The Day Our Treasure Hunt Engine Blew Up at 3 AM (And How We Rebuilt It Right)

The Problem We Were Actually Solving Our event platform at Veltrix ran a treasure hunt game that gave users real-world rewards. It started as a simple Rails app with a PostgreSQL counter column for each hunt. By 3 AM on Black Friday, that counter column became a single point of failure. Every leaderboard update blocked the entire leaderboard query because PostgreSQL row-level locks escalated to table-level for SERIAL columns. Our error rate jumped from 0.2% to 18% under 2000 concurrent writes. The system didnt just slow down; it started failing writes with could not serialize access due to concurrent update deadlocks. We lost $47K in rewards payouts before we could scale up the database. What We Tried First (And Why It Failed) Our first fix was to shard the PostgreSQL counter by hunt ID, splitting the hot row into 1024 partitions. That reduced the lock contention, but introduced new problems. Each hunt now needed its own sequence, and our Rails code had to route writes to the correct shard. The shard routing introduced 400ms extra latency on leaderboard queries because we had to union results across 1024 tables. Meanwhile, PostgreSQL sequences had gaps up to 1024 when nodes restarted, so our reward payouts were off by thousands on high-traffic hunts. Our Redis cache didnt help because the leaderboard queries were point lookups against 1024 tables, and Redis couldnt pipeline those efficiently. The Architecture Decision We ripped out the PostgreSQL counter and replaced it with a Kafka Streams-based event sourcing system called HuntStream. Every hunt action (point earned, reward claimed) became an immutable event in a Kafka topic. We built a materialized view on top of RocksDB that consumed the topic and maintained the current leaderboard state in memory. The materialized view was partitioned by hunt ID, which meant leaderboard queries only hit one RocksDB partition per hunt. We used RocksDBs built-in caching to keep hot leaderboards in memory, and fall back to disk fo