directus-task-management/docs/architecture/performance-and-scalability-design.md

Performance and Scalability Design

Database Optimization

• Indexing Strategy: Composite indexes on frequently queried combinations
• Partitioning: Tasks partitioned by project and date for large datasets
• Caching Layer: Redis cache for frequently accessed task data
• Query Optimization: GraphQL query complexity limits and batching

API Performance

• Pagination: Cursor-based pagination for large result sets
• Field Selection: GraphQL field selection to minimize data transfer
• Caching: HTTP caching headers and ETags for unchanged data
• Rate Limiting: Per-user and per-agent rate limiting

Integration Performance

• Async Processing: Background jobs for heavy operations (sync, reports)
• Batch Operations: Bulk APIs for multiple task operations
• Event-Driven Updates: WebSocket notifications for real-time updates
• Circuit Breakers: Fallback strategies for external system failures