psycopg2cffi VS Pyjion

The only compatibility issue I've run into is database drivers.

For PostgreSQL, psycopg2 is not supported. psycopg2cffi is largely unmaintained, and the 2.9.0 version in PyPI lacks some newer features of psycopg2: the `psycopg2.sql` module and empty result sets raise a RuntimeError in Python 3.7+. The latest commit in on Github does have these changes [1]. Psycopg 3 [2] and pg8000 [3] (as user tlocke mentioned elsewhere) are viable alternates provided you aren't stuck with older versions of PostgreSQL. I'm going to continue to use psycopg2cffi until I can upgrade an old PostgreSQL 9.4 database.

For Microsoft SQL Server, pymssql does not support PyPy [4]. It's under new maintainership so it might gain support in the future. pypyodbc hasn't had any activity since 2022, and no new PyPI release since 2021 [5]. The datatypes returned can differ between libodbc1 versions. On Ubuntu 18.04 in particular: empty string columns are returned as a single space, integer columns are returned as a Decimal. Also, if you encounter a mysterious HY010 error ("Function sequence error"), you may need to upgrade libodbc1 to v2.3.7+ from v2.3.4 using the Microsoft repos.

[1]: https://github.com/chtd/psycopg2cffi

From time to time, I use pgcopy coupled with psycopg2cffi to feed large volumes of data processed by custom parsers written in Python for several formats. The whole process is 4-5x faster with PyPy.

It exists, was created by microsoft employees, and is referenced in the article: https://www.trypyjion.com/

I've actually come across and started using Pyjion recently (https://github.com/tonybaloney/pyjion); how does Pypy compare, both in terms of performance and purpose? There seems to be a lot of overlap...

Python is capable of doing things at runtime that are really hard to statically compile around, such as monkeypatching methods onto existing objects. You can compile it, but it's complicated. One strategy is to use a JIT that can observe application state at runtime and then invalidate code as it becomes obsoleted by changes, but it's complicated. See pyjion for an example.

When I say "Python" I am referring to the standard CPython interpreter which most people use. But there is also PyPy, which includes a Just In Time compile that compiles selected code into machine language on the fly, as needed. pyjion is another JIT compiler that generates machine language on the fly, and you can install it with pip. Or you could work for Facebook and use Cinder. Cython, Nuitka and Pyston are other alternatives.

and if you need more speed you can try https://www.pypy.org/ or https://github.com/tonybaloney/Pyjion or https://www.pyston.org/

Finally, there is also Pyjion which based on its website is “A drop-in JIT Compiler for Python 3.10” (https://www.trypyjion.com/). We will be covering it on a separate writeup. See you next time ;-).

Though some exciting stuff happening over the next few years, Python is getting faster, has been for awhile, and stuff like Pyjion https://www.trypyjion.com/, a drop in C# powered JIT compiler is starting to approach usable. Rust and Python seem to be best buds right now, so more extension libraries in rust, a newer more approachable language than say C/C++ but with a similar speed. Sign me up!

Pyjion Easy to use, small compiler. Increase performance of our 🐌 CPython.