Various style changes and a first test for the rate controller.

streaming/src/main/scala/org/apache/spark/streaming/dstream/InputDStream.scala

@@ -53,11 +53,17 @@ abstract class InputDStream[T: ClassTag] (@transient ssc_ : StreamingContext)
    * A rate estimator configured by the user to compute a dynamic ingestion bound for this stream.
    * @see `RateEstimator`
    */
-  protected [streaming] val rateEstimator = ssc.conf
-                                               .getOption("spark.streaming.RateEstimator")
-                                               .getOrElse("noop") match {
-                                                 case _ => new NoopRateEstimator()
-                                               }
+  protected [streaming] val rateEstimator = newEstimator()
+
+  /**
+   * Return the configured estimator, or `noop` if none was specified.
+   */
+  private def newEstimator() =
+    ssc.conf.get("spark.streaming.RateEstimator", "noop") match {
+      case "noop" => new NoopRateEstimator()
+      case estimator => throw new IllegalArgumentException(s"Unknown rate estimator: $estimator")
+    }
+
 
   // Keep track of the freshest rate for this stream using the rateEstimator
   protected[streaming] val rateController: RateController = new RateController(id, rateEstimator) {

streaming/src/main/scala/org/apache/spark/streaming/dstream/ReceiverInputDStream.scala

@@ -45,9 +45,9 @@ abstract class ReceiverInputDStream[T: ClassTag](@transient ssc_ : StreamingCont
    * Asynchronously maintains & sends new rate limits to the receiver through the receiver tracker.
    */
   override val rateController: RateController = new RateController(id, rateEstimator) {
-      override def publish(rate: Long): Unit =
-        ssc.scheduler.receiverTracker.sendRateUpdate(id, rate)
-    }
+    override def publish(rate: Long): Unit =
+      ssc.scheduler.receiverTracker.sendRateUpdate(id, rate)
+  }
 
   /**
    * Gets the receiver object that will be sent to the worker nodes

streaming/src/main/scala/org/apache/spark/streaming/scheduler/JobScheduler.scala

@@ -67,7 +67,7 @@ class JobScheduler(val ssc: StreamingContext) extends Logging {
     eventLoop.start()
 
     // Estimators receive updates from batch completion
-    ssc.graph.getInputStreams.map(_.rateController).foreach(ssc.addStreamingListener(_))
+    ssc.graph.getInputStreams.foreach(is => ssc.addStreamingListener(is.rateController))
     listenerBus.start(ssc.sparkContext)
     receiverTracker = new ReceiverTracker(ssc)
     inputInfoTracker = new InputInfoTracker(ssc)

streaming/src/main/scala/org/apache/spark/streaming/scheduler/RateController.scala

@@ -19,12 +19,12 @@ package org.apache.spark.streaming.scheduler
 
 import java.util.concurrent.atomic.AtomicLong
 
+import scala.concurrent.{ExecutionContext, Future}
+
 import org.apache.spark.annotation.DeveloperApi
 import org.apache.spark.streaming.scheduler.rate.RateEstimator
 import org.apache.spark.util.ThreadUtils
 
-import scala.concurrent.{ExecutionContext, Future}
-
 /**
  * :: DeveloperApi ::
  * A StreamingListener that receives batch completion updates, and maintains
@@ -38,32 +38,34 @@ private [streaming] abstract class RateController(val streamUID: Int, rateEstima
   protected def publish(rate: Long): Unit
 
   // Used to compute & publish the rate update asynchronously
-  @transient private val executionContext = ExecutionContext.fromExecutorService(
+  @transient
+  implicit private val executionContext = ExecutionContext.fromExecutorService(
     ThreadUtils.newDaemonSingleThreadExecutor("stream-rate-update"))
 
-  private val rateLimit : AtomicLong = new AtomicLong(-1L)
+  private val rateLimit: AtomicLong = new AtomicLong(-1L)
 
-  // Asynchronous computation of the rate update
+  /**
+   * Compute the new rate limit and publish it asynchronously.
+   */
   private def computeAndPublish(time: Long, elems: Long, workDelay: Long, waitDelay: Long): Unit =
     Future[Unit] {
       val newSpeed = rateEstimator.compute(time, elems, workDelay, waitDelay)
       newSpeed foreach { s =>
         rateLimit.set(s.toLong)
         publish(getLatestRate())
       }
-    } (executionContext)
+    }
 
   def getLatestRate(): Long = rateLimit.get()
 
-  override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted){
-      val elements = batchCompleted.batchInfo.streamIdToInputInfo
+  override def onBatchCompleted(batchCompleted: StreamingListenerBatchCompleted) {
+    val elements = batchCompleted.batchInfo.streamIdToInputInfo
 
-    for (
+    for {
       processingEnd <- batchCompleted.batchInfo.processingEndTime;
       workDelay <- batchCompleted.batchInfo.processingDelay;
       waitDelay <- batchCompleted.batchInfo.schedulingDelay;
       elems <- elements.get(streamUID).map(_.numRecords)
-    ) computeAndPublish(processingEnd, elems, workDelay, waitDelay)
+    } computeAndPublish(processingEnd, elems, workDelay, waitDelay)
   }
-
 }

streaming/src/main/scala/org/apache/spark/streaming/scheduler/rate/RateEstimator.scala

@@ -31,16 +31,27 @@ private[streaming] trait RateEstimator extends Serializable {
    * Computes the number of elements the stream attached to this `RateEstimator`
    * should ingest per second, given an update on the size and completion
    * times of the latest batch.
+   *
+   * @param time The timetamp of the current batch interval that just finished
+   * @param elements The number of elements that were processed in this batch
+   * @param processingDelay The time in ms that took for the job to complete
+   * @param schedulingDelay The time in ms that the job spent in the scheduling queue
    */
-  def compute(time: Long, elements: Long,
-      processingDelay: Long, schedulingDelay: Long): Option[Double]
+  def compute(
+      time: Long,
+      elements: Long,
+      processingDelay: Long,
+      schedulingDelay: Long): Option[Double]
 }
 
 /**
- *  The trivial rate estimator never sends an update
+ * The trivial rate estimator never sends an update
  */
 private[streaming] class NoopRateEstimator extends RateEstimator {
 
-  def compute(time: Long, elements: Long,
-      processingDelay: Long, schedulingDelay: Long): Option[Double] = None
+  def compute(
+      time: Long,
+      elements: Long,
+      processingDelay: Long,
+      schedulingDelay: Long): Option[Double] = None
 }

streaming/src/test/scala/org/apache/spark/streaming/scheduler/RateControllerSuite.scala

@@ -0,0 +1,78 @@
+/*
+ * 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.
+ */
+
+package org.apache.spark.streaming.scheduler
+
+import org.scalatest.concurrent.Eventually._
+import org.scalatest.time.SpanSugar._
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.rdd.RDD
+import org.apache.spark.streaming.{StreamingContext, TestOutputStreamWithPartitions, TestSuiteBase, Time}
+import org.apache.spark.streaming.dstream.InputDStream
+import org.apache.spark.streaming.scheduler.rate.RateEstimator
+
+class RateControllerSuite extends TestSuiteBase {
+
+  test("rate controller publishes updates") {
+    val ssc = new StreamingContext(conf, batchDuration)
+    val dstream = new MockRateLimitDStream(ssc)
+    val output = new TestOutputStreamWithPartitions(dstream)
+    output.register()
+    runStreams(ssc, 1, 1)
+
+    eventually(timeout(2.seconds)) {
+      assert(dstream.publishCalls === 1)
+    }
+  }
+}
+
+/**
+ * An InputDStream that counts how often its rate controller `publish` method was called.
+ */
+private class MockRateLimitDStream(@transient ssc: StreamingContext)
+    extends InputDStream[Int](ssc) {
+
+  @volatile
+  var publishCalls = 0
+
+  private object ConstantEstimator extends RateEstimator {
+    def compute(
+        time: Long,
+        elements: Long,
+        processingDelay: Long,
+        schedulingDelay: Long): Option[Double] = {
+      Some(100.0)
+    }
+  }
+
+  override val rateController: RateController = new RateController(id, ConstantEstimator) {
+    override def publish(rate: Long): Unit = {
+      publishCalls += 1
+    }
+  }
+
+  def compute(validTime: Time): Option[RDD[Int]] = {
+    val data = Seq(1)
+    ssc.scheduler.inputInfoTracker.reportInfo(validTime, StreamInputInfo(id, data.size))
+    Some(ssc.sc.parallelize(data))
+  }
+
+  def stop(): Unit = {}
+
+  def start(): Unit = {}
+}