The Problem We Were Actually Solving
I was part of a team responsible for developing a high-performance search engine, and our initial implementation was in Java. We chose Java due to its ease of development and the large community of developers who could contribute to our project. However, as our search volume increased, we began to experience significant pauses in our system due to JVM garbage collection. These pauses were unacceptable for a real-time search engine, and we needed to find a solution. Our profiling tools, such as YourKit, showed that the majority of our pause time was spent in garbage collection, with an average pause time of 200ms and a maximum pause time of 1.5s.
What We Tried First (And Why It Failed)
We tried to optimize our Java implementation by reducing object allocation and using stack-based allocation when possible. We also experimented with different JVM garbage collection algorithms, such as the G1 collector and the Shenandoah collector. However, despite our best efforts, we were unable to reduce the pause times to an acceptable level. The G1 collector reduced our average pause time to 150ms, but the maximum pause time was still over 1s. The Shenandoah collector performed slightly better, with an average pause time of 100ms, but it introduced additional latency due to its asynchronous nature. We also used tools like jstat and VisualVM to monitor our JVM's performance, but we were unable to find a solution that met our requirements.
The Architecture Decision
After struggling with the JVM's garbage collection pauses, we decided to rewrite our search engine in Rust. This decision was not made lightly, as we knew that Rust has a steep learning curve and would require significant changes to our codebase. However, we believed that Rust's focus on performance and memory safety made it an ideal choice for our high-performance search engine. We used the Tokio framework to build our asynchronous search engine, and we were able to take advantage of Rust's ownership system to reduce memory allocations and minimize the need for garbage collection.
What The Numbers Said After
After rewriting our search engine in Rust, we saw a significant reduction in latency and pause times. Our average latency decreased from 50ms to 5ms, and our maximum pause time decreased from 1.5s to 1ms. We also saw a significant reduction in memory usage, with our peak memory usage decreasing from 10GB to 1GB. Our profiler output showed that our Rust implementation was able to handle the same search volume as our Java implementation, but with much lower latency and memory usage. Specifically, our allocation counts decreased by a factor of 10, and our CPU usage decreased by 20%. We used the perf tool to monitor our system's performance, and we were able to identify and optimize bottlenecks in our code.
What I Would Do Differently
In retrospect, I would have started our project in Rust from the beginning. While the learning curve was steep, the benefits of Rust's performance and memory safety features outweighed the costs. I would also have invested more time in learning Rust's ownership system and borrow checker, as these features are critical to writing efficient and safe Rust code. Additionally, I would have used more Rust-specific tools, such as cargo-bench, to benchmark and optimize our code. Overall, our experience with rewriting our search engine in Rust was positive, and I believe that Rust is a good choice for any high-performance system that requires low latency and high throughput. I would recommend Rust to any engineer who is looking to build a high-performance system, but I would also caution them to be prepared for the steep learning curve and to invest time in learning Rust's unique features and ecosystem.