Node.js Cluster Workers FAQ & Answers

Cluster mode spawns multiple Node.js processes (workers) that share the same server port, utilizing all CPU cores. Use cluster for: I/O-intensive applications (web servers, REST APIs), scaling HTTP servers across cores, process isolation (crash in one worker doesn't kill others). Pattern: const cluster = require('cluster'); const numCPUs = require('os').cpus().length; if (cluster.isMaster) {for (let i = 0; i < numCPUs; i++) cluster.fork();} else {app.listen(3000);}. Master process distributes incoming connections to workers via round-robin. Each worker is separate process with own V8 instance and memory space. Workers can't share memory (use Redis for shared state). Memory: Each worker uses ~30-50MB base + application memory. Use PM2 for production cluster management: pm2 start app.js -i max.

Worker threads run JavaScript in parallel threads within same process, sharing memory via SharedArrayBuffer. Use worker threads for: CPU-intensive JavaScript (data processing, compression, encryption), parallel computations, background tasks without blocking event loop. Pattern: const {Worker} = require('worker_threads'); const worker = new Worker('./heavy-task.js', {workerData: inputData}); worker.on('message', (result) => console.log(result)). Each thread shares same V8 instance but has own event loop and heap. Can transfer data via postMessage or share memory via SharedArrayBuffer. Memory efficient: Each thread uses ~2-10MB vs ~30-50MB per cluster process. DON'T use for: I/O operations (event loop already handles efficiently), HTTP servers (use cluster instead). Threads can't share TCP handles (can't share ports).

Cluster processes use 10x more memory than worker threads. Cluster: Each worker is separate process with full Node.js runtime, own V8 heap, own require() cache, own global objects. Memory per worker: ~30-50MB base + application code (100MB total per worker). 8 workers = ~800MB. Worker threads: Share single V8 instance, share require() cache and global code. Memory per thread: ~2-10MB for thread-specific heap. 8 threads = ~100MB total (base) + thread heaps. Example: Express API with 4 workers in cluster = 4 × 100MB = 400MB. Same with 4 worker threads = 100MB + 4 × 5MB = 120MB. Trade-off: Cluster provides process isolation (crash isolation, easier debugging), threads provide memory efficiency. Choose cluster for scaling servers, threads for parallel computations.

Yes, worker threads can share memory using SharedArrayBuffer and Atomics. Pattern: const shared = new SharedArrayBuffer(1024); const worker = new Worker('./worker.js', {workerData: {shared}}); const view = new Int32Array(shared); Atomics.add(view, 0, 1). Main thread and worker both access same memory buffer. Atomics provide thread-safe operations: Atomics.add, Atomics.store, Atomics.load, Atomics.wait, Atomics.notify. Use for: Lock-free data structures, shared counters, inter-thread coordination. Limitations: Only typed arrays (Int32Array, Float64Array), not objects/strings directly. For complex data: Use MessagePort with postMessage (structured clone, copies data). SharedArrayBuffer best for high-frequency numeric data sharing. Cluster processes cannot share memory (use Redis/external store). Worker threads unique feature for shared-memory parallelism.

Yes, always use cluster (or PM2) for production HTTP servers to utilize all CPU cores and provide crash resilience. Benefits: (1) Utilizes all cores (Node.js is single-threaded, cluster creates one process per core), (2) Zero-downtime deploys (restart workers one at a time), (3) Automatic restart on crash (worker dies, master spawns new one), (4) Load distribution (master distributes requests across workers). Implementation: Use PM2 (recommended): pm2 start app.js -i max (spawns worker per core). Or manual cluster: Master process forks workers, workers listen on shared port. 4-core server: 1 process serves 1 core, 4 workers serve 4 cores = 4x throughput. Without cluster: Only 1 core utilized, crash kills entire server. Container environments: Let orchestrator handle scaling (Kubernetes replica sets), run 1 process per container.

Cluster mode spawns multiple Node.js processes (workers) that share the same server port, utilizing all CPU cores. Use cluster for: I/O-intensive applications (web servers, REST APIs), scaling HTTP servers across cores, process isolation (crash in one worker doesn't kill others). Pattern: const cluster = require('cluster'); const numCPUs = require('os').cpus().length; if (cluster.isMaster) {for (let i = 0; i < numCPUs; i++) cluster.fork();} else {app.listen(3000);}. Master process distributes incoming connections to workers via round-robin. Each worker is separate process with own V8 instance and memory space. Workers can't share memory (use Redis for shared state). Memory: Each worker uses ~30-50MB base + application memory. Use PM2 for production cluster management: pm2 start app.js -i max.

Worker threads run JavaScript in parallel threads within same process, sharing memory via SharedArrayBuffer. Use worker threads for: CPU-intensive JavaScript (data processing, compression, encryption), parallel computations, background tasks without blocking event loop. Pattern: const {Worker} = require('worker_threads'); const worker = new Worker('./heavy-task.js', {workerData: inputData}); worker.on('message', (result) => console.log(result)). Each thread shares same V8 instance but has own event loop and heap. Can transfer data via postMessage or share memory via SharedArrayBuffer. Memory efficient: Each thread uses ~2-10MB vs ~30-50MB per cluster process. DON'T use for: I/O operations (event loop already handles efficiently), HTTP servers (use cluster instead). Threads can't share TCP handles (can't share ports).

Cluster processes use 10x more memory than worker threads. Cluster: Each worker is separate process with full Node.js runtime, own V8 heap, own require() cache, own global objects. Memory per worker: ~30-50MB base + application code (100MB total per worker). 8 workers = ~800MB. Worker threads: Share single V8 instance, share require() cache and global code. Memory per thread: ~2-10MB for thread-specific heap. 8 threads = ~100MB total (base) + thread heaps. Example: Express API with 4 workers in cluster = 4 × 100MB = 400MB. Same with 4 worker threads = 100MB + 4 × 5MB = 120MB. Trade-off: Cluster provides process isolation (crash isolation, easier debugging), threads provide memory efficiency. Choose cluster for scaling servers, threads for parallel computations.

Yes, worker threads can share memory using SharedArrayBuffer and Atomics. Pattern: const shared = new SharedArrayBuffer(1024); const worker = new Worker('./worker.js', {workerData: {shared}}); const view = new Int32Array(shared); Atomics.add(view, 0, 1). Main thread and worker both access same memory buffer. Atomics provide thread-safe operations: Atomics.add, Atomics.store, Atomics.load, Atomics.wait, Atomics.notify. Use for: Lock-free data structures, shared counters, inter-thread coordination. Limitations: Only typed arrays (Int32Array, Float64Array), not objects/strings directly. For complex data: Use MessagePort with postMessage (structured clone, copies data). SharedArrayBuffer best for high-frequency numeric data sharing. Cluster processes cannot share memory (use Redis/external store). Worker threads unique feature for shared-memory parallelism.

Yes, always use cluster (or PM2) for production HTTP servers to utilize all CPU cores and provide crash resilience. Benefits: (1) Utilizes all cores (Node.js is single-threaded, cluster creates one process per core), (2) Zero-downtime deploys (restart workers one at a time), (3) Automatic restart on crash (worker dies, master spawns new one), (4) Load distribution (master distributes requests across workers). Implementation: Use PM2 (recommended): pm2 start app.js -i max (spawns worker per core). Or manual cluster: Master process forks workers, workers listen on shared port. 4-core server: 1 process serves 1 core, 4 workers serve 4 cores = 4x throughput. Without cluster: Only 1 core utilized, crash kills entire server. Container environments: Let orchestrator handle scaling (Kubernetes replica sets), run 1 process per container.