[SPARK-15645][Streaming] Fix some typos of Streaming module

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

@@ -32,8 +32,8 @@ import org.apache.spark.util.Utils
 private[spark] class ParallelCollectionPartition[T: ClassTag](
     var rddId: Long,
     var slice: Int,
-    var values: Seq[T])
-    extends Partition with Serializable {
+    var values: Seq[T]
+  ) extends Partition with Serializable {
 
   def iterator: Iterator[T] = values.iterator
 

examples/src/main/scala/org/apache/spark/examples/streaming/CustomReceiver.scala

@@ -29,7 +29,7 @@ import org.apache.spark.streaming.{Seconds, StreamingContext}
 import org.apache.spark.streaming.receiver.Receiver
 
 /**
- * Custom Receiver that receives data over a socket. Received bytes is interpreted as
+ * Custom Receiver that receives data over a socket. Received bytes are interpreted as
  * text and \n delimited lines are considered as records. They are then counted and printed.
  *
  * To run this on your local machine, you need to first run a Netcat server
@@ -50,7 +50,7 @@ object CustomReceiver {
     val sparkConf = new SparkConf().setAppName("CustomReceiver")
     val ssc = new StreamingContext(sparkConf, Seconds(1))
 
-    // Create a input stream with the custom receiver on target ip:port and count the
+    // Create an input stream with the custom receiver on target ip:port and count the
     // words in input stream of \n delimited text (eg. generated by 'nc')
     val lines = ssc.receiverStream(new CustomReceiver(args(0), args(1).toInt))
     val words = lines.flatMap(_.split(" "))

examples/src/main/scala/org/apache/spark/examples/streaming/QueueStream.scala

@@ -43,7 +43,7 @@ object QueueStream {
     reducedStream.print()
     ssc.start()
 
-    // Create and push some RDDs into
+    // Create and push some RDDs into rddQueue
     for (i <- 1 to 30) {
       rddQueue.synchronized {
         rddQueue += ssc.sparkContext.makeRDD(1 to 1000, 10)

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

@@ -52,7 +52,7 @@ import org.apache.spark.util.{CallSite, Utils}
  * `join`. These operations are automatically available on any DStream of pairs
  * (e.g., DStream[(Int, Int)] through implicit conversions.
  *
- * DStreams internally is characterized by a few basic properties:
+ * A DStream internally is characterized by a few basic properties:
  *  - A list of other DStreams that the DStream depends on
  *  - A time interval at which the DStream generates an RDD
  *  - A function that is used to generate an RDD after each time interval

streaming/src/main/scala/org/apache/spark/streaming/receiver/BlockGenerator.scala

@@ -86,13 +86,13 @@ private[streaming] class BlockGenerator(
   /**
    * The BlockGenerator can be in 5 possible states, in the order as follows.
    *
-   *  - Initialized: Nothing has been started
+   *  - Initialized: Nothing has been started.
    *  - Active: start() has been called, and it is generating blocks on added data.
    *  - StoppedAddingData: stop() has been called, the adding of data has been stopped,
    *                       but blocks are still being generated and pushed.
    *  - StoppedGeneratingBlocks: Generating of blocks has been stopped, but
    *                             they are still being pushed.
-   *  - StoppedAll: Everything has stopped, and the BlockGenerator object can be GCed.
+   *  - StoppedAll: Everything has been stopped, and the BlockGenerator object can be GCed.
    */
   private object GeneratorState extends Enumeration {
     type GeneratorState = Value
@@ -148,7 +148,7 @@ private[streaming] class BlockGenerator(
     blockIntervalTimer.stop(interruptTimer = false)
     synchronized { state = StoppedGeneratingBlocks }
 
-    // Wait for the queue to drain and mark generated as stopped
+    // Wait for the queue to drain and mark state as StoppedAll
     logInfo("Waiting for block pushing thread to terminate")
     blockPushingThread.join()
     synchronized { state = StoppedAll }

streaming/src/main/scala/org/apache/spark/streaming/receiver/Receiver.scala

@@ -32,7 +32,7 @@ import org.apache.spark.storage.StorageLevel
  * should define the setup steps necessary to start receiving data,
  * and `onStop()` should define the cleanup steps necessary to stop receiving data.
  * Exceptions while receiving can be handled either by restarting the receiver with `restart(...)`
- * or stopped completely by `stop(...)` or
+ * or stopped completely by `stop(...)`.
  *
  * A custom receiver in Scala would look like this.
  *
@@ -45,7 +45,7 @@ import org.apache.spark.storage.StorageLevel
  *          // Call store(...) in those threads to store received data into Spark's memory.
  *
  *          // Call stop(...), restart(...) or reportError(...) on any thread based on how
- *          // different errors needs to be handled.
+ *          // different errors need to be handled.
  *
  *          // See corresponding method documentation for more details
  *      }
@@ -71,7 +71,7 @@ import org.apache.spark.storage.StorageLevel
  *          // Call store(...) in those threads to store received data into Spark's memory.
  *
  *          // Call stop(...), restart(...) or reportError(...) on any thread based on how
- *          // different errors needs to be handled.
+ *          // different errors need to be handled.
  *
  *          // See corresponding method documentation for more details
  *     }

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

@@ -19,7 +19,6 @@ package org.apache.spark.streaming.scheduler
 
 import scala.util.{Failure, Success, Try}
 
-import org.apache.spark.SparkEnv
 import org.apache.spark.internal.Logging
 import org.apache.spark.rdd.RDD
 import org.apache.spark.streaming.{Checkpoint, CheckpointWriter, Time}
@@ -239,7 +238,7 @@ class JobGenerator(jobScheduler: JobScheduler) extends Logging {
     logInfo("Restarted JobGenerator at " + restartTime)
   }
 
-  /** Generate jobs and perform checkpoint for the given `time`.  */
+  /** Generate jobs and perform checkpointing for the given `time`.  */
   private def generateJobs(time: Time) {
     // Checkpoint all RDDs marked for checkpointing to ensure their lineages are
     // truncated periodically. Otherwise, we may run into stack overflows (SPARK-6847).

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

@@ -20,7 +20,7 @@ package org.apache.spark.streaming.scheduler
 import java.util.concurrent.{CountDownLatch, TimeUnit}
 
 import scala.collection.mutable.HashMap
-import scala.concurrent.{ExecutionContext, Future}
+import scala.concurrent.ExecutionContext
 import scala.language.existentials
 import scala.util.{Failure, Success}