gke-alert-configuration

GKE Alert Configuration

This skill provides guidelines and best practices for creating robust,

high-signal alerting policies for Google Kubernetes Engine workloads using

Google Cloud Managed Service for Prometheus and Terraform. It ensures

comprehensive coverage of the 4 Golden Signals and key cluster health

metrics while minimizing alert noise.

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Critical Rules

• Negative Triggers and Scope Redirection for Non-GKE Standalone Runtimes:
• This skill is strictly scoped to Google Kubernetes Engine (GKE)

workloads, clusters, and services using PromQL and Google Cloud Managed

Service for Prometheus.

• Do not use for non-GKE compute runtimes, such as standalone Compute

Engine virtual machines or standalone Cloud Run services without GKE.

• STOP AND RESPOND DIRECTLY (Do Not Edit Files): When the user

requests alert configuration for non-GKE compute infrastructure:

1. **Do not write, create, edit, or validate any Terraform files on

disk**.

1. Immediately stop and respond directly to the user in chat:

• Explicitly Clarify Out-of-Scope: State clearly that

standalone Compute Engine virtual machine monitoring or

standalone Cloud Run monitoring is out of scope for this

GKE-specific PromQL alerting skill, which is designed

specifically for GKE workloads using Google Cloud Managed

Service for Prometheus and PromQL.

• Do Not Generate GKE PromQL Alerts: Do not create or generate

Kubernetes PromQL alert policies or fabricate Kubernetes

container, pod, or node resources for non-GKE infrastructure.

• Redirect the User: Guide and redirect the user to standard

Google Cloud Monitoring metrics, such as

compute.googleapis.com/instance/cpu/utilization or

run.googleapis.com/request_latencies, using standard

google_monitoring_alert_policy with condition_threshold or

MQL, or recommend the relevant specialized Cloud observability

skill.

• Mandatory kube-state-metrics (KSM) Cost Guardrail:
• Deploying open-source kube-state-metrics in Google Cloud Managed

Service for Prometheus incurs billable metric ingestion costs.

• STOP AND ASK PERMISSION FIRST (Do Not Edit Files): When a requested

alert rule relies on Tier 2 KSM metrics (such as kube_cronjob_*,

kube_pod_status_phase, kube_persistentvolume_, kube_deployment_,

kube_statefulset_, kube_job_, or kube_daemonset_), *do not

write, create, edit, or validate any Terraform files or generate alert

policies before obtaining user approval**.

• Instead, you must immediately stop and respond directly to the user

to:

1. Alert the user that the requested alert requires

kube-state-metrics.

1. Explain the cost impact: Detail that kube-state-metrics incurs

billable sample ingestion costs in Google Cloud Managed Service for

Prometheus.

1. Ask for explicit permission: Ask the user for explicit

permission before assuming, enabling, or generating KSM-dependent

alert configurations.

1. Recommend filtering or allowlisting: Suggest and recommend

filtering or allowlisting only the specific required metrics, such

as using a PodMonitoring resource with metricRelabeling

(action: keep) or KSM --metric-allowlist to minimize ingestion

costs. Provide a concrete allowlist example.

• Always prefer Non-KSM Native Alternatives (Tier 1 cAdvisor or native

GKE metrics documented in

[metrics_and_alerts_catalog.md](references/metrics_and_alerts_catalog.md))

whenever possible, such as using container_memory_working_set_bytes

and container_spec_memory_limit_bytes instead of

kube_pod_container_resource_limits.

• Explicit Tier and Cost Surcharge Identification in Response: In

every response where you generate or recommend an alerting policy, you

must explicitly state its classification tier and cost impact:

• Tier 1 native or standard metric (GKE built-in metrics, cAdvisor

container_*, kubelet volume stats, kubelet node conditions, and

control-plane metrics; see

[metrics_and_alerts_catalog.md](references/metrics_and_alerts_catalog.md)):

State that it is a **Tier 1 native or standard metric with zero KSM

cost surcharge**.

• Tier 2 KSM metric: State that it is a **Tier 2 KSM-dependent

metric** and follow the permission and allowlisting guardrail above.

*(Tip: Generally, metrics with the kube_ prefix that represent

resource state or metadata belong to Tier 2).*

• Plan-Validate-Execute Loop for Approved File Edits: When modifying,

adding, or merging approved Terraform files on disk in a workspace, follow

the three-phase workflow:

1. Plan: Draft a structured change plan (changes.json) containing

proposed policy resource names, PromQL expressions, grouping labels, and

durations.

1. Validate: Run the pre-edit validation script (`python3

scripts/validate_config.py --plan changes.json`) to verify PromQL

grammar, lookback windows, duration rules, and ensure no duplicate

signals exist.

1. Execute: After the plan passes validation, apply or merge changes

in-place into the target Terraform configuration (alerts.tf).

1. Note: When answering questions or providing Terraform snippets

directly in chat where no disk modification is requested, output the

complete, valid Terraform HCL block in your response.

• Configure the 4 Golden Signals and Cluster Health: Always ensure the

target Kubernetes workload or service has the following alerting coverage:

1. Latency (P95 response time)
2. Errors (Multi-Window Multi-Burn-Rate SLO alerts, such as Fast Burn 1

hour / 5 minutes with factor 14.4, Slow Burn 6 hours / 30 minutes with

factor 6.0; do not use simple static ratios)

1. Traffic (Sudden drop or complete metric disappearance using

absent() or default 0 syntax, or overload spikes)

1. Saturation (Memory Limit Utilization Only): When describing or

configuring alert policies for a cluster or project, include ONLY

Memory Saturation (container_memory_working_set_bytes /

container_spec_memory_limit_bytes). Do NOT include CPU saturation

alerts or list container_cpu_usage_seconds_total as an alert metric

because CPU is compressible and throttled by CFS quotas rather than

causing uncompressible fatal termination (OOM).

1. Cluster Health (Pod CrashLooping, Node NotReady)

• PromQL Only (Managed Prometheus): You must use

condition_prometheus_query_language with PromQL. Do NOT use MQL or

standard condition_threshold unless explicitly requested. Google Cloud

Managed Service for Prometheus is the standard telemetry ingestion path for

GKE.

• Terraform Only: Write the generated observability configuration ONLY as

Terraform (.tf) files, such as alerts.tf and variables.tf.

• Dynamic Multi-Resource Alerting (No Hardcoding): You must not hardcode

specific pod names, node names, or service names in alerting conditions

unless explicitly requested. Alerting policies must be written to cover

resources dynamically:

• Always use grouping aggregations (`by (cluster, namespace, service, pod,

container)`) instead of filtering to a single instance. This allows a

single alert policy to dynamically track each service or pod separately.

• Always declare and use Terraform variables for project_id,

cluster_name, and namespace (var.project_id, var.cluster_name,

var.namespace) to make the configuration reusable across environments.

Always define these variables in variables.tf (or within the

configuration) and reference all three in policies or PromQL label

matchers.

• No Redundant Duration Windows on Lookbacks:
• When PromQL expressions already use an aggregated lookback window (such

as increase(...[15m]) > 3 or multi-window SLO burn rates), the query

time window already smooths out transient spikes.

• Adding a Terraform duration on top of a PromQL lookback window increases

the Mean Time to Detect (MTTD) without providing additional smoothing

benefits.

• In these cases, set Terraform duration = "0s" (or "60s"). Do not

enforce duration = "300s" on top of [15m], which delays critical

crashloop alerts by up to 20 minutes total (15 minutes + 5 minutes).

• Use duration = "300s" only on instantaneous gauge conditions, such as

kube_node_status_condition == 0.

• Use SLO Burn Rates Instead of Simple Ratios: For error rate alerting,

always generate Multi-Window Multi-Burn-Rate (MWMBR) SLO alerts (such as

14.4x burn rate over 1 hour and 5 minute windows for a 99% SLO) rather than

simple error rate ratios (rate(5xx)/rate(total) > 0.05), which produce

excessive false alarms on low traffic.

• Robust Traffic Drop Detection (absent() / default 0): When

monitoring for traffic drops to zero, do not use rate(...) == 0 alone

because Prometheus time series disappear completely when no requests occur

(evaluating to an empty vector rather than 0). Use default 0 syntax, such

as sum(rate(...[5m])) default 0 == 0, or absent(...) == 1.

• Notification Channels: By default, never configure any notification

channels without user input. If the user explicitly provides a notification

channel, configure the alerts to use it. Otherwise, you must prompt the user

in your response to ask if they would like to configure one.

• Consult GKE Metrics and Open-Source Alerts Catalog: When designing or

generating evaluation suites or alerting policies, consult

[metrics_and_alerts_catalog.md](references/metrics_and_alerts_catalog.md)

for public GKE metrics (kubernetes.io/) and open-source Kubernetes alerts

(awesome-prometheus-alerts).

• Plain English Response: You must include a plain English explanation for

what the alerts do in your response. Explain what the alert measures, what

the threshold represents, and what a trigger indicates.

• User Labels: Include a user_labels block in all

google_monitoring_alert_policy resources to track policies created by this

skill:

    user_labels = {
      created-with-google-skill = "gke-alert-configuration"
    }

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Alerting Policy Structure in Terraform

Alerting policies must be defined using the google_monitoring_alert_policy

resource with condition_prometheus_query_language. Always declare variables in

variables.tf for project_id, cluster_name, and namespace.

# variables.tf
variable "project_id" {
  type        = string
  description = "Google Cloud Project ID"
}

variable "cluster_name" {
  type        = string
  description = "GKE Cluster Name"
}

variable "namespace" {
  type        = string
  description = "Target Kubernetes Namespace"
  default     = "default"
}

variable "slo_target" {
  type        = number
  description = "SLO Target fraction (for example 0.99 for 99%)"
  default     = 0.99
}

# alerts.tf
# Example: Multi-Window Multi-Burn-Rate (MWMBR) SLO Alert (Fast Burn: 14.4x, 1h & 5m windows)
resource "google_monitoring_alert_policy" "k8s_service_error_rate_slo" {
  project      = var.project_id
  display_name = "[K8s] ${var.cluster_name} - Service Error Rate SLO Fast Burn"
  combiner     = "OR"

  conditions {
    display_name = "Error Budget Fast Burn (14.4x over 1h and 5m)"
    condition_prometheus_query_language {
      query    = <<-EOT
        (
          (
            sum(
              rate(
                http_requests_total{
                  cluster="${var.cluster_name}",
                  namespace="${var.namespace}",
                  status=~"5.."
                }[5m]
              )
            ) by (service, namespace, cluster)
            /
            sum(
              rate(
                http_requests_total{
                  cluster="${var.cluster_name}",
                  namespace="${var.namespace}"
                }[5m]
              )
            ) by (service, namespace, cluster)
          ) > (1 - ${var.slo_target}) * 14.4
        )
        and
        (
          (
            sum(
              rate(
                http_requests_total{
                  cluster="${var.cluster_name}",
                  namespace="${var.namespace}",
                  status=~"5.."
                }[1h]
              )
            ) by (service, namespace, cluster)
            /
            sum(
              rate(
                http_requests_total{
                  cluster="${var.cluster_name}",
                  namespace="${var.namespace}"
                }[1h]
              )
            ) by (service, namespace, cluster)
          ) > (1 - ${var.slo_target}) * 14.4
        )
      EOT
      duration = "0s"
    }
  }
}

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Telemetry Metrics and PromQL Examples

For GKE metrics (kubernetes.io/), community open-source alerts

(awesome-prometheus-alerts), KSM cost guardrails, and non-KSM native

alternatives, you must read and follow:

• [metrics_and_alerts_catalog.md](references/metrics_and_alerts_catalog.md)

For specific PromQL queries corresponding to each of the Golden Signals, you

must read and follow:

• [promql_queries.md](references/promql_queries.md)

For GKE cluster prerequisites, enabling Google Cloud Managed Service for

Prometheus collection, configuring PodMonitoring custom scraping, and enabling

control plane metrics collection (API Server, Controller Manager, Scheduler),

you must read and follow:

• [gke_configuration_prerequisites.md](references/gke_configuration_prerequisites.md)

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Tooling Scripts and Validation Loop

Use the validate_config.py script to validate change plans and Terraform

configurations when working in a repository:

• Pre-Edit Plan Validation: Draft a changes.json plan specifying the

proposed policies, queries, and durations, and validate it before editing:

• Command: python3 scripts/validate_config.py --plan changes.json
• Post-Edit and Directory Validation: Scan existing or modified Terraform

files in a directory to ensure no duplicates or syntax errors exist:

• Command: `python3 scripts/validate_config.py --directory [TARGET_TF_DIR]

--cluster-var "${var.cluster_name}"`

• Single file validation: `python3 scripts/validate_config.py --file

[PATH_TO_TF_FILE]`

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Technical Considerations and Gotchas

• Lookback Windows versus Duration Buffers:
• Do not add large duration = "300s" buffers to alerts that already use

aggregated lookback windows like increase(...[15m]) or multi-window

SLO rates.

• The [15m] window in increase(...[15m]) > 3 already smooths spikes.

Adding duration = "300s" increases MTTD by forcing the restart count

to remain above 3 for an extra 5 continuous minutes, delaying alerts by

up to 20 minutes total.

• Use duration = "0s" or "60s" when using lookback window functions.

Reserve duration = "300s" for raw instantaneous gauge conditions, such

as kube_node_status_condition == 0.

• Memory Saturation Only for Cluster Alerting:
• Do not configure CPU saturation alerts for cluster or workload

monitoring. CPU is compressible (throttled by the CFS scheduler), while

memory is uncompressible (triggers OOMKills).

• Configure Memory Saturation using container_memory_working_set_bytes /

container_spec_memory_limit_bytes.

• Missing Resource Limits Blind Spot (Mandatory Explanation): Saturation

alerts that compare usage to limits (such as

container_spec_memory_limit_bytes) will fail to resolve or return

NaN if workloads do not have explicit Memory limits configured in their

Kubernetes manifests.

• Mandatory Instruction: Whenever you generate, discuss, or recommend

any memory saturation alert comparing usage against limits (including

non-KSM cAdvisor alternatives using

container_spec_memory_limit_bytes), you **must explicitly explain and

warn the user in your response** that container memory limits must be

explicitly configured in the Kubernetes pod specs or manifests

(resources.limits.memory) for the saturation query to resolve (and not

return NaN or fail to resolve).

• Linear Disk Predictions (predict_linear): When forecasting volume

exhaustion using

`predict_linear(kubelet_volume_stats_available_bytes[6h:5m], 4 24 3600)

< 0, explain that predict_linear` uses linear regression over the recent

lookback window (for example, 6 hours) to project when available disk will

drop below 0 (for example, within 4 days). Identify

kubelet_volume_stats_available_bytes as a Tier 1 native kubelet metric

with zero KSM surcharge.

• API Server Error and Client Metrics:
• apiserver_request_total and rest_client_requests_total are Tier 1

Control Plane metrics with zero KSM cost surcharge. Explain that

apiserver_request_total monitors 5xx HTTP error rates across API

server endpoints, while rest_client_requests_total monitors 4xx and

5xx requests sent by REST clients communicating with the API server.

• Traffic Disappearance Gotcha (absent() / default 0):
• When traffic drops completely to zero, Prometheus and GMP stop emitting

the http_requests_total time series.

• sum(rate(...[5m])) == 0 evaluates to an empty vector, preventing the

alert from triggering.

• Always use sum(rate(...[5m])) default 0 == 0 or absent(...) == 1 to

reliably detect total traffic loss.

• CrashLooping versus Normal Restarts: A container restarting occasionally

might be normal, for example job completion or a minor rolling update. Alert

on frequent restarts (such as more than 3 restarts in 15 minutes with

duration = "0s") using kube_pod_container_status_restarts_total rather

than a single restart to avoid noise.

• Node Upgrades: During GKE cluster upgrades, nodes are drained and

restarted, which can trigger "Node NotReady" alerts. Warn the user that

these alerts might fire during maintenance windows, or suggest configuring

maintenance windows if supported.

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Additional Resources

• Google Cloud Managed Service for Prometheus Documentation
• GKE Observability and Monitoring Concepts
• Google Cloud Alerting Policies in Terraform
• Google Cloud Monitoring Pricing
• Google SRE Workbook: Alerting on SLOs
• Awesome Prometheus Alerts Repository