Spark之SparkStreaming案例-kafka

sparkStreaming从kafka中拿取数据 完整代码

package com.chb.spark.streaming;

import java.io.Serializable;
import java.util.Arrays;
import java.util.HashMap;
import java.util.HashSet;
import java.util.Map;
import java.util.Set;

import kafka.serializer.StringDecoder;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.FlatMapFunction;
import org.apache.spark.api.java.function.Function2;
import org.apache.spark.api.java.function.PairFunction;
import org.apache.spark.streaming.Durations;
import org.apache.spark.streaming.api.java.JavaDStream;
import org.apache.spark.streaming.api.java.JavaPairDStream;
import org.apache.spark.streaming.api.java.JavaPairInputDStream;
import org.apache.spark.streaming.api.java.JavaStreamingContext;
import org.apache.spark.streaming.kafka.KafkaUtils;

import scala.Tuple2;

public class KafkaDirectWordCount {
    public static void main(String[] args) {
        SparkConf conf = new SparkConf().setAppName("kafka-wordCount").setMaster("local[2]");

        JavaStreamingContext jssc = new JavaStreamingContext(conf, Durations.seconds(3));

        //首先创建一个kafka的参数
        Map kafkaParams = new HashMap();
        //此处存储boker.list
        kafkaParams.put("metadata.broker.list", "192.168.1.224:9092, 192.168.1.225:9092, 192.168.1.226:9092");

        //可以读取多个topic
        Set topics = new HashSet();
        topics.add("wordcount");

        JavaPairInputDStream lines = KafkaUtils.createDirectStream(
                jssc,
                String.class, //key的类型
                String.class, //value的类型
                StringDecoder.class, //解码器
                StringDecoder.class,
                kafkaParams,
                topics
        );

        /**
         * 输入的lines为Tuple2的元组，
         * 其实key为空， value为line, flatMap目的将line切分成word
         * val words = line.flatMap(_._2.split(" "));
         */
        JavaDStream words = lines.flatMap(new FlatMapFunction() {

            private static final long serialVersionUID = 1L;

            @Override
            public Iterable call(Tuple2 t)
                    throws Exception {
                return  Arrays.asList(t._2.split(" "));
            }
        });
        /**
         * 每个单词映射
         * word=>(word, 1)
         * val pairs words.map(x=>(x, 1));
         */
        JavaPairDStream pairs = words.mapToPair(new PairFunction() {
            private static final long serialVersionUID = 1L;

            @Override
            public Tuple2 call(String t) throws Exception {
                return new Tuple2(t, 1);
            }
        });
        /**
         * 统计每个单词的总数
         * val wcs = pairs.reduceByKey(_+_)
         * public interface Function2 extends Serializable {
         *      R call(T1 v1, T2 v2) throws Exception;
         *  }
         */
        JavaPairDStream wcs = pairs.reduceByKey(new Function2() {
            private static final long serialVersionUID = 1L;

            @Override
            public Integer call(Integer v1, Integer v2) throws Exception {
                return v1 + v2;
            }
        });
        //触发Action操作， 产生job, 打印每个batch的前10个结果
        wcs.print();

        jssc.start();
        jssc.awaitTermination();
        jssc.stop();
        jssc.close();
    }
}

scala代码

package com.spark.stream.scala

import org.apache.spark.SparkConf
import org.apache.spark.SparkContext
import org.apache.spark.streaming.StreamingContext
import org.apache.spark.streaming.Durations
import org.apache.spark.storage.StorageLevel
import org.apache.spark.streaming.dstream.DStream
import org.apache.spark.streaming.kafka.KafkaUtils
import kafka.serializer.StringDecoder

object WordCountKafka {
  def main(args: Array[String]): Unit = {

    val sparkConf = new SparkConf().setAppName("WordCount").setMaster("local[2]") //至少2个线程，一个DRecive接受监听端口数据，一个计算
    val sc = new StreamingContext(sparkConf, Durations.seconds(10));

    // 首先要创建一份kafka参数map
    // 我们这里是不需要zookeeper节点的啊,所以我们这里放broker.list
    val kafkaParams = Map[String, String](  
      "metadata.broker.list" -> "node1:9092,node2:9092,node3:9092"
    )

    // 然后创建一个set,里面放入你要读取的Topic,这个就是我们所说的,它给你做的很好,可以并行读取多个topic
    var topics = Set[String]("wordcount20160423");

    //kafka返回的数据时key/value形式，后面只要对value进行分割就ok了
    val linerdd = KafkaUtils.createDirectStream[String, String, StringDecoder, StringDecoder](  
      sc, kafkaParams, topics)

    val wordrdd = linerdd.flatMap { _._2.split(" ") }
    val resultrdd = wordrdd.map { x => (x, 1) }.reduceByKey { _ + _ }
    //    resultrdd.map(x => println(x._1+"--"+x._2))
    resultrdd.print()
    sc.start()
    sc.awaitTermination()
    sc.stop()

  }
}