# Scaling Strategy Specification

## Overview

Horizontal and vertical scaling strategies for the platform.

## Horizontal Scaling Patterns

### Application Scaling

**Method**: Add more service instances
**Auto-scaling**: Based on CPU, memory, request rate
**Load Balancing**: Distribute traffic across instances

### Database Scaling

**Read Replicas**: Scale read capacity
**Sharding**: Partition data for write scaling
**Connection Pooling**: Efficient connection management

## Database Scaling

### Read Replicas

**Strategy**: Multiple read replicas for read-heavy workloads
**Replication**: Async replication from primary
**Use Case**: API read operations

### Sharding

**Strategy**: Partition data by chain_id
**Implementation**: Database partitioning
**Use Case**: Very large datasets

## Caching Strategy

### Redis Cache

**Use Cases**:
- API response caching
- Session storage
- Rate limiting counters
- Frequently accessed data

### CDN Caching

**Use Cases**:
- Static assets
- API responses (short TTL)

## Load Balancing Configuration

**Method**: Kubernetes service or external load balancer
**Algorithm**: Round-robin or least connections
**Health Checks**: Regular health checks, route to healthy instances

## References

- Infrastructure: See `infrastructure.md`
- CI/CD: See `cicd.md`