Variations & Follow-ups

📖 2 min read 📄 Part 8 of 10

Ad Click Aggregation - Variations and Follow-ups

Common Variations

1. Impression Tracking

  • Track ad impressions (views)
  • Calculate CTR (click-through rate)
  • Viewability tracking
  • Frequency capping

2. Conversion Attribution

  • Track post-click conversions
  • Multi-touch attribution
  • Attribution windows
  • Revenue tracking

3. Real-time Bidding (RTB)

  • Bid request aggregation
  • Win rate calculation
  • Bid optimization
  • Budget pacing

4. Video Ad Analytics

  • Video start/complete tracking
  • Watch time aggregation
  • Quality metrics
  • Engagement tracking

Follow-up Questions

Q: How do you ensure exactly-once semantics? A: Flink checkpointing, idempotent writes, transactional sinks, deduplication with Redis, reconciliation jobs.

Q: How do you detect click fraud? A: Rate limiting per user/IP, bot detection (user-agent, behavior), IP reputation, ML models, pattern analysis.

Q: How do you handle late-arriving clicks? A: Watermarks with grace period, late data side output, batch reconciliation, allowed lateness configuration.

Q: How do you deduplicate clicks? A: Redis cache with click_id (5 min TTL), Bloom filter for pre-check, database unique constraints.

Q: How do you handle high cardinality? A: Pre-aggregation, dimension reduction, sampling for analytics, separate billing pipeline.

Q: How do you ensure billing accuracy? A: Exactly-once processing, reconciliation jobs, audit trails, idempotent operations, transaction logs.

Q: How do you optimize for cost? A: Compression (10:1), tiered storage, sampling for analytics, spot instances, retention policies.

Q: How do you handle traffic spikes? A: Auto-scaling, Kafka buffering, backpressure, rate limiting, circuit breakers.

Edge Cases

Duplicate Clicks

  • Network retries
  • Browser back button
  • Malicious duplication
  • Handling: Deduplication with Redis, click_id tracking

Clock Skew

  • Client time incorrect
  • Server time differences
  • Timezone issues
  • Handling: Server-side timestamps, NTP sync, validation

Fraud Patterns

  • Click farms
  • Bot networks
  • Competitor clicks
  • Handling: ML models, IP reputation, behavior analysis, rate limiting

System Failures

  • Kafka outage
  • Flink job failure
  • Database unavailable
  • Handling: Checkpointing, replay from Kafka, graceful degradation

These variations demonstrate comprehensive understanding of ad click aggregation systems.