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 processin...

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Bibliographic Details
Published in:Procedia computer science Vol. 269; pp. 172 - 181
Main Authors: Imam Sakti, Muhammad Dhitan, Dirgantara, Daniel, Ramadhan, Aldiaz Kusuma, Ibrahim, Muhammad Amien
Format: Journal Article
Language:English
Published: Elsevier B.V 2025
Subjects:
Asynchronous performance
Caching
Connection pooling
Express
Node.js
PostgreSQL
Query optimization
Redis
Asynchronous performance
Express
Caching
PostgreSQL
Redis
Node.js
Connection pooling
Query optimization
ISSN:1877-0509, 1877-0509
Online Access:Get full text
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Summary: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.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2025.08.270