Blog: rust-phf: the perfect hash function

Tobias Bieniek

Senior Software Engineer


This is the story of how we made the conduit-mime-types Rust crate almost infinitely faster, using perfect hash functions and compile-time code generation.

Let's start at the beginning. is the package registry of the Rust programming language, or in simpler terms: the place where you can download all the dependencies of your apps. The server itself is also built with Rust, and specifically with an HTTP framework called conduit.

conduit has a component called conduit-static, which is responsible for efficiently serving static files to the users. conduit-static is itself relying on a package called conduit-mime-types, which is the main focus of this story. The purpose of this package is to map filename extensions to MIME types and vice-versa.

In other words: if you call get_mime_type("xls") the function should return Some("application/") and if you call get_extension("application/") it should return Some("xls").

The most naive implementation would use a list of filename extension and MIME type records:

xls = application/
xlsx = application/vnd.openxmlformats-officedocument.spreadsheetml.sheet
xps = application/

and it would then search through the list to find a matching filename extension or MIME type. While this works fine if you only have few entries in that list, it slows down significantly the more records you add to that list.

One way of improving the situation is to use hashmaps. These data structures calculate a hash of the thing you pass in, and then efficiently look-up the other thing that might be associated with this hash. These hashmaps also become slower the more records you put into them, but their performance is still much, much better compared to linear lists.

How it started

The original implementation of conduit-mime-types was already using two hashmaps, one for extension to MIME type mapping, and a second one for MIME type to extension mapping. Both of these hashmaps were filled by an initialize() function, which read a JSON file and then transformed the data into these mappings.

While this initialization step wasn't particularly slow, it still took quite a few unnecessary milliseconds. In fact, it used to be slow enough that we even found a benchmark in the project which measured the initialization speed. Needless to say that we got nerd sniped by this benchmark to improve the initialization speed.

Step 1: Code generation

While JSON can be parsed at blazing speed these days, it is still slower than not having to parse it at all. Our first step towards a more efficient implementation was to get rid of the JSON parsing step.

How did we get rid of the parsing step? Well, we didn't, but we did move it from run time to development time. We wrote a small script that parsed the JSON file and generated Rust code based on the content of the JSON file. The only thing that was left in the initialization code was transforming the statically bundled MIME type data into the hashmap records. This step resulted in a roughly 80% performance improvement.

You can find the corresponding pull request here

Step 2: Automatic code generation

While this approach was working quite well, it resulted is a small maintenance overhead because the code generation script would have to be run each time the raw JSON data was edited.

One alternative is to automatically generate the code at compile-time. This can be achieved by creating a file next to the Cargo.toml file of your library:

use std::env;
use std::fs::File;
use std::io::{BufWriter, Write};
use std::path::Path;

fn main() {
  let path = Path::new(&env::var("OUT_DIR").unwrap()).join("");
  let mut file = BufWriter::new(File::create(&path).unwrap());
  writeln!(&mut file, "static FOO: &'static str = \"bar\";\n").unwrap();

The main() function of this file will be executed automatically before your library is compiled, and you can use it to generate arbitrary code files that the rest of your code can then import, or rather include!(...).

While this admittedly decreased our build speed, it also meant that the raw JSON data and the generated code file could no longer diverge and cause subtle bugs. In practice the build speed degradation was barely measurable though, since the compilation of the code itself already took a significant amount of time.

Step 3: Perfect hash functions

As we mentioned earlier, we still had an initialization step which transformed that raw data in the generated code file to the hashmap records. This was necessary because Rust currently does not support static hashmaps, which would be necessary to have them generated at compile time. Luckily, there are alternatives.

While looking for a solution to the problem we stumbled upon the rust-phf crate, which has the tagline: "Compile time static maps for Rust". Exactly what we needed!

It was relatively straight-forward to modify our file and take advantage of the phf_codegen crate to generate the necessary two maps at compile time for us. The resulting maps have an API that is roughly similar to the regular hashmaps in Rust, but all we really needed was the .get() method anyway.

We now had gotten rid of all the content of the initialization step, reducing the time to run that step to essentially zero. Through some clever math we determined that by removing the step we had made it infinitely faster, and we could now get rid of the corresponding benchmark too.

Not only that, we also improved the lookup speed. rust-phf is using "perfect hash functions", as the name suggests. This means that it generates hash maps that don't have any collisions, which makes the lookup code much more efficient. The downside of these maps is that they have to recalculate the whole map if you insert any records, but since we were dealing with read-only data this downside was irrelevant to us.

You can take a look at the pull request that introduced rust-phf in conduit-mime-types here.


If you have read-only mappings that you want to use in your Rust code, then the rust-phf project can give you significant speed improvements by moving some of the work to compile time.

We hope that this short story about our work on conduit-mime-types was helpful to you. If you have any questions do not hesitate to contact us. We're happy to help!

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