Responsive Node.js Endpoint Under High Concurrency

Note from the N-iX interview prep — backend performance.

Summary: Use non-blocking async patterns, offload CPU tasks, and scale horizontally.

Key strategies

1. Non-blocking Async I/O:
   • Use async/await for all I/O (DB, external APIs, file system).
   • Avoid Sync functions (readFileSync, crypto.pbkdf2Sync).
   • Use streams for large data (CSV exports) to avoid buffering in memory.
2. CPU-intensive tasks:
   • Move them to Worker Threads or a separate microservice.
   • Use worker_threads or an external job queue (Bull + Redis).
   • Respond immediately with 202 Accepted and process in the background.
3. Connection Pooling:
   • For PostgreSQL, use pg.Pool with a configured max connections.
   • Keep the pool size below the DB’s max to avoid timeouts.
4. Rate Limiting & Backpressure:
   • Apply rate limiting (per IP/user) — see middleware order.
   • Use backpressure signals to slow down clients when overloaded.
5. Horizontal scaling:
   • Run multiple Node instances (cluster mode or PM2).
   • Use a load balancer (NGINX) to distribute requests.
   • Ensure statelessness so any instance can handle any request.
6. Monitoring & Observability:
   • Metrics (req/sec, error rate, latency) via Prometheus.
   • Distributed tracing (OpenTelemetry) to pinpoint bottlenecks.

app.post("/api/report", async (req, res) => {
  const { data } = req.body;
  const job = await queue.add("generate-report", { data });
  res.status(202).json({ jobId: job.id }); // immediate response
});

Real-world result (Seguralta)

Used for monthly commission reports. The endpoint handled 2,500 concurrent requests without blocking the event loop, with average response time < 200ms for the initial 202 Accepted.

Related: middleware chain · index.