MapReduce的基本内容介绍(附代码)
内容导读
互联网集市收集整理的这篇技术教程文章主要介绍了MapReduce的基本内容介绍(附代码),小编现在分享给大家,供广大互联网技能从业者学习和参考。文章包含3792字,纯文字阅读大概需要6分钟。
内容图文
![MapReduce的基本内容介绍(附代码)](/upload/InfoBanner/zyjiaocheng/537/9e5966d26c0140d8852b867cb98daf1f.jpg)
1、WordCount程序
1.1 WordCount源程序
import java.io.IOException; import java.util.Iterator; 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 { public WordCount() { } public static void main(String[] args) throws Exception { Configuration conf = new Configuration(); String[] otherArgs = (new GenericOptionsParser(conf, args)).getRemainingArgs(); if(otherArgs.length < 2) { System.err.println("Usage: wordcount <in> [<in>...] <out>"); System.exit(2); } Job job = Job.getInstance(conf, "word count"); job.setJarByClass(WordCount.class); job.setMapperClass(WordCount.TokenizerMapper.class); job.setCombinerClass(WordCount.IntSumReducer.class); job.setReducerClass(WordCount.IntSumReducer.class); job.setOutputKeyClass(Text.class); job.setOutputValueClass(IntWritable.class); for(int i = 0; i < otherArgs.length - 1; ++i) { FileInputFormat.addInputPath(job, new Path(otherArgs[i])); } FileOutputFormat.setOutputPath(job, new Path(otherArgs[otherArgs.length - 1])); System.exit(job.waitForCompletion(true)?0:1); } public static class TokenizerMapper extends Mapper<Object, Text, Text, IntWritable> { private static final IntWritable one = new IntWritable(1); private Text word = new Text(); public TokenizerMapper() { } public void map(Object key, Text value, Mapper<Object, Text, Text, IntWritable>.Context context) throws IOException, InterruptedException { StringTokenizer itr = new StringTokenizer(value.toString()); while(itr.hasMoreTokens()) { this.word.set(itr.nextToken()); context.write(this.word, one); } } } public static class IntSumReducer extends Reducer<Text, IntWritable, Text, IntWritable> { private IntWritable result = new IntWritable(); public IntSumReducer() { } public void reduce(Text key, Iterable<IntWritable> values, Reducer<Text, IntWritable, Text, IntWritable>.Context context) throws IOException, InterruptedException { int sum = 0; IntWritable val; for(Iterator i$ = values.iterator(); i$.hasNext(); sum += val.get()) { val = (IntWritable)i$.next(); } this.result.set(sum); context.write(key, this.result); } } }
1.2 运行程序,Run As->Java Applicatiion
1.3 编译打包程序,产生Jar文件
2 运行程序
2.1 建立要统计词频的文本文件
wordfile1.txt
Spark Hadoop
Big Data
wordfile2.txt
Spark Hadoop
Big Cloud
2.2 启动hdfs,新建input文件夹,上传词频文件
cd /usr/local/hadoop/
./sbin/start-dfs.sh
./bin/hadoop fs -mkdir input
./bin/hadoop fs -put /home/hadoop/wordfile1.txt input
./bin/hadoop fs -put /home/hadoop/wordfile2.txt input
2.3 查看已上传的词频文件:
hadoop@dblab-VirtualBox:/usr/local/hadoop$ ./bin/hadoop fs -ls .
Found 2 items
drwxr-xr-x - hadoop supergroup 0 2019-02-11 15:40 input
-rw-r--r-- 1 hadoop supergroup 5 2019-02-10 20:22 test.txt
hadoop@dblab-VirtualBox:/usr/local/hadoop$ ./bin/hadoop fs -ls ./input
Found 2 items
-rw-r--r-- 1 hadoop supergroup 27 2019-02-11 15:40 input/wordfile1.txt
-rw-r--r-- 1 hadoop supergroup 29 2019-02-11 15:40 input/wordfile2.txt
2.4 运行WordCount
./bin/hadoop jar /home/hadoop/WordCount.jar input output
屏幕上会输入大段信息
然后可以查看运行结果:
hadoop@dblab-VirtualBox:/usr/local/hadoop$ ./bin/hadoop fs -cat output/*
Hadoop 2
Spark 2
以上就是MapReduce的基本内容介绍(附代码)的详细内容,更多请关注Gxl网其它相关文章!
内容总结
以上是互联网集市为您收集整理的MapReduce的基本内容介绍(附代码)全部内容,希望文章能够帮你解决MapReduce的基本内容介绍(附代码)所遇到的程序开发问题。 如果觉得互联网集市技术教程内容还不错,欢迎将互联网集市网站推荐给程序员好友。
内容备注
版权声明:本文内容由互联网用户自发贡献,该文观点与技术仅代表作者本人。本站仅提供信息存储空间服务,不拥有所有权,不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容, 请发送邮件至 gblab@vip.qq.com 举报,一经查实,本站将立刻删除。
内容手机端
扫描二维码推送至手机访问。