Testing, Debugging, Logging, and Observability

Build reliable Node.js applications with unit tests, integration tests, debugging, structured logging, metrics, traces, and health checks.

Unit and Integration Testing

Unit tests verify small pieces of logic in isolation. Integration tests verify interactions between components such as APIs, databases, queues, and external services.

A good test suite balances speed, coverage, and confidence. Not every line requires a unit test; important business behavior and integration boundaries deserve particular attention.

Node.js Test Example

import assert from 'node:assert/strict';
import test from 'node:test';

function add(a, b) {
  return a + b;
}

test('adds two numbers', () => {
  assert.equal(add(2, 3), 5);
});

💡 Node.js includes a built-in test runner that can be used for straightforward unit tests.

Key Takeaways

Unit tests focus on isolated behavior.
Integration tests validate collaboration between real components.
Tests should verify behavior rather than implementation details.

Knowledge Check

What is an integration test primarily concerned with?

Production Logging

Production applications should use structured logs containing fields such as timestamp, severity, service, request ID, user or correlation identifiers where appropriate, and useful error metadata.

Avoid logging passwords, access tokens, session cookies, API keys, and other sensitive information. JSON logs are convenient for centralized log systems.

Redact Secrets

Never log authorization headers, passwords, cookies, private keys, refresh tokens, or other sensitive credentials.

Structured Logger

function log(level, message, metadata = {}) {
  console.log(JSON.stringify({
    timestamp: new Date().toISOString(),
    level,
    message,
    ...metadata
  }));
}

log('info', 'Request completed', {
  requestId: 'req-123',
  statusCode: 200
});

💡 Structured logs make application events easier to search, filter, aggregate, and analyze.

Key Takeaways

Use structured logs for production systems.
Include correlation identifiers when tracing requests across services.
Redact sensitive values before logging.

Knowledge Check

Which value should normally never be logged in plaintext?

Metrics, Tracing, and Health Checks

Observability combines logs, metrics, and traces to explain system behavior. Metrics answer questions such as how many requests are arriving, how long they take, and how many fail. Distributed tracing follows a request across services.

Health endpoints should distinguish between basic process liveness and readiness to receive traffic.

Observability Pipeline

Key Takeaways

Logs, metrics, and traces provide complementary observability.
Readiness and liveness checks answer different operational questions.
Correlation IDs make distributed debugging significantly easier.

Knowledge Check

What does distributed tracing help you understand?

Unit and Integration Testing

Production Logging

Metrics, Tracing, and Health Checks

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