I started with caching and indexing and felt okay for the first few minutes, but then they pushed on consistency guarantees during high load and I fumbled a bit.
Start by clarifying requirements and constraints (e.g., read/write ratio, consistency level, latency SLA) to frame the design. Then propose a high-level architecture that separates reads and writes, uses caching and sharding for scalability, and employs replication and consensus for consistency. Finally, discuss trade-offs and how you would validate the design under peak load.
Pro tip: Emphasize that consistency is not binary; propose a tunable consistency model (e.g., quorum reads/writes) and explain how it aligns with eBay's business needs (e.g., inventory accuracy vs. search freshness).
Ask about data volume, read/write patterns, latency SLAs, consistency requirements, and peak traffic characteristics (e.g., flash sales).
Sketch a layered design: API gateway, query service, caching layer, distributed database (e.g., sharded SQL or NoSQL), and message queue for async updates.
Explain how to scale reads (replicas, caching) and writes (sharding, partitioning), and how to handle peak traffic (auto-scaling, rate limiting, backpressure).
Discuss replication strategies (sync vs. async), consensus protocols (Raft/Paxos), and how to achieve tunable consistency (e.g., quorum).
Summarize trade-offs (e.g., latency vs. consistency, cost vs. performance) and propose monitoring, load testing, and failure injection to validate the design.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.