Docker
1. Introduction
This chapter is a condensed walk-through of the official Docker Getting Started Guide, making use of an example repository Python-Vscode to demonstrate some basic docker commands. Now what's the main difference between a VM and Docker?
2. Installation
The installation steps may vary between distributions and OS's. Follow the steps to install docker-ce
from the official documentation.
After that, pull the repository that is used in this tutorial from https://github.com/Menziess/Python-Vscode:
3. Run and Deploy
Develop, deploy, and run applications with containers.
Image: a blueprint for a container
Container: a unit of software (instance of an image) that packages all its dependencies so that it run smoothly and reliably
Swarm: a cluster of nodes (running Docker)
Service: a collection of containers running the same image
Stack: multiple services, possibly sharing dependencies, to be scaled and orchestrated together
For example: an application, or a stack, may contain a database service, and a web-app service, described by their respective images, each having three containers (6 in total), running distributed on a swarm.
2.1 Build Image
Verify that the Dockerfile exists:
Build the image from the dockerfile:
Show the image that has been built:
Run the app in detached mode:
Show the running containers:
Stop the running container:
Push container to your own dockerhub repository:
2.2 Scale Service (Multiple Containers - Single Node)
Verify that the docker-compose.yml
file exists:
We initialize a swarm (of one node, our local computer):
Then we deploy our service:
And we scale it by increasing the number of replicas in the .yml file, and simply run the previous command again:
We take down the app, and leave the swarm:
2.3 Distributed Swarm (Containers Across Cluster - Multiple Nodes)
Start two VM's using virtualbox and docker-machine:
Initialize the first VM as a swarm manager, as we did in 3.2:
Copy the command that is show in terminal, and ssh into the second VM, then paste the command to add it as a worker to the swarm:
Show all the nodes in the swarm:
Now you can walk through 3.2 again, and deploy the stack, but on the distributed swarm this time. If you do this, run docker-machine ls
to reveal the VM ip addresses to access the application in the browser.
Finally, we can leave the swarm from within each VM, remove the stack:
2.4 Stack Services (Adding Database)
We will expand our docker-compose.yml
file by adding more services. We will add a docker visualizer and a redis database. The database will require a volume that is stored on the swarm manager called /data
, let's make that folder and redeploy:
Our application should now display the number of visits.
4. Containers For Development
It is sometimes useful to develop apps in a docker container to ensure consistency between different developers' environments.
4.1 Docker Approach
A container can be run with a shared volume, so that code changes are immediately visible, and the container is deleted after use. Create a docker image with debug enabled by setting the ENV FLASK_DEBUG
flag to 1
, then run:
4.2 Traditional Approach
Create a virtual environment with a tool such as 'virtualenv' in the root folder, and run:
This will activate the virtual environment and install dev dependencies and command-line tools. Run the flask app:
5. Useful Dockerfiles
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