3 分布式计算与资源调度

分布式计算概述

什么是分布式计算

由数据得到的结论是广义上的计算，也是计算的目的

分布式计算是指以分布式的形式完成数据的统计，得到结果

• 数据过大无法一台电脑独立计算
• 用数量取胜单台计算

分布式计算模式

分散-汇总模式(MapReduce)

• 分发数据，多台服务器各自负责一部分的数据处理
• 汇总到一台主机

中心调度-步骤执行模式(Spark、Flink)

在计算的中间结果有数据交换的过程

• 由一个节点作为中心调度管理者
• 划为几个步骤
• 安排每台机器的执行与数据交换

MapReduce

是Hadoop组件之一

MapReduce 提供了两个编程接口：

• Map 提供分散数据功能
• Reduce 汇总聚合功能

执行原理

提示

Hive 是基于MapReduce的sql计算框架

YARN

YARN 与 MapReduce 会一起执行，用于资源调度

分布式服务器资源调度，对整个服务器进行统一调度，对资源有规划有管理的使用，能提高效率。

YARN下MapReduce的调度

YARN架构

• RescourceManager：整个集群的资源调度者
• NodeManager：单个服务器的资源调度者

YARN容器

• 预先先占用资源，再分配给任务
• 虚拟化计算，封装运行（NodeManager）

YARN辅助角色

• 代理服务器，提高网络访问的安全
• JobHistoryServer历史服务器，通过收集容器的日志，通过浏览器访问

配置MapReduce与部署YARN

修改部分

集群规划：

MapReduce的配置

添加 mapred-env.sh 配置(/etc/hadoop)

export JAVA_HOME=/export/server/jdk
export HADOOP_JOB_HISTORYSERVER_HEAPSIZE=1000
export HADOOP_MAPREN_ROOT_LOGGER=LNFO,RFA

添加 mapred-site.xml 配置(/etc/hadoop)

<configuration>
        <property>
                <name>mapreduce.framework.name</name>
                <value>yarn</value>
        </property>
        <property>
                <name>mapreduce.jobhistory.address</name>
                <value>node1:10820</value>
        </property>
        <property>
                <name>mapreduce.jobhistory.webapp.address</name>
                <value>node1:19888</value>
        </property>
        <property>
                <name>mapreduce.jobhistory.intermediate-done-dir</name>
                <value>/data/mr-history/tmp</value>
        </property>
        <property>
                <name>mapreduce.jobhistory.done-dir</name>
                <value>/data/mr-history/done</value>
        </property>
        <property>
                <name>yarn.app.mapreduce.am.env</name>
                <value>HADOOP_MAPRED_HOME=$HADOOP_HOME</value>
        </property>
        <property>
                <name>mapreduce.map.env</name>
                <value>HADOOP_MAPRED_HOME=$HADOOP_HOME</value>
        </property>
        <property>
                <name>mapreduce.reduce.env</name>
                <value>HADOOP_MAPRED_HOME=$HADOOP_HOME</value>
        </property>
</configuration>

YARN配置文件

添加 mapred-env.sh 配置(/etc/hadoop)

export JAVA_HOME=/export/server/jdk
export HADOOP_HOME=/export/server/hadoop
export HADOOP_CONF_DIR=$HADOOP_HOME/etc/hadoop
export HADOOP_LOG_DIR=$HADOOP_HOME/logs

添加 yarn-site.xml 配置(/etc/hadoop)

<configuration>
        <property>
                <name>yarn.resourcemanager.hostname</name>
                <value>node1</value>
        </property>
        <property>
                <name>yarn.nodemanager.local-dirs</name>
                <value>/data/nm-local</value>
        </property>
        <property>
                <name>yarn.nodemanager.log-dirs</name>
                <value>/data/nm-log</value>
        </property>
        <property>
                <name>yarn.nodemanager.aux-services</name>
                <value>mapreduce_shuffle</value>
        </property>
        <property>
                <name>yarn.log.server.url</name>
                <value>http://node1:19888/jobhistory/logs</value>
        </property>
        <property>
                <name>yarn.web-proxy.address</name>
                <value>node1:8089</value>
        </property>
        <property>
                <name>yarn.log-aggregation-enable</name>
                <value>true</value>
        </property>
        <property>
                <name>yarn.nodemanager.remote-app-log-dir</name>
                <value>/tmp/logs</value>
        </property>
        <property>
                <name>yarn.resourcemanager.scheduler.class</name>
                <value>org.apache.hadoop.yarn.server.resourcemanager.scheduler.fair.FairScheduler</value>
        </property>
</configuration>

开始计算

提交MapReduce至YARN运行

MapReduce实例代码

• wordcount: 单词计数器
• pi：求圆周率

求单词计数的执行过程

1. 把需要统计的文本放置在hdfs文件系统
2. 执行以下命令进行文件单词统计

[hadoop@node1 ~]$ hadoop jar /export/server/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.5.jar wordcount hdfs://node1:8020/input/ hdfs://node1:8020/output/hl

提示

其中/input/的意思是统计该目录下的所有文件

求PI的执行过程

hadoop jar /export/server/hadoop/share/hadoop/mapreduce/hadoop-mapreduce-examples-3.3.5.jar pi 3 10000

结果：

2023-06-18 14:06:45,427 INFO mapreduce.Job:  map 0% reduce 0%
2023-06-18 14:07:01,534 INFO mapreduce.Job:  map 67% reduce 0%
2023-06-18 14:09:57,239 INFO mapreduce.Job:  map 78% reduce 0%
2023-06-18 14:09:58,243 INFO mapreduce.Job:  map 89% reduce 0%
2023-06-18 14:10:13,294 INFO mapreduce.Job:  map 89% reduce 22%
2023-06-18 14:10:16,305 INFO mapreduce.Job:  map 100% reduce 22%
2023-06-18 14:10:17,309 INFO mapreduce.Job:  map 100% reduce 100%
2023-06-18 14:10:17,314 INFO mapreduce.Job: Job job_1687019957893_0008 completed successfully
2023-06-18 14:10:17,457 INFO mapreduce.Job: Counters: 54
        File System Counters
                FILE: Number of bytes read=72
                FILE: Number of bytes written=1109437
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=783
                HDFS: Number of bytes written=215
                HDFS: Number of read operations=17
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=3
                HDFS: Number of bytes read erasure-coded=0
        Job Counters 
                Launched map tasks=3
                Launched reduce tasks=1
                Data-local map tasks=3
                Total time spent by all maps in occupied slots (ms)=589338
                Total time spent by all reduces in occupied slots (ms)=17633
                Total time spent by all map tasks (ms)=589338
                Total time spent by all reduce tasks (ms)=17633
                Total vcore-milliseconds taken by all map tasks=589338
                Total vcore-milliseconds taken by all reduce tasks=17633
                Total megabyte-milliseconds taken by all map tasks=603482112
                Total megabyte-milliseconds taken by all reduce tasks=18056192
        Map-Reduce Framework
                Map input records=3
                Map output records=6
                Map output bytes=54
                Map output materialized bytes=84
                Input split bytes=429
                Combine input records=0
                Combine output records=0
                Reduce input groups=2
                Reduce shuffle bytes=84
                Reduce input records=6
                Reduce output records=0
                Spilled Records=12
                Shuffled Maps =3
                Failed Shuffles=0
                Merged Map outputs=3
                GC time elapsed (ms)=669
                CPU time spent (ms)=590390
                Physical memory (bytes) snapshot=1938477056
                Virtual memory (bytes) snapshot=12350963712
                Total committed heap usage (bytes)=1853358080
                Peak Map Physical memory (bytes)=625938432
                Peak Map Virtual memory (bytes)=3091316736
                Peak Reduce Physical memory (bytes)=286851072
                Peak Reduce Virtual memory (bytes)=3089543168
        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=354
        File Output Format Counters 
                Bytes Written=97
Job Finished in 220.245 seconds
Estimated value of Pi is 3.14159264764749259810