phillip2019 / dea-airflow

Geoscience Australia DEA Airflow DAGs repository

Deployment Workflow

This repository contains two branches, master and develop.

The master branch requires Pull Requests and code reviews to merge code into it. It deploys automatically to the Production (Sandbox) Airflow deployment.

The develop branch accepts pushes directly, or via Pull Request, and deploys automatically to the Development Airflow.

We're not happy with this strategy, and are looking for an alternative that doesn't have us deploying and inadvertently running code in multiple places by accident, but haven't come up with anything yet.

Development Using Docker

If you have Docker available, by far the easiest development setup is to use Docker Compose. Full instruction is available from here: https://airflow.apache.org/docs/apache-airflow/stable/start/docker.html

First, initialise some environment variables:

mkdir ./dags ./logs ./plugins # you will notice plugins and dags folder already exist
echo -e "AIRFLOW_UID=$(id -u)\nAIRFLOW_GID=0" >> .env

Then start up docker-compose:

docker-compose up airflow-init
docker-compose up

Connect to the Local Airflow Webserver in your browser, and login with Username: airflow, Password: airflow.

Connect to the Local Prometheus webserver in your browser

Connect to the Local statsd-exporter Webserver in your browser

Additional pip libraries

To add more pip libraries to docker-compose, append the library to _PIP_ADDITIONAL_REQUIREMENTS under environment configuration

    _PIP_ADDITIONAL_REQUIREMENTS: ${_PIP_ADDITIONAL_REQUIREMENTS:-airflow-exporter airflow-kubernetes-job-operator authlib flask-appbuilder apache-airflow[statsd] SQLAlchemy kubernetes boto3}

Troubleshooting

if you are experiencing issues with the docker-compose file, please ensure to check your docker-compose version, it is confirmed to work with version 1.29.2

ubuntu@:~/dea-airflow$ docker-compose version
docker-compose version 1.29.2, build 5becea4c
docker-py version: 5.0.0
CPython version: 3.7.10
OpenSSL version: OpenSSL 1.1.0l  10 Sep 2019

Local Editing of DAG's

DAGs can be locally edited and validated. Development can be done in conda or venv according to developer preference. Grab everything airflow and write DAGs. Use autopep8 and pylint to achieve import validation and consistent formatting as the CI pipeline for this repository matures.

pip install apache-airflow[aws,kubernetes,postgres,redis,ssh,celery] -c constraints.txt
pip install pylint pylint-airflow

pylint dags plugins

Pre-commit setup

A pre-commit config is provided to automatically format and check your code changes. This allows you to immediately catch and fix issues before you raise a failing pull request (which run the same checks under Travis).

If you don't use Conda, install pre-commit from pip:

pip install pre-commit

If you do use Conda, install from conda-forge (required because the pip version uses virtualenvs which are incompatible with Conda's environments)

conda install pre_commit

Now install the pre-commit hook to the current repository:

pre-commit install

Your code will now be formatted and validated before each commit. You can also invoke it manually by running pre-commit run --all-files

Integration Test on GITHUB ACTION or locally

This docker-compose.workflow.yaml has an extra postgres endpoint with a copy of odc database.

run it locally

mkdir ./logs
echo -e "AIRFLOW_UID=$(id -u)\nAIRFLOW_GID=0" >> .env
docker-compose -f docker-compose.workflow.yaml up airflow-init
docker-compose -f docker-compose.workflow.yaml up

checking the opendatacube integration test database

docker exec -it dea-airflow_opendatacube_1 bash
PGPASSWORD=opendatacubepassword psql -U opendatacubeusername -d opendatacube -p 5432 -h localhost

Integration test setup

setup connections for db_odc_reader

docker-compose -f docker-compose.workflow.yaml run airflow-worker airflow connections add db_odc_reader --conn-schema opendatacube --conn-login opendatacubeusername --conn-password opendatacubepassword --conn-port 5432 --conn-type postgres --conn-host opendatacube

integration test database

The integration test database contains selected number of products and datasets, if you need to add more products and datasets to the database, please update dbsetup.sh in integration_tests folder.

Once the local database has been updated, create the dump for integration test

PGPASSWORD=opendatacubepassword pg_dump -U opendatacubeusername -h localhost opendatacube >> opendatacube.sql
mv opendatacube.sql ~/dea-airflow/docker/database

some basic sql for checking correctness

select id, name from agdc.dataset_type;