The Problem We Were Actually Solving
The production team had been complaining about the algorithm being too slow and inefficient. They believed that the issue was due to the fact that we were serving a large number of concurrent requests to the algorithm, which was causing it to choke under the load. To mitigate this, our ops team was recommending that we implement some form of client-side caching or database sharding to distribute the load more evenly. The idea was to add more servers to the pool as needed, and let the load balancer handle the routing. Sounds simple enough, but as it turned out, the real challenge lay elsewhere.
What We Tried First (And Why It Failed)
We decided to go with a combination of client-side caching using Redis, and database sharding using our custom-built sharding tool. The plan was to cache the results of the algorithm for a certain period of time, and serve cached results to clients that requested the same route within that time frame. For the database, we would shard the data across multiple machines, each of which would handle a portion of the total load. Sounds like a solid plan, or so we thought.
We ran the setup for a few days, and initially, it seemed to work like a charm. Clients were happy with faster response times, and the ops team was relieved that the servers were breathing a little easier. However, as we delved deeper into the performance data, we realized that things weren't as peachy as we thought. The algorithm was still taking an age to compute, and the Redis cache was getting hit with requests that were way beyond its capacity. Turns out, our clients were clever and had figured out how to bypass the cache and hit the algorithm directly, rendering our whole caching setup useless.
The Architecture Decision
It was at this point that we realized that our problem wasn't just about the algorithm or the caching setup - it was about the configuration of our servers themselves. We were serving too many clients from a single server, which was causing the algorithm to choke under the load. We decided to take a step back and re-evaluate our server configuration. We moved to a microservices-based architecture, where each client was served by a separate, lightweight microservice that handled the computation of the algorithm. This way, we could scale each microservice independently, and avoid overloading any single server.
We also implemented a load balancer that would route requests to the microservices, but with a twist - we configured the load balancer to dynamically allocate servers based on their current load. This way, we could ensure that no single server was overwhelmed, and that the load was distributed evenly across the available servers. The result was a system that was not only faster but also more scalable and efficient.
What The Numbers Said After
After implementing the new architecture, we saw a significant reduction in the time it took to compute the algorithm. Our average response time went down by 30%, and we were able to handle a 50% increase in concurrent requests without breaking a sweat. But what really impressed us was the drastic reduction in the number of errors we saw. We went from a few hundred errors per day to almost zero.
The cost savings were also significant. We were able to scale our servers more efficiently, which meant that we didn't need to add as many new machines to the pool. Our infrastructure costs went down by 20%, and we were able to redirect those savings to other areas of the business.
What I Would Do Differently
Looking back, there are a few things that I would do differently if I had to relive this experience. Firstly, I would have done more thorough testing before rolling out the new architecture. We were so eager to get it up and running that we didn't test it as thoroughly as we should have. This led to some nasty surprises down the line, but we were able to fix them eventually.
Secondly, I would have invested more time in understanding the behavior of our clients. They were clever, and they found ways to bypass our caching setup. If we had understood their behavior better, we could have designed a caching setup that was more robust.
Lastly, I would have invested more time in automating our load balancer configuration. We ended up configuring it manually, which was a nightmare to maintain. In hindsight, we should have invested in a tool that could automate the process, and provide us with more visibility into the load balancer's behavior.
Top comments (0)