四、MapReduce 基础

是一个并行计算框架（计算的数据源比较广泛-HDFS、RDBMS、NoSQL），Hadoop的 MR模块充分利用了HDFS中所有数据节点（datanode）所在机器的内存、CUP以及少量磁盘完成对大数据集的分布式计算。MapReduce将计算分为两个阶段：

1. 通过将一个大的计算任务分割成若干个小任务（计算目标数据集的分割），每一个小任务会分配给所有的计算节点（datanode所在物理机器）完成对局部数据的归类和分析，我们通常把该阶段定义为Map阶段，在Map阶段结束后会在本地系统磁盘存储计算的临时结果；
2. 当Map阶段所有节点完成对局部数据的归类分析后，MR框架会启动Reduce任务完成对Map阶段的局部计算临时结果汇总，把以上阶段成为Reduce阶段。

I、计算流程

II、YARN环境搭建

配置文件

[root@CentOS ~]# vi /usr/hadoop-2.6.0/etc/hadoop/yarn-site.xml

<property>
    <name>yarn.nodemanager.aux-services</name>
    <value>mapreduce_shuffle</value>
</property>
<!--Resource Manager-->
<property>
    <name>yarn.resourcemanager.hostname</name>
    <value>CentOS</value>
</property>

[root@CentOS ~]# mv /usr/hadoop-2.6.0/etc/hadoop/mapred-site.xml.template /usr/hadoop-2.6.0/etc/hadoop/mapred-site.xml

[root@CentOS ~]# vi /usr/hadoop-2.6.0/etc/hadoop/mapred-site.xml

<property>
    <name>mapreduce.framework.name</name>
    <value>yarn</value>
</property>

启动计算服务

[root@CentOS ~]# start-yarn.sh
[root@CentOS ~]# jps
1584 SecondaryNameNode
1364 NameNode
1446 DataNode
5229 Jps

访问:http://centos:8088/

III、HelloWorld of MapReduce 编程

  <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-core</artifactId>
      <version>2.6.0</version>
  </dependency>
  <dependency>
      <groupId>org.apache.hadoop</groupId>
      <artifactId>hadoop-mapreduce-client-jobclient</artifactId>
      <version>2.6.0</version>
  </dependency>

IpMapper

 import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.LongWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Mapper;

import java.io.IOException;

/**
 * @program: hadoop_01
 * @description:
 * @author: luoht
 * @create: 2019-01-04 16:08
 **/

public class IpMapper extends Mapper<LongWritable,Text,Text,IntWritable>{

    /**
     *192.168.0.12 1 001 click 5000 2019-01-04 14:44:00
     * @param key :输入文本行字节偏移量
     * @param value:输入文本行
     * @param context
     * @throws IOException
     * @throws InterruptedException
     */

    @Override
    protected void map(LongWritable key, Text value, Context context) throws IOException, InterruptedException {
        String[] tokens = value.toString().split("");
        String ip = tokens[0];
        context.write(new Text(ip),new IntWritable(1));
    }
}

IpReducer

import org.apache.hadoop.io.IntWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.mapreduce.Reducer;

import java.io.IOException;

/**
 * @program: hadoop_01
 * @description:
 * @author: luoht
 * @create: 2019-01-04 16:13
 **/

public class IpReducer extends Reducer<Text,IntWritable,Text,IntWritable> {
    /**
     *
     * @param key :ip
     * @param values: Int[]{1,1,1,..}
     * @param context
     * @throws IOException
     * @throws InterruptedException
     */

    @Override
    protected void reduce(Text key, Iterable<IntWritable> values, Context context) throws IOException, InterruptedException {
        int total = 0;
        for (IntWritable value : values) {
            total+=value.get();
        }
        context.write(key,new IntWritable(total));

    }
}

封装job

import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.conf.Configured;
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.lib.input.TextInputFormat;
import org.apache.hadoop.mapreduce.lib.output.TextOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;

/**
 * @program: hadoop_01
 * @description:
 * @author: luoht
 * @create: 2019-01-04 16:15
 **/

public class CustomJobSubmiter extends Configured implements Tool {
    @Override
    public int run(String[] strings) throws Exception {
        /*1. 封装job 对象*/
        Configuration conf=getConf();
        Job job = Job.getInstance(conf);
        /*2. 设置数据读入和写出的格式*/
        job.setInputFormatClass(TextInputFormat.class);
        job.setOutputFormatClass(TextOutputFormat.class);
        /*3. 设置处理数据的路径*/
        Path dst = new Path("/tt/test");
        TextOutputFormat.setOutputPath(job,dst);
        /*4. 设置数据计算逻辑*/
        Path src=new Path("/tt/access");
        TextInputFormat.addInputPath(job,src);
        Path dst=new Path("/tt/result");
        TextOutputFormat.setOutputPath(job,dst);
        /*5. 设置Mapper和Reducer输出泛型*/
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(IntWritable.class);

        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(IntWritable.class);
        /*6. 提交任务*/
        job.submit();
        return 0;
    }

    public static void main(String[] args) throws Exception {
        ToolRunner.run(new CustomJobSubmiter(),args);
    }
}