Ketan Singh

Collection of musing and ramblings

Speeding up Python with Rust, what works and what doesn't !

Posted at — Jun 3, 2021

P.S. You can find source here

What works

Not so long ago I encountered lzstring python package which is itself a port from Javascript’s lz-string library. What’s the purpose you ask? Javascipt package was written to compress relatively large amounts of data and store it in localStorage. Depending upon kind of text, its redundancies and compression level of the algorithm I found that it can compress text very well. For Some of the texts I was able to compress text to its 25% of the original size. We can go into details of lzma and brilliance of Igor Pavlov here but that’s not the point of his post, maybe another time. Coming back to python package, which can do pretty much same thing but was quite slow. So I ran some benchmark to get baseline performance numbers using timeit. Its not the best tool out there to benchmark but it will do the job.

import lzstring
import timeit
import random 
import string

X = lzstring.LZString()

for i in range(1, 100):
	big_str = ''.join(random.choices(string.ascii_uppercase + string.digits, k=i*100))

	r1 = timeit.timeit("""
from __main__ import big_str, compressToBase64, decompressFromBase64
a = compressToBase64(big_str)
b = decompressFromBase64(a)
	""", number=100)

This gives us the following chart for input growth vs time it takes for pure python implementation.


How do we speed it up? Most obvious answer few years ago was to use C or C++ to rewrite this particular module. It still is in some cases but extending python with Rust in my opinion blows both C and C++ out of the water. Why? If you ask some Rust evangelist they could probably speak on this topic for entire day. In my opinion it has the following advantage

Now coming back to speeding up lzstring. I was ready to implement crate from scratch however after a brief github-fu I found that someone already wrote the crate in Rust lz-str-rs which saved me from days of rewriting the whole thing, godspeed!!

Now how do we connect it to python? We have couple of options

we can either use PyO3 or rust-cpython. PyO3 began as fork of rust-cpython when rust-cpython wasn’t maintained. Over the time pyo3 has become fundamentally different from rust-cpython. Few advantages of PyO3 include

You can check user manual on how to use pyo3

Now we know how to write pyo3 module and we’ve already found a Rust implementation lzstring. All that’s left is write a wrapper pyo3 around it. For now I will just implement compressToBase64 and decompressFromBase64 from the lzstring .

fn compressToBase64(input: String) -> PyResult<String> {

fn decompressFromBase64(input: String) -> PyResult<String> {
    let result = lz_str::decompress_from_base64(input.as_str());
    if let None = result {
        return Err(exceptions::PyTypeError::new_err("decompression failed"));
    let to_string = String::from_utf16(&*result.unwrap());

    if let Err(e) = to_string {
        return Err(exceptions::PyTypeError::new_err(e.to_string()));


Using this, I build the python package and published it to PyPi

Let’s rewrite the bench for the python module and rerun the benchmark and compare it against python.

from  lzma_pyo3 import  decompressFromBase64, compressToBase64

for i in range(1, 100):
  big_str = ''.join(random.choices(string.ascii_uppercase + string.digits, k=i*100))

  r1 = timeit.timeit("""
from __main__ import big_str, decompressFromBase64, compressToBase64
a = compressToBase64(big_str)
b = decompressFromBase64(a)
	""", number=100)
  runs.append([r1, i*100])



What doesn’t works

This ofcourse doesn’t always work as expected, You need to ensure you do computationally heavy tasks with Rust’s native types. Pyo3 provides us with very convinent types by which you can access python’s datatype in Rust very easily. Following table shows some of the most popular types.

PythonRustRust (Python-native)
strString, Cow, &str&PyUnicode
bytesVec, &[u8]&PyBytes
intAny integer type (i32, u32, usize, etc)&PyLong
floatf32, f64&PyFloat
dict[K, V]HashMap<K, V>, BTreeMap<K, V>, hashbrown::HashMap<K, V>2&PyDict
tuple[T, U](T, U), Vec&PyTuple
set[T]HashSet, BTreeSet, hashbrown::HashSet2&PySet
frozenset[T]HashSet, BTreeSet, hashbrown::HashSet2&PyFrozenSet

Pyo3 also provides FromPyObject trait which you can use with your custom struct to convert types from Python to Rust. If you want to optimize your performance you should first of all convert any Rust (Python-native) to Rust native type as soon as possible in your code. In order for that to work, you cannot mix types in your python datastructures e.g. ['', 123, true] otherwise you will not be able to convert it to Rust native types and will be forced to work with something like &PyList which cannot be optimized as much.

Do note that python is much more efficient on “simpler code”. Consider following code snippets

follow snippet one simply sums up vector of integer and is wrapped with pyo3

fn list_sum(v: Vec<i64>) -> PyResult<i64> {
    return Ok(v.iter().sum());

Usage of python’s native sum() is very similiar to list_sum()

x = random.sample(range(1, 50000), 200)

In all of my benchmark runs I found python to be significantly faster. I think this is mostly because of overhead to convert &PyList to Vec<i64>.

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