I've been exploring how to effectively unit test the performance of code and have come up with a few ideas. First, there's Big-O analysis which I've documented in an article on my site. Second, I've considered measuring algorithmic efficiency in a way that isn't limited to hardware differences. This can be done by utilizing tools like Cachegrind or Callgrind that simulate CPU behavior minimally, allowing for consistent CPU instruction counts across different machines. I plan to write more about this soon, as it involves snapshot testing and accounting for noise, such as variations from Python's randomized hash seed. However, one downside is that this method may not reveal performance changes related to specific CPU functionalities, especially for compiled Python extensions. A practical approach to this is to run benchmarks on a single machine comparing the main branch to your new code in a pull request. I'm curious if anyone else has suggestions or techniques for unit testing code performance?
5 Answers
Why do you refer to this as unit testing instead of benchmarking? Isn’t there a difference? I’m exploring benchmarking techniques in other languages like Zig, but I'm curious how similar approaches could work in Python without adding too much overhead.
Unit tests are designed to run quickly, so checking performance in a one-second test might not be very insightful. Changing your Python version can affect performance counts too, which complicates things. I once sped up an hour-long process to just four seconds without worrying about precise operation counts—sometimes you just need to focus on getting the major changes right without digging too deeply into details.
True, but you're assuming key CPU-bound algorithms represent real bottlenecks. In cases where scalability is crucial, though, measuring is important. By the way, your points about the article being more about scalability than speed are interesting!
While it's true Python isn't optimal for every performance aspect, it offers quicker development time. If the algorithm is the problem, improving it in Python might be more effective before considering a language switch.
If precision in performance is critical, Python may not be the best choice since it has higher-level abstractions. Languages like C++ give you more control. However, with the right libraries, you can still optimize significant tasks in Python.
In IO-bound applications, it might be helpful to limit IO operations to an expected number. This could offer insight into performance if you add more IO operations. Have you seen this in practice? It might provide useful context.
I was initially thinking about CPU-bound applications, but your point makes sense. It does make me wonder what 'limit' really means in this context. Measuring IO operations is another interesting angle to consider.
Benchmarking focuses on speed while unit testing is about validating performance criteria, like whether an algorithm scales with O(n). The two serve different purposes.