Storm的wordcount实战示例

有关strom的具体介绍，本文不再过多叙述，不了解的朋友可参考之前的文章
http://qindongliang.iteye.com/category/361820
本文主要以一个简单的wordcount例子，来了解下storm应用程序的开发，虽然只是一个简单的例子
但麻雀虽小，五脏俱全，主要涉及的内容：

（1）wordcount的拓扑定义
（2）spout的使用
（3）bolt的使用
（4）tick定时器的使用
（5） bolt之间数据传输的坑
简单的数据流程图如下：





提交到storm集群上的拓扑图：






maven项目的pom依赖：

<?xml version="1.0" encoding="UTF-8"?>
<project xmlns="http://maven.apache.org/POM/4.0.0"
         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.jstrom.demo</groupId>
    <artifactId>jstrom-test</artifactId>
    <version>1.0-SNAPSHOT</version>



    <properties>

        <jstorm.version>2.1.1</jstorm.version>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <slf4j.version>1.7.12</slf4j.version>
        <joad-time.version>2.9.4</joad-time.version>
        <storm-kafka.version>0.9.4</storm-kafka.version>
        <kafka.version>0.9.0.0</kafka.version>
        <esper.version>5.4.0</esper.version>



     </properties>






    <dependencies>


        <!-- https://mvnrepository.com/artifact/com.espertech/esper -->



        <!-- https://mvnrepository.com/artifact/joda-time/joda-time -->
        <dependency>
            <groupId>joda-time</groupId>
            <artifactId>joda-time</artifactId>
            <version>${joad-time.version}</version>
        </dependency>

        <!-- https://mvnrepository.com/artifact/org.apache.kafka/kafka_2.11 -->
        <dependency>
            <groupId>org.apache.kafka</groupId>
            <artifactId>kafka_2.11</artifactId>
            <version>${kafka.version}</version>
            <scope>provided</scope>
        </dependency>


        <dependency>
            <groupId>com.alibaba.jstorm</groupId>
            <artifactId>jstorm-core</artifactId>
            <version>${jstorm.version}</version>
            <scope>provided</scope>
        </dependency>
        <!-- https://mvnrepository.com/artifact/org.apache.storm/storm-kafka -->
        <dependency>
            <groupId>org.apache.storm</groupId>
            <artifactId>storm-kafka</artifactId>
            <version>${storm-kafka.version}</version>
            <scope>provided</scope>
        </dependency>





        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-jdk14</artifactId>
            <version>${slf4j.version}</version>
        </dependency>
        <dependency>
            <groupId>org.slf4j</groupId>
            <artifactId>slf4j-nop</artifactId>
            <version>${slf4j.version}</version>
        </dependency>


    </dependencies>

    <build>
        <plugins>
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-compiler-plugin</artifactId>
                <version>2.3.2</version>
                <configuration>
                    <source>1.7</source>
                    <target>1.7</target>
                </configuration>
            </plugin>
            <plugin>
                <artifactId>maven-assembly-plugin</artifactId>
                <configuration>
                    <archive>
                        <manifest>
                            <addClasspath>true</addClasspath>
                            <mainClass>换成自己的主类</mainClass>
                        </manifest>
                    </archive>
                    <descriptorRefs>
                        <descriptorRef>jar-with-dependencies</descriptorRef>
                    </descriptorRefs>
                </configuration>
                <executions>
                    <execution>
                        <id>make-my-jar-with-dependencies</id>
                        <phase>package</phase>
                        <goals>
                            <goal>single</goal>
                        </goals>
                    </execution>
                </executions>
            </plugin>
        </plugins>
    </build>

</project>

（1）Topology主拓扑类：

package com.jstorm.wd;

import backtype.storm.Config;
import backtype.storm.LocalCluster;
import backtype.storm.StormSubmitter;
import backtype.storm.topology.TopologyBuilder;
import backtype.storm.tuple.Fields;

/**
 * Created by QinDongLiang on 2016/9/12.
 */
public class TopologyWordCount {

    public static void main(String[] args) throws  Exception {
        TopologyBuilder builder=new TopologyBuilder();
        //设置数据源
        builder.setSpout("spout",new CreateSentenceSpout(),1);
        //读取spout数据源的数据，进行split业务逻辑
        builder.setBolt("split",new SplitWordBolt(),1).shuffleGrouping("spout");
        //读取split后的数据，进行count (tick周期10秒)
        builder.setBolt("count",new SumWordBolt(),1).fieldsGrouping("split",new Fields("word"));
        //读取count后的数据，进行缓冲打印 （tick周期3秒，仅仅为测试tick使用，所以多加了这个bolt）
        builder.setBolt("show",new ShowBolt(),1).shuffleGrouping("count");
        //读取show后缓冲后的数据，进行最终的打印 （实际应用中，最后一个阶段应该为持久层）
        builder.setBolt("final",new FinalBolt(),1).allGrouping("show");

        Config config=new Config();
        config.setDebug(false);
        //集群模式
        if(args!=null&&args.length>0){
            config.setNumWorkers(2);
            StormSubmitter.submitTopology(args[0],config,builder.createTopology());
        //单机模式
        }else{
            config.setMaxTaskParallelism(1);;
            LocalCluster cluster=new LocalCluster();
            cluster.submitTopology("word-count",config,builder.createTopology());
            Thread.sleep(3000000);
            cluster.shutdown();
        }
    }

}

（2）Spout数据源类

package com.jstorm.wd;

import backtype.storm.spout.SpoutOutputCollector;
import backtype.storm.task.TopologyContext;
import backtype.storm.topology.OutputFieldsDeclarer;
import backtype.storm.topology.base.BaseRichSpout;
import backtype.storm.tuple.Fields;
import backtype.storm.tuple.Values;
import backtype.storm.utils.Utils;
import org.joda.time.DateTime;

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

/**
 * Created by QinDongLiang on 2016/8/31.
 * 创建数据源
 */
public class CreateSentenceSpout extends BaseRichSpout {
    //
    SpoutOutputCollector collector;
    Random random;
    String [] sentences=null;

    @Override
    public void open(Map map, TopologyContext topologyContext, SpoutOutputCollector spoutOutputCollector) {
        this.collector=spoutOutputCollector;//spout_collector
        random=new Random();//
        sentences=new String[]{"hadoop hadoop hadoop java java "};

    }

    @Override
    public void nextTuple() {
        Utils.sleep(10000);
        //获取数据
        String sentence=sentences[random.nextInt(sentences.length)];
        System.out.println("线程名："+Thread.currentThread().getName()+"  "+new DateTime().toString("yyyy-MM-dd HH:mm:ss  ")+"10s发射一次数据："+sentence);
        //向下游发射数据
        this.collector.emit(new Values(sentence));
    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer outputFieldsDeclarer) {
        outputFieldsDeclarer.declare(new Fields("sentence"));
    }
}

（3）Split的bolt类

package com.jstorm.wd;

import backtype.storm.task.OutputCollector;
import backtype.storm.task.TopologyContext;
import backtype.storm.topology.OutputFieldsDeclarer;
import backtype.storm.topology.base.BaseRichBolt;
import backtype.storm.tuple.Fields;
import backtype.storm.tuple.Tuple;
import backtype.storm.tuple.Values;

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

/**
 * 简单的按照空格进行切分后，发射到下一阶段bolt
 * Created by QinDongLiang on 2016/8/31.
 */
public class SplitWordBolt extends BaseRichBolt {

    Map<String,Integer> counts=new HashMap<>();

    private OutputCollector outputCollector;

    @Override
    public void prepare(Map map, TopologyContext topologyContext, OutputCollector outputCollector) {
        this.outputCollector=outputCollector;
    }

    @Override
    public void execute(Tuple tuple) {
        String sentence=tuple.getString(0);
//        System.out.println("线程"+Thread.currentThread().getName());
//        简单的按照空格进行切分后，发射到下一阶段bolt
       for(String word:sentence.split(" ") ){
           outputCollector.emit(new Values(word));//发送split
       }

    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer outputFieldsDeclarer) {
        //声明输出的filed
        outputFieldsDeclarer.declare(new Fields("word"));
    }
}

（4）Sum的bolt类

package com.jstorm.wd;

import backtype.storm.Config;
import backtype.storm.task.OutputCollector;
import backtype.storm.task.TopologyContext;
import backtype.storm.topology.OutputFieldsDeclarer;
import backtype.storm.topology.base.BaseRichBolt;
import backtype.storm.tuple.Fields;
import backtype.storm.tuple.Tuple;
import backtype.storm.tuple.Values;
import backtype.storm.utils.TupleHelpers;
import backtype.storm.utils.Utils;
import org.joda.time.DateTime;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

import java.io.*;
import java.util.HashMap;
import java.util.Map;

/**
 * Created by QinDongLiang on 2016/8/31.
 */
public class SumWordBolt extends BaseRichBolt {

    Map<String,Integer> counts=new HashMap<>();

    private OutputCollector outputCollector;
    final static Logger logger= LoggerFactory.getLogger(SumWordBolt.class);
    @Override
    public void prepare(Map map, TopologyContext topologyContext, OutputCollector outputCollector) {
        this.outputCollector=outputCollector;
    }

    @Override
    public Map<String, Object> getComponentConfiguration() {
        Map<String, Object> conf = new HashMap<String, Object>();
        conf.put(Config.TOPOLOGY_TICK_TUPLE_FREQ_SECS, 10);//加入Tick时间窗口，进行统计
        return conf;
    }

    public static Object deepCopy(Object srcObj) {
        Object cloneObj = null;
        try {
            ByteArrayOutputStream out = new ByteArrayOutputStream();
            ObjectOutputStream oo = new ObjectOutputStream(out);
            oo.writeObject(srcObj);

            ByteArrayInputStream in = new ByteArrayInputStream(out.toByteArray());
            ObjectInputStream oi = new ObjectInputStream(in);
            cloneObj = oi.readObject();
        } catch(IOException e) {
            e.printStackTrace();
        } catch(ClassNotFoundException e) {
            e.printStackTrace();
        }
        return cloneObj;
    }

    @Override
    public void execute(Tuple tuple) {
        //时间窗口定义为10s内的统计数据，统计完毕后，发射到下一阶段的bolt进行处理
        //发射完成后retun结束，开始新一轮的时间窗口计数操作
        if(TupleHelpers.isTickTuple(tuple)){
            System.out.println(new DateTime().toString("yyyy-MM-dd HH:mm:ss")+" 每隔10s发射一次map 大小："+counts.size());
//            Map<String,Integer> copyMap= (Map<String, Integer>) deepCopy(counts);
            outputCollector.emit(new Values(counts));//10S发射一次
//            counts.clear();
           counts=new HashMap<>();//这个地方，不能执行clear方法，可以再new一个对象，否则下游接受的数据，有可能为空 或者深度copy也行，推荐new
            return;
        }

        //如果没到发射时间，就继续统计wordcount
        System.out.println("线程"+Thread.currentThread().getName()+"  map 缓冲统计中......  map size："+counts.size());
        //String word=tuple.getString(0);//如果有多tick，就不用使用这种方式获取tuple里面的数据
        String word=tuple.getStringByField("word");
        Integer count=counts.get(word);
        if(count==null){
            count=0;
        }
         count++;
         counts.put(word,count);


//        System.out.println(word+" =====>  "+count);



    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer outputFieldsDeclarer) {

        outputFieldsDeclarer.declare(new Fields("word_map"));
    }
}

（5）Show的bolt类

/**
 * Created by QinDongLiang on 2016/9/12.
 */
public class ShowBolt extends BaseRichBolt {


    private  OutputCollector outputCollector;

    @Override
    public Map<String, Object> getComponentConfiguration() {
        Map<String, Object> conf = new HashMap<String, Object>();
        conf.put(Config.TOPOLOGY_TICK_TUPLE_FREQ_SECS, 3);//tick时间窗口3秒后，发射到下一阶段的bolt，仅为测试用
        return conf;
    }

    @Override
    public void prepare(Map map, TopologyContext topologyContext, OutputCollector outputCollector) {
        this.outputCollector=outputCollector;
    }

    Map<String,Integer> counts=new HashMap<>();

    @Override
    public void execute(Tuple tuple) {
 //tick时间窗口3秒后，发射到下一阶段的bolt，仅为测试用，故多加了这个bolt逻辑
        if(TupleHelpers.isTickTuple(tuple)){
            System.out.println(new DateTime().toString("yyyy-MM-dd HH:mm:ss")+"  showbolt间隔  应该是 3 秒后 ");
//        System.out.println("what: "+tuple.getValue(0)+"  "+tuple.getFields().toList());
            outputCollector.emit(new Values(counts));
        return;
        }

        counts= (Map<String, Integer>) tuple.getValueByField("word_map");




    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer outputFieldsDeclarer) {

         outputFieldsDeclarer.declare(new Fields("final_result"));
    }
}

（6）Final的bolt类

package com.jstorm.wd;

import backtype.storm.task.OutputCollector;
import backtype.storm.task.TopologyContext;
import backtype.storm.topology.OutputFieldsDeclarer;
import backtype.storm.topology.base.BaseRichBolt;
import backtype.storm.tuple.Tuple;
import org.joda.time.DateTime;

import java.util.Map;

/**
 * Created by QinDongLiang on 2016/9/12.
 * 最终的结果打印bolt
 */
public class FinalBolt extends BaseRichBolt {

    @Override
    public void prepare(Map map, TopologyContext topologyContext, OutputCollector outputCollector) {

    }

    @Override
    public void execute(Tuple tuple) {
//        最终的结果打印bolt
        System.out.println(new DateTime().toString("yyyy-MM-dd HH:mm:ss")+"  final bolt ");
        Map<String,Integer> counts= (Map<String, Integer>) tuple.getValue(0);
        for(Map.Entry<String,Integer> kv:counts.entrySet()){
            System.out.println(kv.getKey()+"  "+kv.getValue());
        }
        //实际应用中，最后一个阶段，大部分应该是持久化到mysql，redis，es，solr或mongodb中
    }

    @Override
    public void declareOutputFields(OutputFieldsDeclarer outputFieldsDeclarer) {

    }
}

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