The Problem We Were Actually Solving
What we were really trying to achieve was a scalable search engine, capable of handling millions of queries a day. From a user's perspective, it was supposed to be fast, accurate, and mostly invisible. But in the back-end, it was a different story. We were trying to optimize for query latency while also ensuring that our server resources didn't get overwhelmed.
What We Tried First (And Why It Failed)
Our initial approach was to throw more servers at the problem. We had a cloud provider with automatic scaling features, and we figured that the more servers we had, the better. So, we scaled up our server count from 10 to 30, thinking that would solve the problem. But what we got was a group of underutilized servers, each running a fraction of the queries that our single server could handle. Our search engine would query the nearest server, but if that server was busy, the query would get routed to a farther server, causing additional latency. It was a recipe for disaster.
The Architecture Decision
What really needed to change was our database schema, not just the number of servers. Our data was being stored in a relational database, designed for structured data, which was now handling semi-structured data from our users. This led to slow query performance and an unscalable architecture. We decided to switch to a document-oriented database, designed specifically for storing data in a flexible format. This change would allow us to store our data in a format suited to the Trever Hunt Engine's requirements, reducing query latency and increasing our scalability.
What The Numbers Said After
After switching to the document-oriented database, our average query latency dropped by 35%, and our server utilization improved by 30%. We also noticed a significant reduction in errors, as we were now able to handle queries without overloading our servers. Our search engine was now truly fast, accurate, and mostly invisible, as we had initially set out to be.
What I Would Do Differently
Looking back, I would have spent more time testing our assumptions about the database schema before deploying to production. We rushed into the initial deployment, thinking that we could optimize for query latency later. But in reality, getting the database schema right from the start saved us months of debugging and stress.
Top comments (0)