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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==
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