What is the Spark master that you are using. Use local[4], not local
if you are running locally.
On Mon, Nov 10, 2014 at 3:01 PM, Something Something
<mailinglist...@gmail.com> wrote:
> I am embarrassed to admit but I can't get a basic 'word count' to work under
> Kafka/Spark streaming. My code looks like this. I don't see any word
> counts in console output. Also, don't see any output in UI. Needless to
> say, I am newbie in both 'Spark' as well as 'Kafka'.
>
> Please help. Thanks.
>
> Here's the code:
>
> public static void main(String[] args) {
> if (args.length < 4) {
> System.err.println("Usage: JavaKafkaWordCount <zkQuorum> <group>
> <topics> <numThreads>");
> System.exit(1);
> }
>
> // StreamingExamples.setStreamingLogLevels();
> // SparkConf sparkConf = new
> SparkConf().setAppName("JavaKafkaWordCount");
>
> // Location of the Spark directory
> String sparkHome = "/opt/mapr/spark/spark-1.0.2/";
>
> // URL of the Spark cluster
> String sparkUrl = "spark://mymachine:7077";
>
> // Location of the required JAR files
> String jarFiles =
> "./spark-streaming-kafka_2.10-1.1.0.jar,./DlSpark-1.0-SNAPSHOT.jar,./zkclient-0.3.jar,./kafka_2.10-0.8.1.1.jar,./metrics-core-2.2.0.jar";
>
> SparkConf sparkConf = new SparkConf();
> sparkConf.setAppName("JavaKafkaWordCount");
> sparkConf.setJars(new String[]{jarFiles});
> sparkConf.setMaster(sparkUrl);
> sparkConf.set("spark.ui.port", "2348");
> sparkConf.setSparkHome(sparkHome);
>
> Map<String, String> kafkaParams = new HashMap<String, String>();
> kafkaParams.put("zookeeper.connect", "myedgenode:2181");
> kafkaParams.put("group.id", "1");
> kafkaParams.put("metadata.broker.list", "myedgenode:9092");
> kafkaParams.put("serializer.class",
> "kafka.serializer.StringEncoder");
> kafkaParams.put("request.required.acks", "1");
>
> // Create the context with a 1 second batch size
> JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new
> Duration(2000));
>
> int numThreads = Integer.parseInt(args[3]);
> Map<String, Integer> topicMap = new HashMap<String, Integer>();
> String[] topics = args[2].split(",");
> for (String topic: topics) {
> topicMap.put(topic, numThreads);
> }
>
> // JavaPairReceiverInputDStream<String, String> messages =
> // KafkaUtils.createStream(jssc, args[0], args[1], topicMap);
> JavaPairDStream<String, String> messages =
> KafkaUtils.createStream(jssc,
> String.class,
> String.class,
> StringDecoder.class,
> StringDecoder.class,
> kafkaParams,
> topicMap,
> StorageLevel.MEMORY_ONLY_SER());
>
>
> JavaDStream<String> lines = messages.map(new Function<Tuple2<String,
> String>, String>() {
> @Override
> public String call(Tuple2<String, String> tuple2) {
> return tuple2._2();
> }
> });
>
> JavaDStream<String> words = lines.flatMap(new
> FlatMapFunction<String, String>() {
> @Override
> public Iterable<String> call(String x) {
> return Lists.newArrayList(SPACE.split(x));
> }
> });
>
> JavaPairDStream<String, Integer> wordCounts = words.mapToPair(
> new PairFunction<String, String, Integer>() {
> @Override
> public Tuple2<String, Integer> call(String s) {
> return new Tuple2<String, Integer>(s, 1);
> }
> }).reduceByKey(new Function2<Integer, Integer, Integer>() {
> @Override
> public Integer call(Integer i1, Integer i2) {
> return i1 + i2;
> }
> });
>
> wordCounts.print();
> jssc.start();
> jssc.awaitTermination();
>
