作者：秋日里的一抹阳光_797 | 来源：互联网 | 2023-09-01 10:52
代码结构BeanWritable:往数据库读写使用的beanControlJobTest:JobControl任务控制DBInputFormatApp:将关系型数据库的数据导入HD
代码结构
BeanWritable:往数据库读写使用的bean
ControlJobTest:JobControl任务控制
DBInputFormatApp:将关系型数据库的数据导入HDFS,其中包含了Map、Reduce,内部静态类
DBOutputFormatApp:将HDFS的结构化数据导入关系型数据库
此处关系型数据库使用Mysql
代码如下
BeanWritable.java
/**
*
*/
package com.zhen.controlJobTest;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapred.lib.db.DBWritable;
/**
* JavaBean
* 需要实现Hadoop序列化接口Writable以及与数据库交互时的序列化接口DBWritable
* 官方API中解释如下：
* public class DBInputFormat
* extends InputFormat implements Configurable
* 即Mapper的Key是LongWritable类型，不可改变；Value是继承自DBWritable接口的自定义JavaBean
*
* @author FengZhen
*/
public class BeanWritable implements Writable, DBWritable {
private int id;
private String name;
private double height;
public void readFields(ResultSet resultSet) throws SQLException {
this.id = resultSet.getInt(1);
this.name = resultSet.getString(2);
this.height = resultSet.getDouble(3);
}
public void write(PreparedStatement preparedStatement) throws SQLException {
preparedStatement.setInt(1, id);
preparedStatement.setString(2, name);
preparedStatement.setDouble(3, height);
}
public void readFields(DataInput dataInput) throws IOException {
this.id = dataInput.readInt();
this.name = dataInput.readUTF();
this.height = dataInput.readDouble();
}
public void write(DataOutput dataOutput) throws IOException {
dataOutput.writeInt(id);
dataOutput.writeUTF(name);
dataOutput.writeDouble(height);
}
public void set(int id,String name,double height){
this.id = id;
this.name = name;
this.height = height;
}
@Override
public String toString() {
return id + "\t" + name + "\t" + height;
}
}
DBInputFormatApp.java
package com.zhen.controlJobTest;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapred.lib.db.DBWritable;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;
import org.apache.hadoop.mapreduce.lib.db.DBInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/**
* @author FengZhen
* 将mysql数据导入hdfs
*/
public class DBInputFormatApp{
/**
* Map
* 当Map的输出key为LongWritable，value为Text时，reduce可以省略不写，默认reduce也是输出LongWritable:Text
* */
public static class DBInputMapper extends Mapper {
private LongWritable outputKey;
private Text outputValue;
@Override
protected void setup(Mapper.Context context)
throws IOException, InterruptedException {
this.outputKey = new LongWritable();
this.outputValue = new Text();
}
@Override
protected void map(LongWritable key, BeanWritable value,
Mapper.Context context)
throws IOException, InterruptedException {
outputKey.set(key.get());;
outputValue.set(value.toString());
context.write(outputKey, outputValue);
}
}
}
DBOutputFormatApp.java
package com.zhen.controlJobTest;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.sql.PreparedStatement;
import java.sql.ResultSet;
import java.sql.SQLException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapred.lib.db.DBWritable;
import org.apache.hadoop.mapreduce.Counter;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;
import org.apache.hadoop.mapreduce.lib.db.DBOutputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
/**
* @author FengZhen
* 将hdfs数据导入mysql
* 使用DBOutputFormat将HDFS路径下的结构化数据写入mysql中，结构化数据如下,第一列为key,后边三列为数据
* 0 1 Enzo 180.66
* 1 2 Din 170.666
*
*/
public class DBOutputFormatApp{
public static class DBOutputMapper extends Mapper{
private NullWritable outputKey;
private BeanWritable outputValue;
@Override
protected void setup(Mapper.Context context)
throws IOException, InterruptedException {
this.outputKey = NullWritable.get();
this.outputValue = new BeanWritable();
}
@Override
protected void map(LongWritable key, Text value,
Mapper.Context context)
throws IOException, InterruptedException {
//插入数据库成功的计数器
final Counter successCounter = context.getCounter("exec", "successfully");
//插入数据库失败的计数器
final Counter faildCounter = context.getCounter("exec", "faild");
//解析结构化数据
String[] fields = value.toString().split("\t");
//DBOutputFormatApp这个MapReduce应用导出的数据包含long类型的key，所以忽略key从1开始
if (fields.length > 3) {
int id = Integer.parseInt(fields[1]);
String name = fields[2];
double height = Double.parseDouble(fields[3]);
this.outputValue.set(id, name, height);
context.write(outputKey, outputValue);
//如果插入数据库成功则递增1，表示成功计数
successCounter.increment(1L);
}else{
//如果插入数据库失败则递增1，表示失败计数
faildCounter.increment(1L);
}
}
}
/**
* 输出的key必须是继承自DBWritable的类型，DBOutputFormat要求输出的key必须是DBWritable类型
* */
public static class DBOutputReducer extends Reducer{
@Override
protected void reduce(NullWritable key, Iterable values,
Reducer.Context context)
throws IOException, InterruptedException {
for (BeanWritable beanWritable : values) {
context.write(beanWritable, key);
}
}
}
}
ControlJobTest.java
/**
*
*/
package com.zhen.controlJobTest;
import java.io.IOException;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.NullWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.db.DBConfiguration;
import org.apache.hadoop.mapreduce.lib.db.DBInputFormat;
import org.apache.hadoop.mapreduce.lib.db.DBOutputFormat;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.jobcontrol.ControlledJob;
import org.apache.hadoop.mapreduce.lib.jobcontrol.JobControl;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import com.zhen.controlJobTest.DBInputFormatApp.DBInputMapper;
import com.zhen.controlJobTest.DBOutputFormatApp.DBOutputMapper;
import com.zhen.controlJobTest.DBOutputFormatApp.DBOutputReducer;
/**
* @author FengZhen
*
*/
public class ControlJobTest {
public static void main(String[] args) throws IOException {
//第一个任务，mysql导入到HDFS
Configuration configuration1 = new Configuration();
//配置当前作业需要使用的JDBC配置
DBConfiguration.configureDB(configuration1, "com.mysql.jdbc.Driver", "jdbc:mysql://localhost:3306/hadoop",
"root", "123qwe");
Job job1 = Job.getInstance(configuration1, DBInputFormatApp.class.getSimpleName());
job1.setJarByClass(DBInputFormatApp.class);
job1.setMapperClass(DBInputMapper.class);
job1.setMapOutputKeyClass(LongWritable.class);
job1.setMapOutputValueClass(Text.class);
job1.setOutputKeyClass(LongWritable.class);
job1.setOutputValueClass(Text.class);
//配置作业的输入数据格式
job1.setInputFormatClass(DBInputFormat.class);
//配置当前作业需要查询的sql语句及接收sql语句的bean
DBInputFormat.setInput(
job1,
BeanWritable.class,
"select * from people",
"select count(1) from people");
FileOutputFormat.setOutputPath(job1, new Path(args[0]));
//第二个任务 HDFS导出到mysql
Configuration configuration2 = new Configuration();
//在创建Configuration的时候紧接着配置数据库连接信息
DBConfiguration.configureDB(configuration2, "com.mysql.jdbc.Driver", "jdbc:mysql://localhost:3306/hadoop", "root", "123qwe");
Job job2 = Job.getInstance(configuration2, DBOutputFormatApp.class.getSimpleName());
job2.setJarByClass(DBOutputFormatApp.class);
job2.setMapperClass(DBOutputMapper.class);
job2.setMapOutputKeyClass(NullWritable.class);
job2.setMapOutputValueClass(BeanWritable.class);
job2.setReducerClass(DBOutputReducer.class);
job2.setOutputFormatClass(DBOutputFormat.class);
job2.setOutputKeyClass(BeanWritable.class);
job2.setOutputValueClass(NullWritable.class);
job2.setInputFormatClass(TextInputFormat.class);
FileInputFormat.setInputPaths(job2, args[0]);
//配置当前作业输出到数据库表、字段信息
DBOutputFormat.setOutput(job2, "people", new String[]{"id","name","height"});
ControlledJob controlledJob1 = new ControlledJob(configuration1);
controlledJob1.setJob(job1);
ControlledJob controlledJob2 = new ControlledJob(configuration2);
controlledJob2.setJob(job2);
//如果两个任务有依赖关系，必须设置此选项
controlledJob2.addDependingJob(controlledJob1);
JobControl jobControl = new JobControl("groupName");
jobControl.addJob(controlledJob1);
jobControl.addJob(controlledJob2);
jobControl.run();
while(true){
boolean allFinished = jobControl.allFinished();
if (allFinished) {
System.exit(0);
}
}
}
}
mysql表结构如下
CREATE TABLE `people` (
`id` int(11) NOT NULL,
`name` varchar(255) DEFAULT NULL,
`height` double DEFAULT NULL
) ENGINE=InnoDB DEFAULT CHARSET=utf8
先插入测试数据
然后将代码打包为jar,传到服务器，执行任务
hadoop jar /Users/FengZhen/Desktop/Hadoop/other/mapreduce_jar/JobControlTest.jar com.zhen.controlJobTest.ControlJobTest
/user/hadoop/mapreduce/mysqlToHdfs/people
此任务包含了两个子任务，一个是将mysql数据导入HDFS,一个是将HDFS的数据导出Mysql，也可以写个简单的mapreduce任务来测试。
如果两个子任务有依赖关系，那么必须要设置
controlledJob2.addDependingJob(controlledJob1);
说明job2依赖于job1，当job1执行完之后才会去执行job2.
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