Testing and Debugging @ docker/docker-py

Testing and Debugging

Motivation

The docker-py SDK undergoes rigorous testing and debugging to ensure its reliability and stability. Understanding these processes empowers developers to contribute effectively and maintain the high quality of the library.

Testing

The docker-py project employs a comprehensive testing framework to validate the functionality and correctness of the codebase.

Unit Testing: The majority of testing focuses on individual components and functions using the unittest framework. This approach isolates specific units of code, enabling granular testing and efficient debugging.
Integration Testing: Integration tests verify the interaction between different components and modules within the SDK.
End-to-End Testing: These tests simulate real-world scenarios, covering the complete workflow of the SDK with the Docker daemon. They help ensure the seamless integration of all parts.

Test Structure:

The test suite resides within the tests directory.
Each test file corresponds to a specific module or feature.
Tests are structured to cover a wide range of scenarios, including positive and negative cases, edge conditions, and error handling.

Examples:

Unit Test Example:

import unittest
          from docker.errors import APIError
          from docker.models.containers import Container
          from docker.models.images import Image
          
          class ContainerTest(unittest.TestCase):
              def test_start(self):
                  container = Container(client=client, id='test-container')
                  container.start()
                  self.assertTrue(container.is_running())
          
              def test_stop(self):
                  container = Container(client=client, id='test-container')
                  container.stop()
                  self.assertFalse(container.is_running())
          
              def test_remove(self):
                  container = Container(client=client, id='test-container')
                  container.remove()
                  with self.assertRaises(APIError):
                      container.inspect()

Integration Test Example:

import unittest
          from docker import Client
          
          class ImageTest(unittest.TestCase):
              def test_build_and_run(self):
                  client = Client()
                  image = client.images.build(path='.', tag='test-image')
                  container = client.containers.create(image='test-image', command=['echo', 'hello'])
                  container.start()
                  output = container.logs()
                  self.assertEqual(output, b'hello\n')

End-to-End Test Example:

import unittest
          from docker import Client
          
          class EndToEndTest(unittest.TestCase):
              def test_deploy_app(self):
                  client = Client()
                  # Build and push an image
                  client.images.build(path='.', tag='test-app:latest')
                  client.images.push('test-app:latest')
          
                  # Run the app in a container
                  container = client.containers.create(image='test-app:latest')
                  container.start()
          
                  # Verify the app is running
                  self.assertTrue(container.is_running())

Test Coverage:

Test coverage is measured using tools such as coverage.py.
The goal is to achieve high code coverage, ensuring that all lines of code are executed by at least one test.

Debugging

Debugging is an essential part of the development process. The docker-py project utilizes various tools and techniques to identify and resolve issues.

Logging: Logging statements are incorporated throughout the codebase to capture relevant information for troubleshooting.
Debugging Tools: Integrated Development Environments (IDEs) like PyCharm provide powerful debugging features, such as breakpoints, stepping through code, and inspecting variables.
Interactive Shell: The Python interpreter can be used to interactively execute code and experiment with different scenarios.
Docker Daemon Logging: The Docker daemon logs provide insights into the behavior of the Docker engine, which can be helpful in diagnosing issues related to container interactions.

Debugging Examples:

Using Logging:

import logging
          
          logger = logging.getLogger(__name__)
          
          def some_function():
              try:
                  # Perform an operation
              except Exception as e:
                  logger.error(f"Error during operation: {e}")

Using Breakpoints in an IDE:
Using the Interactive Shell:

>>> import docker
          >>> client = docker.from_env()
          >>> container = client.containers.get('test-container')
          >>> container.logs()

Error Handling:

The docker-py library incorporates robust error handling to gracefully handle potential errors and exceptions that may occur during interactions with the Docker API.
Error messages are designed to be informative and provide guidance on resolving issues.

Important Note: Debugging practices might evolve based on the specific needs and complexity of the project.

Source: tree/master/tests

Additional References: