spark-kafka-wordcount

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maven

<properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <project.reporting.outputEncoding>UTF-8</project.reporting.outputEncoding>
        <java.version>1.8</java.version>
        <scala.binary.version>2.10</scala.binary.version>
        <spark.version>1.6.1</spark.version>
    </properties>

    <dependencies>
        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-actuator</artifactId>
        </dependency>

        <dependency>
            <groupId>org.springframework.boot</groupId>
            <artifactId>spring-boot-starter-test</artifactId>
            <scope>test</scope>
        </dependency>

        <!-- SPARK -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-core_${scala.binary.version}</artifactId>
            <version>${spark.version}</version>
            <exclusions>
                <exclusion>
                    <groupId>org.slf4j</groupId>
                    <artifactId>slf4j-log4j12</artifactId>
                </exclusion>
                <exclusion>
                    <groupId>log4j</groupId>
                    <artifactId>log4j</artifactId>
                </exclusion>
            </exclusions>
        </dependency>
        <!-- SPARK STREAMING -->
        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming_${scala.binary.version}</artifactId>
            <version>${spark.version}</version>
            <exclusions>
                <exclusion>
                    <artifactId>commons-logging</artifactId>
                    <groupId>commons-logging</groupId>
                </exclusion>
            </exclusions>
        </dependency>

        <dependency>
            <groupId>org.apache.spark</groupId>
            <artifactId>spark-streaming-kafka_${scala.binary.version}</artifactId>
            <version>${spark.version}</version>
        </dependency>

        <!-- JACKSON SCALA -->
        <dependency>
            <groupId>com.fasterxml.jackson.module</groupId>
            <artifactId>jackson-module-scala_${scala.binary.version}</artifactId>
            <version>2.7.3</version>
        </dependency>
        <dependency>
            <groupId>com.fasterxml.jackson.module</groupId>
            <artifactId>jackson-module-jaxb-annotations</artifactId>
            <version>2.7.4</version>
        </dependency>
        <dependency>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-databind</artifactId>
            <version>2.7.4</version>
        </dependency>
        <dependency>
            <groupId>com.fasterxml.jackson.core</groupId>
            <artifactId>jackson-annotations</artifactId>
            <version>2.7.4</version>
        </dependency>

    </dependencies>

kafka

public class DemoProducer {

    private final kafka.javaapi.producer.Producer<Integer, String> producer;
    private final String topic;
    private final Properties props = new Properties();

    public DemoProducer(String topic){
        props.put("serializer.class", "kafka.serializer.StringEncoder");
        props.put("metadata.broker.list", "localhost:9092");
        // Use random partitioner. Don't need the key type. Just set it to Integer.
        // The message is of type String.
        producer = new kafka.javaapi.producer.Producer<Integer, String>(new ProducerConfig(props));
        this.topic = topic;
    }

    public void start(){
        for(int i=0;i<100;i++){
            String messageStr = new String("Message_" + RandomUtils.nextInt(1,10));
            producer.send(new KeyedMessage<Integer, String>(topic, messageStr));
            System.out.println("send:"+messageStr);
        }
    }
}

kafka-spark

public class JavaKafkaWordCount implements Serializable{

    private static final Pattern SPACE = Pattern.compile(" ");

    private JavaKafkaWordCount() {
    }

    public static void main(String[] args) throws Exception {
        String zkHost = "localhost:2181";
        String topic = "wordTopic";
        String group = "spark-demo";

        ch.qos.logback.classic.Logger root = (ch.qos.logback.classic.Logger) LoggerFactory.getLogger(Logger.ROOT_LOGGER_NAME);
        root.setLevel(Level.WARN);

        //sh bin/kafka-topics.sh --create --topic wordTopic --replication-factor 1 --partitions 1 --zookeeper localhost:2181
        DemoProducer demoProducer = new DemoProducer(topic);
        demoProducer.start();

        JavaKafkaWordCount javaKafkaWordCount = new JavaKafkaWordCount();

        javaKafkaWordCount.start(zkHost,group,topic,1);
    }

    public void start(String zkHost,String group,String topicList,int numThreads) throws Exception {
        SparkConf sparkConf = new SparkConf().setMaster("local[2]").setAppName("JavaKafkaWordCount");
        // Create the context with 2 seconds batch size
        JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, new Duration(2000));

        Map<String, Integer> topicMap = new HashMap<>();
        String[] topics = topicList.split(",");
        for (String topic: topics) {
            topicMap.put(topic, numThreads);
        }

        JavaPairReceiverInputDStream<String, String> messages =
                KafkaUtils.createStream(jssc, zkHost,group,topicMap);

        JavaDStream<String> lines = messages.map(new Function<Tuple2<String, String>, String>() {
            @Override
            public String call(Tuple2<String, String> tuple2) {
                System.out.println(tuple2);
                return tuple2._2();
            }
        });

        JavaDStream<String> words = lines.flatMap(new FlatMapFunction<String, String>() {
            @Override
            public Iterable<String> call(String x) {
                return Arrays.asList(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<>(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();
    }
}

输出

(Message_2,15)
(Message_8,9)
(Message_4,13)
(Message_6,10)
(Message_9,8)
(Message_3,12)
(Message_1,11)
(Message_7,8)
(Message_5,14)

ui

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