hadoop入门（WordCount实例详解）

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 { //继承泛型类Mapper         public static class TokenizerMapper     extends Mapper<Object, Text, Text, IntWritable>{ //定义hadoop数据类型IntWritable实例one，并且赋值为1 private final static IntWritable one = new IntWritable(1); //定义hadoop数据类型Text实例word private Text word = new Text(); //实现map函数    public void map(Object key, Text value, Context context                  ) throws IOException, InterruptedException { //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’)”    StringTokenizer itr = new StringTokenizer(value.toString()); //循环条件表示返回是否还有分隔符。    while (itr.hasMoreTokens()) { /***** 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 ****/      word.set(itr.nextToken()); //hadoop全局类context输出函数write;      context.write(word, one);    } } } //继承泛型类Reducer public static class IntSumReducer     extends Reducer<Text,IntWritable,Text,IntWritable> { //实例化IntWritable private IntWritable result = new IntWritable(); //实现reduce public void reduce(Text key, Iterable<IntWritable> values,                     Context context                     ) throws IOException, InterruptedException {    int sum = 0; //循环values，并记录单词个数    for (IntWritable val : values) {      sum += val.get();    } //Java数据类型sum，转换为hadoop数据类型result    result.set(sum); //输出结果到hdfs    context.write(key, result); } } public static void main(String[] args) throws Exception { //实例化Configuration Configuration conf = new 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); } //实例化job Job job = Job.getInstance(conf, "word count"); //为了能够找到wordcount这个类 job.setJarByClass(wordcount.class); //指定map类型 job.setMapperClass(TokenizerMapper.class); /******** 指定CombinerClass类 这里很多人对 CombinerClass不理解 ************/ job.setCombinerClass(IntSumReducer.class); //指定reduce类 job.setReducerClass(IntSumReducer.class); //rduce输出Key的类型，是Text job.setOutputKeyClass(Text.class); // rduce输出Value的类型 job.setOutputValueClass(IntWritable.class); //添加输入路径 for (int i = 0; i < otherArgs.length - 1; ++i) {    FileInputFormat.addInputPath(job, new Path(otherArgs)); } //添加输出路径 FileOutputFormat.setOutputPath(job,    new Path(otherArgs[otherArgs.length - 1])); //提交job System.exit(job.waitForCompletion(true) ? 0 : 1); } }