dag-factory is a library for dynamically generating Apache Airflow DAGs from YAML configuration files.
To install dag-factory run pip install dag-factory. It requires Python 3.6.0+ and Apache Airflow 2.0+.
After installing dag-factory in your Airflow environment, there are two steps to creating DAGs. First, we need to create a YAML configuration file. For example:
example_dag1:
default_args:
owner: 'example_owner'
start_date: 2018-01-01 # or '2 days'
end_date: 2018-01-05
retries: 1
retry_delay_sec: 300
schedule_interval: '0 3 * * *'
concurrency: 1
max_active_runs: 1
dagrun_timeout_sec: 60
default_view: 'tree' # or 'graph', 'duration', 'gantt', 'landing_times'
orientation: 'LR' # or 'TB', 'RL', 'BT'
description: 'this is an example dag!'
on_success_callback_name: print_hello
on_success_callback_file: /usr/local/airflow/dags/print_hello.py
on_failure_callback_name: print_hello
on_failure_callback_file: /usr/local/airflow/dags/print_hello.py
tasks:
task_1:
operator: airflow.operators.bash_operator.BashOperator
bash_command: 'echo 1'
task_2:
operator: airflow.operators.bash_operator.BashOperator
bash_command: 'echo 2'
dependencies: [task_1]
task_3:
operator: airflow.operators.bash_operator.BashOperator
bash_command: 'echo 3'
dependencies: [task_1]Then in the DAGs folder in your Airflow environment you need to create a python file like this:
from airflow import DAG
import dagfactory
dag_factory = dagfactory.DagFactory("/path/to/dags/config_file.yml")
dag_factory.clean_dags(globals())
dag_factory.generate_dags(globals())And this DAG will be generated and ready to run in Airflow!
If you have several configuration files you can import them like this:
# 'airflow' word is required for the dagbag to parse this file
from dagfactory import load_yaml_dags
load_yaml_dags(globals_dict=globals(), suffix=['dag.yaml'])
The package airflow.sensors.http_sensor works with all supported versions of Airflow. In Airflow 2.0+, the new package name can be used in the operator value: airflow.providers.http.sensors.http
The following example shows response_check logic in a python file:
task_2:
operator: airflow.sensors.http_sensor.HttpSensor
http_conn_id: 'test-http'
method: 'GET'
response_check_name: check_sensor
response_check_file: /path/to/example1/http_conn.py
dependencies: [task_1]The response_check logic can also be provided as a lambda:
task_2:
operator: airflow.sensors.http_sensor.HttpSensor
http_conn_id: 'test-http'
method: 'GET'
response_check_lambda: 'lambda response: "ok" in reponse.text'
dependencies: [task_1]- Construct DAGs without knowing Python
- Construct DAGs without learning Airflow primitives
- Avoid duplicative code
- Everyone loves YAML! ;)
Contributions are welcome! Just submit a Pull Request or Github Issue.