Scaling Considerations

📖 2 min read 📄 Part 6 of 10

Web Analytics Tool - Scaling Considerations

Horizontal Scaling Strategy

Ingestion Layer Scaling

  • Auto-scaling groups for tracking endpoints (100-1000 nodes)
  • Kafka cluster scaling (100+ brokers, 10K partitions)
  • Load balancer distribution across regions
  • CDN for SDK delivery and static assets

Processing Layer Scaling

  • Flink cluster auto-scaling based on lag
  • Spark cluster for batch jobs (5000+ nodes)
  • Separate clusters for real-time vs batch
  • Resource isolation per workload

Storage Layer Scaling

  • ClickHouse sharding by website_id (500+ nodes)
  • Druid cluster scaling (200+ nodes)
  • Redis cluster for caching (200+ nodes)
  • S3 for unlimited cold storage

Query Layer Scaling

  • Query node pools by query type
  • Read replicas for popular queries
  • Query result caching
  • Connection pooling

Performance Optimization

Data Ingestion

  • Batch event processing (20 events per request)
  • Compression (gzip, snappy)
  • Async processing with Kafka
  • Event validation at edge

Query Performance

  • Materialized views for common queries
  • Pre-aggregated data in Druid
  • Query result caching (80% hit rate)
  • Sampling for large datasets

Storage Optimization

  • Columnar storage format
  • Data compression (4:1 ratio)
  • Partitioning by date
  • TTL-based data lifecycle

Bottleneck Mitigation

Hot Partition Problem

  • Consistent hashing for distribution
  • Sub-partitioning by hour
  • Separate high-traffic websites
  • Dynamic rebalancing

Query Overload

  • Query queue management
  • Rate limiting per user
  • Query timeout enforcement
  • Priority queuing

Write Amplification

  • Batch writes to reduce I/O
  • Async replication
  • Write-ahead logging
  • Compaction scheduling

Cost Optimization

Compute Costs

  • Spot instances for batch processing (70% savings)
  • Reserved instances for base load
  • Auto-scaling for variable load
  • Right-sizing instances

Storage Costs

  • Tiered storage (hot/warm/cold)
  • Data compression
  • Lifecycle policies
  • S3 Intelligent-Tiering

Network Costs

  • Regional data processing
  • CDN for static content
  • Compression for data transfer
  • VPC peering for internal traffic

This scaling strategy ensures the system can handle billions of events while maintaining performance and controlling costs.