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}}
</ref>
==Improving Performance==
===Horizontal Scaling===
* Load balancing - Distribute incoming requests across multiple server instances using load balancers
* Stateless design - Ensure your API doesn't store any state on local servers so that it could be scaled up easily
* 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/partitioning - 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
* Data archiving - Archive old data to keep active datasets smaller and queries faster.
===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
* Database cache - Stored copy of a query's results that acts as a cache for faster read access
===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
* Response compression - Enable compression to reduce bandwidth usage
===Asynchronous Processing===
* Message queues - Use systems like Apache Kafka, handle tasks asynchronously and avoid blocking
* Event-driven architecture - decouple services using events to reduce direct dependencies
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