I recently implemented psycopg3 in my AWS Lambda function as a Layer to successfully query my AWS RDS database. What made this process manageable was the abundance of additional documentation for psycopg3—both from official sources and community-driven content (e.g., forum posts, videos). This was invaluable for understanding how to make it work in this specific environment.
However, I believe psqlpy has the potential to surpass psycopg3 in terms of usability and market adoption, particularly for AWS Lambda use cases. With a few targeted improvements, you could make psqlpy much easier for end users to integrate into AWS Lambda environments.
Suggestions:
Improve AWS Lambda compatibility: Simplifying the process for using psqlpy as an AWS Lambda Layer would be a significant advantage.
Reasons:
Even the current solutions (e.g., psycopg3) lack sufficient documentation for Lambda integration, which leaves users spending unnecessary time troubleshooting. By addressing this, psqlpy could easily capture a larger user base.
This is an easy-to-implement improvement ("low-hanging fruit") that would open the door to greater adoption, especially as AWS Lambda is such a widely-used service.
Next Steps:
Create streamlined instructions for AWS Lambda integration: Provide clear, step-by-step documentation or a guide on how to download and structure psqlpy for AWS Lambda Layers.
Provide a command or script for packaging psqlpy: An example of a command I used for psycopg3 that sets up the correct directory structure for Lambda is below. This makes it easy to generate a zip file and upload it to AWS as a Layer:
Provide an AWS Lambda example: A sample Lambda function using psqlpy to query a database would be incredibly useful. AWS requires the main file to be named lambda_function.py and the function inside to be named lambda_handler. Having a working example would save developers significant time during setup.
By implementing these suggestions, I believe psqlpy could achieve much greater adoption, particularly within the AWS Lambda ecosystem.
I hope these suggestions help, and I’m happy to discuss further if needed. I look forward to supporting this project as it continues to evolve.
I recently implemented psycopg3 in my AWS Lambda function as a Layer to successfully query my AWS RDS database. What made this process manageable was the abundance of additional documentation for psycopg3—both from official sources and community-driven content (e.g., forum posts, videos). This was invaluable for understanding how to make it work in this specific environment.
However, I believe psqlpy has the potential to surpass psycopg3 in terms of usability and market adoption, particularly for AWS Lambda use cases. With a few targeted improvements, you could make psqlpy much easier for end users to integrate into AWS Lambda environments.
Suggestions: Improve AWS Lambda compatibility: Simplifying the process for using psqlpy as an AWS Lambda Layer would be a significant advantage.
Reasons:
Even the current solutions (e.g., psycopg3) lack sufficient documentation for Lambda integration, which leaves users spending unnecessary time troubleshooting. By addressing this, psqlpy could easily capture a larger user base.
This is an easy-to-implement improvement ("low-hanging fruit") that would open the door to greater adoption, especially as AWS Lambda is such a widely-used service.
Next Steps:
Create streamlined instructions for AWS Lambda integration: Provide clear, step-by-step documentation or a guide on how to download and structure psqlpy for AWS Lambda Layers.
Provide a command or script for packaging psqlpy: An example of a command I used for psycopg3 that sets up the correct directory structure for Lambda is below. This makes it easy to generate a zip file and upload it to AWS as a Layer:
By implementing these suggestions, I believe psqlpy could achieve much greater adoption, particularly within the AWS Lambda ecosystem.
I hope these suggestions help, and I’m happy to discuss further if needed. I look forward to supporting this project as it continues to evolve.