Monitoring Expert
Observability and performance specialist implementing comprehensive monitoring, alerting, tracing, and performance testing systems.
Core Workflow
- Assess — Identify what needs monitoring (SLIs, critical paths, business metrics)
- Instrument — Add logging, metrics, and traces to the application (see examples below)
- Collect — Configure aggregation and storage (Prometheus scrape, log shipper, OTLP endpoint); verify data arrives before proceeding
- Visualize — Build dashboards using RED (Rate/Errors/Duration) or USE (Utilization/Saturation/Errors) methods
- Alert — Define threshold and anomaly alerts on critical paths; validate no false-positive flood before shipping
Quick-Start Examples
Structured Logging (Node.js / Pino)
import pino from 'pino';
const logger = pino({ level: 'info' });
// Good — structured fields, includes correlation ID
logger.info({ requestId: req.id, userId: req.user.id, durationMs: elapsed }, 'order.created');
// Bad — string interpolation, no correlation
console.log(`Order created for user ${userId}`);Prometheus Metrics (Node.js)
import { Counter, Histogram, register } from 'prom-client';
const httpRequests = new Counter({
name: 'http_requests_total',
help: 'Total HTTP requests',
labelNames: ['method', 'route', 'status'],
});
const httpDuration = new Histogram({
name: 'http_request_duration_seconds',
help: 'HTTP request latency',
labelNames: ['method', 'route'],
buckets: [0.05, 0.1, 0.3, 0.5, 1, 2, 5],
});
// Instrument a route
app.use((req, res, next) => {
const end = httpDuration.startTimer({ method: req.method, route: req.path });
res.on('finish', () => {
httpRequests.inc({ method: req.method, route: req.path, status: res.statusCode });
end();
});
next();
});
// Expose scrape endpoint
app.get('/metrics', async (req, res) => {
res.set('Content-Type', register.contentType);
res.end(await register.metrics());
});OpenTelemetry Tracing (Node.js)
import { NodeSDK } from '@opentelemetry/sdk-node';
import { OTLPTraceExporter } from '@opentelemetry/exporter-trace-otlp-http';
import { trace } from '@opentelemetry/api';
const sdk = new NodeSDK({
traceExporter: new OTLPTraceExporter({ url: 'http://jaeger:4318/v1/traces' }),
});
sdk.start();
// Manual span around a critical operation
const tracer = trace.getTracer('order-service');
async function processOrder(orderId) {
const span = tracer.startSpan('order.process');
span.setAttribute('order.id', orderId);
try {
const result = await db.saveOrder(orderId);
span.setStatus({ code: SpanStatusCode.OK });
return result;
} catch (err) {
span.recordException(err);
span.setStatus({ code: SpanStatusCode.ERROR });
throw err;
} finally {
span.end();
}
}Prometheus Alerting Rule
groups:
- name: api.rules
rules:
- alert: HighErrorRate
expr: |
rate(http_requests_total{status=~"5.."}[5m])
/ rate(http_requests_total[5m]) > 0.05
for: 2m
labels:
severity: critical
annotations:
summary: "Error rate above 5% on {{ $labels.route }}"k6 Load Test
import http from 'k6/http';
import { check, sleep } from 'k6';
export const options = {
stages: [
{ duration: '1m', target: 50 }, // ramp up
{ duration: '5m', target: 50 }, // sustained load
{ duration: '1m', target: 0 }, // ramp down
],
thresholds: {
http_req_duration: ['p(95)<500'], // 95th percentile < 500 ms
http_req_failed: ['rate<0.01'], // error rate < 1%
},
};
export default function () {
const res = http.get('https://api.example.com/orders');
check(res, { 'status is 200': (r) => r.status === 200 });
sleep(1);
}Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Logging | references/structured-logging.md | Pino, JSON logging |
| Metrics | references/prometheus-metrics.md | Counter, Histogram, Gauge |
| Tracing | references/opentelemetry.md | OpenTelemetry, spans |
| Alerting | references/alerting-rules.md | Prometheus alerts |
| Dashboards | references/dashboards.md | RED/USE method, Grafana |
| Performance Testing | references/performance-testing.md | Load testing, k6, Artillery, benchmarks |
| Profiling | references/application-profiling.md | CPU/memory profiling, bottlenecks |
| Capacity Planning | references/capacity-planning.md | Scaling, forecasting, budgets |
Constraints
MUST DO
- Use structured logging (JSON)
- Include request IDs for correlation
- Set up alerts for critical paths
- Monitor business metrics, not just technical
- Use appropriate metric types (counter/gauge/histogram)
- Implement health check endpoints
MUST NOT DO
- Log sensitive data (passwords, tokens, PII)
- Alert on every error (alert fatigue)
- Use string interpolation in logs (use structured fields)
- Skip correlation IDs in distributed systems