5 ms·

To be fair, Chroma is also written in Python. And while LanceDB and others are written in Rust, that doesn't automatically give it super powers.

PossiblyKyle 3y ago
Fair point, then you could claim it's similar to this DB with its reliance on Faiss. Despite that, Chroma at this point is more feature rich. I was mostly referring to this https://thedataquarry.com/posts/vector-db-1/ https://thedataquarry.com/posts/vector-db-1/ You are not wrong about the performance from Rust, but LanceDB is inherently written with performance in mind. SIMD support for both x86 and ARM, and an underlying vector storage approach that's built for speed (Lance)
- marcinzm 3y ago
  >more feature rich Not necessarily a good thing when the product is made by a VC backed startup that may die or pivot in six months leaving you the need to maintain it yourself.
- dmezzetti 3y ago
  I've seen a number of projects come over the last couple years. I'm the author of txtai (https://github.com/neuml/txtai https://github.com/neuml/txtai) which I started in 2020. How you approach performance is the key point. You can write performant code in any language. For example, for standard keyword search, I wrote a component to make sparse/keyword search just as efficient as Apache Lucene in Python. https://neuml.hashnode.dev/building-an-efficient-sparse-keyword-index-in-python https://neuml.hashnode.dev/building-an-efficient-sparse-keyw....
6r17 3y ago
Python programmer for 15 years and i picked up rust to write an oAuth gateway not long ago ; i wrote it in python beforehand - rust DOES give you superpowers ; especially if you compare it to something like python that isn't nowhere as fast and has no typing
- DSingularity 3y ago
  Python does have typing. Although it doesn’t feel as “first class” like as rust or golang it gets the job done.
- dmezzetti 3y ago
  There are plenty of examples of Python libraries that can be performant such as NumPy and PyTorch (which both rely on C/C++). Some libraries such as Hugging Face's tokenizers even use Rust. I referenced this article below but will reference it again here too. https://neuml.hashnode.dev/building-an-efficient-sparse-keyword-index-in-python https://neuml.hashnode.dev/building-an-efficient-sparse-keyw.... You can write performant code in any language if you try.
  - iopq 3y ago
    So to make Python fast you just need to write a library in another language, brilliant
    - dmezzetti 3y ago
      If you read the article referenced, I discussed a number of ways to write performant Python such as using this package (https://docs.python.org/3/library/array.html https://docs.python.org/3/library/array.html).
    - deleted 3y ago
      [deleted]
  - aldanor 3y ago
    NumPy is a C library with Python frontend, moreover lots of functionality based on other existing C libraries like blas etc. PyTorch, quoting themselves, is a Python binding into a monolithic C++ framework; also optionally depending on existing libs like mkl etc. > You can write performant code in any language if you try. Unfortunately, only to a certain extent. Sure, if you just need to multiply a handful of matrices and you want your blas ops to be blas'ed where the sheer size of data outweighs any of your actual code, it doesn't really matter. Once you need to implement lower-level logic, ie traversing and processing the data in some custom way, especially without eating extra memory, you're out of luck with Python/numpy and the rest.

PossiblyKyle 3y ago

Fair point, then you could claim it's similar to this DB with its reliance on Faiss. Despite that, Chroma at this point is more feature rich. I was mostly referring to this https://thedataquarry.com/posts/vector-db-1/ https://thedataquarry.com/posts/vector-db-1/ You are not wrong about the performance from Rust, but LanceDB is inherently written with performance in mind. SIMD support for both x86 and ARM, and an underlying vector storage approach that's built for speed (Lance)

marcinzm 3y ago
>more feature rich Not necessarily a good thing when the product is made by a VC backed startup that may die or pivot in six months leaving you the need to maintain it yourself.
dmezzetti 3y ago
I've seen a number of projects come over the last couple years. I'm the author of txtai (https://github.com/neuml/txtai https://github.com/neuml/txtai) which I started in 2020. How you approach performance is the key point. You can write performant code in any language. For example, for standard keyword search, I wrote a component to make sparse/keyword search just as efficient as Apache Lucene in Python. https://neuml.hashnode.dev/building-an-efficient-sparse-keyword-index-in-python https://neuml.hashnode.dev/building-an-efficient-sparse-keyw....

6r17 3y ago

Python programmer for 15 years and i picked up rust to write an oAuth gateway not long ago ; i wrote it in python beforehand - rust DOES give you superpowers ; especially if you compare it to something like python that isn't nowhere as fast and has no typing

DSingularity 3y ago
Python does have typing. Although it doesn’t feel as “first class” like as rust or golang it gets the job done.
dmezzetti 3y ago
There are plenty of examples of Python libraries that can be performant such as NumPy and PyTorch (which both rely on C/C++). Some libraries such as Hugging Face's tokenizers even use Rust. I referenced this article below but will reference it again here too. https://neuml.hashnode.dev/building-an-efficient-sparse-keyword-index-in-python https://neuml.hashnode.dev/building-an-efficient-sparse-keyw.... You can write performant code in any language if you try.
- iopq 3y ago
  So to make Python fast you just need to write a library in another language, brilliant
  - dmezzetti 3y ago
    If you read the article referenced, I discussed a number of ways to write performant Python such as using this package (https://docs.python.org/3/library/array.html https://docs.python.org/3/library/array.html).
  - deleted 3y ago
    [deleted]
- aldanor 3y ago
  NumPy is a C library with Python frontend, moreover lots of functionality based on other existing C libraries like blas etc. PyTorch, quoting themselves, is a Python binding into a monolithic C++ framework; also optionally depending on existing libs like mkl etc. > You can write performant code in any language if you try. Unfortunately, only to a certain extent. Sure, if you just need to multiply a handful of matrices and you want your blas ops to be blas'ed where the sheer size of data outweighs any of your actual code, it doesn't really matter. Once you need to implement lower-level logic, ie traversing and processing the data in some custom way, especially without eating extra memory, you're out of luck with Python/numpy and the rest.