Streamlining Asynchronous System Testing with Flask and pytest
Mor Dabastany???
Automation and Infrastructure lead || Python and Playwright advocate
For developers/qe engineers dealing with asynchronous systems, testing can be a significant challenge. These systems often respond with only a request ID, making it difficult to verify the actual response data. However, by leveraging the power of Flask and pytest, we can create a robust testing framework that simplifies the process.
The Asynchronous System Challenge
In an asynchronous system, when a request is sent, the system typically responds with a request ID rather than the actual data. This ID is then used to query the system for the result at a later time. This behavior can make testing difficult, as the traditional request-response cycle is disrupted.
Introducing Flask as a Mock System Flask
Flask is a lightweight Python web framework, can be used as a mock system to simulate the asynchronous system's behavior. In our testing setup, the system under test will send its responses to Flask instead of the actual system or a callback route will be notified. Flask will act as a piggyback instance, storing the responses for later verification.
Flask and pytest Integration
It's no secret that I find pytest to be is a powerful and flexible testing framework for written in Python, making it an ideal choice for our testing needs (Pytest Parametrization: Injecting Data into Tests | LinkedIn). By integrating Flask with pytest, we can create a streamlined testing process that leverages the strengths of both tools.
Setting up the Flask Mock System
Let's start by creating a Flask application that will act as our mock system. This application will have endpoints to receive responses from the system under test and retrieve them later using the request ID.
# mock_system.py
from flask import Flask, request, jsonify
app = Flask(__name__)
responses = {}
@app.route('/receive', methods=['POST'])
def receive_response():
data = request.get_json()
request_id = data['request_id']
response = data['response']
responses[request_id] = response
return jsonify({'status': 'success'})
@app.route('/retrieve/<request_id>', methods=['GET'])
def retrieve_response(request_id):
if request_id in responses:
return jsonify(responses[request_id])
else:
return jsonify({'error': 'Request ID not found'}), 404
In this example, we create a Flask application with two endpoints:
Writing Tests with pytest and Integration with Flask Mock
Next, we can use pytest to write our tests. Within each test, we can send requests to the system under test and capture the request IDs. Using these IDs, we can then query the Flask mock system to retrieve the responses and assert their correctness.
# tests/test_async_system.py
import pytest
import requests
BASE_URL = 'https://localhost:5000'
@pytest.fixture(scope='module')
def mock_system():
from mock_system import app
app.testing = True
with app.test_client() as client:
yield client
def test_async_operation(mock_system):
# Send request to the system under test
request_id = send_request_to_system()
# Wait for the response to be available
... <-- some logic here
# Retrieve the response from the mock system
response = mock_system.get(f'{BASE_URL}/retrieve/{request_id}')
data = response.get_json()
# Assert the response data
assert data['status'] == 'success'
assert data['result'] == 'expected_result'
In this example, we create a pytest test case test_async_operation that simulates testing an asynchronous operation:
Benefits of This Approach
By combining Flask and pytest, we gain several advantages:
Conclusion
Testing asynchronous systems can be challenging, but by leveraging the power of Flask and pytest, we can create a robust testing framework that streamlines the process. With Flask acting as a mock system and pytest handling the testing logic, we can ensure the reliability and correctness of our code while benefiting from rapid feedback and scalability. The provided code examples demonstrate a practical implementation of this approach, allowing you to adapt it to your specific testing needs.
#python #pytest #automation #QA #QE