Python-Vscode

1. Introduction

This repository is a condensed walkthrough of the official Docker Getting Started Guide, using python in vscode, with docker and python extensions, a setup.py file, and some good practices.

2. Develop

2.1 Using Docker

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:

docker run -p 80:80 --rm menziess/python-vscode

2.2 Using Virtual Environment

Clone this repository and create a virtual environment with a tool such as 'virtualenv' in the root folder, and run:

source ./development.sh

This will activate the virtual environment and install dev dependencies and command-line tools.

Run the flask app:

flask run

Run tests manually:

python -m pytest test/

3. Run and Deploy

Develop, deploy, and run applications with containers.

1. Image: a blueprint for a container
2. Container: a unit of software (instance of an image) that packages all its dependencies so that it run smoothly and reliably
3. Swarm: a cluster of nodes (running Docker)
4. Service: a collection of containers running the same image
5. 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.

3.1 Build Image

Verify that the Dockerfile exists

$ ls
Dockerfile		app.py			requirements.txt

Build the image from the dockerfile

docker build -t menziess/python-vscode:latest .

Show the image that has been built

$ docker image ls

REPOSITORY             TAG                 IMAGE ID
menziess/python-vscode latest              326387cea398

Run the app in detached mode

docker run -p 80:80 menziess/python-vscode

Show the running containers

$ docker container ls
CONTAINER ID        IMAGE                  COMMAND             CREATED
1fa4ab2cf395        menziess/python-vscode "python app.py"     28 seconds ago

Stop the running container

docker container stop 1fa4ab2cf395

Push container

docker login
docker push menziess/python-vscode

3.2 Scale Service (Multiple Containers - Single Node)

Verify that the docker-compose.yml file exists

$ ls
Dockerfile		app.py			requirements.txt      docker-compose.yml

We initialize a swarm (of one node, our local computer)

docker swarm init

Then we deploy our service

docker stack deploy -c docker-compose.yml getstartedlab

And we scale it by increasing the number of replicas in the yml file, and simply run the previous command again

docker stack deploy -c docker-compose.yml getstartedlab

We take down the app, and leave the swarm

docker stack rm getstartedlab
docker swarm leave --force

3.3 Distributed Swarm (Containers Across Cluster - Multiple Nodes)

Start two VM's using virtualbox and docker-machine

docker-machine create --driver virtualbox myvm1
docker-machine create --driver virtualbox myvm2

Initialize the first VM as a swarm manager, as we did in 3.2

docker-machine ls
docker-machine ssh myvm1 "docker swarm init --advertise-addr <myvm1 ip>"

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

docker-machine ssh myvm2 "<the command that is shown>"

Show all the nodes in the swarm

docker-machine ssh myvm1 "docker node ls"

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

docker-machine ssh myvm2 "docker swarm leave"
docker-machine ssh myvm1 "docker swarm leave --force"

docker stack rm getstartedlab
docker-machine stop myvm1
docker-machine stop myvm2

3.4 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

docker-machine ssh myvm1 "mkdir ./data"
docker stack deploy -c docker-compose.yml getstartedlab

Our application should now display the number of visits.