Observability

Do NOT trigger this skill for:

• Pure infrastructure provisioning (Terraform, Kubernetes YAML) - those are ops/IaC concerns
• Application performance profiling of CPU/memory at the code level (use a performance-engineering skill)

Key principles

1. Structured logging always - Every log line should be machine-parseable JSON with consistent fields. Plain-text logs cannot be queried, filtered, or aggregated at scale. Correlation IDs are non-negotiable.

2. USE for resources, RED for services - Resources (CPU, memory, connections) are measured with Utilization/Saturation/Errors. Services (APIs, queues) are measured with Rate/Errors/Duration. Knowing which method applies tells you which metrics to instrument before you write a single line of code.

3. Instrument at boundaries - Service ingress/egress, database calls, external HTTP calls, and message queue produce/consume operations are the minimum instrumentation surface. Everything else is optional until proven necessary.

4. Alert on symptoms, not causes - Alert when users are impacted (high error rate, high latency). Do not page on CPU at 80% or a memory warning - those are causes to investigate, not symptoms to wake someone up for.

5. SLOs drive decisions - Every reliability trade-off should reference an error budget. If budget is healthy, ship features. If budget is burning, stop and fix reliability. SLOs without error budgets are just numbers on a slide.

Core concepts

The three pillars

Cardinality

Every unique combination of label values in a metric creates a new time series in your metrics backend. user_id as a metric label will create millions of time series and kill Prometheus. Keep metric label cardinality under ~100 unique values per label. Use logs or traces for high-cardinality data (user IDs, request IDs, emails).

Exemplars

Exemplars are trace IDs embedded in metric data points. When you see a p99 spike on a histogram, an exemplar lets you jump directly to a trace that caused it. OpenTelemetry and Prometheus support exemplars natively. Enable them - they are the bridge between metrics and traces.

Context propagation

Context propagation is the mechanism by which a trace ID flows through service boundaries. The W3C traceparent header is the standard format. Every service must: extract the header on ingress, attach it to async context, and inject it into all outbound calls. Failing to propagate breaks trace continuity silently.

SLI / SLO / Error budget

• SLI (Service Level Indicator): A measurement of service behavior users care about. Example: successful_requests / total_requests
• SLO (Service Level Objective): A target for an SLI over a time window. Example: 99.9% of requests succeed within 300ms, measured over 30 days
• Error budget: 1 - SLO. For a 99.9% SLO, the budget is 0.1% - about 43 minutes of downtime per month. Burn rate measures how fast you consume it.

Common tasks

Set up structured logging

Use pino for Node.js (fastest), winston for flexibility. Always include a correlation ID middleware that attaches traceId to every log automatically.

// logger.ts - pino with correlation ID support
import pino from 'pino';

export const logger = pino({
  level: process.env.LOG_LEVEL ?? 'info',
  base: {
    service: process.env.SERVICE_NAME ?? 'unknown',
    version: process.env.SERVICE_VERSION ?? '0.0.0',
  },
  timestamp: pino.stdTimeFunctions.isoTime,
  redact: ['req.headers.authorization', 'body.password', 'body.token'],
});

// Express middleware - binds traceId to every child logger in the request scope
export function loggerMiddleware(req: Request, res: Response, next: NextFunction) {
  const traceId = req.headers['traceparent'] as string
    ?? req.headers['x-request-id'] as string
    ?? crypto.randomUUID();

  req.log = logger.child({ traceId, method: req.method, path: req.path });
  res.setHeader('x-request-id', traceId);
  next();
}

// Usage in a route handler
app.post('/orders', async (req, res) => {
  req.log.info({ orderId: body.id }, 'Processing order');
  try {
    const result = await orderService.create(body);
    req.log.info({ orderId: result.id, durationMs: Date.now() - start }, 'Order created');
    res.json(result);
  } catch (err) {
    req.log.error({ err, orderId: body.id }, 'Order creation failed');
    res.status(500).json({ error: 'internal_error' });
  }
});

Instrument with OpenTelemetry

Use the Node.js SDK with auto-instrumentation for HTTP, Express, and common DB clients. Add manual spans only for business-critical operations.

// instrumentation.ts - must be loaded before any other module (Node --require flag)
import { NodeSDK } from '@opentelemetry/sdk-node';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';
import { OTLPMetricExporter } from '@opentelemetry/exporter-metrics-otlp-http';
import { PeriodicExportingMetricReader } from '@opentelemetry/sdk-metrics';
import { getNodeAutoInstrumentations } from '@opentelemetry/auto-instrumentations-node';
import { ParentBasedSampler, TraceIdRatioBased } from '@opentelemetry/sdk-trace-node';

const sdk = new NodeSDK({
  serviceName: process.env.SERVICE_NAME ?? 'my-service',
  traceExporter: new OTLPTraceExporter({
    url: process.env.OTEL_EXPORTER_OTLP_ENDPOINT ?? 'http://localhost:4318/v1/traces',
  }),
  metricReader: new PeriodicExportingMetricReader({
    exporter: new OTLPMetricExporter(),
    exportIntervalMillis: 15_000,
  }),
  sampler: new ParentBasedSampler({
    root: new TraceIdRatioBased(0.1), // 10% head-based sampling
  }),
  instrumentations: [getNodeAutoInstrumentations()],
});

sdk.start();
process.on('SIGTERM', () => sdk.shutdown());

// Manual span for a business operation
import { trace, SpanStatusCode } from '@opentelemetry/api';

const tracer = trace.getTracer('order-service');

async function processPayment(orderId: string, amount: number) {
  return tracer.startActiveSpan('payment.process', async (span) => {
    span.setAttributes({ 'order.id': orderId, 'payment.amount': amount });
    try {
      const result = await stripe.charges.create({ amount, currency: 'usd' });
      span.setStatus({ code: SpanStatusCode.OK });
      return result;
    } catch (err) {
      span.setStatus({ code: SpanStatusCode.ERROR, message: (err as Error).message });
      span.recordException(err as Error);
      throw err;
    } finally {
      span.end();
    }
  });
}

Load instrumentation.ts before your app with node --require ./dist/instrumentation.js server.js. See references/opentelemetry-setup.md for exporters, processors, and Python setup.

Define SLIs and SLOs

Define SLIs from the user's perspective first, then map to metrics you can measure.

# slos.yaml - document alongside your service