AdvancedTechnical
5 min
Handling Distributed Cache Invalidation
CachingScalabilitySystem Design
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Interview Question
In a high-traffic microservices system using a distributed cache, how do you handle cache invalidation without breaking consistency?
Key Points to Cover
- Apply cache-aside vs write-through vs write-back patterns appropriately
- Use TTLs and versioned keys for expiring stale data
- Leverage pub/sub channels for cross-node invalidation
- Design idempotent writers for consistency guarantees
Evaluation Rubric
Explains cache strategies correctly30% weight
Maintains data integrity across nodes30% weight
Handles cache pressure during high traffic20% weight
Prevents data race conditions20% weight
Hints
- 💡Redis pub/sub, eventual consistency, key versioning.
Common Pitfalls to Avoid
- ⚠️Over-reliance on a single invalidation strategy without considering the nuances of different data access patterns.
- ⚠️Implementing overly aggressive TTLs that lead to frequent cache misses and increased database load.
- ⚠️Neglecting to handle race conditions during cache updates and invalidations, leading to inconsistent states.
- ⚠️Failing to account for network partitions or message broker failures, which can disrupt pub/sub based invalidation.
- ⚠️Caching entire datasets or large objects that are difficult to invalidate granularly, leading to broad, potentially unnecessary invalidation events.
Potential Follow-up Questions
- ❓When would you bypass the cache?
- ❓How to test stale data handling?
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