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程序源码
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.LongWritable; 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.input.TextInputFormat; import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat; import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat; public class WordCount { public static class WordCountMap extends Mapper<LongWritable, Text, Text, IntWritable> { private final IntWritable one = new IntWritable(1); private Text word = new Text(); public void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException { String line = value.toString(); StringTokenizer token = new StringTokenizer(line); while (token.hasMoreTokens()) { word.set(token.nextToken()); context.write(word, one); } } } public static class WordCountReduce extends Reducer<Text, IntWritable, Text, IntWritable> { public void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException { int sum = 0; for (IntWritable val : values) { sum += val.get(); } context.write(key, new IntWritable(sum)); } } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); Job job = new Job(conf); job.setJarByClass(WordCount.class); job.setJobName("wordcount"); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); job.setMapperClass(WordCountMap.class); job.setReducerClass(WordCountReduce.class); job.setInputFormatClass(TextInputFormat.class); job.setOutputFormatClass(TextOutputFormat.class); FileInputFormat.addInputPath(job, new Path(args[0])); FileOutputFormat.setOutputPath(job, new Path(args[1])); job.waitForCompletion(true); } }
1 编译源码
javac -classpath /opt/hadoop-1.2.1/hadoop-core-1.2.1.jar:/opt/hadoop-1.2.1/lib/commons-cli-1.2.jar -d ./word_count_class/ WordCount.java
将源码编译成class文件并放在当前文件夹下的word_count_class目录,当然,首先需要创建该目录
2 将源码打成jar包
进入源码目录
jar -cvf wordcount.jar *
3 上传输入文件
先在hadoop中为本次任务创建一个输入文件存放目录
hadoop fs -mkdir input_wordcount
将input目录下的所有文本文件上传到hadoop中的input_wordcount目录下
hadoop fs -put input/* input_wordcount/
4 上传jar并执行
hadoop jar word_count_class/wordcount.jar WordCount input_wordcount output_wordcount
5 查看计算结果
程序输出目录
hadoop fs -ls output_wordcount
程序输出内容
hadoop fs -cat output_wordcount/part-r-00000
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原文地址:http://www.cnblogs.com/sherrykid/p/4604717.html