Get Started, Part 2: Containers

Prerequisites

• Install Docker.
• Read the orientation in Part 1.
• Give your environment a quick test run to make sure you’re all set up:

  docker run hello-world
  

Introduction

It’s time to begin building an app the Docker way. We’ll start at the bottom of the hierarchy of such an app, which is a container, which we cover on this page. Above this level is a service, which defines how containers behave in production, covered in Part 3. Finally, at the top level is the stack, defining the interactions of all the services, covered in Part 5.

• Stack
• Services
• Container (you are here)

Your new development environment

In the past, if you were to start writing a Python app, your first order of business was to install a Python runtime onto your machine. But, that creates a situation where the environment on your machine has to be just so in order for your app to run as expected; ditto for the server that runs your app.

With Docker, you can just grab a portable Python runtime as an image, no installation necessary. Then, your build can include the base Python image right alongside your app code, ensuring that your app, its dependencies, and the runtime, all travel together.

These portable images are defined by something called a Dockerfile.

Define a container with a Dockerfile

Dockerfile will define what goes on in the environment inside your container. Access to resources like networking interfaces and disk drives is virtualized inside this environment, which is isolated from the rest of your system, so you have to map ports to the outside world, and be specific about what files you want to “copy in” to that environment. However, after doing that, you can expect that the build of your app defined in this Dockerfile will behave exactly the same wherever it runs.

Dockerfile

Create an empty directory and put this file in it, with the name Dockerfile. Take note of the comments that explain each statement.

# Use an official Python runtime as a base image
FROM python:2.7-slim

# Set the working directory to /app
WORKDIR /app

# Copy the current directory contents into the container at /app
ADD . /app

# Install any needed packages specified in requirements.txt
RUN pip install -r requirements.txt

# Make port 80 available to the world outside this container
EXPOSE 80

# Define environment variable
ENV NAME World

# Run app.py when the container launches
CMD ["python", "app.py"]

This Dockerfile refers to a couple of things we haven’t created yet, namely app.py and requirements.txt. Let’s get those in place next.

The app itself

Grab these two files and place them in the same folder as Dockerfile. This completes our app, which as you can see is quite simple. When the above Dockerfile is built into an image, app.py and requirements.txt will be present because of that Dockerfile’s ADD command, and the output from app.py will be accessible over HTTP thanks to the EXPOSE command.

requirements.txt

Flask
Redis

app.py

from flask import Flask
from redis import Redis, RedisError
import os
import socket

# Connect to Redis
redis = Redis(host="redis", db=0)

app = Flask(__name__)

@app.route("/")
def hello():
    try:
        visits = redis.incr('counter')
    except RedisError:
        visits = "<i>cannot connect to Redis, counter disabled</i>"

    html = "<h3>Hello {name}!</h3>" \
           "<b>Hostname:</b> {hostname}<br/>" \
           "<b>Visits:</b> {visits}"
    return html.format(name=os.getenv('NAME', "world"), hostname=socket.gethostname(), visits=visits)

if __name__ == "__main__":
 app.run(host='0.0.0.0', port=80)

Now we see that pip install requirements.txt installs the Flask and Redis libraries for Python, and the app prints the environment variable NAME, as well as the output of a call to socket.gethostname(). Finally, because Redis isn’t running (as we’ve only installed the Python library, and not Redis itself), we should expect that the attempt to use it here will fail and produce the error message.

Note: Accessing the name of the host when inside a container retrieves the container ID, which is like the process ID for a running executable.

Build the App

That’s it! You don’t need Python or anything in requirements.txt on your system, nor will building or running this image install them on your system. It doesn’t seem like you’ve really set up an environment with Python and Flask, but you have.

Here’s what ls should show:

$ ls
Dockerfile  app.py   requirements.txt

Now run the build command. This creates a Docker image, which we’re going to tag using -t so it has a friendly name.

docker build -t friendlyhello .

Where is your built image? It’s in your machine’s local Docker image registry:

$ docker images

REPOSITORY            TAG                 IMAGE ID
friendlyhello         latest              326387cea398

Run the app

Run the app, mapping your machine’s port 4000 to the container’s EXPOSEd port 80 using -p:

docker run -p 4000:80 friendlyhello

You should see a notice that Python is serving your app at http://0.0.0.0:80. But that message coming from inside the container, which doesn’t know you mapped port 80 of that container to 4000, making the correct URL http://localhost:4000. Go there, and you’ll see the “Hello World” text, the container ID, and the Redis error message.

Note: This port remapping of 4000:80 is to demonstrate the difference between what you EXPOSE within the Dockerfile, and what you publish using docker run -p. In later steps, we’ll just map port 80 on the host to port 80 in the container and use http://localhost.

Hit CTRL+C in your terminal to quit.

Now let’s run the app in the background, in detached mode:

docker run -d -p 4000:80 friendlyhello

You get the long container ID for your app and then are kicked back to your terminal. Your container is running in the background. You can also see the abbreviated container ID with docker ps (and both work interchangeably when running commands):

$ docker ps
CONTAINER ID        IMAGE               COMMAND             CREATED
1fa4ab2cf395        friendlyhello       "python app.py"     28 seconds ago

You’ll see that CONTAINER ID matches what’s on http://localhost:4000.

Now use docker stop to end the process, using the CONTAINER ID, like so:

docker stop 1fa4ab2cf395

Share your image

To demonstrate the portability of what we just created, let’s upload our build and run it somewhere else. After all, you’ll need to learn how to push to registries to make deployment of containers actually happen.

A registry is a collection of repositories, and a repository is a collection of images – sort of like a GitHub repository, except the code is already built. An account on a registry can create many repositories. The docker CLI is preconfigured to use Docker’s public registry by default.

Note: We’ll be using Docker’s public registry here just because it’s free and pre-configured, but there are many public ones to choose from, and you can even set up your own private registry using Docker Trusted Registry.

If you don’t have a Docker account, sign up for one at cloud.docker.com. Make note of your username.

Log in your local machine.

docker login

Now, publish your image. The notation for associating a local image with a repository on a registry, is username/repository:tag. The :tag is optional, but recommended; it’s the mechanism that registries use to give Docker images a version. So, putting all that together, enter your username, and repo and tag names, so your existing image will upload to your desired destination:

docker tag friendlyhello username/repository:tag

Upload your tagged image:

docker push username/repository:tag

Once complete, the results of this upload are publicly available. From now on, you can use docker run and run your app on any machine with this command:

docker run -p 4000:80 username/repository:tag

Note: If you don’t specify the :tag portion of these commands, the tag of :latest will be assumed, both when you build and when you run images.

No matter where docker run executes, it pulls your image, along with Python and all the dependencies from requirements.txt, and runs your code. It all travels together in a neat little package, and the host machine doesn’t have to install anything but Docker to run it.

Conclusion of part one

That’s all for this page. In the next section, we will learn how to scale our application by running this container in a service.