MapReduce的基本思想

先举一个简单的例子: 打个比方我们有三个人斗地主, 要数数牌够不够, 一种最简单的方法可以找一个人数数是不是有54张(传统单机计算); 还可以三个人各分一摞牌数各自的(Map阶段), 三个人的总数加起来汇总(Reduce阶段).

所以MapReduce的思想即: “分治”+”汇总”. 大数据量下, 一台机器处理不了的数据, 就用多台机器, 以分布式集群的形式来处理.

关于Map与Reduce有很多文章将这两个词直译为映射和规约, 其实Map的思想就是各自负责一块实行分治, Reduce的思想即: 将分治的结果汇总. 干嘛翻译的这么生硬呢(故意让人觉得大数据很神秘么?)

MapReduce的编程入门

还是很简单的模式: 包含8个步骤

我们那最简单的单词计数来举例(号称大数据的HelloWorld), 先让大家跑起来看看现象再说.

按照MapReduce思想有两个主要步骤, Mapper与Reducer, 剩余的东西Hadoop都帮助我们实现了, 先入门实践再了解原理;

MapReducer有两种运行模式: 1,集群模式(生产环境);2,本地模式(试验学习)

前提:

1, 下载一个Hadoop的安装包, 放到本地, 并配置到环境变量里面;

2, 下载一个hadoop.dll放到hadoop的bin目录下

创建Maven工程, 导入依赖

	  <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-common</artifactId>
      <version>2.10.1</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-client</artifactId>
      <version>2.10.1</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-hdfs</artifactId>
      <version>2.10.1</version>
    </dependency>
    <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-core</artifactId>
      <version>2.10.1</version>
    </dependency>

数据文件D:\Source\data\demo_result1\xx.txt

hello,world,hadoop
hive,sqoop,flume,hello
kitty,tom,jerry,world
hadoop

开始编写代码

第一步, 创建Mapper类

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;
import java.io.IOException;
public class BaseMapper extends Mapper<LongWritable, Text, Text, LongWritable> {
    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] words = value.toString().split(",");
        Text keyout = new Text();
        LongWritable valueout = new LongWritable(1);
        for (String word : words) {
            keyout.set(word);
            context.write(keyout, valueout);
        }
    }
}

第二步, 创建Reducer类

import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;
import java.io.IOException;
public class BaseReducer extends Reducer<Text, LongWritable, Text, LongWritable> {
    @Override
    protected void reduce(Text key, Iterable<LongWritable> values, Context context) throws IOException, InterruptedException {
        int x = 0;
        for (LongWritable value : values) {
            x += value.get();
        }
        context.write(key, new LongWritable(x));
    }
}

第三步, 创建Job启动类

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
import org.apache.hadoop.fs.Path;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Job;
import org.apache.hadoop.mapreduce.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
public class MainJob extends Configured implements Tool {
    @Override
    public int run(String[] strings) throws Exception {
        Job job = Job.getInstance(super.getConf(), MainJob.class.getName());
		//集群运行时候: 要打包
        job.setJarByClass(MainJob.class);
        //1, 读取输入文件解析类
        job.setInputFormatClass(TextInputFormat.class);
        TextInputFormat.setInputPaths(job,new Path("D:\\Source\\data\\data_in"));
        //2, 设置Mapper类
        job.setMapperClass(BaseMapper.class);
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);
		 //3, 设置shuffle阶段的分区, 排序, 规约, 分组
        //7, 设置Reducer类
        job.setReducerClass(BaseReducer.class);
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);
        //8, 设置文件输出类以及输出地址
        job.setOutputFormatClass(TextOutputFormat.class);
        TextOutputFormat.setOutputPath(job,new Path("D:\\Source\\data\\demo_result1"));				
      	//启动MapReduceJob
        boolean completion = job.waitForCompletion(true);
        return completion?0:1;
    }
    public static void main(String[] args) {
        MainJob mainJob = new MainJob();
        try {
            Configuration configuration = new Configuration();
            configuration.set("mapreduce.framework.name","local");
            configuration.set("yarn.resourcemanager.hostname","local");
            int run = ToolRunner.run(configuration, mainJob, args);
            System.exit(run);
        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}