lxml VS rust-cpython

lxml

Parsel is a library build to extract data from XML/HTML documents with XPath and CSS selectors support, and could be combined with regular expressions. It's usees lxml parser under the hood by default.

So, now that I no longer work in that industry, and I had some free time, I created a lazy parsing package using lxml instead of the xml parser in the standard library, which can help people who want to have a python only parsing solution.

I got curious about this now so I did some tests on my own, and it appears that the XML parser implementation in Python does indeed strip all newline characters from attributes. Whether this is according to XML standard I do not know; I also briefly tried an alternative XML implementation for Python and it behaves the same, so I would assume that this is standard behavior, but I'm not knowledgable enough about XML to say for certain.

I use lxml for the XML parsing and pyodbc as the ODBC library. We have a small team so I just keep it as simple as possible: 1. A cursor yields the XML documents from a SQL query as a stream 2. A generator function parses the XML document and yields the rows (you could parallelize this step) 3. Stream each of the resulting rows to a single CSV file 4. Scoop up the resulting CSV file into the target database (usually with the DB engine's loader; bulk insert isn't so fast over ODBC) It ends up being a straight forward, low-overhead approach.

Generally, I'm already using Python to glue together things like OpenCV or libxml, which do the heavy-lifting, and taking advantage of how things like Qt's QImage release Python's Global Interpreter Lock, allowing me to load and process images on a background thread, so the Python code itself is usually already I/O-bound, but yes. If the Python code would become a bottleneck, it helps with that too.

lxml is an XML parser however, it also supports HTML parsing. It's blazing fast and supports XPath. I think it isn't as beginner friendly to use, though it has detailed documentation. It works less well with heavily broken HTML documents and the encoding detection isn't as good as the one of BS4.

According to lxml benchmarking docs, Python’s built in xml parser wouldn’t behave that bad either: https://github.com/lxml/lxml/blob/master/doc/performance.txt

I've never used pyo3, just cpython, but the latter at least let me do things like:

For interoperating with Python, check out PyO3 or rust-cpython. (More generally, see Rust Interop and Are We Extending Yet?)

You probably should learn Rust. Aside from the process of learning new and different languages making you a better programmer, rust-cpython, PyO3, or Interoptopus make it easy to expose Python APIs from your Rust code. (eg. So it's easy to compile the same codebase as both a Python module and a WebAssembly module.)

Have you looked into abi_stable, flapigen, interoptopus, cbindgen, PyO3, or rust-cpython?

Personally, I'm not willing to compromise on my GUI look and feel, so I use PyQt or PySide to write my GUIs against the QWidget API (I'm a KDE user and Python is the only language with mature memory-safe bindings to Qt) and, if the project can be structured with a frontend-backend separation, I use rust-cpython or PyO3 to write a backend in Rust that the Python frontend can import. Sort of using Python/Rust as a QWidget analogue to the QML/C++ architecture promoted for Qt Quick. (Which I don't use because it's still too incomplete on Kubuntu 20.04 LTS.)

also, check out rust-cpython, PyO3, and maturin, among other things. They're really nice options for using Rust for its strengths and Python for its strengths within the same project.

This section uses flapigen to expand the foreign_class macro into many cpython functions as an extension module, and cargo compiles it as a cdylib. If you want to see what that looks like, install cargo-expand and run cargo expand. You'll get a lot of generated rust code.

For Python specifically, In addition to using rust-cpython or PyO3, maturin makes it really comfortable to build, package, and publish Rust code into Python packages and, if your niche doesn't quite fit, there's setuptools-python which might do it.

hlua or rlua are what you want for Lua, rust-cpython or PyO3 for Python, rutie for Ruby, and possibly deno_core for JavaScript.

I've done it using cpython and pyo3 for performance in scientific computing. Compared to C++ I found using Rust got rid of most debugging across the FFI boundary. Eliminating memory errors and providing clear compilation errors when multithreading made more a difference for me than type safety, though it's not unrelated.