Optimizing Asynchronous Performance in Node.js with Express and PostgreSQL

Modern web applications demand high scalability, yet Node.js–Express–PostgreSQL stacks often struggle under heavy concurrent loads due to inefficient request handling and database interaction. This paper evaluates the effectiveness of three optimization techniques connection pooling, batch processing, and Redis caching to mitigate these issues. Using metrics like response time, throughput, and memory usage, experiments were conducted on a Node.js API connected to a PostgreSQL database. The results demonstrate that Redis caching was the most impactful method. By serving frequently accessed data from memory, it drastically reduced read-latency spikes (e.g., from 30.32s down to 1.66s) and boosted write-operation throughput by 50%. In parallel, connection pooling stabilized system resources (CPU at 5–10%; memory usage between 79–297 MB), and batch processing increased throughput up to 321 req/s.Ultimately, while all methods enhanced scalability, the findings show that prioritizing an in- memory caching strategy is indispensable for building low-latency backends. For optimal performance under high concurrency, combining Redis with connection pooling and batching proves to be a highly effective approach.