讲给Java工程师的史上最通俗易懂Storm教程：纯手敲WordCount程序

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         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>

    <groupId>com.player3</groupId>
    <artifactId>storm-wordcount</artifactId>
    <version>1.0-SNAPSHOT</version>

    <dependencies>
        <dependency>
            <groupId>org.apache.storm</groupId>
            <artifactId>storm-core</artifactId>
            <version>1.1.0</version>
        </dependency>
        <dependency>
            <groupId>commons-collections</groupId>
            <artifactId>commons-collections</artifactId>
            <version>3.2.1</version>
        </dependency>

    </dependencies>

    <build>
        <sourceDirectory>src/main/java</sourceDirectory>

        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-shade-plugin</artifactId>
                <configuration>
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            <plugin>
                <groupId>org.codehaus.mojo</groupId>
                <artifactId>exec-maven-plugin</artifactId>
                <version>1.2.1</version>
                <executions>
                    <execution>
                        <goals>
                            <goal>exec</goal>
                        </goals>
                    </execution>
                </executions>
                <configuration>
                    <executable>java</executable>
                    <includeProjectDependencies>true</includeProjectDependencies>
                    <includePluginDependencies>false</includePluginDependencies>
                    <classpathScope>compile</classpathScope>
                    <mainClass>com.player3.storm.WordCountTopology</mainClass>
                </configuration>
            </plugin>
        </plugins>
    </build>
</project>

　　

package com.player3.storm;

import org.apache.storm.Config;
import org.apache.storm.LocalCluster;
import org.apache.storm.StormSubmitter;
import org.apache.storm.spout.SpoutOutputCollector;
import org.apache.storm.task.OutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.TopologyBuilder;
import org.apache.storm.topology.base.BaseRichBolt;
import org.apache.storm.topology.base.BaseRichSpout;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Tuple;
import org.apache.storm.tuple.Values;
import org.apache.storm.utils.Utils;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.util.HashMap;
import java.util.Map;
import java.util.Random;

/**
 * 单词计数拓扑
 *
 * 我认识很多java工程师，都是会一些大数据的技术的，不会太精通，没有那么多的时间去研究
 * storm的课程，我就只是讲到，最基本的开发，就够了，java开发广告计费系统，大量的流量的引入和接入，就是用storm做得
 * 用storm，主要是用它的成熟的稳定的易于扩容的分布式系统的特性
 * java工程师，来说，做一些简单的storm开发，掌握到这个程度差不多就够了
 *
 * @author Administrator
 *
 */
public class WordCountTopology {

	/**
	 * spout
	 *
	 * spout，继承一个基类，实现接口，这个里面主要是负责从数据源获取数据
	 *
	 * 我们这里作为一个简化，就不从外部的数据源去获取数据了，只是自己内部不断发射一些句子
	 *
	 * @author Administrator
	 *
	 */
	public static class RandomSentenceSpout extends BaseRichSpout {

		private static final long serialVersionUID = 3699352201538354417L;

		private static final Logger LOGGER = LoggerFactory.getLogger(RandomSentenceSpout.class);

		private SpoutOutputCollector collector;
		private Random random;

		/**
		 * open方法
		 *
		 * open方法，是对spout进行初始化的
		 *
		 * 比如说，创建一个线程池，或者创建一个数据库连接池，或者构造一个httpclient
		 *
		 */
		@SuppressWarnings("rawtypes")
		public void open(Map conf, TopologyContext context,
				SpoutOutputCollector collector) {
			// 在open方法初始化的时候，会传入进来一个东西，叫做SpoutOutputCollector
			// 这个SpoutOutputCollector就是用来发射数据出去的
			this.collector = collector;
			// 构造一个随机数生产对象
			this.random = new Random();
		}

		/**
		 * nextTuple方法
		 *
		 * 这个spout类，之前说过，最终会运行在task中，某个worker进程的某个executor线程内部的某个task中
		 * 那个task会负责去不断的无限循环调用nextTuple()方法
		 * 只要的话呢，无限循环调用，可以不断发射最新的数据出去，形成一个数据流
		 *
		 */
		public void nextTuple() {
			Utils.sleep(100);
			String[] sentences = new String[]{"the cow jumped over the moon", "an apple a day keeps the doctor away",
					"four score and seven years ago", "snow white and the seven dwarfs", "i am at two with nature"};
			String sentence = sentences[random.nextInt(sentences.length)];
			LOGGER.info("【发射句子】sentence=" + sentence);
			// 这个values，你可以认为就是构建一个tuple
			// tuple是最小的数据单位，无限个tuple组成的流就是一个stream
			collector.emit(new Values(sentence));
		}

		/**
		 * declareOutputFielfs这个方法
		 *
		 * 很重要，这个方法是定义一个你发射出去的每个tuple中的每个field的名称是什么
		 *
		 */
		public void declareOutputFields(OutputFieldsDeclarer declarer) {
			declarer.declare(new Fields("sentence"));
		}

	}

	/**
	 * 写一个bolt，直接继承一个BaseRichBolt基类
	 *
	 * 实现里面的所有的方法即可，每个bolt代码，同样是发送到worker某个executor的task里面去运行
	 *
	 * @author Administrator
	 *
	 */
	public static class SplitSentence extends BaseRichBolt {

		private static final long serialVersionUID = 6604009953652729483L;

		private OutputCollector collector;

		/**
		 * 对于bolt来说，第一个方法，就是prepare方法
		 *
		 * OutputCollector，这个也是Bolt的这个tuple的发射器
		 *
		 */
		@SuppressWarnings("rawtypes")
		public void prepare(Map conf, TopologyContext context, OutputCollector collector) {
			this.collector = collector;
		}

		/**
		 * execute方法
		 *
		 * 就是说，每次接收到一条数据后，就会交给这个executor方法来执行
		 *
		 */
		public void execute(Tuple tuple) {
			String sentence = tuple.getStringByField("sentence");
			String[] words = sentence.split(" ");
			for(String word : words) {
				collector.emit(new Values(word));
			}
		}

		/**
		 * 定义发射出去的tuple，每个field的名称
		 */
		public void declareOutputFields(OutputFieldsDeclarer declarer) {
			declarer.declare(new Fields("word"));
		}

	}

	public static class WordCount extends BaseRichBolt {

		private static final long serialVersionUID = 7208077706057284643L;

		private static final Logger LOGGER = LoggerFactory.getLogger(WordCount.class);

		private OutputCollector collector;
		private Map<String, Long> wordCounts = new HashMap<String, Long>();

		@SuppressWarnings("rawtypes")
		public void prepare(Map conf, TopologyContext context, OutputCollector collector) {
			this.collector = collector;
		}

		public void execute(Tuple tuple) {
			String word = tuple.getStringByField("word");

			Long count = wordCounts.get(word);
			if(count == null) {
				count = 0L;
			}
			count++;

			wordCounts.put(word, count);

			LOGGER.info("【单词计数】" + word + "出现的次数是" + count);

			collector.emit(new Values(word, count));
		}

		public void declareOutputFields(OutputFieldsDeclarer declarer) {
			declarer.declare(new Fields("word", "count"));
		}

	}

	public static void main(String[] args) {
		// 在main方法中，会去将spout和bolts组合起来，构建成一个拓扑
		TopologyBuilder builder = new TopologyBuilder();

		// 这里的第一个参数的意思，就是给这个spout设置一个名字
		// 第二个参数的意思，就是创建一个spout的对象
		// 第三个参数的意思，就是设置spout的executor有几个
		builder.setSpout("RandomSentence", new RandomSentenceSpout(), 2);
		builder.setBolt("SplitSentence", new SplitSentence(), 5)
				.setNumTasks(10)
				.shuffleGrouping("RandomSentence");
		// 这个很重要，就是说，相同的单词，从SplitSentence发射出来时，一定会进入到下游的指定的同一个task中
		// 只有这样子，才能准确的统计出每个单词的数量
		// 比如你有个单词，hello，下游task1接收到3个hello，task2接收到2个hello
		// 5个hello，全都进入一个task
		builder.setBolt("WordCount", new WordCount(), 10)
				.setNumTasks(20)
				.fieldsGrouping("SplitSentence", new Fields("word"));

		Config config = new Config();

		// 说明是在命令行执行，打算提交到storm集群上去
		if(args != null && args.length > 0) {
			config.setNumWorkers(3);
			try {
				StormSubmitter.submitTopology(args[0], config, builder.createTopology());
			} catch (Exception e) {
				e.printStackTrace();
			}
		} else {
			// 说明是在eclipse里面本地运行
			config.setMaxTaskParallelism(20);

			LocalCluster cluster = new LocalCluster();
			cluster.submitTopology("WordCountTopology", config, builder.createTopology());

			Utils.sleep(60000);

			cluster.shutdown();
		}
	}

}