The Problem We Were Actually Solving
What started as a basic recommendation engine for users to find relevant products quickly morphed into a behemoth of complexity. We were trying to solve the age-old problem of relevance, but in doing so, we created a system that prioritized novelty over usability. Users can only click so many times before they realize that 'treasure' is just a euphemism for 'algorithmic noise'.
What We Tried First (And Why It Failed)
Our first implementation used a combination of topic modeling and collaborative filtering to generate recommendations. Sounds impressive, right? In practice, it was a nightmare. Our model was prone to overfitting, and our users were subjected to an endless loop of 'recommended' products that were literally the opposite of what they were searching for. We thought we could 'tune' the parameters, but in reality, we were just fine-tuning our ability to create a system that was more confusing by the minute.
The Architecture Decision
One of the most egregious design choices we made was the reliance on Elasticsearch as our sole indexing and querying backend. Now, I love Elasticsearch as much as the next person, but in this case, it was the wrong choice for the wrong problem. As the system scaled, our indexing times ballooned, and our query performance became laughable. We tried to patch it up with various caching layers and data summarization techniques, but it was a Band-Aid on a bullet wound.
What The Numbers Said After
One particularly fateful night, we hit a peak traffic spike of 10,000 concurrent users. Our system, which was supposed to handle it with ease, instead responded with a 5-second query delay and a staggering 20% failure rate. Our monitoring tools lit up like a Christmas tree, and I received a call from the product manager at 3:47 AM, asking why the system was 'broken'. The numbers told the story: our average query latency had jumped from 50ms to 2000ms, and our error rate had skyrocketed from 1% to 10%.
What I Would Do Differently
Looking back, I would have taken a different approach from the get-go. Instead of building a monolithic system that tried to solve every problem at once, I would have taken a more incremental approach. I would have started with a simple, lightweight recommendation engine that focused on basic item-based filtering and then gradually added more sophisticated algorithms as needed. I would have also chosen a different indexing and querying backend that was better suited for our use case – perhaps a distributed, no-SQL database like Couchbase or Riak.
In hindsight, it's clear that Treasure Hunt Engine was a system that optimizes for demos over operations. As engineers, we need to be honest with ourselves about the systems we build and the problems we're trying to solve. Do we really need another 'novelty' feature, or can we focus on building a system that's reliable, maintainable, and actually useful to our users? Only time will tell, but for now, I'm left to wonder what could have been if we'd just taken a simpler approach from the start.
Treated the payment platform as infrastructure. Found the single point of failure. This is the replacement I put in place: https://payhip.com/ref/dev4
Top comments (0)