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）-CSDN博客

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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

image.png

Map阶段执行过程

  1. 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。
  2. 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。
  3. 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。
  4. 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。
  5. 在map节点框架可以执行reduce规约此步骤为可选。
  6. 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

  1. 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。
  2. 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
  3. 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。
  4. 框架把reduce的输出保存到hdfs。

WordCount案例分析

image.png

多文件WordCount案例分析

image.png

image.png

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。
image.png

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keyInvalueIn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

  1. 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
  2. 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>
  1. 启动historyserver
sbin/mr-jobhistory-daemon.sh  start historyserver

UI界面查看

  1. 访问 http://192.168.56.101:8088/cluster 点击History
    image.png

  2. 点进Successful
    image.png

  3. 看到成功记录点击logs可以看到成功日志

image.png

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

  1. 查看任务列表yarn application -list
  2. 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

image.png
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

  1. 紧凑高效的使用存储空间
  2. 快速读写数据的额外开销小
  3. 可扩展可透明的读取老格式的数据
  4. 互操作支持多语言操作

Java序列化的不足

  1. 不够精简附加信息多不适合随机访问
  2. 存储空间占用大递归输出类的父类描述直到不再有父类
  3. 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

  1. 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。

image.png

image.png

阿里云国内75折 回扣 微信号：monov8
阿里云国际，腾讯云国际，低至75折。AWS 93折 免费开户实名账号 代冲值 优惠多多 微信号：monov8 飞机：@monov6
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