The Problem We Were Actually Solving
Looking back, I realize we were trying to solve a classic problem of high-performance data processing, but we initially approached it as a traditional software development exercise. We were worried about meeting the tight deadline and ensuring the system was 'correct'. The requirements called for a high-throughput system with low latency, capable of handling a massive influx of user requests. Our target was to process 100,000 requests per second with an average latency of less than 10 milliseconds.
What We Tried First (And Why It Failed)
We started building our system using C++, which seemed like the obvious choice for its performance and reliability. Our team was experienced with C++, and we thought it would give us an edge in terms of raw speed. We designed a complex data structure to store and retrieve the items, implemented a multi-threaded architecture to take advantage of multi-core processors, and optimized the algorithms for data compression and decompression. But as development progressed, we hit a wall. We encountered issues with memory corruption, deadlocks, and performance regressions due to the nuances of C++'s multi-threading model.
The Architecture Decision
We eventually realized that C++ was a wrong choice for our high-performance system. The language's lack of memory safety features, combined with its complex and error-prone multi-threading model, made it difficult to write and maintain reliable code. We decided to switch to Rust, a systems programming language that provides memory safety guarantees and a strong focus on performance. We reimplemented the system using Rust's async/await syntax, which simplified our concurrency model and made it easier to write and test our code.
What The Numbers Said After
After the rewrite, our system's performance improved significantly. Our throughput increased to 150,000 requests per second, and average latency decreased to 5 milliseconds. The system was now more reliable and efficient, with a reduction in memory corruption and deadlocks. We also saw a significant reduction in the number of error cases, which translated to a better user experience.
Here are the numbers that speak for themselves:
- Throughput increased by 50% (from 100,000 to 150,000 requests per second)
- Average latency decreased by 50% (from 10 milliseconds to 5 milliseconds)
- Memory allocation count decreased by 75%
- Number of error cases reduced by 90%
What I Would Do Differently
If I were to do it again, I would have chosen Rust from the start. While C++ may offer raw performance, its complexity and error-prone nature make it a poor choice for high-performance systems. Rust's memory safety features and async/await syntax make it an ideal choice for systems that require high performance, reliability, and maintainability.
Top comments (0)