One of the biggest complaints when I started using Ruby on Rails (in 2008) was it was slow. To speed it up, one could create a C extension. Today, I hear the same complaints being lodged against my go-to language, Python. Do we need to learn C?
While “fast” is relative to the application and horizontal scaling is easy when deploying to cloud platforms, there are times when seconds, even milliseconds, matter.
If you’ve hit a wall optimizing your Python code, it may be time to go lower level. And for that, Rust is a solid option.
I’ve been reading about Rust for a while and wanted to see how to use it with Python. For that, I turned to Google, and found the article, Speed up your Python using Rust by Bruno Rocha.
Running the Code (On Mac/Linux)
To test this out on your Mac do the following:
- Clone this repo and cd into the directory.
- Install Python 3.7.x, create a new virtual environment, and install the requirements.
- Install Rust.
- Change into the
pyext-myrustlibdirectory and build the library:
cargo build --release
- Change back into the root directory
- Copy the new library into the root directory and change it to a
.sofile for use:
cp pyext-myrustlib/target/release/libmyrustlib.dylib ./myrustlib.so
- Run the benchmarks:
How Much Faster is Rust Than Pure Python?
Let’s take a look…
For the simple benchmark we’re using a function that counts pairs of repeated characters in a string of 1 million random characters (sounds like an interview problem…).
Now there are a few ways to parse that string, so we’ll use the following:
Here’s how they perform on my MacBook Pro: macOS Mojave, 2.2 GHz Intel Core i7, 16 GB RAM:
|Name (time in us)||Min||Max||Mean|
|test_rust||440.6270 (1.0)||926.0550 (1.0)||486.1527 (1.0)|
|test_python_numpy||1,105.9690 (2.51)||2,461.2190 (2.66)||1,221.5940 (2.51)|
|test_regex||26,473.0460 (60.08)||34,816.4480 (37.60)||27,283.4936 (56.12)|
|test_pure_python||42,791.8280 (97.12)||45,206.2730 (48.82)||43,268.9481 (89.00)|
|test_python_comprehension||55,309.7680 (125.53)||59,225.5910 (63.95)||56,470.0048 (116.16)|
|test_python_itertools||63,602.1140 (144.34)||76,422.4370 (82.52)||66,260.3507 (136.30)|
Result: Using Mean as the comparision, Rust beats pure Python in this one instance by a lot: it’s 56x faster than Python regex, and 89x faster than pure Python. However, it’s only 2.5x faster than numpy!
This also shows that while a list comprehension is easier to read, it’s not the best implementation for this example.
Using Rust to speed up specific parts of your Python application can be a great and relatively easy way to go. Try out rust-cpython for yourself, or another available option (which I will be trying soon), PyO3.
Start with my example on Github.