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: gke:get_cluster, gke:list_k8s_events, gke:get_k8s_logs,

> gke:get_k8s_cluster_info, gke: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

# Enable golden path monitoring suite
gcloud container clusters update <CLUSTER_NAME> --region <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 <CLUSTER_NAME> --region <REGION> \
  --enable-managed-prometheus \
  --quiet

# Enable Dataplane V2 observability metrics
gcloud container clusters update <CLUSTER_NAME> --region <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:

kubectl top pods --all-namespaces --sort-by=cpu
kubectl top nodes
kubectl top pods --containers -n <NAMESPACE>  # per-container breakdown

Cloud Logging (gcloud-only)

Querying cluster logs (no MCP equivalent — use gcloud logging read):

# System component logs
gcloud logging read \
  'resource.type="k8s_cluster" AND resource.labels.cluster_name="<CLUSTER_NAME>"' \
  --project <PROJECT_ID> --limit 50 \
  --quiet

# Workload logs for a specific namespace
gcloud logging read \
  'resource.type="k8s_container" AND resource.labels.cluster_name="<CLUSTER_NAME>" AND resource.labels.namespace_name="<NAMESPACE>"' \
  --project <PROJECT_ID> --limit 50 \
  --quiet

# Audit logs (who did what)
gcloud logging read \
  'resource.type="k8s_cluster" AND logName:"cloudaudit.googleapis.com"' \
  --project <PROJECT_ID> --limit 50 \
  --quiet

Diagnostic Settings

For security monitoring and troubleshooting, enable control-plane audit logs:

# View current logging config
gcloud container clusters describe <CLUSTER_NAME> --region <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:

• Always explicitly name Google Cloud Monitoring as the platform to

implement these alerts and dashboards.

• 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:

• 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.
• compute.googleapis.com/instance_group/size (filtered by instance_group_name="gke-<cluster_name>-.*"): 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:

# Reduce to system-only monitoring
gcloud container clusters update <CLUSTER_NAME> --region <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"

Supporting Links

• GKE system metrics
• GKE Observability Documentation
• Google Cloud Managed Service for Prometheus
• Cloud Logging Query Language (LQL)
• Google Cloud Monitoring Alerts