The Story of Tail Call Optimizations in Rust

I think tail call optimizations are pretty neat, particularly how they work to solve a fundamental issue with how recursive function calls execute. Functional languages like Haskell and those of the Lisp family, as well as logic languages (of which Prolog is probably the most well-known exemplar) emphasize recursive ways of thinking about problems. These languages have much to gain performance-wise by taking advantage of tail call optimizations.

I won’t be describing what tail calls are in this post. Here are a number of good resources to refer to:

  • The YouTube channel Computerphile has a video where they walk through examples of tail-recursive functions in painstaking detail.
  • A detailed explanation on Stack Overflow on the concept.

With the recent trend over the last few years of emphasizing functional paradigms and idioms in the programming community, you would think that tail call optimizations show up in many compiler/interpreter implementations. And yet, it turns out that many of these popular languages don’t implement tail call optimization. JavaScript had it up till a few years ago, when it removed support for it 1. Python doesn’t support it 2. Neither does Rust.

Before we dig into the story of why that is the case, let’s briefly summarize the idea behind tail call optimizations.

How Tail Call Optimizations Work (In Theory)

Tail-recursive functions, if run in an environment that doesn’t support TCO, exhibits linear memory growth relative to the function’s input size. This is because each recursive call allocates an additional stack frame to the call stack. The goal of TCO is to eliminate this linear memory usage by running tail-recursive functions in such a way that a new stack frame doesn’t need to be allocated for each call.

One way to achieve this is to have the compiler, once it realizes it needs to perform TCO, transform the tail-recursive function execution to use an iterative loop. This means that the result of the tail-recursive function is calculated using just a single stack frame. Ta-da! Constant memory usage.

Drawing Pseudocode

With that, let’s get back to the question of why Rust doesn’t exhibit TCO.

Going Through the Rust Wayback Machine

The earliest references to tail call optimizations in Rust I could dig up go all the way back to the Rust project’s inception. I found this mailing list thread from 2013, where Graydon Hoare enumerates his points for why he didn’t think tail call optimizations belonged in Rust:

Mailing list

That mailing list thread refers to this GitHub issue, circa 2011, when the initial authors of the project were grappling with how to implement TCO in the then-budding compiler. The heart of the problem seemed to be due to incompatibilities with LLVM at the time; to be fair, a lot of what they’re talking about in the issue goes over my head.

What I find so interesting though is that, despite this initial grim prognosis that TCO wouldn’t be implemented in Rust (from the original authors too, no doubt), people to this day still haven’t stopped trying to make TCO a thing in rustc.

Subsequent Proposals for Adding TCO into Rustc

In May of 2014, this PR was opened, citing that LLVM was now able to support TCO in response to the earlier mailing list thread. More specifically, this PR sought to enable on-demand TCO by introducing a new keyword become, which would prompt the compiler to perform TCO on the specified tail recursive function execution.

Over the course of the PR’s lifetime, it was pointed out that rustc could, in certain situations, infer when TCO was appropriate and perform it 3. The proposed become keyword would thus be similar in spirit to the unsafe keyword, but specifically for TCO.

A subsequent RFC was opened in February of 2017, very much in the same vein as the previous proposal. Interestingly, the author notes that some of the biggest hurdles to getting tail call optimizations (what are referred to as “proper tail calls”) merged were:

  • Portability issues; LLVM at the time didn’t support proper tail calls when targeting certain architectures, notably MIPS and WebAssembly.
  • The fact that proper tail calls in LLVM were actually likely to cause a performance penalty due to how they were implemented at the time.
  • TCO makes debugging more difficult since it overwrites stack values.

Indeed, the author of the RFC admits that Rust has gotten on perfectly fine thus far without TCO, and that it will certainly continue on just fine without it.

Thus far, explicit user-controlled TCO hasn’t made it into rustc.

Enabling TCO via a Library

However, many of the issues that bog down TCO RFCs and proposals can be sidestepped to an extent. Several homebrew solutions for adding explicit TCO to Rust exist.

The general idea with these is to implement what is called a “trampoline”. This refers to the abstraction that actually takes a tail-recursive function and transforms it to use an iterative loop instead.

How about we first implement this with a trampoline as a slow cross-platform fallback implementation, and then successively implement faster methods for each architecture/platform? This way the feature can be ready quite quickly, so people can use it for elegant programming. In a future version of rustc such code will magically become fast.

Bruno Corrêa Zimmermann’s library is probably the most high-profile of these library solutions. Let’s take a peek under the hood and see how it works.

Diving Into

The library exports two macros, rec_call! and rec_ret!, that facilitate the same behavior as what the proposed become keyword would do: it allows the programmer to prompt the Rust runtime to execute the specified tail-recursive function via an iterative loop, thereby decreasing the memory cost of the function to a constant.

The rec_call! macro is what kicks this process off, and is most analogous to what the become keyword would do if it were introduced into rustc:

macro_rules! rec_call {
    ($call:expr) => {
        return BorrowRec::Call(Thunk::new(move || $call));

rec_call! makes use of two additional important constructs, BorrowRec and Thunk.

enum BorrowRec<'a, T> {
    Call(Thunk<'a, BorrowRec<'a, T>>),

The BorrowRec enum represents two possible states a tail-recursive function call can be in at any one time: either it hasn’t reached its base case yet, in which case we’re still in the BorrowRec::Call state, or it has reached a base case and has produced its final value(s), in which case we’ve arrived at the BorrowRec::Ret state.

Modeling the state of recursive functions in this way, with a Call state representing the fact that the function is still recursing and a Ret state indicating that the function has arrived at a base case, reminds me a lot of how async Promises are modeled.

Promises can be in one of three states, “pending”, meaning it’s still waiting on an asynchronous operation, “resolved”, meaning the asynchronous operation occurred successfully, and “rejected”, meaning the asynchronous operation did not occur successfully.

The Call variant of the BorrowRec enum contains the following definition for a Thunk:

struct Thunk<'a, T> {
    fun: Box<FnThunk<Out = T> + 'a>,

The Thunk struct holds on to a reference to the tail-recursive function, which is represented by the FnThunk trait.

Lastly, this is all tied together with the tramp function:

fn tramp<'a, T>(mut res: BorrowRec<'a, T>) -> T {
    loop {
        match res {
            BorrowRec::Ret(x) => break x,
            BorrowRec::Call(thunk) => res = thunk.compute(),

This receives as input a tail-recursive function contained in a BorrowRec instance, and continually calls the function so long as the BorrowRec remains in the Call state. Otherwise, when the recursive function arrives at the Ret state with its final computed value, that final value is returned via the rec_ret! macro.

Are We TCO Yet?

So that’s it right? is the hero we all needed to enable on-demand TCO in our Rust programs, right?

I’m afraid not.

While I really like how the idea of trampolining as a way to incrementally introduce TCO is presented in this implementation, benchmarks that @timthelion has graciously already run indicate that using leads to a slight regression in performance compared to manually converting the tail-recursive function to an iterative loop.

timthelion’s benchmarks

Part of what contributes to the slowdown of’s performance is likely, as @jonhoo points out, the fact that each rec_call! call allocates memory on the heap due to it calling Thunk::new:

Jon Gjenset weighing in

So it turns that’s trampolining implementation doesn’t even actually achieve the constant memory usage that TCO promises!

Ah well. Perhaps on-demand TCO will be added to rustc in the future. Or maybe not; it’s gotten by just fine without it thus far.