Dockerizing a web app + using Docker Compose (part 1 of 3)

by Andrzej Bakonski

A couple months ago we decided to move Veryfi’s Python-based web app onto Microsoft Azure. The process was complicated and involved several stages. First I had to Dockerize the app, then move it into a Docker Swarm setup, and finally set up a CI/CD pipeline using Jenkins and BitBucket. Most of this was new to me, so the learning curve was steep. I had limited experience with Python and knew of Docker and Jenkins, but had yet to dive into the deep end. After completing the task, I thought I could share my research and process with the Veryfi community.

I’ve compiled a three-part series that will cover these topics:

  1. Dockerizing a web app, using Docker Compose for orchestrating multi-container infrastructure
  2. Deploying to Docker Swarm on Microsoft Azure
  3. CI/CD using BitBucket, Jenkins, Azure Container Registry

This is the first post in the series.

I won’t go into a full blown explanation of Docker — there are plenty of articles online that answer that question, and a good place to start is here. One brief (and incomplete) description is that Docker creates something similar to Virtual Machines, only that Docker containers run on the host machine’s OS, rather than on a VM. Each Docker container should ideally contain one service and an application can comprise of multiple containers. With this approach, individual containers (services) can be easily swapped out or scaled out, independently of others. For example, our main web app currently runs on 3 instances of the main Python app container, and they all speak to one single Redis container.

So why did we decide to implement Docker at Veryfi?

The main benefits to us have been portability and the ease of scale and orchestration. We use Macs internally and deploy to Linux servers on Azure and AWS and wanted to ensure we don’t run into the “well it worked on my machine…” scenario. It had to be easy to port to whatever infrastructure we wanted to use. We wanted to be able to scale easily — both Azure and AWS have container services that make this easy with Docker. Simply put, we wanted to make it all easy. And as I’ll explain in the third post in this series, we made things super easy by implementing CI/CD.

Dockerizing an app

Note: the example included in this section can be found in this GitHub repo: https://github.com/abakonski/docker-flask
The example here is a minimal, “Hello World” app.

Docker containers are defined by Docker images, which are essentially templates for the environment that a container will run in, as well as the service(s) that will be running within them. A Docker image is defined by a Dockerfile, which outlines what gets installed, how it’s configured etc. This file always first defines the base image that will be used.

Docker images comprise multiple layers. For example, our web app image is based on the “python:3.6” image (https://github.com/docker-library/python/blob/d3c5f47b788adb96e69477dadfb0baca1d97f764/3.6/jessie/Dockerfile). This Python image is based on several layers of images containing various Debian Jessie build dependencies, which are ultimately based on a standard Debian Jessie image. It’s also possible to base a Docker image on “scratch” — an empty image that is the very top-level base image of all other Docker images, which allows for a completely customizable image, from OS to the services and any other software.

In addition to defining the base image, the Dockerfile also defines things like:

  • Environment variables
  • Package/dependency install steps
  • Port configuration
  • Environment set up, including copying application code to the image and any required file system changes
  • A command to start the service that will run for the duration of the Docker container’s life

This is an example Dockerfile:

FROM python:3.6

# Set up environment variables
ENV NGINX_VERSION '1.10.3-1+deb9u1'

# Install dependencies
RUN apt-key adv --keyserver hkp://pgp.mit.edu:80 --recv-keys 573BFD6B3D8FBC641079A6ABABF5BD827BD9BF62 \
    && echo "deb http://httpredir.debian.org/debian/ stretch main contrib non-free" >> /etc/apt/sources.list \
    && echo "deb-src http://httpredir.debian.org/debian/ stretch main contrib non-free" >> /etc/apt/sources.list \
    && apt-get update -y \
    && apt-get install -y -t stretch openssl nginx-extras=${NGINX_VERSION} \
    && apt-get install -y nano supervisor \
    && rm -rf /var/lib/apt/lists/*


# Expose ports
EXPOSE 80

# Forward request and error logs to Docker log collector
RUN ln -sf /dev/stdout /var/log/nginx/access.log \
    && ln -sf /dev/stderr /var/log/nginx/error.log

# Make NGINX run on the foreground
RUN if ! grep --quiet "daemon off;" /etc/nginx/nginx.conf ; then echo "daemon off;" >> /etc/nginx/nginx.conf; fi;

# Remove default configuration from Nginx
RUN rm -f /etc/nginx/conf.d/default.conf \
    && rm -rf /etc/nginx/sites-available/* \
    && rm -rf /etc/nginx/sites-enabled/*

# Copy the modified Nginx conf
COPY /conf/nginx.conf /etc/nginx/conf.d/

# Custom Supervisord config
COPY /conf/supervisord.conf /etc/supervisor/conf.d/supervisord.conf

# COPY requirements.txt and RUN pip install BEFORE adding the rest of your code, this will cause Docker's caching mechanism
# to prevent re-installinig all of your dependencies when you change a line or two in your app
COPY /app/requirements.txt /home/docker/code/app/
RUN pip3 install -r /home/docker/code/app/requirements.txt

# Copy app code to image
COPY /app /app
WORKDIR /app

# Copy the base uWSGI ini file to enable default dynamic uwsgi process number
COPY /app/uwsgi.ini /etc/uwsgi/
RUN mkdir -p /var/log/uwsgi


CMD ["/usr/bin/supervisord"]

Here’s a cheat sheet of the commands used in the above example:

  • FROM — this appears at the top of all Dockerfiles and defines the image that this new Docker image will be based on. This could be a public image (see https://hub.docker.com/) or a local, custom image
  • ENV — this command sets environment variables that are available within the context of the Docker container
  • EXPOSE — this opens ports into the Docker container so traffic can be sent into them. These will still need to be listened to from within the container, (i.e. NginX could be configured to listen to port 80). Without this EXPOSE command, no traffic from outside the container will be able to get through on those ports
  • RUN — this command will run shell commands inside the container (when the image is being built)
  • COPY — this copies files from the host machine to the container
  • CMD — this is the command that will execute on container launch and will dictate the life of the container. If it’s a service, such as NginX, the container will continue to run for as long as NginX is up. If it’s a quick command (i.e. “echo ‘Hello world’”), then the container will stop running as soon as the command has executed and exited

The Docker image resulting from the above Dockerfile will be based on the Python 3.6 image and contain NginX and a copy of the app code. The Python dependencies are all listed in requirements.txt and are installed as part of the process. NginX, uWSGI and supervisord are all configured as part of this process as well.

This setup breaks the rule of thumb for the “ideal” way of using Docker, in that one container runs more than one service (i.e. NginX and uWSGI). It was a case-specific decision to keep things simple. Of course, there could be a separate container running just NginX and one running uWSGI, but for the time being, I’ve left the two in one container.

These services are both run and managed with the help of supervisord. Here’s the supervisord config file that ensures NginX and uWSGI are both running:

[supervisord]
nodaemon=true

[program:uwsgi]
# Run uWSGI with custom ini file
command=/usr/local/bin/uwsgi --ini /etc/uwsgi/uwsgi.ini
stdout_logfile=/dev/stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr
stderr_logfile_maxbytes=0

[program:nginx]
# NginX will use a custom conf file (ref: Dockerfile)
command=/usr/sbin/nginx
stdout_logfile=/dev/stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr
stderr_logfile_maxbytes=0

Launching a Docker container

I’m not including the instructions on installing Docker in this post (a good place to get started is here)

With the above project set up and Docker installed, the next step is to actually launch a Docker container based on the above image definition.

Frist, the Docker image must be built. In this example, I’ll tag (name) the image as “myapp”. In whatever terminal/shell is available on the machine you’re using (I’m running the Mac terminal), run the following command:

$ docker build -t myapp .

Next, run a container based on the above image using one of the following commands:

# run Docker container in interactive terminal mode - this will print logs to the terminal stdout, hitting command+C (or Ctrl+C etc) will kill the container
$ docker run -ti -p 80:80 myapp

# run Docker container quietly in detached/background mode - the container will need to be killed with the "docker kill" command (see next code block below)
$ docker run -d -p 80:80 myapp

The above commands will direct traffic to port 80 on the host machine to the Docker container’s port 80. The Python app should now be accessible on port 80 on localhost (i.e. open http://localhost/ in a browser on the host machine).

Here are some helpful commands to see what’s going on with the Docker container and perform any required troubleshooting:

# list running Docker containers
$ docker ps

# show logs for a specific container
$ docker logs [container ID]

# connect to a Docker container's bash terminal
$ docker exec -it [container ID] bash

# stop a running container
$ docker kill [container ID]

# remove a container
$ docker rm [container ID]

# get a list of available Docker commands
$ docker --help

Docker Compose

Note: the example included in this section is contained in this GitHub repo: https://github.com/abakonski/docker-compose-flask
As above, the example here is minimal.

The above project is a good start, but it’s a very limited example of what Docker can do. The next step in setting up a microservice infrastructure is through the use of Docker Compose. Typically, most apps will comprise multiple services that interact with each other. Docker Compose is a pretty simple way of orchestrating exactly that. The concept is that you describe the environment in a YAML file (usually named docker-compose.yml) and launch the entire environment with just one or two commands.

This YAML file describes things like:

  • The containers that need to run (i.e. the various services)
  • The various storage mounts and the containers that have access to them — this makes it possible for various services to have shared access to files and folders
  • The various network connections over which containers can communicate with each other
  • Other configuration parameters that will allow containers to work together
version: '3'

services:
  redis:
    image: "redis:alpine"
    ports:
      - "6379:6379"
    networks:
      - mynet

  web:
    build: .
    image: myapp:latest
    ports:
      - "80:80"
    networks:
      - mynet

networks:
  mynet:

The above YAML file defines two Docker images that our containers will be based on, and one network that both containers will be connected to so that they can “talk” to each other.

In this example, the first container will be created based on the public “redis:alpine” image. This is a generic image that runs a Redis server. The “ports” setting is used to open a port on the container and map it to a host port. The syntax for ports is “HOST:CONTAINER”. In this example we forward the host port 6379 to the same port in the container. Lastly, we tell Docker compose to put the Redis container on the “mynet” network, which is defined at the bottom of the file.

The second container defined will be based on a custom local image, namely the one that’s outlined in the first section of this article. The “build” setting here simply tells Docker Compose to build the Dockerfile that is sitting in the same directory as the YAML file (./Dockerfile) and tag that image with the value of “image” — in this case “myapp:latest”. The “web” container is also going to run on the “mynet” network, so it will be able to communicate with the Redis container and the Redis service running within it.

Finally, there is a definition for the “mynet” network at the bottom of the YAML file. This is set up with the default configuration.

This is a very basic setup, just to get a basic example up and running. There is a ton of info on Docker Compose YAML files here.

Once the docker-compose.yml file is ready, build it (in this case only the “web” project will actually be built, as the “redis” image will just be pulled from the public Docker hub repo). Then bring up the containers and network:

# build all respective images
$ docker-compose build

# create containers, network, etc
$ docker-compose up

# as above, but in detached mode
$ docker-compose up -d

Refer to the Docker commands earlier in this article for managing the containers created by Docker Compose. When in doubt, use the “–help” argument, as in:

# general Docker command listing and help
$ docker --help

# Docker network help
$ docker network --help

# Help with specific Docker commands
$ docker <command> --help

# Docker Compose help
$ docker-compose --help

So there you have it — a “Hello World” example of Docker and Docker Compose.

Just remember that this is a starting point. Anyone diving into Docker for the first time will find themselves sifting through the official Docker docs and StackOverflow forums etc, but hopefully this post is a useful intro. Stay tuned for my follow-up posts that will cover deploying containers into Docker Swarm on Azure and then setting up a full pipeline into Docker Swarm using Jenkins and BitBucket.

If you have any feedback, questions or insights, feel free to reach out in the comments.