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Python Bytes

Python Bytes

By: Michael Kennedy and Brian Okken
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 Python Bytes is a weekly podcast hosted by Michael Kennedy and Brian Okken. The show is a short discussion on the headlines and noteworthy news in the Python, developer, and data science space.Copyright 2016-2025 Politics & Government
Episodes
  • #436 Slow tests go last

    #436 Slow tests go last
    Jun 16 2025
    Topics covered in this episode: * Free-threaded Python no longer “experimental” as of Python 3.14*typed-ffmpegpyleak* Optimizing Test Execution: Running live_server Tests Last with pytest*ExtrasJokeWatch on YouTube About the show Sponsored by PropelAuth: pythonbytes.fm/propelauth66 Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky)Brian: @brianokken@fosstodon.org / @brianokken.bsky.socialShow: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Free-threaded Python no longer “experimental” as of Python 3.14 “PEP 779 ("Criteria for supported status for free-threaded Python") has been accepted, which means free-threaded Python is now a supported build!” - Hugo van KemenadePEP 779 – Criteria for supported status for free-threaded PythonAs noted in the discussion of PEP 779, “The Steering Council (SC) approves PEP 779, with the effect of removing the “experimental” tag from the free-threaded build of Python 3.14.”We are in Phase II then.“We are confident that the project is on the right path, and we appreciate the continued dedication from everyone working to make free-threading ready for broader adoption across the Python community.”“Keep in mind that any decision to transition to Phase III, with free-threading as the default or sole build of Python is still undecided, and dependent on many factors both within CPython itself and the community. We leave that decision for the future.”How long will all this take? According to Thomas Wouters, a few years, at least: “In other words: it'll be a few years at least. It can't happen before 3.16 (because we won't have Stable ABI support until 15) and may well take longer.” Michael #2: typed-ffmpeg typed-ffmpeg offers a modern, Pythonic interface to FFmpeg, providing extensive support for complex filters with detailed typing and documentation.Inspired by ffmpeg-python, this package enhances functionality by addressing common limitations, such as lack of IDE integration and comprehensive typing, while also introducing new features like JSON serialization of filter graphs and automatic FFmpeg validation.Features : Zero Dependencies: Built purely with the Python standard library, ensuring maximum compatibility and security.User-Friendly: Simplifies the construction of filter graphs with an intuitive Pythonic interface.Comprehensive FFmpeg Filter Support: Out-of-the-box support for most FFmpeg filters, with IDE auto-completion.Integrated Documentation: In-line docstrings provide immediate reference for filter usage, reducing the need to consult external documentation.Robust Typing: Offers static and dynamic type checking, enhancing code reliability and development experience.Filter Graph Serialization: Enables saving and reloading of filter graphs in JSON format for ease of use and repeatability.Graph Visualization: Leverages graphviz for visual representation, aiding in understanding and debugging.Validation and Auto-correction: Assists in identifying and fixing errors within filter graphs.Input and Output Options Support: Provide a more comprehensive interface for input and output options, including support for additional codecs and formats.Partial Evaluation: Enhance the flexibility of filter graphs by enabling partial evaluation, allowing for modular construction and reuse.Media File Analysis: Built-in support for analyzing media files using FFmpeg's ffprobe utility, providing detailed metadata extraction with both dictionary and dataclass interfaces. Michael #3: pyleak Detect leaked asyncio tasks, threads, and event loop blocking with stack trace in Python. Inspired by goleak.Use as context managers or function dectoratorsWhen using no_task_leaks, you get detailed stack trace information showing exactly where leaked tasks are executing and where they were created.Even has great examples and a pytest plugin. Brian #4: Optimizing Test Execution: Running live_server Tests Last with pytest Tim Kamanin“When working with Django applications, it's common to have a mix of fast unit tests and slower end-to-end (E2E) tests that use pytest's live_server fixture and browser automation tools like Playwright or Selenium. ”Tim is running E2E tests last for Faster feedback from quick testsTo not tie up resources early in the test suite.He did this with custom “e2e” markerImplementing a pytest_collection_modifyitems hook function to look for tests using the live_server fixture, and for them automatically add the e2e marker to those testsmove those tests to the endThe reason for the marker is to be able to Just run e2e tests with -m e2eAvoid running them sometimes with -m "not ...
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    37 mins
  • #435 Stop with .folders in my ~/

    #435 Stop with .folders in my ~/
    Jun 9 2025
    Topics covered in this episode: platformdirspoethepoet - “Poe the Poet is a batteries included task runner that works well with poetry or with uv.”Python Pandas Ditches NumPy for Speedier PyArrowpointblank: Data validation made beautiful and powerfulExtrasJokeWatch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python TrainingThe Complete pytest CoursePatreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky)Brian: @brianokken@fosstodon.org / @brianokken.bsky.socialShow: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Michael #1: platformdirs A small Python module for determining appropriate platform-specific dirs, e.g. a "user data dir".Why the community moved on from appdirs to platformdirsAt AppDirs: Note: This project has been officially deprecated. You may want to check out pypi.org/project/platformdirs/ which is a more active fork of appdirs. Thanks to everyone who has used appdirs. Shout out to ActiveState for the time they gave their employees to work on this over the years.Better than AppDirs: Works today, works tomorrow – new Python releases sometimes change low-level APIs (win32com, pathlib, Apple sandbox rules). platformdirs tracks those changes so your code keeps running.First-class typing – no more types-appdirs stubs; editors autocomplete paths as Path objects.Richer directory set – if you need a user’s Downloads folder or a per-session runtime dir, there’s a helper for it.Cleaner internals – rewritten to use pathlib, caching, and extensive test coverage; all platforms are exercised in CI.Community stewardship – the project lives in the PyPA orbit and gets security/compatibility patches quickly. Brian #2: poethepoet - “Poe the Poet is a batteries included task runner that works well with poetry or with uv.” from Bob BelderbosTasks are easy to define and are defined in pyproject.toml Michael #3: Python Pandas Ditches NumPy for Speedier PyArrow Pandas 3.0 will significantly boost performance by replacing NumPy with PyArrow as its default engine, enabling faster loading and reading of columnar data.Recently talked with Reuven Lerner about this on Talk Python too.In the next version, v3.0, PyArrow will be a required dependency, with pyarrow.string being the default type inferred for string data.PyArrow is 10 times faster.PyArrow offers columnar storage, which eliminates all that computational back and forth that comes with NumPy. PyArrow paves the way for running Pandas, by default, on Copy on Write mode, which improves memory and performance usage. Brian #4: pointblank: Data validation made beautiful and powerful “With its … chainable API, you can … validate your data against comprehensive quality checks …” Extras Brian: Ruff rulesRuff users, what rules are using and what are you ignoring?Python 3.14.0b2 - did we already cover this?Transferring your Mastodon account to another server, in case anyone was thinking about doing thatI’m trying out Fathom Analytics for privacy friendly analytics Michael: Polars for Power Users: Transform Your Data Analysis Game Course Joke: Does your dog bite?
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    26 mins
  • #434 Most of OpenAI’s tech stack runs on Python

    #434 Most of OpenAI’s tech stack runs on Python
    Jun 2 2025
    Topics covered in this episode: Making PyPI’s test suite 81% fasterPeople aren’t talking enough about how most of OpenAI’s tech stack runs on PythonPyCon Talks on YouTubeOptimizing Python Import PerformanceExtrasJokeWatch on YouTube About the show Sponsored by Digital Ocean: pythonbytes.fm/digitalocean-gen-ai Use code DO4BYTES and get $200 in free credit Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky)Brian: @brianokken@fosstodon.org / @brianokken.bsky.socialShow: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too. Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Brian #1: Making PyPI’s test suite 81% faster Alexis ChallandeThe PyPI backend is a project called WarehouseIt’s tested with pytest, and it’s a large project, thousands of tests.Steps for speedup Parallelizing test execution with pytest-xdist 67% time reduction--numprocesses=auto allows for using all coresDB isolation - cool example of how to config postgress to give each test worker it’s on dbThey used pytest-sugar to help with visualization, as xdist defaults to quite terse outputUse Python 3.12’s sys.monitoring to speed up coverage instrumentation 53% time reductionNice example of using COVERAGE_CORE=sysmonOptimize test discovery Always use testpathsSped up collection time. 66% reduction (collection was 10% of time)Not a huge savings, but it’s 1 line of configEliminate unnecessary imports Use python -X importtimeExamine dependencies not used in testing.Their example: ddtrace A tool they use in production, but it also has a couple pytest plugins includedThose plugins caused ddtrace to get imported Using -p:no ddtrace turns off the plugin bitsNotes from Brian: I often get questions about if pytest is useful for large projects.Short answer: Yes!Longer answer: But you’ll probably want to speed it upI need to extend this article with a general purpose “speeding up pytest” post or series. -p:no can also be used to turn off any plugin, even builtin ones. Examples include nice to have developer focused pytest plugins that may not be necessary in CICI reporting plugins that aren’t needed by devs running tests locally Michael #2: People aren’t talking enough about how most of OpenAI’s tech stack runs on Python Original article: Building, launching, and scaling ChatGPT ImagesTech stack: The technology choices behind the product are surprisingly simple; dare I say, pragmatic! Python: most of the product’s code is written in this language.FastAPI: the Python framework used for building APIs quickly, using standard Python type hints. As the name suggests, FastAPI’s strength is that it takes less effort to create functional, production-ready APIs to be consumed by other services.C: for parts of the code that need to be highly optimized, the team uses the lower-level C programming languageTemporal: used for asynchronous workflows and operations inside OpenAI. Temporal is a neat workflow solution that makes multi-step workflows reliable even when individual steps crash, without much effort by developers. It’s particularly useful for longer-running workflows like image generation at scale Michael #3: PyCon Talks on YouTube Some talks that jumped out to me: Keynote by Cory Doctorow503 days working full-time on FOSS: lessons learnedGoing From Notebooks to Scalable Systems And my Talk Python conversation around it. (edited episode pending)Unlearning SQLThe Most Bizarre Software Bugs in History The PyArrow revolution in Pandas And my Talk Python episode about it.What they don't tell you about building a JIT compiler for CPython And my Talk Python conversation around it (edited episode pending)Design Pressure: The Invisible Hand That Shapes Your Code Marimo: A Notebook that "Compiles" Python for Reproducibility and Reusability And my Talk Python episode about it.GPU Programming in Pure Python And my Talk Python conversation around it (edited episode pending)Scaling the Mountain: A Framework for Tackling Large-Scale Tech Debt Brian #4: Optimizing Python Import Performance Mostly pay attention to #'s 1-3This is related to speeding up a test suite, speeding up necessary imports.Finding what’s slow Use python -X importtime Ex: python -X importtime ptyestTechniques Lazy imports move slow-to-import imports into functions/methodsAvoiding circular imports hopefully you’re doing that alreadyOptimize __init__.py files Avoid unnecessary imports, heavy computations, complex logicNotes from Brian Some questions remain open for me Does module aliasing really help much?This applies to testing in a big way Test collection imports your test suite, so anything imported at the top level of a file gets imported at test collection time, even if ...
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    29 mins
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