Storm部署与运行

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部署Storm集群。

环境配置

Strom

下载

• 首先从官网下载Strom压缩包，这里以最新的Strom1.2.2作为演示。

• 解压到/usr/local：

  • sudo tar xzvf apache-storm-1.2.2.tar.gz -C /usr/local
    

• 查看解压文件：

  • 

• 需要重命名：

  • sudo mv /usr/local/apache-storm-1.2.2/ /usr/local/storm
    

• 修改拥有者：

  • sudo chown -R hadoop:hadoop storm
    

• 添加到环境变量

  • vim ~/.bashrc
    export STROM=/usr/local/storm
    source ~/.bashrc
    

  • 

修改配置文件

• 进入软件目录，修改文件storm.yaml

  • cd /usr/local/storm/
    sudo vim conf/storm.yaml
    

• 修改为本地server：

  • storm.zookeeper.servers:
    	- "localhost"
    	
     nimbus.seeds: ["localhost"]
    
     supervisor.slots.ports:
         - 6700
         - 6701
         - 6702
         - 6703
    
     storm.zookeeper.port: 2181
    
     storm.local.dir: "/usr/local/storm/storm-local"
    
     storm.health.check.dir: "healthchecks"
    
     storm.health.check.timeout.ms: 5000
    

  • 

Python

• 检查是否有高于2.6版本的python：
• 

启动storm

修改执行脚本

• 参考这里修改脚本，主要是修改python的路径

• 先查找自己的python路径：

  • ls /usr/bin/python*
    

  • 

• 找到执行脚本，在./bin/storm中，根据之前的python路径修改

  • 

启动

• 执行命令：

  • nohup storm nimbus >/dev/null 2>&1 &
    nohup storm supervisor >/dev/null 2>&1 &
    nohup storm ui >/dev/null 2>&1 &
    

  • 

  • 这里的2>&1表示就是把所有标准输出（&1）和标准出错（2）都扔到垃圾桶里面，最后的&表示后台执行

• 这里可能需要几分钟才能完成启动，使用jps命令查看可以看到nimbus, supervisor, core：

  • 

• 在浏览器中localhost:8080 上查看集群情况：

  • 

• 成功！

测试运行

示例

• /usr/local/storm/example目录下的storm-starter里有很多Storm的项目，比如DRPC、Word Count。

• 在该目录路径下使用maven来打包Stormjar包

• 首先找到该目录：

  • 

• 我们发现该目录是用maven进行打包的：

  • 

• 因此，我们可以使用IDEA来打包，也可以直接使用mvn：

  • mvn clean install -Dmaven.test.skip=true 
    mvn package
    

Stream Join的简单实例

WordCount

SentenceSpout.java

该文件为模拟外部输入

import java.util.Map;
import java.util.UUID;

import org.apache.storm.spout.SpoutOutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.BaseRichSpout;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Values;
import org.apache.storm.utils.Utils;

public class SentenceSpout extends BaseRichSpout {

    private SpoutOutputCollector spoutOutputCollector;
    private String[] sentences = {"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"};

    public void open(Map map, TopologyContext topologycontext, SpoutOutputCollector spoutoutputcollector) {
        this.spoutOutputCollector = spoutoutputcollector;
    }

    public void nextTuple() {
        for (String sentence : sentences) {
            Values values = new Values(sentence);
            UUID msgId = UUID.randomUUID();
            this.spoutOutputCollector.emit(values, msgId);
        }
        Utils.sleep(1000);
    }

    public void declareOutputFields(OutputFieldsDeclarer outputfieldsdeclarer) {
        outputfieldsdeclarer.declare(new Fields("sentence"));
    }

}

SplitSentenceBolt.java

分割字符的bolt

import java.util.Map;

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

public class SplitSentenceBolt extends BaseRichBolt {

    private OutputCollector outputCollector;

    public void execute(Tuple tuple) {
        String sentence = tuple.getStringByField("sentence");
        String[] words = sentence.split(" ");
        for (String word : words) {
            this.outputCollector.emit(new Values(word));
        }
    }

    public void prepare(Map map, TopologyContext topologycontext, OutputCollector outputcollector) {
        this.outputCollector = outputcollector;
    }

    public void declareOutputFields(OutputFieldsDeclarer outputfieldsdeclarer) {
        outputfieldsdeclarer.declare(new Fields("word"));
    }

}

WordCountBolt.java

统计次数的bolt

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

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

public class WordCountBolt extends BaseRichBolt {

    private OutputCollector outputCollector;
    private HashMap<String, Integer> counts = null;

    public void prepare(Map map, TopologyContext topologycontext, OutputCollector outputcollector) {
        this.outputCollector = outputcollector;
        this.counts = new HashMap<String, Integer>();
    }

    public void execute(Tuple tuple) {
        String word = tuple.getStringByField("word");
        Integer count = counts.get(word);
        if (count == null) {
            count = 0;
        }
        count++;
        this.counts.put(word, count);
        this.outputCollector.emit(new Values(word, count));
        this.outputCollector.ack(tuple);
        System.out.println(word + ": " + count);
    }

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

}

WordCountTopology.java

主函数

import org.apache.storm.Config;
import org.apache.storm.LocalCluster;
import org.apache.storm.topology.TopologyBuilder;
import org.apache.storm.tuple.Fields;

public class WordCountTopology {

    public static void main(String[] args) throws Exception {

        SentenceSpout sentenceSpout = new SentenceSpout();
        SplitSentenceBolt splitSentenceBolt = new SplitSentenceBolt();
        WordCountBolt wordCountBolt = new WordCountBolt();

        TopologyBuilder builder = new TopologyBuilder();
        builder.setSpout("sentenceSpout-1", sentenceSpout);
        builder.setBolt("splitSentenceBolt-1", splitSentenceBolt).shuffleGrouping("sentenceSpout-1");
        builder.setBolt("wordCountBolt-1", wordCountBolt).fieldsGrouping("splitSentenceBolt-1", new Fields("word"));

        Config config = new Config();
        LocalCluster cluster = new LocalCluster();

        cluster.submitTopology("wordCountTopology-1", config, builder.createTopology());
        Thread.sleep(999999999);
        cluster.shutdown();
    }

}

运行

• 使用已经编译好的WordCountjar包，提交任务：

  • storm jar wordcount.jar WordCountTopology wc
    

  • 这里WordCountTopoloy为主class，wc为任务别名

• 可以看见，WordCount的统计是不断变化的：

  • 

Stream Join

SimpleJoinBolt.java

这里主要处理join的过程

import org.apache.storm.Config;
import org.apache.storm.generated.GlobalStreamId;
import org.apache.storm.task.OutputCollector;
import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.BaseRichBolt;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Tuple;
import org.apache.storm.utils.TimeCacheMap;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.HashSet;
import java.util.List;
import java.util.Map;
import java.util.Set;



public class SimpleJoinBolt extends BaseRichBolt {
    private OutputCollector _collector;
    private Fields _outFields;
    private Fields _idFields;
    int _numSources;
    Map<String, GlobalStreamId> _fieldLocations;

    //在内存中保留近期活跃的对象
    //由于bolt在接收两个数据源的流数据时，同一id两个数据流很可能不会再一个时间点同时发出，因此需要对数据流先进行缓存，直到所有
    //id相同的数据源都被后被聚合处理，做完聚合处理后再将对应的tuple信息从缓存中删除。在
    TimeCacheMap<List<Object>,Map<GlobalStreamId, Tuple>> _pending;

    //传进的Fields是聚合后将被输出的字段
    public SimpleJoinBolt(Fields outFields){
        this._outFields=outFields;
    }
    public void execute(Tuple tuple) {
        // TODO Auto-generated method stub
        //从tuple中获取_idFields字段，如果不存在于等待被处理的队列_pending中，则加入一行
        List<Object> id=tuple.select(_idFields);
        GlobalStreamId streamId=new GlobalStreamId(tuple.getSourceComponent(),tuple.getSourceStreamId());
        //打印当前处理元组的来源Spout
        System.out.println("元组来源："+tuple.getSourceComponent());
        //打印当前元组
        System.out.println("接收的元组："+tuple.getFields().get(0)+" = "+tuple.getValues().get(0)+" , "+tuple.getFields().get(1)+" = "+tuple.getValues().get(1));
        //如果当前pending中还不存在join key为此id的元组，则将该条记录加入
        if(!_pending.containsKey(id)){
            _pending.put(id, new HashMap<GlobalStreamId,Tuple>());
        }
        //从_pending队列中获取当前GlobalStreamId对应的HashMap对象
        Map<GlobalStreamId,Tuple> parts=_pending.get(id);
        //如果streamId已经包含其中，则抛出异常，接收到同一个spout中的两条一样id的tuple，否则将该streamId加入parts中
        if(parts.containsKey(streamId)){
            throw new RuntimeException("Received same side of single join twice");
        }
        parts.put(streamId, tuple);

        //如果parts中已经包含了聚合数据源的个数，则从_pending队列中移除这条记录
        if(parts.size()==_numSources){
            _pending.remove(id);
            List<Object> joinResult=new ArrayList<Object>();
            for(String outField:_outFields){
                GlobalStreamId loc=_fieldLocations.get(outField);
                joinResult.add(parts.get(loc).getValueByField(outField));
            }
            //输出聚合结果
            System.out.print("两条关系流中id值为"+id.get(0)+"的元组均已收到，聚合结果为：");
            for(Object obj:joinResult){
                System.out.print(obj+" ");
            }
            System.out.println();

            //多锚定
            _collector.emit(new ArrayList<Tuple>(parts.values()),joinResult);
            for (Tuple part : parts.values()) {
                _collector.ack(part);
            }
        }else{
            System.out.println("只从一个关系流中收取到id值为"+id+"的元组，不可进行join操作");
        }
    }

    public void prepare(Map conf, TopologyContext context, OutputCollector collector) {
        // TODO Auto-generated method stub
        _fieldLocations = new HashMap<String, GlobalStreamId>();
        this._collector=collector;
        //创建TimeCacheMap对象，设置超时回调接口，用于tuple处理失败时fail消息
        int timeout=((Number)conf.get(Config.TOPOLOGY_MESSAGE_TIMEOUT_SECS)).intValue();
        _pending=new TimeCacheMap<List<Object>,Map<GlobalStreamId,Tuple>>(timeout,new ExpireCallback());
        //记录数据源的数据个数
        _numSources=context.getThisSources().size();

        Set<String> idFields=null;
        //遍历TopologyContext中不同的数据源:genderSpout和ageSpout
        System.out.println(context.getThisSources().keySet());
        for(GlobalStreamId source:context.getThisSources().keySet()){
            //得到公共的Fields字段id,保存到_idFields中
            Fields fields=context.getComponentOutputFields(source.get_componentId(),source.get_streamId());
            //fields:[id,gender],[id,age]
            Set<String> setFields=new HashSet<String>(fields.toList());
            if(idFields==null){
                idFields=setFields;
            }else{
                //求交集
                idFields.retainAll(setFields);
                System.out.println(idFields);
            }
            //同时将_outFields中字段所在数据源记录下来，保存到一张HashMap _fieldLocations中，以便聚合后获取对应的字段值
            for(String outfield:_outFields){
                for(String sourcefield:fields){
                    if(outfield.equals(sourcefield)){
                        _fieldLocations.put(outfield, source);
                    }
                }
            }
            //打印结果:gender=GlobalStreamId(componentId=gender-spout,streamId=default)
            //age=GlobalStreamId(componentId=age-spout,streamId=default)
            System.out.println(_fieldLocations);

        }
        _idFields=new Fields(new ArrayList<String>(idFields));
        if(_fieldLocations.size()!=_outFields.size()){
            throw new RuntimeException("Cannot find all outfields among sources");
        }
    }

    public void declareOutputFields(OutputFieldsDeclarer declarer) {
        // TODO Auto-generated method stub
        declarer.declare(_outFields);
    }

    private class ExpireCallback implements TimeCacheMap.ExpiredCallback<List<Object>, Map<GlobalStreamId,Tuple>>{

        public void expire(List<Object> key, Map<GlobalStreamId, Tuple> tuples) {
            // TODO Auto-generated method stub
            for(Tuple tuple:tuples.values()){
                _collector.fail(tuple);
            }
        }

    }

}

SingleJoinExample.java

这里处理两个spout和setBolt过程

import org.apache.storm.Config;
import org.apache.storm.LocalCluster;
import org.apache.storm.testing.FeederSpout;
import org.apache.storm.topology.TopologyBuilder;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Values;
import org.apache.storm.utils.Utils;

public class SingleJoinExample {
    public static void main(String[] args) {
        FeederSpout genderSpout = new FeederSpout(new Fields("id", "gender"));
        FeederSpout ageSpout = new FeederSpout(new Fields("id", "age"));

        TopologyBuilder builder = new TopologyBuilder();
        builder.setSpout("gender", genderSpout);
        builder.setSpout("age", ageSpout);
        builder.setBolt("join", new SimpleJoinBolt(new Fields("gender", "age")))
                .fieldsGrouping("gender", new Fields("id"))
                .fieldsGrouping("age", new Fields("id"));

        Config conf = new Config();
        conf.setMaxTaskParallelism(3);

        LocalCluster cluster = new LocalCluster();
        cluster.submitTopology("join-bolt", conf, builder.createTopology());

        for (int i = 0; i < 100; i++) {
            String gender;
            if (i % 2 == 0) {
                gender = "male";
            }
            else {
                gender = "female";
            }
            genderSpout.feed(new Values(i, gender));
        }

        for (int i = 100; i >= 0; i--) {
            ageSpout.feed(new Values(i, i + 20));
        }

        Utils.sleep(2000);
        cluster.shutdown();
    }
}

运行

首先生成JAR包，然后执行：

storm jar ds.jar SingleJoinExample join

运行结果为：

可以看见，当接收到的元祖没有匹配的id时，会等待另一个元祖到来，然后再聚合。

Reference

• Async-loop-died
• Storm处理Stream Join的简单实例