Aron T
07/24/2024, 8:04 PMMarvin
07/24/2024, 8:05 PMschema
parameter that varies based on the environment.
2. Environment Variables: You can use environment variables to differentiate between development and production settings. Your flow can read these environment variables to determine which schema to use.
3. Secrets Management: Utilize Prefect's secret management capabilities to store and retrieve schema settings securely.
### Example Steps
1. Define the Flow with Environment-Aware Configuration
python
from prefect import flow, task
@task
def get_schema():
import os
return os.getenv("DATABASE_SCHEMA", "default_schema")
@task
def perform_db_operation(schema):
# Your code that uses the schema
pass
@flow
def my_flow():
schema = get_schema()
perform_db_operation(schema)
if __name__ == "__main__":
my_flow()
2. Set Environment Variables
- In your development environment:
sh
export DATABASE_SCHEMA="dev_schema"
- In your production environment:
sh
export DATABASE_SCHEMA="prod_schema"
3. Create Deployments with Different Tags or Parameters
- For development:
sh
prefect deployment build my_flow.py:my_flow -n dev-deployment --apply
- For production:
sh
prefect deployment build my_flow.py:my_flow -n prod-deployment --apply
4. Use Prefect Cloud or Server to Manage Your Deployments
- You can then trigger these deployments using Prefect Cloud/Server with appropriate tags or directly via CLI specifying the deployment name.
Documentation and Resources
- Prefect's guide on Deployments
- Prefect's Testing Guide
By using these strategies, you can dynamically configure your flows to use different schemas based on the environment, ensuring a clean separation between development and production settings.
If you need additional details or code samples, feel free to ask!Bring your towel and join one of the fastest growing data communities. Welcome to our second-generation open source orchestration platform, a completely rethought approach to dataflow automation.
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