +13,8 @@ and Spark Streaming for stream processing.
 ## Online Documentation
 
 You can find the latest Spark documentation, including a programming
-guide, on the [project web page](http://spark.apache.org/documentation.html).
+guide, on the [project web page](http://spark.apache.org/documentation.html)
+and [project wiki](https://cwiki.apache.org/confluence/display/SPARK).
 This README file only contains basic setup instructions.
 
 ## Building Spark

core/src/main/resources/org/apache/spark/ui/static/additional-metrics.js

@@ -39,6 +39,8 @@ $(function() {
         var column = "table ." + $(this).attr("name");
         $(column).hide();
     });
+    // Stripe table rows after rows have been hidden to ensure correct striping.
+    stripeTables();
 
     $("input:checkbox").click(function() {
         var column = "table ." + $(this).attr("name");

core/src/main/resources/org/apache/spark/ui/static/table.js

@@ -28,8 +28,3 @@ function stripeTables() {
         });
     });
 }
-
-/* Stripe all tables after pages finish loading. */
-$(function() {
-    stripeTables();
-});

core/src/main/resources/org/apache/spark/ui/static/webui.css

@@ -120,6 +120,20 @@ pre {
   border: none;
 }
 
+.stacktrace-details {
+  max-height: 300px;
+  overflow-y: auto;
+  margin: 0;
+  transition: max-height 0.5s ease-out, padding 0.5s ease-out;
+}
+
+.stacktrace-details.collapsed {
+  max-height: 0;
+  padding-top: 0;
+  padding-bottom: 0;
+  border: none;
+}
+
 span.expand-additional-metrics {
   cursor: pointer;
 }

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

@@ -66,7 +66,6 @@ private[spark] class ExecutorAllocationManager(sc: SparkContext) extends Logging
   // 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)
-  verifyBounds()
 
   // How long there must be backlogged tasks for before an addition is triggered
   private val schedulerBacklogTimeout = conf.getLong(
@@ -77,9 +76,14 @@ private[spark] class ExecutorAllocationManager(sc: SparkContext) extends Logging
     "spark.dynamicAllocation.sustainedSchedulerBacklogTimeout", schedulerBacklogTimeout)
 
   // How long an executor must be idle for before it is removed
-  private val removeThresholdSeconds = conf.getLong(
+  private val executorIdleTimeout = conf.getLong(
     "spark.dynamicAllocation.executorIdleTimeout", 600)
 
+  // During testing, the methods to actually kill and add executors are mocked out
+  private val testing = conf.getBoolean("spark.dynamicAllocation.testing", false)
+
+  validateSettings()
+
   // Number of executors to add in the next round
   private var numExecutorsToAdd = 1
 
@@ -103,17 +107,14 @@ private[spark] class ExecutorAllocationManager(sc: SparkContext) extends Logging
   // Polling loop interval (ms)
   private val intervalMillis: Long = 100
 
-  // Whether we are testing this class. This should only be used internally.
-  private val testing = conf.getBoolean("spark.dynamicAllocation.testing", false)
-
   // Clock used to schedule when executors should be added and removed
   private var clock: Clock = new RealClock
 
   /**
-   * Verify that the lower and upper bounds on the number of executors are valid.
+   * Verify that the settings specified through the config are valid.
    * If not, throw an appropriate exception.
    */
-  private def verifyBounds(): Unit = {
+  private def validateSettings(): Unit = {
     if (minNumExecutors < 0 || maxNumExecutors < 0) {
       throw new SparkException("spark.dynamicAllocation.{min/max}Executors must be set!")
     }
@@ -124,6 +125,22 @@ private[spark] class ExecutorAllocationManager(sc: SparkContext) extends Logging
       throw new SparkException(s"spark.dynamicAllocation.minExecutors ($minNumExecutors) must " +
         s"be less than or equal to spark.dynamicAllocation.maxExecutors ($maxNumExecutors)!")
     }
+    if (schedulerBacklogTimeout <= 0) {
+      throw new SparkException("spark.dynamicAllocation.schedulerBacklogTimeout must be > 0!")
+    }
+    if (sustainedSchedulerBacklogTimeout <= 0) {
+      throw new SparkException(
+        "spark.dynamicAllocation.sustainedSchedulerBacklogTimeout must be > 0!")
+    }
+    if (executorIdleTimeout <= 0) {
+      throw new SparkException("spark.dynamicAllocation.executorIdleTimeout must be > 0!")
+    }
+    // Require external shuffle service for dynamic allocation
+    // Otherwise, we may lose shuffle files when killing executors
+    if (!conf.getBoolean("spark.shuffle.service.enabled", false) && !testing) {
+      throw new SparkException("Dynamic allocation of executors requires the external " +
+        "shuffle service. You may enable this through spark.shuffle.service.enabled.")
+    }
   }
 
   /**
@@ -254,7 +271,7 @@ private[spark] class ExecutorAllocationManager(sc: SparkContext) extends Logging
     val removeRequestAcknowledged = testing || sc.killExecutor(executorId)
     if (removeRequestAcknowledged) {
       logInfo(s"Removing executor $executorId because it has been idle for " +
-        s"$removeThresholdSeconds seconds (new desired total will be ${numExistingExecutors - 1})")
+        s"$executorIdleTimeout seconds (new desired total will be ${numExistingExecutors - 1})")
       executorsPendingToRemove.add(executorId)
       true
     } else {
@@ -329,8 +346,8 @@ private[spark] class ExecutorAllocationManager(sc: SparkContext) extends Logging
   private def onExecutorIdle(executorId: String): Unit = synchronized {
     if (!removeTimes.contains(executorId) && !executorsPendingToRemove.contains(executorId)) {
       logDebug(s"Starting idle timer for $executorId because there are no more tasks " +
-        s"scheduled to run on the executor (to expire in $removeThresholdSeconds seconds)")
-      removeTimes(executorId) = clock.getTimeMillis + removeThresholdSeconds * 1000
+        s"scheduled to run on the executor (to expire in $executorIdleTimeout seconds)")
+      removeTimes(executorId) = clock.getTimeMillis + executorIdleTimeout * 1000
     }
   }
 

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

@@ -22,6 +22,7 @@ import java.net.{Authenticator, PasswordAuthentication}
 import org.apache.hadoop.io.Text
 
 import org.apache.spark.deploy.SparkHadoopUtil
+import org.apache.spark.network.sasl.SecretKeyHolder
 
 /**
  * Spark class responsible for security.
@@ -84,7 +85,7 @@ import org.apache.spark.deploy.SparkHadoopUtil
  *            Authenticator installed in the SecurityManager to how it does the authentication
  *            and in this case gets the user name and password from the request.
  *
- *  - ConnectionManager -> The Spark ConnectionManager uses java nio to asynchronously
+ *  - BlockTransferService -> The Spark BlockTransferServices uses java nio to asynchronously
  *            exchange messages.  For this we use the Java SASL
  *            (Simple Authentication and Security Layer) API and again use DIGEST-MD5
  *            as the authentication mechanism. This means the shared secret is not passed
@@ -98,7 +99,7 @@ import org.apache.spark.deploy.SparkHadoopUtil
  *            of protection they want. If we support those, the messages will also have to
  *            be wrapped and unwrapped via the SaslServer/SaslClient.wrap/unwrap API's.
  *
- *            Since the connectionManager does asynchronous messages passing, the SASL
+ *            Since the NioBlockTransferService does asynchronous messages passing, the SASL
  *            authentication is a bit more complex. A ConnectionManager can be both a client
  *            and a Server, so for a particular connection is has to determine what to do.
  *            A ConnectionId was added to be able to track connections and is used to
@@ -107,6 +108,10 @@ import org.apache.spark.deploy.SparkHadoopUtil
  *            and waits for the response from the server and does the handshake before sending
  *            the real message.
  *
+ *            The NettyBlockTransferService ensures that SASL authentication is performed
+ *            synchronously prior to any other communication on a connection. This is done in
+ *            SaslClientBootstrap on the client side and SaslRpcHandler on the server side.
+ *
  *  - HTTP for the Spark UI -> the UI was changed to use servlets so that javax servlet filters
  *            can be used. Yarn requires a specific AmIpFilter be installed for security to work
  *            properly. For non-Yarn deployments, users can write a filter to go through a
@@ -139,7 +144,7 @@ import org.apache.spark.deploy.SparkHadoopUtil
  *  can take place.
  */
 
-private[spark] class SecurityManager(sparkConf: SparkConf) extends Logging {
+private[spark] class SecurityManager(sparkConf: SparkConf) extends Logging with SecretKeyHolder {
 
   // key used to store the spark secret in the Hadoop UGI
   private val sparkSecretLookupKey = "sparkCookie"
@@ -337,4 +342,8 @@ private[spark] class SecurityManager(sparkConf: SparkConf) extends Logging {
    * @return the secret key as a String if authentication is enabled, otherwise returns null
    */
   def getSecretKey(): String = secretKey
+
+  // Default SecurityManager only has a single secret key, so ignore appId.
+  override def getSaslUser(appId: String): String = getSaslUser()
+  override def getSecretKey(appId: String): String = getSecretKey()
 }

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

@@ -217,6 +217,12 @@ class SparkConf(loadDefaults: Boolean) extends Cloneable with Logging {
      */
     getAll.filter { case (k, _) => isAkkaConf(k) }
 
+  /**
+   * Returns the Spark application id, valid in the Driver after TaskScheduler registration and
+   * from the start in the Executor.
+   */
+  def getAppId: String = get("spark.app.id")
+
   /** Does the configuration contain a given parameter? */
   def contains(key: String): Boolean = settings.contains(key)
 

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

@@ -313,6 +313,8 @@ class SparkContext(config: SparkConf) extends SparkStatusAPI with Logging {
   val applicationId: String = taskScheduler.applicationId()
   conf.set("spark.app.id", applicationId)
 
+  env.blockManager.initialize(applicationId)
+
   val metricsSystem = env.metricsSystem
 
   // The metrics system for Driver need to be set spark.app.id to app ID.
@@ -558,6 +560,8 @@ class SparkContext(config: SparkConf) extends SparkStatusAPI with Logging {
 
 
   /**
+   * :: Experimental ::
+   *
    * Get an RDD for a Hadoop-readable dataset as PortableDataStream for each file
    * (useful for binary data)
    *
@@ -600,6 +604,8 @@ class SparkContext(config: SparkConf) extends SparkStatusAPI with Logging {
   }
 
   /**
+   * :: Experimental ::
+   *
    * Load data from a flat binary file, assuming the length of each record is constant.
    *
    * @param path Directory to the input data files

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

@@ -276,7 +276,7 @@ object SparkEnv extends Logging {
     val blockTransferService =
       conf.get("spark.shuffle.blockTransferService", "netty").toLowerCase match {
         case "netty" =>
-          new NettyBlockTransferService(conf)
+          new NettyBlockTransferService(conf, securityManager)
         case "nio" =>
           new NioBlockTransferService(conf, securityManager)
       }
@@ -285,8 +285,9 @@ object SparkEnv extends Logging {
       "BlockManagerMaster",
       new BlockManagerMasterActor(isLocal, conf, listenerBus)), conf, isDriver)
 
+    // NB: blockManager is not valid until initialize() is called later.
     val blockManager = new BlockManager(executorId, actorSystem, blockManagerMaster,
-      serializer, conf, mapOutputTracker, shuffleManager, blockTransferService)
+      serializer, conf, mapOutputTracker, shuffleManager, blockTransferService, securityManager)
 
     val broadcastManager = new BroadcastManager(isDriver, conf, securityManager)