The Problem We Were Actually Solving
At first glance, the problem seemed simple: build a fast and accurate search engine for a treasure hunt page. However, as we dug deeper, we realized that accuracy was just one of many requirements. The real problem was to minimize timeouts and maximize the quality of search results within a short time frame. The e-commerce platform's massive product catalog and our limited processing power created a ticking time bomb: a single misconfiguration could lead to a catastrophic failure of the entire system.
What We Tried First (And Why It Failed)
Initially, we built our search engine using a combination of Elasticsearch and OpenCV. We thought that the cutting-edge library would provide the best results in a short amount of time. However, we soon encountered a slew of issues. The sheer complexity of OpenCV's architecture caused long processing times and unpredictable behavior. Moreover, the library's tendency to "hallucinate" and produce irrelevant search results led us further away from our goal. With the OpenCV integration gone, we decided to pivot to an alternative solution.
The Architecture Decision
After reevaluating our requirements, we decided to use a simple yet effective combination of Apache Lucene and a custom indexing solution. We built a custom indexing job that ran overnight to update our search index. This solution allowed us to minimize latency and maintain high accuracy. Lucene, on the other hand, handled the actual search queries with remarkable efficiency. We also implemented an in-memory caching layer to reduce the load on our Elasticsearch cluster. When faced with a particularly tricky configuration, I had to make a choice between sacrificing accuracy for speed or the other way around.
What The Numbers Said After
After deploying the new solution, we noticed a significant decrease in timeouts and latency. Our average search response time dropped from 500ms to under 100ms, allowing users to find the treasures they sought within a few seconds. Moreover, our search accuracy saw a modest improvement, from 80% to 90%, due to the better indexing solution. While not perfect, our solution passed the crucial test: it didn't fail catastrophically under load.
What I Would Do Differently
If I were to do this project again, I would focus more on the caching strategy and experiment with different indexing solutions. I would also invest more time in testing our solution with different volumes of data and user workflows. Our custom indexing solution was effective, but it also introduced new challenges when it came to data drift and maintenance. I would also explore hybrid solutions that combine the strengths of different technologies, such as using a graph database in conjunction with Lucene.
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