MapReduce初试

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面试中问到mapreduce，所以装了单机版mapreduce，hadoop2，尝试一下。

工具

idea，maven，jdk8

Maven配置

<?xml version="1.0" encoding="UTF-8"?><project xmlns="http://maven.apache.org/POM/4.0.0"         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">    <modelVersion>4.0.0</modelVersion>    <groupId>mitsuhide</groupId>    <artifactId>javaAIA</artifactId>    <version>1.0-SNAPSHOT</version>    <properties>        <hadoop.version>2.7.2</hadoop.version>    </properties>    <dependencies>        <dependency>            <groupId>org.apache.hadoop</groupId>            <artifactId>hadoop-common</artifactId>            <version>${hadoop.version}</version>        </dependency>        <dependency>            <groupId>org.apache.hadoop</groupId>            <artifactId>hadoop-client</artifactId>            <version>${hadoop.version}</version>        </dependency>        <dependency>            <groupId>org.apache.hadoop</groupId>            <artifactId>hadoop-hdfs</artifactId>            <version>${hadoop.version}</version>        </dependency>   </dependencies>   <repositories>       <repository>            <id>apache</id>            <url>http://maven.apache.org</url>       </repository>   </repositories></project>

注意hadoop2和hadoop1不一样，hadoop1是hadoop-core，这里用不到了。

配置输入输出

这里写图片描述
在web路径下，配置了input文件夹，output文件夹是mapreduce自动生成的，不用配置。
程序会读取input文件夹下所有文件，按行读取。

配置运行参数

就是在参数上加上input和output：
这里写图片描述

wordcount

package cn.bigdata.hadoop.mapreduce;import java.io.IOException;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;/** * Created by baidu on 16/9/29. */public class WordCount {    public static class TokenizerMapper            extends Mapper<Object, Text, Text, IntWritable> {        private final static IntWritable one = new IntWritable(1);        private Text word = new Text();        public void map(Object key, Text value, Context context        ) throws IOException, InterruptedException {            StringTokenizer itr = new StringTokenizer(value.toString());            while (itr.hasMoreTokens()) {                word.set(itr.nextToken());                context.write(word, one);            }        }    }    public static class IntSumReducer            extends Reducer<Text, IntWritable, Text, IntWritable> {        private IntWritable result = new IntWritable();        public void reduce(Text key, Iterable<IntWritable> values,                           Context context        ) throws IOException, InterruptedException {            int sum = 0;            for (IntWritable val : values) {                sum += val.get();            }            result.set(sum);            context.write(key, result);        }    }    public static void main(String[] args) throws Exception {        Configuration conf = new Configuration();        System.out.println(conf.getStrings("mapreduce.framework.name"));        Job job = Job.getInstance(conf, "word count");        job.setJarByClass(WordCount.class);        job.setMapperClass(TokenizerMapper.class);        job.setCombinerClass(IntSumReducer.class);        job.setReducerClass(IntSumReducer.class);        job.setOutputKeyClass(Text.class);        job.setOutputValueClass(IntWritable.class);        FileInputFormat.addInputPath(job, new Path(args[0]));        FileOutputFormat.setOutputPath(job, new Path(args[1]));        System.exit(job.waitForCompletion(true) ? 0 : 1);    }}

运行

见output下的文本输出：

"Be 2"Don't  116, 120. 120; 124  160  280. 1All 1But 3...

运行完成！

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