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;            }        }    }}