41–50 of 150 posts
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Maybe one day, when CPython guys start taking JIT seriously and finally get rid of GIL.
Isn't multiprocessing + caching like Redis a good enough workaround? I know that it works well for web applications. Not sure about other domains.
Luckily I mostly write IO bound code, so sticking with GILed threads is an option for me.
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Any good resources on learning how to solve problems by leveraging strong type systems?
There's "Real World OCaml" (new) and "Real World Haskell" (a bit out of date). But my favourite introductory text, which is not free, is Graham Hutton's "Programming in Haskell".
conda create -n py1 python=1.0
source activate py1
python
Python 1.0.1 (Mar 26 2014)
Copyright 1991-1994 Stichting Mathematisch Centrum, Amsterdam
>>>
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It would be great to still have static constraints as an option though. I absolutely love the free form in the prototype phase, but once I've got that one nailed and want to make it maintainable... well, I always miss it at that time.
Yea thats what mypy is for, and that fits my workflow too: Rapidly prototype once the API has stabilised, I add the typehints. For unfammilliar codebases using MonkeyType is now a option too.
Maybe a flag to the interpreter that would raise an exception each time it detects an inconsistency at runtime. I can imaging turning this feature on in a staging environment.
The most successful language in the last twenty years.
Arguably, that would be Java, which was introduced in 1995. For whatever hey are worth, most language popularity indexes (TIOBE, LangPop) place Java above Python with quite a large margin. Java may not get much exposure here or in startups, but if you go out in the real world of 'boring' business applications, Java is pretty much the defacto language (together with C#).
Was there ever a DOS binary? That would be worth firing up a VM for.
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Java's tooling support blows Python's tooling support right out of the water. The static type system of Java allows faster refactoring and IMHO makes development of large applications easier and less error prone. I use Python for simple scripts and often for data transformation. But e.g. at work we have a few million lines of C# that I would never ever want to manage if it was written in Python.
I always find it peculiar that people seem to defend a million line code base, like that is something to be proud off. The days I walk out of the office with less code in some mature project's repo are the days I know I accomplished the most.
If I had to deploy 10 microservices with 2 level caches and internal network routing on a whole server rack or in "the cloud", our customers would laugh in our face and choose one of our competitors that deploys on one or two on-site servers that can sit somewhere in the office and require minimal, if any, maintenance besides security updates.
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There's "Real World OCaml" (new) and "Real World Haskell" (a bit out of date). But my favourite introductory text, which is not free, is Graham Hutton's "Programming in Haskell".
Thanks for the info. Just curious, what sort of problems are strongly typed languages most suited for? In the sense that which class of problems can be solved using them leading to elegant solutions.
Haskell's effect typing also helps us. For example, for reproducible calculations, it is critical that no data is pulled-in via a backdoor (i.e. a side effect). Even reading and using the current system datetime is something we want to prevent. This can be expressed in the types using Haskell.
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Yea thats what mypy is for, and that fits my workflow too: Rapidly prototype once the API has stabilised, I add the typehints. For unfammilliar codebases using MonkeyType is now a option too.
I started using type hints and it greatly improves the readability and sometimes catches a mistake. But would love for it to be more. Maybe a flag to the interpreter that would raise an exception each time it detects an inconsistency at runtime. I can imaging turning this feature on in a staging environment.