Ad Click Aggregation - Interview Tips
Interview Approach
1. Clarify Requirements (5 minutes)
Key Questions:
- Click volume: Clicks per second?
- Accuracy: Exactly-once for billing?
- Latency: Real-time or batch acceptable?
- Fraud: How important is fraud detection?
- Dimensions: What aggregation dimensions?
2. High-Level Design (10 minutes)
Components:
- Click ingestion (CDN, API)
- Message queue (Kafka)
- Stream processing (Flink)
- Storage (ClickHouse)
- Fraud detection
- Dashboard/API
3. Deep Dive (20 minutes)
Focus Areas:
- Exactly-once semantics
- Deduplication strategy
- Fraud detection approach
- Aggregation windows
- Scaling strategy
4. Tradeoffs (10 minutes)
Discuss:
- Exactly-once vs at-least-once
- Real-time vs batch
- Accuracy vs latency
- Cost vs performance
Common Pitfalls
Don't
- Ignore exactly-once requirement for billing
- Forget about fraud detection
- Overlook deduplication
- Ignore late-arriving data
- Forget about cost optimization
Do
- Emphasize billing accuracy
- Discuss fraud prevention
- Consider deduplication carefully
- Think about failure modes
- Mention reconciliation
Key Topics
Exactly-Once Processing
- Flink checkpointing
- Idempotent writes
- Transactional sinks
- Deduplication
Fraud Detection
- Rate limiting
- Bot detection
- IP reputation
- ML models
Aggregation
- Tumbling windows
- State management
- Late data handling
- Pre-aggregation
Scaling
- Kafka partitioning
- Flink parallelism
- Storage sharding
- Cost optimization
Strong Signals
Technical Depth
- Understand stream processing
- Know exactly-once semantics
- Familiar with fraud detection
- Understand distributed systems
System Thinking
- Consider billing accuracy
- Think about fraud
- Discuss failure modes
- Consider cost
Sample Answers
Q: How do you ensure exactly-once for billing? A: Flink exactly-once with checkpointing, idempotent writes to ClickHouse, deduplication with Redis, reconciliation jobs.
Q: How do you detect fraud? A: Rate limiting per user/IP, bot detection via user-agent and behavior, IP reputation, ML models, pattern analysis.
Q: How do you handle 10M clicks/sec? A: Kafka with 10K partitions, Flink cluster with 500 nodes, ClickHouse with 100 nodes, horizontal scaling, compression.
This structured approach demonstrates comprehensive understanding of ad click aggregation systems.