Design: Cache-Database Consistency with Concurrent Writers
Context
You operate a high-throughput service utilizing a cache (e.g., Redis) positioned in front of a relational database. Multiple application servers are expected to concurrently update the same logical record (identified by a key).
Problem
Your task is to design a system that effectively prevents and resolves cache-database update conflicts and race conditions, all while maintaining high performance.
Requirements
Design how to prevent and resolve conflicts across the following critical areas:
- Write Ordering and Atomicity: Ensure updates are processed in a defined order and as indivisible units.
- Versioning/ETags and Compare-and-Set (CAS): Incorporate mechanisms for optimistic concurrency control.
- Distributed Locks: Specify when and how distributed locks should be employed.
- TTLs and Cache Invalidation vs. Write-Through/Write-Back: Detail the chosen cache update strategy and its implications.
- Idempotent Updates and Retries: Design for operations that can be safely repeated without unintended side effects.
- Recovery after Partial Failures: Outline strategies for system recovery when components partially fail.
Deliverables
Your design must specify the following:
- APIs: Define read and write APIs, including relevant headers or RPC fields.
- Data Model: Describe the data structures for both the database and the cache.
- Consistency Guarantees: Clearly state the consistency model(s) provided by your design.
- Trade-offs: Discuss the architectural trade-offs made and the conditions under which each option would be chosen.
Provide detailed read and write flows, explicitly including conflict resolution and failure handling mechanisms.
AI interview prep
Core dilemma
Achieving strong cache-database consistency and preventing data corruption under high concurrent write load, while maintaining high performance, availability, and fault tolerance in a distributed system.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Design: Cache-Database Consistency with Concurrent Writers
Context
You operate a high-throughput service utilizing a cache (e.g., Redis) positioned in front of a relational database. Multiple application servers are expected to concurrently update the same logical record (identified by a key).
Problem
Your task is to design a system that effectively prevents and resolves cache-database update conflicts and race conditions, all while maintaining high performance.
Requirements
Design how to prevent and resolve conflicts across the following critical areas:
- Write Ordering and Atomicity: Ensure updates are processed in a defined order and as indivisible units.
- Versioning/ETags and Compare-and-Set (CAS): Incorporate mechanisms for optimistic concurrency control.
- Distributed Locks: Specify when and how distributed locks should be employed.
- TTLs and Cache Invalidation vs. Write-Through/Write-Back: Detail the chosen cache update strategy and its implications.
- Idempotent Updates and Retries: Design for operations that can be safely repeated without unintended side effects.
- Recovery after Partial Failures: Outline strategies for system recovery when components partially fail.
Deliverables
Your design must specify the following:
- APIs: Define read and write APIs, including relevant headers or RPC fields.
- Data Model: Describe the data structures for both the database and the cache.
- Consistency Guarantees: Clearly state the consistency model(s) provided by your design.
- Trade-offs: Discuss the architectural trade-offs made and the conditions under which each option would be chosen.
Provide detailed read and write flows, explicitly including conflict resolution and failure handling mechanisms.