【hadoop学习】——MapReduce的WordCount案例代码简析

我会带着你远行 2022-11-16 05:44 226阅读 0赞

一、Map阶段部分：  
获取文本中的所有单词，作为key，每个key对应的value为1，输出格式为<key,1>.  
具体步骤：获取输入文本的每一行数据由Text类型转为String类型，将其以空格或逗号为分隔符切割为一个个单词放入String类型数组中，再将该数组中元素转为Text类型循环写出，每一个输出的key对应的value值为1.  
【注意】：  
①每一行数据都要调用一次map方法；  
②将要输出的String类型的数据转Text类型时，将new Text()得到的outK放在map方法外面作为WordCountMapper类的成员变量；  
③map阶段不对数据进行聚合，每个单词对应的输出value为1；  
④context为map和reducer与系统代码进行交互的桥梁。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N3NTIwbGtt_size_16_color_FFFFFF_t_70]  
二、Reduce部分：  
将输入的每一个key的value集合值进行累加，将累加和作为输出的key的value进行输出。  
注意：每一个key都会调用一次reduce方法，得到该key的value值。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N3NTIwbGtt_size_16_color_FFFFFF_t_70 1]  
三、Driver部分：  
通过job将map和reducer连接起来（第3步）。  
第4步设置最终输出kv类型的目的是有时不使用reduce时要转化为对应输出类型。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3N3NTIwbGtt_size_16_color_FFFFFF_t_70 2]  
\---->Hadoop 2.7.6 中的 WordCount 程序源码：

/**
     * 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 com;
    import org.apache.hadoop.fs.FileSystem;
    import org.apache.hadoop.conf.Configuration;
    import org.apache.hadoop.fs.Path;
    import org.apache.hadoop.io.IntWritable;
    import org.apache.hadoop.io.Text;
    import org.apache.hadoop.mapreduce.Job;
    import org.apache.hadoop.mapreduce.Mapper;
    import org.apache.hadoop.mapreduce.Reducer;
    import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
    import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;
    import org.apache.hadoop.util.GenericOptionsParser;
    
    import java.io.IOException;
    import java.util.StringTokenizer;
    
    public class WordCount {
        
    
      public static class TokenizerMapper 
           extends Mapper<Object, Text, Text, IntWritable>{
        
    
        private final static IntWritable one = new IntWritable(1);
        private Text word = new Text();
    
        public void map(Object key, Text value, Context context
                        ) throws IOException, InterruptedException {
        
          StringTokenizer itr = new StringTokenizer(value.toString());
          while (itr.hasMoreTokens()) {
        
            word.set(itr.nextToken());
            context.write(word, one);
          }
        }
      }
    
      public static class IntSumReducer 
           extends Reducer<Text,IntWritable,Text,IntWritable> {
        
        private IntWritable result = new IntWritable();
    
        public void reduce(Text key, Iterable<IntWritable> values, 
                           Context context
                           ) throws IOException, InterruptedException {
        
          int sum = 0;
          for (IntWritable val : values) {
        
            sum += val.get();
          }
          result.set(sum);
          context.write(key, result);
        }
      }
    
      public static void main(String[] args) throws Exception {
        
        Configuration conf = new Configuration();
        String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();
        if (otherArgs.length < 2) {
        
          System.err.println("Usage: wordcount <in> [<in>...] <out>");
          System.exit(2);
        }
        Job job = Job.getInstance(conf, "word count");
        job.setJarByClass(WordCount.class);
        job.setMapperClass(TokenizerMapper.class);
        job.setCombinerClass(IntSumReducer.class);
        job.setReducerClass(IntSumReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        for (int i = 0; i < otherArgs.length - 1; ++i) {
        
          FileInputFormat.addInputPath(job, new Path(otherArgs[i]));
        }
        FileOutputFormat.setOutputPath(job,
          new Path(otherArgs[otherArgs.length - 1]));
        System.exit(job.waitForCompletion(true) ? 0 : 1);
      }
    }

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