万物容器化…

docker in docker

既然已经有了kvm in kvm，那么docker也是需要能够嵌套虚拟化的；
脑洞：how about kvm in docker?
自己打算基于centos镜像构建docker in docker镜像，发现在容器内创建容器需要获取root权限，加上–privileged也没有解决，想到docker hub 官网上面肯定已经有造好的轮子，执行指令docker search docker发现确实存在docker in docker 的镜像，docker pull docker。

使用docker in docker 基础镜像：

docker run -it --privileged --name some-docker -d docker:dind
docker logs some-docker
docker run -it --rm --link some-docker:docker docker sh
# 发现已经进入到容器内部的命令行，测试一下：
docker run -it --name busybox busybox
hostname     # 发现容器id和some-docker的id不一样，表示已经进入了docker 的 dicker镜像

之后可以重复这个过程，也就是说只要你愿意可以建立多层docker in docker~

hadoop on docker

大象在鲸鱼上跳舞：

#拉取镜像，这里拉取的别人的精简版hadoop镜像，没有使用官网镜像，该镜像的初始版本还得过奖
sudo docker pull kiwenlau/hadoop:1.0
#下载启动hadoop容器集群源码：
git clone https://github.com/kiwenlau/hadoop-cluster-docker
cd hadoop-cluster-docker/
#创建docker网络，目的是为了hadoop的master和slave可以使用这个网络，进行服务发现，相当于DNS服务器
sudo docker network create --driver=bridge hadoop
#运行docker容器：
./start-container.sh
#进入到hadoop-master容器中，启动hadoop：
./start-hadoop.sh

启动hadoop集群后的输出信息：

Starting namenodes on [hadoop-master]
hadoop-master: Warning: Permanently added 'hadoop-master,172.18.0.2' (ECDSA) to the list of known hosts.
hadoop-master: starting namenode, logging to /usr/local/hadoop/logs/hadoop-root-namenode-hadoop-master.out
hadoop-slave2: Warning: Permanently added 'hadoop-slave2,172.18.0.4' (ECDSA) to the list of known hosts.
hadoop-slave1: Warning: Permanently added 'hadoop-slave1,172.18.0.3' (ECDSA) to the list of known hosts.
hadoop-slave2: starting datanode, logging to /usr/local/hadoop/logs/hadoop-root-datanode-hadoop-slave2.out
hadoop-slave1: starting datanode, logging to /usr/local/hadoop/logs/hadoop-root-datanode-hadoop-slave1.out
Starting secondary namenodes [0.0.0.0]
0.0.0.0: Warning: Permanently added '0.0.0.0' (ECDSA) to the list of known hosts.
0.0.0.0: starting secondarynamenode, logging to /usr/local/hadoop/logs/hadoop-root-secondarynamenode-hadoop-master.out
starting yarn daemons
starting resourcemanager, logging to /usr/local/hadoop/logs/yarn--resourcemanager-hadoop-master.out
hadoop-slave2: Warning: Permanently added 'hadoop-slave2,172.18.0.4' (ECDSA) to the list of known hosts.
hadoop-slave1: Warning: Permanently added 'hadoop-slave1,172.18.0.3' (ECDSA) to the list of known hosts.
hadoop-slave2: starting nodemanager, logging to /usr/local/hadoop/logs/yarn-root-nodemanager-hadoop-slave2.out
hadoop-slave1: starting nodemanager, logging to /usr/local/hadoop/logs/yarn-root-nodemanager-hadoop-slave1.out

运行word-count案例：

root@hadoop-master:~# ./run-wordcount.sh
17/07/03 13:23:50 INFO client.RMProxy: Connecting to ResourceManager at hadoop-master/172.18.0.2:8032
17/07/03 13:23:51 INFO input.FileInputFormat: Total input paths to process : 2
17/07/03 13:23:52 INFO mapreduce.JobSubmitter: number of splits:2
17/07/03 13:23:53 INFO mapreduce.JobSubmitter: Submitting tokens for job: job_1499088095628_0001
17/07/03 13:23:53 INFO impl.YarnClientImpl: Submitted application application_1499088095628_0001
17/07/03 13:23:53 INFO mapreduce.Job: The url to track the job: http://hadoop-master:8088/proxy/application_1499088095628_0001/
17/07/03 13:23:53 INFO mapreduce.Job: Running job: job_1499088095628_0001
17/07/03 13:24:30 INFO mapreduce.Job: Job job_1499088095628_0001 running in uber mode : false
17/07/03 13:24:30 INFO mapreduce.Job:  map 0% reduce 0%
17/07/03 13:24:47 INFO mapreduce.Job:  map 50% reduce 0%
17/07/03 13:24:48 INFO mapreduce.Job:  map 100% reduce 0%
17/07/03 13:24:58 INFO mapreduce.Job:  map 100% reduce 100%
17/07/03 13:24:59 INFO mapreduce.Job: Job job_1499088095628_0001 completed successfully
17/07/03 13:24:59 INFO mapreduce.Job: Counters: 49
  File System Counters
      FILE: Number of bytes read=56
      FILE: Number of bytes written=352398
      FILE: Number of read operations=0
      FILE: Number of large read operations=0
      FILE: Number of write operations=0
      HDFS: Number of bytes read=258
      HDFS: Number of bytes written=26
      HDFS: Number of read operations=9
      HDFS: Number of large read operations=0
      HDFS: Number of write operations=2
  Job Counters
      Launched map tasks=2
      Launched reduce tasks=1
      Data-local map tasks=2
      Total time spent by all maps in occupied slots (ms)=30737
      Total time spent by all reduces in occupied slots (ms)=6406
      Total time spent by all map tasks (ms)=30737
      Total time spent by all reduce tasks (ms)=6406
      Total vcore-milliseconds taken by all map tasks=30737
      Total vcore-milliseconds taken by all reduce tasks=6406
      Total megabyte-milliseconds taken by all map tasks=31474688
      Total megabyte-milliseconds taken by all reduce tasks=6559744
  Map-Reduce Framework
      Map input records=2
      Map output records=4
      Map output bytes=42
      Map output materialized bytes=62
      Input split bytes=232
      Combine input records=4
      Combine output records=4
      Reduce input groups=3
      Reduce shuffle bytes=62
      Reduce input records=4
      Reduce output records=3
      Spilled Records=8
      Shuffled Maps =2
      Failed Shuffles=0
      Merged Map outputs=2
      GC time elapsed (ms)=518
      CPU time spent (ms)=7700
      Physical memory (bytes) snapshot=710238208
      Virtual memory (bytes) snapshot=2512601088
      Total committed heap usage (bytes)=559415296
  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=26
  File Output Format Counters
      Bytes Written=26
input file1.txt:
Hello Hadoop
input file2.txt:
Hello Docker
wordcount output:
Docker    1
Hadoop    1
Hello    2

通过网络访问hadoop-master容器和hadoop-slave容器：http://host-ip:8088；访问存在于主节点的ResourceManager UI：
访问http://host-ip:50070，查看从节点NameNode UI：

感想

容器化后应用使用的虚拟网络，管理容器集群所有容器应用的整套网络感觉不简单；
重复造dockerfile的轮子好像也没啥收获；
应用容器化是趋势；

参考

docker run docker
Docker in Docker!
基于Docker快速搭建多节点Hadoop集群
 基于Docker搭建Hadoop集群之升级版

everything in docker

everything in docker

docker in docker

hadoop on docker

感想

参考

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