Lark VS nearley

Lark supports, and recommends, writing and storing the grammar in a .lark file. We have syntax highlighting support in all major IDEs, and even in github itself. For example, here is Lark's built-in grammar for Python: https://github.com/lark-parser/lark/blob/master/lark/grammar...

You can also test grammars "live" in our online IDE: https://www.lark-parser.org/ide/

The rationale is that it's more terse and has less visual clutter than a DSL over Python, which makes it easier to read and write.

First, let me introduce myself. My name is Erez. You may know some of the Python libraries I wrote in the past: Lark, Preql and Data-diff.

It's not super maintained but you might enjoy building something with ppci, Pure Python Compiler Infrastructure. It has some front-ends and some back-ends. There's also PeachPy for an assembler. People like using Lark for parsing, I hear.

lark, a parsing library where I am somewhat involved has a really nice solution to this: Rules starting with _ are inlined in a post processing step.

Lark is a good library to assist with this.

If I may ask, did you consider Lark, and if so, why wasn't it fit for your purposes?

A grammar provides the high level constructs you need to define the "shape" of your data, and it largely takes care of the rest. Grammar libraries exist in other language (eg. lark or Parsimonius in Python) and they weren't created just to make XML parsing easier.

I made a solid attempt at an Earley parser framework of my own, but apparently to get the most reliable performance from Earley parsing you need to implement Joop Leo's improvement for right-recursive grammars, which nobody has been able to adequately explain to me. I've read Kegler's open letter to Vaillant, I've tried to read other implementations, I've even tried to beat my head against the original academic paper, but I don't have the background knowledge to make sense of it all.

While I suspect I would learn more writing a tokenizer and parsing logic myself I find grammars much easier to read and maintain.

ANTLR is pretty good and is supported across several languages and something I had previously used for some quick Elasticsearch query syntax munging in Python. It also means you can often start from an already existing grammar.

The JS version of ANTLR didn't seem to work for me so for the SQL/JSONPath stuff ended up using the Moo lever and Nearly parser which was rather pleasant. https://nearley.js.org

Parsers in nearley.js [1] are written in a very readable EBNF-like DSL; then they get desugared down to a JS file that's a lot like your snippet.

[1] https://github.com/kach/nearley