We'd been running the Veltrix-powered treasure hunt engine for six months, and it had been a resounding success. The documentation said it was scalable, and I'd watched as our traffic grew from a few thousand users to tens of thousands without a hiccup. Then, one fateful night, our logs started filling up with error messages and our users started complaining about a "server not found" error.
The Problem We Were Actually Solving
We were trying to create a seamless user experience for our treasure hunt game, where users could search for hidden clues and pieces of treasure. But what we were actually solving was a problem that had nothing to do with scaling our infrastructure or optimizing our database queries – we were solving the problem of how to convince our business stakeholders that our technology was worth investing in.
What We Tried First (And Why It Failed)
The Veltrix documentation told us to use the "cache-as-you-go" strategy, where we would cache frequently accessed game data in a Redis instance. We figured this would reduce the load on our database and make the game more responsive for our users. But as our traffic grew, it became clear that we were just pushing the problem down the line – instead of caching frequently accessed data, we were now caching infrequently accessed data, which led to a 30% spike in Redis errors.
The Architecture Decision
So we decided to pivot and go with a full-on caching strategy using a combination of Redis and an in-memory caching layer provided by our application server. We thought this would reduce the load on our database and make the game even more responsive for our users. But what we didn't realize was that we were now creating a dependency on our application server's memory, which was not designed to handle such a large cache. As our cache grew, it started to swamp the server's memory, causing it to page out to disk and leading to a 90% spike in page faults.
What The Numbers Said After
After running our new caching strategy for two weeks, we noticed a 40% drop in database queries, which was great. But we also saw a 60% increase in Redis errors and a 90% spike in page faults, which was not great. The numbers were telling us that we'd fixed one problem, but created another one that was more severe.
What I Would Do Differently
If I had it all to do over again, I would have listened more closely to the warning signs that our "cache-as-you-go" strategy was not working. I would have done more testing and analysis to understand the root causes of our problems, rather than just trying to fix the symptoms. And I would have chosen a caching strategy that was more robust and fault-tolerant, rather than one that created a dependency on our application server's memory.
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