docker

docker pull sequenceiq/spark:1.6.0
docker run -it -p 8088:8088 -p 8042:8042 -p 4040:4040 -h sandbox sequenceiq/spark:1.6.0 bash

maven

<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>

版本

<scala.binary.version>2.10</scala.binary.version>
<spark.version>1.6.1</spark.version>

主要需要引入jackson的scala版本，否则报错如下：

Exception in thread "main" java.lang.VerifyError: class com.fasterxml.jackson.module.scala.ser.ScalaIteratorSerializer overrides final method withResolved.(Lcom/fasterxml/jackson/databind/BeanProperty;Lcom/fasterxml/jackson/databind/jsontype/TypeSerializer;Lcom/fasterxml/jackson/databind/JsonSerializer;)Lcom/fasterxml/jackson/databind/ser/std/AsArraySerializerBase;

streaming

/**
     * nc -lk 9999
     * http://192.168.0.102:4040
     */
    public void start(){
        ch.qos.logback.classic.Logger root = (ch.qos.logback.classic.Logger)LoggerFactory.getLogger(Logger.ROOT_LOGGER_NAME);
        root.setLevel(Level.WARN);
        SparkConf sparkConf = new SparkConf().setMaster("local[2]").setAppName("NetworkWordCount");
        JavaStreamingContext streamingContext = new JavaStreamingContext(sparkConf, Durations.seconds(1));

        // Create a DStream that will connect to hostname:port, like localhost:9999
        JavaReceiverInputDStream<String> lines = streamingContext.socketTextStream("localhost", 9999);

        // Split each line into words
        JavaDStream<String> words = lines.flatMap(
                new FlatMapFunction<String, String>() {
                    @Override public Iterable<String> call(String x) {
                        LOGGER.debug("flatMap called -> [{}]", x);
                        return Arrays.asList(x.split(" "));
                    }
                });

        // Count each word in each batch
        JavaPairDStream<String, Integer> pairs = words.mapToPair(
                new PairFunction<String, String, Integer>() {
                    @Override public Tuple2<String, Integer> call(String s) {
                        return new Tuple2<String, Integer>(s, 1);
                    }
                });
        JavaPairDStream<String, Integer> wordCounts = pairs.reduceByKey(
                new Function2<Integer, Integer, Integer>() {
                    @Override public Integer call(Integer i1, Integer i2) {
                        return i1 + i2;
                    }
                });

        // Print the first ten elements of each RDD generated in this DStream to the console
        wordCounts.print();

        streamingContext.start();              // Start the computation
        streamingContext.awaitTermination();   // Wait for the computation to terminate
    }

run

启动netcat

nc -lk 9999

运行，然后输入空格间隔的字符串，然后打开spark-ui
http://192.168.0.102:4040/

docs

• sequenceiq/docker-spark

• JavaKafkaWordCount