👀 Views: 45 💬 Answers: 1 📅 Created: 2025-09-27

flask sqlalchemy performance python

I'm trying to figure out I'm prototyping a solution and I'm relatively new to this, so bear with me. Building an application that relies on heavy data manipulation and rendering in the frontend, I've been using Flask with SQLAlchemy for the backend. The current implementation retrieves a large dataset from the database, which has started to cause performance issues during peak load times. The way data is fetched isn't efficient; for instance, the current query looks something like this: ```python @app.route('/data') def get_data(): data = db.session.query(MyModel).all() return jsonify([item.to_dict() for item in data]) ``` Fetching all records at once seems to be the bottleneck here. I've tried using pagination to limit the number of entries returned, which helped somewhat, but I need a more efficient way to handle this without sacrificing the user experience. Here's what I attempted: 1. Implemented pagination using Flask-SQLAlchemy's `paginate` method, but even then, users can still experience lag when navigating through pages. 2. Cached results with Flask-Caching, but that only seemed to partially alleviate the slowness. 3. Explored applying filters directly in the query to reduce the amount of data fetched, but that required multiple client-side requests for better granularity. I want to understand how to optimize this further. Should I look into asynchronous calls, or is there a better way to structure my SQLAlchemy queries to load data more efficiently? Any insights or patterns that could help improve the performance would be greatly appreciated. I'm working on a web app that needs to handle this. This is part of a larger application I'm building. Cheers for any assistance! This issue appeared after updating to Python 3.10. Is there a simpler solution I'm overlooking?