In Python REST APIs, how do I profile bottlenecks?

In Python REST APIs, profiling bottlenecks involves analyzing the time taken by different parts of your application to identify areas that can be optimized. Here are some common methods to profile your Python REST APIs:

  • cProfile: Use the built-in cProfile module to measure where time is being spent in your code.
  • Line Profiler: This tool allows you to see how much time is spent on each line of code.
  • Memory Profiler: Useful for analyzing memory usage, which can impact performance.
  • Flame Graphs: Visual representation of profiled stack traces can help identify bottlenecks at a glance.

Here's a brief example of using cProfile to profile a simple REST API using Flask:

import cProfile from flask import Flask app = Flask(__name__) @app.route('/api/data') def get_data(): # Simulate some processing time result = heavy_computation() return {'data': result} def heavy_computation(): # Simulated heavy computation sum = 0 for i in range(10000): sum += i ** 2 return sum if __name__ == '__main__': cProfile.run('app.run(debug=True)')

Python REST API profiling cProfile performance optimization Flask bottlenecks line profiler memory profiler

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