Streamlining Asynchronous System Testing with Flask and pytest

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:

• /receive: This endpoint accepts POST requests containing a request ID and a response. It stores the response in a dictionary, using the request ID as the key.
• /retrieve/<request_id>: This endpoint accepts GET requests with a request ID as a parameter. It retrieves the corresponding response from the dictionary and returns it as a JSON response. If the request ID is not found, it returns a 404 error.

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:

1. We define a mock_system fixture that sets up the Flask application in testing mode and yields a test client for making requests to the mock system.
2. Inside the test case, we send a request to the system under test and capture the request ID.
3. We wait for the response to be available - some delay or other methods could be added there.
4. We use the mock_system fixture to send a GET request to the /retrieve/<request_id> endpoint of the Flask mock system, retrieving the response data.
5. Finally, we assert that the response data meets our expectations.

Benefits of This Approach

By combining Flask and pytest, we gain several advantages:

1. Simplified Testing: The Flask mock system abstracts away the complexities of the actual asynchronous system, allowing us to focus on testing the core functionality of our code.
2. Improved Reliability: With Flask acting as a controlled environment, we can ensure consistent test results and eliminate potential issues caused by the actual system's availability or state.
3. Rapid Feedback: pytest's fast execution and comprehensive reporting capabilities provide quick feedback, enabling us to identify and fix issues more efficiently.
4. Scalability: As our system grows, our testing framework can easily scale by adding more test cases and taking advantage of pytest's parallelization and distributed testing capabilities, by utilizing more and more fixtures of the same mocked service.

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