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MapReduce原理分析

什么是MapReduce

前言：如果想知道一堆牌中有多少张红桃，直接的方式是一张张的检查，并数出有多少张红桃。
而MapReduce的方法是，给所有的节点分配这堆牌，让每个节点计算自己手中有几张是红桃，然后将这个数汇总，得到结果。

概述

官方介绍：MapReduce是一种分布式计算模型，由Google提出，主要用于搜索领域，解决海量数据的计算问题。
MapReduce是分布式运行的，由俩个阶段组成：Map和Reduce。
MapReduce框架都有默认实现，用户只需要覆盖map()和reduce()俩个函数，即可实现分布式计算。

原理分析

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Map阶段执行过程

框架会把输入文件划分为很多InputSplit，默认每个hdfs的block对应一个InputSplit。通过RecordReader类，将每个InputSplit解析为一个个键值对<K1,V1>。默认每一个行会被解析成一个键值对。
框架会调用Mapper类中的map()函数，map函数的形参是<k1,v1>，输出是<k2,v2>。一个inputSplit对应一个map task。
框架对map函数输出的<k2,v2>进行分区。不同分区中的<k2,v2>由不同的reduce task处理，默认只有一个分区。
框架对每个分区中的数据，按照k2进行排序、分组。分组指的是相同k2的v2分为一组。
在map节点，框架可以执行reduce规约，此步骤为可选。
框架会把map task输出的<k2,v2>写入linux的磁盘文件

Reduce阶段执行过程

框架对多个map任务的输出，按照不同的分区，通过网络copy到不同的reduce节点，这个过程称为shuffle。
框架对reduce端接收到的相同分区的<k2,v2>数据进行合并、排序、分组
框架调用reduce类中的reduce方法，输入<k2,[v2…]>，输出<k3,v3>。一个<k2,[v2…]>调用一次reduce函数。
框架把reduce的输出保存到hdfs。

WordCount案例分析

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多文件WordCount案例分析

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Shuffle过程详解

shuffle是一个过程，贯穿map和reduce，通过网络将map产生的数据放到reduce。
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Map与Reduce的WordsCount案例（与日志查看）

引入依赖

<?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 https://maven.apache.org/xsd/maven-4.0.0.xsd">
    <modelVersion>4.0.0</modelVersion>
    <parent>
        <groupId>org.springframework.boot</groupId>
        <artifactId>spring-boot-starter-parent</artifactId>
        <version>2.7.14</version>
        <relativePath/> <!-- lookup parent from repository -->
    </parent>
    <groupId>com.hx</groupId>
    <artifactId>hadoopDemo1</artifactId>
    <version>0.0.1-SNAPSHOT</version>
    <name>hadoopDemo1</name>
    <description>Demo project for Spring Boot</description>
    <properties>
        <java.version>1.8</java.version>
    </properties>
    <dependencies>
        <dependency>
            <groupId>org.apache.hadoop</groupId>
            <artifactId>hadoop-client</artifactId>
            <version>3.3.0</version>
            <scope>provided</scope>
        </dependency>
    </dependencies>
</project>

编码

import org.apache.hadoop.conf.Configuration;
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.Mapper;
import org.apache.hadoop.mapreduce.Reducer;
import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
import org.apache.hadoop.mapreduce.lib.output.FileOutputFormat;

import java.io.IOException;

/**
 * @author Huathy
 * @date 2023-10-21 21:17
 * @description 组装任务
 */
public class WordCountJob {
    public static void main(String[] args) throws Exception {
        System.out.println("inputPath  => " + args[0]);
        System.out.println("outputPath  => " + args[1]);
        String path = args[0];
        String path2 = args[1];

        // job需要的配置参数
        Configuration configuration = new Configuration();
        // 创建job
        Job job = Job.getInstance(configuration, "wordCountJob");
        // 注意：这一行必须设置，否则在集群的时候将无法找到Job类
        job.setJarByClass(WordCountJob.class);
        // 指定输入文件
        FileInputFormat.setInputPaths(job, new Path(path));
        FileOutputFormat.setOutputPath(job, new Path(path2));

        job.setMapperClass(WordMap.class);
        job.setReducerClass(WordReduce.class);
        // 指定map相关配置
        job.setMapOutputKeyClass(Text.class);
        job.setMapOutputValueClass(LongWritable.class);
        // 指定reduce
        job.setOutputKeyClass(Text.class);
        job.setOutputValueClass(LongWritable.class);

        // 提交任务
        job.waitForCompletion(true);
    }

    /**
     * @author Huathy
     * @date 2023-10-21 21:39
     * @description 创建自定义映射类
     * 定义输入输出类型
     */
    public static class WordMap extends Mapper<LongWritable, Text, Text, LongWritable> {
        /**
         * 需要实现map函数
         * 这个map函数就是可以接受keyIn，valueIn，产生keyOut、ValueOut
         *
         * @param k1
         * @param v1
         * @param context
         * @throws IOException
         * @throws InterruptedException
         */
        @Override
        protected void map(LongWritable k1, Text v1, Context context) throws IOException, InterruptedException {
            // k1表示每行的行首偏移量，v1表示每一行的内容
            // 对获取到的每一行数据进行切割，把单词切割出来
            String[] words = v1.toString().split("\W");
            // 迭代切割的单词数据
            for (String word : words) {
                // 将迭代的单词封装为<k2,v2>的形式
                Text k2 = new Text(word);
                System.out.println("k2: " + k2.toString());
                LongWritable v2 = new LongWritable(1);
                // 将<k2,v2>输出
                context.write(k2, v2);
            }
        }
    }

    /**
     * @author Huathy
     * @date 2023-10-21 22:08
     * @description 自定义的reducer类
     */
    public static class WordReduce extends Reducer<Text, LongWritable, Text, LongWritable> {
        /**
         * 针对v2s的数据进行累加求和，并且把最终的数据转为k3,v3输出
         *
         * @param k2
         * @param v2s
         * @param context
         * @throws IOException
         * @throws InterruptedException
         */
        @Override
        protected void reduce(Text k2, Iterable<LongWritable> v2s, Context context) throws IOException, InterruptedException {
            long sum = 0L;
            for (LongWritable v2 : v2s) {
                sum += v2.get();
            }
            // 组装K3,V3
            LongWritable v3 = new LongWritable(sum);
            System.out.println("k3: " + k2.toString() + " -- v3: " + v3.toString());
            context.write(k2, v3);
        }
    }

}

运行命令与输出日志

[root@cent7-1 hadoop-3.2.4]# hadoop jar wc.jar WordCountJob  hdfs://cent7-1:9000/hello.txt  hdfs://cent7-1:9000/out /home/hadoop-3.2.4/wc.jar
inputPath  => hdfs://cent7-1:9000/hello.txt
outputPath  => hdfs://cent7-1:9000/out
set jar => /home/hadoop-3.2.4/wc.jar
2023-10-22 15:30:34,183 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
2023-10-22 15:30:35,183 WARN mapreduce.JobResourceUploader: Hadoop command-line option parsing not performed. Implement the Tool interface and execute your application with ToolRunner to remedy this.
2023-10-22 15:30:35,342 INFO mapreduce.JobResourceUploader: Disabling Erasure Coding for path: /tmp/hadoop-yarn/staging/root/.staging/job_1697944187818_0010
2023-10-22 15:30:36,196 INFO input.FileInputFormat: Total input files to process : 1
2023-10-22 15:30:37,320 INFO mapreduce.JobSubmitter: number of splits:1
2023-10-22 15:30:37,694 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1697944187818_0010
2023-10-22 15:30:37,696 INFO mapreduce.JobSubmitter: Executing with tokens: []
2023-10-22 15:30:38,033 INFO conf.Configuration: resource-types.xml not found
2023-10-22 15:30:38,034 INFO resource.ResourceUtils: Unable to find 'resource-types.xml'.
2023-10-22 15:30:38,188 INFO impl.YarnClientImpl: Submitted application application_1697944187818_0010
2023-10-22 15:30:38,248 INFO mapreduce.Job: The url to track the job: http://cent7-1:8088/proxy/application_1697944187818_0010/
2023-10-22 15:30:38,249 INFO mapreduce.Job: Running job: job_1697944187818_0010
2023-10-22 15:30:51,749 INFO mapreduce.Job: Job job_1697944187818_0010 running in uber mode : false
2023-10-22 15:30:51,751 INFO mapreduce.Job:  map 0% reduce 0%
2023-10-22 15:30:59,254 INFO mapreduce.Job:  map 100% reduce 0%
2023-10-22 15:31:08,410 INFO mapreduce.Job:  map 100% reduce 100%
2023-10-22 15:31:09,447 INFO mapreduce.Job: Job job_1697944187818_0010 completed successfully
2023-10-22 15:31:09,578 INFO mapreduce.Job: Counters: 54
	File System Counters
		FILE: Number of bytes read=129
		FILE: Number of bytes written=479187
		FILE: Number of read operations=0
		FILE: Number of large read operations=0
		FILE: Number of write operations=0
		HDFS: Number of bytes read=139
		HDFS: Number of bytes written=35
		HDFS: Number of read operations=8
		HDFS: Number of large read operations=0
		HDFS: Number of write operations=2
		HDFS: Number of bytes read erasure-coded=0
	Job Counters 
		Launched map tasks=1
		Launched reduce tasks=1
		Data-local map tasks=1
		Total time spent by all maps in occupied slots (ms)=4916
		Total time spent by all reduces in occupied slots (ms)=5821
		Total time spent by all map tasks (ms)=4916
		Total time spent by all reduce tasks (ms)=5821
		Total vcore-milliseconds taken by all map tasks=4916
		Total vcore-milliseconds taken by all reduce tasks=5821
		Total megabyte-milliseconds taken by all map tasks=5033984
		Total megabyte-milliseconds taken by all reduce tasks=5960704
	Map-Reduce Framework
		Map input records=4
		Map output records=8
		Map output bytes=107
		Map output materialized bytes=129
		Input split bytes=94
		Combine input records=0
		Combine output records=0
		Reduce input groups=5
		Reduce shuffle bytes=129
		Reduce input records=8
		Reduce output records=5
		Spilled Records=16
		Shuffled Maps =1
		Failed Shuffles=0
		Merged Map outputs=1
		GC time elapsed (ms)=259
		CPU time spent (ms)=2990
		Physical memory (bytes) snapshot=528863232
		Virtual memory (bytes) snapshot=5158191104
		Total committed heap usage (bytes)=378011648
		Peak Map Physical memory (bytes)=325742592
		Peak Map Virtual memory (bytes)=2575839232
		Peak Reduce Physical memory (bytes)=203120640
		Peak Reduce Virtual memory (bytes)=2582351872
	Shuffle Errors
		BAD_ID=0
		CONNECTION=0
		IO_ERROR=0
		WRONG_LENGTH=0
		WRONG_MAP=0
		WRONG_REDUCE=0
	File Input Format Counters 
		Bytes Read=45
	File Output Format Counters 
		Bytes Written=35
[root@cent7-1 hadoop-3.2.4]#

MapReduce任务日志查看

开启yarn日志聚合功能，将散落在nodemanager节点的日志统一收集管理，方便查看
修改yarn-site.xml中的yarn.log-aggregation-enable和yarn.log.server.url

<property>
    <name>yarn.log-aggregation-enable</name>
    <value>true</value>
</property>
<property>
    <name>yarn.log.server.url</name>
    <value>http://cent7-1:19888/jobhistory/logs/</value>
</property>

启动historyserver：

sbin/mr-jobhistory-daemon.sh  start historyserver

UI界面查看

访问 http://192.168.56.101:8088/cluster ，点击History
点进Successful
看到成功记录，点击logs可以看到成功日志

Hadoop3.0大数据处理学习3（MapReduce原理分析、日志归集、序列化机制、Yarn资源调度器）,Hadoop,大数据,学习,mapreduce,大数据

停止Hadoop集群中的任务

Ctrl+C退出终端，并不会结束任务，因为任务已经提交到了Hadoop

查看任务列表：yarn application -list
结束任务进程：yarn application -kill [application_Id]

# 查看正在进行的任务列表
[root@cent7-1 hadoop-3.2.4]# yarn application -list
2023-10-22 16:18:38,756 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
Total number of applications (application-types: [], states: [SUBMITTED, ACCEPTED, RUNNING] and tags: []):1
                Application-Id	    Application-Name	    Application-Type	      User	     Queue	             State	       Final-State	       Progress	                       Tracking-URL
application_1697961350721_0002	        wordCountJob	           MAPREDUCE	      root	   default	          ACCEPTED	         UNDEFINED	             0%	                                N/A
# 结束任务
[root@cent7-1 hadoop-3.2.4]# yarn application -kill application_1697961350721_0002
2023-10-22 16:18:55,669 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
Killing application application_1697961350721_0002
2023-10-22 16:18:56,795 INFO impl.YarnClientImpl: Killed application application_1697961350721_0002

Hadoop序列化机制

序列化机制作用

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上面可以看出，Hadoop运行的时候大多数IO操作。我们在编写Hadoop的Map和Reduce代码的时候，用的都是Hadoop官方提供的数据类型，Hadoop官方对序列化做了优化，只会序列化核心内容来减少IO开销。

Hadoop序列化机制的特点

紧凑：高效的使用存储空间
快速：读写数据的额外开销小
可扩展：可透明的读取老格式的数据
互操作：支持多语言操作

Java序列化的不足

不够精简，附加信息多，不适合随机访问
存储空间占用大，递归输出类的父类描述，直到不再有父类
扩展性差，Hadoop中的Writable可以方便用户自定义

资源管理器（Yarn）详解

Yarn目前支持三种调度器：（针对任务的调度器）
- FIFO Scheduler：先进先出调度策略（工作中存在实时任务和离线任务，先进先出可能不太适合业务）
- CapacityScheduler：可以看作是FIFO的多队列版本。可以分成多个队列，每个队列里面是先进先出的。
- FairScheduler：多队列，多用户共享资源。公平任务调度（建议使用）。

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