| Name | hawkflowairflow JSON |
| Version |
0.0.3
JSON |
| download |
| home_page | |
| Summary | HawkFlow.ai apache airflow integration |
| upload_time | 2023-10-02 09:44:18 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.7 |
| license | LICENSE |
| keywords |
hawkflow
airflow
monitoring
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# HawkFlow.ai apache airflow integration
1. First, sign up to hawkflow for free: https://hawkflow.ai/ and get an API key
2. Install the pip package `pip install hawkflowairflow`
3. Add this to the top of your DAG
```python
from hawkflowairflow import hawkflow_callbacks
hawkflow_callbacks.HF_API_KEY = "YOUR_HAWKFLOW_API_KEY_HERE"
```
4. Add these two lines to default_args in your DAG:
```
default_args={
"on_success_callback": hawkflow_callbacks.hawkflow_success_callback,
"on_execute_callback": hawkflow_callbacks.hawkflow_start_callback
}
```
All done. Now when your DAG runs, you will see the output in the HawkFlow UI. https://app.hawkflow.ai/login
### <span style="color:#D10000">Known Issues</span>
If you are on an <span style="color:red">ARM mac</span> and notice that your DAG is just hanging. You may need to put this
at the top of your DAG. Airflow is running as a different user on your mac, and the security is blocking outgoing requests.
```
import os
os.environ['NO_PROXY'] = '*'
```
### More examples
More examples: [HawkFlow.ai Python examples](https://github.com/hawkflow/hawkflow-examples/tree/master/python)
Read the docs: [HawkFlow.ai documentation](https://docs.hawkflow.ai/)
## What is HawkFlow.ai?
HawkFlow.ai is a new monitoring platform that makes it easier than ever to make monitoring part of your development process.
Whether you are an Engineer, a Data Scientist, an Analyst, or anyone else that writes code, HawkFlow.ai helps you and your team take ownership of monitoring.
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