I'm currently assessing the technology choices for my upcoming projects and finding it challenging to justify using compiled languages like Java, C++, or Rust for typical backend or heavy-compute tasks, except for specific use cases like game engines or kernel development.
My reasoning is primarily based on two observations:
1. The performance gap seems to be narrowing. With tools like Numba, particularly when leveraging NoGIL and crafting pre-allocated loops, you can achieve 70-90% of the performance of native C or C++ for CPU-intensive tasks. Plus, there's a lot you can accomplish with basic mathematical operations.
2. Development time is significantly shorter with Python. It allows for quicker cycles since there's less boilerplate code involved. Moreover, large language models (LLMs) appear to work best with Python due to its extensive training data and concise syntax, which enhances context utilization. Naturally, one still needs to know the logic and architecture of their program.
If a project can be developed in Python in around 100 hours with approximately 80% of native performance (thanks to JIT compilation for essential processes and implementing complex algorithms), versus 300 hours in Java or C++ for only a slight performance increase, the return on investment leans heavily in favor of Python.
I'm keen to hear from experienced developers: Beyond low-level constraints like embedded systems or game engines, are there drawbacks to this "Optimized Python" strategy in real-world enterprise or high-scale applications? Are there potential architectural limitations, concurrency challenges, or maintainability issues that I might be missing which absolutely require a statically typed compiled language rather than a hybrid Python method? It seems to me there's a breakthrough here that many may not be aware of yet, similar to niches in fintech where optimized Python is utilized for testing and research.
5 Answers
If your team is already proficient in Java, for instance, it might make sense to stick with it even for simple projects. You also have to keep in mind that features like NoGIL are still quite niche, and most Python developers won't have experience with them. If you require fast computing, don’t expect to write low-level code easily when building an API or web app with frameworks like Flask or Django.
The choice of language heavily depends on the project's scope and requirements. While I enjoy Python, I wouldn't choose it for enterprise-grade software required to be maintained on-premises for extended periods. Additionally, consider the libraries and frameworks your project will utilize; some may only be robust and mature in specific languages, presenting a significant practical challenge regardless of performance metrics.
Honestly, in real-world applications, the way you write your code usually matters far more for performance than the programming language itself. Most bottlenecks come from database interactions, I/O operations, or rendering, rather than from logic processing. I’ve seen poorly performing Java applications and fast executing Python ones. When it comes down to it, if you're not hitting a unique case where your application's core logic is the actual bottleneck, sticking with Python and optimizing it can save a lot of time and effort overall.
Sure, there are rare instances when you might need to switch languages for performance, but they're not everyday demands.
From my experience, Numba can hit a wall when it comes to large input arrays and complex algorithms—especially in scientific computing. The JIT compilation process may lead to significant delays and can even cause memory overloads too. Languages like Rust or C++ just run smoothly because they don't have to deal with that runtime compilation. In some cases, using Rust through PyO3 bindings can outperform Numba by two to five times due to better optimization.
Can you share a specific instance? I'm currently working with 20+ GB arrays in Numba, and it seems to handle them well.
Exactly! When things get mathematically complex, like with Monte Carlo simulations, the performance truly starts to shine in compiled languages. Python struggles in those areas.
For most projects, Python provides sufficient performance. You only really need to dive into languages like Rust or C++ if you're facing significant performance crunches. Numba really helps for light performance boosts without having to completely rewrite your application.
Yeah! It just seems like rewriting everything in a faster language is a huge jump in development time for a marginal performance gain. Why take that step unless absolutely necessary?
Right! If the bottleneck isn't in the core logic, the productivity gains from using Python typically outweigh any marginal performance benefits from switching languages.