Top-K Analysis System - Scaling Considerations
Horizontal Scaling
Stream Processing
- Flink parallelism by dimension
- Key-based partitioning
- State partitioning
- Incremental aggregation
Storage Scaling
- Redis cluster sharding
- Replication for availability
- Read replicas for queries
- Consistent hashing
Query Scaling
- API node pool
- Load balancing
- Result caching
- Connection pooling
Performance Optimization
Ingestion
- Batch processing
- Async updates
- Compression
- Efficient serialization
Counting
- Count-Min Sketch (space-efficient)
- Heavy Hitters algorithm
- Incremental updates
- Approximate counting
Query
- Redis sorted sets
- Pre-computed top-K
- Query result caching
- Parallel queries
Bottleneck Mitigation
High Event Rate
- Kafka partitioning
- Flink parallelism
- Sampling for non-critical
- Backpressure handling
Memory Pressure
- Probabilistic structures
- Periodic cleanup
- Sliding windows
- State compression
Query Load
- Caching
- Read replicas
- Rate limiting
- Query optimization
Cost Optimization
Compute
- Right-sizing
- Auto-scaling
- Spot instances
- Resource limits
Storage
- Memory optimization
- Compression
- Retention policies
- Tiered storage
This scaling strategy ensures efficient top-K analysis at massive scale.