Apple·Software Engineer·Onsite - System Design / Architecture·Senior

Senior

Jun 2026

Summary

Apple system design round focused entirely on a high-throughput ad click ingestion and analytics platform. One long question, lots of rabbit holes, and I left feeling like I'd covered maybe 60% of what they actually wanted.

Questions Asked (1)

Design a system that ingests ad click events at very high throughput and exposes aggregated metrics per ad, campaign, and advertiser across multiple time windows (last minute, last hour, last day, all-time). Cover the ingestion pipeline, streaming aggregation with windowed counts, real-time and historical storage, the click event schema, fraud and bot filtering, and how you handle late or out-of-order events.

System DesignTechnical Trade-offsData Modeling

Author's notes

This was basically the entire interview.

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AI HintsAI Generated

Suggested Approach

Start by clarifying requirements (scale, latency, accuracy, cost) and then walk through the end-to-end architecture: ingestion, stream processing with windowed aggregations, storage for real-time and historical queries, and handling of late/out-of-order events. Emphasize trade-offs between latency, accuracy, and cost, and explain how fraud filtering integrates into the pipeline.

Pro tip: Proactively discuss how you would handle late events and ensure exactly-once semantics, as these are common pitfalls in streaming systems. Also, mention that you would monitor data quality and system health with metrics like event lag and aggregation accuracy.

1. Clarify Requirements and Scale

Ask questions to understand expected throughput (e.g., millions of clicks per second), latency requirements (real-time vs. near-real-time), accuracy needs (exact vs. approximate), and query patterns. This shapes the entire design.

2. Design Ingestion Pipeline

Propose a scalable ingestion layer using a distributed message queue (e.g., Kafka) to handle high throughput, with partitioning by ad/campaign ID for parallelism. Include a schema for click events and discuss serialization formats (e.g., Avro, Protobuf).

3. Stream Processing and Windowed Aggregation

Describe a stream processing engine (e.g., Flink, Spark Streaming) that computes windowed counts (tumbling/sliding windows) for last minute, hour, day, and all-time. Explain how to handle late/out-of-order events using watermarks and allowed lateness, and how to maintain state efficiently.

4. Storage and Serving Layer

Design a dual-storage approach: a fast, scalable store for real-time aggregates (e.g., Redis, Cassandra) and a durable store for historical data (e.g., HDFS, S3 with a query engine like Presto). Discuss how to serve queries across time windows and ensure consistency.

5. Fraud and Bot Filtering

Integrate fraud detection in the pipeline, either inline (e.g., rule-based filters, ML models) or as a separate stream that flags and filters events. Discuss trade-offs between latency and accuracy, and how to handle false positives/negatives.

Key Points to Mention

Use of a distributed message queue (e.g., Kafka) for ingestion with partitioning and replication for fault tolerance.
Stream processing with event-time windows, watermarks, and allowed lateness to handle out-of-order events.
Exactly-once processing semantics and idempotent writes to avoid double-counting.
Dual storage: low-latency store for real-time aggregates and a data lake/warehouse for historical queries.
Fraud detection techniques: rule-based (e.g., IP blacklists, click frequency) and ML-based (e.g., anomaly detection).
Scalability and cost trade-offs: approximate algorithms (e.g., HyperLogLog) for cardinality, and tiered storage.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.