Handle cache-update conflicts in distributed services
Overview
This question evaluates a candidate's competency in distributed systems and data consistency, focusing on cache–database consistency, concurrent write handling, versioning/CAS, distributed locking, cache invalidation strategies, idempotency, and failure recovery in high-throughput services.
Design: Cache–Database Consistency with Concurrent Writers
Context
You operate a high-throughput service using a cache (e.g., Redis) in front of a relational database. Multiple application servers may concurrently update the same logical record (key). You must prevent and resolve cache–database update conflicts and race conditions while keeping performance high.
Requirements
Design how to prevent and resolve conflicts across these areas:
- Write ordering and atomicity
- Versioning/ETags and compare-and-set (CAS)
- Distributed locks (when and how)
- TTLs and cache invalidation vs write-through/write-back
- Idempotent updates and retries
- Recovery after partial failures
Specify:
- APIs (read/write), including headers or RPC fields
- Data model (DB and cache)
- Consistency guarantees
- Trade-offs and when to choose each option
Provide read and write flows, including conflict resolution and failure handling.
Solution
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