Docker

Back in 2017 I learned about a ton of new and exciting technologies that I had never used or even heard of. One of those technologies is docker. If you don’t know of docker, then let me give some background information. “Docker is an open platform for developing, shipping, and running applications.” [source]. Before we can dive further into what this really means we need to first come to an understanding of containerization. According to the docker website, “a container is a standard unit of software that packages up code and all its dependencies so the application runs quickly and reliably from one computing environment to another.” [source].

One of the biggest things to remember is that containers are not virtual machines. “Containers and virtual machines have similar resource isolation and allocation benefits, but function differently because containers virtualize the operating system instead of hardware. Containers are more portable and efficient.” [source]

A question I was once asked when I gave a talk at a Python meetup was “how easy is it to dockerize an existing project?” This is a great question, as one of the biggest make or breaks with technology is its capability of integrating with existing code bases. Though I can’t say for larger code bases, I can say for my projects, adding docker is simple. This is coming from the perspective of Python projects, which have whats called requirements.txt files that contain all the dependancies. All that needs adding is a Dockerfile, which an example can be found below.

FROM python:3.5
ADD . /code
WORKDIR /code
EXPOSE 9123
RUN pip install -r requirements.txt
CMD ["python3", "run.py"]

Let’s break the above down line by line.

FROM gets the python 3.5 image and uses it for the base.

ADD copies the current directory (.) to the filesystem of the image at the /code path.

WORKDIR sets the working directory for the RUN and CMD instructions that follow.

EXPOSE instructs the docker container to listen on the specified port at runtime.

RUN executes any given command(s). In this case we are executing a pip install.

CMD provides defaults for an executing container. In this case the default is running python3 run.py.

With this one file, the application is now dockerized. All that needs done is executing the following commands.

sudo docker build . --tag=hello_world

sudo docker run hello_world

Now that’s what I call Spicy.