Node.js Structured Logging in Production: Pino, Correlation IDs, and Log Aggregation

console.log works until it doesn't. In production, you need to answer questions like: "Why did this request fail for user 4821 at 3:14am?" — and console.log('error:', err) gives you nothing to search, filter, or correlate.

This guide covers structured logging with Pino — the fastest Node.js logger — including request correlation, child loggers, log level management, and shipping logs to your aggregation stack.

Why Pino

Node.js logging libraries range from the veteran (winston) to the minimal (debug). Pino wins for production on throughput:

Library	ops/sec	Notes
pino	~7,000,000	JSON, minimal overhead
winston	~200,000	Flexible, popular
bunyan	~300,000	JSON, older API
console.log	~1,000,000	No structure, no levels

Pino achieves speed by writing JSON synchronously to stdout and delegating all formatting, filtering, and transport to a separate worker process. Your application code never blocks on log I/O.

Basic Setup

npm install pino pino-pretty

// lib/logger.js
const pino = require('pino');

const logger = pino({
  level: process.env.LOG_LEVEL || 'info',

  // In development: pretty-print with colors
  // In production: JSON (pino default)
  transport: process.env.NODE_ENV !== 'production'
    ? { target: 'pino-pretty', options: { colorize: true } }
    : undefined,

  // Redact sensitive fields before logging
  redact: {
    paths: ['req.headers.authorization', 'body.password', 'body.token', '*.creditCard'],
    censor: '[REDACTED]',
  },

  // Base fields on every log entry
  base: {
    pid: process.pid,
    hostname: require('os').hostname(),
    service: process.env.SERVICE_NAME || 'api',
    version: process.env.npm_package_version || 'unknown',
  },
});

module.exports = logger;

Every log entry is now valid JSON:

{
  "level": 30,
  "time": 1711771200000,
  "pid": 12345,
  "hostname": "api-pod-7f9b",
  "service": "api",
  "version": "2.4.1",
  "msg": "Server started"
}

Downstream tools (Loki, Datadog, CloudWatch Insights) can query on any field.

Request Correlation IDs

The single most valuable thing you can add to production logging: a correlation ID that ties every log entry for a single request together.

npm install cls-hooked uuid

// lib/correlation.js
const { createNamespace } = require('cls-hooked');

const ns = createNamespace('request');

function correlationMiddleware(req, res, next) {
  // Accept from upstream (tracing gateway, load balancer) or generate
  const correlationId =
    req.headers['x-correlation-id'] ||
    req.headers['x-request-id'] ||
    require('crypto').randomUUID();

  res.setHeader('x-correlation-id', correlationId);

  ns.run(() => {
    ns.set('correlationId', correlationId);
    ns.set('userId', null); // Set after auth
    next();
  });
}

function getCorrelationId() {
  return ns.get('correlationId');
}

function setUserId(userId) {
  ns.set('userId', userId);
}

module.exports = { correlationMiddleware, getCorrelationId, setUserId };

Now create a child logger that injects correlation context on every entry:

// lib/request-logger.js
const pino = require('pino');
const { getCorrelationId } = require('./correlation');
const baseLogger = require('./logger');

// Proxy that injects correlation ID lazily
const requestLogger = new Proxy(baseLogger, {
  get(target, prop) {
    if (['info', 'warn', 'error', 'debug', 'trace', 'fatal'].includes(prop)) {
      return (obj, msg, ...args) => {
        const correlationId = getCorrelationId();
        const context = correlationId ? { correlationId } : {};

        if (typeof obj === 'string') {
          target[prop]({ ...context }, obj, msg, ...args);
        } else {
          target[prop]({ ...context, ...obj }, msg, ...args);
        }
      };
    }
    return target[prop];
  }
});

module.exports = requestLogger;

Every log line now carries correlationId without any changes to your business logic:

{"level":30,"time":1711771203000,"service":"api","correlationId":"a3f8-4b21","msg":"User profile fetched"}
{"level":50,"time":1711771203042,"service":"api","correlationId":"a3f8-4b21","err":{"message":"DB timeout"},"msg":"Database query failed"}

One correlationId query in Grafana/Datadog shows every log line for the failing request.

HTTP Request Logging with pino-http

npm install pino-http

// app.js
const express = require('express');
const pinoHttp = require('pino-http');
const logger = require('./lib/logger');
const { correlationMiddleware } = require('./lib/correlation');

const app = express();

// Correlation ID must come BEFORE pino-http
app.use(correlationMiddleware);

app.use(pinoHttp({
  logger,

  // Attach correlation ID to request log
  genReqId: (req) => req.headers['x-correlation-id'] || require('crypto').randomUUID(),

  // Log at 'warn' level for 4xx, 'error' for 5xx
  customLogLevel: (req, res, err) => {
    if (err || res.statusCode >= 500) return 'error';
    if (res.statusCode >= 400) return 'warn';
    return 'info';
  },

  // Customize what's logged per request
  serializers: {
    req: (req) => ({
      method: req.method,
      url: req.url,
      userAgent: req.headers['user-agent'],
      // Never log Authorization or Cookie
    }),
    res: (res) => ({
      statusCode: res.statusCode,
      contentLength: res.headers?.['content-length'],
    }),
  },

  // Skip health check noise
  autoLogging: {
    ignore: (req) => req.url === '/health' || req.url === '/ready',
  },
}));

You'll see structured entries like:

{
  "level": 30,
  "time": 1711771200123,
  "reqId": "a3f8-4b21",
  "req": { "method": "GET", "url": "/api/users/42" },
  "res": { "statusCode": 200 },
  "responseTime": 24,
  "msg": "request completed"
}

Child Loggers for Service Context

Child loggers inherit parent fields and add their own — perfect for domain-specific context:

// services/payment-service.js
const logger = require('../lib/logger');

const log = logger.child({ component: 'payment' });

class PaymentService {
  async processPayment(userId, amount, currency) {
    // Child of child — each layer adds context
    const reqLog = log.child({ userId, amount, currency });

    reqLog.info('Payment processing started');

    try {
      const result = await stripe.charges.create({ amount, currency });
      reqLog.info({ chargeId: result.id }, 'Payment succeeded');
      return result;
    } catch (err) {
      // Serialize error correctly — pino handles Error objects natively
      reqLog.error({ err, stripeCode: err.code }, 'Payment failed');
      throw err;
    }
  }
}

Every log from PaymentService carries { component: 'payment', userId, amount, currency } automatically. Searching for component:payment AND level:error shows all payment failures without touching application code.

Log Level Management

Don't hardcode log levels. Runtime log level changes let you debug production issues without redeploys:

// lib/log-level-controller.js
const logger = require('./logger');

// Express route for ops team
function registerLogLevelRoute(app) {
  // GET /admin/log-level
  app.get('/admin/log-level', (req, res) => {
    res.json({ level: logger.level });
  });

  // PUT /admin/log-level  { "level": "debug" }
  app.put('/admin/log-level', (req, res) => {
    const { level } = req.body;
    const validLevels = ['trace', 'debug', 'info', 'warn', 'error', 'fatal'];

    if (!validLevels.includes(level)) {
      return res.status(400).json({ error: 'Invalid level', valid: validLevels });
    }

    const previous = logger.level;
    logger.level = level;

    logger.info({ previous, current: level }, 'Log level changed');
    res.json({ previous, current: level });
  });
}

module.exports = { registerLogLevelRoute };

Protect this endpoint with your admin auth middleware. Bumping to debug in production reveals verbose context for a specific incident, then bumping back to info reduces noise.

Shipping Logs to Aggregation Stacks

Pino writes to stdout by default — the right approach for containers. Your log aggregation layer reads stdout.

Grafana Loki

npm install pino-loki

// logger with Loki transport
const logger = pino({
  level: process.env.LOG_LEVEL || 'info',
  transport: {
    targets: [
      // Always write to stdout (for container logs)
      { target: 'pino/file', options: { destination: 1 } },
      // Also ship to Loki
      {
        target: 'pino-loki',
        options: {
          host: process.env.LOKI_HOST || 'http://loki:3100',
          labels: {
            app: process.env.SERVICE_NAME || 'api',
            env: process.env.NODE_ENV || 'production',
          },
          // Batch settings
          interval: 5, // flush every 5 seconds
          replaceTimestamp: false,
        },
      },
    ],
  },
});

AWS CloudWatch Logs (via stdout + awslogs driver)

{
  "logDriver": "awslogs",
  "options": {
    "awslogs-group": "/ecs/my-service",
    "awslogs-region": "us-east-1",
    "awslogs-stream-prefix": "api"
  }
}

CloudWatch Insights can now query structured JSON directly:

fields @timestamp, service, correlationId, @message
| filter level = "50"
| sort @timestamp desc
| limit 20

Datadog

npm install pino-datadog-transport

transport: {
  target: 'pino-datadog-transport',
  options: {
    ddClientConf: {
      authMethods: { apiKeyAuth: process.env.DD_API_KEY },
    },
    ddServerConf: { site: 'datadoghq.com' },
    service: process.env.SERVICE_NAME,
    ddsource: 'nodejs',
  },
}

Trace-Log Correlation with OpenTelemetry

If you're running distributed tracing (see our OpenTelemetry guide), inject trace context into every log entry:

// lib/traced-logger.js
const { trace, context } = require('@opentelemetry/api');
const baseLogger = require('./logger');

function withTraceContext() {
  const span = trace.getActiveSpan();
  if (!span) return {};

  const { traceId, spanId, traceFlags } = span.spanContext();
  return {
    trace_id: traceId,
    span_id: spanId,
    trace_flags: `0${traceFlags.toString(16)}`,
  };
}

// Mixin that adds OTel context to every log call
const tracedLogger = new Proxy(baseLogger, {
  get(target, prop) {
    if (['info', 'warn', 'error', 'debug', 'trace', 'fatal'].includes(prop)) {
      return (obj, msg, ...args) => {
        const traceCtx = withTraceContext();
        if (typeof obj === 'string') {
          target[prop]({ ...traceCtx }, obj, msg, ...args);
        } else {
          target[prop]({ ...traceCtx, ...obj }, msg, ...args);
        }
      };
    }
    return target[prop];
  }
});

module.exports = tracedLogger;

Now Grafana can jump from a slow trace directly to the correlated log lines.

Error Serialization

Pino ships with a built-in error serializer — always pass errors as the err field:

// ✅ Correct — pino serializes err natively
logger.error({ err }, 'Database query failed');

// ❌ Wrong — loses stack trace and error properties
logger.error(`Database query failed: ${err.message}`);

// ✅ For custom error properties
logger.error({
  err,
  query: sanitizedQuery,
  duration: queryDuration
}, 'Slow query exceeded threshold');

Pino's error serializer captures message, stack, type, and any custom properties on the Error object.

Production Checklist

Practice	Why
JSON output in production	Queryable by Loki/Datadog/CloudWatch
Correlation ID on every request	Reconstruct any request from logs alone
Redact auth headers and passwords	Prevent credential leaks in log stores
Skip health check logging	Reduces noise by 30%+ in k8s
pino-http for HTTP request logs	Automatic timing, status code, method
Child loggers per component	Instant filtering by component
Runtime log level control	Debug production without redeploys
Error as err field	Full stack trace and error properties
Trace ID injection	Jump from trace to logs in Grafana

Summary

The path from console.log to production-grade logging:

1. Install Pino and point it at stdout — it's JSON by default and the fastest option
2. Add correlation IDs via middleware + CLS — every log entry tied to a request
3. Use child loggers per service/component — free filtering in any log aggregator
4. Redact sensitive fields at the logger level — no credential leaks regardless of what code logs
5. Ship to an aggregator (Loki, Datadog, CloudWatch) — stdout is the right interface, your infra handles the rest

The compounding benefit: once you have structured logs with correlation IDs and trace context, debugging a production incident becomes a query instead of a grep session through hundreds of lines of unstructured text.

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This is article 58 in the Node.js Production Series — deep-dive guides on running Node.js at scale.