hadoop实例 RandomWriter

参考文献：http://www.hadooper.cn/dct/page/65778

1.概述

RandomWriter（随机写）例子利用 Map/Reduce把数据随机的写到dfs中。每个map输入单个文件名，然后随机写BytesWritable的键和值到DFS顺序文件。map没有产生任何输出，所以reduce没有执行。产生的数据是可以配置的。配置变量如下

名字	默认值	描述
test.randomwriter.maps_per_host	10	每个节点运行的map任务数
test.randomwrite.bytes_per_map	1073741824	每个map任务产生的数据量
test.randomwrite.min_key	10	minimum size of the key in bytes
test.randomwrite.max_key	1000	maximum size of the key in bytes
test.randomwrite.min_value	0	minimum size of the value
test.randomwrite.max_value	20000	maximum size of the value

test.randomwriter.maps_per_host表示每个工作节点（datanode）上运行map的次数。默认情况下，只有一个数据节点，那么就有10个map，每个map的数据量为1G，因此要将10G数据写入到hdfs中。我配置的试验环境中只有2个工作节点，不过我希望每个工作节点只有1个map任务。

test.randomwrite.bytes_per_map我原本以为是随机写输出的测试文件的大小，默认为1G=1*1024*1024*1024，但是我将这个数据改成1*1024*1024以后，输出的测试文件还是1G，这让我很不解。（PS:2011-11-2，今天知道这个参数表示没个map任务产生的数据量，如果将其改为1*1024*1024，那么就表示没个map任务产生的数据量为1MB。）（PS：2011-11-3，修改参数test.randomwrite.bytes_per_map并不能更改每个map任务产生的数据量，还是1G，不管我将这个参数设定为什么值。不过修改参数：test.randomwriter.maps_per_host是有效的。测试发现将该参数设为1和2都测试通过。问题：在哪里修改test.randomwrite.bytes_per_map才能真正修改map任务产生的数据量。！）

2.代码实例

其中test.randomwrite.bytes_per_map=1*1024*1024，test.randomwriter.maps_per_host=1。

/**
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * 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,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.apache.hadoop.examples;

import java.io.IOException;
import java.util.Date;
import java.util.Random;

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.BytesWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.io.WritableComparable;
import org.apache.hadoop.mapred.ClusterStatus;
import org.apache.hadoop.mapred.FileOutputFormat;
import org.apache.hadoop.mapred.FileSplit;
import org.apache.hadoop.mapred.InputFormat;
import org.apache.hadoop.mapred.InputSplit;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.MapReduceBase;
import org.apache.hadoop.mapred.Mapper;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.RecordReader;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.SequenceFileOutputFormat;
import org.apache.hadoop.mapred.lib.IdentityReducer;
import org.apache.hadoop.util.GenericOptionsParser;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

/**
 * This program uses map/reduce to just run a distributed job where there is
 * no interaction between the tasks and each task write a large unsorted
 * random binary sequence file of BytesWritable.
 * In order for this program to generate data for terasort with 10-byte keys
 * and 90-byte values, have the following config:
 * <xmp>
 * <?xml version="1.0"?>
 * <?xml-stylesheet type="text/xsl" href="configuration.xsl"?>
 * <configuration>
 *   <property>
 *     <name>test.randomwrite.min_key</name>
 *     <value>10</value>
 *   </property>
 *   <property>
 *     <name>test.randomwrite.max_key</name>
 *     <value>10</value>
 *   </property>
 *   <property>
 *     <name>test.randomwrite.min_value</name>
 *     <value>90</value>
 *   </property>
 *   <property>
 *     <name>test.randomwrite.max_value</name>
 *     <value>90</value>
 *   </property>
 *   <property>
 *     <name>test.randomwrite.total_bytes</name>
 *     <value>1099511627776</value>
 *   </property>
 * </configuration></xmp>
 * 
 * Equivalently, {@link RandomWriter} also supports all the above options
 * and ones supported by {@link GenericOptionsParser} via the command-line.
 */
public class RandomWriter extends Configured implements Tool {
  
  /**
   * User counters
   */
  static enum Counters { RECORDS_WRITTEN, BYTES_WRITTEN }
  
  /**
   * A custom input format that creates virtual inputs of a single string
   * for each map.
   */
  static class RandomInputFormat implements InputFormat<Text, Text> {

    /** 
     * Generate the requested number of file splits, with the filename
     * set to the filename of the output file.
     */
    public InputSplit[] getSplits(JobConf job, 
                                  int numSplits) throws IOException {
      InputSplit[] result = new InputSplit[numSplits];
      Path outDir = FileOutputFormat.getOutputPath(job);
      for(int i=0; i < result.length; ++i) {
        result[i] = new FileSplit(new Path(outDir, "dummy-split-" + i), 0, 1, 
                                  (String[])null);
      }
      return result;
    }

    /**
     * Return a single record (filename, "") where the filename is taken from
     * the file split.
     */
    static class RandomRecordReader implements RecordReader<Text, Text> {
      Path name;
      public RandomRecordReader(Path p) {
        name = p;
      }
      public boolean next(Text key, Text value) {
        if (name != null) {
          key.set(name.getName());
          name = null;
          return true;
        }
        return false;
      }
      public Text createKey() {
        return new Text();
      }
      public Text createValue() {
        return new Text();
      }
      public long getPos() {
        return 0;
      }
      public void close() {}
      public float getProgress() {
        return 0.0f;
      }
    }

    public RecordReader<Text, Text> getRecordReader(InputSplit split,
                                        JobConf job, 
                                        Reporter reporter) throws IOException {
      return new RandomRecordReader(((FileSplit) split).getPath());
    }
  }

  static class Map extends MapReduceBase
    implements Mapper<WritableComparable, Writable,
                      BytesWritable, BytesWritable> {
    
    private long numBytesToWrite;
    private int minKeySize;
    private int keySizeRange;
    private int minValueSize;
    private int valueSizeRange;
    private Random random = new Random();
    private BytesWritable randomKey = new BytesWritable();
    private BytesWritable randomValue = new BytesWritable();
    
    private void randomizeBytes(byte[] data, int offset, int length) {
      for(int i=offset + length - 1; i >= offset; --i) {
        data[i] = (byte) random.nextInt(256);
      }
    }
    
    /**
     * Given an output filename, write a bunch of random records to it.
     */
    public void map(WritableComparable key, 
                    Writable value,
                    OutputCollector<BytesWritable, BytesWritable> output, 
                    Reporter reporter) throws IOException {
      int itemCount = 0;
      while (numBytesToWrite > 0) {
        int keyLength = minKeySize + 
          (keySizeRange != 0 ? random.nextInt(keySizeRange) : 0);
        randomKey.setSize(keyLength);
        randomizeBytes(randomKey.getBytes(), 0, randomKey.getLength());
        int valueLength = minValueSize +
          (valueSizeRange != 0 ? random.nextInt(valueSizeRange) : 0);
        randomValue.setSize(valueLength);
        randomizeBytes(randomValue.getBytes(), 0, randomValue.getLength());
        output.collect(randomKey, randomValue);
        numBytesToWrite -= keyLength + valueLength;
        reporter.incrCounter(Counters.BYTES_WRITTEN, keyLength + valueLength);
        reporter.incrCounter(Counters.RECORDS_WRITTEN, 1);
        if (++itemCount % 200 == 0) {
          reporter.setStatus("wrote record " + itemCount + ". " + 
                             numBytesToWrite + " bytes left.");
        }
      }
      reporter.setStatus("done with " + itemCount + " records.");
    }
    
    /**
     * Save the values out of the configuaration that we need to write
     * the data.
     */
    @Override
    public void configure(JobConf job) {
      numBytesToWrite = job.getLong("test.randomwrite.bytes_per_map",
                                    1*1024*1024);
      minKeySize = job.getInt("test.randomwrite.min_key", 10);
      keySizeRange = 
        job.getInt("test.randomwrite.max_key", 1000) - minKeySize;
      minValueSize = job.getInt("test.randomwrite.min_value", 0);
      valueSizeRange = 
        job.getInt("test.randomwrite.max_value", 20000) - minValueSize;
    }
    
  }
  
  /**
   * 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 -1;
    }
    
    Path outDir = new Path(args[0]);
    JobConf job = new JobConf(getConf());
    
    job.setJarByClass(RandomWriter.class);
    job.setJobName("random-writer");
    FileOutputFormat.setOutputPath(job, outDir);
    
    job.setOutputKeyClass(BytesWritable.class);
    job.setOutputValueClass(BytesWritable.class);
    
    job.setInputFormat(RandomInputFormat.class);
    job.setMapperClass(Map.class);        
    job.setReducerClass(IdentityReducer.class);
    job.setOutputFormat(SequenceFileOutputFormat.class);
    
    JobClient client = new JobClient(job);
    ClusterStatus cluster = client.getClusterStatus();
    int numMapsPerHost = job.getInt("test.randomwriter.maps_per_host", 1);
    long numBytesToWritePerMap = job.getLong("test.randomwrite.bytes_per_map",
                                             1*1024*1024);
    if (numBytesToWritePerMap == 0) {
      System.err.println("Cannot have test.randomwrite.bytes_per_map set to 0");
      return -2;
    }
    long totalBytesToWrite = job.getLong("test.randomwrite.total_bytes", 
         numMapsPerHost*numBytesToWritePerMap*cluster.getTaskTrackers());
    int numMaps = (int) (totalBytesToWrite / numBytesToWritePerMap);
    if (numMaps == 0 && totalBytesToWrite > 0) {
      numMaps = 1;
      job.setLong("test.randomwrite.bytes_per_map", totalBytesToWrite);
    }
    
    job.setNumMapTasks(numMaps);
    System.out.println("Running " + numMaps + " maps.");
    
    // reducer NONE
    job.setNumReduceTasks(0);
    
    Date startTime = new Date();
    System.out.println("Job started: " + startTime);
    JobClient.runJob(job);
    Date endTime = new Date();
    System.out.println("Job ended: " + endTime);
    System.out.println("The job took " + 
                       (endTime.getTime() - startTime.getTime()) /1000 + 
                       " seconds.");
    
    return 0;
  }
  
  public static void main(String[] args) throws Exception {
    int res = ToolRunner.run(new Configuration(), new RandomWriter(), args);
    System.exit(res);
  }

}

输出信息：

11/10/17 13:27:46 WARN conf.Configuration: DEPRECATED: hadoop-site.xml found in the classpath. Usage of hadoop-site.xml is deprecated. Instead use core-site.xml, mapred-site.xml and hdfs-site.xml to override properties of core-default.xml, mapred-default.xml and hdfs-default.xml respectively
Running 2 maps.
Job started: Mon Oct 17 13:27:47 CST 2011
11/10/17 13:27:47 INFO mapred.JobClient: Running job: job_201110171322_0001
11/10/17 13:27:48 INFO mapred.JobClient:  map 0% reduce 0%
11/10/17 13:29:58 INFO mapred.JobClient:  map 50% reduce 0%
11/10/17 13:30:05 INFO mapred.JobClient:  map 100% reduce 0%
11/10/17 13:30:07 INFO mapred.JobClient: Job complete: job_201110171322_0001
11/10/17 13:30:07 INFO mapred.JobClient: Counters: 8
11/10/17 13:30:07 INFO mapred.JobClient:   Job Counters 
11/10/17 13:30:07 INFO mapred.JobClient:     Launched map tasks=3
11/10/17 13:30:07 INFO mapred.JobClient:   org.apache.hadoop.examples.RandomWriter$Counters
11/10/17 13:30:07 INFO mapred.JobClient:     BYTES_WRITTEN=2147504078
11/10/17 13:30:07 INFO mapred.JobClient:     RECORDS_WRITTEN=204528
11/10/17 13:30:07 INFO mapred.JobClient:   FileSystemCounters
11/10/17 13:30:07 INFO mapred.JobClient:     HDFS_BYTES_WRITTEN=2154580318
11/10/17 13:30:07 INFO mapred.JobClient:   Map-Reduce Framework
11/10/17 13:30:07 INFO mapred.JobClient:     Map input records=2
11/10/17 13:30:07 INFO mapred.JobClient:     Spilled Records=0
11/10/17 13:30:07 INFO mapred.JobClient:     Map input bytes=0
11/10/17 13:30:07 INFO mapred.JobClient:     Map output records=204528
Job ended: Mon Oct 17 13:30:07 CST 2011
The job took 140 seconds.

在hdfs上产生了两个文件，在/home/hadoop/rand目录下，分别是part-00000（1Gb,r3）和part-00001（1Gb,r3）

作者：xwdreamer

出处：http://www.cnblogs.com/xwdreamer

欢迎任何形式的转载，但请务必注明出处。

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原文地址：https://www.cnblogs.com/xwdreamer/p/2296957.html