During the previous parts in this series, I introduced Apache Airflow in general, demonstrated my docker dev stack, and built out a simple linear DAG definition. I want to wrap up the series by showing a few other common DAG patterns I regularly use.
In order to follow along, get the source code!
unzip airflow-template.zip cd airflow-template docker-compose up -d docker-compose logs airflow_webserver
This will take a few minutes to get everything initialized, but once its up you will see something like this:
If you've read this far you should have a reasonable understanding of the Apache Airflow layout and be up and running with your own docker dev environment. Well done! This part in the series will cover building an actual simple pipeline in Airflow.
Start building by getting the source code!
The simplest DAG is simply having a list of tasks, where each task depends upon its previous task. If you've spun up the airflow instance and taken a look, it looks like this:
Now, if you're asking why I would choose making an ice cream sundae as my DAG, you may need to reevaluate your priorities.
Generally, if you order ice cream, the lovely deliverer of the ice cream will first as you what kind of cone (or cup, you heathen) you want, then your flavor (or flavors!), what toppings, and then will put them all together into sweet, creamy, cold, deliciousness.
You would accomplish this awesomeness with the following Airflow code:
In this part of the series I will cover how to get a nice Apache Airflow instance up and running with docker. You won't need to have anything installed locally besides docker, which is fantastic, because configuring all these pieces individually would be kind of awful!
This is the exact same setup and configuration I use for my own Apache Airflow instances. When I run Apache Airflow in production I don't use Postgres in a docker container, as that is not recommended, but this setup is absolutely perfect for dev and will very closely match your production requirements!
Following along with a blog post is great, but the best way to learn is to just jump in and start building. Get the Apache Airflow Docker Dev Stack here.
Getting an instance Apache Airflow up and running looks very similar to a Celery instance. This is because Airflow uses Celery behind the scenes to execute tasks. Read more...
Briefly, Apache Airflow is a workflow management system (WMS). It groups tasks into analyses, and defines a logical template for when these analyses should be run. Then it gives you all kinds of amazing logging, reporting, and a nice graphical view of your analyses. I'll let you hear it directly from the folks at Apache Airflow
Apache Airflow is a platform to programmatically author, schedule and monitor workflows.
Use airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command line utilities make performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative.
Source - ...