Ad Click Aggregation - Scaling Considerations
Horizontal Scaling
Ingestion Scaling
- Load balancer distribution
- Auto-scaling ingestion nodes
- Kafka partitioning (10K partitions)
- Geographic distribution
Processing Scaling
- Flink parallelism (10K tasks)
- State partitioning
- Incremental checkpoints
- Resource isolation
Storage Scaling
- ClickHouse sharding by advertiser_id
- Replication factor 3
- Add nodes for capacity
- Query node pools
Performance Optimization
Ingestion
- Batch processing
- Async validation
- Redis deduplication
- Connection pooling
Aggregation
- Pre-aggregation in Flink
- Tumbling windows
- State backend optimization
- Exactly-once semantics
Storage
- Columnar storage
- Compression (10:1)
- Materialized views
- Partition pruning
Bottleneck Mitigation
High Click Rate
- Horizontal scaling
- Kafka partitioning
- Backpressure handling
- Rate limiting
Fraud Detection
- Async processing
- Bloom filters
- ML model optimization
- Caching
Query Load
- Query result caching
- Pre-aggregated data
- Read replicas
- Connection pooling
Cost Optimization
Compute
- Spot instances for processing
- Right-sizing
- Auto-scaling
- Reserved capacity
Storage
- Compression
- Retention policies
- Tiered storage
- Lifecycle management
This scaling strategy ensures accurate click aggregation at massive scale.