# Gke Observability > Configures GKE observability, including Cloud Logging, Cloud Monitoring, and managed Prometheus. Use when configuring GKE monitoring, setting up GKE logging, or configuring Prometheus metrics collection. Don't use to configure local application logging frameworks or external APMs outside GKE. - Skill: `tuyv/gke-observability` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add tuyv/gke-observability` - Raw SKILL.md: https://api.skillmd.com/api/skills/tuyv/gke-observability/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - Author: tuyv (https://skillmd.com/u/tuyv) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tuyv/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 **Say this whenever you hand over a `--monitoring` command:** 1. **Control-plane metrics are NOT enabled by default.** State this outright in your answer — do not leave it implied by the fact that you are supplying an enable command. `API_SERVER`, `SCHEDULER`, and `CONTROLLER_MANAGER` are off on every new cluster and collect nothing until explicitly turned on, and the same is true of `DCGM`, `CADVISOR`, `KUBELET`, and kube-state (`POD`, `DEPLOYMENT`, `STATEFULSET`, `DAEMONSET`, `HPA`, `STORAGE`, `JOBSET`). `SYSTEM` is the only package on by default. A user asking "why are there no API server metrics" has almost always simply never enabled them. 2. **The flag replaces, it does not append.** The set supplied to `--monitoring` overrides the previous setting entirely, so omitting a component silently turns it off. Always pass the full desired list, and always include `SYSTEM` — it cannot be disabled while monitoring is on, and never on Autopilot. 3. **These metrics bill per sample ingested** via Managed Service for Prometheus. Enabling the full suite on a large cluster is a real cost increase; mention it rather than presenting the list as free. > **The gcloud flag and the API field use different spellings for the same > components.** Do not copy names between them: > > Component | `gcloud --monitoring=` | `monitoringConfig` API enum > ---------------- | ---------------------- | --------------------------- > System | `SYSTEM` | `SYSTEM_COMPONENTS` > API server | `API_SERVER` | `APISERVER` > Controller mgr | `CONTROLLER_MANAGER` | `CONTROLLER_MANAGER` > > The remaining components share a spelling. Using an API enum in the CLI flag > (or the reverse) fails the command — this is a common and confusing error. ```bash # Enable golden path monitoring suite gcloud container clusters update --region \ --monitoring=SYSTEM,API_SERVER,SCHEDULER,CONTROLLER_MANAGER,STORAGE,POD,DEPLOYMENT,STATEFULSET,DAEMONSET,HPA,JOBSET,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_attempt_duration_seconds` | Scheduler | Scheduling performance | | `kubernetes.io/node/cpu/core_usage_time` | Cloud Monitoring | 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 > **Prerequisite:** The `kube_*` series above (e.g., `kube_pod_status_phase`, > `kube_pod_container_status_restarts_total`, `kube_node_status_condition`) > come from **kube-state-metrics**, which GKE does not collect by default. > Deploy the Managed Prometheus kube-state-metrics package first. ### 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. ### Node Health (Production Rules) A comprehensive assessment of node health relies on analyzing these two metrics together: 1. **`kubernetes.io/node/status_condition`** (filtered by `status_condition="Ready"`): Use this to track healthy nodes. Note that it will only report values for nodes that have successfully bootstrapped. 2. **`compute.googleapis.com/instance_group/size`** (filtered by `instance_group_name="gke--.*"`): Use this to track the total number of nodes in a specific cluster. Note that it does not differentiate between healthy and unhealthy nodes. ## 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. **Recent additions:** - **Managed OpenTelemetry for GKE (Preview)**: Managed in-cluster OTLP endpoint plus auto-instrumentation for traces, metrics, and logs. Requires GKE 1.34.1-gke.2178000+; enable with `gcloud beta container clusters update ... --managed-otel-scope=COLLECTION_AND_INSTRUMENTATION_COMPONENTS`. - **PSI (Pressure Stall Information) metrics**: cAdvisor `container_pressure_{cpu,memory,io}_{waiting,stalled}_seconds_total` series (beta in Kubernetes 1.34) can be collected via a Managed Prometheus `ClusterNodeMonitoring` resource; GKE's documented collection path requires GKE 1.35+. ## 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" ``` ## Supporting Links - [GKE system metrics](https://docs.cloud.google.com/monitoring/api/metrics_kubernetes) - [GKE Observability Documentation](https://cloud.google.com/kubernetes-engine/docs/concepts/observability) - [Google Cloud Managed Service for Prometheus](https://cloud.google.com/stackdriver/docs/managed-prometheus) - [Cloud Logging Query Language (LQL)](https://cloud.google.com/logging/docs/view/logging-query-language) - [Google Cloud Monitoring Alerts](https://cloud.google.com/monitoring/alerts)