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https://github.com/ajbosco/dag-factory
Dynamically generate Apache Airflow DAGs from YAML configuration files
https://github.com/ajbosco/dag-factory
airflow apache-airflow dags python
Last synced: 21 days ago
JSON representation
Dynamically generate Apache Airflow DAGs from YAML configuration files
- Host: GitHub
- URL: https://github.com/ajbosco/dag-factory
- Owner: ajbosco
- License: mit
- Created: 2018-11-21T00:20:36.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-05-31T04:15:54.000Z (about 1 month ago)
- Last Synced: 2024-06-02T18:52:42.861Z (about 1 month ago)
- Topics: airflow, apache-airflow, dags, python
- Language: Python
- Size: 330 KB
- Stars: 1,087
- Watchers: 24
- Forks: 167
- Open Issues: 52
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Funding: .github/FUNDING.yml
- License: LICENSE
Lists
- awesome-apache-airflow - dag-factory - A library for dynamically generating Apache Airflow DAGs from YAML configuration files. (Libraries, Hooks, Utilities)
- my-awesome-stars - ajbosco/dag-factory - Dynamically generate Apache Airflow DAGs from YAML configuration files (Python)
README
# dag-factory
[](https://github.com/ajbosco/dag-factory/actions?workflow=build)
[](https://codecov.io/github/ajbosco/dag-factory?branch=master)
[](https://pypi.org/project/dag-factory/)
[](https://github.com/ambv/black)
[](https://pepy.tech/project/dag-factory)
*dag-factory* is a library for dynamically generating [Apache Airflow](https://github.com/apache/incubator-airflow) DAGs from YAML configuration files.
- [Installation](#installation)
- [Usage](#usage)
- [Benefits](#benefits)
- [Contributing](#contributing)
## Installation
To install *dag-factory* run `pip install dag-factory`. It requires Python 3.6.0+ and Apache Airflow 2.0+.
## Usage
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:
```yaml
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:
```python
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:
```python
# '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'])
```
## Notes
### HttpSensor (since 0.10.0)
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:
```yaml
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:
```yaml
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]
```
## Benefits
* Construct DAGs without knowing Python
* Construct DAGs without learning Airflow primitives
* Avoid duplicative code
* Everyone loves YAML! ;)
## Contributing
Contributions are welcome! Just submit a Pull Request or Github Issue.