本文将大数据学习门槛降到了地平线

从删库到跑路

Hadoop介绍

Hadoop-大数据开源世界的亚当夏娃。
核心是HDFS数据存储系统,和MapReduce分布式计算框架。

HDFS

原理是把大块数据切碎,

每个碎块复制三份,分开放在三个廉价机上,一直保持有三块可用的数据互为备份。使用的时候只从其中一个备份读出来,这个碎块数据就有了。

存数据的叫datenode(格子间),管理datenode的叫namenode(执伞人)。

MapReduce

原理是大任务先分堆处理-Map,再汇总处理结果-Reduce。分和汇是多台服务器并行进行,才能体现集群的威力。难度在于如何把任务拆解成符合MapReduce模型的分和汇,以及中间过程的输入输出<k,v> 都是什么。

单机版Hadoop介绍

对于学习hadoop原理和hadoop开发的人来说,搭建一套hadoop系统是必须的。但

  • 配置该系统是非常头疼的,很多人配置过程就放弃了。
  • 没有服务器供你使用

这里介绍一种免配置的单机版hadoop安装使用方法,可以简单快速的跑一跑hadoop例子辅助学习、开发和测试。
要求笔记本上装了Linux虚拟机,虚拟机上装了docker。

安装

使用docker下载sequenceiq/hadoop-docker:2.7.0镜像并运行。

[root@bogon ~]# docker pull sequenceiq/hadoop-docker:2.7.0  
2.7.0: Pulling from sequenceiq/hadoop-docker860d0823bcab: Pulling fs layer e592c61b2522: Pulling fs layer

下载成功输出

Digest: sha256:a40761746eca036fee6aafdf9fdbd6878ac3dd9a7cd83c0f3f5d8a0e6350c76a
Status: Downloaded newer image for sequenceiq/hadoop-docker:2.7.0

启动

[root@bogon ~]# docker run -it sequenceiq/hadoop-docker:2.7.0 /etc/bootstrap.sh -bash --privileged=true
Starting sshd:                                             [  OK  ]
Starting namenodes on [b7a42f79339c]
b7a42f79339c: starting namenode, logging to /usr/local/hadoop/logs/hadoop-root-namenode-b7a42f79339c.out
localhost: starting datanode, logging to /usr/local/hadoop/logs/hadoop-root-datanode-b7a42f79339c.out
Starting secondary namenodes [0.0.0.0]
0.0.0.0: starting secondarynamenode, logging to /usr/local/hadoop/logs/hadoop-root-secondarynamenode-b7a42f79339c.out
starting yarn daemons
starting resourcemanager, logging to /usr/local/hadoop/logs/yarn--resourcemanager-b7a42f79339c.out
localhost: starting nodemanager, logging to /usr/local/hadoop/logs/yarn-root-nodemanager-b7a42f79339c.out

启动成功后命令行shell会自动进入Hadoop的容器环境,不需要执行docker exec。在容器环境进入/usr/local/hadoop/sbin,执行./start-all.sh和./mr-jobhistory-daemon.sh start historyserver,如下

bash-4.1# cd /usr/local/hadoop/sbin
bash-4.1# ./start-all.sh
This script is Deprecated. Instead use start-dfs.sh and start-yarn.sh

Starting namenodes on [b7a42f79339c]
b7a42f79339c: namenode running as process 128. Stop it first.

localhost: datanode running as process 219. Stop it first.
Starting secondary namenodes [0.0.0.0]
0.0.0.0: secondarynamenode running as process 402. Stop it first.

starting yarn daemons
resourcemanager running as process 547. Stop it first.
localhost: nodemanager running as process 641. Stop it first.  

bash-4.1# ./mr-jobhistory-daemon.sh start historyserver
chown: missing operand after `/usr/local/hadoop/logs'
Try `chown --help' for more information.
starting historyserver, logging to /usr/local/hadoop/logs/mapred--historyserver-b7a42f79339c.out

Hadoop启动完成,如此简单。

要问分布式部署有多麻烦,数数光配置文件就有多少个吧!我亲眼见过一个hadoop老鸟,因为新换的服务器hostname主机名带横线“-”,配了一上午,环境硬是没起来。

运行自带的例子

回到Hadoop主目录,运行示例程序

bash-4.1# cd /usr/local/hadoop
bash-4.1# bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.0.jar grep input output 'dfs[a-z.]+' 
20/07/05 22:34:41 INFO client.RMProxy: Connecting to ResourceManager at /0.0.0.0:8032
20/07/05 22:34:43 INFO input.FileInputFormat: Total input paths to process : 31
20/07/05 22:34:43 INFO mapreduce.JobSubmitter: number of splits:31
20/07/05 22:34:44 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1594002714328_0001
20/07/05 22:34:44 INFO impl.YarnClientImpl: Submitted application application_1594002714328_0001
20/07/05 22:34:45 INFO mapreduce.Job: The url to track the job: http://b7a42f79339c:8088/proxy/application_1594002714328_0001/
20/07/05 22:34:45 INFO mapreduce.Job: Running job: job_1594002714328_0001
20/07/05 22:35:04 INFO mapreduce.Job: Job job_1594002714328_0001 running in uber mode : false
20/07/05 22:35:04 INFO mapreduce.Job:  map 0% reduce 0%
20/07/05 22:37:59 INFO mapreduce.Job:  map 11% reduce 0%
20/07/05 22:38:05 INFO mapreduce.Job:  map 12% reduce 0%

mapreduce计算完成,有如下输出

20/07/05 22:55:26 INFO mapreduce.Job: Counters: 49
        File System Counters
                FILE: Number of bytes read=291
                FILE: Number of bytes written=230541
                FILE: Number of read operations=0
                FILE: Number of large read operations=0
                FILE: Number of write operations=0
                HDFS: Number of bytes read=569
                HDFS: Number of bytes written=197
                HDFS: Number of read operations=7
                HDFS: Number of large read operations=0
                HDFS: Number of write operations=2
        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)=5929
                Total time spent by all reduces in occupied slots (ms)=8545
                Total time spent by all map tasks (ms)=5929
                Total time spent by all reduce tasks (ms)=8545
                Total vcore-seconds taken by all map tasks=5929
                Total vcore-seconds taken by all reduce tasks=8545
                Total megabyte-seconds taken by all map tasks=6071296
                Total megabyte-seconds taken by all reduce tasks=8750080
        Map-Reduce Framework
                Map input records=11
                Map output records=11
                Map output bytes=263
                Map output materialized bytes=291
                Input split bytes=132
                Combine input records=0
                Combine output records=0
                Reduce input groups=5
                Reduce shuffle bytes=291
                Reduce input records=11
                Reduce output records=11
                Spilled Records=22
                Shuffled Maps =1
                Failed Shuffles=0
                Merged Map outputs=1
                GC time elapsed (ms)=159
                CPU time spent (ms)=1280
                Physical memory (bytes) snapshot=303452160
                Virtual memory (bytes) snapshot=1291390976
                Total committed heap usage (bytes)=136450048
        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=437
        File Output Format Counters 
                Bytes Written=197

hdfs命令查看输出结果

bash-4.1# bin/hdfs dfs -cat output/*
6       dfs.audit.logger
4       dfs.class
3       dfs.server.namenode.
2       dfs.period
2       dfs.audit.log.maxfilesize
2       dfs.audit.log.maxbackupindex
1       dfsmetrics.log
1       dfsadmin
1       dfs.servers
1       dfs.replication
1       dfs.file

例子讲解

grep是一个在输入中计算正则表达式匹配的mapreduce程序,筛选出符合正则的字符串以及出现次数。

shell的grep结果会显示完整的一行,这个命令只显示行中匹配的那个字符串
grep input output 'dfs[a-z.]+'   

正则表达式dfs[a-z.]+,表示字符串要以dfs开头,后面是小写字母或者换行符n之外的任意单个字符都可以,数量一个或者多个。
输入是input里的所有文件,

bash-4.1# ls -lrt
total 48
-rw-r--r--. 1 root root  690 May 16  2015 yarn-site.xml
-rw-r--r--. 1 root root 5511 May 16  2015 kms-site.xml
-rw-r--r--. 1 root root 3518 May 16  2015 kms-acls.xml
-rw-r--r--. 1 root root  620 May 16  2015 httpfs-site.xml
-rw-r--r--. 1 root root  775 May 16  2015 hdfs-site.xml
-rw-r--r--. 1 root root 9683 May 16  2015 hadoop-policy.xml
-rw-r--r--. 1 root root  774 May 16  2015 core-site.xml
-rw-r--r--. 1 root root 4436 May 16  2015 capacity-scheduler.xml

结果输出到output。
计算流程如下

稍有不同的是这里有两次reduce,第二次reduce就是把结果按照出现次数排个序。map和reduce流程开发者自己随意组合,只要各流程的输入输出能衔接上就行。

管理系统介绍

Hadoop提供了web界面的管理系统,

端口号用途
50070Hadoop Namenode UI端口
50075Hadoop Datanode UI端口
50090Hadoop SecondaryNamenode 端口
50030JobTracker监控端口
50060TaskTrackers端口
8088Yarn任务监控端口
60010Hbase HMaster监控UI端口
60030Hbase HRegionServer端口
8080Spark监控UI端口
4040Spark任务UI端口

加命令参数

docker run命令要加入参数,才能访问UI管理页面

docker run -it -p 50070:50070 -p 8088:8088 -p 50075:50075  sequenceiq/hadoop-docker:2.7.0 /etc/bootstrap.sh -bash --privileged=true

执行这条命令后在宿主机浏览器就可以查看系统了,当然如果Linux有浏览器也可以查看。我的Linux没有图形界面,所以在宿主机查看。

50070 Hadoop Namenode UI端口

50075 Hadoop Datanode UI端口

8088 Yarn任务监控端口


已完成和正在运行的mapreduce任务都可以在8088里查看,上图有gerp和wordcount两个任务。

一些问题

一、./sbin/mr-jobhistory-daemon.sh start historyserver必须执行,否则运行任务过程中会报

20/06/29 21:18:49 INFO ipc.Client: Retrying connect to server: 0.0.0.0/0.0.0.0:10020. Already tried 9 time(s); retry policy is RetryUpToMaximumCountWithFixedSleep(maxRetries=10, sleepTime=1000 MILLISECONDS)
java.io.IOException: java.net.ConnectException: Call From 87a4217b9f8a/172.17.0.1 to 0.0.0.0:10020 failed on connection exception: java.net.ConnectException: Connection refused; For more details see:  http://wiki.apache.org/hadoop/ConnectionRefused

二、./start-all.sh必须执行否则报形如
Unknown Job job_1592960164748_0001错误

三、docker run命令后面必须加--privileged=true,否则运行任务过程中会报java.io.IOException: Job status not available

四、注意,Hadoop 默认不会覆盖结果文件,因此再次运行上面实例会提示出错,需要先将 ./output 删除。或者换成output01试试?

总结

本文方法可以低成本的完成Hadoop的安装配置,对于学习理解和开发测试都有帮助的。如果开发自己的Hadoop程序,需要将程序打jar包上传到share/hadoop/mapreduce/目录,执行

bin/hadoop jar share/hadoop/mapreduce/yourtest.jar

来运行程序观察效果。

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