1 files changed, 50 insertions, 0 deletions
diff --git a/examples/src/main/scala/spark/streaming/examples/StatefulNetworkWordCount.scala b/examples/src/main/scala/spark/streaming/examples/StatefulNetworkWordCount.scala
new file mode 100644
index 0000000000..51c3c9f9b4
--- /dev/null
+++ b/examples/src/main/scala/spark/streaming/examples/StatefulNetworkWordCount.scala
@@ -0,0 +1,50 @@
+package spark.streaming.examples
+
+import spark.streaming._
+import spark.streaming.StreamingContext._
+
+/**
+ * Counts words cumulatively in UTF8 encoded, '\n' delimited text received from the network every second.
+ * Usage: StatefulNetworkWordCount <master> <hostname> <port>
+ *   <master> is the Spark master URL. In local mode, <master> should be 'local[n]' with n > 1.
+ *   <hostname> and <port> describe the TCP server that Spark Streaming would connect to receive data.
+ *
+ * To run this on your local machine, you need to first run a Netcat server
+ *    `$ nc -lk 9999`
+ * and then run the example
+ *    `$ ./run spark.streaming.examples.StatefulNetworkWordCount local[2] localhost 9999`
+ */
+object StatefulNetworkWordCount {
+  def main(args: Array[String]) {
+    if (args.length < 3) {
+      System.err.println("Usage: StatefulNetworkWordCount <master> <hostname> <port>\n" +
+        "In local mode, <master> should be 'local[n]' with n > 1")
+      System.exit(1)
+    }
+
+    val updateFunc = (values: Seq[Int], state: Option[Int]) => {
+      val currentCount = values.foldLeft(0)(_ + _)
+
+      val previousCount = state.getOrElse(0)
+
+      Some(currentCount + previousCount)
+    }
+
+    // Create the context with a 1 second batch size
+    val ssc = new StreamingContext(args(0), "NetworkWordCumulativeCountUpdateStateByKey", Seconds(1),
+      System.getenv("SPARK_HOME"), Seq(System.getenv("SPARK_EXAMPLES_JAR")))
+    ssc.checkpoint(".")
+
+    // Create a NetworkInputDStream on target ip:port and count the
+    // words in input stream of \n delimited test (eg. generated by 'nc') 
+    val lines = ssc.socketTextStream(args(1), args(2).toInt)
+    val words = lines.flatMap(_.split(" "))
+    val wordDstream = words.map(x => (x, 1))
+
+    // Update the cumulative count using updateStateByKey
+    // This will give a Dstream made of state (which is the cumulative count of the words)
+    val stateDstream = wordDstream.updateStateByKey[Int](updateFunc)
+    stateDstream.print()
+    ssc.start()
+  }
+}