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  [...] ");            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 {        private static final IntWritable one = new IntWritable(1);        private Text word = new Text();        public TokenizerMapper() {        }        public void map(Object key, Text value, Mapper.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 {        private IntWritable result = new IntWritable();        public IntSumReducer() {        }        public void reduce(Text key, Iterable values, Reducer.Context context) throws IOException, InterruptedException {            int sum = 0;            IntWritableval;            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

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