# Gke Observability > Configures GKE observability with Cloud Logging, Cloud Monitoring, and managed Prometheus for monitoring, logging, and metrics collection. - Skill: `google/gke-observability` (Agent Skill) - Install (CLI): `npx skillmds@latest add google/gke-observability` - Raw SKILL.md: https://api.skillmd.com/api/skills/google/gke-observability/raw - Safety review: CAUTION (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra, Cloud Platforms, Monitoring & Observability - Tags: Cloud Logging, Cloud Monitoring, Gcloud, Gke, Google Kubernetes Engine, Kubectl, Observability, Prometheus - Author: Google (https://skillmd.com/u/google), verified publisher - Updated: 2026-07-06 - Page: https://skillmd.com/skills/google/gke-observability --- # GKE Observability This reference covers monitoring, logging, and metrics configuration for GKE. The golden path enables comprehensive observability including control-plane metrics. > **MCP Tools:** `get_cluster`, `list_k8s_events`, `get_k8s_logs`, > `get_k8s_cluster_info`, `describe_k8s_resource`. **CLI-only:** `gcloud > container clusters update --monitoring=...`, `gcloud logging read` ## Golden Path Observability Defaults Setting | Golden Path Value | Notes --------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------- | ----- `loggingConfig` components | SYSTEM_COMPONENTS, WORKLOADS | Full workload logging `monitoringConfig` components | SYSTEM_COMPONENTS, STORAGE, POD, DEPLOYMENT, STATEFULSET, DAEMONSET, HPA, JOBSET, CADVISOR, KUBELET, DCGM, APISERVER, SCHEDULER, CONTROLLER_MANAGER | Full suite including control-plane `managedPrometheusConfig.enabled` | `true` | Google-managed Prometheus `advancedDatapathObservabilityConfig.enableMetrics` | `true` | Dataplane V2 flow metrics `loggingService` | `logging.googleapis.com/kubernetes` | Cloud Logging `monitoringService` | `monitoring.googleapis.com/kubernetes` | Cloud Monitoring ### Control-Plane Metrics (Golden Path Addition) The golden path adds three control-plane monitoring components not present in default clusters: | Component | What It Monitors | | -------------------- | ----------------------------------------------------- | | `APISERVER` | API server request latency, error rates, admission | : : webhook performance : | `SCHEDULER` | Scheduling latency, pending pods, scheduling failures | | `CONTROLLER_MANAGER` | Controller work queue depth, reconciliation latency | These are critical for diagnosing cluster-level issues (slow API responses, scheduling delays, stuck controllers). ## Enabling Full Monitoring ```bash # Enable golden path monitoring suite gcloud container clusters update --region \ --monitoring=SYSTEM,API_SERVER,SCHEDULER,CONTROLLER_MANAGER,STORAGE,POD,DEPLOYMENT,STATEFULSET,DAEMONSET,HPA,CADVISOR,KUBELET,DCGM \ --quiet # Enable Managed Prometheus gcloud container clusters update --region \ --enable-managed-prometheus \ --quiet # Enable Dataplane V2 observability metrics gcloud container clusters update --region \ --enable-dataplane-v2-flow-observability \ --quiet ``` ## Managed Prometheus Golden path enables Google Managed Prometheus for metrics collection and querying. **Querying metrics:** - Use Cloud Monitoring Metrics Explorer in the console - Use PromQL via the Prometheus UI or API - Grafana dashboards via Managed Grafana **Key GKE metrics:** | Metric | Source | Use | | --------------------------------------- | ------------------ | ------------- | | `container_cpu_usage_seconds_total` | cAdvisor | Pod CPU usage | | `container_memory_working_set_bytes` | cAdvisor | Pod memory | : : : usage : | `kube_pod_status_phase` | kube-state-metrics | Pod lifecycle | | `apiserver_request_duration_seconds` | API Server | Control plane | : : : latency : | `scheduler_scheduling_duration_seconds` | Scheduler | Scheduling | : : : performance : | `node_cpu_seconds_total` | Kubelet | Node CPU | | `DCGM_FI_DEV_GPU_UTIL` | DCGM | GPU | : : : utilization : ## Live Resource Usage (kubectl-only) No MCP or gcloud equivalent exists for live resource usage. Use `kubectl top`: ```bash kubectl top pods --all-namespaces --sort-by=cpu kubectl top nodes kubectl top pods --containers -n # per-container breakdown ``` ## Cloud Logging (gcloud-only) **Querying cluster logs** (no MCP equivalent — use `gcloud logging read`): ```bash # System component logs gcloud logging read \ 'resource.type="k8s_cluster" AND resource.labels.cluster_name=""' \ --project --limit 50 \ --quiet # Workload logs for a specific namespace gcloud logging read \ 'resource.type="k8s_container" AND resource.labels.cluster_name="" AND resource.labels.namespace_name=""' \ --project --limit 50 \ --quiet # Audit logs (who did what) gcloud logging read \ 'resource.type="k8s_cluster" AND logName:"cloudaudit.googleapis.com"' \ --project --limit 50 \ --quiet ``` ## Diagnostic Settings For security monitoring and troubleshooting, enable control-plane audit logs: ```bash # View current logging config gcloud container clusters describe --region \ --format="yaml(loggingConfig)" \ --quiet ``` ## Alerting Set up alerts for critical conditions: Condition | Metric | Threshold ----------------------- | --------------------------------------------------- | --------- High API server latency | `apiserver_request_duration_seconds` | P99 > 5s Pod crash loops | `kube_pod_container_status_restarts_total` | > 5 in 10min Node not ready | `kube_node_status_condition` | condition=Ready, status!=True High GPU utilization | `DCGM_FI_DEV_GPU_UTIL` | > 95% sustained PVC near capacity | `kubelet_volume_stats_used_bytes / capacity` | > 85% Scheduling failures | `scheduler_schedule_attempts_total{result="error"}` | > 0 ### Proposing Dashboards & Alerts (Production Rules) When designing or proposing alerting and dashboard strategies for GKE: 1. **Always explicitly name Google Cloud Monitoring** as the platform to implement these alerts and dashboards. 2. **Always include API server latency** (via `apiserver_request_duration_seconds` metric) on the dashboard as a critical indicator of control plane health, alongside node CPU/Memory and pod crash loops. ## Cost Considerations Monitoring and logging have associated costs: - **Cloud Logging**: Charged per GiB ingested beyond free tier (50 GiB/project/month) - **Cloud Monitoring**: Free for GKE system metrics; custom metrics charged per time series - **Managed Prometheus**: Charged per samples ingested To reduce costs in non-production: ```bash # Reduce to system-only monitoring gcloud container clusters update --region \ --monitoring=SYSTEM \ --quiet ``` ## Distributed Tracing & Continuous Profiling (Recommended) **Not golden path defaults** — recommended for production microservice architectures and performance-sensitive workloads. - **Cloud Trace**: Add OpenTelemetry SDK to your app with the `opentelemetry-operations-go` (or equivalent) exporter. Traces appear in Cloud Trace console. Identifies cross-service latency bottlenecks. - **Cloud Profiler**: Add the Cloud Profiler agent to your app. Profiles CPU and memory usage in production with low overhead. Identifies hotspots and compares across versions. ## LQL Query Examples Common Logging Query Language patterns for GKE troubleshooting: ``` # Error logs for a specific container resource.type="k8s_container" AND resource.labels.container_name="my-app" AND severity>=ERROR # OOMKilled events resource.type="k8s_event" AND jsonPayload.reason="OOMKilling" # Pod scheduling failures resource.type="k8s_event" AND jsonPayload.reason="FailedScheduling" # Audit logs (who did what) resource.type="k8s_cluster" AND logName:"cloudaudit.googleapis.com" ```