Running MDS-in-a-box in Docker

I didn’t really set out to learn Docker when I started the MDS-in-a-box project, but as it turns out, Docker is quite a good fit. Part of this is because I desired to run the project in a Github Action, which is a very similar paradigm, and also because I have the notion (TBD) of running a bunch of simulations in AWS Batch. The goal of this post is to show a quick demo and then summarize what I learned – which frankly will also serve as a quick reference for me when I use Docker again.

Running the project in Docker

Once Docker Desktop is installed, building the project is trivial with two ‘make’ scripts.

make docker-build
make docker-run-superset

This takes a few minutes, but once its complete you have a full operational analytics stack running inside your machine.

The first rule of Docker

I learned this one the hard way, as I attempted to add to my existing container. The environment was only based on Python, and I needed to add Node support to it. I tried and tried to modify the dockerfile to get Node working – which leads to the first rule of Docker:

Thou Shalt Use An Existing Base Image

As it turns out, a quick googling revealed that there was already an awesome set of python+node base images. Shout out to this repo which is what I ended up using: Python with Node.js.

Now that I had the Docker container “working” – I needed to actually figure out which docker commands to use.

Docker Quick Reference

These are the commands that I learned and used over and over again as I triaged my way through adding another component to my environment. It is not exhaustive but designed to be a practical list of key commands to help you get started with Docker, too.

  1. docker build – use this to build the image defined in your working directory. In my project, I’m also giving it a name (-t mdsbox) and defining where to save it, so the full command is ‘docker build -t mdsbox .
  2. docker run – use this to run your image as a container once its built. You also pass in your environmental variables as part of docker run, so this command gets a bit long. Unfortunately, this is the first command that you see when learning Docker, which makes it look more imposing and scary than it actually is. The general syntax is ‘docker run <docker config> <CLI command>‘.
  3. docker ps – use this command to see which containers are running. This is so you know which containers to stop or to access (via docker exec) within the CLI.
  4. docker stop – this command stops a container. If you run a container from the terminal, you can’t stop it or exit like a process running in the terminal (i.e. with Ctrl+D), so you need to use ‘docker stop <container name>‘ instead!
  5. docker exec – this command lets you run a command on a running container. I found this be absolutely huge for debugging as you can get right into the terminal on your container and futz around with it. The command I used to access it is ‘docker exec -it <container name> /bin/bash‘ which drops you into the terminal.
  6. –publish – I’m including this Docker flag, since this is the flag you invoke to make your application visible on the network. Used in context, it looks something like this: ‘docker run –publish 3000:3000 <container name>‘. It is simply mapping port 3000 on the host to port 3000 on the container.

There are some notable exclusions, like ‘docker pull‘ but this reference is merely to help get started with MDS-in-a-box. By the way, you can check out the latest deployed version at!

As a note, I want to thank Pedram Navid & Greg Wilson for being my Docker shepherds – I definitely was stuck a few times and your guidance was incredibly helpful in getting things unstuck!