c-questdb-client VS Numba

Hi, I'm the original author of the QuestDB Python client library and benchmark.

It all started when we had one of our users needing to insert quite a bit of data into our database quickly from Pandas. They had a dataframe that took 25 minutes to serialize row-by-row iterating through the dataframe. The culprit was .iterrows(). Now it's a handful of seconds.

This took a few iterations: At first I thought this could all be handled by Python buffer protocol, but that turned out to create a whole bunch of copies, so for a number of dtypes the code now uses Arrow when it's zero-copy.

The main code is in Cython (and the fact that one can inspect the generated C is pretty neat) with supporting code in Rust. The main serialization logic is in Rust and it's in a separate repo: https://github.com/questdb/c-questdb-client/tree/main/questd....

The main code is in Cython (and the fact that one can inspect the generated C is pretty neat) with auxilliary code in Rust. The main serialization logic is in Rust and it's in a separate repo: https://github.com/questdb/c-questdb-client/tree/main/questdb-rs.

Around the same time, I discovered Numba and was fascinated by how easily it could bring huge performance improvements to Python code.

Simulations are, at least in my experience, numba’s [0] wheelhouse.

[0]: https://numba.pydata.org/

That's very cool. Numba introduces just-in-time compilation to Python via decorators and its sole reason for being is to turn everything it can into abstract syntax trees.

A super-fast backtesting engine built in NumPy and accelerated with Numba.

Regarding speed, I don't agree this is a good argument against Python. For example, it seems no one here has yet mentioned numba, a Python JIT compiler. With a simple decorator you can compile a function to machine code with speeds on par with C. Numba also allows you to easily write cuda kernels for GPU computation. I've never had to drop down to writing C or C++ to write fast and performant Python code that does computationally demanding tasks thanks to numba.

Just for reference,

* Nuitka[0] "is a Python compiler written in Python. It's fully compatible with Python 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, and 3.11."

* Pypy[1] "is a replacement for CPython" with builtin optimizations such as on the fly JIT compiles.

* Cython[2] "is an optimising static compiler for both the Python programming language and the extended Cython programming language... makes writing C extensions for Python as easy as Python itself."

* Numba[3] "is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code."

* Pyston[4] "is a performance-optimizing JIT for Python, and is drop-in compatible with ... CPython 3.8.12"

[0] https://github.com/Nuitka/Nuitka

[1] https://www.pypy.org/

[2] https://cython.org/

[3] https://numba.pydata.org/

[4] https://github.com/pyston/pyston

For the benefit of future readers: https://numba.pydata.org/

Taichi (similar to numba) is a python library that allows you to write high speed code within python. So your program consists of slow python that gets interpreted regularly, and fast python (fully type annotated and restricted to a subset of the language) that gets parallellized and jitted for CPU or GPU. And you can mix the two within the same source file.