Eliminating lock starvation in a high-concurrency service
before100%
after0%
How replacing a single global Redis lock with an append-only event log removed lock starvation and write races in a high-concurrency service.
/distributed-systems
Distributed systems that stay correct at peak traffic
High-concurrency systems fail in the gaps: a global lock that starves writers, a race between two services, a shard that runs hot. I design the data models and coordination patterns that remove those gaps, then prove them under real traffic.
What I've done
- Replaced a single global lock with an append-only event log in a high-concurrency service, taking lock starvation from 100% to 0% at peak.
- Led the design of a lock-free data pipeline for real-time model ingestion.
- Designed a sharding and hybrid storage migration (object storage + relational) for high-frequency event data.
- Decoupled message delivery across 30+ microservices with zone-aware routing, validated through fault injection.
- Led monolith decompositions, including replacing a monolithic request flow with a plug-in API gateway.