kafka+storm+hbase如何实现计算WordCount

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kafka+storm+hbase实现计算WordCount。

（1）表名：wc

（2）列族：result

（3）RowKey：word

（4）Field：count

1、解决：

（1）第一步：首先准备kafka、storm和hbase相关jar包。依赖如下：

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"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">

  4.0.0

  com

  kafkaSpout

  0.0.1-SNAPSHOT

   

    

        

            org.apache.storm

            storm-core

            0.9.3

        

        

            org.apache.storm

            storm-kafka

            0.9.3

        

        

            org.apache.kafka

            kafka_2.10

            0.8.1.1

            

                

                    org.apache.zookeeper

                    zookeeper

                

                

                    log4j

                    log4j

                

            

        

        

            org.apache.hbase

            hbase-client

            0.99.2

            

                

                    org.slf4j

                    slf4j-log4j12

                

                

                    org.apache.zookeeper

                    zookeeper

                

            

        

         

       

 

         com.google.protobuf

 

         protobuf-java

 

         2.5.0

 

        

 

        

            org.apache.curator

            curator-framework

            2.5.0

            

                

                    log4j

                    log4j

                

                

                    org.slf4j

                    slf4j-log4j12

                

            

        

                                                                              

           

            jdk.tools

            jdk.tools

            1.7

            system

            C:\Program Files\Java\jdk1.7.0_51\lib\tools.jar

            

         

    

  

    

        

            central

            http://repo1.maven.org/maven2/

            

                false

            

            

                true

            

        

        

            clojars

            https://clojars.org/repo/

            

                true

            

            

                true

            

        

        

            scala-tools

            http://scala-tools.org/repo-releases

            

                true

            

            

                true

            

        

        

            conjars

            http://conjars.org/repo/

            

                true

            

            

                true

            

        

    

 

    

        

            

                org.apache.maven.plugins

                maven-compiler-plugin

                3.1

                

                    1.6

                    1.6

                    UTF-8

                    true

                    true

                

            

            

                maven-assembly-plugin

                

                    

                        jar-with-dependencies

                    

                    

                        

                            

                        

                    

                

                

                    

                        make-assembly

                        package

                        

                            single

                        

                    

                

            

        

    

 

（2）将kafka发来的数据通过levelSplit的bolt进行分割处理，然后再发送到下一个Bolt中。代码如下：

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package com.kafka.spout;

 

import java.util.regex.Matcher;

import java.util.regex.Pattern;

import backtype.storm.topology.BasicOutputCollector;

import backtype.storm.topology.OutputFieldsDeclarer;

import backtype.storm.topology.base.BaseBasicBolt;

import backtype.storm.tuple.Fields;

import backtype.storm.tuple.Tuple;

import backtype.storm.tuple.Values;

  

public class LevelSplit extends BaseBasicBolt {

  

    public void execute(Tuple tuple, BasicOutputCollector collector) {

        String words = tuple.getString(0).toString();//the cow jumped over the moon

        String []va=words.split(" ");

        for(String word : va)

        {

            collector.emit(new Values(word));

        }

         

    }

    

    public void declareOutputFields(OutputFieldsDeclarer declarer) {

        declarer.declare(new Fields("word"));

    }

 

}

（3）将levelSplit的Bolt发来的数据到levelCount的Bolt中进行计数处理，然后发送到hbase（Bolt）中。代码如下：

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package com.kafka.spout;

 

import java.util.HashMap;

import java.util.Map;

import java.util.Map.Entry;

 

import backtype.storm.topology.BasicOutputCollector;

import backtype.storm.topology.OutputFieldsDeclarer;

import backtype.storm.topology.base.BaseBasicBolt;

import backtype.storm.tuple.Fields;

import backtype.storm.tuple.Tuple;

import backtype.storm.tuple.Values;

  

public class LevelCount extends BaseBasicBolt {

    Map counts = new HashMap();

 

    public void execute(Tuple tuple, BasicOutputCollector collector) {

        // TODO Auto-generated method stub

        String word = tuple.getString(0);

        Integer count = counts.get(word);

        if (count == null)

            count = 0;

        count++;

        counts.put(word, count);

 

        for (Entry e : counts.entrySet()) {

            //sum += e.getValue();

            System.out.println(e.getKey()

                                + "----------->" +e.getValue());

        }

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

    }

 

    public void declareOutputFields(OutputFieldsDeclarer declarer) {

        // TODO Auto-generated method stub

         declarer.declare(new Fields("word", "count"));

    }

}

（4）准备连接kafka和hbase条件以及设置整个拓扑结构并且提交拓扑。代码如下：

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package com.kafka.spout;

  

import java.util.HashMap;

import java.util.Map;

 

import com.google.common.collect.Maps;

 

//import org.apache.storm.guava.collect.Maps;

  

import backtype.storm.Config;

import backtype.storm.LocalCluster;

import backtype.storm.StormSubmitter;

import backtype.storm.generated.AlreadyAliveException;

import backtype.storm.generated.InvalidTopologyException;

import backtype.storm.spout.SchemeAsMultiScheme;

import backtype.storm.topology.TopologyBuilder;

import backtype.storm.tuple.Fields;

import backtype.storm.utils.Utils;

import storm.kafka.BrokerHosts;

import storm.kafka.KafkaSpout;

import storm.kafka.SpoutConfig;

import storm.kafka.ZkHosts;

   

public class StormKafkaTopo {

    public static void main(String[] args) {

                  

        BrokerHosts brokerHosts = new ZkHosts("zeb,yjd,ylh");

        SpoutConfig spoutConfig = new SpoutConfig(brokerHosts, "yjd", "/storm", "kafkaspout");

        Config conf = new Config();  

        spoutConfig.scheme =  new SchemeAsMultiScheme(new MessageScheme());   

         

        SimpleHBaseMapper mapper = new SimpleHBaseMapper();

        mapper.withColumnFamily("result");

        mapper.withColumnFields(new Fields("count"));

        mapper.withRowKeyField("word");

         

        Map map = Maps.newTreeMap();

        map.put("hbase.rootdir", "hdfs://zeb:9000/hbase");

        map.put("hbase.zookeeper.quorum", "zeb:2181,yjd:2181,ylh:2181");

         

        // hbase-bolt

        HBaseBolt hBaseBolt = new HBaseBolt("wc", mapper).withConfigKey("hbase.conf");

 

        conf.setDebug(true);

        conf.put("hbase.conf", map);

          

        TopologyBuilder builder = new TopologyBuilder();

        builder.setSpout("spout", new KafkaSpout(spoutConfig));

        builder.setBolt("split", new LevelSplit(), 1).shuffleGrouping("spout");

        builder.setBolt("count", new LevelCount(), 1).fieldsGrouping("split", new Fields("word"));

        builder.setBolt("hbase", hBaseBolt, 1).shuffleGrouping("count");

         

        if(args != null && args.length > 0) {

            //提交到集群运行

            try {

                StormSubmitter.submitTopology(args[0], conf, builder.createTopology());

            } catch (AlreadyAliveException e) {

                e.printStackTrace();

            } catch (InvalidTopologyException e) {

                e.printStackTrace();

            }

        } else {

            //本地模式运行

            LocalCluster cluster = new LocalCluster();

            cluster.submitTopology("Topotest1121", conf, builder.createTopology());

            Utils.sleep(1000000);

            cluster.killTopology("Topotest1121");

            cluster.shutdown();

        }          

    }

}

（5）在kafka端用控制台生产数据，如下：

kafka+storm+hbase如何实现计算WordCount

2、运行结果截图：

 kafka+storm+hbase如何实现计算WordCount

3、遇到的问题：

（1）把所有的工作做好后，提交了拓扑，运行代码。发生了错误1，如下：

 kafka+storm+hbase如何实现计算WordCount

解决：原来是因为依赖版本要统一的问题，最后将版本修改一致后，成功解决。

（2）发生了错误2，如下：

 kafka+storm+hbase如何实现计算WordCount

解决：原来是忘记开hbase中的HMaster和HRegionServer。启动后问题成功解决。

感谢你能够认真阅读完这篇文章，希望小编分享的“kafka+storm+hbase如何实现计算WordCount”这篇文章对大家有帮助，同时也希望大家多多支持创新互联，关注创新互联行业资讯频道，更多相关知识等着你来学习!


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