MapReduce的基本内容是什么

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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。

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