Testing your software

1. Fast tests for slow services: why you should use verified fakes
   Sometimes your Python tests need to talk to something slow and not under your control. Learn how to write fast and realistic tests, without resorting to mocks.

2. Why Pylint is both useful and unusable, and how you can use it
   You want to find bugs in your Python code before as you write your code. PyLint is a great tool for this, but it has some problems you’ll need to work around.

3. Stuck with slow tests? Speed up your feedback loop
   Sometimes you can’t speed up your Python test suite. What you can do, however, is find failures faster with linters, partial testing, and more.

4. When your CI is taking forever on AWS, it might be EBS
   When running tests or builds on AWS, a bad EBS configuration can slow everything down; learn how to identify the problem and speed up your build.

5. Realistic, easy, and fast enough: database tests with Docker
   Realistic tests require a real database—but that can be difficult and slow. But Docker makes it simple, and some tweaks can make faster.

6. When C extensions crash: easier debugging for your Python application
   If your Python test suite segfaults in C code, debugging is difficult. But an easy configuration tweak can help you pinpoint the responsible code.

7. Goodbye to Flake8 and PyLint: faster linting with Ruff
   Ruff is a new linter that is vastly faster than PyLint and flake8—with many of the same checks.

8. Catching memory leaks with your test suite
   If you have a good test suite, you may be able use pytest fixtures to identify memory and other resource leaks.