I am not running locally. The Spark master is:
"spark://<machine name>:7077"
On Mon, Nov 10, 2014 at 3:47 PM, Tathagata Das <tathagata.das1...@gmail.com>
wrote:
> 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();
> >
>
