AdvancedTechnical
5 min
Ensuring Data Consistency Across Microservices
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Interview Question
Your application spans multiple microservices with separate databases. How would you ensure data consistency while maintaining scalability?
Key Points to Cover
- Differentiate between strong, eventual, and causal consistency
- Use distributed transactions only when necessary (e.g., 2PC)
- Prefer event-driven models with idempotent consumers
- Implement compensating transactions for rollback scenarios
- Leverage CDC pipelines to synchronize cross-service data
Evaluation Rubric
Explains consistency vs availability trade-offs30% weight
Identifies appropriate distributed data patterns30% weight
Ensures idempotency and resilience20% weight
Designs for high scalability20% weight
Hints
- 💡CAP theorem, Saga pattern, CDC pipelines.
Common Pitfalls to Avoid
- ⚠️Over-reliance on distributed transactions (2PC) leading to performance bottlenecks and reduced availability.
- ⚠️Assuming all data requires strong consistency, leading to unnecessary complexity and tight coupling.
- ⚠️Lack of idempotency in event consumers, causing duplicate data or incorrect state updates.
- ⚠️Not implementing proper error handling and retry mechanisms for event consumption, leading to data loss.
- ⚠️Failing to consider the network partition problem and how it impacts distributed systems when designing for consistency.
Potential Follow-up Questions
- ❓When would you use two-phase commit?
- ❓How do you detect event replay issues?
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