A while ago at $work, we had a performance issue with one of our core Python libraries.
This particular library forms the backbone of our 3D processing pipeline. It’s a rather big and complex library which uses NumPy and other scientific Python packages to do a wide range of mathematical and geometrical operations.
Our system also has to work onprem with limited CPU resources, and while at first it performed well, as the number of concurrent physical users grew we started running into problems and our system struggled to keep up with the load.
We came to the conclusion that we had to make our system at least 50 times faster to handle the increased workload, and we figured that Rust could help us achieve that.
Because the performance problems we encountered are pretty common, we can recreate & solve them right here, in a (notsoshort) article.
So grab a cup of tea (or coffee) and I’ll walk you through (a) the basic underlying problem and (b) a few iterations of optimizations we can apply to solve this problem.
If you want to jump straight to the final code, just to go to the summary.
Our running example
Let’s create a small library, which will exhibit our original performance issues (but does completely arbitrary work).
Imagine you have a list of polygons and a of list points, all in 2D. For business reasons, we want to “match” each point to a single polygon.
Our imaginary library is going to:
 Start with an initial list of points and polygons (all in 2D).
 For each point, find a much smaller subset of polygons that are closest to it, based on distance from the center.
 Out of those polygons, select the “best” one (we are going to use “smallest area” as “best”).
In code, that’s going to look like this (The full code can be found here):
from typing import List, Tuple
import numpy as np
from dataclasses import dataclass
from functools import cached_property
Point = np.array
@dataclass
class Polygon:
x: np.array
y: np.array
@cached_property
def center(self) > Point: ...
def area(self) > float: ...
def find_close_polygons(polygon_subset: List[Polygon], point: Point, max_dist: float) > List[Polygon]:
...
def select_best_polygon(polygon_sets: List[Tuple[Point, List[Polygon]]]) > List[Tuple[Point, Polygon]]:
...
def main(polygons: List[Polygon], points: np.ndarray) > List[Tuple[Point, Polygon]]:
...
The key difficulty (performance wise) is this mix of Python objects and numpy arrays.
We are going to analyze this in depth in a minute.
It’s worth noting that converting parts of / everything to vectorized numpy might be possible for this toy library, but will be nearly impossible for the real library while making the code much less readable and modifiable, and the gains are going to be limited (here’s a partially vertorized version, which is faster but far from the results we are going to achieve).
Also, using any JITbased tricks (PyPy / numba) results in very small gains (as we will measure, just to make sure).
Why not just Rewrite It (all) In Rust™?
As compelling as a complete rewrite was, it had a few problems:
 The library was already using numpy for a lot of its calculations, so why should we expect Rust to be better?
 It is big and complex and very business critical and highly algorithmic, so that would take ~months of work, and our poor onprem server is dying today.
 A bunch of friendly researchers are actively working on said library, implementing better algorithms and doing a lot of experiments. They aren’t going to be very happy to learn a new programming language, waiting for things to compile and fighting with the borrow checker. They would appreciate us not moving their cheese too far.
Dipping our toes
It is time to introduce our friend the profiler.
Python has a built in Profiler (cProfile
), but in this case it’s not really the right tool for the job:
 It’ll introduce a lot of overhead to all the Python code, and none for native code, so our results might be biased.
 We won’t be able to see into native frames, meaning we aren’t going to be able to see into our Rust code.
We are going to use pyspy
(GitHub).
pyspy
is a sampling profiler which can see into native frames.
They also mercifully publish prebuilt wheels to pypi, so we can just pip install pyspy
and get to work.
We also need something to measure.
# measure.py
import time
import poly_match
import os
# Reduce noise, actually improve perf in our case.
os.environ["OPENBLAS_NUM_THREADS"] = "1"
polygons, points = poly_match.generate_example()
# We are going to increase this as the code gets faster and faster.
NUM_ITER = 10
t0 = time.perf_counter()
for _ in range(NUM_ITER):
poly_match.main(polygons, points)
t1 = time.perf_counter()
took = (t1  t0) / NUM_ITER
print(f"Took and avg of {took * 1000:.2f}ms per iteration")
It’s not very scientific, but it’s going to take us very far.
“Good benchmarking is hard. Having said that, do not stress too much about having a perfect benchmarking setup, particularly when you start optimizing a program.”
~ Nicholas Nethercote, in “The Rust Performance Book”
Running this script will give us our baseline:
$ python measure.py
Took an avg of 293.41ms per iteration
For the original library, we used 50 different examples to make sure all cases are covered.
This matched the overall system perf, meaning we can start working on crushing this number.
Side note: We can also measure using PyPy (we’ll also add a warmup to allow the JIT to do its magic).
$ conda create n pypyenv c condaforge pypy numpy && conda activate pypyenv
$ pypy measure_with_warmup.py
Took an avg of 1495.81ms per iteration
Measure first
So, let’s find out what is so slow here.
$ pyspy record native o profile.svg  python measure.py
pyspy> Sampling process 100 times a second. Press ControlC to exit.
Took an avg of 365.43ms per iteration
pyspy> Stopped sampling because process exited
pyspy> Wrote flamegraph data to 'profile.svg'. Samples: 391 Errors: 0
Already, we can see that the overhead is pretty small.
Just for comparison, using cProfile
we get this:
$ python m cProfile measure.py
Took an avg of 546.47ms per iteration
7551778 function calls (7409483 primitive calls) in 7.806 seconds
...
We get this nice, reddish graph called a flamegraph:
Each box is a function, and we can see the relative time we spend in each function,
including the functions it is calling to (going down the graph/stack).
Try clicking on a the norm
box to zoom into it.
Here, the main takeaways are:
 The vast majority of time is spent in
find_close_polygons
.  Most of that time is spend doing
norm
, which is a numpy function.
So, let’s have a look at find_close_polygons
:
def find_close_polygons(
polygon_subset: List[Polygon], point: np.array, max_dist: float
) > List[Polygon]:
close_polygons = []
for poly in polygon_subset:
if np.linalg.norm(poly.center  point) < max_dist:
close_polygons.append(poly)
return close_polygons
We are going to rewrite this function in Rust.
Before diving into the details, it’s important to notice a few things here:
 This function accepts & returns complex objects (
Polygon
,np.array
).  The size of the objects is nontrivial (so copying stuff might cost us).
 This function is called “a lot” (so overhead we introduce is probably going to matter).
My first Rust module
pyo3
is a crate for interacting between Python and Rust.
It has exceptionally good documentation, and they explain the basic setup here.
We are going to call our crate poly_match_rs
, and add function called find_close_polygons
.
mkdir poly_match_rs && cd "$_"
pip install maturin
maturin init bindings pyo3
maturin develop
Starting out, our crate is going to look like this:
use pyo3::prelude::*;
#[pyfunction]
fn find_close_polygons() > PyResult<()> {
Ok(())
}
#[pymodule]
fn poly_match_rs(_py: Python, m: &PyModule) > PyResult<()> {
m.add_function(wrap_pyfunction!(find_close_polygons, m)?)?;
Ok(())
}
We also need to remember to execute maturin develop
every time we change the Rust library.
And thats it! Let’s call our new function and see what happens.
>>> poly_match_rs.find_close_polygons(polygons, point, max_dist)
E TypeError: poly_match_rs.poly_match_rs.find_close_polygons() takes no arguments (3 given)
v1  A naive Rust translation
We’ll start with matching the expected API.
PyO3 is pretty smart about Python to Rust conversions, so that’s going to be pretty easy:
#[pyfunction]
fn find_close_polygons(polygons: Vec<PyObject>, point: PyObject, max_dist: f64) > PyResult<Vec<PyObject>> {
Ok(vec![])
}
PyObject
is (as the name suggest) a generic “anything goes” Python object.
We’ll try to interact with it in a bit.
This should make the program run (albeit incorrectly).
I’m going to just copy and paste the original Python function, and fix the syntax.
#[pyfunction]
fn find_close_polygons(polygons: Vec<PyObject>, point: PyObject, max_dist: f64) > PyResult<Vec<PyObject>> {
let mut close_polygons = vec![];
for poly in polygons {
if norm(poly.center  point) < max_dist {
close_polygons.push(poly)
}
}
Ok(close_polygons)
}
Cool, but this won’t compile:
% maturin develop
...
error[E0609]: no field `center` on type `Py<PyAny>`
> src/lib.rs:8:22

8  if norm(poly.center  point) < max_dist {
 ^^^^^^ unknown field
error[E0425]: cannot find function `norm` in this scope
> src/lib.rs:8:12

8  if norm(poly.center  point) < max_dist {
 ^^^^ not found in this scope
error: aborting due to 2 previous errors ] 58/59: poly_match_rs
We need three crates to implement our function:
# For Rustnative array operations.
ndarray = "0.15"
# For a `norm` function for arrays.
ndarraylinalg = "0.16"
# For accessing numpycreated objects, based on `ndarray`.
numpy = "0.18"
First, lets turn the opaque and generic point: PyObject
into something we can work with.
Just like we asked PyO3 for a “Vec
of PyObject
s”, we can ask for a numpyarray,
and it’ll autoconvert the argument for us.
use numpy::PyReadonlyArray1;
#[pyfunction]
fn find_close_polygons(
// An object which says "I have the GIL", so we can access Pythonmanaged memory.
py: Python<'_>,
polygons: Vec<PyObject>,
// A reference to a numpy array we will be able to access.
point: PyReadonlyArray1<f64>,
max_dist: f64,
) > PyResult<Vec<PyObject>> {
// Convert to `ndarray::ArrayView1`, a fully operational native array.
let point = point.as_array();
...
}
Because point
is now an ArrayView1
, we can actually use it. For example:
// Make the `norm` function available.
use ndarray_linalg::Norm;
assert_eq!((point.to_owned()  point).norm(), 0.);
Now we just need to get the center of each polygon, and “cast” it to an ArrayView1
.
In PyO3, this looks like this:
let center = poly
.getattr(py, "center")? // Pythonstyle getattr, requires a GIL token (`py`).
.extract::<PyReadonlyArray1<f64>>(py)? // Tell PyO3 what to convert the result to.
.as_array() // Like `point` before.
.to_owned(); // We need one of the sides of the `` to be "owned".
It’s a bit of a mouthful, but overall the result is a pretty clear linetoline translation of the original code:
1use pyo3::prelude::*;
2
3use ndarray_linalg::Norm;
4use numpy::PyReadonlyArray1;
5
6#[pyfunction]
7fn find_close_polygons(
8 py: Python<'_>,
9 polygons: Vec<PyObject>,
10 point: PyReadonlyArray1<f64>,
11 max_dist: f64,
12) > PyResult<Vec<PyObject>> {
13 let mut close_polygons = vec![];
14 let point = point.as_array();
15 for poly in polygons {
16 let center = poly
17 .getattr(py, "center")?
18 .extract::<PyReadonlyArray1<f64>>(py)?
19 .as_array()
20 .to_owned();
21
22 if (center  point).norm() < max_dist {
23 close_polygons.push(poly)
24 }
25 }
26
27 Ok(close_polygons)
28}
vs the original:
def find_close_polygons(
polygon_subset: List[Polygon], point: np.array, max_dist: float
) > List[Polygon]:
close_polygons = []
for poly in polygon_subset:
if np.linalg.norm(poly.center  point) < max_dist:
close_polygons.append(poly)
return close_polygons
We expect this version to have some advantage over the original function, but how much?
$ (cd ./poly_match_rs/ && maturin develop)
$ python measure.py
Took an avg of 609.46ms per iteration
So.. Is Rust just super slow?
No! We just forgot to ask for speed!
If we run with maturin develop release
we get much better results:
$ (cd ./poly_match_rs/ && maturin develop release)
$ python measure.py
Took an avg of 23.44ms per iteration
Now that is a nice speedup!
We also want to see into our native code, so we are going to enable debug symbols in release. While we are at it, we might as well ask for maximum speed.
# added to Cargo.toml
[profile.release]
debug = true # Debug symbols for our profiler.
lto = true # Linktime optimization.
codegenunits = 1 # Slower compilation but faster code.
v2  Rewrite even more in Rust
Now, using the native
flag in pyspy
is going to show us both Python and our new native code.
Running pyspy
again
$ pyspy record native o profile.svg  python measure.py
pyspy> Sampling process 100 times a second. Press ControlC to exit.
we get this flamegraph (nonred colors are added to so we can refer to them):
Looking at the profiler output, we can see a few interesting things:
 The relative size of
find_close_polygons::...::trampoline
(the symbol Python directly calls) and__pyfunction_find_close_polygons
(our actual implementation). Hovering, they are 95% vs 88% of samples, so the overhead is pretty small.
 The actual logic (
if (center  point).norm() < max_dist { ... }
) which islib_v1.rs:22
(very small box on the right), is about 9% of the total runtime. So x10 improvement should still be possible!
 Most of the time is spent in
lib_v1.rs:16
, which ispoly.getattr(...).extract(...)
and if we zoom in we can see is really justgetattr
and getting the underlying array usingas_array
.
The conclusion here is that we need to focus on solving the 3rd point,
and the way to do that is to Rewrite Polygon
in Rust.
Let’s look at our target:
@dataclass
class Polygon:
x: np.array
y: np.array
_area: float = None
@cached_property
def center(self) > np.array:
centroid = np.array([self.x, self.y]).mean(axis=1)
return centroid
def area(self) > float:
if self._area is None:
self._area = 0.5 * np.abs(
np.dot(self.x, np.roll(self.y, 1))  np.dot(self.y, np.roll(self.x, 1))
)
return self._area
We’ll want to keep the existing API as much as possible,
but we don’t really need area
to be that fast (for now).
The actual class might have additional complex stuff,
like a merge
method which uses ConvexHull
from scipy.spatial
.
To cut costs (and limit the scope of this already long article),
we will only move the “core” functionality of Polygon
to Rust,
and subclass that from Python to implement the rest of the API.
Our struct
is going to look like this:
// `Array1` is a 1d array, and the `numpy` crate will play nicely with it.
use ndarray::Array1;
// `subclass` tells PyO3 to allow subclassing this in Python.
#[pyclass(subclass)]
struct Polygon {
x: Array1<f64>,
y: Array1<f64>,
center: Array1<f64>,
}
Now we need to actually implement it.
We want to expose poly.{x, y, center}
as:
 Properties.
 numpy Arrays.
We also need a constructor so Python can create new Polygon
s.
use numpy::{PyArray1, PyReadonlyArray1, ToPyArray};
#[pymethods]
impl Polygon {
#[new]
fn new(x: PyReadonlyArray1<f64>, y: PyReadonlyArray1<f64>) > Polygon {
let x = x.as_array();
let y = y.as_array();
let center = Array1::from_vec(vec![x.mean().unwrap(), y.mean().unwrap()]);
Polygon {
x: x.to_owned(),
y: y.to_owned(),
center,
}
}
// the `Py<..>` in the return type is a way of saying "an Object owned by Python".
#[getter]
fn x(&self, py: Python<'_>) > PyResult<Py<PyArray1<f64>>> {
Ok(self.x.to_pyarray(py).to_owned()) // Create a Pythonowned, numpy version of `x`.
}
// Same for `y` and `center`.
}
We need to add our new struct as a class to the module:
#[pymodule]
fn poly_match_rs(_py: Python, m: &PyModule) > PyResult<()> {
m.add_class::<Polygon>()?; // new.
m.add_function(wrap_pyfunction!(find_close_polygons, m)?)?;
Ok(())
}
And now we can update the Python code to use it:
class Polygon(poly_match_rs.Polygon):
_area: float = None
def area(self) > float:
...
We can compile it and it’ll actually work, but it’ll be much slower!
(Remember that x
, y
, and center
will now need to create a new numpy array on each access).
To actually improve performance, we need to extract
our original Rustbased Polygon
from the list of PythonPolygon
s.
PyO3 is very flexible with this type of operation, so there are a few ways we could do it.
One limit we have is that we also need to return PythonPolygon
s, and we don’t want to do any cloning of the actual data.
It’s possible to manually call .extract::<Polygon>(py)?
on each PyObject
s, but we ask PyO3 to give us Py<Polygon>
directly.
This is a reference to a Pythonowned object,
which we expect to contain an instance (or a subclass, in our case) of a native pyclass
struct.
45#[pyfunction]
46fn find_close_polygons(
47 py: Python<'_>,
48 polygons: Vec<Py<Polygon>>, // References to Pythonowned objects.
49 point: PyReadonlyArray1<f64>,
50 max_dist: f64,
51) > PyResult<Vec<Py<Polygon>>> { // Return the same `Py` references, unmodified.
52 let mut close_polygons = vec![];
53 let point = point.as_array();
54 for poly in polygons {
55 let center = poly.borrow(py).center // Need to use the GIL (`py`) to borrow the underlying `Polygon`.
56 .to_owned();
57
58 if (center  point).norm() < max_dist {
59 close_polygons.push(poly)
60 }
61 }
62
63 Ok(close_polygons)
64}
Let’s see what we get using this code:
$ python measure.py
Took an avg of 6.29ms per iteration
We are nearly there! Just x2 to go!
v3  Avoid allocations
Let’s fire up the profiler one more time.
 We start to see
select_best_polygon
, which now calls some Rust code (when it gets thex
&y
vectors) We could fix that, but that’s a very small potential improvement (maybe 10%)
 We see we spend about 20% the time on
extract_argument
(underlib_v2.rs:48
), so we are still paying quite a lot on overhead! But most of the time is in
PyIterator::next
andPyTypeInfo::is_type_of
, which aren’t easy to fix.
 But most of the time is in
 We see a bunch of time spent allocating stuff!
lib_v2.rs:58
is ourif
, and we seedrop_in_place
andto_owned
. The actual line is about 35% of the overall time, which is a lot more than we expect: this should be the “fast bit” with all the data in place.
Let’s tackle the last point.
This our problematic snippet:
let center = poly.borrow(py).center
.to_owned();
if (center  point).norm() < max_dist { ... }
What we want is to avoid that to_owned
.
But we need an owned object for norm
, so we’ll have to implement that manually.
(The reason we can improve on ndarray
here is that we know that our array is actually just 2 f32
s).
This would look like this:
use ndarray_linalg::Scalar;
let center = &poly.as_ref(py).borrow().center;
if ((center[0]  point[0]).square() + (center[1]  point[1]).square()).sqrt() < max_dist {
close_polygons.push(poly)
}
But, alas, the borrow checker is unhappy with us:
error[E0505]: cannot move out of `poly` because it is borrowed
> src/lib.rs:58:33

55  let center = &poly.as_ref(py).borrow().center;
 
 
 borrow of `poly` occurs here
 a temporary with access to the borrow is created here ...
...
58  close_polygons.push(poly);
 ^^^^ move out of `poly` occurs here
59  }
60  }
  ... and the borrow might be used here, when that temporary is dropped and runs the `Drop` code for type `PyRef`
As usual, the borrow checker is correct: we are doing memory crimes.
The simpler fix is to Just Clone, and close_polygons.push(poly.clone())
compiles.
This is actually a very cheap clone, because we only incr
the reference count of the Python object.
However, in this case we can also shorten the borrow by doing a classic Rust trick:
let norm = {
let center = &poly.as_ref(py).borrow().center;
((center[0]  point[0]).square() + (center[1]  point[1]).square()).sqrt()
};
if norm < max_dist {
close_polygons.push(poly)
}
Because poly
is only borrowed in the inner scope, once we reach close_polygons.push
the compiler
can know that we no longer hold that reference, and will happily compile the new version.
And finally, we have
$ python measure.py
Took an avg of 2.90ms per iteration
Which is 100x improvement over the original code.
Summary
We started out with this Python code:
@dataclass
class Polygon:
x: np.array
y: np.array
_area: float = None
@cached_property
def center(self) > np.array:
centroid = np.array([self.x, self.y]).mean(axis=1)
return centroid
def area(self) > float:
...
def find_close_polygons(
polygon_subset: List[Polygon], point: np.array, max_dist: float
) > List[Polygon]:
close_polygons = []
for poly in polygon_subset:
if np.linalg.norm(poly.center  point) < max_dist:
close_polygons.append(poly)
return close_polygons
# Rest of file (main, select_best_polygon).
We profiled it using pyspy
, and even our most naive, linetoline translation of find_close_polygons
resulted in more than a x10 improvement.
We did a few additional iterations of profilewritemeasure iterations until we finally we gained a x100 improvement in runtime, while keeping the same API as the original library.
Version  Avg time per iteration (ms)  Multiplier 

Baseline implementation (Python)  293.41  1x 
Naive linetoline Rust translation of find_close_polygons  23.44  12.50x 
Polygon implementation in Rust  6.29  46.53x 
Optimized allocation implementation in Rust  2.90  101.16x 
The final python code looks like this
import poly_match_rs
from poly_match_rs import find_close_polygons
class Polygon(poly_match_rs.Polygon):
_area: float = None
def area(self) > float:
...
# Rest of file unchanged (main, select_best_polygon).
which calls this Rust code:
use pyo3::prelude::*;
use ndarray::Array1;
use ndarray_linalg::Scalar;
use numpy::{PyArray1, PyReadonlyArray1, ToPyArray};
#[pyclass(subclass)]
struct Polygon {
x: Array1<f64>,
y: Array1<f64>,
center: Array1<f64>,
}
#[pymethods]
impl Polygon {
#[new]
fn new(x: PyReadonlyArray1<f64>, y: PyReadonlyArray1<f64>) > Polygon {
let x = x.as_array();
let y = y.as_array();
let center = Array1::from_vec(vec![x.mean().unwrap(), y.mean().unwrap()]);
Polygon {
x: x.to_owned(),
y: y.to_owned(),
center,
}
}
#[getter]
fn x(&self, py: Python<'_>) > PyResult<Py<PyArray1<f64>>> {
Ok(self.x.to_pyarray(py).to_owned())
}
// Same for `y` and `center`.
}
#[pyfunction]
fn find_close_polygons(
py: Python<'_>,
polygons: Vec<Py<Polygon>>,
point: PyReadonlyArray1<f64>,
max_dist: f64,
) > PyResult<Vec<Py<Polygon>>> {
let mut close_polygons = vec![];
let point = point.as_array();
for poly in polygons {
let norm = {
let center = &poly.as_ref(py).borrow().center;
((center[0]  point[0]).square() + (center[1]  point[1]).square()).sqrt()
};
if norm < max_dist {
close_polygons.push(poly)
}
}
Ok(close_polygons)
}
#[pymodule]
fn poly_match_rs(_py: Python, m: &PyModule) > PyResult<()> {
m.add_class::<Polygon>()?;
m.add_function(wrap_pyfunction!(find_close_polygons, m)?)?;
Ok(())
}
Takeaways
Rust (with the help of pyo3) unlocks true native performance for everyday Python code, with minimal compromises.
Python is a superb API for researchers, and crafting fast building blocks with Rust is an extremely powerful combination.
Profiling is super interesting, and it pushes you to truly understand everything that’s happening in your code.
And finally: computers are crazy fast. The next time you wait for something to complete, consider firing up a profiler, you might learn something new 🚀