I jumped straight to a distributed queue setup and then realized mid-explanation that ordering across multiple producers is actually the tricky part.
Start by clarifying the requirements: what 'order' means (global vs per-source), expected throughput, latency, durability, and scale. Then propose a design that uses a monotonic sequence number or timestamp assigned at ingestion, with a distributed log like Kafka or a database with auto-increment, and discuss trade-offs between ordering guarantees, scalability, and fault tolerance.
Pro tip: Emphasize that strict global ordering often requires a single writer or consensus, which limits scalability; propose a hybrid approach like per-partition ordering with a global merge, and discuss how to handle out-of-order messages due to network delays.
Ask about ordering scope (global vs per-source), throughput, latency, durability, and consistency needs. This ensures you design the right system.
Propose an architecture: ingestion service assigns sequence numbers, messages stored in a distributed log (e.g., Kafka) or database, consumers read in order. Mention components like load balancers, producers, and storage.
Explain how to assign order: use a centralized sequencer, timestamp with logical clocks, or per-partition ordering with a global merge. Discuss trade-offs (e.g., single point of failure vs scalability).
Address partitioning, replication, and handling failures. How to maintain order during scaling or node failures? Consider consensus algorithms like Raft or Paxos for coordination.
Compare approaches: strict global ordering vs eventual ordering, latency vs throughput, and cost. Mention when to relax ordering for performance.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.