This repo is a tool intended for Airflow developers who prefer using virtualenv to docker. It makes it easy to change your configuration from one executor or backend to another.

It's assumed that you already have the database backends running in your host machine.

If you just want to change one aspect of your configuration:

# change database backend
use-mysql
use-postgres
use-sqlite
use-mssql

# change dags folder
use-example-dag
use-dag

# change executor
use-local-executor
use-kubernetes-executor
use-sequential-executor
use-celery-executor

To change more than one attribute in one line, you can use the aenv command. For example, if you want to use kubernetes executor with mysql backend and only example_bash_operator.py, you can enter this:

aenv --kubernetes --mysql --example-dag example_bash_operator.py

This is helpful when you want to use the same configuration in multiple terminal windows.

To print your configuration:

# print relevant airflow env vars
echo-airflow-env

As an Airflow developer, I frequently need to switch database backends and executors. I strongly prefer working in a virtualenv over docker because of the speed, lighter resource footprint, and better debugging. And I found these utilities very helpful for this kind of workflow.

The lower-level helpers like use-mysql and use-sqlite are great when you just want to change one aspect of your setup. For setting up the same configuration in multiple terminal sessions, that's why the aenv wrapper exists.

Add aenv.sh to your shell profile, e.g.:

source ~/code/aenv/aenv.sh

If your airflow repo is not located at ~/code/airflow, set this var before sourcing aenv.sh:

export AIRFLOW_ROOT_DIR=/path/to/airflow-repo  # only need to set if different from ~/code/airflow

To use k8s executor locally with a virtualenv scheduler, you need to coordinate a few things. You need to to get your dags in the image (ideally without rebuilding) and you need to get your logs out of the container. To do this you create hostpath persistent volumes, and we provide helper functions for this.

Note: By default, the helpers will mount ~/airflow/logs as the logs dir and $AIRFLOW_ROOT_DIR/airflow/example_dags as the dags folder. But you can change this by setting variables AIRFLOW_HOST_PATH_DAGS and AIRFLOW_HOST_PATH_DAGS prior to sourcing aenv.sh.

To create the volumes and volume claims:

# create shared volumes for k8s executor and KPO
create-dags-volume
create-logs-volume

# you'll need to delete them and recreate if you want to change
delete-dags-volume
delete-logs-volume

The included pod_template.yaml references the PVCs so that you can share logs and dags between your k8s pods and your host. Your environment will be configured to use this template file when you switch to kubernetes executor. This lets you access your k8s task logs from your webserver (running on host in virtualenv). And it lets you change dag code without rebuilding your image.

Other k8s helpers:

# switch the default image used for k8s executor
use-image  # default: `use-image local latest`

# remove up leftover pods
delete-pods-errored
delete-pods-completed

Default assumption is that you want to use docker image local:latest.

You can change this with env vars or with helper use-image some-image some-tag or just set the normal env vars.

On a mac, it's easy enough to get mysql, postgres and sqlite installed and leave them running in the background on the host.

But to run MSSQL you need to run it in docker. Included here is a function to spin up an instance for you:

# spins up a mssql container with port 1433 and admin password `$MSSQL_DEV_PASSWORD` (default: Abc123456)
# also uses a docker volume for persistent
start-mssql  

Before you use airflow with mssql, you need to set up the database.

First connect to the server:

mssql-cli -U sa -P Abc123456 -S localhost

Then create the database and set isolation level:

CREATE DATABASE	testing;
ALTER DATABASE testing SET READ_COMMITTED_SNAPSHOT ON;