Update (Dec 10, 2021): I have added some extra information worth reading at the bottom of this post.

The Rust compiler uses hash tables heavily, and the choice of hash function used for these hash tables makes a big difference to the compiler’s speed.

By default, Rust hash tables use Siphash 1-3, a hash function that is high quality but fairly slow. In contrast, the Rust compiler uses as hash function called FxHasher, which is surprisingly simple yet effective.

Rust hashing basics

To put a type into a hash table requires computing a hash value for it. The computation of a hash value of a type in Rust has two parts.

First, there is the Hash trait. This defines how a type should be traversed by the hash function, but does not specify the hash function itself.

The following example shows how this is implemented for a simple type.

struct Person {
    name: String,
    phone: u64,

impl std::hash::Hash for Person {
    fn hash<H: std::hash::Hasher>(&self, state: &mut H) {

It’s quite mechanical: you just call hash() on every field. In fact, it’s so mechanical that the compiler can generate the Hash impl for you if you annotate a type with #[derive(Hash)].

Once you get down to scalar types like integers, the second part comes into play: the Hasher trait, which defines the actual hash function. It works on byte slices, and possibly also integers.

Putting these together: to hash a value of a type (e.g. in order to insert it in a hash table), the Hasher is created, then the Hash implementation calls into the Hasher one or more times, and then the Hasher’s finish() method is called to produce the final u64 value that is the actual hash value used by the hash table. This is a simplified description but it’s enough to give the basic idea.

SipHasher13: A typical hash function

Let’s look at the core of the SipHasher13 implementation. Here’s how the hasher is initialised.

pub fn new() -> SipHasher13 {
    SipHasher13::new_with_keys(0, 0)

fn new_with_keys(key0: u64, key1: u64) -> Hasher<S> {
    let mut state = Hasher {
	k0: key0,
	k1: key1,
	length: 0,
	state: State { v0: 0, v1: 0, v2: 0, v3: 0 },
	tail: 0,
	ntail: 0,
	_marker: PhantomData,

fn reset(&mut self) {
    self.length = 0;
    self.state.v0 = self.k0 ^ 0x736f6d6570736575;
    self.state.v1 = self.k1 ^ 0x646f72616e646f6d;
    self.state.v2 = self.k0 ^ 0x6c7967656e657261;
    self.state.v3 = self.k1 ^ 0x7465646279746573;
    self.ntail = 0;

Not too complicated, but there are a number of fields and some magic constants.

Next is the code that does the actual hashing. Don’t worry too much about the details here. (Especially given that I’ve removed comments for brevity). Just note that it’s reasonably complicated, with many arithmetic and bit operations, multiple conditions, a loop, and a call to another function S::c_rounds() which isn’t shown here but does more bit shuffling.

fn write(&mut self, msg: &[u8]) {
    let length = msg.len();
    self.length += length;
    let mut needed = 0;
    if self.ntail != 0 {
	needed = 8 - self.ntail;
	self.tail |= unsafe { u8to64_le(msg, 0, cmp::min(length, needed)) } << (8 * self.ntail);
	if length < needed {
	    self.ntail += length;
	} else {
	    self.state.v3 ^= self.tail;
	    S::c_rounds(&mut self.state);
	    self.state.v0 ^= self.tail;
	    self.ntail = 0;
    let len = length - needed;
    let left = len & 0x7;
    let mut i = needed;
    while i < len - left {
	let mi = unsafe { load_int_le!(msg, i, u64) };
	self.state.v3 ^= mi;
	S::c_rounds(&mut self.state);
	self.state.v0 ^= mi;
	i += 8;
    self.tail = unsafe { u8to64_le(msg, i, left) };
    self.ntail = left;

I like the optimism of that #[inline] attribute!

Finally, we have the code for finalisation. It’s straight-line code this time, with a number of operations, and calls to two other bit-shuffling functions.

fn finish(&self) -> u64 {
    let mut state = self.state;
    let b: u64 = ((self.length as u64 & 0xff) << 56) | self.tail;
    state.v3 ^= b;
    S::c_rounds(&mut state);
    state.v0 ^= b;
    state.v2 ^= 0xff;
    S::d_rounds(&mut state);
    state.v0 ^ state.v1 ^ state.v2 ^ state.v3

Other hash functions

While every hash function is different, SipHasher13 is pretty representative of many of them, with state containing multiple variables and lots of bit-shuffling for the actual hashing.

The fasthash crate aggregates a number of popular hash functions that are available in Rust. Getting a sense of the speeds of different hash functions is difficult. In my experience, it is an exaggeration to say that every hash function implementation claims to be faster than all the others… but not that much of an exaggeration.


FxHasher is based on a hash function used within Firefox. (Indeed, the Fx is short for “Firefox”.)

Consider its core. The following snippet shows initialisation, the main hash operation, and finalisation.

fn default() -> FxHasher {
    FxHasher { hash: 0 }

const K: usize = 0x517cc1b727220a95;

fn add_to_hash(&mut self, i: usize) {
    self.hash = self.hash.rotate_left(5).bitxor(i).wrapping_mul(K);

fn finish(&self) -> u64 {
    self.hash as u64

It is brutally simple. Initialisation sets a single variable to zero. Hashing a value is just a rotate, an xor, and a multiply. Finalisation is a no-op.

(Are you wondering where the constant 0x517cc1b727220a95 comes from? 0xffff_ffff_ffff_ffff / 0x517c_c1b7_2722_0a95 = π.)

In terms of hashing quality, it is mediocre. If you run it through a hash quality tester it will fail a number of the tests. For example, if you hash any sequence of N zeroes, you get zero. And yet, for use in hash tables within the Rust compiler, it’s hard to beat. (Fortunately, the compiler is not an interesting target for HashDoS attacks.)

Why is this? A lot of hash function are designed to process large amounts of data. But the most common case in the Rust compiler is hashing a struct with one or two integer or pointer fields. FxHasher is incredibly fast for these small inputs, partly because its functions are so small that they can be reliably inlined. (Bigger functions are less likely to be inlined, even if marked with #[inline]. Also, a number of Rust hashing libraries are wrappers around C libraries, where inlining is not possible. Inlining of intrinsics is also problematic.)

FxHasher can finish hashing a struct with two fields before SipHasher13 is done initializing. The quality is good enough that it only results in slightly more collisions in hash tables than higher quality hash functions, and the raw hashing speed more than makes up for this.

Also, FxHasher is deliberately simplistic in how it handles fields. SipHasher13 works in 64-bit chunks. If it is given a u32 followed by four u8s, it will combine them and process them much like a single u64. In contrast, FxHasher will just cast each integer to usize and run add_to_hash() five times. (And on 32-bit platforms u64 inputs are split in two). Rust’s hashing structure permits this behaviour, and it’s a good choice when each add_to_hash() is just a rotate, an xor, and a multiply. That is faster and simpler than trying to accumulate regular sized chunks of data before hashing. (Note: this is why SipHasher13 above has a write() method that takes a byte slice but FxHasher has an add_to_hash() method that takes a usize. See the full implementations for details.)

Making it faster

Recently I’ve been trying to improve upon FxHasher, and I haven’t had much success. I’m not benchmarking FxHasher directly, the metric I use is “does this make the compiler faster?”

Here are some things I’ve tried.

  • Higher quality algorithms, like SipHasher13: range from slightly slower to much slower.
  • Initialise with one, instead of zero: negligible differences.
  • Different multiplication constants: sometimes negligible differences, sometimes terrible results.
  • Remove the multiply: disastrously slow, due to many more collisions.
  • Move the multiply from add_to_hash to finish: very bad, due to more collisions.
  • Remove the rotate_left: tiny improvements on quite a few benchmarks, but moderate regressions on a smaller number, and not worthwhile.
  • Change the order from rotate/xor/multiply to xor/multiple/rotate: slightly slower.

The only thing that was a clear win was to change the #[inline] attributes to #[inline(always)], which slightly sped up a couple of benchmarks. Although the methods are usually inlined, there must have been one or two performance-sensitive places where they weren’t. Update: this turned out to be a measurement error, and #[inline(always)] makes no difference.

After all this, my appreciation for FxHasher has grown. It’s like a machete: simple to the point of crudeness, yet unbeatable for certain use cases. Impressive!


There might be people—including some who have forgotten more about hash functions than I will ever know—who are furious at my simplistic treatment of this topic. If you know of a change to FxHasher or an alternative algorithm that might be faster or better, I’d love to hear about it via email, or Twitter, or wherever else. I just want to make the compiler faster. Thanks!


There was some good discussion about this post on Reddit. Reddit user CAD1997 pointed out that FxHasher’s handling of the high bits of inputs is poor, because the multiply effectively throws a lot of them away. This means it performs badly when hashing a 64-bit integer with low entropy in the low bits.

This was demonstrated when llogiq and I tried out a micro-optimization idea from glandium for combining a struct with two 32-bit fields into a single 64-bit value to be hashed, rather than hashing them separately. The struct in question is this one:

pub struct DefId {
    pub krate: CrateNum,
    pub index: DefIndex,

krate and index are both 32-bit integers. krate is a low-entropy value, typically taking on a small number of distinct values. index is a high-entropy value, typically taking on a large number of distinct values.

If they are combined like this, with the high-entropy field in the low bits:

((self.krate as u64) << 32) | (self.index as u64)

it’s a tiny win compared to hashing them separately.

If they are combined like this, with the high-entropy field in the high bits:

((self.index as u64) << 32) | (self.krate as u64)

it’s a huge slowdown due to a massive increase in hash table collisions.

So it’s good to be aware of this weakness when using FxHasher. But why does FxHasher still do well in the Rust compiler? First, the compiler is (almost?) always built as a 64-bit binary, so FxHasher is working with 64-bit inputs and hash values. Second, the values hashed are in three groups.

  • Most common are integers. These are (almost?) all 32-bit integers or smaller, in which the upper bits are all zero.
  • Next most common are pointers. These have very low entropy in the upper bits because most memory allocations occur in a small number of distinct sections of the address space, such as the heap and the stack.
  • Least common are strings. These are hashed by FxHasher in 64-bit chunks, and so the hash quality won’t be good, but it seems they are rare enough that it doesn’t really hurt performance.

Nonetheless, I am considering using an idea from the ahash crate. For its fallback variants it can use a clever folded multiply operation that mixes bits well without throwing them away, because the overflow bits from the multiply get XORed back into the result. And it turns out you can do this surprisingly cheaply on common platforms. ahash’s fallback variants do some additional initialisation and finalisation work that probably wouldn’t benefit the compiler, so I wouldn’t use ahash directly. But changing FxHasher::add_to_hash() to use the folded multiply will likely give a hash function that is as fast while avoiding the potential performance cliffs.

I wrote the original post in the hope of learning about improvements, so I consider this a good outcome!