# Discussion on "Why I no longer recommend Julia" by Yuri Vishnevsky

**URL:** <https://discourse.julialang.org/t/discussion-on-why-i-no-longer-recommend-julia-by-yuri-vishnevsky/81151>\
**Category:** Community\
**Tags:** discussion\
**Created:** [May 16, 2022, 2:33pm UTC](https://discourse.julialang.org/t/discussion-on-why-i-no-longer-recommend-julia-by-yuri-vishnevsky/81151 "2022-05-16T14:33:38Z")\
**Posts on this page:** 1\
**Showing post:** 247

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**Author:** ![Benny](https://avatars.discourse-cdn.com/v4/letter/b/49beb7/32.png) [@Benny](https://discourse.julialang.org/u/Benny)\
**Post date:** [June 16, 2022, 11:16am UTC](https://discourse.julialang.org/t/discussion-on-why-i-no-longer-recommend-julia-by-yuri-vishnevsky/81151/247 "2022-06-16T11:16:47Z")

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> [@Bruno\_Amorim](#):
>
> Well for-loops are horribly slow in python, while in Julia they are as fast as they can be. So that must be it! Except that there is [numba](https://numba.pydata.org/), which solves that problem. (actually, it were the limitations of numba 6 years ago that made me move to Julia, but it seems that numba has greatly improved since then).

I use Numba to write simple fast loops when vectorized code wouldn’t make much sense, but there’s a lot of stuff I can’t write. Yes, there is active development and much room for improvement (gufuncs can’t be called in `jit` functions yet, ufuncs don’t specialize as readily as `jit`), but a fundamental limitation is the need to adhere to Python and NumPy. For example, instead of named `struct`s, Numpy arrays have unnamed structured dtypes (similar to Julia’s `eltype`). But dtypes aren’t bona fide Python types, so you can’t make an instance of one; you can index a structured array, but the element is an instance of `numpy.void` no matter the particular structure. Likewise, Numba is incapable of making a `vectorize`d ufunc that dispatches on or returns a structured element, and there doesn’t seem to be any active attempt to make it work somehow (Github issue #5329).

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