Handling High Write Traffic
While most systems are read-heavy, some of the most challenging problems in distributed systems involve handling massive write loads. Every GPS ping from an Uber driver, every click tracked by analytics, every log line from a server, every IoT sensor reading, these are all writes that must be captured reliably at enormous scale.
Here's the uncomfortable truth about writes: you can't cache your way out of them. With reads, you can add more replicas, layer caches in front of caches, and serve slightly stale data when needed. Writes don't offer the same luxury. Every write must eventually hit persistent storage, and that storage becomes your bottleneck. The strategies that work brilliantly for reads simply don't apply.
This fundamental asymmetry between reads and writes is why write-heavy systems require a completely different mental model. The database that handles 100,000 reads per second might only handle 10,000 writes. The architecture that scales reads horizontally hits a wall when you try to scale writes the same way.
Handling high write traffic is a critical pattern in system design interviews. Problems like designing a logging system, analytics pipeline, IoT platform, or real-time bidding system all require deep understanding of write optimization. The interviewer isn't just looking for buzzwords. They want to see that you understand the fundamental constraints and the trade-offs involved in each approach.