This article misleads you by conflating regular expressions with specific implementations like PCRE, which also does non-regex string matches. Annoyingly, the article does a good job of explaining what a regex is and what the limitations of regex are relative to PCRE, so the author should understand that what they are talking about when they talk about NP-complete string matching is not regex, but PCRE-specific features.

The distinction matters because regex absolutely can't match HTML, and because regex, unlike PCRE expressions, have guaranteed O(1) space and O(n) time complexity when matching a string of length n. When you use PCRE features for string matching, that may degrade to exponential time which makes it useless. For example, you can do denial of service PCRE attacks, but not denial of service regex attacks (unless you can query with some megabyte-large regex).

It might just be a me problem, but I've always been wary of regexes. They're not too bad to write, but reading them back and understanding what's actually going on can get a bit hairy. Plus, all of the subtle differences between regex libraries seems like a bit of a footgun.

Obviously they have their place, but I know a lot of the older guys seemed to love them way more than the young.

Something that seems obvious but not always implied by people's comments is that people are rarely trying to match an entire document with a regular expression so it doesn't really matter that "HTML is not a regular language".

If I am trying to e.g. count div tags with a regex like "<div" or whatever, then clearly this would work in 99.9% of cases and probably achieve what the poster is looking for.

As soon as you also add character classes to ignore various parts of the document that you are not interested in like "<div[^>]*>" or whatever it is, then it is eminently useful even if the bit we are ignoring is not fully regular.

One lovely thing about regex is how fast it is. I was asked to parse a massive CAN Bus log file for how many times some event had logged. This was the early 2000s and the file was 6GB, which was pretty big. I tried .Net's string.StartsWith or something and that took ages to run through the file. I did the same thing with a regex and it finished in like 5 seconds (HDD, not SSD!). I don't know how the magic works but it is very impressive.

Some people, when confronted with a problem, think "I know, I'll get my agent to solve it with regular expressions."

Now they have three problems.

"Doom Using Regular Expressions" https://news.ycombinator.com/item?id=49094081

Before the AI craze, I'd gotten quite good at writing regexes. Regexr was quite useful for decoding and composing them. I feel like they're going to become a lost art.

While true in principle, writing grammars in regexes is problematic in practice: the syntax for the more advanced features (named submatches, lookahead, backreferences, etc.) is pretty complex, and refactoring the expression means you're working within a string literal, with no help whatsoever from your editor or IDE.

My "go to" solution for parsing (and validating/matching) non-trivial grammars is a library that wraps regexes and allows you to structure the grammar with entities above substrings of a string literal (including arbitrary code for transformations). PyParsing for Python, scala-parser-combinators for Scala, Grammar in Raku, PetitParser in Smalltalk, PEGs in Janet, parser combinators in F#, and so on. These are mostly internal/embedded DSLs, which makes them much easier to use than the typical lexer/parser generators, while giving you all the power to structure and evolve the grammar easily.

For simple grammars, a well-written library adds little overhead over plain regexes. However, grammars rarely stay simple - very often, during the course of development, you find edge cases or the need for extensions. If you started with a structured parser, you're fine: there are specific ways of evolving the grammar, and you can use normal refactoring tools to perform them. If you started with a regex, you quickly end up with a monster regex literal that becomes more brittle and harder to change with each modification.

One important property I look for in parsing libraries is the support for left-recursion. Memoizing/packrat parser generators can handle it gracefully, which is important, because if I'm implementing a published grammar, I want to encode it as closely to the original as possible. For the same reason, I prefer having dedicated tools for associativity and precedence (so that I don't have to invent names for intermediate levels).

TL;DR: yes, regexes are much more expressive than the "regular" in the name would imply, but they still have their limits. For parsing things, it's better to start with something that can work in the simple case fast (so no lex/yacc-style codegen from 2 separate external DSLs), but which also provides enough structure that adding good error handling, extending the grammar, attaching arbitrary code transformations, etc. won't be a big problem later.

Just like jq there will be some lad along to tell us that "I don't like the syntax and find it confusing" not realising that's the exact superpower it presents is it's terseness is a key property to it's adoption. jq and regex really are sort of handy one liners that you invoke in other scripts and you explain what they do in your script with a comment.

Regexes are great, they seem like magic when you use them right. They can solve your problems even if you don't use them right. Just make sure not to mix the flavors.

The last thing we need is huge regexs made by AI

Been burned by this exact thing before

> The regular expression is very simple

OT but this me-problem makes me angry every time I read it. Nothing is simple, otherwise it is trivial and not worth mentioning. I can't read over this without thinking that I'm not smart enough to wrap my head around something instantly.

Every time I cut-n-paste a regex into code, I comment with the url of the spell book page I copied so future me can answer, "WTF does this do again?"