62 lines
1.3 KiB
Markdown
62 lines
1.3 KiB
Markdown
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# Scaling Strategy Specification
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## Overview
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Horizontal and vertical scaling strategies for the platform.
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## Horizontal Scaling Patterns
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### Application Scaling
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**Method**: Add more service instances
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**Auto-scaling**: Based on CPU, memory, request rate
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**Load Balancing**: Distribute traffic across instances
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### Database Scaling
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**Read Replicas**: Scale read capacity
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**Sharding**: Partition data for write scaling
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**Connection Pooling**: Efficient connection management
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## Database Scaling
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### Read Replicas
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**Strategy**: Multiple read replicas for read-heavy workloads
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**Replication**: Async replication from primary
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**Use Case**: API read operations
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### Sharding
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**Strategy**: Partition data by chain_id
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**Implementation**: Database partitioning
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**Use Case**: Very large datasets
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## Caching Strategy
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### Redis Cache
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**Use Cases**:
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- API response caching
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- Session storage
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- Rate limiting counters
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- Frequently accessed data
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### CDN Caching
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**Use Cases**:
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- Static assets
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- API responses (short TTL)
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## Load Balancing Configuration
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**Method**: Kubernetes service or external load balancer
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**Algorithm**: Round-robin or least connections
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**Health Checks**: Regular health checks, route to healthy instances
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## References
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- Infrastructure: See `infrastructure.md`
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- CI/CD: See `cicd.md`
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