Node.js backend development
Node.js Performance Optimization: 10 Things Developers Should Know
Node.js Performance Optimization: 10 Things Developers Should Know requires decisions about event-loop delay, CPU profiles, asynchronous I/O, connection reuse, payload size, memory and backpressure. This guide explains the architecture, delivery and production practices needed to achieve a performance plan based on profiles and load tests rather than micro-benchmarks.
Protect the Node.js event loop
Node.js handles many connections efficiently when each callback does a small amount of work. Synchronous filesystem, compression, crypto, large JSON processing and expensive loops can block every request sharing the process.
Measure event-loop delay and CPU profiles under realistic load. Move CPU-heavy work to worker threads or a separate service, bound input sizes and apply backpressure instead of accepting unlimited concurrent work.
Structure Node.js around domains
Keep route handlers thin and place business operations in modules with explicit interfaces. Use TypeScript strict mode plus runtime schemas because compile-time types do not validate JSON, headers, queue messages or environment variables.
Standardize errors, pagination, logging and configuration. A modular monolith is a strong default for a small team and leaves room to extract a service when ownership or scaling makes the boundary valuable.
Run PostgreSQL as the system of record
Use migrations, constraints, transactions and parameterized queries. Design indexes around observed filters and ordering, inspect execution plans and avoid offset pagination for large changing datasets.
Configure connection pools and statement timeouts, monitor slow queries and vacuum behavior, back up data and test restoration. Application scaling should respect database connection and write capacity.
Add Redis for a measured caching need
The cache-aside pattern reads Redis first, falls back to the source database and stores the result with an appropriate TTL. Define invalidation behavior and include tenant and version information in keys so cached data cannot cross security boundaries.
Set memory limits and an eviction policy, monitor hit rate and stale-data incidents, and protect hot keys from stampedes. The application must remain correct when Redis is empty or unavailable.
Scale Node.js without losing work
Keep API processes stateless and place sessions or shared coordination in an external store only when needed. Use health checks, graceful shutdown and load balancing so deployments stop accepting new traffic while in-flight requests finish.
Queues can buffer background work, but backpressure must continue through the system. Check database pools, external rate limits and cache capacity before adding instances because downstream services often become the real bottleneck.