Start by clarifying requirements (scale, latency, accuracy) and then propose a high-level architecture with WebSocket ingestion, a stream processing layer, and a storage/query layer. Focus on the core algorithmic challenge of maintaining top-K busiest cells efficiently under high throughput, and discuss trade-offs between different approaches.
Pro tip: Emphasize that the top-K problem can be solved with a combination of a hash map for counts and a min-heap of size K, but also mention that for sliding windows or approximate results, data structures like count-min sketch or t-digest can be used. Show awareness of the CAP theorem and how it affects consistency vs. availability in a real-time system.
Ask about the number of drivers, update frequency, expected QPS, latency requirements, and accuracy needs. This will guide design decisions.
Outline components: WebSocket servers for ingestion, a stream processing system (e.g., Kafka + Flink), a storage layer for cell counts (e.g., Redis), and a query service for top-K.
Explain how to map geographic coordinates to cell IDs (e.g., geohash, S2, or custom grid). Discuss partitioning strategies to distribute load across nodes.
Describe algorithms for maintaining top-K busiest cells: using a min-heap of size K with a hash map for counts, or approximate methods for scalability. Discuss sliding windows and time decay.
Discuss trade-offs: exact vs. approximate, push vs. pull for updates, consistency vs. availability, and how to handle failures and scale.
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Start by defining the problem both algorithms solve: achieving consensus in a distributed system. Then explain each algorithm's core mechanism and roles, and finally compare their design philosophies, ease of understanding, and practical trade-offs.
Pro tip: Emphasize that Raft was designed for understandability, while Paxos is more general but harder to implement correctly. Mention real-world systems like etcd (Raft) and Chubby (Paxos) to show practical awareness.
Briefly explain why consensus is needed in distributed systems: to ensure all nodes agree on a single value despite failures.
Describe the roles (proposers, acceptors, learners) and the two-phase protocol (prepare/promise, accept/accepted). Mention that Paxos is notoriously difficult to understand and implement.
Describe Raft's leader election, log replication, and safety mechanisms. Highlight its strong leadership and simpler state space compared to Paxos.
Contrast their design goals: Paxos is foundational and flexible but complex; Raft is engineered for understandability and used in production systems like etcd and Consul.
Explain when to choose each: Paxos for high-performance, custom consensus (e.g., Google Spanner); Raft for easier implementation and maintenance (e.g., Kubernetes, Uber's own systems).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.