Agent skill

service-mesh-observability

Implement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/aiskillstore/marketplace/tree/main/skills/sickn33/service-mesh-observability

SKILL.md

Service Mesh Observability

Complete guide to observability patterns for Istio, Linkerd, and service mesh deployments.

Do not use this skill when

  • The task is unrelated to service mesh observability
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.
  • If detailed examples are required, open resources/implementation-playbook.md.

Use this skill when

  • Setting up distributed tracing across services
  • Implementing service mesh metrics and dashboards
  • Debugging latency and error issues
  • Defining SLOs for service communication
  • Visualizing service dependencies
  • Troubleshooting mesh connectivity

Core Concepts

1. Three Pillars of Observability

┌─────────────────────────────────────────────────────┐
│                  Observability                       │
├─────────────────┬─────────────────┬─────────────────┤
│     Metrics     │     Traces      │      Logs       │
│                 │                 │                 │
│ • Request rate  │ • Span context  │ • Access logs   │
│ • Error rate    │ • Latency       │ • Error details │
│ • Latency P50   │ • Dependencies  │ • Debug info    │
│ • Saturation    │ • Bottlenecks   │ • Audit trail   │
└─────────────────┴─────────────────┴─────────────────┘

2. Golden Signals for Mesh

Signal Description Alert Threshold
Latency Request duration P50, P99 P99 > 500ms
Traffic Requests per second Anomaly detection
Errors 5xx error rate > 1%
Saturation Resource utilization > 80%

Templates

Template 1: Istio with Prometheus & Grafana

yaml
# Install Prometheus
apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus
  namespace: istio-system
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
    scrape_configs:
      - job_name: 'istio-mesh'
        kubernetes_sd_configs:
          - role: endpoints
            namespaces:
              names:
                - istio-system
        relabel_configs:
          - source_labels: [__meta_kubernetes_service_name]
            action: keep
            regex: istio-telemetry
---
# ServiceMonitor for Prometheus Operator
apiVersion: monitoring.coreos.com/v1
kind: ServiceMonitor
metadata:
  name: istio-mesh
  namespace: istio-system
spec:
  selector:
    matchLabels:
      app: istiod
  endpoints:
    - port: http-monitoring
      interval: 15s

Template 2: Key Istio Metrics Queries

promql
# Request rate by service
sum(rate(istio_requests_total{reporter="destination"}[5m])) by (destination_service_name)

# Error rate (5xx)
sum(rate(istio_requests_total{reporter="destination", response_code=~"5.."}[5m]))
  / sum(rate(istio_requests_total{reporter="destination"}[5m])) * 100

# P99 latency
histogram_quantile(0.99,
  sum(rate(istio_request_duration_milliseconds_bucket{reporter="destination"}[5m]))
  by (le, destination_service_name))

# TCP connections
sum(istio_tcp_connections_opened_total{reporter="destination"}) by (destination_service_name)

# Request size
histogram_quantile(0.99,
  sum(rate(istio_request_bytes_bucket{reporter="destination"}[5m]))
  by (le, destination_service_name))

Template 3: Jaeger Distributed Tracing

yaml
# Jaeger installation for Istio
apiVersion: install.istio.io/v1alpha1
kind: IstioOperator
spec:
  meshConfig:
    enableTracing: true
    defaultConfig:
      tracing:
        sampling: 100.0  # 100% in dev, lower in prod
        zipkin:
          address: jaeger-collector.istio-system:9411
---
# Jaeger deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: jaeger
  namespace: istio-system
spec:
  selector:
    matchLabels:
      app: jaeger
  template:
    metadata:
      labels:
        app: jaeger
    spec:
      containers:
        - name: jaeger
          image: jaegertracing/all-in-one:1.50
          ports:
            - containerPort: 5775   # UDP
            - containerPort: 6831   # Thrift
            - containerPort: 6832   # Thrift
            - containerPort: 5778   # Config
            - containerPort: 16686  # UI
            - containerPort: 14268  # HTTP
            - containerPort: 14250  # gRPC
            - containerPort: 9411   # Zipkin
          env:
            - name: COLLECTOR_ZIPKIN_HOST_PORT
              value: ":9411"

Template 4: Linkerd Viz Dashboard

bash
# Install Linkerd viz extension
linkerd viz install | kubectl apply -f -

# Access dashboard
linkerd viz dashboard

# CLI commands for observability
# Top requests
linkerd viz top deploy/my-app

# Per-route metrics
linkerd viz routes deploy/my-app --to deploy/backend

# Live traffic inspection
linkerd viz tap deploy/my-app --to deploy/backend

# Service edges (dependencies)
linkerd viz edges deployment -n my-namespace

Template 5: Grafana Dashboard JSON

json
{
  "dashboard": {
    "title": "Service Mesh Overview",
    "panels": [
      {
        "title": "Request Rate",
        "type": "graph",
        "targets": [
          {
            "expr": "sum(rate(istio_requests_total{reporter=\"destination\"}[5m])) by (destination_service_name)",
            "legendFormat": "{{destination_service_name}}"
          }
        ]
      },
      {
        "title": "Error Rate",
        "type": "gauge",
        "targets": [
          {
            "expr": "sum(rate(istio_requests_total{response_code=~\"5..\"}[5m])) / sum(rate(istio_requests_total[5m])) * 100"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "thresholds": {
              "steps": [
                {"value": 0, "color": "green"},
                {"value": 1, "color": "yellow"},
                {"value": 5, "color": "red"}
              ]
            }
          }
        }
      },
      {
        "title": "P99 Latency",
        "type": "graph",
        "targets": [
          {
            "expr": "histogram_quantile(0.99, sum(rate(istio_request_duration_milliseconds_bucket{reporter=\"destination\"}[5m])) by (le, destination_service_name))",
            "legendFormat": "{{destination_service_name}}"
          }
        ]
      },
      {
        "title": "Service Topology",
        "type": "nodeGraph",
        "targets": [
          {
            "expr": "sum(rate(istio_requests_total{reporter=\"destination\"}[5m])) by (source_workload, destination_service_name)"
          }
        ]
      }
    ]
  }
}

Template 6: Kiali Service Mesh Visualization

yaml
# Kiali installation
apiVersion: kiali.io/v1alpha1
kind: Kiali
metadata:
  name: kiali
  namespace: istio-system
spec:
  auth:
    strategy: anonymous  # or openid, token
  deployment:
    accessible_namespaces:
      - "**"
  external_services:
    prometheus:
      url: http://prometheus.istio-system:9090
    tracing:
      url: http://jaeger-query.istio-system:16686
    grafana:
      url: http://grafana.istio-system:3000

Template 7: OpenTelemetry Integration

yaml
# OpenTelemetry Collector for mesh
apiVersion: v1
kind: ConfigMap
metadata:
  name: otel-collector-config
data:
  config.yaml: |
    receivers:
      otlp:
        protocols:
          grpc:
            endpoint: 0.0.0.0:4317
          http:
            endpoint: 0.0.0.0:4318
      zipkin:
        endpoint: 0.0.0.0:9411

    processors:
      batch:
        timeout: 10s

    exporters:
      jaeger:
        endpoint: jaeger-collector:14250
        tls:
          insecure: true
      prometheus:
        endpoint: 0.0.0.0:8889

    service:
      pipelines:
        traces:
          receivers: [otlp, zipkin]
          processors: [batch]
          exporters: [jaeger]
        metrics:
          receivers: [otlp]
          processors: [batch]
          exporters: [prometheus]
---
# Istio Telemetry v2 with OTel
apiVersion: telemetry.istio.io/v1alpha1
kind: Telemetry
metadata:
  name: mesh-default
  namespace: istio-system
spec:
  tracing:
    - providers:
        - name: otel
      randomSamplingPercentage: 10

Alerting Rules

yaml
apiVersion: monitoring.coreos.com/v1
kind: PrometheusRule
metadata:
  name: mesh-alerts
  namespace: istio-system
spec:
  groups:
    - name: mesh.rules
      rules:
        - alert: HighErrorRate
          expr: |
            sum(rate(istio_requests_total{response_code=~"5.."}[5m])) by (destination_service_name)
            / sum(rate(istio_requests_total[5m])) by (destination_service_name) > 0.05
          for: 5m
          labels:
            severity: critical
          annotations:
            summary: "High error rate for {{ $labels.destination_service_name }}"

        - alert: HighLatency
          expr: |
            histogram_quantile(0.99, sum(rate(istio_request_duration_milliseconds_bucket[5m]))
            by (le, destination_service_name)) > 1000
          for: 5m
          labels:
            severity: warning
          annotations:
            summary: "High P99 latency for {{ $labels.destination_service_name }}"

        - alert: MeshCertExpiring
          expr: |
            (certmanager_certificate_expiration_timestamp_seconds - time()) / 86400 < 7
          labels:
            severity: warning
          annotations:
            summary: "Mesh certificate expiring in less than 7 days"

Best Practices

Do's

  • Sample appropriately - 100% in dev, 1-10% in prod
  • Use trace context - Propagate headers consistently
  • Set up alerts - For golden signals
  • Correlate metrics/traces - Use exemplars
  • Retain strategically - Hot/cold storage tiers

Don'ts

  • Don't over-sample - Storage costs add up
  • Don't ignore cardinality - Limit label values
  • Don't skip dashboards - Visualize dependencies
  • Don't forget costs - Monitor observability costs

Resources

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