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Editor to share with you why to use docker, I believe that most people do not know much, so share this article for your reference, I hope you can learn a lot after reading this article, let's go to know it!

# # operating docker docker can be simply regarded as a git of a virtual machine image. There are several common commands:

Load a mirror image

Docker pull user/image:tag

Show the local mirror

Docker images

Open a container with an image and run the command line interactively

Docker run-it imageId / bin/bash end container press Ctrl+D, exit container press Ctrl+Q+P

List containers for all statu

Docker ps-la

Start a container in interactive mode

Docker start-I containerId

# # AWS ECS AWS regards Elastic Beantalks as a container service, and now supports loading docker images. When users just want to take the image and run, AWS recommends using Elastic Beantalks.

AWS ECS provides a more complex environment: you can create multiple instance cluster, you can create multiple task on each cluster, you can use ELB flexibly, and you can do source/container file system mapping.

Note that each task should be a complete system, such as a whole LAMP.

# # Why docker? The biggest advantage of docker is fast, low-cost virtualization, which can deploy dozens or even hundreds of almost identical environments on a host machine. In big data's field, it is more likely that multiple instance join together to form a large cluster to gain more powerful capabilities-hadoop cluster, spark cluster, akka cluster are all like this. My thinking is that the two are two opposite directions in the use of hardware resources: one is small into many, and the other is big into small.

So when should we consider using docker? (except that it is really impossible to install this kind of hard work with software)

Multi-tenant environment / PAAS, which is the best understood application scenario, such as a database platform such as Leancloud

It is easy to understand the development, testing and deployment of the environment, but it is difficult to achieve it in big data's field, because one docker cannot put down the whole environment

Multi-version maintenance, upgrade deployment, code rollback

Deployment of data processing components, especially scalable worker, such as ETL. Speaking of which, the crawler under our Rails can indeed be packaged as docker. The deployment of worker is automated anyway, so it feels like the only benefit may be that dockfile is slightly easier to write than ansible playbook (TBD).

So is docker useful in the field of big data, for example, can it cooperate with hadoop? Of course, in addition to this high-end technology, at least we can deploy a multi-node hadoop cluster on a local machine, right?

The above is all the contents of the article "Why use docker". Thank you for reading! I believe we all have a certain understanding, hope to share the content to help you, if you want to learn more knowledge, welcome to follow the industry information channel!

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