spark JavaDirectKafkaWordCount 例子分析

spark  JavaDirectKafkaWordCount 例子分析：

1、

KafkaUtils.createDirectStream(
        jssc,
        String.class,
        String.class,
        StringDecoder.class,
        StringDecoder.class,
        kafkaParams,
        topicsSet
    );
后面参数意思： 源码是这样

 @param ssc StreamingContext object
 * @param kafkaParams Kafka <a href="http://kafka.apache.org/documentation.html#configuration">
 *    configuration parameters</a>. Requires "metadata.broker.list" or "bootstrap.servers"
 *    to be set with Kafka broker(s) (NOT zookeeper servers) specified in
 *    host1:port1,host2:port2 form.
 * @param fromOffsets Per-topic/partition Kafka offsets defining the (inclusive)
 *    starting point of the stream
 * @param messageHandler Function for translating each message and metadata into the desired type
 * @tparam K type of Kafka message key
 * @tparam V type of Kafka message value
 * @tparam KD type of Kafka message key decoder
 * @tparam VD type of Kafka message value decoder
 * @tparam R type returned by messageHandler
 * @return DStream of R
 */
def createDirectStream[
  K: ClassTag,
  V: ClassTag,
  KD <: Decoder[K]: ClassTag,
  VD <: Decoder[V]: ClassTag,
  R: ClassTag] (
    ssc: StreamingContext,
    kafkaParams: Map[String, String],
    fromOffsets: Map[TopicAndPartition, Long],
    messageHandler: MessageAndMetadata[K, V] => R
): InputDStream[R] = {
  val cleanedHandler = ssc.sc.clean(messageHandler)
  new DirectKafkaInputDStream[K, V, KD, VD, R](
    ssc, kafkaParams, fromOffsets, cleanedHandler)
}

2、数据在输入到输出经历几个阶段：先map返回JavaDStream<String>类型

　　　　　　　　　　　　　　　　　然后flatMap 返回JavaDStream<String>类型

　　　　　　　　　　　　　　　　　在 然后mapToPair返回JavaPairDStream<String, Integer>

　　　　　　　　　　　　　　　　　最后reduceByKey 获得两数之和　　

 

 

完整例子请看尾部完整代码

import java.util.HashMap;
import java.util.HashSet;
import java.util.Arrays;
import java.util.regex.Pattern;

import scala.Tuple2;

import com.google.common.collect.Lists;
import kafka.serializer.StringDecoder;

import org.apache.spark.SparkConf;
import org.apache.spark.api.java.function.*;
import org.apache.spark.streaming.api.java.*;
import org.apache.spark.streaming.kafka.KafkaUtils;
import org.apache.spark.streaming.Durations;

/**
 * Consumes messages from one or more topics in Kafka and does wordcount.
 * Usage: JavaDirectKafkaWordCount <brokers> <topics>
 *   <brokers> is a list of one or more Kafka brokers
 *   <topics> is a list of one or more kafka topics to consume from
 *
 * Example:
 *    $ bin/run-example streaming.JavaDirectKafkaWordCount broker1-host:port,broker2-host:port topic1,topic2
 */

public final class JavaDirectKafkaWordCount {
  private static final Pattern SPACE = Pattern.compile(" ");

  public static void main(String[] args) {
    if (args.length < 2) {
      System.err.println("Usage: JavaDirectKafkaWordCount <brokers> <topics>\n" +
          "  <brokers> is a list of one or more Kafka brokers\n" +
          "  <topics> is a list of one or more kafka topics to consume from\n\n");
      System.exit(1);
    }

    StreamingExamples.setStreamingLogLevels();

    String brokers = args[0];
    String topics = args[1];

    // Create context with a 2 seconds batch interval
    SparkConf sparkConf = new SparkConf().setAppName("JavaDirectKafkaWordCount");
    JavaStreamingContext jssc;
     jssc = new (sparkConf, Durations.seconds(2));

    HashSet<String> topicsSet = new HashSet<String>(Arrays.asList(topics.split(",")));
    HashMap<String, String> kafkaParams = new HashMap<String, String>();
    kafkaParams.put("metadata.broker.list", brokers);

    // Create direct kafka stream with brokers and topics
    JavaPairInputDStream<String, String> messages = KafkaUtils.createDirectStream(
        jssc,
        String.class,
        String.class,
        StringDecoder.class,
        StringDecoder.class,
        kafkaParams,
        topicsSet
    );

    // Get the lines, split them into words, count the words and print
    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();

    // Start the computation
    jssc.start();
    jssc.awaitTermination();
  }
}