Structured logging: JSON logs for machine-readable observability

Text-based logs are difficult to search, filter, and aggregate in log management systems. Finding all errors for a specific user or request requires complex regex.

Use structured JSON logging with a library like pino:

import pino from 'pino';

const logger = pino({
  level: process.env.LOG_LEVEL || 'info',
  // Pretty print in development
  transport: process.env.NODE_ENV === 'development'
    ? { target: 'pino-pretty' }
    : undefined,
  base: {
    service: 'myapp',
    version: process.env.APP_VERSION,
    env: process.env.NODE_ENV,
  },
});

// Use structured fields, not string interpolation
// BAD:
logger.info(`User ${userId} created order ${orderId}`);

// GOOD:
logger.info({ userId, orderId, amount }, 'Order created');

// Output: {"level":"info","userId":"u_123","orderId":"o_456","amount":1999,"msg":"Order created"}

// Request logging middleware
app.use((req, res, next) => {
  const start = Date.now();
  res.on('finish', () => {
    logger.info({
      method: req.method,
      path: req.path,
      status: res.statusCode,
      durationMs: Date.now() - start,
      correlationId: req.headers['x-correlation-id'],
    }, 'HTTP request');
  });
  next();
});

JSON logs allow log aggregators (Datadog, CloudWatch, Loki) to index individual fields. You can query 'all errors where userId = X in the last hour' in seconds instead of grep-ing through files.