The question itself wasn't crazy hard but the vibe in the room made it harder than it needed to be.
Start by clarifying the requirements: what exactly are we counting, what scale (events per second, data volume), what accuracy is needed, and what are the latency and consistency requirements. Then propose a high-level architecture that separates the write path (ingestion) from the read path (querying), using scalable components like distributed counters, stream processing, and time-series databases. Finally, discuss trade-offs between accuracy, latency, cost, and complexity, and how you would handle failures and scale.
Pro tip: Demonstrate awareness of real-world constraints by mentioning that perfect accuracy is often unnecessary; approximate counting with probabilistic data structures can drastically reduce resource usage while meeting business needs. Also, highlight the importance of monitoring and alerting on the system itself to detect anomalies.
Ask questions to understand the scale (e.g., events per second, data retention), accuracy requirements (exact vs approximate), latency needs (real-time vs batch), and consistency guarantees. Also consider the specific use case: rate limiting, statistics, or counting.
Propose a distributed system with separate ingestion and query paths. Use a scalable message queue (e.g., Kafka) for ingestion, stream processing (e.g., Flink, Spark Streaming) for aggregation, and a storage layer optimized for time-series or counters (e.g., Redis, Cassandra, Druid).
Detail how counting/rate limiting works: for rate limiting, discuss algorithms like token bucket, sliding window, and their distributed implementation using Redis or a dedicated service. For counting, discuss sharding, aggregation, and using probabilistic data structures (e.g., HyperLogLog, Count-Min Sketch) when exact counts are not needed.
Explain how the system scales horizontally (e.g., partitioning by key, adding nodes) and handles failures (replication, checkpointing, idempotent writes). Discuss how to avoid hot spots and ensure even load distribution.
Compare design choices: exact vs approximate counting, push vs pull for metrics, centralized vs distributed rate limiting. Discuss cost, complexity, and performance implications, and justify your decisions based on the requirements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.