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）

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）"/>

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）

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

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

多文件WordCount案例分析

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。

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=".0.0" xmlns:xsi=""xsi:schemaLocation=".0.0 .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jobJob 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reducejob.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*/@Overrideprotected 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*/@Overrideprotected 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,V3LongWritable 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: 54File System CountersFILE: Number of bytes read=129FILE: Number of bytes written=479187FILE: Number of read operations=0FILE: Number of large read operations=0FILE: Number of write operations=0HDFS: Number of bytes read=139HDFS: Number of bytes written=35HDFS: Number of read operations=8HDFS: Number of large read operations=0HDFS: Number of write operations=2HDFS: Number of bytes read erasure-coded=0Job Counters Launched map tasks=1Launched reduce tasks=1Data-local map tasks=1Total time spent by all maps in occupied slots (ms)=4916Total time spent by all reduces in occupied slots (ms)=5821Total time spent by all map tasks (ms)=4916Total time spent by all reduce tasks (ms)=5821Total vcore-milliseconds taken by all map tasks=4916Total vcore-milliseconds taken by all reduce tasks=5821Total megabyte-milliseconds taken by all map tasks=5033984Total megabyte-milliseconds taken by all reduce tasks=5960704Map-Reduce FrameworkMap input records=4Map output records=8Map output bytes=107Map output materialized bytes=129Input split bytes=94Combine input records=0Combine output records=0Reduce input groups=5Reduce shuffle bytes=129Reduce input records=8Reduce output records=5Spilled Records=16Shuffled Maps =1Failed Shuffles=0Merged Map outputs=1GC time elapsed (ms)=259CPU time spent (ms)=2990Physical memory (bytes) snapshot=528863232Virtual memory (bytes) snapshot=5158191104Total committed heap usage (bytes)=378011648Peak Map Physical memory (bytes)=325742592Peak Map Virtual memory (bytes)=2575839232Peak Reduce Physical memory (bytes)=203120640Peak Reduce Virtual memory (bytes)=2582351872Shuffle ErrorsBAD_ID=0CONNECTION=0IO_ERROR=0WRONG_LENGTH=0WRONG_MAP=0WRONG_REDUCE=0File Input Format Counters Bytes Read=45File 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>

3. 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
   

2. 点进Successful
   

3. 看到成功记录，点击logs可以看到成功日志

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: []):1Application-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

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）。