Start by clarifying requirements: write throughput, read latency, time range granularity, and consistency needs. Then propose a layered architecture: ingestion via a distributed log (e.g., Kafka) for durability and buffering, stream processing to pre-aggregate counts into time buckets, and a storage layer optimized for time-series reads (e.g., a columnar store or a custom bucketed store). Finally, discuss trade-offs around consistency, cost, and complexity.
Pro tip: Emphasize that pre-aggregation at write time is key to low read latency, but you must handle late-arriving data and out-of-order timestamps gracefully—perhaps with a watermarking strategy and a separate late-data path.
Ask about expected write QPS, number of clients, read latency SLA, time range granularity (e.g., per-minute, per-hour), and consistency requirements (e.g., eventual vs. strong).
Propose a pipeline: clients send increments to a distributed message queue (e.g., Kafka) for durability and buffering; stream processors consume and aggregate into time buckets; aggregated data is stored in a read-optimized store.
Design a schema for time-bucketed counts (e.g., per-minute buckets per metric) and decide on pre-aggregation levels (e.g., minute, hour, day) to balance storage and read performance.
Discuss strategies like watermarks, allowed lateness, and a separate late-data path that updates aggregates, ensuring correctness without sacrificing read latency.
Explain how to serve arbitrary time range queries efficiently: use a query layer that merges pre-aggregated buckets, possibly with caching, and returns results with low latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.