The Problem We Were Actually Solving
From what I could tell, we weren't just dealing with a slow search algorithm or inefficient database queries. Our users were simply looking for different ways to filter their results, and our serverless setup wasn't optimized for these ad-hoc queries. It seemed like every time our users asked for something different, our system would have to start from scratch, re-run the query, and cache the results all over again. This was causing a significant increase in latency, memory usage, and ultimately, downtime.
What We Tried First (And Why It Failed)
My initial approach was to implement caching at the database level, hoping to alleviate some of the load on our serverless functions. We installed Redis and configured it to store frequently requested search results. This seemed like a straightforward solution, but what I failed to consider was the additional complexity it introduced. Now we had to manage yet another system, with its own set of configuration files, update scripts, and potential points of failure.
The Architecture Decision
After re-evaluating our setup, I decided to implement a more robust caching strategy that leveraged our existing infrastructure. We integrated our search engine with our content delivery network (CDN) to cache search results at the edge, where they could be served directly to users without having to hit our serverless functions. This reduced the load on our system significantly, and search times were now consistently under 2 seconds.
What The Numbers Said After
With the new caching strategy in place, our system was now able to handle searches with an average latency of 150ms, a 95% reduction from the previous 30 minutes. Our users were no longer experiencing downtime due to slow searches, and our team was able to focus on more critical tasks.
What I Would Do Differently
If I were to do it all over again, I would have implemented the CDN caching strategy from the start. The added complexity of managing Redis and its associated overhead would have been avoided, and we could have saved ourselves a significant amount of development time and resources. Additionally, I would have considered implementing a hybrid caching strategy that utilized both Redis and the CDN, depending on the specific requirements of our search queries. This would have allowed us to get the best of both worlds and achieve even better results.
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