I started with the event schema and API shape, which felt safe, but I spent way too long there and had to rush through the hard parts.
Start by clarifying requirements: scale (e.g., 1B views/day), latency, accuracy, and privacy constraints. Then design a high-level architecture covering client-side event collection, ingestion pipeline, counting service, storage, and bot filtering, diving into trade-offs for high write volume and data modeling.
Pro tip: Emphasize idempotency and exactly-once semantics in event processing to avoid double-counting, and discuss how to handle late-arriving events with a lambda architecture or stream processing with watermarks.
Ask questions to understand expected traffic (e.g., 1B views/day), latency requirements, accuracy needs, and privacy regulations. Define what constitutes a 'view' (e.g., 3 seconds on page).
Propose a lightweight JavaScript tracker that batches events and sends them asynchronously via beacon API or XHR to avoid blocking page load. Include mechanisms to handle offline scenarios and retries.
Outline a scalable ingestion layer (e.g., API gateway, Kafka) to handle high write volume. Describe stream processing (e.g., Flink, Spark Streaming) for real-time aggregation, bot filtering, and deduplication.
Choose storage for raw events (e.g., S3, HDFS) and aggregated counts (e.g., Cassandra, Redis). Explain how to serve view counts with low latency and handle updates.
Discuss techniques for bot detection (e.g., user-agent analysis, behavioral patterns) and privacy measures (e.g., anonymization, consent management, data retention policies).
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.