Metrics Monitoring System - Scaling Considerations
Horizontal Scaling
Scraper Scaling
- Multiple scraper instances with consistent hashing
- Target distribution across scrapers
- Auto-scaling based on target count
- Health checking and failover
Storage Scaling
- Sharding by metric hash
- Replication factor of 3
- Add nodes for capacity
- Automatic rebalancing
Query Scaling
- Query node pool
- Load balancing across nodes
- Query result caching
- Read replicas
Performance Optimization
Write Path
- Batch writes (1000 samples)
- Compression (10:1 ratio)
- Async replication
- Write-ahead logging
Read Path
- Query result caching
- Downsampling for long ranges
- Parallel query execution
- Index optimization
Storage
- Columnar format
- Delta encoding
- Block compression
- Tiered storage
Bottleneck Mitigation
High Cardinality
- Label limits
- Metric relabeling
- Cardinality monitoring
- Drop high-cardinality metrics
Query Load
- Query timeout
- Rate limiting
- Query complexity limits
- Result size limits
Storage Growth
- Retention policies
- Automatic downsampling
- Compaction
- Archival to object storage
Cost Optimization
Compute
- Spot instances for non-critical
- Right-sizing
- Auto-scaling
- Reserved instances
Storage
- Tiered storage
- Compression
- Retention policies
- Object storage for archives
This scaling strategy ensures the monitoring system can grow with demand while controlling costs.