Pytest With Eric

Learn to write production level Python Unit Tests with Pytest

Imagine your project contains 100, if not thousands of tests. When you run pytest, the test runner executes them sequentially, taking ages to complete the tests.

What if there was a better way? A way to use resources effectively, run tests in parallel and reduce the overall test execution time and CI/CD pipeline.

This would make testing more efficient, provide faster feedback on code changes, and improve the overall development process, all while reducing your CI/CD running costs.

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Have you ever found it challenging trying to run Pytest or Python scripts via Poetry?

Perhaps Poetry fails to detect your virtual environment or uses the default Pytest version instead of the one you want.

How do you ensure Poetry runs your code within the virtual environment?

What about handling dependency management and lock files in Poetry? How do you group dependencies and manage them effectively?

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As a software developer it’s not uncommon that your code depends on external systems, databases, or APIs.

So how do you write tests for code that’s heavily reliant on these systems, totally out of your control?

Perhaps you’ve heard of mocking, patching or even stubbing and have little to no idea what it means or how to use it.

Should you write mocks, or patches? What’s the difference between them? Which one should you use?

This can be quite confusing, especially if you’re new to testing.

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You know that annoying feeling when trying to run your code and unrelated warnings pop up?

Like a DeprecationWarning from an external library that you have no control over.

While useful, warnings can clutter your console output and obscure important results.

How do you silence warnings in Pytest? Should you disable ALL warnings? What about important ones?

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In modern computing, it’s a common belief that computers execute tasks in parallel, effortlessly juggling multiple operations at once.

True parallelism is largely the domain of supercomputers and quantum computing.

But what about the everyday applications that form the backbone of our digital lives?

Asynchronous, or async, allows a program to efficiently execute its tasks (executing other tasks one while is waiting). This is also called concurrency.

When it comes to testing async code, things can get a bit tricky.

How can you ensure your async code behaves as expected? How do you test the interaction of different async tasks and functions without getting tangled in the complexities of event loops and await statements?

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Have you ever had to wade through hundreds or thousands of tests to run a few specific ones?

Running pytest in the terminal, executes ALL the tests which not only takes a long time, but is also inefficient.

Maybe you can run a test in a specific directory but that too carries overhead and is hard to do repeatedly for tests in different directories.

So how do you filter tests in Pytest? How can you tell Pytest to run only the tests you’re interested in? Something like a regular expression?

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Dealing with failing tests is a misery.

Imagine your new code breaks old tests and you have no idea why, preventing you from merging your latest code.

Your tests are slow and break every time you change the code even slightly, feeling more like a massive dumbbell you dread when merging PRs, rather than a net or signal to protect or guide you.

On the other side of the spectrum, maybe you don’t even know what to test.

Should you test every function in your application? What if the code interacts with a database or external service? Should you use mocks?

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Have you heard about Test-Driven Development (TDD) but have no idea what it means?

Maybe you know what it is in theory, but never applied it in practice.

Does the expectation of writing tests before code cause stress or anxiety that you’re wasting time in lost productivity?

What about if you’re working at a start-up and the code will be irrelevant in a few months? — Should you still practice TDD?

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Have you ever wanted to run a single unit test only to find yourself running the entire test suite every time, wasting valuable time and resources?

Running a single test is entirely possible with Pytest, but it’s not as straightforward or user-friendly.

What if you want to define configuration for your tests, such as environment variables, custom arguments, verbosity and run them with a single click repeatedly?

How about if you wanted to add a debugger or line breakpoints to your tests?

You’d kinda struggle to do all of this in the terminal without some advanced command-line flags, what if there was an easier way?

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Have you ever thought about testing your software behavior from a user’s perspective?

You’re probably familiar with unit tests, maybe even integration tests and regression tests. But what about the entire feature and its behavior?

For example, the user logs in, adds products to a shopping cart, and checks out.

How do you validate this entire end-to-end scenario? There could be infinite scenarios.

How to bridge the gap between product engineers, QA testers, and developers — to make sure you’re speaking the same language?

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Have you ever struggled with testing command-line arguments for your applications or scripts?

Perhaps you’ve build a robust application, with a database and REST API and interfaced via command-line (CLI).

You’ve tested the database and REST API, but what about the CLI?

How do you test that your code correctly handles missing arguments, wrong data types, or invalid strings or characters?

Command Line Arguments are a prime error candidate for errors, given their immense interaction with the end user. Hence, its crucial to ensure your application correctly processes user inputs and handles errors gracefully.

How do you do this without redefining all arguments in your tests? How do you abstract that layer?

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As a website test automation engineer, have you ever struggled with repeatedly doing the same UI/UX actions?

Human manual testing is error-prone, time-consuming and in fact… just boring.

It also lacks scale - testing time grows linearly with complexity and number of tests.

Feedback is slow and fairly manual, introducing many unknown variables due to human senses and memory.

So how can you improve this? How can you automate Web Browser UI testing?

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Have you ever wondered how to customize the behaviour of your test suite in Pytest?

Perhaps you want to set a configfile path or read it, before executing your tests?

Maybe set some global variables for Dev or Prod or send Slack notifications after the test suite has finished running?

How do you achieve this in Pytest?

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Have you ever had tests fail due to irrelevant or outdated code?

Perhaps adding to the confusion of debugging and troubleshooting along with unnecessary test execution time.

Or maybe you have code in development that’s not ready to be tested yet.

In such cases, you might want to exclude certain tests from your test suite.

But how do you do it in Pytest? You can of course run pytest in the relevant directories but that’s not very efficient.

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Almost every backend system uses a database today. After all, you need to persist system and user information.

But with that comes a challenge, your code needs to be able to efficiently read and write from a database. And it’s your job to thoroughly test it.

Databases are capable of smoothly handling read/writes, transactions and rollbacks.

So how do you test your interactions with the database?

How do you leverage the inbuilt database features to write robust I/O operations?

What about handling errors, rollbacks, transactions or network failures? Can you test for these?

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Have you ever spent more time fiddling with test settings than actually writing tests?

Maybe you’ve been through the Pytest docs and seen a bunch of config (short for Configuration) files like pytest.ini, tox.ini, pyproject.toml, and setup.cfg and wondered what they are and how to use them?

Or perhaps you’re familiar with these files but did you know you can also define Pytest config in files like pyproject.toml and setup.cfg?

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Say you’re working on a large test suite with a bunch of tests, each with its own set of requirements and dependencies, across different environments.

You want to run initialization commands in different environments, set up global variables, maybe slack notifications or ping an external API via a webhook to say your tests have begun.

How do you do this in Pytest? How do you tune Pytest to automatically execute tasks before running your tests?

After all you don’t want to write another script to do this. That’s just more code to maintain and more things to go wrong.

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Imagine this, you’ve just joined a company and your first task is to optimise the existing test suite and improve the CI/CD run time.

Or maybe you’re a veteran Python developer and with success, your test suite has grown substantially to 1000 tests, maybe 2000 tests?

Now it’s getting out of control and each run takes 5 mins or more. CI/CD Pipelines cost a small fortune.

You’re testing backend APIs, data ETL processes, user authentication, disk writes and so on.

How do you optimize test run time? Where do you start?

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With time-dependent code, a key challenge arises in testing it across various time and dates.

Consider a scenario where you’re working on a global transaction application. This application involves several time-dependent activities such as recording transaction time, date, the timezone of the transaction, and more.

How do you ensure your application functions correctly when a transaction occurs outside your country?

One approach might be to manually adjust the time, date, and timezone of your system. However, this method is non-deterministic, time-consuming, and impractical for running individual tests.

So, what’s the solution?

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With Pytest, one of the tricker aspects can be understanding and controlling the standard output (stdout) and standard error (stderr).

Often, during testing, your code generates output or errors that are directed to the console.

This can lead to cluttered and obscure test outputs, making it challenging to decipher test results and debug effectively.

So how do you control what’s shown on the console and make your tests easier to debug?

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