背景介绍：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。但首先，配置该系统是非常头疼的，可能很多人配置过程就放弃了。另外，很可能没有多个服务器供你使用，或者你没有一台性能强劲的电脑可以跑多个虚拟机。本文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例子帮助学习和开发测试。前提是笔记本上安装了Linux虚拟机，并且虚拟机上安装了docker。
参考旧文
Oracle VM virtualbox安装Linux，并访问外网，和宿主机互通
下面开始：
使用docker下载sequenceiq/hadoop-docker:2.7.0镜像并运行，下载最新版的镜像运行中遇到了问题，2.7.0版本可以成功。
[root@bogon ~]# docker pull sequenceiq/hadoop-docker:2.7.0命令到此结束，后面是输出2.7.0: Pulling from sequenceiq/hadoop-docker860d0823bcab: Pulling fs layer e592c61b2522: Pulling fs layer
下载成功会看见下面输出
Digest: sha256:a40761746eca036fee6aafdf9fdbd6878ac3dd9a7cd83c0f3f5d8a0e6350c76aStatus: Downloaded newer image for sequenceiq/hadoop-docker:2.7.0
用下面命令运行Hadoop镜像
docker run -it sequenceiq/hadoop-docker:2.7.0 /etc/bootstrap.sh -bash --privileged=true
[root@bogon ~]# docker run -it sequenceiq/hadoop-docker:2.7.0 /etc/bootstrap.sh -bash --privileged=trueStarting sshd: [ OK ]Starting namenodes on [b7a42f79339c]b7a42f79339c: starting namenode, logging to /usr/local/hadoop/logs/hadoop-root-namenode-b7a42f79339c.outlocalhost: starting datanode, logging to /usr/local/hadoop/logs/hadoop-root-datanode-b7a42f79339c.outStarting secondary namenodes [0.0.0.0]0.0.0.0: starting secondarynamenode, logging to /usr/local/hadoop/logs/hadoop-root-secondarynamenode-b7a42f79339c.outstarting yarn daemonsstarting resourcemanager, logging to /usr/local/hadoop/logs/yarn--resourcemanager-b7a42f79339c.outlocalhost: 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，不需要执行docker exec了。在容器中进入/usr/local/hadoop/sbin，执行./start-all.sh和./mr-jobhistory-daemon.sh start historyserver，如下
cd /usr/local/hadoop/sbin
./start-all.sh
./mr-jobhistory-daemon.sh start historyserver
bash-4.1# cd /usr/local/hadoop/sbinbash-4.1# ./start-all.shThis script is Deprecated. Instead use start-dfs.sh and start-yarn.shStarting 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 daemonsresourcemanager running as process 547. Stop it first.localhost: nodemanager running as process 641. Stop it first.bash-4.1# ./mr-jobhistory-daemon.sh start historyserverchown: 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
回到Hadoop主目录cd /usr/local/hadoop，运行示例程序。
bin/hadoop jar share/hadoop/mapreduce/hadoop-mapreduce-examples-2.7.0.jar grep input output 'dfs[a-b.]+'
这个示例程序的功能是将 input 文件夹中的所有文件作为输入，筛选当中符合正则表达式 dfs[a-z.]+ 的单词并统计出现的次数，最后输出结果到 output 文件夹中。
bash-4.1# cd /usr/local/hadoopbash-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 /0.0.0.0:803220/07/05 22:34:43 INFO input.FileInputFormat: Total input paths to process : 3120/07/05 22:34:43 INFO mapreduce.JobSubmitter: number of splits:3120/07/05 22:34:44 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1594002714328_000120/07/05 22:34:44 INFO impl.YarnClientImpl: Submitted application application_1594002714328_000120/07/05 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_000120/07/05 22:35:04 INFO mapreduce.Job: Job job_1594002714328_0001 running in uber mode : false20/07/05 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处理完成，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
使用命令查看输出结果
bash-4.1# bin/hdfs dfs -cat output/*6 dfs.audit.logger4 dfs.class3 dfs.server.namenode.2 dfs.period2 dfs.audit.log.maxfilesize2 dfs.audit.log.maxbackupindex1 dfsmetrics.log1 dfsadmin1 dfs.servers1 dfs.replication1 dfs.file
Hadoop提供了web界面的管理系统，如果想在docker使用，run命令要加入参数，
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。
注意：
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？
这些问题在我的环境出现的，如果其他版本操作系统不知道是否有这些问题。我是用的Redhat7.6。
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，bin/hadoop jar share/hadoop/mapreduce/你的程序.jar来运行程序观察效果。
