![]() ![]() They install in dist-packages (and might be a little older FWIW). The apt packages are pre-compiled and install much faster. numpy and pandas are installed via apt instead of pip because building them takes a long time on the Pi (~2hrs in my test □). Will update, upgrade and install necessary packages from the apt repository.Īdds dist-packages to the PYTHONPATH. I chose it because it’s slim, stable, and familiar. I’m using python:3.7-slim-buster as the base image for our project. Here’s a brief summary of the Dockerfile content: Command entrypoint.sh /entrypoint.sh EXPOSE 8080 ENV AIRFLOW_HOME =/app/airflow ENV AIRFLOW_CORE_LOAD_EXAMPLES =FalseĮNV AIRFLOW_CORE_LOAD_DEFAULT_CONNECTIONS =FalseĮNV AIRFLOW_CORE_FERNET_KEY =this-should-be-unique-and-secret ENV AIRFLOW_WEBSERVER_EXPOSE_CONFIG =True Python3-numpy ENV PYTHONPATH = " $ :/usr/lib/python3/dist-packages" RUN pip install -U pip setuptools wheel \ & apt-get install -yqq -no-install-recommends \ With that said, let’s make sure we’re all up to date by runningįROM python:3.7-slim-buster RUN apt-get update -yqq \ In this post I’m using the -raspbian-buster-lite.img distribution of Buster Lite on a Raspberry Pi 3 Model B V1.2. If you need some help check out Installing Raspian & Configuring for Headless Access. I recommend installing a fresh copy of Raspian on a new SD card. Building Airflow and Starting the Containerįirst things first - it’s always best to start with a clean slate.It has served wonders in me learning about both Docker and Airflow. No magic sauce here □□♀️.įor a much more thorough example of running Airflow in a Docker container, I recommend checking out the amazing puckel/docker-airflow repo. Hopefully that way you can take a simple example, conceptualize its workings and build something cool from there. This is meant to be the simplest, bare bones, example of running Airflow on a Raspberry Pi. By running it yourself you’re guaranteed to run into errors □, get frustrated □, and ultimately learn something you otherwise wouldn’t □. Learning by doing is the best way of understanding how something works, and can help give you an advantage in real work situations where you might be running Airflow on a SaaS platform. A Raspberry Pi is cheap, Airflow is free, and I’ve got work that needs done!.Having said that, why run it on a Raspberry Pi? The answer to that is two fold Convenient storage and retrieval of credentials & variables within the Airflow ecosystem.It ships with a nice GUI for monitoring and triggering workflows, and.There’s a wide variety of out of the box Operators for doing work,.It’s feature rich and has a large community of developers. Think: “Do this, then that, and then finally this”. Running Airflow & Docker on a Raspberry Pi May 24, 2020Īpache Airflow is a great open source pipeline orchestration platform. ![]()
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