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An AI assistant is planned. Until it is genuinely useful we would rather point you at the page that actually answers your question.
Node.js runs JavaScript on the server using a single-threaded event loop with non-blocking I/O. That model suits workloads dominated by waiting — database queries, API calls, file operations — because one process can hold thousands of in-flight operations without a thread for each.
The same model is a poor fit for sustained CPU work. A long synchronous computation blocks the event loop and stalls every other request in that process. Knowing which side of that line a workload sits on is the main architectural decision.
For most web backends the work is I/O-bound, which is exactly where Node performs well. It handles high concurrency on modest hardware, and the ecosystem for HTTP services, queues and database access is mature.
The organisational argument is shared language. One language across frontend and backend means shared types, shared validation logic and engineers who can work either side. On smaller teams that flexibility is worth a lot.
Our approach
We keep the event loop free. CPU-heavy work goes to worker threads or a separate service, and we monitor event loop lag directly — it is the earliest signal that a Node service is in trouble, and it is not visible in CPU or memory graphs.
We handle errors at the boundaries. Unhandled promise rejections and swallowed async errors are the most common source of silent failure in Node systems. Every async boundary has explicit handling, and the process is configured to crash rather than continue in an unknown state.
We validate every input at the edge with a schema. TypeScript types disappear at runtime, so anything arriving from a request, a queue or a third party is parsed and validated before it enters the application. Trusting a declared type on external data is a common and expensive mistake.
Capabilities
REST and GraphQL services on NestJS or Fastify with validation, authentication and structured errors.
Queues and workers with retries, dead-letter handling and idempotency for jobs that must not run twice.
WebSocket services with connection state, reconnection and horizontal scaling across instances.
Typed database access with connection pooling, transactions and query patterns that hold up at volume.
Third-party API clients with retries, circuit breakers and timeouts, so one slow dependency cannot cascade.
Structured logging, tracing and event loop metrics — the signals that make Node problems diagnosable.
Stack
Process
Establishing whether the work is I/O-bound or CPU-bound, since that determines the architecture.
Deciding what belongs in one service and what should be separate, avoiding premature fragmentation.
Schema, indexes and API contracts agreed before implementation.
Schema validation at every external boundary and explicit handling at async boundaries.
Realistic traffic against realistic data volumes, watching event loop lag as the primary signal.
Structured logs, traces and alerts on latency and error rate before the first release.
Use cases
Services powering web and mobile clients where most time is spent waiting on data.
Systems coordinating several third-party APIs, where concurrency and failure isolation matter.
Chat, notifications, live dashboards and collaborative editing over persistent connections.
Batch jobs, imports and exports that must be reliable, resumable and safe to retry.
Outcomes
FAQ
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