使用JobControl控制MapReduce任务

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<T extends DBWritable>
 *   extends InputFormat<LongWritable, T> 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();
        this.name = resultSet.getString();
        this.height = resultSet.getDouble();
    }

    public void write(PreparedStatement preparedStatement) throws SQLException {
        preparedStatement.setInt(, id);
        preparedStatement.setString(, name);
        preparedStatement.setDouble(, 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<LongWritable, BeanWritable, LongWritable, Text> {

        private LongWritable outputKey;
        private Text outputValue;

        @Override
        protected void setup(Mapper<LongWritable, BeanWritable, LongWritable, Text>.Context context)
                throws IOException, InterruptedException {
            this.outputKey = new LongWritable();
            this.outputValue = new Text();
        }

        @Override
        protected void map(LongWritable key, BeanWritable value,
                Mapper<LongWritable, BeanWritable, LongWritable, Text>.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<LongWritable, Text, NullWritable, BeanWritable>{
        private NullWritable outputKey;
        private BeanWritable outputValue;

        @Override
        protected void setup(Mapper<LongWritable, Text, NullWritable, BeanWritable>.Context context)
                throws IOException, InterruptedException {
            this.outputKey = NullWritable.get();
            this.outputValue = new BeanWritable();
        }
        @Override
        protected void map(LongWritable key, Text value,
                Mapper<LongWritable, Text, NullWritable, BeanWritable>.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 > ) {
                int id = Integer.parseInt(fields[]);
                String name = fields[];
                double height = Double.parseDouble(fields[]);
                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<NullWritable, BeanWritable, BeanWritable, NullWritable>{
        @Override
        protected void reduce(NullWritable key, Iterable<BeanWritable> values,
                Reducer<NullWritable, BeanWritable, BeanWritable, NullWritable>.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[]));

        //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[]);
        //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();
            }
        }

    }

}

mysql表结构如下

CREATE TABLE `people` (
  `id` int() NOT NULL,
  `name` varchar() 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.