November 16th, 2017

Red Hat Developers: Speed up your Python using Rust

Programing, Python, by admin.

What is Rust?

Rust is a systems programming language that runs blazingly fast, prevents segfaults, and guarantees thread safety.


  • zero-cost abstractions
  • move semantics
  • guaranteed memory safety
  • threads without data races
  • trait-based generics
  • pattern matching
  • type inference
  • minimal runtime
  • efficient C bindings

Description is taken from

Why does it matter for a Python developer?

The better description of Rust I heard from Elias (a member of the Rust Brazil Telegram Group).

Rust is a language that allows you to build high level abstractions, but without giving up low-level control – that is, control of how data is represented in memory, control of which threading model you want to use etc.
Rust is a language that can usually detect, during compilation, the worst parallelism and memory management errors (such as accessing data on different threads without synchronization, or using data after they have been deallocated), but gives you a hatch escape in the case you really know what you’re doing.
Rust is a language that, because it has no runtime, can be used to integrate with any runtime; you can write a native extension in Rust that is called by a program node.js, or by a python program, or by a program in ruby, lua etc. and, however, you can script a program in Rust using these languages. — “Elias Gabriel Amaral da Silva”

There is a bunch of Rust packages out there to help you extending Python with Rust.

I can mention Milksnake created by Armin Ronacher (the creator of Flask) and also PyO3 The Rust bindings for Python interpreter.

See a complete reference list at the bottom of this article.

Let’s see it in action

For this post, I am going to use Rust Cpython, it’s the only one I have tested, it is compatible with stable version of Rust and found it straightforward to use.

NOTE: PyO3 is a fork of rust-cpython, comes with many improvements, but works only with the nightly version of Rust, so I prefered to use the stable for this post, anyway the examples here must work also with PyO3.

Pros: It is easy to write Rust functions and import from Python and as you will see by the benchmarks it worth in terms of performance.

Cons: The distribution of your project/lib/framework will demand the Rust module to be compiled on the target system because of variation of environment and architecture, there will be a compiling stage which you don’t have when installing Pure Python libraries, you can make it easier using rust-setuptools or using the MilkSnake to embed binary data in Python Wheels.

Python is sometimes slow

Yes, Python is known for being “slow” in some cases and the good news is that this doesn’t really matter depending on your project goals and priorities. For most projects, this detail will not be very important.

However, you may face the rare case where a single function or module is taking too much time and is detected as the bottleneck of your project performance, often happens with string parsing and image processing.


Let’s say you have a Python function which does a string processing, take the following easy example of counting pairs of repeated chars, but have in mind that this example can be reproduced with other string processing functions or any other generally slow process in Python.

# How many subsequent-repeated group of chars are in the given string? 
abCCdeFFghiJJklmnopqRRstuVVxyZZ... {millions of chars here} 1 2 3 4 5 6

Python is slow for doing large string processing, so you can use pytest-benchmark to compare a Pure Python (with Iterator Zipping) function versus a Regexp implementation.

# Using a Python3.6 environment
$ pip3 install pytest pytest-benchmark

Then write a new Python program called

import re
import string
import random # Python ZIP version
def count_doubles(val): total = 0
 # there is an improved version later on this post for c1, c2 in zip(val, val[1:]): if c1 == c2: total += 1 return total # Python REGEXP version
double_re = re.compile(r'(?=(.)\1)') def count_doubles_regex(val): return len(double_re.findall(val)) # Benchmark it
# generate 1M of random letters to test it
val = ''.join(random.choice(string.ascii_letters) for i in range(1000000)) def test_pure_python(benchmark): benchmark(count_doubles, val) def test_regex(benchmark): benchmark(count_doubles_regex, val)

Run pytest to compare:

$ pytest =============================================================================
platform linux -- Python 3.6.0, pytest-3.2.3, py-1.4.34, pluggy-0.4.
benchmark: 3.1.1 (defaults: timer=time.perf_counter disable_gc=False min_roun
rootdir: /Projects/rustpy, inifile:
plugins: benchmark-3.1.1
collected 2 items .. -----------------------------------------------------------------------------
Name (time in ms) Min Max Mean -----------------------------------------------------------------------------
test_regex 24.6824 (1.0) 32.3960 (1.0) 27.0167 (1.0) test_pure_python 51.4964 (2.09) 62.5680 (1.93) 52.8334 (1.96) -----------------------------------------------------------------------------

Lets take the Mean for comparison:

  • Regexp – 27.0167 <– less is better
  • Python Zip – 52.8334

Extending Python with Rust

Create a new crate

crate is how we call Rust Packages.

Having rust installed (recommended way is Rust is also available on Fedora and RHEL repositories by the rust-toolset

I used rustc 1.21.0

In the same folder run:

cargo new pyext-myrustlib

It creates a new Rust project in that same folder called pyext-myrustlib containing the Cargo.toml (cargo is the Rust package manager) and also a src/ (where we write our library implementation).

Edit Cargo.toml

It will use the rust-cpython crate as dependency and tell cargo to generate a dylib to be imported from Python.

name = "pyext-myrustlib"
version = "0.1.0"
authors = ["Bruno Rocha <>"] [lib]
name = "myrustlib"
crate-type = ["dylib"] [dependencies.cpython]
version = "0.1"
features = ["extension-module"]

Edit src/

What we need to do:

  1. Import all macros from cpython crate.
  2. Take Python and PyResult types from CPython into our lib scope.
  3. Write the count_doubles function implementation in Rust, note that this is very similar to the Pure Python version except for:
    • It takes a Python as first argument, which is a reference to the Python Interpreter and allows Rust to use the Python GIL.
    • Receives a &str typed val as reference.
    • Returns a PyResult which is a type that allows the rise of Python exceptions.
    • Returns an PyResult object in Ok(total) (Result is an enum type that represents either success (Ok) or failure (Err)) and as our function is expected to return a PyResult the compiler will take care of wrapping our Ok on that type. (note that our PyResult expects a u64 as return value).
  4. Using py_module_initializer! macro we register new attributes to the lib, including the __doc__ and also we add the count_doubles attribute referencing our Rust implementation of the function.
    • Attention to the names libmyrustlib, initlibmyrustlib, and PyInit.
    • We also use the try! macro, which is the equivalent to Python’stry.. except.
    • Return Ok(()) – The () is an empty result tuple, the equivalent of None in Python.
extern crate cpython; use cpython::{Python, PyResult}; fn count_doubles(_py: Python, val: &str) -> PyResult<u64> { let mut total = 0u64; // There is an improved version later on this post for (c1, c2) in val.chars().zip(val.chars().skip(1)) { if c1 == c2 { total += 1; } } Ok(total)
} py_module_initializer!(libmyrustlib, initlibmyrustlib, PyInit_myrustlib, |py, m | { try!(m.add(py, "__doc__", "This module is implemented in Rust")); try!(m.add(py, "count_doubles", py_fn!(py, count_doubles(val: &str)))); Ok(())

Now let’s build it with cargo

$ cargo build --release Finished release [optimized] target(s) in 0.0 secs $ ls -la target/release/libmyrustlib*
target/release/* <-- Our dylib is here

Now let’s copy the generated .so lib to the same folder where our is located.

NOTE: on Fedora you must get a .so in other system you may get a .dylib and you can rename it changing extension to .so.

$ cd ..
$ ls pyext-myrustlib/ $ cp pyext-myrustlib/target/release/ $ ls pyext-myrustlib/

Having the in the same folder or added to your Python path allows it to be directly imported, transparently as it was a Python module.


Importing from Python and comparing the results

Edit your now importing our Rust implemented version and adding a benchmark for it.

import re
import string
import random
import myrustlib # <-- Import the Rust implemented module ( def count_doubles(val): """Count repeated pair of chars ins a string""" total = 0 for c1, c2 in zip(val, val[1:]): if c1 == c2: total += 1 return total double_re = re.compile(r'(?=(.)\1)') def count_doubles_regex(val): return len(double_re.findall(val)) val = ''.join(random.choice(string.ascii_letters) for i in range(1000000)) def test_pure_python(benchmark): benchmark(count_doubles, val) def test_regex(benchmark): benchmark(count_doubles_regex, val) def test_rust(benchmark): # <-- Benchmark the Rust version benchmark(myrustlib.count_doubles, val)


$ pytest
platform linux -- Python 3.6.0, pytest-3.2.3, py-1.4.34, pluggy-0.4.
benchmark: 3.1.1 (defaults: timer=time.perf_counter disable_gc=False min_round
rootdir: /Projects/rustpy, inifile:
plugins: benchmark-3.1.1
collected 3 items ... -----------------------------------------------------------------------------
Name (time in ms) Min Max Mean -----------------------------------------------------------------------------
test_rust 2.5555 (1.0) 2.9296 (1.0) 2.6085 (1.0) test_regex 25.6049 (10.02) 27.2190 (9.29) 25.8876 (9.92) test_pure_python 52.9428 (20.72) 56.3666 (19.24) 53.9732 (20.69) -----------------------------------------------------------------------------

Lets take the Mean for comparison:

  • Rust – 2.6085 <– less is better
  • Regexp – 25.8876
  • Python Zip – 53.9732

Rust implementation can be 10x faster than Python Regex and 21x faster than Pure Python Version.

Interesting that Regex version is only 2x faster than Pure Python ?

NOTE: That numbers makes sense only for this particular scenario, for other cases that comparison may be different.

Updates and Improvements

After this article has been published I got some comments on r/python and also on r/rust

The contributions came as Pull Requests and you can send a new if you think the functions can be improved.

Thanks to: Josh Stone we got a better implementation for Rust which iterates the string only once and also the Python equivalent.

Thanks to: Purple Pixie we got a Python implementation using itertools, however this version is not performing any better and still needs improvements.

Iterating only once

fn count_doubles_once(_py: Python, val: &str) -> PyResult<u64> { let mut total = 0u64; let mut chars = val.chars(); if let Some(mut c1) = { for c2 in chars { if c1 == c2 { total += 1; } c1 = c2; } } Ok(total)
def count_doubles_once(val): total = 0 chars = iter(val) c1 = next(chars) for c2 in chars: if c1 == c2: total += 1 c1 = c2 return total

Python with itertools

import itertools def count_doubles_itertools(val): c1s, c2s = itertools.tee(val) next(c2s, None) total = 0 for c1, c2 in zip(c1s, c2s): if c1 == c2: total += 1 return total

New Results

Name (time in ms) Min Max Mean -------------------------------------------------------------------------------
test_rust_once 1.0072 (1.0) 1.7659 (1.0) 1.1268 (1.0) test_rust 2.6228 (2.60) 4.5545 (2.58)  2.9367 (2.61) test_regex 26.0261 (25.84) 32.5899 (18.45) 27.2677 (24.20)
test_pure_python_once 38.2015 (37.93) 43.9625 (24.90) 39.5838 (35.13)
test_pure_python 52.4487 (52.07) 59.4220 (33.65) 54.8916 (48.71)
test_itertools 58.5658 (58.15) 66.0683 (37.41) 60.8705 (54.02)

The new Rust implementation is 3x better than the old, but the python-itertools version is even slower than the pure python

After adding the improvements to iterate the list of chars only once, Rust still has advantage from 1.1268 to 39.583


NOTE: If you want to propose changes or improvements send a PR, I still want to see better Python implementations using numpy, numba or Cython and also there is an incomplete version with rust regex crate:


Rust may not be yet the general purpose language of choice by its level of complexity and may not be the better choice yet to write common simple applications such as web sites and test automation scripts.

However, for specific parts of the project where Python is known to be the bottleneck and your natural choice would be implementing a C/C++ extension, writing this extension in Rust seems easy and better to maintain.

There are still many improvements to come in Rust and lots of others crates to offer Python <--> Rust integration. Even if you are not including the language in your tool belt right now, it is really worth to keep an eye open to the future!


The code snippets for the examples showed here are available in GitHub repo:

The examples in this publication are inspired by Extending Python with Rust talk by Samuel Cormier-Iijima in Pycon Canada. video here:

Also by My Python is a little Rust-y by Dan Callahan in Pycon Montreal. video here:

Other references:

Join Community:

Join Rust community, you can find group links in

If you speak Portuguese, I recommend you to join and there is the on Youtube.


Bruno Rocha

  • Senior Quality Engineer at Red Hat
  • Teaching Python and Flask at
  • Fellow Member of Python Software Foundation
  • Member of RustBR study group

More info: and

Whether you are new to Containers or have experience, downloading this cheat sheet can assist you when encountering tasks you haven’t done lately.


The post Speed up your Python using Rust appeared first on RHD Blog.

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