Logging Observability

Logging and Observability

Structured Logging

Always use structured logging (JSON format) instead of unstructured text. Structured logs are machine-parseable, searchable, and can be aggregated across services.

Standard Log Entry Format

{
  "timestamp": "2024-03-15T14:30:22.123Z",
  "level": "INFO",
  "message": "Order processed successfully",
  "service": "order-service",
  "version": "1.4.2",
  "correlationId": "req-abc123",
  "userId": "user-456",
  "orderId": "ord-789",
  "durationMs": 234,
  "environment": "production"
}

Required Fields

Every log entry must include:

Recommended Fields

Implementation Examples

// Node.js with pino
const pino = require('pino');
const logger = pino({
  level: process.env.LOG_LEVEL || 'info',
  formatters: { level: (label) => ({ level: label.toUpperCase() }) },
  timestamp: pino.stdTimeFunctions.isoTime,
  base: { service: 'order-service', version: process.env.APP_VERSION || 'unknown' },
});

logger.info({ orderId: 'ord-789', durationMs: 234 }, 'Order processed successfully');
logger.error({ err, orderId: 'ord-789' }, 'Failed to process order');

# Python with structlog
import structlog
structlog.configure(processors=[
    structlog.processors.TimeStamper(fmt="iso"),
    structlog.processors.add_log_level,
    structlog.processors.JSONRenderer(),
])
logger = structlog.get_logger(service="order-service")
logger.info("order_processed", order_id="ord-789", duration_ms=234)

Log Levels Usage Guide

Choose the correct log level based on the audience and urgency:

Level Selection Rules

• If it means waking someone up at 3 AM, it is ERROR.
• If it means something is degraded but not broken, it is WARN.
• If it is the normal outcome of a user or system action, it is INFO.
• If it would help debug a problem but is too noisy for production, it is DEBUG.
• If you would only want it while stepping through code, it is TRACE.

What to Log and What NOT to Log

Log These Events

Never Log These

Correlation IDs and Request Tracing

A correlation ID (also called request ID or trace ID) links all log entries for a single request, even across multiple services.

Middleware Implementation

const { randomUUID } = require('crypto');

function correlationIdMiddleware(req, res, next) {
  const correlationId = req.headers['x-correlation-id'] || randomUUID();
  req.correlationId = correlationId;
  res.setHeader('x-correlation-id', correlationId);
  req.log = logger.child({ correlationId }); // Bind to logger context
  next();
}
app.use(correlationIdMiddleware);

// Usage in route handlers
app.get('/api/orders/:id', async (req, res) => {
  req.log.info({ orderId: req.params.id }, 'Fetching order');
  const order = await orderService.getById(req.params.id);
  res.json(order);
});

Propagation Across Services

Forward the correlation ID in outgoing requests:

async function callPaymentService(orderId, correlationId) {
  return fetch('https://payment-service/api/charge', {
    method: 'POST',
    headers: { 'Content-Type': 'application/json', 'x-correlation-id': correlationId },
    body: JSON.stringify({ orderId }),
  }).then(r => r.json());
}

Distributed Tracing

Core Concepts

OpenTelemetry Setup

const { NodeSDK } = require('@opentelemetry/sdk-node');
const { getNodeAutoInstrumentations } = require('@opentelemetry/auto-instrumentations-node');
const { OTLPTraceExporter } = require('@opentelemetry/exporter-trace-otlp-http');

const sdk = new NodeSDK({
  traceExporter: new OTLPTraceExporter({
    url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT || 'http://localhost:4318/v1/traces',
  }),
  instrumentations: [getNodeAutoInstrumentations()],
  serviceName: 'order-service',
});
sdk.start();

Custom Spans

const { trace } = require('@opentelemetry/api');
const tracer = trace.getTracer('order-service');

async function processOrder(orderId) {
  return tracer.startActiveSpan('processOrder', async (span) => {
    try {
      span.setAttribute('order.id', orderId);
      const order = await tracer.startActiveSpan('validateOrder', async (child) => {
        const result = await validateOrder(orderId);
        child.end();
        return result;
      });
      await tracer.startActiveSpan('chargePayment', async (child) => {
        await chargePayment(order);
        child.end();
      });
      span.setStatus({ code: trace.SpanStatusCode.OK });
    } catch (error) {
      span.setStatus({ code: trace.SpanStatusCode.ERROR, message: error.message });
      span.recordException(error);
      throw error;
    } finally {
      span.end();
    }
  });
}

Metrics

Metric Types

Implementation Example (Prometheus Client)

const client = require('prom-client');

// Counter
const httpRequestsTotal = new client.Counter({
  name: 'http_requests_total',
  help: 'Total number of HTTP requests',
  labelNames: ['method', 'path', 'status'],
});

// Gauge
const activeConnections = new client.Gauge({
  name: 'active_connections',
  help: 'Number of active database connections',
});

// Histogram
const requestDuration = new client.Histogram({
  name: 'http_request_duration_seconds',
  help: 'HTTP request duration in seconds',
  labelNames: ['method', 'path'],
  buckets: [0.01, 0.05, 0.1, 0.25, 0.5, 1, 2.5, 5, 10],
});

// Middleware to collect metrics
app.use((req, res, next) => {
  const end = requestDuration.startTimer({ method: req.method, path: req.route?.path || req.path });
  res.on('finish', () => {
    end();
    httpRequestsTotal.inc({ method: req.method, path: req.route?.path || req.path, status: res.statusCode });
  });
  next();
});

// Metrics endpoint
app.get('/metrics', async (req, res) => {
  res.set('Content-Type', client.register.contentType);
  res.end(await client.register.metrics());
});

Alert Design Principles

Well-designed alerts reduce noise and improve response times.

Principles

Alert Template

alert: HighErrorRate
expr: rate(http_requests_total{status=~"5.."}[5m]) / rate(http_requests_total[5m]) > 0.05
for: 5m
labels:
  severity: critical
  team: backend
annotations:
  summary: "High 5xx error rate on {{ $labels.service }}"
  description: |
    Error rate is {{ $value | humanizePercentage }} (threshold: 5%).
    Service: {{ $labels.service }}
    Environment: {{ $labels.environment }}
  dashboard: "https://grafana.example.com/d/http-overview"
  runbook: "https://wiki.example.com/runbooks/high-error-rate"

Health Check Endpoints

Implement three standard health check endpoints:

Implementation

app.get('/health', (req, res) => {
  res.json({ status: 'ok', version: process.env.APP_VERSION });
});

app.get('/ready', async (req, res) => {
  const checks = {
    database: await ping(db, 'SELECT 1'),
    cache: await ping(redis, 'PING'),
  };
  const allHealthy = Object.values(checks).every(c => c.status === 'ok');
  res.status(allHealthy ? 200 : 503).json({ status: allHealthy ? 'ready' : 'not_ready', checks });
});

app.get('/live', (req, res) => {
  res.json({ status: 'ok', uptime: process.uptime() });
});

async function ping(client, command) {
  try {
    await client.query(command);
    return { status: 'ok' };
  } catch (error) {
    return { status: 'error', message: error.message };
  }
}

Log Aggregation Platforms

Choosing a Platform

• Small team, AWS-native: CloudWatch Logs with Insights queries.
• Multi-cloud or hybrid: Datadog or self-hosted ELK.
• Already using Grafana for metrics: Add Loki for logs.
• Enterprise with compliance needs: Splunk or Datadog.

Observability Checklist

When implementing observability for a new service:

• Structured JSON logging with consistent field names.
• Log levels used appropriately (see table above).
• Correlation IDs generated and propagated across services.
• No secrets, PII, or credentials in log output.
• Health check endpoints implemented (/health, /ready, /live).
• Key metrics exposed (request rate, error rate, duration histograms).
• Distributed tracing configured with OpenTelemetry.
• Alerts defined for error rate, latency, and availability.
• Every alert linked to a runbook.
• Dashboards created for service overview and key flows.
• Log retention and rotation configured.
• Log aggregation pipeline verified end-to-end.

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