Examples with ClusterStatus org.apache.hadoop.mapred.ClusterStatus used on opensource projects

Example 1 with ClusterStatus

use of org.apache.hadoop.mapred.ClusterStatus in project hadoop by apache.

the class Join method run.

/**
   * The main driver for sort program.
   * Invoke this method to submit the map/reduce job.
   * @throws IOException When there is communication problems with the 
   *                     job tracker.
   */
@SuppressWarnings("unchecked")
public int run(String[] args) throws Exception {
    Configuration conf = getConf();
    JobClient client = new JobClient(conf);
    ClusterStatus cluster = client.getClusterStatus();
    int num_reduces = (int) (cluster.getMaxReduceTasks() * 0.9);
    String join_reduces = conf.get(REDUCES_PER_HOST);
    if (join_reduces != null) {
        num_reduces = cluster.getTaskTrackers() * Integer.parseInt(join_reduces);
    }
    Job job = Job.getInstance(conf);
    job.setJobName("join");
    job.setJarByClass(Sort.class);
    job.setMapperClass(Mapper.class);
    job.setReducerClass(Reducer.class);
    Class<? extends InputFormat> inputFormatClass = SequenceFileInputFormat.class;
    Class<? extends OutputFormat> outputFormatClass = SequenceFileOutputFormat.class;
    Class<? extends WritableComparable> outputKeyClass = BytesWritable.class;
    Class<? extends Writable> outputValueClass = TupleWritable.class;
    String op = "inner";
    List<String> otherArgs = new ArrayList<String>();
    for (int i = 0; i < args.length; ++i) {
        try {
            if ("-r".equals(args[i])) {
                num_reduces = Integer.parseInt(args[++i]);
            } else if ("-inFormat".equals(args[i])) {
                inputFormatClass = Class.forName(args[++i]).asSubclass(InputFormat.class);
            } else if ("-outFormat".equals(args[i])) {
                outputFormatClass = Class.forName(args[++i]).asSubclass(OutputFormat.class);
            } else if ("-outKey".equals(args[i])) {
                outputKeyClass = Class.forName(args[++i]).asSubclass(WritableComparable.class);
            } else if ("-outValue".equals(args[i])) {
                outputValueClass = Class.forName(args[++i]).asSubclass(Writable.class);
            } else if ("-joinOp".equals(args[i])) {
                op = args[++i];
            } else {
                otherArgs.add(args[i]);
            }
        } catch (NumberFormatException except) {
            System.out.println("ERROR: Integer expected instead of " + args[i]);
            return printUsage();
        } catch (ArrayIndexOutOfBoundsException except) {
            System.out.println("ERROR: Required parameter missing from " + args[i - 1]);
            // exits
            return printUsage();
        }
    }
    // Set user-supplied (possibly default) job configs
    job.setNumReduceTasks(num_reduces);
    if (otherArgs.size() < 2) {
        System.out.println("ERROR: Wrong number of parameters: ");
        return printUsage();
    }
    FileOutputFormat.setOutputPath(job, new Path(otherArgs.remove(otherArgs.size() - 1)));
    List<Path> plist = new ArrayList<Path>(otherArgs.size());
    for (String s : otherArgs) {
        plist.add(new Path(s));
    }
    job.setInputFormatClass(CompositeInputFormat.class);
    job.getConfiguration().set(CompositeInputFormat.JOIN_EXPR, CompositeInputFormat.compose(op, inputFormatClass, plist.toArray(new Path[0])));
    job.setOutputFormatClass(outputFormatClass);
    job.setOutputKeyClass(outputKeyClass);
    job.setOutputValueClass(outputValueClass);
    Date startTime = new Date();
    System.out.println("Job started: " + startTime);
    int ret = job.waitForCompletion(true) ? 0 : 1;
    Date end_time = new Date();
    System.out.println("Job ended: " + end_time);
    System.out.println("The job took " + (end_time.getTime() - startTime.getTime()) / 1000 + " seconds.");
    return ret;
}

Example 2 with ClusterStatus

use of org.apache.hadoop.mapred.ClusterStatus in project hadoop by apache.

the class RandomTextWriter method run.

/**
   * This is the main routine for launching a distributed random write job.
   * It runs 10 maps/node and each node writes 1 gig of data to a DFS file.
   * The reduce doesn't do anything.
   * 
   * @throws IOException 
   */
public int run(String[] args) throws Exception {
    if (args.length == 0) {
        return printUsage();
    }
    Configuration conf = getConf();
    JobClient client = new JobClient(conf);
    ClusterStatus cluster = client.getClusterStatus();
    int numMapsPerHost = conf.getInt(MAPS_PER_HOST, 10);
    long numBytesToWritePerMap = conf.getLong(BYTES_PER_MAP, 1 * 1024 * 1024 * 1024);
    if (numBytesToWritePerMap == 0) {
        System.err.println("Cannot have " + BYTES_PER_MAP + " set to 0");
        return -2;
    }
    long totalBytesToWrite = conf.getLong(TOTAL_BYTES, numMapsPerHost * numBytesToWritePerMap * cluster.getTaskTrackers());
    int numMaps = (int) (totalBytesToWrite / numBytesToWritePerMap);
    if (numMaps == 0 && totalBytesToWrite > 0) {
        numMaps = 1;
        conf.setLong(BYTES_PER_MAP, totalBytesToWrite);
    }
    conf.setInt(MRJobConfig.NUM_MAPS, numMaps);
    Job job = Job.getInstance(conf);
    job.setJarByClass(RandomTextWriter.class);
    job.setJobName("random-text-writer");
    job.setOutputKeyClass(Text.class);
    job.setOutputValueClass(Text.class);
    job.setInputFormatClass(RandomWriter.RandomInputFormat.class);
    job.setMapperClass(RandomTextMapper.class);
    Class<? extends OutputFormat> outputFormatClass = SequenceFileOutputFormat.class;
    List<String> otherArgs = new ArrayList<String>();
    for (int i = 0; i < args.length; ++i) {
        try {
            if ("-outFormat".equals(args[i])) {
                outputFormatClass = Class.forName(args[++i]).asSubclass(OutputFormat.class);
            } else {
                otherArgs.add(args[i]);
            }
        } catch (ArrayIndexOutOfBoundsException except) {
            System.out.println("ERROR: Required parameter missing from " + args[i - 1]);
            // exits
            return printUsage();
        }
    }
    job.setOutputFormatClass(outputFormatClass);
    FileOutputFormat.setOutputPath(job, new Path(otherArgs.get(0)));
    System.out.println("Running " + numMaps + " maps.");
    // reducer NONE
    job.setNumReduceTasks(0);
    Date startTime = new Date();
    System.out.println("Job started: " + startTime);
    int ret = job.waitForCompletion(true) ? 0 : 1;
    Date endTime = new Date();
    System.out.println("Job ended: " + endTime);
    System.out.println("The job took " + (endTime.getTime() - startTime.getTime()) / 1000 + " seconds.");
    return ret;
}

Example 3 with ClusterStatus

use of org.apache.hadoop.mapred.ClusterStatus in project hadoop by apache.

the class RandomWriter method run.

/**
   * This is the main routine for launching a distributed random write job.
   * It runs 10 maps/node and each node writes 1 gig of data to a DFS file.
   * The reduce doesn't do anything.
   * 
   * @throws IOException 
   */
public int run(String[] args) throws Exception {
    if (args.length == 0) {
        System.out.println("Usage: writer <out-dir>");
        ToolRunner.printGenericCommandUsage(System.out);
        return 2;
    }
    Path outDir = new Path(args[0]);
    Configuration conf = getConf();
    JobClient client = new JobClient(conf);
    ClusterStatus cluster = client.getClusterStatus();
    int numMapsPerHost = conf.getInt(MAPS_PER_HOST, 10);
    long numBytesToWritePerMap = conf.getLong(BYTES_PER_MAP, 1 * 1024 * 1024 * 1024);
    if (numBytesToWritePerMap == 0) {
        System.err.println("Cannot have" + BYTES_PER_MAP + " set to 0");
        return -2;
    }
    long totalBytesToWrite = conf.getLong(TOTAL_BYTES, numMapsPerHost * numBytesToWritePerMap * cluster.getTaskTrackers());
    int numMaps = (int) (totalBytesToWrite / numBytesToWritePerMap);
    if (numMaps == 0 && totalBytesToWrite > 0) {
        numMaps = 1;
        conf.setLong(BYTES_PER_MAP, totalBytesToWrite);
    }
    conf.setInt(MRJobConfig.NUM_MAPS, numMaps);
    Job job = Job.getInstance(conf);
    job.setJarByClass(RandomWriter.class);
    job.setJobName("random-writer");
    FileOutputFormat.setOutputPath(job, outDir);
    job.setOutputKeyClass(BytesWritable.class);
    job.setOutputValueClass(BytesWritable.class);
    job.setInputFormatClass(RandomInputFormat.class);
    job.setMapperClass(RandomMapper.class);
    job.setReducerClass(Reducer.class);
    job.setOutputFormatClass(SequenceFileOutputFormat.class);
    System.out.println("Running " + numMaps + " maps.");
    // reducer NONE
    job.setNumReduceTasks(0);
    Date startTime = new Date();
    System.out.println("Job started: " + startTime);
    int ret = job.waitForCompletion(true) ? 0 : 1;
    Date endTime = new Date();
    System.out.println("Job ended: " + endTime);
    System.out.println("The job took " + (endTime.getTime() - startTime.getTime()) / 1000 + " seconds.");
    return ret;
}

Example 4 with ClusterStatus

use of org.apache.hadoop.mapred.ClusterStatus in project hive by apache.

the class Driver method getClusterStatus.

/**
   * Return the status information about the Map-Reduce cluster
   */
public ClusterStatus getClusterStatus() throws Exception {
    ClusterStatus cs;
    try {
        JobConf job = new JobConf(conf);
        JobClient jc = new JobClient(job);
        cs = jc.getClusterStatus();
    } catch (Exception e) {
        e.printStackTrace();
        throw e;
    }
    LOG.info("Returning cluster status: " + cs.toString());
    return cs;
}

Example 5 with ClusterStatus

use of org.apache.hadoop.mapred.ClusterStatus in project incubator-systemml by apache.

the class InfrastructureAnalyzer method getClusterUtilization.

/**
	 * Gets the fraction of running map/reduce tasks to existing
	 * map/reduce task slots. 
	 * 
	 * NOTE: on YARN the number of slots is a spurious indicator 
	 * because containers are purely scheduled based on memory. 
	 * 
	 * @param mapOnly if true, only look at map tasks
	 * @return cluster utilization (current / capacity)
	 * @throws IOException if IOException occurs
	 */
public static double getClusterUtilization(boolean mapOnly) throws IOException {
    //in local mode, the cluster utilization is always 0.0 
    JobConf job = ConfigurationManager.getCachedJobConf();
    JobClient client = new JobClient(job);
    ClusterStatus stat = client.getClusterStatus();
    double ret = 0.0;
    if (//if in cluster mode
    stat != null) {
        if (mapOnly) {
            int capacity = stat.getMaxMapTasks();
            int current = stat.getMapTasks();
            ret = ((double) current) / capacity;
        } else {
            int capacity = stat.getMaxMapTasks() + stat.getMaxReduceTasks();
            int current = stat.getMapTasks() + stat.getReduceTasks();
            ret = ((double) current) / capacity;
        }
    }
    return ret;
}