将大数据学习门槛降到了地平线

将大数据学习门槛降到了地平线

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

MapReduce

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

管理系统介绍

Hadoop提供了web界面的管理系统，

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: 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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