Structured logging with pino: JSON logs, log levels, and child loggers

Application logs are unstructured plaintext strings. Searching for a specific error in production requires grep with complex regex patterns, and aggregating statistics (how many 500 errors per hour) is impossible without parsing log lines.

Use pino for structured JSON logging. Every log line becomes a machine-parseable JSON object with consistent fields.

const pino = require('pino');

const logger = pino({
  level: process.env.LOG_LEVEL || 'info',
  // In development, use pino-pretty for human-readable output
  // In production, output raw JSON
  transport: process.env.NODE_ENV !== 'production'
    ? { target: 'pino-pretty', options: { colorize: true } }
    : undefined,
  base: {
    service: 'order-service',
    version: process.env.APP_VERSION,
    environment: process.env.NODE_ENV,
  },
});

// Child loggers inherit fields and add their own
const requestLogger = logger.child({ requestId: req.id, userId: req.user?.id });
requestLogger.info({ orderId: '123' }, 'Order created successfully');
// Output: {"level":30,"service":"order-service","requestId":"abc","userId":"u1","orderId":"123","msg":"Order created successfully"}

// Log errors with the err field — pino serializes Error objects correctly
try { await processOrder(); }
catch (err) { requestLogger.error({ err }, 'Order processing failed'); }

JSON logs are trivially queryable by log aggregation systems (Loki, Elasticsearch, Datadog). Adding a field to every log line in a request context is O(1) with child loggers — no global state needed.

Setting up production-grade logging in a Node.js service