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。
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）。
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。
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
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。
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
[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。
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
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。
Hadoop提供了web界面的管理系统，
端口号 用途 50070 Hadoop Namenode UI端口 50075 Hadoop Datanode UI端口 50090 Hadoop SecondaryNamenode 端口 50030 JobTracker监控端口 50060 TaskTrackers端口 8088 Yarn任务监控端口 60010 Hbase HMaster监控UI端口 60030 Hbase HRegionServer端口 8080 Spark监控UI端口 4040 Spark任务UI端口
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。
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、./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？
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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