Data Consistency During Failover and Multi Region Replication Questions
Handling consistency challenges when failing over between regions. Understand synchronous replication (slower, consistent) vs. asynchronous replication (faster, potential data loss). Discuss split-brain scenarios (if communication between regions breaks, how do you prevent two independent systems each thinking they're the primary?). At Staff level, show understanding of tradeoffs and practical operational considerations.
HardSystem Design
17 practiced
Design a robust fencing mechanism that works across heterogeneous systems in your stack: Kubernetes leader pods, a relational DB primary, and a message queue leader. Address atomicity of fencing, cross-system ordering guarantees, failure modes, and automated recovery procedures.
EasyTechnical
30 practiced
Create a concise runbook for a planned failover from primary region to secondary for a service composed of a stateless frontend and a stateful backend. The service SLO is 99.95% uptime, RTO 15 minutes, RPO 1 minute. Include preconditions, verification steps, failover commands, and post-failover validation to ensure data consistency.
MediumSystem Design
24 practiced
Design a multi-region read strategy that provides low read latency and strong read-after-write consistency for users who may move between regions frequently (e.g., mobile users traveling). Describe caching, write-forwarding, session tokens, and the trade-offs in latency, availability, and complexity.
MediumSystem Design
18 practiced
Design an architecture to provide global session stickiness for write operations while allowing reads from any region. Include session token design, storage for session mapping (e.g., regional store vs global store), implications for failover, cache invalidation, and capacity planning for session stores.
HardTechnical
16 practiced
Design anti-entropy processes (e.g., Merkle trees) at petabyte scale across regions. Discuss partitioning strategies, incremental hashing, bandwidth-efficient transfers, scheduling to avoid congestion, prioritizing hot objects, and monitoring to ensure convergence. Include cost and performance trade-offs.
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