Storm之——Storm+Kafka+Flume+Zookeeper+MySQL实现数据实时分析(程序案例篇)

改文章为转载,出处为http://blog.csdn.net/l1028386804/article/details/79441007,本文已按照作者要求转载

转载请注明出处：http://blog.csdn.net/l1028386804/article/details/79441007

一、前言

本博文是基于《Storm之——Storm+Kafka+Flume+Zookeeper+MySQL实现数据实时分析(环境搭建篇)》,请先阅读《Storm之——Storm+Kafka+Flume+Zookeeper+MySQL实现数据实时分析(环境搭建篇)》

首先我们启动服务器上的Storm、Kafka、Flume、Zookeeper和MySQL，具体参见博文《Storm之——Storm+Kafka+Flume+Zookeeper+MySQL实现数据实时分析(环境搭建篇)》。

二、简单介绍

为了方便，这里我们只是简单的向/home/flume/log.log中追加单词，每行一个单词，利用Storm接收每个单词，将单词计数更新到数据库，具体的逻辑为，如果数据库中没有相关单词，则将数据插入数据库，如果存在相关单词，则更新数据库中的计数。具体SQL逻辑参见博文《MySQL之——实现无数据插入，有数据更新》

三、程序实现

1、创建项目

创建Maven项目结构如下：

2、配置pom.xml

     
     
<?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.lyz </groupId>
<artifactId>storm-kafka-mysql </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>org.apache.storm </groupId>
<artifactId>storm-kafka </artifactId>
<version>1.1.0 </version>
</dependency>
<dependency>
<groupId>redis.clients </groupId>
<artifactId>jedis </artifactId>
<version>2.7.3 </version>
</dependency>

<dependency>
<groupId>mysql </groupId>
<artifactId>mysql-connector-java </artifactId>
<version>5.1.28 </version>
</dependency>

<dependency>
<groupId>c3p0 </groupId>
<artifactId>c3p0 </artifactId>
<version>0.9.1.2 </version>
</dependency>

<dependency>
<groupId>org.apache.kafka </groupId>
<artifactId>kafka_2.12 </artifactId>
<version>1.0.0 </version>
<exclusions>
<exclusion>
<groupId>org.apache.zookeeper </groupId>
<artifactId>zookeeper </artifactId>
</exclusion>
<exclusion>
<groupId>log4j </groupId>
<artifactId>log4j </artifactId>
</exclusion>
<exclusion>
<groupId>org.slf4j </groupId>
<artifactId>slf4j-log4j12 </artifactId>
</exclusion>
</exclusions>
</dependency>

<dependency>
<groupId>org.apache.kafka </groupId>
<artifactId>kafka-clients </artifactId>
<version>1.0.0 </version>
</dependency>

</dependencies>
<build>
<plugins>
<plugin>
<artifactId>maven-assembly-plugin </artifactId>
<configuration>
<descriptorRefs>
<descriptorRef>jar-with-dependencies </descriptorRef>
</descriptorRefs>
<archive>
<manifest>
<!--告诉运行的主类是哪个，注意根据自己的情况，下面的包名做相应的修改-->
<mainClass>com.lyz.storm.StormTopologyDriver </mainClass>
</manifest>
</archive>
</configuration>
<executions>
<execution>
<id>make-assembly </id>
<phase>package </phase>
<goals>
<goal>single </goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins </groupId>
<artifactId>maven-compiler-plugin </artifactId>
<configuration>
<source>1.8 </source>
<target>1.8 </target>
</configuration>
</plugin>
</plugins>
</build>
</project>
     
     

3、实现单词分割计数的MySplitBolt类

     
     
package com.lyz.storm.bolt;

import org.apache.storm.topology.BasicOutputCollector;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.BaseBasicBolt;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Tuple;
import org.apache.storm.tuple.Values;

/
* 这个Bolt模拟从kafkaSpout接收数据，并把数据信息发送给MyWordCountAndPrintBolt的过程。
* @author liuyazhuang

/
public class MySplitBolt extends BaseBasicBolt {

private static final long serialVersionUID = 4482101012916443908L;

@Override
public void execute(Tuple input, BasicOutputCollector collector) {
//1、数据如何获取
//如果StormTopologyDriver中的spout配置的是MyLocalFileSpout，则用的是declareOutputFields中的juzi这个key
//byte[] juzi = (byte[]) input.getValueByField("juzi");
//2、这里用这个是因为StormTopologyDriver这个里面的spout用的是KafkaSpout，而KafkaSpout中的declareOutputFields返回的是bytes，所以下面用bytes，这个地方主要模拟的是从kafka中获取数据
byte[] juzi = ( byte[]) input.getValueByField( "bytes");
//2、进行切割
String[] strings = new String(juzi).split( " ");
//3、发送数据
for (String word : strings) {
//Values对象帮我们生成一个list
collector.emit( new Values(word, 1));
}
}

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

4、实现入库操作的MyWordCountAndPrintBolt类

     
     
package com.lyz.storm.bolt;

import java.sql.Connection;
import java.sql.SQLException;
import java.sql.Statement;
import java.util.Map;

import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.BasicOutputCollector;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.BaseBasicBolt;
import org.apache.storm.tuple.Tuple;

import com.lyz.storm.db.DBProvider;


/
* 用于统计分析，并且把统计分析的结果存储到mysql中。
* @author liuyazhuang

/
public class MyWordCountAndPrintBolt extends BaseBasicBolt {

private static final long serialVersionUID = 5564341843792874197L;
private DBProvider provider;
@Override
public void prepare(Map stormConf, TopologyContext context) {
//连接redis---代表可以连接任何事物
provider = new DBProvider();
super.prepare(stormConf,context);
}

@Override
public void execute(Tuple input, BasicOutputCollector collector) {
String word = (String) input.getValueByField( "word");
Integer num = (Integer) input.getValueByField( "num");
Connection conn = null;
Statement stmt = null;
try {
conn = provider.getConnection();
stmt = conn.createStatement() ;
stmt.executeUpdate( "INSERT INTO word_count (word, count) VALUES ('" + word + "', " + num + ") ON DUPLICATE KEY UPDATE count = count + " + num) ;
} catch (SQLException e) {
e.printStackTrace();
} finally{
if(stmt != null){
try {
stmt.close();
stmt = null;
} catch (Exception e2) {
e2.printStackTrace();
}
}
if(conn != null){
try {
conn.close();
conn = null;
} catch (Exception e2) {
e2.printStackTrace();
}
}
}
}

@Override
public void declareOutputFields(OutputFieldsDeclarer outputFieldsDeclarer) {
//todo 不需要定义输出的字段
}
}
     
     

5、实现操作数据库的DBProvider类

     
     
package com.lyz.storm.db;

import java.beans.PropertyVetoException;
import java.sql.Connection;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;

import com.mchange.v2.c3p0.ComboPooledDataSource;

/
* JDBC操作数据库
* @author liuyazhuang

/
public class DBProvider {

private static ComboPooledDataSource source ;
private static final String DB_DRIVER = "com.mysql.jdbc.Driver";
private static final String DB_URL = "jdbc:mysql://127.0.0.1:3306/sharding_0?useUnicode=true&characterEncoding=UTF-8&useOldAliasMetadataBehavior=true";
private static final String USER = "root";
private static final String PASSWORD = "root";
private static Connection connection;

static{
try {
source = new ComboPooledDataSource();
source.setDriverClass(DB_DRIVER);
source.setJdbcUrl(DB_URL);
source.setUser(USER);
source.setPassword(PASSWORD);
source.setInitialPoolSize( 10);
source.setMaxPoolSize( 20);
source.setMinPoolSize( 5);
source.setAcquireIncrement( 1);
source.setMaxIdleTime( 3);
source.setMaxStatements( 3000);
source.setCheckoutTimeout( 2000);
} catch (PropertyVetoException e) {
e.printStackTrace();
}
}

/
* 获取数据库连接

@return 数据库连接
/
public Connection getConnection() throws SQLException {
connection = source.getConnection();
return connection;
}


//关闭操作
public static void closeConnection(Connection con){
if(con!= null){
try {
con.close();
} catch (SQLException e) {
e.printStackTrace();
}
}
}

public static void closeResultSet(ResultSet rs){
if(rs!= null){
try {
rs.close();
} catch (SQLException e) {
e.printStackTrace();
}
}
}

public static void closePreparedStatement(PreparedStatement ps){
if(ps!= null){
try {
ps.close();
} catch (SQLException e) {
e.printStackTrace();
}
}
}
}
     
     

6、实现程序的入口类StormTopologyDriver

     
     
package com.lyz.storm;

import org.apache.storm.Config;
import org.apache.storm.LocalCluster;
import org.apache.storm.StormSubmitter;
import org.apache.storm.generated.StormTopology;
import org.apache.storm.kafka.KafkaSpout;
import org.apache.storm.kafka.SpoutConfig;
import org.apache.storm.kafka.ZkHosts;
import org.apache.storm.topology.TopologyBuilder;

import com.lyz.storm.bolt.MySplitBolt;
import com.lyz.storm.bolt.MyWordCountAndPrintBolt;

/*
* 这个Driver使Kafka、strom、mysql进行串联起来。

这个代码执行前需要创建kafka的topic,创建代码如下：
* [root@liuyazhuang kafka]# bin/kafka-topics.sh --create --zookeeper liuyazhuang1:2181 --replication-factor 1 -partitions 3 --topic wordCount

接着还要向kafka中传递数据，打开一个shell的producer来模拟生产数据
* [root@liuyazhuang kafka]# bin/kafka-console-producer.sh --broker-list liuyazhuang:9092 --topic wordCount
* 接着输入数据

@author liuyazhuang
*/
public class StormTopologyDriver {

public static void main(String[] args) throws Exception {
//1、准备任务信息
TopologyBuilder topologyBuilder = new TopologyBuilder();
SpoutConfig spoutConfig = new SpoutConfig( new ZkHosts( "192.168.209.121:2181"), "wordCount", "/wordCount", "wordCount");
topologyBuilder.setSpout( "KafkaSpout", new KafkaSpout(spoutConfig), 2);
topologyBuilder.setBolt( "bolt1", new MySplitBolt(), 4).shuffleGrouping( "KafkaSpout");
topologyBuilder.setBolt( "bolt2", new MyWordCountAndPrintBolt(), 2).shuffleGrouping( "bolt1");

//2、任务提交
Config config = new Config();
config.setNumWorkers( 2);
StormTopology stormTopology = topologyBuilder.createTopology();

if(args != null && args.length > 0){
StormSubmitter.submitTopology(args[ 0], config, topologyBuilder.createTopology());
} else{
//本地模式
LocalCluster localCluster = new LocalCluster();
localCluster.submitTopology( "wordcount",config,stormTopology);
}

}
}
     
     

7、创建数据库

执行如下脚本创建数据库

     
     
create database sharding_0;
CREATE TABLE word_count (
id int( 11) NOT NULL AUTO_INCREMENT,
word varchar( 255) DEFAULT '',
count int( 11) DEFAULT NULL,
PRIMARY KEY ( id),
UNIQUE KEY word ( word) USING BTREE
) ENGINE= InnoDB AUTO_INCREMENT= 233 DEFAULT CHARSET=utf8;
     
     

至此，我们的程序案例编写完成。

四、温馨提示

大家可以到链接http://download.csdn.net/download/l1028386804/10269075下载完整的Storm+Kafka+Flume+Zookeeper+MySQL实现数据实时分析(程序案例篇)源代码

转载请注明出处：http://blog.csdn.net/l1028386804/article/details/79441007

一、前言

本博文是基于《Storm之——Storm+Kafka+Flume+Zookeeper+MySQL实现数据实时分析(环境搭建篇)》,请先阅读《Storm之——Storm+Kafka+Flume+Zookeeper+MySQL实现数据实时分析(环境搭建篇)》

首先我们启动服务器上的Storm、Kafka、Flume、Zookeeper和MySQL，具体参见博文《Storm之——Storm+Kafka+Flume+Zookeeper+MySQL实现数据实时分析(环境搭建篇)》。

二、简单介绍

为了方便，这里我们只是简单的向/home/flume/log.log中追加单词，每行一个单词，利用Storm接收每个单词，将单词计数更新到数据库，具体的逻辑为，如果数据库中没有相关单词，则将数据插入数据库，如果存在相关单词，则更新数据库中的计数。具体SQL逻辑参见博文《MySQL之——实现无数据插入，有数据更新》

三、程序实现

1、创建项目

创建Maven项目结构如下：

2、配置pom.xml

  
  
<?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.lyz </groupId>
<artifactId>storm-kafka-mysql </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>org.apache.storm </groupId>
<artifactId>storm-kafka </artifactId>
<version>1.1.0 </version>
</dependency>
<dependency>
<groupId>redis.clients </groupId>
<artifactId>jedis </artifactId>
<version>2.7.3 </version>
</dependency>

<dependency>
<groupId>mysql </groupId>
<artifactId>mysql-connector-java </artifactId>
<version>5.1.28 </version>
</dependency>

<dependency>
<groupId>c3p0 </groupId>
<artifactId>c3p0 </artifactId>
<version>0.9.1.2 </version>
</dependency>

<dependency>
<groupId>org.apache.kafka </groupId>
<artifactId>kafka_2.12 </artifactId>
<version>1.0.0 </version>
<exclusions>
<exclusion>
<groupId>org.apache.zookeeper </groupId>
<artifactId>zookeeper </artifactId>
</exclusion>
<exclusion>
<groupId>log4j </groupId>
<artifactId>log4j </artifactId>
</exclusion>
<exclusion>
<groupId>org.slf4j </groupId>
<artifactId>slf4j-log4j12 </artifactId>
</exclusion>
</exclusions>
</dependency>

<dependency>
<groupId>org.apache.kafka </groupId>
<artifactId>kafka-clients </artifactId>
<version>1.0.0 </version>
</dependency>

</dependencies>
<build>
<plugins>
<plugin>
<artifactId>maven-assembly-plugin </artifactId>
<configuration>
<descriptorRefs>
<descriptorRef>jar-with-dependencies </descriptorRef>
</descriptorRefs>
<archive>
<manifest>
<!--告诉运行的主类是哪个，注意根据自己的情况，下面的包名做相应的修改-->
<mainClass>com.lyz.storm.StormTopologyDriver </mainClass>
</manifest>
</archive>
</configuration>
<executions>
<execution>
<id>make-assembly </id>
<phase>package </phase>
<goals>
<goal>single </goal>
</goals>
</execution>
</executions>
</plugin>
<plugin>
<groupId>org.apache.maven.plugins </groupId>
<artifactId>maven-compiler-plugin </artifactId>
<configuration>
<source>1.8 </source>
<target>1.8 </target>
</configuration>
</plugin>
</plugins>
</build>
</project>
  
  

3、实现单词分割计数的MySplitBolt类

  
  
package com.lyz.storm.bolt;

import org.apache.storm.topology.BasicOutputCollector;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.BaseBasicBolt;
import org.apache.storm.tuple.Fields;
import org.apache.storm.tuple.Tuple;
import org.apache.storm.tuple.Values;

/
* 这个Bolt模拟从kafkaSpout接收数据，并把数据信息发送给MyWordCountAndPrintBolt的过程。
* @author liuyazhuang

/
public class MySplitBolt extends BaseBasicBolt {

private static final long serialVersionUID = 4482101012916443908L;

@Override
public void execute(Tuple input, BasicOutputCollector collector) {
//1、数据如何获取
//如果StormTopologyDriver中的spout配置的是MyLocalFileSpout，则用的是declareOutputFields中的juzi这个key
//byte[] juzi = (byte[]) input.getValueByField("juzi");
//2、这里用这个是因为StormTopologyDriver这个里面的spout用的是KafkaSpout，而KafkaSpout中的declareOutputFields返回的是bytes，所以下面用bytes，这个地方主要模拟的是从kafka中获取数据
byte[] juzi = ( byte[]) input.getValueByField( "bytes");
//2、进行切割
String[] strings = new String(juzi).split( " ");
//3、发送数据
for (String word : strings) {
//Values对象帮我们生成一个list
collector.emit( new Values(word, 1));
}
}

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

4、实现入库操作的MyWordCountAndPrintBolt类

  
  
package com.lyz.storm.bolt;

import java.sql.Connection;
import java.sql.SQLException;
import java.sql.Statement;
import java.util.Map;

import org.apache.storm.task.TopologyContext;
import org.apache.storm.topology.BasicOutputCollector;
import org.apache.storm.topology.OutputFieldsDeclarer;
import org.apache.storm.topology.base.BaseBasicBolt;
import org.apache.storm.tuple.Tuple;

import com.lyz.storm.db.DBProvider;


/
* 用于统计分析，并且把统计分析的结果存储到mysql中。
* @author liuyazhuang

/
public class MyWordCountAndPrintBolt extends BaseBasicBolt {

private static final long serialVersionUID = 5564341843792874197L;
private DBProvider provider;
@Override
public void prepare(Map stormConf, TopologyContext context) {
//连接redis---代表可以连接任何事物
provider = new DBProvider();
super.prepare(stormConf,context);
}

@Override
public void execute(Tuple input, BasicOutputCollector collector) {
String word = (String) input.getValueByField( "word");
Integer num = (Integer) input.getValueByField( "num");
Connection conn = null;
Statement stmt = null;
try {
conn = provider.getConnection();
stmt = conn.createStatement() ;
stmt.executeUpdate( "INSERT INTO word_count (word, count) VALUES ('" + word + "', " + num + ") ON DUPLICATE KEY UPDATE count = count + " + num) ;
} catch (SQLException e) {
e.printStackTrace();
} finally{
if(stmt != null){
try {
stmt.close();
stmt = null;
} catch (Exception e2) {
e2.printStackTrace();
}
}
if(conn != null){
try {
conn.close();
conn = null;
} catch (Exception e2) {
e2.printStackTrace();
}
}
}
}

@Override
public void declareOutputFields(OutputFieldsDeclarer outputFieldsDeclarer) {
//todo 不需要定义输出的字段
}
}
  
  

5、实现操作数据库的DBProvider类

  
  
package com.lyz.storm.db;

import java.beans.PropertyVetoException;
import java.sql.Connection;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;

import com.mchange.v2.c3p0.ComboPooledDataSource;

/
* JDBC操作数据库
* @author liuyazhuang

/
public class DBProvider {

private static ComboPooledDataSource source ;
private static final String DB_DRIVER = "com.mysql.jdbc.Driver";
private static final String DB_URL = "jdbc:mysql://127.0.0.1:3306/sharding_0?useUnicode=true&characterEncoding=UTF-8&useOldAliasMetadataBehavior=true";
private static final String USER = "root";
private static final String PASSWORD = "root";
private static Connection connection;

static{
try {
source = new ComboPooledDataSource();
source.setDriverClass(DB_DRIVER);
source.setJdbcUrl(DB_URL);
source.setUser(USER);
source.setPassword(PASSWORD);
source.setInitialPoolSize( 10);
source.setMaxPoolSize( 20);
source.setMinPoolSize( 5);
source.setAcquireIncrement( 1);
source.setMaxIdleTime( 3);
source.setMaxStatements( 3000);
source.setCheckoutTimeout( 2000);
} catch (PropertyVetoException e) {
e.printStackTrace();
}
}

/
* 获取数据库连接

@return 数据库连接
/
public Connection getConnection() throws SQLException {
connection = source.getConnection();
return connection;
}


//关闭操作
public static void closeConnection(Connection con){
if(con!= null){
try {
con.close();
} catch (SQLException e) {
e.printStackTrace();
}
}
}

public static void closeResultSet(ResultSet rs){
if(rs!= null){
try {
rs.close();
} catch (SQLException e) {
e.printStackTrace();
}
}
}

public static void closePreparedStatement(PreparedStatement ps){
if(ps!= null){
try {
ps.close();
} catch (SQLException e) {
e.printStackTrace();
}
}
}
}
  
  

6、实现程序的入口类StormTopologyDriver

  
  
package com.lyz.storm;

import org.apache.storm.Config;
import org.apache.storm.LocalCluster;
import org.apache.storm.StormSubmitter;
import org.apache.storm.generated.StormTopology;
import org.apache.storm.kafka.KafkaSpout;
import org.apache.storm.kafka.SpoutConfig;
import org.apache.storm.kafka.ZkHosts;
import org.apache.storm.topology.TopologyBuilder;

import com.lyz.storm.bolt.MySplitBolt;
import com.lyz.storm.bolt.MyWordCountAndPrintBolt;

/*
* 这个Driver使Kafka、strom、mysql进行串联起来。

这个代码执行前需要创建kafka的topic,创建代码如下：
* [root@liuyazhuang kafka]# bin/kafka-topics.sh --create --zookeeper liuyazhuang1:2181 --replication-factor 1 -partitions 3 --topic wordCount

接着还要向kafka中传递数据，打开一个shell的producer来模拟生产数据
* [root@liuyazhuang kafka]# bin/kafka-console-producer.sh --broker-list liuyazhuang:9092 --topic wordCount
* 接着输入数据

@author liuyazhuang
*/
public class StormTopologyDriver {

public static void main(String[] args) throws Exception {
//1、准备任务信息
TopologyBuilder topologyBuilder = new TopologyBuilder();
SpoutConfig spoutConfig = new SpoutConfig( new ZkHosts( "192.168.209.121:2181"), "wordCount", "/wordCount", "wordCount");
topologyBuilder.setSpout( "KafkaSpout", new KafkaSpout(spoutConfig), 2);
topologyBuilder.setBolt( "bolt1", new MySplitBolt(), 4).shuffleGrouping( "KafkaSpout");
topologyBuilder.setBolt( "bolt2", new MyWordCountAndPrintBolt(), 2).shuffleGrouping( "bolt1");

//2、任务提交
Config config = new Config();
config.setNumWorkers( 2);
StormTopology stormTopology = topologyBuilder.createTopology();

if(args != null && args.length > 0){
StormSubmitter.submitTopology(args[ 0], config, topologyBuilder.createTopology());
} else{
//本地模式
LocalCluster localCluster = new LocalCluster();
localCluster.submitTopology( "wordcount",config,stormTopology);
}

}
}
  
  

7、创建数据库

执行如下脚本创建数据库

  
  
create database sharding_0;
CREATE TABLE word_count (
id int( 11) NOT NULL AUTO_INCREMENT,
word varchar( 255) DEFAULT '',
count int( 11) DEFAULT NULL,
PRIMARY KEY ( id),
UNIQUE KEY word ( word) USING BTREE
) ENGINE= InnoDB AUTO_INCREMENT= 233 DEFAULT CHARSET=utf8;
  
  

至此，我们的程序案例编写完成。

四、温馨提示

大家可以到链接http://download.csdn.net/download/l1028386804/10269075下载完整的Storm+Kafka+Flume+Zookeeper+MySQL实现数据实时分析(程序案例篇)源代码