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{{hatnote|"Api.php" redirects here. For the Wikipedia API, see {{Plain link|[Special:ApiHelp]}}.|selfref=true}}

An '''application programming interface''' ('''API''') is a connection or fetching, in technical terms, between [[computer]]s or between [[computer program]]s. It is a type of software [[Interface (computing)|interface]], offering a service to other pieces of [[software]].<ref name=":0">{{cite book |last=Reddy |first=Martin |author-link= |date=2011 |title=API Design for C++ |url=https://books.google.com/books?id=IY29LylT85wC |publisher=Elsevier Science |page=1 |isbn=9780123850041}}</ref> A document or standard that describes how to build such a connection or interface is called an ''API specification''. A computer system that meets this standard is said to ''implement'' or ''expose'' an API. The term API may refer either to the specification or to the implementation.

In contrast to a [[user interface]], which connects a computer to a person, an application programming interface connects computers or pieces of software to each other. It is not intended to be used directly by a person (the [[end user]]) other than a [[computer programmer]]<ref name=":0" /> who is incorporating it into software. An API is often made up of different parts which act as tools or services that are available to the programmer. A program or a programmer that uses one of these parts is said to ''call'' that portion of the API. The calls that make up the API are also known as [[subroutine]]s, methods, requests, or [[communication endpoint|endpoint]]s. An API specification ''defines'' these calls, meaning that it explains how to use or implement them.

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===Operating systems===

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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 Strategies===

Load balancing - Distribute incoming requests across multiple server instances using load balancers like HAProxy, NGINX, or cloud solutions (AWS ALB, Google Cloud Load Balancer).

Stateless design - Ensure your API doesn't store session state on individual servers. Use external stores like Redis or databases for session management so any server can handle any request.

Auto-scaling - Implement automatic server provisioning based on metrics like CPU usage, memory consumption, or request queue length. Cloud platforms make this easier with services like AWS Auto Scaling Groups.

Microservices architecture - Break monolithic APIs into smaller, independent services that can be scaled individually based on demand patterns.

Database Scaling

Read replicas - Use read-only database replicas to distribute read queries across multiple database instances.

Database sharding - Partition your data across multiple database instances based on criteria like user ID, geographic ___location, or data type.

Connection pooling - Use connection pools to efficiently manage database connections and prevent connection exhaustion.

Query optimization - Ensure proper indexing, optimize N+1 queries, and use database-specific optimization techniques.

Caching Strategies

Application-level caching - Cache frequently requested data in memory using Redis, Memcached, or in-process caches.

HTTP caching - Implement proper cache headers (Cache-Control, ETag) to enable browser and CDN caching.

CDN usage - Use Content Delivery Networks to cache static content and API responses closer to users geographically.

Database query caching - Cache expensive database queries and invalidate appropriately when data changes.

Asynchronous Processing

Message queues - Use systems like RabbitMQ, Apache Kafka, or AWS SQS to handle time-consuming tasks asynchronously.

Background jobs - Process non-critical operations (emails, reports, data processing) in background workers.

Event-driven architecture - Decouple services using events to reduce direct dependencies and improve scalability.

API Design for Scale

Pagination - Implement cursor-based or offset-based pagination to handle large result sets efficiently.

Rate limiting - Implement throttling to prevent abuse and ensure fair resource usage across clients.

Field selection - Allow clients to specify which fields they need to reduce payload size and processing time.

Bulk operations - Provide endpoints for batch operations to reduce the number of round trips.

Performance Optimization

Response compression - Enable gzip compression to reduce bandwidth usage.

Keep-alive connections - Use HTTP keep-alive to reuse connections and reduce overhead.

Efficient serialization - Consider using efficient serialization formats like Protocol Buffers for internal services.

Resource optimization - Monitor and optimize memory usage, garbage collection, and CPU utilization.

Infrastructure and Deployment

Containerization - Use Docker and orchestration platforms like Kubernetes for easier scaling and deployment.

Circuit breakers - Implement circuit breaker patterns to prevent cascading failures when dependencies are unavailable.

Health checks - Implement comprehensive health checks for load balancers and monitoring systems.

Blue-green deployments - Use deployment strategies that allow zero-downtime updates and quick rollbacks.

Monitoring and Observability

Metrics collection - Track key performance indicators like response times, throughput, error rates, and resource utilization.

Distributed tracing - Use tools like Jaeger or Zipkin to trace requests across multiple services.

Alerting - Set up alerts for critical metrics and anomalies to enable proactive responses.

Performance profiling - Regularly profile your application to identify bottlenecks and optimization opportunities.

Data Management

Data archiving - Archive old data to keep active datasets smaller and queries faster.

Data partitioning - Partition data by time, geography, or other relevant criteria to improve query performance.

Eventual consistency - Accept eventual consistency where strong consistency isn't required to improve performance.

Security at Scale

API gateways - Use API gateways to handle authentication, rate limiting, and routing at scale.

Token-based authentication - Use stateless authentication tokens that don't require server-side session storage.

Input validation - Implement efficient input validation to prevent malicious requests from consuming resources.

Cost Optimization

Resource right-sizing - Monitor actual usage and adjust server sizes accordingly.

Spot instances - Use cheaper spot/preemptible instances for non-critical workloads.

Reserved capacity - Purchase reserved instances for predictable baseline load.

==Release policies==