Django: introducing django-msgspec
It’s another day, another new package day here. Say hello to django-msgspec, a package of drop-in replacements for Django and Django REST Framework (DRF) components backed by msgspec.
It’s another day, another new package day here. Say hello to django-msgspec, a package of drop-in replacements for Django and Django REST Framework (DRF) components backed by msgspec.
git stash -p optimization in Git 2.55I got another commit merged in Git 2.55 (2026-06-29), yay! The release note reads:
ls-treeTo list all files in your repository as they were at a given commit, use git ls-tree with its -r and --name-only options:
(e) or + glob qualifiersZsh glob qualifiers can select files by many built-in attributes: type, size, modification time, and more. But when no built-in qualifier fits, you can bring in the e or + qualifiers to run arbitrary code, making globs a fully programmable file filter. With great power comes great responsibility!
git log with --perl-regexpgit log has an --author option to limit the output to commits from matching authors:
(m0) glob qualifiersMy downloads directory is a junk pile that I just never get around to fully clearing. So if I’ve just downloaded a bunch of files, I normally want to work with them and ignore the rest of the files.
time-machine is my library for mocking the current date and time in Python tests. Its headline advantage over freezegun, the library that inspired it, is speed.
time-machine is my library for mocking the current date and time in Python tests. I’ve just released version 3.3.0 with a decent number of changes, so here’s a lovely summary for you.
ValueError: day of month directive '%d' may not be used without a year directiveTake this code, parsing the date of an annually recurring event that only has a month and day, such as a work anniversary:
TypeError: NotImplemented should not be used in a boolean contextTake this class, which implements __eq__() and derives __ne__() from it:
unittest.mock calls with attached mocksWhen you use a unittest.mock mock, you often make assertions on the calls it received, such as through the mock_calls list. But sometimes you want to assert on the order of calls across multiple mocked functions, for example to check that steps in a process happen in the right sequence.
unittest.mock’s wrapsTesting terminology distinguishes between different kinds of test doubles, including:
Did you know that each Django release has a “code word” associated with it? It’s hidden in plain sight, in the announcement blog post describing the list of features coming in the next version. I think this is a lovely little tradition.
I recently migrated one of my client projects from the legacy django-csp package to Django 6.0’s built-in Content Security Policy (CSP) support (release note). This security header is a powerful tool for preventing unwanted content from being loaded on your site, so configuration correctness is paramount. The migration was fairly straightforward, but a few pages had complicated overrides, so I wanted to be sure that no CSP headers had been changed by my swapping of CSP implementations.
Just as cars painted red are known to be faster, libraries implemented in Rust are also known to be faster. Today’s example is orjson, a Rusty replacement for Python’s built-in json module, boasting 10x faster serialization and 2x faster deserialization.
gc.get_objects()Here’s a lovely hack that I’ve used when machete-mode debugging.
WeakKeyDictionaryIn several programs, I’ve wanted to solve the problem of associating extra data with an object. For example, in django-upgrade, the individual “fixer” functions often want to store extra data per visited ast.Module object.
Back in January, Firefox’s Security & Privacy Newsletter for 2025 Q4 piqued my interest with this mention:
Back in February, I encountered an out-of-memory error while migrating a client project to Python 3.14. The issue occurred when running Django’s database migration command (migrate) on a limited-resource server, and seemed to be caused by the new incremental garbage collection algorithm in Python 3.14.
I’ve made another package! Like icu4py, which I made in February, it was sponsored by my client Rippling. And like tprof, which I made in January, it’s a profiling tool!
I made a new package! Thank you to my client Rippling for inspiring and sponsoring its development.
Memory usage can be hard to keep under control in Python projects. The language doesn’t make it explicit where memory is allocated, module imports can have significant costs, and it’s all too easy to create a global data structure that accidentally grows unbounded, leaking memory. Django projects can be particularly susceptible to memory bloat, as they may import many large dependencies like numpy, even if they’re only used in a few places.
(om[1]) glob qualifiersI’ve recently been using memray, a memory profiler for Python, to optimize the startup memory usage (and time) for a client. It’s a fantastic tool, but its flexibility hampers getting instant feedback. It takes three commands to profile a program, generate a flame graph from the profile, and open the flame graph in the browser:
Profilers measure the performance of a whole program to identify where most of the time is spent. But once you’ve found a target function, re-profiling the whole program to see if your changes helped can be slow and cumbersome. The profiler introduces overhead to execution and you have to pick out the stats for the one function you care about from the report. I have often gone through this loop while optimizing client or open source projects, such as when I optimized Django’s system checks framework (previous post).
Last month, I held another quiz at the December edition of Django London. The December quiz is an annual tradition at our meetup, a way of making this final event of the year more relaxed and giving away some nice prizes. This was the seventh quiz that I’ve presented, and the eighth overall.