独立于hadoop的Map减少了一个接一个执行的作业

k3fezbri  于 2021-05-30  发布在  Hadoop
关注(0)|答案(3)|浏览(358)

是否可以执行独立的map reduce作业(而不是在reducer输出的链接中)
成为Map器的输入。
可以一个接一个地执行。

57hvy0tb

57hvy0tb1#

在驱动程序代码中调用两个方法runfirstjob,runsecondjob。就像这样。这只是一个提示,根据需要进行修改

public class ExerciseDriver {

static Configuration conf;

public static void main(String[] args) throws Exception {

    ExerciseDriver ED = new ExerciseDriver();
    conf = new Configuration();
    FileSystem fs = FileSystem.get(conf);

    if(args.length < 4) {
        System.out.println("Too few arguments. Arguments should be:  <hdfs input folder> <hdfs output folder> <N configurable Integer Value>");
        System.exit(0);
    }

    String pathin1stmr = args[0];
    String pathout1stmr = args[1];
    String pathin2ndmr = args[2];
    String pathout2ndmr = args[3];

    ED.runFirstJob(pathin1stmr, pathout1stmr);

    ED.runSecondJob(pathin2ndmr, pathout2ndmr);

}

public int runFirstJob(String pathin, String pathout)  

 throws Exception {

    Job job = new Job(conf);
    job.setJarByClass(ExerciseDriver.class);
    job.setMapperClass(ExerciseMapper1.class);
    job.setCombinerClass(ExerciseCombiner.class);
    job.setReducerClass(ExerciseReducer1.class);
    job.setInputFormatClass(ParagrapghInputFormat.class);
    job.setOutputFormatClass(TextOutputFormat.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(IntWritable.class); 
    FileInputFormat.addInputPath(job, new Path(pathin));
    FileOutputFormat.setOutputPath(job, new Path(pathout));

   job.submit();  

   job.getMaxMapAttempts();

   /*
   JobContextImpl jc = new JobContextImpl();
   TaskReport[] maps = jobclient.getMapTaskReports(job.getJobID());

    */

    boolean success = job.waitForCompletion(true);
    return success ? 0 : -1;

}

  public int runSecondJob(String pathin, String pathout) throws Exception { 
    Job job = new Job(conf);
    job.setJarByClass(ExerciseDriver.class);
    job.setMapperClass(ExerciseMapper2.class);
    job.setReducerClass(ExerciseReducer2.class);
    job.setInputFormatClass(KeyValueTextInputFormat.class);
    job.setOutputFormatClass(TextOutputFormat.class);
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(Text.class);    
    FileInputFormat.addInputPath(job,new Path(pathin));
    FileOutputFormat.setOutputPath(job, new Path(pathout));
    boolean success = job.waitForCompletion(true);
    return success ? 0 : -1;
}

 }
2vuwiymt

2vuwiymt2#

You can go with Parallel job running. Sample code is given below

Configuration conf = new Configuration();
Path Job1InputDir = new Path(args[0]);
Path Job2InputDir = new Path(args[1]);
Path Job1OutputDir = new Path(args[2]);
Path Job2OutputDir = new Path(args[3]);
Job Job1= submitJob(conf, Job1InputDir , Job1OutputDir );
Job Job2= submitJob(conf, Job2InputDir , Job2OutputDir );
// While both jobs are not finished, sleep
while (!Job1.isComplete() || !Job2.isComplete()) {
Thread.sleep(5000);
}
if (Job1.isSuccessful()) {
System.out.println(" job1 completed successfully!");
} else {
System.out.println(" job1 failed!");
}
if (Job2.isSuccessful()) {
System.out.println("Job2 completed successfully!");
} else {
System.out.println("Job2 failed!");
}
System.exit(Job1.isSuccessful() &&
Job2.isSuccessful() ? 0 : 1);
}
tpxzln5u

tpxzln5u3#

如果您想一个接一个地执行,那么您可以按照下面的链接链接作业:
http://unmeshasreeveni.blogspot.in/2014/04/chaining-jobs-in-hadoop-mapreduce.html

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