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* Auto-scaling - adjusts the number of computing resources to match the current workload demand
* Microservices architecture - Break monolithic APIs into smaller, independent services that can be scaled individually based on demand patterns
===Database Scaling===
* Replica Server - Increase read-only database replicas to distribute read queries across multiple database instances
* Database sharding - Partition your data across multiple database instances based on criteria
* Connection pooling - Use connection pools to efficiently manage database connections
* Query optimization - Create indexes on columns frequently used in WHERE clauses, JOIN conditions, and ORDER BY clauses
* Use NoSQL - New nodes can be added on the fly without worrying about the consistency of data
===Caching Strategies===
Memory Cache - Cache frequently requested data in memory using Redis, Memcached
Content Delivery Networks - cache static content and API responses closer to users geographically
Client-Side Caching - Let the browser or mobile app cache responses, use Last-Modified headers
===API Design for Scale===
Pagination - Implement cursor-based or offset-based pagination to handle large result sets efficiently
Rate limiting - Implement throttling ensure fair resource usage across clients
Field selection - Allow clients to specify which fields they need to reduce payload size and processing time
Multiple operations - Provide endpoints for batch operations to reduce the number of api calls
==Documentation==
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