最近一段时间大数据很火,我有稍微有点java基础,自然选择了由java编写的hadoop框架,wordCount是hadoop中类似于java中helloWorld的存在,自然不能错过。
package hadoop.wordcount.com; 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; public class WordCount { /** * Hadoop mapreduce中的map,用来把数据转化为map * @author admin * */ public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable>{ // IntWritable是hadoop中定义的类型,相当于java中的int,这行代码相当于 int one=1; private final static IntWritable one = new IntWritable(1); // Text是hadoop中定义的类型,相当于java中的String,这行代码相当于 String text=""; private Text word = new Text(); /** * hadoop中继承Mapper需要实现map()方法 * key 转化为map时输入的key,类型与Mapper第一个参数一致 * value 转化为map时输入的value,类型与Mapper第二个参数一致 */ public void map(Object key, Text value, Context context ) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); // 遍历输入的value,并将它们写入上下文 while (itr.hasMoreTokens()) { word.set(itr.nextToken()); context.write(word, one); } } } /** * hadoop mapreduce中的Reducer,对数据的具体操作写在这里面 * @author admin * */ public static class IntSumReducer extends Reducer<Text,IntWritable,Text,IntWritable> { private IntWritable result = new IntWritable(); /** * 在这里添加对数据的操作 * key为输入类型 * values为输出类型 * */ 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();// 读取配置文件 Job job = Job.getInstance(conf, "word count");// 新建一个任务 job.setJarByClass(WordCount.class);// 主类 job.setMapperClass(TokenizerMapper.class);// mapper job.setCombinerClass(IntSumReducer.class); job.setReducerClass(IntSumReducer.class);// reducer job.setOutputKeyClass(Text.class);// 输出结果的key类型 job.setOutputValueClass(IntWritable.class);// 输出结果的value类型 // 要读取的数据,此处内容根据你hadoop实际配置而定 FileInputFormat.addInputPath(job, new Path("hdfs://dtj007:9000/dtj007/djt.txt")); // 要输出数据的路径,此处内容根据你hadoop实际配置而定 FileOutputFormat.setOutputPath(job, new Path("hdfs://dtj007:9000/dtj007/wordcount-out")); System.exit(job.waitForCompletion(true) ? 0 : 1);// 提交任务 } }
运行完毕以后可以在你linux配置的hadoop目录下使用:
bin/hadoop fs -text /你在wordCount中配置的输出路径/part-r-00000
命令进行查看
原文:http://www.cnblogs.com/dtj007/p/5120321.html