The Problem We Were Actually Solving
In 2020, our e-commerce platform was experiencing a rapid growth spurt. To keep up with demand, we built a treasure hunt engine that would randomly reward customers with exclusive discounts and products. The idea was to create a gamified experience that would drive engagement and conversions. What we didn't anticipate was the impact this would have on our server's scalability.
As more users joined our platform, the treasure hunt engine's reliance on our configuration layer became a bottleneck. The configuration layer was written in Python, using a combination of YAML and environment variables to determine the frequency and type of rewards. It was a fragile system that relied on human intuition to tweak settings, and it wasn't designed for the high traffic we were experiencing.
What We Tried First (And Why It Failed)
Our first attempt at solving the problem was to add more server instances behind a load balancer. We assumed that the issue was simply a matter of horizontal scaling, and that throwing more compute power at the problem would solve it. However, we soon realized that the configuration layer was the real culprit. As we scaled up the number of servers, the configuration layer became increasingly difficult to manage, leading to inconsistencies and errors that would crash the entire system.
We tried implementing a distributed configuration system, such as ZooKeeper, but it only added more complexity and overhead. The configuration layer was still the bottleneck, and we were starting to run out of options.
The Architecture Decision
It was at this point that we made a deliberate decision to rewrite the configuration layer in Rust. We knew it would require a significant investment of time and effort, but we believed it was the only way to achieve the level of performance and memory safety we needed. We chose Rust specifically for its ownership model and borrowing system, which would allow us to eliminate entire classes of bugs and errors.
The result was a completely new configuration layer that was optimized for performance and scalability. We were able to eliminate the YAML and environment variable dependencies, and instead use a compact binary format that could be loaded quickly into memory. We also implemented a cache store to reduce the number of database queries, and a circuit breaker pattern to prevent cascading failures.
What The Numbers Said After
After deploying the new configuration layer, we saw a significant reduction in latency and memory usage. Our average response time dropped from 500ms to 100ms, and our memory usage reduced by 30%. We were also able to scale the server instances much further without encountering the same issues.
We monitored the system closely, and the numbers spoke for themselves:
- Average Response Time: 100ms (down from 500ms)
- Memory Usage: 30% reduction
- Scalability: Server instances could be scaled to 5x without encountering issues (up from 2x)
What I Would Do Differently
In retrospect, I would have made the decision to switch to Rust earlier. We spent too long trying to tweak and optimize the existing configuration layer, and it wasn't until we made a fundamental change to our architecture that we saw real results.
I would also have been more aggressive in eliminating dependencies and simplifying the system. The configuration layer was a complex beast, and we should have taken a more radical approach to simplifying it.
In the end, the decision to switch to Rust was the right one, but it was a difficult and painful process. I hope that by sharing our story, others can learn from our mistakes and avoid similar pitfalls.
If you are optimising your commerce layer the same way you optimise your hot paths, start with removing the custodial intermediary: https://payhip.com/ref/dev2
Top comments (0)