truffleruby
mypy
truffleruby | mypy | |
---|---|---|
25 | 112 | |
2,963 | 17,569 | |
0.1% | 0.9% | |
9.9 | 9.7 | |
4 days ago | 3 days ago | |
Ruby | Python | |
GNU General Public License v3.0 or later | 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.
truffleruby
- TruffleRuby 24.0.0
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Mir: Strongly typed IR to implement fast and lightweight interpreters and JITs
I think it would be worth mentioning GraalVM and https://github.com/oracle/truffleruby in competitors section.
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GraalVM for JDK 21 is here
GitHub page has some info: https://github.com/oracle/truffleruby#current-status
My question is, how viable is TruffleRuby vs JRuby?
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Making Python 100x faster with less than 100 lines of Rust
I wonder why GraalVM is not more often used for these speed critical cases: https://www.graalvm.org/python/
Is the problem the Oracle involvement? (Same for ruby https://www.graalvm.org/ruby/)
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Ruby 3.2βs YJIT is Production-Ready
Looks like itβs still a WIP
https://github.com/oracle/truffleruby/commits?author=eregon
- Implement Pattern Matching in TruffleRuby (GSoC)
- TruffleRuby β GraalVM Community Edition 22.2.0
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Modern programming languages require generics
this comes at the cost of boxing ints inside Integer, though.
So, if you ignore for a moment primitives types, whenever you have generics, everything boils down to a single method accepting Objects and returning Objects. What the JVM does is to do runtime profiling of what actually you are passing to the generic method, and generate optimized routines for the "best case". In theory this is the best of the two worlds, because like in general you will have a single implementation of the method (avoiding duplication of the code), but if you use it in an hot spot you get the optimized code.
In a way, it is quite wasteful, because you throw away a lot of information at compile time, just to get it back (and maybe not all of it) at runtime through profiling, but in practice it works quite well.
A side effect of this is this makes the JVM a wonderful VM for running dynamic languages like Ruby and Python, because that information is _not_ there at compile time. In particular GraalVM/TruffleVM and exposes this functionality to dynamic language implementations, allowing very good performance (according to they website [1][2], Ruby and Python on TruffleVM are about 8x faster than the official implementation, and JS in line with V8)
[1] https://www.graalvm.org/ruby/
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GraalVM 22.1: Developer experience improvements, Apple Silicon builds, and more
I opened a ticket some time ago about performance with Jekyll and liquid templates. At least in that case, yjit was way faster. I'm happy to retest though. Anything that would make my jekyll builds faster would help.
https://github.com/oracle/truffleruby/issues/2363
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Ruby YJIT Ported to Rust
Here's a benchmark [1] done in Jan'22 against many ruby implementations, truffleRuby [2] seems to be way ahead in most, and at least ahead in all. Why truffleRuby isn't talk about much here?
[1] https://eregon.me/blog/2022/01/06/benchmarking-cruby-mjit-yj...
[2] https://github.com/oracle/truffleruby
mypy
- The GIL can now be disabled in Python's main branch
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Polars β 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?
JRuby - JRuby, an implementation of Ruby on the JVM
pyright - Static Type Checker for Python
artichoke - π Artichoke is a Ruby made with Rust
ruff - An extremely fast Python linter and code formatter, written in Rust.
graalpython - A Python 3 implementation built on GraalVM
pyre-check - Performant type-checking for python.
ruby-packer - Packing your Ruby application into a single executable.
black - The uncompromising Python code formatter
graaljs - A ECMAScript 2023 compliant JavaScript implementation built on GraalVM. With polyglot language interoperability support. Running Node.js applications!
pytype - A static type analyzer for Python code
clj-kondo - Static analyzer and linter for Clojure code that sparks joy
pydantic - Data validation using Python type hints