kafka+storm+hbase如何實現計算WordCount

發布時間：2021-12-09 10:25:16 來源：億速云閱讀：124 作者：小新欄目：大數據

這篇文章主要介紹了kafka+storm+hbase如何實現計算WordCount，具有一定借鑒價值，感興趣的朋友可以參考下，希望大家閱讀完這篇文章之后大有收獲，下面讓小編帶著大家一起了解一下。

kafka+storm+hbase實現計算WordCount。

（1）表名：wc

（2）列族：result

（3）RowKey：word

（4）Field：count

1、解決：

（1）第一步：首先準備kafka、storm和hbase相關jar包。依賴如下：

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<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</groupId>

<artifactId>kafkaSpout</artifactId>

<version>0.0.1-SNAPSHOT</version>

<dependencies>

<dependency>

<groupId>org.apache.storm</groupId>

<artifactId>storm-core</artifactId>

<version>0.9.3</version>

</dependency>

<dependency>

<groupId>org.apache.storm</groupId>

<artifactId>storm-kafka</artifactId>

<version>0.9.3</version>

</dependency>

<dependency>

<groupId>org.apache.kafka</groupId>

<artifactId>kafka_2.10</artifactId>

<version>0.8.1.1</version>

<exclusions>

<exclusion>

<groupId>org.apache.zookeeper</groupId>

<artifactId>zookeeper</artifactId>

</exclusion>

<exclusion>

<groupId>log4j</groupId>

<artifactId>log4j</artifactId>

</exclusion>

</exclusions>

</dependency>

<dependency>

<groupId>org.apache.hbase</groupId>

<artifactId>hbase-client</artifactId>

<version>0.99.2</version>

<exclusions>

<exclusion>

<groupId>org.slf4j</groupId>

<artifactId>slf4j-log4j12</artifactId>

</exclusion>

<exclusion>

<groupId>org.apache.zookeeper</groupId>

<artifactId>zookeeper</artifactId>

</exclusion>

</exclusions>

</dependency>

<dependency>

<groupId>com.google.protobuf</groupId>

<artifactId>protobuf-java</artifactId>

<version>2.5.0</version>

</dependency>

<dependency>

<groupId>org.apache.curator</groupId>

<artifactId>curator-framework</artifactId>

<version>2.5.0</version>

<exclusions>

<exclusion>

<groupId>log4j</groupId>

<artifactId>log4j</artifactId>

</exclusion>

<exclusion>

<groupId>org.slf4j</groupId>

<artifactId>slf4j-log4j12</artifactId>

</exclusion>

</exclusions>

</dependency>

<dependency>

<groupId>jdk.tools</groupId>

<artifactId>jdk.tools</artifactId>

<version>1.7</version>

<scope>system</scope>

<systemPath>C:\Program Files\Java\jdk1.7.0_51\lib\tools.jar</systemPath>

</dependency>

</dependencies>

<repositories>

<repository>

<id>central</id>

<url>http://repo1.maven.org/maven2/</url>

<snapshots>

<enabled>false</enabled>

</snapshots>

<releases>

<enabled>true</enabled>

</releases>

</repository>

<repository>

<id>clojars</id>

<url>https://clojars.org/repo/</url>

<snapshots>

<enabled>true</enabled>

</snapshots>

<releases>

<enabled>true</enabled>

</releases>

</repository>

<repository>

<id>scala-tools</id>

<url>http://scala-tools.org/repo-releases</url>

<snapshots>

<enabled>true</enabled>

</snapshots>

<releases>

<enabled>true</enabled>

</releases>

</repository>

<repository>

<id>conjars</id>

<url>http://conjars.org/repo/</url>

<snapshots>

<enabled>true</enabled>

</snapshots>

<releases>

<enabled>true</enabled>

</releases>

</repository>

</repositories>

<build>

<plugins>

<plugin>

<groupId>org.apache.maven.plugins</groupId>

<artifactId>maven-compiler-plugin</artifactId>

<version>3.1</version>

<configuration>

<source>1.6</source>

<target>1.6</target>

<encoding>UTF-8</encoding>

<showDeprecation>true</showDeprecation>

<showWarnings>true</showWarnings>

</configuration>

</plugin>

<plugin>

<artifactId>maven-assembly-plugin</artifactId>

<configuration>

<descriptorRefs>

<descriptorRef>jar-with-dependencies</descriptorRef>

</descriptorRefs>

<archive>

<manifest>

<mainClass></mainClass>

</manifest>

</archive>

</configuration>

<executions>

<execution>

<id>make-assembly</id>

<phase>package</phase>

<goals>

<goal>single</goal>

</goals>

</execution>

</executions>

</plugin>

</plugins>

</build>

</project>

（2）將kafka發來的數據通過levelSplit的bolt進行分割處理，然后再發送到下一個Bolt中。代碼如下：

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）將levelSplit的Bolt發來的數據到levelCount的Bolt中進行計數處理，然后發送到hbase（Bolt）中。代碼如下：

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

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<String, Integer> 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）準備連接kafka和hbase條件以及設置整個拓撲結構并且提交拓撲。代碼如下：

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<String, Object> 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) {

//提交到集群運行

try {

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

} catch (AlreadyAliveException e) {

e.printStackTrace();

} catch (InvalidTopologyException e) {

e.printStackTrace();

}

} else {

//本地模式運行

LocalCluster cluster = new LocalCluster();

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

Utils.sleep(1000000);

cluster.killTopology("Topotest1121");

cluster.shutdown();

}

（5）在kafka端用控制臺生產數據，如下：

kafka+storm+hbase如何實現計算WordCount

2、運行結果截圖：

kafka+storm+hbase如何實現計算WordCount

3、遇到的問題：

（1）把所有的工作做好后，提交了拓撲，運行代碼。發生了錯誤1，如下：

kafka+storm+hbase如何實現計算WordCount

解決：原來是因為依賴版本要統一的問題，最后將版本修改一致后，成功解決。

（2）發生了錯誤2，如下：

kafka+storm+hbase如何實現計算WordCount

解決：原來是忘記開hbase中的HMaster和HRegionServer。啟動后問題成功解決。

感謝你能夠認真閱讀完這篇文章，希望小編分享的“kafka+storm+hbase如何實現計算WordCount”這篇文章對大家有幫助，同時也希望大家多多支持億速云，關注億速云行業資訊頻道，更多相關知識等著你來學習!

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