Airflow declarative DAGs via YAML.
Compatibility:
Python 2.7 / 3.5+
Airflow 1.10.4+
Key Features
Declarative DAGs in plain text YAML helps a lot to understand how DAG will looks like. Made for humans, not programmers.
It makes extremely hard to turn your DAGs into code mess. Even if you make complicated YAMLs generator the result would be readable for humans.
No more guilty about coupling business logic with task management system (Airflow). They now could coexists separated.
Static analysis becomes a trivial task.
It’s a good abstraction to create your own scheduler/worker compatible with original Airflow one.
Examples
Check tests/dags directory for example of DAGs which will works and which won’t. Use src/airflow_declarative/schema.py module for the reference about YAML file schema. It should be self descriptive.
Don’t be shy to experiment: trafaret-config will help you to understand what had gone wrong and why and where.
Usage
We provide support for two installation options:
As a complementary side package for the upstream Airflow.
As a built-in Airflow functionality using patches for Airflow.
Upstream Airflow
The idea is to put a Python script to the dags_folder which would load the declarative dags via airflow_declarative. More details: Installation using Upstream Airflow.
import os
import airflow_declarative
# Assuming that the yaml dags are located in the same directory
# as this Python module:
root = os.path.dirname(__file__)
dags_list = [
airflow_declarative.from_path(os.path.join(root, item))
for item in os.listdir(root)
if item.endswith((".yml", ".yaml"))
]
globals().update({dag.dag_id: dag for dags in dags_list for dag in dags})Patched Airflow
We provide ready to use patches in the patches directory. To use them you will need to apply a patch to a corresponding Airflow version and then build it yourself. More details: Installation using Patched Airflow.
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