3 API Performance Optimization Documentation

Table of Contents

• 1. Pagination
• 2. Asynchronous Logging
• 3. Caching
• 4. Payload Compression
• 5. Connection Pooling
• Summary
• Conclusion

1. Pagination

Purpose

Pagination helps manage and reduce API load by sending only the necessary data per request, avoiding the transfer of large data volumes all at once.

Implementation

1. Pagination Types:
2. Offset-based: Sends a limited number of records per page with limit and offset parameters. Example: GET /items?limit=10&offset=20.

3. Cursor-based: Uses a reference ID for the next page, ensuring better consistency with large datasets. Example: GET /items?cursor=abc123.

4. Common Parameters:

5. limit: Defines the maximum number of records per page.
6. offset or cursor: Specifies the starting point for the next page of data.

Best Practices

• Limit the maximum number of records per page to prevent large payloads.
• Allow clients to specify the number of records within a predefined range.

2. Asynchronous Logging

Purpose

Synchronous logging can be time-consuming and, if done within the request-response cycle, impacts performance. Asynchronous logging helps avoid blocking, freeing the server to respond quickly to the client.

Implementation

• Asynchronous Logging Libraries: Use libraries like asyncio or configure asynchronous logging in frameworks and languages that support it.
• Logging Queues: Set up a queue system (such as Kafka or RabbitMQ) to record logs outside the request cycle.

Best Practices

• Set log levels properly (debug, info, warning, error) and avoid verbose logging in production.
• Log only essential information to prevent overload and excessive storage usage.

3. Caching

Purpose

Caching stores responses for frequently accessed data, reducing server load and decreasing response times.

Implementation

• Cache Layer: Use tools like Redis or Memcached for caching data.
• HTTP Caching Headers:
• Cache-Control: Defines cache rules for clients and proxies.
• ETag: Allows clients to validate if cached data is still valid.
• Expires: Specifies an expiration date for the cache.

Best Practices

• Consider data volatility when setting cache lifetimes. More dynamic data should have shorter cache durations, while static data can be cached for longer.
• Set up an invalidation system for frequently changing data to prevent stale cache.

4. Payload Compression

Purpose

Payload compression reduces the size of data transferred between client and server, speeding up response times and lowering bandwidth usage.

Implementation

• Compression Methods: Enable GZIP or Brotli on the server to compress JSON and other payload formats.
• Compression Configuration: Most frameworks allow automatic compression for specific content types.

Best Practices

• Compress payloads that exceed a minimum size (e.g., 1 KB) to avoid unnecessary overhead on small data.
• Ensure clients are compatible with the chosen compression through the Accept-Encoding header in the request.

5. Connection Pooling

Purpose

Connection pooling improves efficiency by reusing network connections, reducing latency, and enhancing scalability in high-concurrency scenarios.

Implementation

• Libraries and Tools: Use tools that manage pooling automatically, such as requests.Session in Python or database frameworks with built-in connection pooling.
• Pool Configuration: Set pool size and connection lifetime, adjusting according to system load.

Best Practices

• Monitor pool size and adjust based on load and server resources.
• Configure timeouts for idle connections to prevent resource waste.

Summary

Conclusion

Applying these practices helps reduce resource consumption, improve user experience, and increase API scalability. Proper selection and configuration of each technique depend on the specific environment and application requirements.