SparkStream WordCount代码详解

下面我要引用的代码来自于sparkStream的官网案例，案例内容为从kafka读取数据进行wordcount计算

package org.apache.spark.examples.streaming;

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

import scala.Tuple2;

import org.apache.kafka.clients.consumer.ConsumerConfig;
import org.apache.kafka.clients.consumer.ConsumerRecord;
import org.apache.kafka.common.serialization.StringDeserializer;

import org.apache.spark.SparkConf;
import org.apache.spark.streaming.api.java.*;
import org.apache.spark.streaming.kafka010.ConsumerStrategies;
import org.apache.spark.streaming.kafka010.KafkaUtils;
import org.apache.spark.streaming.kafka010.LocationStrategies;
import org.apache.spark.streaming.Durations;

/**
 * Consumes messages from one or more topics in Kafka and does wordcount.
 * Usage: JavaDirectKafkaWordCount <brokers> <groupId> <topics>
 *   <brokers> is a list of one or more Kafka brokers
 *   <groupId> is a consumer group name to consume from topics
 *   <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 \
 *      consumer-group topic1,topic2
 */

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

  public static void main(String[] args) throws Exception {
    if (args.length < 3) {
      System.err.println("Usage: JavaDirectKafkaWordCount <brokers> <groupId> <topics>\n" +
                         "  <brokers> is a list of one or more Kafka brokers\n" +
                         "  <groupId> is a consumer group name to consume from topics\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 groupId = args[1];
    String topics = args[2];

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

    Set<String> topicsSet = new HashSet<>(Arrays.asList(topics.split(",")));
    Map<String, Object> kafkaParams = new HashMap<>();
    kafkaParams.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, brokers);
    kafkaParams.put(ConsumerConfig.GROUP_ID_CONFIG, groupId);
    kafkaParams.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
    kafkaParams.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);

    // Create direct kafka stream with brokers and topics
    JavaInputDStream<ConsumerRecord<String, String>> messages = KafkaUtils.createDirectStream(
        jssc,
        LocationStrategies.PreferConsistent(),
        ConsumerStrategies.Subscribe(topicsSet, kafkaParams));

    // Get the lines, split them into words, count the words and print
    JavaDStream<String> lines = messages.map(ConsumerRecord::value);
    JavaDStream<String> words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator());
    JavaPairDStream<String, Integer> wordCounts = words.mapToPair(s -> new Tuple2<>(s, 1))
        .reduceByKey((i1, i2) -> i1 + i2);
    wordCounts.print();

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

我们来一步步的看：

首先 

    SparkConf sparkConf = new SparkConf().setAppName("JavaDirectKafkaWordCount");
    JavaStreamingContext jssc = new JavaStreamingContext(sparkConf, Durations.seconds(2));
这个是实例化一个SparkStream的对象

其次定义kafka的相关信息

Set<String> topicsSet = new HashSet<>(Arrays.asList(topics.split(",")));
    Map<String, Object> kafkaParams = new HashMap<>();
    kafkaParams.put(ConsumerConfig.BOOTSTRAP_SERVERS_CONFIG, brokers);
    kafkaParams.put(ConsumerConfig.GROUP_ID_CONFIG, groupId);
    kafkaParams.put(ConsumerConfig.KEY_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);
    kafkaParams.put(ConsumerConfig.VALUE_DESERIALIZER_CLASS_CONFIG, StringDeserializer.class);

然后从kafka的topic里读取数据

    // Create direct kafka stream with brokers and topics
    JavaInputDStream<ConsumerRecord<String, String>> messages = KafkaUtils.createDirectStream(
        jssc,
        LocationStrategies.PreferConsistent(),
        ConsumerStrategies.Subscribe(topicsSet, kafkaParams));

获取单行数据

JavaDStream<String> lines = messages.map(ConsumerRecord::value);

拼接数据

JavaDStream<String> words = lines.flatMap(x -> Arrays.asList(SPACE.split(x)).iterator());

将数据转为键值对的形式

JavaPairDStream<String, Integer> wordCounts = words.mapToPair(s -> new Tuple2<>(s, 1))
        .reduceByKey((i1, i2) -> i1 + i2);

启动流式计算

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