MapReduce的基础内容是什么
发布时间：2022-02-19 14:24:03 所属栏目：MySql教程 来源：互联网
导读：这篇文章将为大家详细讲解有关MapReduce的基本内容是什么，小编觉得挺实用的，因此分享给大家做个参考，希望大家阅读完这篇文章后可以有所收获。 1、WordCount程序 1.1 WordCount源程序 import java.io.IOException; import java.util.Iterator; import java
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这篇文章将为大家详细讲解有关MapReduce的基本内容是什么，小编觉得挺实用的，因此分享给大家做个参考，希望大家阅读完这篇文章后可以有所收获。 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 关于MapReduce的基本内容是什么就分享到这里了，希望以上内容可以对大家有一定的帮助，可以学到更多知识。如果觉得文章不错，可以把它分享出去让更多的人看到。 （编辑：92站长网） 【声明】本站内容均来自网络，其相关言论仅代表作者个人观点，不代表本站立场。若无意侵犯到您的权利，请及时与联系站长删除相关内容! |
