Observability Engineer – Full-Stack Monitoring Expert

⚠️ Chunking Rule

Large monitoring stacks (Prometheus + Grafana + OpenTelemetry + logs) = 1000+ lines. Generate ONE component per response: Metrics → Dashboards → Alerting → Tracing → Logs.

Purpose

Design and implement comprehensive observability systems covering metrics, logs, traces, and reliability engineering.

When to Use

• Set up Prometheus monitoring
• Create Grafana dashboards
• Implement distributed tracing (Jaeger, Tempo)
• Define SLIs/SLOs and error budgets
• Configure alerting systems
• Prevent alert fatigue
• Debug microservices latency

Core Concepts

Three Pillars of Observability

┌─────────────────────────────────────────────────────────────┐
│                    OBSERVABILITY                             │
├─────────────────┬─────────────────┬─────────────────────────┤
│    METRICS      │     LOGS        │        TRACES           │
├─────────────────┼─────────────────┼─────────────────────────┤
│ Prometheus      │ Loki/ELK        │ Jaeger/Tempo            │
│ What happened?  │ Why happened?   │ How requests flow?      │
│ Aggregated data │ Event details   │ Request journey         │
└─────────────────┴─────────────────┴─────────────────────────┘

RED Method (Services)

• Rate – Requests per second
• Errors – Error rate percentage
• Duration – Latency/response time

USE Method (Resources)

• Utilization – % time resource is busy
• Saturation – Queue length/wait time
• Errors – Error count

Prometheus Setup

Installation (Kubernetes)

helm repo add prometheus-community https://prometheus-community.github.io/helm-charts
helm install prometheus prometheus-community/kube-prometheus-stack \
  --namespace monitoring --create-namespace \
  --set prometheus.prometheusSpec.retention=30d

Key Configuration

# prometheus.yml
global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'kubernetes-pods'
    kubernetes_sd_configs:
      - role: pod
    relabel_configs:
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
        action: keep
        regex: true

Recording Rules

groups:
  - name: api_metrics
    rules:
      - record: job:http_requests:rate5m
        expr: sum by (job) (rate(http_requests_total[5m]))
      - record: job:http_requests_error_rate:percentage
        expr: (sum by (job) (rate(http_requests_total{status=~"5.."}[5m])) / sum by (job) (rate(http_requests_total[5m]))) * 100

Grafana Dashboards

Dashboard Design Principles

┌─────────────────────────────────────┐
│  Critical Metrics (Big Numbers)     │
├─────────────────────────────────────┤
│  Key Trends (Time Series)           │
├─────────────────────────────────────┤
│  Detailed Metrics (Tables/Heatmaps) │
└─────────────────────────────────────┘

Essential Queries

# Request rate
sum(rate(http_requests_total[5m])) by (service)

# Error rate %
(sum(rate(http_requests_total{status=~"5.."}[5m])) / sum(rate(http_requests_total[5m]))) * 100

# P95 Latency
histogram_quantile(0.95, sum(rate(http_request_duration_seconds_bucket[5m])) by (le, service))

Distributed Tracing

OpenTelemetry Setup (Node.js)

const { NodeTracerProvider } = require('@opentelemetry/sdk-trace-node');
const { JaegerExporter } = require('@opentelemetry/exporter-jaeger');
const { registerInstrumentations } = require('@opentelemetry/instrumentation');
const { HttpInstrumentation } = require('@opentelemetry/instrumentation-http');

const provider = new NodeTracerProvider();
provider.addSpanProcessor(new BatchSpanProcessor(new JaegerExporter()));
provider.register();

registerInstrumentations({
  instrumentations: [new HttpInstrumentation()],
});

Context Propagation

traceparent: 00-0af7651916cd43dd8448eb211c80319c-b7ad6b7169203331-01

Jaeger Deployment

# Kubernetes
apiVersion: jaegertracing.io/v1
kind: Jaeger
metadata:
  name: jaeger
spec:
  strategy: production
  storage:
    type: elasticsearch

SLIs/SLOs

Defining SLOs

slos:
  - name: api_availability
    target: 99.9%  # 43.2 min downtime/month
    window: 28d
    sli: sum(rate(http_requests_total{status!~"5.."}[28d])) / sum(rate(http_requests_total[28d]))

  - name: api_latency_p95
    target: 99%    # 99% requests < 500ms
    window: 28d
    sli: sum(rate(http_request_duration_seconds_bucket{le="0.5"}[28d])) / sum(rate(http_request_duration_seconds_count[28d]))

Error Budget

Error Budget = 1 - SLO Target
Example: 99.9% SLO → 0.1% error budget → 43.2 min/month

Burn Rate Alerts

rules:
  - alert: SLOErrorBudgetBurnFast
    expr: slo:http_availability:burn_rate_1h > 14.4 and slo:http_availability:burn_rate_5m > 14.4
    for: 2m
    labels:
      severity: critical
    annotations:
      summary: "Fast error budget burn - consuming 2% budget in 1 hour"

Alert Fatigue Prevention

Multi-Window Alerting

# Combine short + long windows to reduce false positives
- alert: HighLatency
  expr: |
    (job:http_request_duration:p95_5m > 1 AND job:http_request_duration:p95_1h > 0.8)
  for: 5m

Severity Levels

Best Practices

1. Start with RED/USE methods for consistent metrics
2. Use recording rules for expensive queries
3. Implement multi-window alerts to reduce noise
4. Set achievable SLOs (don’t aim for 100%)
5. Track error budget consistently
6. Correlate traces with metrics using trace IDs
7. Sample traces appropriately (1-10% in production)
8. Add context to spans (user_id, request_id)

Related Skills

• devops – Infrastructure provisioning