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API Performance

REST API Performance principles​

Efficient Resource Utilization

  • Minimize unnecessary API requests by using caching, batch requests, and other techniques to reduce server load
  • Use asynchronous processing to reduce resource utilization
  • Use a streamlined API architecture that allows for efficient resource utilization

Efficient Logic, queries

  • Use an efficient query structure that optimizes the API performance
  • Utilize indexing to improve query performance
  • Utilize stored procedures to minimize the amount of data that is sent over the network

Efficient Storage, read/write

  • Use efficient storage technologies such as NoSQL databases to minimize the need for disk access
  • Utilize stream processing to reduce the amount of data stored on disk
  • Use compression techniques to reduce the size of data stored on disk

Concurrency (Hardware/Software)

  • Utilize multithreading to reduce the need for context switching between tasks
  • Use concurrent programming techniques such as locks and semaphores to efficiently manage concurrent processes
  • Utilize distributed systems to scale up concurrent processing

Caching (Hardware/Software)

  • Utilize caching techniques such as in-memory data stores and query caches to improve response times

REST API Performance tips​

  • Use caching to reduce response time and server load.
  • Use efficient algorithms and data structures to minimize processing time.
  • Use appropriate HTTP response codes to indicate the status of the request.
  • Avoid overuse of nested resources and filtering.
  • Use pagination for large result sets.
  • Optimize the payload size by compressing data or removing unused fields.
  • Use versioning to maintain backward compatibility.
  • Use Asynchronous processing for long-running tasks.
  • Monitor API usage and performance regularly.
  • Implement proper error handling and logging for debugging.
  • Design with security in mind, protecting against common vulnerabilities such as SQL injection, cross-site scripting, and cross-site request forgery.
  • Use standard protocols and data formats, such as HTTP and JSON, for consistency and ease of integration.
  • Avoid using complex query parameters and instead use a simple, consistent URL structure.
  • Test performance under heavy load and optimize accordingly.
  • Offer flexible ways to authenticate, such as OAuth or API keys.
  • Offer both synchronous and asynchronous options for processing requests.
  • Consider using a load balancer to distribute incoming requests and prevent a single point of failure.
  • Implement rate limiting to prevent abuse and protect the system from overuse.
  • Offer multiple response formats, such as JSON, XML, or CSV, for flexibility.
  • Optimize database queries for speed and efficiency.

GraphQL API Performance tips​

  • Use batching to reduce the number of round-trips to the server.
  • Use pagination for large result sets.
  • Avoid deep nesting of fields and use aliases to rename fields.
  • Use data loaders for efficient batch data fetching.
  • Use caching to reduce response time and server load.
  • Validate inputs to minimize server-side processing.
  • Consider using a specialized GraphQL server and cache solutions.
  • Implement proper error handling and logging for debugging.
  • Test performance under heavy load and optimize accordingly.
  • Avoid over-fetching or under-fetching of data by designing the schema and queries carefully.
  • Design with security in mind, such as protecting against malicious queries and excessive resource usage.
  • Offer flexible ways to authenticate, such as JSON web tokens or API keys.
  • Use subscriptions for real-time updates and minimize the usage of long-polling or WebSockets.
  • Implement optimized query execution, such as by using persisted queries or batching.
  • Use fragmentation to split large schemas into smaller, manageable parts.
  • Monitor performance and track resolver execution time to identify performance bottlenecks.