Moved Java streaming examples to examples/src/main/java/spark/streaming/... and fixed logging in NetworkInputTracker to highlight errors when receiver deregisters/shuts down.

3 files changed, 0 insertions, 174 deletions

diff --git a/examples/src/main/scala/spark/streaming/examples/JavaFlumeEventCount.java b/examples/src/main/scala/spark/streaming/examples/JavaFlumeEventCount.java
deleted file mode 100644
index cddce16e39..0000000000
--- a/examples/src/main/scala/spark/streaming/examples/JavaFlumeEventCount.java
+++ /dev/null
@@ -1,50 +0,0 @@
-package spark.streaming.examples;
-
-import spark.api.java.function.Function;
-import spark.streaming.*;
-import spark.streaming.api.java.*;
-import spark.streaming.dstream.SparkFlumeEvent;
-
-/**
- *  Produces a count of events received from Flume.
- *
- *  This should be used in conjunction with an AvroSink in Flume. It will start
- *  an Avro server on at the request host:port address and listen for requests.
- *  Your Flume AvroSink should be pointed to this address.
- *
- *  Usage: JavaFlumeEventCount <master> <host> <port>
- *
- *    <master> is a Spark master URL
- *    <host> is the host the Flume receiver will be started on - a receiver
- *           creates a server and listens for flume events.
- *    <port> is the port the Flume receiver will listen on.
- */
-public class JavaFlumeEventCount {
-  public static void main(String[] args) {
-    if (args.length != 3) {
-      System.err.println("Usage: JavaFlumeEventCount <master> <host> <port>");
-      System.exit(1);
-    }
-
-    String master = args[0];
-    String host = args[1];
-    int port = Integer.parseInt(args[2]);
-
-    Duration batchInterval = new Duration(2000);
-
-    JavaStreamingContext sc = new JavaStreamingContext(master, "FlumeEventCount", batchInterval);
-
-    JavaDStream<SparkFlumeEvent> flumeStream = sc.flumeStream("localhost", port);
-
-    flumeStream.count();
-
-    flumeStream.count().map(new Function<Long, String>() {
-      @Override
-      public String call(Long in) {
-        return "Received " + in + " flume events.";
-      }
-    }).print();
-
-    sc.start();
-  }
-}
diff --git a/examples/src/main/scala/spark/streaming/examples/JavaNetworkWordCount.java b/examples/src/main/scala/spark/streaming/examples/JavaNetworkWordCount.java
deleted file mode 100644
index 4299febfd6..0000000000
--- a/examples/src/main/scala/spark/streaming/examples/JavaNetworkWordCount.java
+++ /dev/null
@@ -1,62 +0,0 @@
-package spark.streaming.examples;
-
-import com.google.common.collect.Lists;
-import scala.Tuple2;
-import spark.api.java.function.FlatMapFunction;
-import spark.api.java.function.Function2;
-import spark.api.java.function.PairFunction;
-import spark.streaming.Duration;
-import spark.streaming.api.java.JavaDStream;
-import spark.streaming.api.java.JavaPairDStream;
-import spark.streaming.api.java.JavaStreamingContext;
-
-/**
- * Counts words in UTF8 encoded, '\n' delimited text received from the network every second.
- * Usage: NetworkWordCount <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.JavaNetworkWordCount local[2] localhost 9999`
- */
-public class JavaNetworkWordCount {
-  public static void main(String[] args) {
-    if (args.length < 2) {
-      System.err.println("Usage: NetworkWordCount <master> <hostname> <port>\n" +
-          "In local mode, <master> should be 'local[n]' with n > 1");
-      System.exit(1);
-    }
-
-    // Create the context with a 1 second batch size
-    JavaStreamingContext ssc = new JavaStreamingContext(
-        args[0], "NetworkWordCount", new Duration(1000));
-
-    // Create a NetworkInputDStream on target ip:port and count the
-    // words in input stream of \n delimited test (eg. generated by 'nc')
-    JavaDStream<String> lines = ssc.networkTextStream(args[1], Integer.parseInt(args[2]));
-    JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
-      @Override
-      public Iterable<String> call(String x) {
-        return Lists.newArrayList(x.split(" "));
-      }
-    });
-    JavaPairDStream<String, Integer> wordCounts = words.map(
-      new PairFunction<String, String, Integer>() {
-        @Override
-        public Tuple2<String, Integer> call(String s) throws Exception {
-          return new Tuple2<String, Integer>(s, 1);
-        }
-      }).reduceByKey(new Function2<Integer, Integer, Integer>() {
-        @Override
-        public Integer call(Integer i1, Integer i2) throws Exception {
-          return i1 + i2;
-        }
-      });
-
-    wordCounts.print();
-    ssc.start();
-
-  }
-}
diff --git a/examples/src/main/scala/spark/streaming/examples/JavaQueueStream.java b/examples/src/main/scala/spark/streaming/examples/JavaQueueStream.java
deleted file mode 100644
index 43c3cd4dfa..0000000000
--- a/examples/src/main/scala/spark/streaming/examples/JavaQueueStream.java
+++ /dev/null
@@ -1,62 +0,0 @@
-package spark.streaming.examples;
-
-import com.google.common.collect.Lists;
-import scala.Tuple2;
-import spark.api.java.JavaRDD;
-import spark.api.java.function.Function2;
-import spark.api.java.function.PairFunction;
-import spark.streaming.Duration;
-import spark.streaming.api.java.JavaDStream;
-import spark.streaming.api.java.JavaPairDStream;
-import spark.streaming.api.java.JavaStreamingContext;
-
-import java.util.LinkedList;
-import java.util.List;
-import java.util.Queue;
-
-public class JavaQueueStream {
-  public static void main(String[] args) throws InterruptedException {
-    if (args.length < 1) {
-      System.err.println("Usage: JavaQueueStream <master>");
-      System.exit(1);
-    }
-
-    // Create the context
-    JavaStreamingContext ssc = new JavaStreamingContext(args[0], "QueueStream", new Duration(1000));
-
-    // Create the queue through which RDDs can be pushed to
-    // a QueueInputDStream
-    Queue<JavaRDD<Integer>> rddQueue = new LinkedList<JavaRDD<Integer>>();
-
-    // Create and push some RDDs into the queue
-    List<Integer> list = Lists.newArrayList();
-    for (int i = 0; i < 1000; i++) {
-      list.add(i);
-    }
-
-    for (int i = 0; i < 30; i++) {
-      rddQueue.add(ssc.sc().parallelize(list));
-    }
-
-
-    // Create the QueueInputDStream and use it do some processing
-    JavaDStream<Integer> inputStream = ssc.queueStream(rddQueue);
-    JavaPairDStream<Integer, Integer> mappedStream = inputStream.map(
-        new PairFunction<Integer, Integer, Integer>() {
-          @Override
-          public Tuple2<Integer, Integer> call(Integer i) throws Exception {
-            return new Tuple2<Integer, Integer>(i % 10, 1);
-          }
-        });
-    JavaPairDStream<Integer, Integer> reducedStream = mappedStream.reduceByKey(
-      new Function2<Integer, Integer, Integer>() {
-        @Override
-        public Integer call(Integer i1, Integer i2) throws Exception {
-          return i1 + i2;
-        }
-    });
-
-    reducedStream.print();
-    ssc.start();
-  }
-}