Error recovery with parser combinators (using nom)

As the COVID-19 pandemic continues to ravage the globe, lots of people are stuck at home, either working remotely or sitting around without much to do. The previous afternoon, I had stumbled across an online announcement that the ACM Digital Library has been made free to all to read and download to help foster research, discovery, and learning during this time of crisis. Feeling curious, and having previously wanted to read certain research papers from the ACM DL previously, I took the opportunity to peruse through its library and read as much content as I could. As I was doing so, I stumbled across a very useful paper called “Syntax error recovery in parsing expression grammars” by (Medeiros, S. and Fabio Mascarenhas, 2018) that I would like to share, and I’ll be testing some of its concepts using a prototype parser written in Rust with the help of the nom crate.

Some background§

Language parsing is a very broad and interesting topic, with a swathe of varying approaches and tools to choose from depending on the requirements of the task at hand, but the basic premise is simple: the goal of a parser is to consume some data as input, break it down into its component parts according to some grammar, and derive meaning or understanding from it (wiki). I personally happen to enjoy working with parsing expression grammars (PEGs) and parser combinators when writing my own projects.

In case you are not familiar, PEG is a kind of declarative formal language for describing other languages in terms of string pattern matching. That is, PEG allows the parser author to declare the grammar of the language they wish to parse using sets of expressions like those shown below:

expr    ← sum
sum     ← product (('+' / '-') product)*
product ← value (('*' / '/') value)*
value   ← [0-9]+ / '(' expr ')'

These PEG rules would then be able to describe the rules to a simple arithmetic language that behaves like this:

InputParsed syntax tree
1 + 2Sum(Value(1), Value(2))
1 + 2 * 3Sum(Value(1), Product(Value(2), Value(3)))
(1 + 2) * 3Product(Sum(Value(1), Value(2)), Value(3))

Any PEG expression can be converted directly into a recursive descent parser, either automatically using a parser generator or crafted by hand in the programming language of your choice.

I really enjoy using parser combinator frameworks like nom as a nice middle ground between the two options, since they grant you the freedom and flexibility of writing your parser fully in the host language (in this example, Rust), but the resulting code is succinct, fairly declarative, and looks somewhat PEG-ish, if you tilt your head and squint hard enough.

fn expr(input: &str) -> IResult<&str, &str> {

fn sum(input: &str) -> IResult<&str, &str> {
    let op = alt((char('+'), char('-')));
    recognize(pair(product, many0(pair(op, product))))(input)

fn product(input: &str) -> IResult<&str, &str> {
    let op = alt((char('*'), char('/')));
    recognize(pair(value, many0(pair(op, value))))(input)

fn value(input: &str) -> IResult<&str, &str> {
    recognize(alt((digit1, delimited(char('('), expr, char(')')))))(input)

Each of the four parsers above corresponds to a PEG rule, and since each one is represented as a pure function, they compose nicely in code and each one can easily be tested in isolation from the others, e.g. with inline unit tests. All in all, I enjoy working with PEG and parser combinators!


I’ve been hacking on a parser and language server for the Nix programming language as a side project (GitHub) for some time now, and this extended period of being stuck at home renewed my interest in working on it. This language server aims to supply code analysis, and auto-completion for compatible third-party text editors and IDEs. This project has been very challenging for me to work on, in a good way, because language servers tend to have very strict requirements of their underlying parsers.

Most compilers and static analysis tools are batch programs which act like a dumb pipe, consuming source code in one end and spitting an executable out the other (yes, incremental compilation and artifact caching bends this analogy a bit, but the basic premise still holds). This means that their parsers and resulting abstract syntax trees are optimized for very different things than what an interactive IDE would want.

Since the user is continuously modifying the source text and entering keystrokes into their editor, the parser providing syntax checking for their editor is very frequently exposed to incomplete or downright invalid snippets of code more often than not. This means that halting parsing and bailing with an error message whenever the first error is encountered, like many traditional parsers do, is simply not an option.

rust-analyzer in action
Parser producing a best-effort syntax tree from incomplete code (credit)

Instead, the parser needs to be as fault-tolerant as possible, always producing a syntax tree of some kind on every single parse and deriving as much syntactic and semantic meaning as it can from user input, however malformed it might be. Your editor should still be able to provide meaningful code completion, hover documentation, go-to-definition, and symbol searching regardless of whether there is a missing semicolon somewhere halfway down the page.

A naive approach§

When I first started working on this project, I had chosen to implement my Nix parser in Rust using nom 5.0, since that was the tool I was most comfortable using for writing parsers at the time.

As I was writing up my parsers, I very quickly realized that bailing early from parsing with an Err(nom::Err::Error(_)) or Err(nom::Error::Failure(_)) wasn’t a good idea for emitting errors. The former triggers a backtrack, which I didn’t always want, and the latter would halt parsing altogether with an error, which I never wanted. Err(nom::Error::Incomplete(_)) sounded promising due to the name, but it too ended up being useless given the design constraints I had in mind. I needed some way to log that a non-fatal parse error had been encountered and resume parsing as though nothing had happened, but unfortunately, there seemed to be nothing in the vast nom parser combinator toolbox that could help me deal with this.

Given that nom parser combinators are pure functions whose signatures are structured like this:

impl Fn(Input) -> IResult<Input, Output, Error>

which maps to:

impl Fn(Input) -> Result<(Remaining, Output), Error>

I decided to carry these non-fatal parse errors through the Output instead of returning them through Result::Err(nom::Error::Error(_)) using a custom data structure which I had named Partial. This was a monadic data structure which was essentially:

pub struct Partial<T> {
    value: Option<T>,
    errors: Vec<Error>,

impl<T> Partial<T> {
    pub fn map<U, F>(self, f: F) -> Partial<U>
        F: FnOnce(T) -> U
    { ... }

    pub fn flat_map<U, F>(mut self, f: F) -> Partial<U>
        F: FnOnce(T) -> Partial<U>
    { ... }

    pub fn value(&self) -> Option<&T> {

    pub fn errors(&self) -> &[Error] {

    pub fn verify(self) -> Result<T, Vec<Error>> {

This data structure was complemented with a bunch of custom nom combinators, e.g. map_partial(), expect_terminated(), and skip_if_err(), which would allow me to compose these fault-tolerant parsers together while accumulating errors in the errors field.

The consumer of this data structure would then choose to either:

  1. Assert that they need a valid AST without errors by calling expr.verify(), transforming the Partial<T> into a Result<T, Vec<Error>>. This option would be useful for traditonal batch compiler authors, as well as for testing and debugging.
  2. Extract and examine the contents of the value and errors field separately. This is what the language server would do: publish the accumulated errors to the user’s editor in the form of diagnostics and then perform further analysis on the syntax tree contained in value.

All the parser combinators would have this function signature instead:

impl Fn(Input) -> IResult<Input, Partial<Output>, Error>

While this approach seemed to work well initially, it spiralled out of control once the parser grew beyond a certain size. The number of Partial specific combinators grew, the parser logic got hairier, more imperative, and trickier to debug, and the performance implications of carrying around a heavy stack of errors from function to function were astonishingly awful. It didn’t look and feel that much like PEG anymore.

I will admit I learned a lot about a breadth of topics during this time, from benchmarking functions with criterion to generating flamegraphs with cargo-flamegraph, and going to extreme lengths to avoid heap allocations to make the parser as fast as possible. I used nom_locate to retain string span information and be as zero-copy as possible when constructing the syntax tree. But ultimately, I couldn’t fix all the warts and fundamental flaws. I needed a new approach.

The paper’s solution§

Finally, back to the paper that originally inspired this article! I shelved this project some months ago due to work and personal life matters, but came back to it last month with some fresh ideas and a better intuition of where to look. Discouraged by the previous setbacks, I was questioning whether parser combinators in general were flexible enough to express parsers which were both permissive and fault-tolerant, while also emitting good hand-crafted diagnostics. But then I stumbled upon the “Syntax error recovery in parsing expression grammars” (2018) paper while scouring the ACM DL search engine for interesting articles last night.

The authors of this paper actually managed to get pretty great results parsing the Lua programming language using a set of extended PEGs, producing excellent tailor-made diagnostics rivaling the automatic error recovering capabilities of their control, a top-down LL parser generated by ANTLR. Their techniques are similar to those outlined in this excellent blog post by @matklad, prominent author of rust-analyzer and rowan, a library for lossless concrete syntax trees.

And they managed to do all of this while not bailing out on the first parse error and still producing some kind of syntax tree 100% of the time in all the cases they tested. And the final result still looks and feels like PEG. Quite promising stuff! 😍

I was immediately excited by this paper since I knew that any error recovery strategy for PEG could potentially be applicable in a parser combinator library like nom, given that both approaches employ recursive descent. If you’re interested in the specific error recovery strategies used, I would strongly recommend you read the entire paper for yourself.

I would also recommend looking at LPegLabel, a reference implementation of a PEG parser generator using these techniques developed by authors Medeiros and Mascarenhas, if you’d like a more concrete example.

In general, though, it boils down to a few key principles:

  1. Parsing should never fail. If some kind of syntax tree isn’t produced, it’s considered a bug. Basically, the output of the top-level parser should be a (T, Vec<Error>), not Result<T, Vec<Error>>. Also, your syntax tree should provide a fallback Error node type for representing invalid, unparseable expressions.
  2. The PEG rules describing your language are loosened and extended to include recovery expressions annotated by “labels” which basically ensures that parsing never fails. These recovery expressions emit error messages when evaluated but will silently allow parsing to continue unabated. Sometimes they skip forward a few tokens, but often the cursor just stays where it is. I’ll demonstrate a very basic recovery expression with implemented with nom later on.
  3. Synchronization tokens like ), }, and ; are used to skip ahead through the text when necessary to avoid recovery expressions emitting spurious errors down the line after an earlier one has already fired.

The first and third concepts aren’t really anything new in the academic space. Infallible parsing, special syntax tree nodes for marking errors, and the use of synchronization tokens for error recovery are common tactics used to great effect in hand-written recursive descent parsers, but this paper applies them nicely to PEG parsers (which in turn, I would apply to parser combinators) without sacrificing their declarative nature. It also provides a small library of handy recovery expressions you can use in different situations to either emit high quality errors or suppress them.

Let’s take a look at a real world example of a fault-tolerant parser written in Rust using the nom 5.0 parser combinator library.


The full source code for this demo can be found here if you’d like to read the whole thing, but the idea is to apply the most basic error recovery strategies outlined in the paper for PEGs using parser combinators.

Below are some Rust types and traits that we will use throughout our example:

use std::ops::Range;

/// This used in place of `&str` or `&[u8]` in our `nom` parsers.
type LocatedSpan<'a> = nom_locate::LocatedSpan<&'a str, State<'a>>;
/// Convenient type alias for `nom::IResult<I, O>` reduced to `IResult<O>`.
type IResult<'a, T> = nom::IResult<LocatedSpan<'a>, T>;

trait ToRange {
    fn to_range(&self) -> Range<usize>;

impl<'a> ToRange for LocatedSpan<'a> {
    fn to_range(&self) -> Range<usize> {
        let start = self.location_offset();
        let end = start + self.fragment().len();

/// Error containing a text span and an error message to display.
struct Error(Range<usize>, String);

/// Carried around in the `LocatedSpan::extra` field in
/// between `nom` parsers.
#[derive(Clone, Debug)]
struct State<'a>(&'a RefCell<Vec<Error>>);

impl<'a> State<'a> {
    /// Pushes an error onto the errors stack from within a `nom`
    /// parser combinator while still allowing parsing to continue.
    pub fn report_error(&self, error: Error) {

Our top-level parse() function is defined as follows:

pub fn parse(source: &str) -> (Expr, Vec<Error>) {
    /// Store our error stack external to our `nom` parser here. It
    /// is wrapped in a `RefCell` so parser functions down the line
    /// can remotely push errors onto it as they run.
    let errors = RefCell::new(Vec::new());
    let input = LocatedSpan::new_extra(source, State(&errors));
    let (_, expr) = all_consuming(source_file)(input).expect("parser cannot fail");
    (expr, errors.into_inner())

Notice how we .expect() on our all-consuming source_file() parser. Remember, if we fail to produce some kind of syntax tree and consume all of the input 100% of the time, that’s considered a bug in the parser.

For the sake of example, I’ve implemented only one recovery expression outlined in the paper in the form of a custom parser combinator I call expect(). It looks like this:

/// Evaluate `parser` and wrap the result in a `Some(_)`. Otherwise,
/// emit the  provided `error_msg` and return a `None` while allowing
/// parsing to continue.
fn expect<'a, F, E, T>(parser: F, error_msg: E) -> impl Fn(LocatedSpan<'a>) -> IResult<Option<T>>
    F: Fn(LocatedSpan<'a>) -> IResult<T>,
    E: ToString,
    move |input| match parser(input) {
        Ok((remaining, out)) => Ok((remaining, Some(out))),
        Err(nom::Err::Error((input, _))) | Err(nom::Err::Failure((input, _))) => {
            let err = Error(input.to_range(), error_msg.to_string());
            input.extra.report_error(err); // Push error onto stack.
            Ok((input, None)) // Parsing failed, but keep going.
        Err(err) => Err(err),

This is the realm where parser combinator libraries really shine. This expect() combinator can be composed with other parser functions and produce results which closely map to their PEG counterparts. Below is an example parser capable of parsing a parenthesized expression which uses expect() to report errors:

fn paren(input: LocatedSpan) -> IResult<Expr> {
    // This approach of using `expect()` to annotate a parser
    // with a message follows the original paper's definition of
    // labels annotating certain parts of the PEG grammar.
    let paren = delimited(
        expect(expr, "expected expression after `(`"),
        expect(char(')'), "missing `)`"),

    map(paren, |inner| {


The final results of this toy implementation were quite striking, consistently producing some very pretty parse results. Given a very trivial AST that looks like this:

/// `foo`, `foo_bar`, `foo123`
struct Ident(String);

enum Expr {
    /// `(foo)`
    /// `foo`
    /// An unparseable, invalid expression.

The following outputs were produced by calling parse():

InputProduced syntax treeErrors
(foo))Paren(Ident(Ident("foo")))[Error(5..6, "expected EOF")]
(%Paren(Error)[Error(1..2, "unexpected `%`"), Error(2..2, "missing `)`")]
(Paren(Error)[Error(1..1, "expected expression after `(`"), Error(1..1, "missing `)`")]
%Error[Error(0..1, "unexpected `%`")]
()Paren(Error)[Error(1..2, "expected expression after `(`")]

These results are markedly better than what I had gotten with nom previously when I was relying on the built-in custom error management facilities, and the logic is significantly more declarative and understandable than the Partial<T> approach. And best of all, the final parsers are much shorter, easier to reason about, and are more directly analogous to their PEG equivalents, which makes the project much more maintainable in the long run.

Future work§

The example shown above was intentionally designed very simply in order to demonstrate the core concepts from Medeiros’ and Mascarenhas’ 2018 paper applied to parser combinators with nom. In order to support parsing a complex programming language like Nix, I will need to translate more of the recovery expressions described in the paper to nom combinators. I will also need to investigate richer forms of error representation, possibly containing multiple spans, warnings, lints, etc.

I should also add that the parser used in my actual project does not use LocatedSpan, but instead processes a custom Tokens<'a> type. Because of this, I can’t integrate the code used in this example into my project as-is. I will need to adapt it to work with this custom type, a topic which is considered out of scope for this particular post.

I also didn’t cover incremental parsing nor concrete syntax trees (that much) in this guide, and I plan for nix-parser to produce a lossless concrete syntax tree (courtesy of rowan) instead of an abstract syntax tree like the example.


Implementing a parser with good error recovery strategies and rich, user-friendly diagnostics is as much an art as it is a science (I think the Rust compiler devs would agree). This is even more true when it comes to parsers catering to the needs of language servers, REPLs, and other interactive uses where you need to be very tolerant to parse errors and provide meaningful diagnostics in response to messy and incomplete input. I learned many valuable things on this journey, and I’m still learning further as I go along. For one, I need to brush up on my formal methods and re-read the paper a few more times to fully digest the information.

I’m incredibly grateful to the ACM for having made their Digital Library open to the public during this global pandemic, and I’m also grateful to Sérgio Medeiros (UFRN, Brazil) and Fabio Mascarenhas (UFRJ, Brazil) for having produced the original research paper. I’m glad to have stumbled across it, and I learned some nice lessons out of it. If you’re a fan of PEG parsers and/or parser combinators and you haven’t read this paper yet, please do. It’s pretty neat!

In the meantime, I’ll be casually hacking away on nix-language-server whenever I have some spare time, armed with plenty of useful knowledge and principles I didn’t have before. Maybe I’ll actually get to producing meaningful auto-completions and semantic analysis out of it for once, as soon as I can focus on traversing the syntax tree itself and building a usable interpreter for evaluating the language. 😛