handlers
mypy
handlers | mypy | |
---|---|---|
4 | 112 | |
1,509 | 17,569 | |
- | 0.9% | |
0.0 | 9.7 | |
over 1 year ago | 4 days ago | |
Go | Python | |
BSD 2-clause "Simplified" License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
handlers
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Go Gin vs Echo Thoughts/ Opinions
When you use a router that supports http.Handler you don't have to worry about maintaining special middleware for that library. There are so many well maintained middleware libraries for the http.Handler like https://github.com/gorilla/handlers
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Noob here. Need recommendation for best REST API framework.
To add to this, gorilla also offers some middleware. And its super easy to import your own and wrap it.
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Go is not an easy language
Study the generic reader/writer implementations in the io module. (On my system, those sources are in /usr/lib/go/src/io.) The io.Reader and io.Writer interfaces are very simple, but very powerful because of how they allow composition. A shell pipeline like `cat somefile.dat | base64 -d | gzip -d | jq .` can be quite directly translated into chained io.Readers and io.Writers.
Another example of this is how HTTP middlewares chain together, see for example all the middlewares in https://github.com/gorilla/handlers. All of these exhibit one particular quality of idiomatic Go code: a preference for composition over inheritance.
Another quality of idiomatic Go code is that concurrent algorithms prefer channels over locking mechanisms (unless the performance penalty of using channels is too severe). I don't have immediate examples coming to mind on this one though, since the use of channels and mutexes tends to be quite intertwined with the algorithm in question.
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Securing a Go-Backed Scrappy Twitter API with Magic
gorilla/handlers
mypy
- The GIL can now be disabled in Python's main branch
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Polars – A bird's eye view of Polars
It's got type annotations and mypy has a discussion about it here as well: https://github.com/python/mypy/issues/1282
- Static Typing for Python
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Python 3.13 Gets a JIT
There is already an AOT compiler for Python: Nuitka[0]. But I don't think it's much faster.
And then there is mypyc[1] which uses mypy's static type annotations but is only slightly faster.
And various other compilers like Numba and Cython that work with specialized dialects of Python to achieve better results, but then it's not quite Python anymore.
[0] https://nuitka.net/
[1] https://github.com/python/mypy/tree/master/mypyc
- Introducing Flask-Muck: How To Build a Comprehensive Flask REST API in 5 Minutes
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WeveAllBeenThere
In Python there is MyPy that can help with this. https://www.mypy-lang.org/
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It's Time for a Change: Datetime.utcnow() Is Now Deprecated
It's funny you should say this.
Reading this article prompted me to future-proof a program I maintain for fun that deals with time; it had one use of utcnow, which I fixed.
And then I tripped over a runtime type problem in an unrelated area of the code, despite the code being green under "mypy --strict". (and "100% coverage" from tests, except this particular exception only occured in a "# pragma: no-cover" codepath so it wasn't actually covered)
It turns out that because of some core decisions about how datetime objects work, `datetime.date.today() < datetime.datetime.now()` type-checks but gives a TypeError at runtime. Oops. (cause discussed at length in https://github.com/python/mypy/issues/9015 but without action for 3 years)
One solution is apparently to use `datetype` for type annotations (while continuing to use `datetime` objects at runtime): https://github.com/glyph/DateType
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What's New in Python 3.12
PEP 695 is great. I've been using mypy every day at work in last couple years or so with very strict parameters (no any type etc) and I have experience writing real life programs with Rust, Agda, and some Haskell before, so I'm familiar with strict type systems. I'm sure many will disagree with me but these are my very honest opinions as a professional who uses Python types every day:
* Some types are better than no types. I love Python types, and I consider them required. Even if they're not type-checked they're better than no types. If they're type-checked it's even better. If things are typed properly (no any etc) and type-checked that's even better. And so on...
* Having said this, Python's type system as checked by mypy feels like a toy type system. It's very easy to fool it, and you need to be careful so that type-checking actually fails badly formed programs.
* The biggest issue I face are exceptions. Community discussed this many times [1] [2] and the overall consensus is to not check exceptions. I personally disagree as if you have a Python program that's meticulously typed and type-checked exceptions still cause bad states and since Python code uses exceptions liberally, it's pretty easy to accidentally go to a bad state. E.g. in the linked github issue JukkaL (developer) claims checking things like "KeyError" will create too many false positives, I strongly disagree. If a function can realistically raise a "KeyError" the program should be properly written to accept this at some level otherwise something that returns type T but 0.01% of the time raises "KeyError" should actually be typed "Raises[T, KeyError]".
* PEP 695 will help because typing things particularly is very helpful. Often you want to pass bunch of Ts around but since this is impractical some devs resort to passing "dict[str, Any]"s around and thus things type-check but you still get "KeyError" left and right. It's better to have "SomeStructure[T]" types with "T" as your custom data type (whether dataclass, or pydantic, or traditional class) so that type system has more opportunities to reject bad programs.
* Overall, I'm personally very optimistic about the future of types in Python!
[1] https://github.com/python/mypy/issues/1773
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Mypy 1.6 Released
# is fixed: https://github.com/python/mypy/issues/12987.
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Ask HN: Why are all of the best back end web frameworks dynamically typed?
You probably already know but you can add type hints and then check for consistency with https://github.com/python/mypy in python.
Modern Python with things like https://learnpython.com/blog/python-match-case-statement/ + mypy + Ruff for linting https://github.com/astral-sh/ruff can get pretty good results.
I found typed dataclasses (https://docs.python.org/3/library/dataclasses.html) in python using mypy to give me really high confidence when building data representations.
What are some alternatives?
go-patterns - Curated list of Go design patterns, recipes and idioms
pyright - Static Type Checker for Python
chi - lightweight, idiomatic and composable router for building Go HTTP services
ruff - An extremely fast Python linter and code formatter, written in Rust.
schema - Package gorilla/schema fills a struct with form values.
pyre-check - Performant type-checking for python.
scrappy-twitter-api-server - Scrappy Twitter API is a Go-backend project that is secured by the Magic SDK. This Go server allows all users to READ tweets. However, to POST or DELETE tweets an access token is required. This access token can be generated via https://scrappy-twitter-api-client-xi.vercel.app/.
black - The uncompromising Python code formatter
sessions - Package gorilla/sessions provides cookie and filesystem sessions and infrastructure for custom session backends.
pytype - A static type analyzer for Python code
ent - An entity framework for Go
pydantic - Data validation using Python type hints