MapReduce的基本内容介绍（附代码）

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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 itemsdrwxr-xr-x- hadoop supergroup0 2019-02-11 15:40 input-rw-r--r--1 hadoop supergroup5 2019-02-10 20:22 test.txthadoop@dblab-VirtualBox:/usr/local/hadoop$ ./bin/hadoop fs -ls ./inputFound 2 items-rw-r--r--1 hadoop supergroup27 2019-02-11 15:40 input/wordfile1.txt-rw-r--r--1 hadoop supergroup29 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/*Hadoop2Spark2以上就是MapReduce的基本内容介绍（附代码）的详细内容，更多请关注小潘博客其它相关文章！