Further fixes to raw text sender, plus an app that uses it

streaming/src/main/scala/spark/streaming/DStream.scala

@@ -176,7 +176,7 @@ extends Logging with Serializable {
   def mapPartitions[U: ClassManifest](mapPartFunc: Iterator[T] => Iterator[U]) = 
     new MapPartitionedDStream(this, ssc.sc.clean(mapPartFunc))
 
-  def reduce(reduceFunc: (T, T) => T) = this.map(x => (1, x)).reduceByKey(reduceFunc, 1).map(_._2)
+  def reduce(reduceFunc: (T, T) => T) = this.map(x => (null, x)).reduceByKey(reduceFunc, 1).map(_._2)
 
   def count() = this.map(_ => 1).reduce(_ + _)
   

streaming/src/main/scala/spark/streaming/JobManager.scala

@@ -12,7 +12,7 @@ class JobManager(ssc: StreamingContext, numThreads: Int = 1) extends Logging {
       SparkEnv.set(ssc.env)
       try {
         val timeTaken = job.run()
-        logInfo("Total delay: %.4f s for job %s; execution was %.4f s".format(
+        logInfo("Total delay: %.5f s for job %s (execution: %.5f s)".format(
           (System.currentTimeMillis() - job.time) / 1000.0, job.id, timeTaken / 1000.0))
       } catch {
         case e: Exception =>

streaming/src/main/scala/spark/streaming/RawInputDStream.scala

@@ -22,7 +22,8 @@ import spark.storage.StorageLevel
 class RawInputDStream[T: ClassManifest](
     @transient ssc: StreamingContext,
     host: String,
-    port: Int)
+    port: Int,
+    storageLevel: StorageLevel)
   extends NetworkInputDStream[T](ssc) with Logging {
 
   val streamId = id
@@ -49,7 +50,7 @@ class RawInputDStream[T: ClassManifest](
           val buffer = queue.take()
           val blockId = "input-" + streamId + "-" + nextBlockNumber
           nextBlockNumber += 1
-          env.blockManager.putBytes(blockId, buffer, StorageLevel.MEMORY_ONLY_2)
+          env.blockManager.putBytes(blockId, buffer, storageLevel)
           actor ! BlockPublished(blockId)
         }
       }

streaming/src/main/scala/spark/streaming/StreamingContext.scala

@@ -4,6 +4,7 @@ import spark.RDD
 import spark.Logging
 import spark.SparkEnv
 import spark.SparkContext
+import spark.storage.StorageLevel
 
 import scala.collection.mutable.ArrayBuffer
 import scala.collection.mutable.Queue
@@ -64,6 +65,16 @@ class StreamingContext (
     inputStreams += inputStream
     inputStream
   }
+  
+  def createRawNetworkStream[T: ClassManifest](
+      hostname: String,
+      port: Int,
+      storageLevel: StorageLevel = StorageLevel.MEMORY_ONLY_2
+    ): DStream[T] = {
+    val inputStream = new RawInputDStream[T](this, hostname, port, storageLevel)
+    inputStreams += inputStream
+    inputStream
+  }
  
   /*
   def createHttpTextStream(url: String): DStream[String] = {

streaming/src/main/scala/spark/streaming/examples/CountRaw.scala

@@ -0,0 +1,32 @@
+package spark.streaming.examples
+
+import spark.util.IntParam
+import spark.storage.StorageLevel
+import spark.streaming._
+import spark.streaming.StreamingContext._
+
+object CountRaw {
+  def main(args: Array[String]) {
+    if (args.length < 4) {
+      System.err.println("Usage: WordCountNetwork <master> <numStreams> <hostname> <port>")
+      System.exit(1)
+    }
+
+    val Array(master, IntParam(numStreams), hostname, IntParam(port)) = args
+
+    // Create the context and set the batch size
+    val ssc = new StreamingContext(master, "CountRaw")
+    ssc.setBatchDuration(Seconds(1))
+
+    // Make sure some tasks have started on each node
+    ssc.sc.parallelize(1 to 1000, 1000).count()
+    ssc.sc.parallelize(1 to 1000, 1000).count()
+    ssc.sc.parallelize(1 to 1000, 1000).count()
+
+    val rawStreams = (1 to numStreams).map(_ =>
+      ssc.createRawNetworkStream[String](hostname, port, StorageLevel.MEMORY_ONLY_2)).toArray
+    val union = new UnifiedDStream(rawStreams)
+    union.map(_.length).reduce(_ + _).foreachRDD(r => println("Byte count: " + r.collect().mkString))
+    ssc.start()
+  }
+}

streaming/src/main/scala/spark/streaming/examples/WordCount2.scala

@@ -85,7 +85,7 @@ object WordCount2 {
     //warmup(ssc.sc)
 
     val data = ssc.sc.textFile(file, mapTasks.toInt).persist(
-      new StorageLevel(false, true, true, 2))  // Memory only, deserialized, 2 replicas
+      new StorageLevel(false, true, false, 2))  // Memory only, serialized, 2 replicas
     println("Data count: " + data.count())
     println("Data count: " + data.count())
     println("Data count: " + data.count())

streaming/src/main/scala/spark/streaming/util/RawTextSender.scala

@@ -1,5 +1,6 @@
 package spark.streaming.util
 
+import java.nio.ByteBuffer
 import spark.util.{RateLimitedOutputStream, IntParam}
 import java.net.ServerSocket
 import spark.{Logging, KryoSerializer}
@@ -33,7 +34,12 @@ object RawTextSender extends Logging {
     bufferStream.trim()
     val array = bufferStream.array
 
+    val countBuf = ByteBuffer.wrap(new Array[Byte](4))
+    countBuf.putInt(array.length)
+    countBuf.flip()
+
     val serverSocket = new ServerSocket(port)
+    logInfo("Listening on port " + port)
 
     while (true) {
       val socket = serverSocket.accept()
@@ -41,6 +47,7 @@ object RawTextSender extends Logging {
       val out = new RateLimitedOutputStream(socket.getOutputStream, bytesPerSec)
       try {
         while (true) {
+          out.write(countBuf.array)
           out.write(array)
         }
       } catch {