Package org.apache.hadoop.mapreduce

Examples of org.apache.hadoop.mapreduce.Job.submit()

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    job.setJarByClass(MRRSleepJob.class);
    job.setMaxMapAttempts(1); // speed up failures
    job.getConfiguration().setBoolean(MRRSleepJob.MAP_FATAL_ERROR, true);
    job.getConfiguration().set(MRRSleepJob.MAP_ERROR_TASK_IDS, "*");

    job.submit();
    boolean succeeded = job.waitForCompletion(true);
    Assert.assertFalse(succeeded);
    Assert.assertEquals(JobStatus.State.FAILED, job.getJobState());

    // FIXME once counters and task progress can be obtained properly
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    job.setJarByClass(MRRSleepJob.class);
    job.setMaxMapAttempts(3); // speed up failures
    job.getConfiguration().setBoolean(MRRSleepJob.MAP_THROW_ERROR, true);
    job.getConfiguration().set(MRRSleepJob.MAP_ERROR_TASK_IDS, "0");

    job.submit();
    boolean succeeded = job.waitForCompletion(true);
    Assert.assertTrue(succeeded);
    Assert.assertEquals(JobStatus.State.SUCCEEDED, job.getJobState());

    // FIXME once counters and task progress can be obtained properly
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    // Submit the request
    try {
      if (submitJobFile != null) {
        Job job = Job.getInstance(new JobConf(submitJobFile));
        job.submit();
        System.out.println("Created job " + job.getJobID());
        exitCode = 0;
      } else if (getStatus) {
        Job job = cluster.getJob(JobID.forName(jobid));
        if (job == null) {
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    String jobName = String.format("Writing final topic/term distributions from %s to %s", modelInput, output);
    log.info("About to run: {}", jobName);

    Job job = prepareJob(modelInput, output, SequenceFileInputFormat.class, CVB0TopicTermVectorNormalizerMapper.class,
        IntWritable.class, VectorWritable.class, SequenceFileOutputFormat.class, jobName);
    job.submit();
    return job;
  }

  private Job writeDocTopicInference(Configuration conf, Path corpus, Path modelInput, Path output)
    throws IOException, ClassNotFoundException, InterruptedException {
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        modelUris[i] = statuses[i].getPath().toUri();
      }
      DistributedCache.setCacheFiles(modelUris, conf);
      setModelPaths(job, modelInput);
    }
    job.submit();
    return job;
  }

  public static Path modelPath(Path topicModelStateTempPath, int iterationNumber) {
    return new Path(topicModelStateTempPath, "model-" + iterationNumber);
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      job.setReducerClass(MatrixColumnMeansReducer.class);
      job.setMapOutputKeyClass(NullWritable.class);
      job.setMapOutputValueClass(VectorWritable.class);
      job.setOutputKeyClass(IntWritable.class);
      job.setOutputValueClass(VectorWritable.class);
      job.submit();
      job.waitForCompletion(true);

      Path tmpFile = new Path(outputVectorTmpPath, "part-r-00000");
      SequenceFileValueIterator<VectorWritable> iterator =
        new SequenceFileValueIterator<VectorWritable>(tmpFile, true, initialConf);
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     * UpperTriangular) so even if there're thousands of mappers, one reducer
     * should cope just fine.
     */
    job.setNumReduceTasks(1);

    job.submit();
    job.waitForCompletion(false);

    if (!job.isSuccessful()) {
      throw new IOException("YtY job unsuccessful.");
    }
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    job.setMapperClass(SplitInputMapper.class);
    job.setReducerClass(SplitInputReducer.class);
    job.setSortComparatorClass(SplitInputComparator.class);
    job.setOutputKeyClass(keyClass);
    job.setOutputValueClass(valueClass);
    job.submit();
    boolean succeeded = job.waitForCompletion(true);
    if (!succeeded) {
      throw new IllegalStateException("Job failed!");
    }
  }
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                                        job.getConfiguration());
        }
      }
    }

    job.submit();
    job.waitForCompletion(false);

    if (!job.isSuccessful()) {
      throw new IOException("Bt job unsuccessful.");
    }
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                                        job.getConfiguration());
        }
      }
    }

    job.submit();
    job.waitForCompletion(false);

    if (!job.isSuccessful()) {
      throw new IOException("ABt job unsuccessful.");
    }
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