Hadoop自带Sort例子分析

Posted 2020-10-14

 

 

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 * Licensed to the Apache Software Foundation (ASF) under one
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 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you under the Apache License, Version 2.0 (the
 * "License"); you may not use this file except in compliance
 * with the License.  You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
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 * See the License for the specific language governing permissions and
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package org.apache.hadoop.examples;

import java.io.IOException;
import java.net.URI;
import java.util.*;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.mapreduce.filecache.DistributedCache;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.mapred.ClusterStatus;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapreduce.*;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.input.SequenceFileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
import org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat;
import org.apache.hadoop.mapreduce.lib.partition.InputSampler;
import org.apache.hadoop.mapreduce.lib.partition.TotalOrderPartitioner;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

/**
 * This is the trivial map/reduce program that does absolutely nothing
 * other than use the framework to fragment and sort the input values.
 *
 * To run: bin/hadoop jar build/hadoop-examples.jar sort
 *            [-r <i>reduces</i>]
 *            [-inFormat <i>input format class</i>]
 *            [-outFormat <i>output format class</i>]
 *            [-outKey <i>output key class</i>]
 *            [-outValue <i>output value class</i>]
 *            [-totalOrder <i>pcnt</i> <i>num samples</i> <i>max splits</i>]
 *            <i>in-dir</i> <i>out-dir</i>
 */
public class Sort<K,V> extends Configured implements Tool {
  public static final String REDUCES_PER_HOST =
    "mapreduce.sort.reducesperhost";
  private Job job = null;

  static int printUsage() {
    System.out.println("sort [-r <reduces>] " +
                       "[-inFormat <input format class>] " +
                       "[-outFormat <output format class>] " +
                       "[-outKey <output key class>] " +
                       "[-outValue <output value class>] " +
                       "[-totalOrder <pcnt> <num samples> <max splits>] " +
                       "<input> <output>");
    ToolRunner.printGenericCommandUsage(System.out);
    return 2;
  }

  /**
   * 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.
   */
  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 sort_reduces = conf.get(REDUCES_PER_HOST);
    if (sort_reduces != null) {
       num_reduces = cluster.getTaskTrackers() *
                       Integer.parseInt(sort_reduces);
    }
    Class<? extends InputFormat> inputFormatClass =
      SequenceFileInputFormat.class;
    Class<? extends OutputFormat> outputFormatClass =
      SequenceFileOutputFormat.class;
    Class<? extends WritableComparable> outputKeyClass = BytesWritable.class;
    Class<? extends Writable> outputValueClass = BytesWritable.class;
    List<String> otherArgs = new ArrayList<String>();
    InputSampler.Sampler<K,V> sampler = null;
    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 ("-totalOrder".equals(args[i])) {
          double pcnt = Double.parseDouble(args[++i]);
          int numSamples = Integer.parseInt(args[++i]);
          int maxSplits = Integer.parseInt(args[++i]);
          if (0 >= maxSplits) maxSplits = Integer.MAX_VALUE;
          sampler =
            new InputSampler.RandomSampler<K,V>(pcnt, numSamples, maxSplits);
        } 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]);
        return printUsage(); // exits
      }
    }
    // Set user-supplied (possibly default) job configs
    job = Job.getInstance(conf);
    job.setJobName("sorter");
    job.setJarByClass(Sort.class);

    job.setMapperClass(Mapper.class);
    job.setReducerClass(Reducer.class);

    job.setNumReduceTasks(num_reduces);

    job.setInputFormatClass(inputFormatClass);
    job.setOutputFormatClass(outputFormatClass);

    job.setOutputKeyClass(outputKeyClass);
    job.setOutputValueClass(outputValueClass);

    // Make sure there are exactly 2 parameters left.
    if (otherArgs.size() != 2) {
      System.out.println("ERROR: Wrong number of parameters: " +
          otherArgs.size() + " instead of 2.");
      return printUsage();
    }
    FileInputFormat.setInputPaths(job, otherArgs.get(0));
    FileOutputFormat.setOutputPath(job, new Path(otherArgs.get(1)));

    if (sampler != null) {
      System.out.println("Sampling input to effect total-order sort...");
      job.setPartitionerClass(TotalOrderPartitioner.class);
      Path inputDir = FileInputFormat.getInputPaths(job)[0];
      inputDir = inputDir.makeQualified(inputDir.getFileSystem(conf));
      Path partitionFile = new Path(inputDir, "_sortPartitioning");
      TotalOrderPartitioner.setPartitionFile(conf, partitionFile);
      InputSampler.<K,V>writePartitionFile(job, sampler);
      URI partitionUri = new URI(partitionFile.toString() +
                                 "#" + "_sortPartitioning");
      DistributedCache.addCacheFile(partitionUri, conf);
    }

    System.out.println("Running on " +
        cluster.getTaskTrackers() +
        " nodes to sort from " +
        FileInputFormat.getInputPaths(job)[0] + " into " +
        FileOutputFormat.getOutputPath(job) +
        " with " + num_reduces + " reduces.");
    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;
  }



  public static void main(String[] args) throws Exception {
    int res = ToolRunner.run(new Configuration(), new Sort(), args);
    System.exit(res);
  }

  /**
   * Get the last job that was run using this instance.
   * @return the results of the last job that was run
   */
  public Job getResult() {
    return job;
  }
}

 

看了源码的第一印象就是，我啥时候写MapReduce也这么规范，这么屌......