Hadoop中wordCount功能实现Demo

wordCount是hadoop下mapReduce下的一个基本的入门程序，用来统计一系列文本中单词出现的次数（最基本的用法）。Mapreduce是一个分布式运算程序的编程框架，是用户开发“基于hadoop的数据分析应用”的核心框架。采用“分而治之”的办法，Mapper负责“分”，Reducer负责统计汇总。

#自定义Mapper部分

package com.vm.mapreduce;import java.io.IOException;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Mapper;/** * Map部分 *  * @author 1 * */public class MyMapper extends Mapper<LongWritable, Text, Text, LongWritable> {@Overrideprotected void map(LongWritable key, Text value, Mapper<LongWritable, Text, Text, LongWritable>.Context context)throws IOException, InterruptedException {// value表示，每一行的文本内容String line = value.toString();String[] split = line.split(" ");// 以空格来分割单词for (String word : split) {context.write(new Text(word), new LongWritable(1L));}}}

#自定以Reducer部分

package com.vm.mapreduce;import java.io.IOException;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Reducer;/** * Reducer部分 *  * @author 1 * */public class MyReducer extends Reducer<Text, LongWritable, Text, LongWritable> {@Overrideprotected void reduce(Text key, Iterable<LongWritable> values,Reducer<Text, LongWritable, Text, LongWritable>.Context context) throws IOException, InterruptedException {long count = 0L;for (LongWritable value : values) {count += value.get();}context.write(key, new LongWritable(count));}}

#代码测试

package com.vm.mapreduce;import java.net.URI;import org.apache.hadoop.conf.Configuration;import org.apache.hadoop.conf.Configured;import org.apache.hadoop.fs.FileSystem;import org.apache.hadoop.fs.Path;import org.apache.hadoop.io.LongWritable;import org.apache.hadoop.io.Text;import org.apache.hadoop.mapreduce.Job;import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;import org.apache.hadoop.util.Tool;import org.apache.hadoop.util.ToolRunner;/** * 测试代码2 *  * @author 1 * */public class WordCount2 extends Configured implements Tool {//文件输入路径public static final String INPUT_PATH = "hdfs://mini1:9000/wordcount/input/words.txt";//文件输出路径public static final String OUTPUT_PATH = "hdfs://mini1:9000/wordcount/output8";@Overridepublic int run(String[] arg0) throws Exception {// 判断并删除已经存在的路径和文件System.setProperty("HADOOP_USER_NAME", "hadoop");FileSystem fs = FileSystem.get(new URI(OUTPUT_PATH), getConf(), "hadoop");Path outPath = new Path(OUTPUT_PATH);if (fs.exists(outPath)) {fs.delete(outPath, true);System.out.println("删除路径。。。。OK");}@SuppressWarnings("deprecation")Job job = new Job(getConf(), "wdCount");// 指定输入目录FileInputFormat.setInputPaths(job, new Path(INPUT_PATH));// 置顶自定义的mapper类job.setMapperClass(MyMapper.class);// 置顶mapper输出的<k,v>类型job.setMapOutputKeyClass(Text.class);job.setMapOutputValueClass(LongWritable.class);// 置顶要运行的Reducer类job.setReducerClass(MyReducer.class);// 置顶reduce的输出的<k,v>类型job.setOutputKeyClass(Text.class);job.setOutputValueClass(LongWritable.class);// 设置输出目录FileOutputFormat.setOutputPath(job, new Path(OUTPUT_PATH));// *********提交作业***********boolean success = job.waitForCompletion(true);System.exit(success ? 0 : 1);return success ? 0 : 1;}public static void main(String[] args) throws Exception {Configuration conf = new Configuration();int res = ToolRunner.run(conf, new WordCount2(), args);System.out.println("=================");System.exit(res);}}