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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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==Design==
The design of an API has significant impact on its usage.<ref name="Clarke4"/> The principle of [[information hiding]] describes the role of programming interfaces as enabling [[modular programming]] by hiding the implementation details of the modules so that users of modules need not understand the complexities inside the modules.<ref name="Parnas72">{{Cite journal |last=Parnas |first=D.L. |date=1972 |title=On the Criteria To Be Used in Decomposing Systems into Modules |url=https://www.win.tue.nl/~wstomv/edu/2ip30/references/criteria_for_modularization.pdf |journal=Communications of the ACM |volume=15 |issue=12 |pages=1053–1058 |doi=10.1145/361598.361623|s2cid=53856438 }}</ref> Thus, the design of an API attempts to provide only the tools a user would expect.<ref name="Clarke4" /> The design of programming interfaces represents an important part of [[software architecture]], the organization of a complex piece of software.<ref name="GarlanShaw94">{{Cite journal |last1=Garlan |first1=David |last2=Shaw |first2=Mary |date=January 1994 |title=An Introduction to Software Architecture |url=https://www.cs.cmu.edu/afs/cs/project/able/ftp/intro_softarch/intro_softarch.pdf |journal=Advances in Software Engineering and Knowledge Engineering |volume=1 |access-date=8 August 2016}}</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
==Release policies==
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