The High-Stakes Problem

The "C10k problem"—handling ten thousand concurrent connections on a single server—was once the benchmark for high-performance networking. In 2025, modern hardware renders the raw connection count trivial, yet Node.js applications frequently choke well below this threshold.

The bottleneck is rarely raw CPU speed or available RAM. The bottleneck is the architecture of the Node.js runtime itself. Node.js relies on a single-threaded event loop. While this model excels at I/O-heavy operations by offloading tasks to the OS kernel, it introduces a critical fragility: Event Loop Lag.

If your main thread is blocked for even 50 milliseconds, you aren't just delaying one request; you are queuing every incoming connection behind it. At 10,000 concurrent requests, a 1% failure in loop efficiency cascades into a total service denial (DoS) or unacceptable P99 latency spikes.

To handle 10k concurrency effectively, we must move beyond standard npm start implementations and engineer the runtime environment to maximize the underlying hardware.

Technical Deep Dive: The Solution & Code

To saturate 10Gbps network interfaces or handle 10k concurrent WebSockets/HTTP requests, we must optimize three distinct layers: CPU utilization, the Libuv thread pool, and socket management.

1. Scaling Beyond the Single Thread (Clustering)

A single Node.js instance runs on a single CPU core. On a 64-core server, a default Node app leaves 63 cores idle. We utilize the cluster module to fork the process, allowing the OS to distribute incoming connections across workers via Round Robin (or OS-specific load balancing).

However, basic clustering is insufficient. We need zero-downtime reloads and worker resilience.

// server.js
import cluster from 'node:cluster';
import http from 'node:http';
import os from 'node:os';
import process from 'node:process';

if (cluster.isPrimary) {
  const numCPUs = os.cpus().length;
  console.log(`Primary ${process.pid} is running. Forking ${numCPUs} workers.`);

  // Fork workers.
  for (let i = 0; i < numCPUs; i++) {
    cluster.fork();
  }

  // Resiliency: Replace dead workers immediately
  cluster.on('exit', (worker, code, signal) => {
    console.warn(`Worker ${worker.process.pid} died. Forking replacement...`);
    cluster.fork();
  });
} else {
  // Workers share the TCP connection in this server
  const server = http.createServer((req, res) => {
    // Simulate non-blocking I/O
    res.writeHead(200);
    res.end('Processed by ' + process.pid);
  });

  // Critical: Increase backlog for high concurrency
  server.listen(8000, () => {
    // Note: The backlog parameter (511 default) might need increasing
    // depending on OS sysctl settings (net.core.somaxconn)
  });

  console.log(`Worker ${process.pid} started`);
}

2. Tuning the Libuv Thread Pool

Node.js handles file I/O, DNS lookups, and crypto operations outside the main event loop using the Libuv thread pool. The default pool size is 4.

If you have 10,000 requests and 50 of them require pbkdf2 hashing or fs.readFile, the remaining 9,950 requests requiring pool access will hang, even if the CPU is idle.

We must increase this variable before the thread pool is instantiated (i.e., at the very start of execution).

// index.js (Entry point)
import process from 'node:process';
import os from 'node:os';

// Rule of thumb: CPU Cores + 1, or higher for heavy I/O
// For 10k concurrent requests involving FS/Crypto, we push this limit.
process.env.UV_THREADPOOL_SIZE = Math.min(os.cpus().length * 2, 128); 

import './server.js'; // Import the application logic after setting env

3. Connection Pooling & Keep-Alive Optimization

At 10,000 requests, the overhead of establishing TCP handshakes (SYN/SYN-ACK) is massive. We must utilize persistent connections. However, the default Node.js http.Agent has conservative defaults.

For inter-service communication (microservices) under high load, you must implement a custom agent to prevent socket exhaustion.

import http from 'node:http';

const agent = new http.Agent({
  keepAlive: true,
  // Default is 1000ms. We increase this to reduce handshake overhead.
  keepAliveMsecs: 10000, 
  // Default is Infinity, but in practice, you want to limit this to avoid
  // running out of file descriptors on the host machine.
  maxSockets: 1024, 
  // Maximum number of requests to schedule on the socket before closing.
  maxTotalSockets: 2048, 
});

// Apply this agent to outgoing requests
const req = http.request({
  hostname: 'api.internal-service',
  port: 80,
  agent: agent,
}, (res) => {
  // Handle response
});

Architecture & Performance Benefits

Implementing these optimizations changes the fundamental behavior of the application under load.

  1. Latency Determinism: By parallelizing the event loop across cores via Clustering, P99 latency stabilizes. A heavy calculation on Worker A does not block the Event Loop of Worker B.
  2. Throughput Maximization: Tuning UV_THREADPOOL_SIZE ensures that the C++ bindings in Node.js do not become the bottleneck, allowing the JavaScript layer to accept requests as fast as the network card allows.
  3. Resource Efficiency: Proper Keep-Alive settings reduce CPU usage spent on SSL/TLS handshakes, which are computationally expensive.

This architecture moves the bottleneck away from the application runtime and onto the infrastructure (Load Balancers/Database), which is the correct state for a scalable system.

How CodingClave Can Help

While the code samples above outline the mechanics of concurrency, integrating these patterns into a production environment is fraught with risk. Implementing cluster logic incorrectly can lead to race conditions, shared state corruption, and "zombie" processes. Furthermore, tuning thread pools without deep profiling can lead to context-switching overhead that degrades performance rather than improving it.

Achieving 10,000 concurrent requests is not a coding exercise; it is an architectural discipline.

CodingClave specializes in high-scale Node.js architecture. We assist enterprise teams in transitioning from monolithic, single-core implementations to distributed, multi-threaded systems capable of enterprise-grade throughput.

We invite you to book a technical consultation. Let us audit your current infrastructure and provide a roadmap to scalability that ensures stability under the heaviest loads.