資源文件file.txthello hadoophello wordthis is my first hadoop program
分析：一個文檔中每行的單詞通過空格分割后獲取，經過map階段可以將所有的單詞整理成如下形式：key:hello value:1key:hadoop value:1key:hello value:1key:word value:1key:this value:1key:is value:1key:my value:1key:first value:1key:hadoop value:1key:program value:1經過hadoop整理后以如下形式輸入到Reduce中：key:hello value:{1,1}key:hadoop value: {1,1}key:word value:{1}key:this value: {1}key:is value:{1}key:myvalue: {1}key:first value:{1}key:program value:{1}所以Reduce接受的時候是以Iterable<IntWritable> values最為值。在Reduce中我們就可以將value中的值迭代相加就可以得出該單詞出現的次數。實現：package com.bwzy.hadoop; import java.io.File; import java.io.IOException; import java.util.Iterator; import java.util.StringTokenizer; import org.apache.hadoop.conf.Configured; import org.apache.hadoop.fs.Path; import org.apache.hadoop.io.*; import org.apache.hadoop.mapreduce.*; import org.apache.hadoop.mapreduce.lib.input.FileInputFormat; import org.apache.hadoop.mapreduce.lib.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; import org.apache.hadoop.util.Tool; import org.apache.hadoop.util.ToolRunner; public class WordCount extends Configured implements Tool {public static class Mapextendsorg.apache.hadoop.mapreduce.Mapper<LongWritable, Text, Text, IntWritable> {private final static IntWritable one = new IntWritable(1);private Text word = new Text();public void map(LongWritable key,Text value,Context context){String line = value.toString();StringTokenizer tokenizer = new StringTokenizer(line);//讀取每一行數據，并將該行數據以空格分割（StringTokenizer默認是以空格分割字符串）while (tokenizer.hasMoreTokens()) {word.set(tokenizer.nextToken());try {context.write(word, one);//輸出給Reduce} catch (IOException e) {e.printStackTrace();} catch (InterruptedException e) {e.printStackTrace();}}}}public static class Reduce extends Reducer<Text, IntWritable, Text, IntWritable> {public void reduce(Text key, Iterable<IntWritable> values,Context context) throws IOException, InterruptedException{int sum = 0; //合并每個單詞出現的次數for (IntWritable val : values) {sum+=val.get();}context.write(key, new IntWritable(sum));}}public static void main(String[] args) throws Exception {int ret = ToolRunner.run(new WordCount(), args);System.exit(ret);}@Overridepublic int run(String[] arg0) throws Exception {Job job = new Job(getConf());job.setJobName("wordcount");job.setOutputKeyClass(Text.class);//key--設置輸出格式job.setOutputValueClass(IntWritable.class);//value--設置輸出格式job.setMapperClass(Map.class);job.setReducerClass(Reduce.class);job.setInputFormatClass(TextInputFormat.class);job.setOutputFormatClass(TextOutputFormat.class);FileInputFormat.setInputPaths(job, new Path(arg0[0]));FileOutputFormat.setOutputPath(job, new Path(arg0[1]));boolean success = job.waitForCompletion(true);return success?0:1;} }
運行：1：將程序打包選中打包的類-->右擊-->Export-->java-->JAR file--填入保存路徑-->完成2：將jar包拷貝到hadoop的目錄下。（因為程序中用到來hadoop的jar包）3：將資源文件（存儲這單詞的文件,假設在/home/user/Document/file1.txt）上傳到定義的hdfs目錄下創建hdfs目錄命令（在hadoop已經成功啟動的前提下）：hadoop fs -mkdir /自定義/自定義/input
上傳本地資源文件到hdfs上：hadop fs -put -copyFromLocal /home/user/Document/file1.txt /自定義/自定義/input
4：運行MapReduce程序：hadoop jar /home/user/hadoop-1.0.4/WordCount.jar com.bwzy.hadoop.WordCount /自定義/自定義/input /自定義/自定義/output
說明：hadoop運行后會自動創建/自定義/自定義/output目錄，在該目錄下會有兩個文件，其中一個文件中存放來MapReduce運行的結果。如果重新運行該程序，需要將/自定義/自定義/output目錄刪除，否則系統認為該結果已經存在了。5：運行的結果為hello 2hadoop 2word 1this 1
is 1my 1first 1program 1

轉載于:https://blog.51cto.com/3157689/1350178

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