Best Practices for Building a High-Performance Data API in Three Steps
Traditional API development needs to deal with server deployment, load balancing and other complex issues, Tinybird provides out-of-the-box solutions:
- Automated API generation: Publish SQL queries directly to REST endpoints with JSON/CSV support
- Dynamic parameter support: Use the
{{参数名}}Syntax enables flexible filtering - Multi-level caching mechanism: Automatically cache hotspot query results and invalidate them automatically when the backend is updated
Realization path:
- Write an analysis query (example):
SELECT * FROM user_clicks WHERE date > {{start_date}} LIMIT {{limit}} - Posted via CLI:
tb pipe publish user_activity.pipe --endpoint /v1/activity - Set up access control:
tb endpoint auth /v1/activity --jwtEnabling JWT Authentication
A social platform used this solution to go live with a real-time interactive data API in just 1 day, with a peak processing capacity of 15,000 RPM and an average latency of <100ms.
This answer comes from the articleTinybird: a platform for rapidly building real-time data analytics APIsThe
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