Design Ad Click Aggregator

What is Ad Click Aggregator?

An Ad Click Aggregator is a real-time system that processes millions of ad clicks per second to detect fraud, generate analytics, and provide insights to advertisers. It's similar to systems used by Google Ads, Facebook Ads, or any digital advertising platform.

Real-time fraud detection and analytics processing under massive click volume is what makes systems like Ad Click Aggregator unique. By understanding this system, you can tackle interview questions for similar real-time analytics platforms, since the core design challenges—stream processing, anomaly detection, high throughput, and low latency—remain the same.

Functional Requirements

Core (Interview Focussed)

• Real-time Click Processing: Process millions of clicks per second with sub-second latency.
• Fraud Detection: Detect suspicious click patterns and bot traffic in real-time.
• Analytics Aggregation: Generate real-time metrics like CTR, conversion rates, and cost per click.
• Click Tracking: Track and store click events with metadata for analysis.

Out of Scope

• Ad serving and placement
• User authentication and authorization
• Payment processing
• Historical data archival
• Machine learning model training

Non-Functional Requirements

Core (Interview Focussed)

• High throughput: Process millions of clicks per second.
• Low latency: Detect fraud and generate analytics within seconds.
• Scalability: Handle traffic spikes during peak advertising hours.
• Accuracy: Minimize false positives in fraud detection.

Out of Scope

• Data retention policies
• Compliance and privacy regulations

💡 Interview Tip: Focus on high throughput, low latency, and accuracy. Interviewers care most about stream processing, real-time analytics, and fraud detection algorithms.

Core Entities

💡 Interview Tip: Focus on Clicks, Analytics, and FraudEvent as they drive real-time processing, aggregation, and fraud detection.

Core APIs

Click Processing

• POST /clicks { ad_id, user_id, ip_address, user_agent, timestamp } – Record a click event
• GET /clicks/{click_id} – Get details of a specific click

Analytics

• GET /analytics/{ad_id}?time_window=&metric_type= – Get real-time analytics for an ad
• GET /analytics/campaign/{campaign_id} – Get campaign-level analytics

Fraud Detection

• GET /fraud/events?advertiser_id=&time_range= – Get fraud events for an advertiser
• POST /fraud/whitelist { ip_address, advertiser_id } – Whitelist an IP address

High-Level Design

Key Components

• Click Ingestion: High-throughput API to receive click events
• Stream Processor: Real-time processing engine for click analysis
• Fraud Detection Engine: ML-based system to detect suspicious patterns
• Analytics Aggregator: Real-time metrics calculation and storage
• Cache Layer: Fast access to recent analytics and fraud data
• Database: Persistent storage for clicks, analytics, and fraud events
• Message Queue: Decouple click ingestion from processing

Mapping Core Functional Requirements to Components

Detailed Design

Click Ingestion Service

Purpose: High-throughput API to receive and validate click events.

Key Design Decisions:

• Load Balancing: Distribute incoming clicks across multiple ingestion servers
• Validation: Basic validation of click data before processing
• Batching: Batch clicks for efficient downstream processing
• Circuit Breaker: Prevent system overload during traffic spikes

Algorithm: Click validation and batching

1. Validate click data (required fields, format)
2. Add metadata (server timestamp, request_id)
3. Batch clicks by time window (e.g., 100ms)
4. Send batch to message queue
5. Return acknowledgment to client

Stream Processing Engine

Purpose: Real-time processing of click streams for analytics and fraud detection.

Key Design Decisions:

• Windowing: Process clicks in time windows (e.g., 1-minute windows)
• Parallel Processing: Process multiple streams in parallel
• State Management: Maintain sliding windows for fraud detection
• Backpressure: Handle traffic spikes gracefully

Algorithm: Real-time click aggregation

1. Receive click batches from message queue
2. Group clicks by ad_id and time window
3. Calculate metrics (count, unique users, CTR)
4. Update analytics cache
5. Send to fraud detection engine
6. Store in database for persistence

Fraud Detection Engine

Purpose: Detect suspicious click patterns and bot traffic in real-time.

Key Design Decisions:

• Rule-based Detection: Fast detection using predefined rules
• ML-based Detection: Advanced pattern recognition
• Threshold Management: Dynamic thresholds based on historical data
• Real-time Alerts: Immediate notification of fraud events

Algorithm: Fraud detection rules

1. Check IP frequency (clicks per IP per minute)
2. Check user agent patterns (bot signatures)
3. Check click timing patterns (too regular)
4. Check geographic anomalies (impossible travel)
5. Calculate fraud score
6. Generate fraud event if score > threshold

Analytics Aggregator

Purpose: Calculate and store real-time analytics metrics.

Key Design Decisions:

• Incremental Updates: Update metrics incrementally
• Multiple Time Windows: Support different aggregation periods
• Cache Strategy: Keep recent metrics in cache
• Data Consistency: Ensure accurate metrics across components

Algorithm: Real-time metrics calculation

1. Receive aggregated click data
2. Calculate metrics:
   - Click count
   - Unique users
   - Click-through rate (CTR)
   - Cost per click (CPC)
3. Update cache with new metrics
4. Store in database for persistence
5. Send to dashboard/API consumers

Database Design

Click Events Table

Indexes:

• idx_ad_timestamp on (ad_id, timestamp) - Fast queries by ad and time
• idx_ip_timestamp on (ip_address, timestamp) - Fraud detection queries

Analytics Table

Indexes:

• idx_ad_time on (ad_id, time_window) - Fast analytics by ad and time
• idx_type_time on (metric_type, time_window) - Queries by metric type

Fraud Events Table

Indexes:

• idx_timestamp on (timestamp) - Time-based queries
• idx_fraud_type on (fraud_type) - Queries by fraud type

Scalability Considerations

Horizontal Scaling

• Click Ingestion: Scale horizontally with load balancers
• Stream Processing: Partition streams by ad_id for parallel processing
• Fraud Detection: Scale ML inference with model serving infrastructure
• Analytics: Shard analytics by time windows and ad_id

Caching Strategy

• Redis: Cache recent analytics and fraud data
• CDN: Cache static analytics dashboards
• Application Cache: Cache frequently accessed fraud rules

Performance Optimization

• Connection Pooling: Efficient database connections
• Batch Processing: Process clicks in batches for efficiency
• Async Processing: Non-blocking click processing pipeline
• Resource Monitoring: Monitor CPU, memory, and network usage

Monitoring and Observability

Key Metrics

• Throughput: Clicks processed per second
• Latency: End-to-end processing time
• Fraud Detection Rate: Percentage of clicks flagged as fraud
• System Health: CPU, memory, and disk usage

Alerting

• High Latency: Alert when processing time exceeds threshold
• Fraud Spike: Alert when fraud rate increases significantly
• System Errors: Alert on processing failures
• Resource Exhaustion: Alert on high resource usage

Trade-offs and Considerations

Consistency vs. Availability

• Choice: Eventual consistency for analytics, strong consistency for fraud detection
• Reasoning: Analytics can tolerate slight delays, fraud detection needs immediate accuracy

Latency vs. Throughput

• Choice: Optimize for throughput with batching
• Reasoning: Fraud detection needs to process high volumes quickly

Accuracy vs. Performance

• Choice: Use rule-based detection for speed, ML for accuracy
• Reasoning: Balance between real-time detection and accurate fraud identification

Common Interview Questions

Q: How would you handle a sudden spike in click volume?

A: Implement auto-scaling, circuit breakers, and backpressure mechanisms to handle traffic spikes gracefully.

Q: How do you ensure fraud detection accuracy?

A: Use multiple detection methods (rules + ML), continuous model retraining, and feedback loops from confirmed fraud cases.

Q: How would you scale this system globally?

A: Deploy regional processing centers, use geo-distributed databases, and implement data replication strategies.

Q: How do you handle false positives in fraud detection?

A: Implement confidence scoring, manual review workflows, and feedback mechanisms to improve detection accuracy.

Key Takeaways

1. Real-time Processing: Stream processing is essential for handling high-volume click data
2. Fraud Detection: Multiple detection methods provide better accuracy and coverage
3. Scalability: Horizontal scaling and partitioning are crucial for handling traffic spikes
4. Performance: Caching and batch processing optimize system performance
5. Monitoring: Comprehensive monitoring ensures system reliability and performance