1. Database Indexing:
- I analyzed our MongoDB query patterns and added appropriate indexes to speed up frequent read and write operations. This reduced query response times significantly, particularly for the most commonly accessed endpoints.
2. Horizontal Scaling:
- I set up a load balancer to distribute incoming requests across multiple instances of our Node.js application. This ensured that our API could handle increased traffic without becoming a bottleneck.
3. Caching Layer:
- I introduced a Redis-based caching layer to store frequently accessed data, such as configuration settings and user session data. This reduced the load on our primary database and improved response times for cached requests.
4. Rate Limiting:
- To prevent abuse and ensure fair usage, I implemented rate limiting using a library like
express-rate-limit. This helped to throttle excessive requests from individual clients, protecting the API from potential denial-of-service attacks.
5. Asynchronous Processing:
- For long-running tasks, I moved processing to background jobs using a message queue (bulljs). This ensured that the API could respond quickly to client requests by offloading intensive tasks to separate worker processes.
6. Performance Monitoring and Alerts:
- I set up performance monitoring using tools like New Relic and Sentry to track key metrics such as response times, error rates, and server load. I also configured alerts to notify the team of any performance degradation or anomalies.
These optimizations collectively improved the API's ability to handle a high volume of requests, increasing throughput, while maintaining low latency and reliability.