Significant Latency in Flask API due to Heavy Data Processing - How to Optimize?
I've spent hours debugging this and I'm experiencing significant latency in my Flask API when processing large datasets from a PostgreSQL database. The API endpoint takes over 10 seconds to respond when fetching and processing around 50,000 records, even though the database query itself completes in about 1 second. I'm using Flask 2.0 and SQLAlchemy 1.4, and I've tried to optimize the data processing by using list comprehensions, but the performance is still inadequate. Here's a simplified version of the code: ```python from flask import Flask, jsonify from sqlalchemy import create_engine, text app = Flask(__name__) database_url = 'postgresql://user:password@localhost/dbname' engine = create_engine(database_url) @app.route('/fetch-data') def fetch_data(): with engine.connect() as connection: result = connection.execute(text('SELECT * FROM large_table')) data = [dict(row) for row in result] # Processing large data here return jsonify(data) ``` While using the `dict(row)` to convert rows to dictionaries seems straightforward, it appears to be the bottleneck. I've tried using `pandas` to handle the data, but it didn't yield a significant speedup either. I also considered utilizing pagination, but since the API is meant to return all records at once for the frontend, that may not be feasible. I've checked the server's performance metrics, and they seem normal, with CPU and memory usage being low during the API call. What optimizations can I implement to reduce the response time without altering the requirements? The stack includes Python and several other technologies. Hoping someone can shed some light on this.