Distributed Systems: Communication, Consensus, and Replication

Design a leader-follower replication strategy, explaining failover and read scaling trade-offs.

Compare strong, eventual, and causal consistency models with practical examples.

Explain the distributed consensus problem and the role of the Raft algorithm in solving it.

Implement vector clocks to track causality between distributed events.

Explain the architecture of a log-based message queue (e.g., Kafka) and its delivery semantics (at-least-once, exactly-once).

Explain the split-brain problem and how quorum-based strategies can prevent it.

Implement the two-phase commit (2PC) protocol and analyze its blocking failure modes.

Apply the Saga pattern for managing long-running, distributed transactions across microservices.