Python mock AWS SSM

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04-09-2024

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Mocking AWS SSM in Python: A Comprehensive Guide

Testing your code that interacts with AWS services like System Manager Parameter Store (SSM) can be tricky. You don't want to depend on a live connection to AWS in your test environment, which could introduce flakiness and make your tests slow. This is where mocking comes in handy.

This article delves into the process of mocking AWS SSM in Python, addressing the challenges faced by a developer in the Stack Overflow example. We'll explore how to leverage the popular moto library and analyze potential pitfalls, providing practical solutions for achieving reliable testing.

Understanding the Challenge

The Stack Overflow post highlights a common issue: moto fails to isolate the test environment and still fetches real data from the live SSM. This behavior stems from the way moto manages its mock services.

How to Mock AWS SSM Effectively

To properly mock SSM, you need to ensure that your code interacts exclusively with the mocked service during testing. Here's a revised example incorporating best practices:

import os
import boto3
from moto import mock_ssm
import pytest

from handler import lambda_handler

@pytest.fixture
def aws_credentials():
    os.environ["AWS_ACCESS_KEY_ID"] = "testing"
    os.environ["AWS_SECRET_ACCESS_KEY"] = "testing"
    os.environ["AWS_SECURITY_TOKEN"] = "testing"
    os.environ["AWS_SESSION_TOKEN"] = "testing"

@mock_ssm
def test_ssm(aws_credentials):  # Inject credentials for moto
    ssm_client = boto3.client('ssm', region_name='us-east-1')  # Explicitly define region
    
    # Mock the parameter before invoking your lambda handler
    ssm_client.put_parameter(
        Name="/mypath/password",
        Description="A test parameter",
        Value="this is it!",
        Type="SecureString",
    )
    
    resp = lambda_handler({"param": "/mypath/password"}, "")
    assert resp["Parameter"]["Value"] == "this is it!"

Explanation:

1. Credentials for moto: The aws_credentials fixture sets up necessary environment variables for moto to function correctly.
2. Explicit Region: Specifying the region explicitly (e.g., 'us-east-1') ensures that moto correctly simulates the chosen AWS region.
3. Mocking Before Invocation: The crucial step is to mock the parameter within the test function before invoking your lambda handler. This ensures that your code interacts with the mocked version of SSM during the test.

Additional Tips for Successful Mocking

• Isolate Dependencies: For more complex tests involving multiple AWS services, consider using patch from the unittest.mock library to isolate and mock specific functions or classes within your code.
• Use a Testing Framework: Utilizing a testing framework like pytest offers convenient features for managing fixtures, mocking, and asserting test outcomes.
• Test Different Scenarios: Go beyond basic success cases. Test edge cases like invalid parameters, access errors, and throttling scenarios to build robust and reliable unit tests.

Conclusion

Mocking AWS SSM in Python is crucial for efficient and reliable testing. By following the best practices outlined in this article, you can confidently ensure your code interacts with SSM as expected, even without depending on live AWS services during testing. This approach fosters faster development cycles, improves code quality, and ultimately leads to more robust and predictable applications.