Node.js Performance Optimization: Common Techniques, Key Metrics, and Bottlenecks

Q: The asker wants to learn about Node.js performance optimization, including common techniques, key metrics, and bottlenecks.

A: The answer outlines broad areas such as CPU, memory, I/O, server, and event‑loop optimization, then lists specific practices:

Use streams for large file reads.

Manage streams with pipeline.

Create a multi‑core cluster to increase CPU utilization.

Store large objects in in‑memory databases like Redis.

Tune GC by adjusting the semi‑space.

Employ Nginx for load balancing and serve static assets from a dedicated server.

Use tools such as node‑clinic for detailed performance diagnosis.

Typical performance bottlenecks involve CPU, memory, disk I/O, and business‑logic heavy operations such as intensive computation or massive file reads.

Key performance indicators include Event Loop Delay, memory usage, CPU usage, network and disk utilization, I/O throughput, disk queue length, as well as response time, throughput, and service stability when running Node.js servers.