The Problem We Were Actually Solving
Looking back, I realized we were trying to solve the wrong problem. Our initial design was based on batch processing, where we'd update the search index every hour. The problem was, our users were active 24/7, and their search requests were piling up. By the time we finished updating the index, it was already outdated. We needed something faster, but we didn't know what.
What We Tried First (And Why It Failed)
We tried to scale our batch processing by adding more nodes to the cluster. We added 10 more machines, thinking it would solve the problem. However, it only made things worse. Our pipeline latency went from 12 minutes to 25 minutes, and our query cost increased by 30%. It was a disaster. We were now burning more CPU and memory, and still, our users were getting outdated search results.
The Architecture Decision
I decided to take a step back and reassess our architecture. I realized that we needed a streaming-based solution that would update the search index in real-time. We implemented Apache Flink, which allowed us to process events as soon as they occurred. We also added a caching layer to reduce the query cost. It was a huge risk, but it paid off. Our pipeline latency dropped to 5 seconds, and our query cost decreased by 50%.
What The Numbers Said After
After switching to the streaming architecture, our numbers looked like this: pipeline latency went from 12 to 5 seconds, query cost decreased from $0.25 to $0.12 per query, and our freshness SLA of 15 minutes was met 99.99% of the time. Our users were happy, and our server didn't seem to mind either.
What I Would Do Differently
If I were to do it differently, I would have invested more time in monitoring and debugging the batch processing pipeline. I would have also considered implementing a canary deployment to test the new streaming architecture before rolling it out to production. These small steps would have saved me a lot of headaches and avoided the last-minute changes that introduced more errors. But, as they say, it's always easier to learn from your mistakes.
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