CAP Theorem, Distributed Systems Design Decisions, and Multithreading

Context

Provide a structured answer that both explains the concepts and shows how you have applied them in real systems. Use concrete examples, trade-offs, and specific techniques you used to build, operate, and optimize distributed and multithreaded systems.

Part 1 — CAP Theorem and Practical Application

1. Explain the CAP theorem (define Consistency, Availability, Partition Tolerance).
2. In a system you designed, during a network partition, which two properties did you prioritize and why?
3. What trade-offs did you accept (e.g., stale reads, write unavailability, failover behavior), and how did you mitigate or monitor them?

Part 2 — Distributed Systems Experience

Describe:

• Architectures you used (e.g., microservices, event-driven, leader–follower, leaderless/quorum, stream processing).
• Key challenges you faced and how you addressed them:
  • Consistency models and correctness (e.g., linearizability, eventual consistency, session consistency, transactions).
  • Partition tolerance and failure modes (e.g., AZ/regional splits, retries, idempotency, backpressure).
  • Scaling and performance (e.g., sharding, hot keys, caching, queues).
  • Fault tolerance and resilience (e.g., circuit breakers, hedged requests, bulkheads).
  • Observability and operations (e.g., metrics, tracing, SLOs, on-call, runbooks).

Part 3 — Multithreaded Applications

Describe:

• Synchronization primitives you used (e.g., mutexes, read–write locks, semaphores, atomics, condition variables, barriers, channels/queues).
• How you avoided deadlocks and data races (design patterns, lock ordering, immutability, tooling).
• How you debugged concurrency issues (tools, techniques, reproductions).
• Performance optimizations (reducing contention, lock-free/low-contention structures, batching, avoiding false sharing).