wordcount源代码详解

package wordcount;
import java.io.IOException;
import java.util.StringTokenizer;

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;

public class wordcount {
        public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{   //继承泛型类Mapper
                private final static IntWritable one = new IntWritable(1);  //定义hadoop数据类型IntWritable实例one，并且赋值为1
                private Text word = new Text();                                    //定义hadoop数据类型Text实例word
 
                public void map(Object key, Text value, Context context) throws IOException, InterruptedException { //实现map函数
                        StringTokenizer itr = new StringTokenizer(value.toString());//Java的字符串分解类，默认分隔符“空格”、“制表符(‘\t’)”、“换行符(‘\n’)”、“回车符(‘\r’)”

                        while (itr.hasMoreTokens()) {  //循环条件表示返回是否还有分隔符。
                                word.set(itr.nextToken());   // nextToken()：返回从当前位置到下一个分隔符的字符串，word.set()：Java数据类型与hadoop数据类型转换
                                context.write(word, one);   //hadoop全局类context输出函数write;
                        }
         }

}

public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> {    //继承泛型类Reducer
         private IntWritable result = new IntWritable();   //实例化IntWritable
         public void reduce(Text key, Iterable<IntWritable> values, Context context ) throws IOException, InterruptedException {  //实现reduce
                    int sum = 0;
                    for (IntWritable val : values)    //循环values，并记录单词个数
                               sum += val.get();
                    result.set(sum);   //Java数据类型sum，转换为hadoop数据类型result
                    context.write(key, result);   //输出结果到hdfs
          }
}

public static void main(String[] args) throws Exception {
         Configuration conf = new Configuration();   //实例化Configuration
/***********
GenericOptionsParser是hadoop框架中解析命令行参数的基本类。 getRemainingArgs();返回数组【一组路径】
*********/
/**********
函数实现
public String[] getRemainingArgs() {
    return (commandLine == null) ? new String[]{} : commandLine.getArgs();
  }

/********
//总结上面：返回数组【一组路径】
String[] otherArgs = new GenericOptionsParser(conf, args).getRemainingArgs();

//如果只有一个路径，则输出需要有输入路径和输出路径
if (otherArgs.length < 2) {
   System.err.println("Usage: wordcount <in> [<in>...] <out>");
   System.exit(2);
}

Job job = Job.getInstance(conf, "word count");   //实例化job
job.setJarByClass(wordcount.class);   //为了能够找到wordcount这个类
job.setMapperClass(TokenizerMapper.class);   //指定map类型
/********
指定CombinerClass类
这里很多人对CombinerClass不理解
************/
job.setCombinerClass(IntSumReducer.class);
job.setReducerClass(IntSumReducer.class);  //指定reduce类
job.setOutputKeyClass(Text.class); //rduce输出Key的类型，是Text
job.setOutputValueClass(IntWritable.class);  // rduce输出Value的类型

for (int i = 0; i < otherArgs.length - 1; ++i)
   FileInputFormat.addInputPath(job, new Path(otherArgs));  //添加输入路径

FileOutputFormat.setOutputPath(job, new Path(otherArgs[otherArgs.length - 1]));   //添加输出路径
System.exit(job.waitForCompletion(true) ? 0 : 1);  //提交job
}
}

转载于:https://www.cnblogs.com/hare/p/6709190.html