This is the kind of question where you think you know where to start and then realize halfway through that latency changes almost every decision.
Start by clarifying requirements and scale, then propose a streaming architecture that prioritizes low latency at every layer. Focus on trade-offs between latency, accuracy, and cost, and explain how you would handle high cardinality and real-time aggregations.
Pro tip: Emphasize the importance of defining latency SLOs and designing for graceful degradation under load, as Apple values reliability and user experience. Also, mention how you would measure and monitor end-to-end latency to ensure the system meets its goals.
Ask questions to understand expected viewer counts, chat message rates, latency targets, data retention, and accuracy requirements. Establish the scale (e.g., millions of concurrent viewers) and the need for real-time vs. near-real-time.
Propose a streaming data pipeline: ingestion (e.g., Kafka, Kinesis), stream processing (e.g., Flink, Spark Streaming), and serving layer (e.g., in-memory databases, Redis). Ensure each component supports low latency.
Discuss how to model viewer counts and chat volume as time-series data, using windowed aggregations (e.g., tumbling windows for counts per second). Address challenges like late data and exactly-once semantics.
Detail techniques to minimize latency: in-memory processing, push-based updates to clients, edge computing, and avoiding disk I/O. Consider using WebSockets or SSE for real-time delivery.
Discuss trade-offs between latency, accuracy, and cost. Explain how to scale horizontally, handle backpressure, and ensure fault tolerance. Mention monitoring and alerting for latency SLOs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.