erezsh
commented
7 years ago Hi, thanks! The simple solution is that the parse-trees that lark produces are reduce-able to a parser's state-machine. If you use the Transformer class on a tree, it will call your methods at the exact same order you would get in a traditional parser, and your return-values will be treated the same. So at the very least, a parse-tree is equivalent to a state-machine. But: 1) Trees allow you to see the "state-machine" visually: At once, instead of in steps. You can also see the tree fold step-by-step as you process it. 2) Trees allow your computation to be aware of previous and future states, so they are more powerful computationally. 3) You can process a tree in steps. That means you can separate your processing into several logical steps if you like. A state-machine requires that each method performs a complete computation.
I am not aware of any benefits for state-machines, besides performance. But Lark solves that too: Once you write a complete transformer, you can hand it off to Lark and it will execute it as a state-machine, without building a tree (for example, when parsing json, it uses about half the memory)
charles-esterbrook
Hey, it looks very interesting!
You make this statement 2 times: "Trees are always simpler to work with than state-machines."
Could you back this up with some resource or examples? I'm very curious.