Parser Generator DSLs

Building Parsers from Babel Macros and JS template literals.

Ever since I started working on styled-components I’ve been fascinated by parsers. Writing a plugin for Webpack or Babel for the first time felt like pure magic, especially if the plugin doesn’t just transpile some code for compatibility reasons or adds some metadata but instead generates entirely new code or enables functionality that just isn’t feasibly implemented as runtime-only code.

These days as JavaScript developers parsers are all around us. When we’re starting up a Webpack or Rollup process Acorn parses our code in the background. When we’re using a CSS-in-JS library then there’s a good chance stylis is parsing our CSS code. When we’re using GraphQL the reference implementation’s parser diligently does its work in the background.

To me, nothing exemplifies this omnipresence of parsers more than “Babel Macros”, which is a Babel plugin that in itself runs other plugins, which are embedded in special npm packages, called “macros”. Very meta. With macros, a package can feasibly appear as if it was just a JS library but use the full power of compile-time transpilation without us having to update our Babel config. For example, eval.macro evaluates JS code inside tagged template literals during compile-time. Tagged template literals were even meant for embedding domain-specific languages (“DSLs”) into JavaScript, as Dr. Axel Rauschmayer writes in his post on them:

“In ECMAScript 6, template strings are a syntactic construct that facilitates the implementation of embedded DSLs in JavaScript.”

Given that macros can be used ad hoc to transpile any code, including tagged template literals, just by using importing a package, plugins like eval.macro — which itself technically embeds JS into JS — can pre-compile some of their functionality:

import eval from 'eval.macro';

// using the macro this:
const val = eval`7 * 6`;
// …turns into this:
const val = 42;

Parsers are indeed all around us and frequently grant us amazing new abilities. But if we look at the state of the JavaScript ecosystem from the perspective of native, compiled languages we may notice that we can go a step further than this with metacompilers, a fancy category of programs that also includes parser generators. Parser Generators — an example in JavaScript being peg.js — allow us to write a parser in a DSL, which often looks similar to regular expressions, and subsequently generate the parser’s code automatically.

This on its own is pretty useful knowledge, but knowing Babel Macros, I was wondering whether it was feasible to create a macro that allowed me to write a parsing grammar in a tagged template literal and transpile it to a parser. If a parser generator in JS was able to output compact code that is still reasonably fast, it’d make itself very useful to create small & quick DSLs that can be run in the browser. Let’s look at how I built RegHex!

Creating an Implementation Plan

When jumping into a complex project like this, I typically start at both ends of the process and ask, “What should the library’s API look like?” and “What are the small implementation details I need some investigation first?”
However, planning and starting are two steps that due to procrastination take me quite… a considerable amount of time.

For this project, I came up with the rough idea for it about a year ago in 2019. I then wrote the first API design draft in April 2020, and implemented the library a month later in May 2020. I was pretty excited about the idea and have no excuses for this, so let’s just move on. The first draft for the API design looked something like the following:

const identifier = match('identifier')`

const string = match('string')`
  ( ${/"[^"]*"/} | ${/'[^']*'/} )

const values = match('values')`
  ( ${identifier} | ${string} )*

// Input: "string"
// Output: [['"string"', .tag: 'string'], .tag: 'values']

// Input: ident
// Output: [['ident', .tag: 'identifier'], .tag: 'values']

The API’s general idea is to expose a match function that is called with a parsing grammar’s name. It then is called as a tagged template literal with a regular expression-like grammar, which contains interpolations with either regular expressions or other grammars, which are used to recursively parse bits of the input. The output of match can then be used to start parsing a string and will return an abstract syntax tree (“AST”), nested nodes describing the parsed contents of the input.

To control the logic of how inputs are parsed around the interpolations “operators” similar to a regular expression syntax are used, while regular expressions will match at the current index of the input. Since regular expression syntax is common in parser generators and is familiar to many, it felt rather natural to write, e.g. | for matching something else if the first part of a group didn’t match, or * to allow for multiple matches.

Parser Combinators

This initial draft revealed a crucial feature of my planned API. Because I chose to embed the parsing grammar into JS code via a tagged template literal, the draft started looking like a “parser combinator”. In short, parser combinators are functions that accept other parsers as inputs and return a new parser. In this case, match’s template optionally accepts other match parsers as interpolations.

The grammar in the draft API states that either an
    identifier or a string matches as often as possible, while
    the two are grammars of their own too.

This allows a larger parser to be gradually composed from smaller, reusable bits of grammar, which also narrows down the task of generating parsing code to one small match tag at a time. As an API, this is what made tagged template literals a really compelling choice. To me, this felt similar to how styled-components splits up CSS for separate components, each rendering a single element with their own styles. 🤩 Tagged template literals naturally force a library’s API to simplify and split up the exposed API surface.

Sticky Regular Expressions

So far, the parser generator works by creating smaller pieces of grammar, defined by interpolating other small matching grammars or regular expressions. Intuitively, the most straightforward approach to generate code from this would be to only transpile the pieces of grammar in the template string, and to reuse the interpolated regular expressions as is. Doing so without skipping over any characters in the input string is crucial however.

Executing a regular expression typically scans over an input string until a match is found, which is both costly and counter to how a parser works. Instead of scanning the string, what the parser generator needs is to attempt to match the regular expression only at an exact location. Luckily, in ECMAScript 6 support for the “sticky flag” was added, which does just that:

const regex = new RegExp('hi', 'y');
const input = 'oh hi';

regex.lastIndex = 0;
regex.exec(input); // null

regex.lastIndex = 3;
regex.exec(input); // ['hi', index: 3]

regex.lastIndex; // 5

The lastIndex property now indicates where the regular expression should be executed, rather than where it should start searching the input string. As usual, the index is also moved along if the regular expression has successfully matched a part of the input string. This lends itself very well to building a continuous parser combinator that consists mostly of regular expressions and some branches and loops, and enables the parser generator to only transpile our custom DSL rather than also reimplementing regular expressions.

The Parser Generator’s DSL’s Parser

Starting the implementation of this parser generator, this is where things become meta. Since match’s tagged template API I’ve outlined is in itself a language that looks like regular expression syntax, a small parser for this DSL is necessary. Luckily, the syntax that this particular DSL has to support is rather small and hence the parser ended up being quite easy to write and compact too. RegHex’s DSL was set to support quantifiers, various types of groups, and alternations. As I’ve decided in the design process early on, it would be whitespace insignificant fore readability. Here’s a small overview of the operators that ended up in the DSL’s syntax:

?, +, *A quantifier may be used to change how many matches are accepted, in order: one or none, one or more, or any amount.
|An alternation can be used to match either one thing or another, falling back when the first interpolation fails.
(?: )A non-capturing group is like a regular group, but the interpolations matched inside it don’t appear in the parser’s output.
(?= )A positive lookahead checks whether interpolations match, and if so continues the matcher without changing the input. If it matches, it’s essentially ignored.
(?! )A negative lookahead checks whether interpolations don’t match, and if so continues the matcher without changing the input. If the interpolations do match the matcher is aborted.

Arguably, the most important feature of this syntax listed above is undoubtedly alternations, since a parser that can’t match several alternative patterns, won’t be able to express any useful grammar. Let’s look at an example of the RegHex DSL in action with a grammar that matches strings of repeated “this”s and “that”s:

const thisThat = match('thisThat')`
  (?: ${/and/})
    ((?! ${/.*that/}) ${/this/}+)
    | ((?! ${/.*this/}) ${/that/}+)

This snippet will only match a given input if it starts with “and”. However, “and” is in a non-capturing group and won’t be output to the AST node. It then matches either a repetition of “this” or a repetition of “that”. There are negative lookahead groups which will make sure that we don’t unnecessarily start parsing the entire string if any of the repeated sequences contain both “this” and “that”.

Given that this is very similar to the behaviour of regular expressions and fairly concise this made me pretty optimistic in that this would be a usable syntax and API.

A diagram of our longer grammar of the matcher as shown in the code snippet above.

Generating the parsing code in Babel

Finally, the last part of the endeavour was writing the Babel plugin code which would pick up all written grammars and replace them with parsing code. While writing the Babel code itself wasn’t too hard, since I had some experience writing Babel plugins before, because one of the goals was to generate small & quick code for the DSL, deciding what the generated parsing code’s patterns should be proved to be a little tricky. I decided early on to generate code that is as compact as possible, which meant to me that each usage of match should be compiled to only a single function.

There are a lot of places in the parsing grammar where one part of the match may be unsuccessful and will jump to another — mostly due to alternations around groups. This means that there may be chained interpolations that the generated function will start to match and, after failing, may give up on while skipping to the next part of the grammar after an alternation.

import match from 'reghex/macro';

const parser = match('parsed')`
  (${1} ${2}+) | ${3}

For instance, given the above code, the grammar may match 1 and then repeatedly 2. However if at any point of the first part this grammar fails to match against the input string, the function will still need to attempt to match 3. It needs to skip to | ${3} and ensure that any results from the previously failed attempt are discarded.

Labelled Statements to the rescue!

Investigating some potential patterns for this problem, I discovered that just a single solution seems to exist to have this amount of control in a single function, and this solution is — what I’d consider — quite an archaic language feature in JavaScript: Labelled Block Statements.

We may not see this feature very often in handwritten JavaScript code, but any loop or block may be annotated using a label. Once we have a label we may break out of it by calling break.

loop1: for (let i = 0; i < 3; i++) {
   loop2: for (let j = 0; j < 3; j++) {
      if (i === 1 && j === 1) break loop1;

This is quite useful for generated code that is trying to avoid additional functions in its code output. However, if you use labels in your code a reviewer may likely just either complain or even exclaim in confusion. 🤷 As MDN puts it in their explanation of labelled statements:

“Labelled loops or blocks are very uncommon. Usually, function calls can be used instead of loop jumps.”

The Output Code

The Babel code I wrote wasn’t much of a surprise at this point — although it did end up becoming messy — and splits each part of the grammar into a separate “Node Class”, specialised in accepting a part of the parsed meta DSL and outputting generated code just for this part of the grammar. Some classes of nodes are QuantifierNode, AlternationNode, or GroupNode.

The resulting output of the earlier example looks like the following. I’ve annotated and modified the code snippet to be easier to read:

// A helper function to execute sticky regexes and return the matched string
import { _exec } from "reghex";

const parser = function _parsed(state) {
  // This creates the AST node, which is an array with a tag
  const node = [];
  node.tag = "parsed";

  let last_index = state.index;
  let match;

  // This "block_0" contains the first group of our grammar
  block_0: {
    var index_0 = state.index;
    var length_0 = node.length;

    // We first attempt to match "1"
    if (match = _exec(state, /1/y)) {
    } else {
      // If we fail, we restore the node as it was, and break out of "block_0"
      node.length = length_0;
      state.index = index_0;
      break block_0;

    // We then attempt to match "2" repeatedly
    loop_1: for (var i = 0; true; i++) {
      var index_1 = state.index;

      // Note that the matching itself is done the same, even in a loop
      if (match = _exec(state, /2/y)) {
      } else {
        // If we had results we're good and can end the loop
        if (iter_1) {
          state.index = index_1;
          break loop_1;

        // If we didn't, we again restore the node, and break out of "block_0"
        node.length = length_0;
        state.index = index_0;
        break block_0;

    // If we get here the first group matched. Success!
    return node;

  // This is the second part of our grammar, where we attempt to match "3"
  if (match = _exec(state, /3/y)) {
  } else {
    // there's nothing left anymore since the grammar is over
    // restore the the index from before any matchers ran and return
    state.index = last_index;

  // We matched a "3". Success!
  return node;

In general, each sequence that RegHex generates follows this same pattern of setting up a block, adding the prior index and node length to it, and then adding in the rest of the grammar recursively. It passes on a break statement to the lower nodes, which those can use to break out of a block.

The trickiest part here is to even read the code that the generator outputs, as it’s not very readable to humans. However, I believe it’s the most compact code that can be generated given the requirements — and Google’s Closure Compiler, which is an optimising JavaScript compiler, agrees! Closure Compiler actually outputs labelled block statements too when inlining some functions into loops. In fact, when I tried to handcode the above example with inline functions instead, Closure Compiler turned those back into labelled block statements.

How does RegHex do?

From an implementation standpoint, I’m quite pleased with the result. The API that I ended up with uses a lot of different ideas and language features all in a single project, which has been a rewarding learning experience and exercise. If you’ve read this far this post has already covered four broad topics:

  • Tagged Template Literals
  • Parser Combinators
  • Sticky Regular Expressions
  • Labelled Break Statements

To then put RegHex through its paces, I generated a parser for the GraphQL query language, which has been published as graphql-parse. The grammar ended up being about 300 lines long and given that the generated implementation is really compact the compiled output comes in at only about 2.6kB (minified and gzipped).

Because it looks nice and fits. The graphql-parse bundle in one image. Ok, old joke thanks to Jason Miller.

Running some benchmarks reveals that, probably due to the non-trivial overhead of regular expressions in JavaScript however, the performance of this parser is only a third of the reference implementation. That being said, this is a little better than I expected given that RegHex is still a good tool for prototyping and creates a rather small parser implementation at a fraction of the effort for developers.

What does the future hold?

It’s worth noting that RegHex could also support parsing entire tagged template literals, which would increase the level of meta-ness even more, since it’d allow RegHex to generate its own DSL’s parser, which would make it somewhat of a self-hosting parser.

RegHex also doesn’t support errors very well, since it doesn’t unroll a nice stack to where parsing ultimately failed, which means it may not give any useful hints as to what went wrong at all. Adding this could be a major improvement for using its generated parsers in a build tool or server-side code.

However, given how many concepts RegHex’s code base touches, it was still a worthwhile experience to work on this proof of concept, despite its current shortcomings.
Check out the code, if you’d like to learn more even more about RegHex!

The RegHex Logo