MapReduce使用JobControl管理实例

import java.io.IOException;
import java.util.StringTokenizer;

import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.jobcontrol.ControlledJob;
import org.apache.hadoop.mapreduce.lib.jobcontrol.JobControl;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

public class JobCtrlTest {

    // 第一个Job的map函数
    public static class Map_First extends
            Mapper<Object, Text, Text, IntWritable> {
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

        public void map(Object key, Text value, Context context)
                throws IOException, InterruptedException {
            StringTokenizer itr = new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) {
                word.set(itr.nextToken());
                context.write(word, one);
            }
        }
    }

    // 第一个Job的reduce函数
    public static class Reduce_First extends
            Reducer<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values,
                Context context) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable value : values) {
                sum += value.get();
            }
            result.set(sum);

            context.write(key, result);
        }
    }

    // 第二个job的map函数
    public static class Map_Second extends
            Mapper<Object, Text, Text, IntWritable> {
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();

        public void map(Object key, Text value, Context context)
                throws IOException, InterruptedException {
            StringTokenizer itr = new StringTokenizer(value.toString());
            while (itr.hasMoreTokens()) {
                word.set(itr.nextToken());
                context.write(word, one);
            }
        }
    }

    // 第二个Job的reduce函数
    public static class Reduce_Second extends
            Reducer<Text, IntWritable, Text, IntWritable> {
        private IntWritable result = new IntWritable();

        public void reduce(Text key, Iterable<IntWritable> values,
                Context context) throws IOException, InterruptedException {
            int sum = 0;
            for (IntWritable value : values) {
                sum += value.get();
            }
            result.set(sum);
            context.write(key, result);
        }
    }

    // 启动函数
    public static void main(String[] args) throws IOException {

        JobConf conf = new JobConf(JobCtrlTest.class);

        // 第一个job的配置
        Job job1 = Job.getInstance(conf, "join1");
        job1.setJarByClass(JobCtrlTest.class);

        job1.setMapperClass(Map_First.class);
        job1.setReducerClass(Reduce_First.class);

        job1.setMapOutputKeyClass(Text.class);// map阶段的输出的key
        job1.setMapOutputValueClass(IntWritable.class);// map阶段的输出的value

        job1.setOutputKeyClass(Text.class);// reduce阶段的输出的key
        job1.setOutputValueClass(IntWritable.class);// reduce阶段的输出的value

        // 加入控制容器
        ControlledJob ctrljob1 = new ControlledJob(conf);
        ctrljob1.setJob(job1);
        // job1的输入输出文件路径
        FileInputFormat.addInputPath(job1, new Path(args[0]));
        FileOutputFormat.setOutputPath(job1, new Path(args[1]));

        // 第二个作业的配置
        Job job2 = Job.getInstance(conf, "Join2");
        job2.setJarByClass(JobCtrlTest.class);

        job2.setMapperClass(Map_Second.class);
        job2.setReducerClass(Reduce_Second.class);

        job2.setMapOutputKeyClass(Text.class);// map阶段的输出的key
        job2.setMapOutputValueClass(IntWritable.class);// map阶段的输出的value

        job2.setOutputKeyClass(Text.class);// reduce阶段的输出的key
        job2.setOutputValueClass(IntWritable.class);// reduce阶段的输出的value

        // 作业2加入控制容器
        ControlledJob ctrljob2 = new ControlledJob(conf);
        ctrljob2.setJob(job2);

        // 设置多个作业直接的依赖关系
        // 如下所写：
        // 意思为job2的启动，依赖于job1作业的完成

        ctrljob2.addDependingJob(ctrljob1);

        // 输入路径是上一个作业的输出路径，因此这里填args[1],要和上面对应好
        FileInputFormat.addInputPath(job2, new Path(args[1]));

        // 输出路径从新传入一个参数，这里需要注意，因为我们最后的输出文件一定要是没有出现过得
        // 因此我们在这里new Path(args[2])因为args[2]在上面没有用过，只要和上面不同就可以了
        FileOutputFormat.setOutputPath(job2, new Path(args[2]));

        // 主的控制容器，控制上面的总的两个子作业
        JobControl jobCtrl = new JobControl("myctrl");

        // 添加到总的JobControl里，进行控制
        jobCtrl.addJob(ctrljob1);
        jobCtrl.addJob(ctrljob2);

        // 在线程启动，记住一定要有这个
        Thread t = new Thread(jobCtrl);
        t.start();

        while (true) {

            if (jobCtrl.allFinished()) {// 如果作业成功完成，就打印成功作业的信息
                System.out.println(jobCtrl.getSuccessfulJobList());
                jobCtrl.stop();
                break;
            }
        }
    }
}