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.
Install
npx skills add https://github.com/wshobson/agents/tree/main/plugins/cloud-infrastructure/skills/service-mesh-observability
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install wshobson-agents@llmmart
git clone https://github.com/wshobson/agents.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole wshobson/agents collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
Service Mesh Observability
Complete guide to observability patterns for Istio, Linkerd, and service mesh deployments.
When to Use This Skill
- 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 and detailed worked examples
Full template library and detailed worked examples live in references/details.md. Read that file when you need the concrete templates.
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
Files (agents)
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references
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details.md 7.5 KB
# service-mesh-observability — templates and worked examples ## 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" ```
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SKILL.md 2.8 KB
--- name: service-mesh-observability description: 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. --- # Service Mesh Observability Complete guide to observability patterns for Istio, Linkerd, and service mesh deployments. ## When to Use This Skill - 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 and detailed worked examples Full template library and detailed worked examples live in `references/details.md`. Read that file when you need the concrete templates. ## 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
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