Performance Degradation with Concurrent Requests in Flask Using SQLAlchemy ORM
I keep running into I'm experimenting with I'm refactoring my project and I'm experiencing significant performance degradation in my Flask application when handling concurrent requests that involve SQLAlchemy ORM... After deploying my application, I noticed that under load, response times for API calls increased dramatically, often exceeding 2 seconds for simple queries. My setup uses Flask 2.0.1 and SQLAlchemy 1.4.22. I have an endpoint that fetches user data based on their IDs. Here’s a simplified version of the code: ```python from flask import Flask, jsonify from flask_sqlalchemy import SQLAlchemy app = Flask(__name__) app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///test.db' app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False db = SQLAlchemy(app) class User(db.Model): id = db.Column(db.Integer, primary_key=True) name = db.Column(db.String(50), nullable=False) @app.route('/users/<int:user_id>', methods=['GET']) def get_user(user_id): user = User.query.get(user_id) if user is None: return jsonify({'error': 'User not found'}), 404 return jsonify({'id': user.id, 'name': user.name}) ``` When I hit this endpoint with multiple concurrent requests (using a tool like Apache Benchmark), I notice that instead of returning results quickly, it starts to queue up requests, and the average response time shoots up as more users hit the endpoint. I have already tried optimizing the query by adding indexes to the user ID column, but there seems to be no noticeable improvement. Additionally, I checked the database connection pooling settings and increased the pool size from the default (5) to 20. However, this just seemed to delay the onset of the slowdown under load. When monitoring the application, I see that many requests are stuck waiting on locks, which leads me to believe it might be related to how SQLAlchemy handles sessions and transactions in a concurrent context. Is there a specific configuration or approach I might be missing that can help improve the performance of my Flask app when handling concurrent SQLAlchemy requests? Any insights would be greatly appreciated. I'm working with Python in a Docker container on Debian. Has anyone else encountered this? Thanks for taking the time to read this! For reference, this is a production microservice. Any pointers in the right direction?