Version: Next

Karmada Metrics Reference

This document provides a comprehensive reference of all Prometheus metrics exported by Karmada components. For guidance on using these metrics for monitoring and observability, see the Karmada Observability Guide.

Karmada Metrics Architecture

Karmada exports Prometheus metrics from the following components:

karmada-apiserver
karmada-controller-manager
karmada-scheduler
karmada-scheduler-estimator
karmada-agent
karmada-webhook
karmada-descheduler

Metric Conventions

Naming

Karmada-specific metrics: Prefixed with karmada_ (e.g., karmada_scheduler_schedule_attempts_total)
Subsystem metrics: Include subsystem name (e.g., karmada_scheduler_estimator_*)
Standard controller-runtime metrics: Use standard prefixes (e.g., workqueue_*, controller_runtime_*)
Units: Time metrics use _seconds, byte metrics use _bytes
Base units: CPU in cores, memory in bytes, duration in seconds

Types

Counter: Monotonically increasing value (e.g., total requests, errors). Use rate() or increase() in queries.
Gauge: Value that can go up or down (e.g., queue depth, resource utilization). Use directly in queries.
Histogram: Observations bucketed by value (e.g., latency). Use histogram_quantile() for percentiles.

Stability Levels

Karmada follows Kubernetes metric stability conventions:

STABLE: No explicit stability annotation in source code. Guaranteed consistent naming and semantics across versions. Breaking changes require minimum 2 release cycles notice. Safe for production use.
ALPHA: Explicitly marked with StabilityLevel: metrics.ALPHA in source code. May change or be removed without notice. Use with caution in production.

Metrics by Category

Scheduler Metrics

These metrics track scheduling operations and performance.

karmada_scheduler_schedule_attempts_total

Component: karmada-scheduler
Type: Counter
Stability: STABLE
Labels:
- result: success or error
- schedule_type: ResourceBinding or ClusterResourceBinding
Description: Number of attempts to schedule a ResourceBinding or ClusterResourceBinding. High error rate indicates scheduling failures preventing workload placement.

Example queries:

# Scheduling success rate
rate(karmada_scheduler_schedule_attempts_total{result="success"}[5m])
  /
rate(karmada_scheduler_schedule_attempts_total[5m])

# Scheduling error rate
rate(karmada_scheduler_schedule_attempts_total{result="error"}[5m])
  /
rate(karmada_scheduler_schedule_attempts_total[5m])

karmada_scheduler_e2e_scheduling_duration_seconds

Component: karmada-scheduler
Type: Histogram
Stability: STABLE
Labels:
- result: success or error
- schedule_type: ResourceBinding or ClusterResourceBinding
Description: End-to-end scheduling latency from when binding enters queue to placement decision. Use to monitor overall scheduling performance.
Buckets: Exponential from 0.001s to ~32s

Example queries:

# P95 scheduling latency
histogram_quantile(0.95, rate(karmada_scheduler_e2e_scheduling_duration_seconds_bucket[5m]))

# P99 scheduling latency
histogram_quantile(0.99, rate(karmada_scheduler_e2e_scheduling_duration_seconds_bucket[5m]))

karmada_scheduler_scheduling_algorithm_duration_seconds

Component: karmada-scheduler
Type: Histogram
Stability: STABLE
Labels:
- schedule_step: Scheduling step (Filter, Score, Select, AssignReplicas, etc.)
Description: Latency for each step of the scheduling algorithm. Use to identify slow steps in the scheduling pipeline.
Buckets: Exponential from 0.001s to ~32s

Example query:

# Latency by step (P95)
histogram_quantile(0.95, rate(karmada_scheduler_scheduling_algorithm_duration_seconds_bucket[5m])) by (schedule_step)

karmada_scheduler_queue_incoming_bindings_total

Component: karmada-scheduler
Type: Counter
Stability: STABLE
Labels:
- event: Add, Update, or Delete
Description: Number of bindings added to scheduling queues by event type. Use to understand workload churn and queue activity.

Example query:

# Queue incoming rate by event
rate(karmada_scheduler_queue_incoming_bindings_total[5m]) by (event)

karmada_scheduler_framework_extension_point_duration_seconds

Component: karmada-scheduler
Type: Histogram
Stability: STABLE
Labels:
- extension_point: Extension point name (Filter, Score, etc.)
- result: success or error
Description: Latency for running all plugins at a specific extension point. Use to identify slow extension points.
Buckets: Exponential from 0.0001s to ~4s

Example query:

# Extension point latency (P95)
histogram_quantile(0.95, rate(karmada_scheduler_framework_extension_point_duration_seconds_bucket[5m])) by (extension_point)

karmada_scheduler_plugin_execution_duration_seconds

Component: karmada-scheduler
Type: Histogram
Stability: STABLE
Labels:
- plugin: Plugin name
- extension_point: Extension point name
- result: success or error
Description: Duration for executing a specific plugin at an extension point. Use to identify slow plugins.
Buckets: Exponential from 0.00001s to ~0.3s

Example query:

# Top 10 slowest plugins (P95)
topk(10, histogram_quantile(0.95, rate(karmada_scheduler_plugin_execution_duration_seconds_bucket[5m])) by (plugin))

scheduler_pending_bindings

Component: karmada-scheduler
Type: Gauge
Stability: ALPHA
Labels:
- queue: active, backoff, or unschedulable
Description: Number of pending bindings in each scheduling queue. Bindings in unschedulable queue indicate resources that cannot be placed due to constraints.

Example query:

# Unschedulable bindings
scheduler_pending_bindings{queue="unschedulable"}

Scheduler Estimator Metrics

These metrics track cluster capacity estimation operations.

karmada_scheduler_estimator_estimating_request_total

Component: karmada-scheduler-estimator
Type: Counter
Stability: STABLE
Labels:
- result: success or error
- type: Estimation type
Description: Number of scheduler estimator requests. High error rate indicates inability to assess cluster capacity, leading to poor placement decisions.

Example query:

# Estimator error rate
rate(karmada_scheduler_estimator_estimating_request_total{result="error"}[5m])
  /
rate(karmada_scheduler_estimator_estimating_request_total[5m])

karmada_scheduler_estimator_estimating_algorithm_duration_seconds

Component: karmada-scheduler-estimator
Type: Histogram
Stability: STABLE
Labels:
- result: success or error
- type: Estimation type
- step: Algorithm step
Description: Latency for each step of the estimation algorithm. Impacts overall scheduling latency.
Buckets: Exponential from 0.001s to ~1s

Example query:

# P95 estimation latency by step
histogram_quantile(0.95, rate(karmada_scheduler_estimator_estimating_algorithm_duration_seconds_bucket[5m])) by (step)

karmada_scheduler_estimator_estimating_plugin_extension_point_duration_seconds

Component: karmada-scheduler-estimator
Type: Histogram
Stability: STABLE
Labels:
- estimating_plugin_extension_point: Extension point name
Description: Latency for running all plugins at a specific estimator extension point.
Buckets: Exponential from 0.0001s to ~4s

karmada_scheduler_estimator_estimating_plugin_execution_duration_seconds

Component: karmada-scheduler-estimator
Type: Histogram
Stability: STABLE
Labels:
- estimating_plugin: Plugin name
- estimating_plugin_extension_point: Extension point name
Description: Duration for executing a specific plugin at an estimator extension point.
Buckets: Exponential from 0.00001s to ~0.3s

Cluster Health Metrics

These metrics track member cluster state and resource capacity.

cluster_ready_state

Component: karmada-controller-manager
Type: Gauge
Stability: STABLE
Labels:
- member_cluster: Cluster name
Description: Cluster readiness state. Value of 1 indicates ready, 0 indicates not ready. Critical metric - value of 0 blocks all scheduling and workload operations to that cluster.

Example query:

# Not-ready clusters
cluster_ready_state == 0

cluster_node_number

Component: karmada-controller-manager
Type: Gauge
Stability: STABLE
Labels:
- member_cluster: Cluster name
Description: Total number of nodes in the cluster. Use with cluster_ready_node_number to calculate node health ratio.

cluster_ready_node_number

Component: karmada-controller-manager
Type: Gauge
Stability: STABLE
Labels:
- member_cluster: Cluster name
Description: Number of ready nodes in the cluster. Low ratio vs total nodes indicates node health issues.

Example query:

# Node readiness ratio per cluster
cluster_ready_node_number / cluster_node_number

# Clusters with less than 80% ready nodes
(cluster_ready_node_number / cluster_node_number) < 0.8

cluster_memory_allocatable_bytes

Component: karmada-controller-manager
Type: Gauge
Stability: STABLE
Labels:
- member_cluster: Cluster name
Description: Allocatable cluster memory in bytes. Represents total memory capacity available for scheduling.

cluster_cpu_allocatable_number

Component: karmada-controller-manager
Type: Gauge
Stability: STABLE
Labels:
- member_cluster: Cluster name
Description: Allocatable CPU in cores. Represents total CPU capacity available for scheduling.

cluster_pod_allocatable_number

Component: karmada-controller-manager
Type: Gauge
Stability: STABLE
Labels:
- member_cluster: Cluster name
Description: Allocatable pod capacity. Represents maximum number of pods that can be scheduled.

cluster_memory_allocated_bytes

Component: karmada-controller-manager
Type: Gauge
Stability: STABLE
Labels:
- member_cluster: Cluster name
Description: Allocated cluster memory in bytes. Sum of memory requests for all scheduled pods.

cluster_cpu_allocated_number

Component: karmada-controller-manager
Type: Gauge
Stability: STABLE
Labels:
- member_cluster: Cluster name
Description: Allocated CPU in cores. Sum of CPU requests for all scheduled pods.

Example query:

# CPU utilization by cluster
(cluster_cpu_allocated_number / cluster_cpu_allocatable_number) * 100

# Clusters with high CPU utilization (>85%)
(cluster_cpu_allocated_number / cluster_cpu_allocatable_number) > 0.85

cluster_pod_allocated_number

Component: karmada-controller-manager
Type: Gauge
Stability: STABLE
Labels:
- member_cluster: Cluster name
Description: Number of allocated pods in the cluster. Count of pods currently scheduled.

cluster_sync_status_duration_seconds

Component: karmada-controller-manager
Type: Histogram
Stability: STABLE
Labels:
- member_cluster: Cluster name
Description: Duration to sync cluster status once. High latency means delayed detection of cluster state changes.

Example query:

# P95 cluster status sync latency
histogram_quantile(0.95, rate(cluster_sync_status_duration_seconds_bucket[5m])) by (member_cluster)

Resource Propagation Metrics

These metrics track the propagation of resources from Karmada control plane to member clusters.

Propagation Pipeline: Policy Match → Policy Apply → Binding Sync → Work Sync → Resource Create/Update

resource_match_policy_duration_seconds

Component: karmada-controller-manager
Type: Histogram
Stability: STABLE
Labels: None
Description: Duration to find a matched PropagationPolicy or ClusterPropagationPolicy for a resource template. First step in propagation pipeline - delays here impact the entire process.
Buckets: Exponential from 0.001s to ~4s

Example query:

# P95 policy matching latency
histogram_quantile(0.95, rate(resource_match_policy_duration_seconds_bucket[5m]))

resource_apply_policy_duration_seconds

Component: karmada-controller-manager
Type: Histogram
Stability: STABLE
Labels:
- result: success or error
Description: Duration to apply a PropagationPolicy or ClusterPropagationPolicy to a resource template.
Buckets: Exponential from 0.001s to ~4s

karmada_policy_apply_attempts_total

Component: karmada-controller-manager
Type: Counter
Stability: STABLE
Labels:
- result: success or error
Description: Number of attempts to apply a PropagationPolicy or ClusterPropagationPolicy. High error rate indicates policy application failures preventing propagation.

Example query:

# Policy application error rate
rate(karmada_policy_apply_attempts_total{result="error"}[5m])
  /
rate(karmada_policy_apply_attempts_total[5m])

policy_preemption_total

Component: karmada-controller-manager
Type: Counter
Stability: STABLE
Labels:
- result: success or error
Description: Number of preemptions where a resource is taken over by a higher-priority propagation policy. High error rate indicates policy conflict issues.

karmada_binding_sync_work_duration_seconds

Component: karmada-controller-manager
Type: Histogram
Stability: STABLE
Labels:
- result: success or error
Description: Duration to sync Work objects for a ResourceBinding or ClusterResourceBinding. Second step in propagation - converts bindings to executable work.
Buckets: Exponential from 0.001s to ~4s

Example query:

# P95 binding-to-work sync latency
histogram_quantile(0.95, rate(karmada_binding_sync_work_duration_seconds_bucket[5m]))

karmada_work_sync_workload_duration_seconds

Component: karmada-controller-manager, karmada-agent
Type: Histogram
Stability: STABLE
Labels:
- result: success or error
Description: Duration to sync the workload to a target member cluster. Critical metric - measures end-to-end sync time from Work object to cluster.
Buckets: Exponential from 0.001s to ~4s

Example query:

# P95 work-to-cluster sync latency
histogram_quantile(0.95, rate(karmada_work_sync_workload_duration_seconds_bucket[5m]))

karmada_create_resource_to_cluster

Component: karmada-controller-manager, karmada-agent
Type: Counter
Stability: STABLE
Labels:
- result: success or error
- apiversion: Resource API version
- kind: Resource kind
- member_cluster: Target cluster name
- recreate: true or false (indicates recreating a previously deleted resource)
Description: Number of resource creation operations to member clusters. Errors indicate connectivity or permission issues.

Example query:

# Resource creation error rate by cluster
sum by (member_cluster) (
  rate(karmada_create_resource_to_cluster{result="error"}[5m])
)

karmada_update_resource_to_cluster

Component: karmada-controller-manager, karmada-agent
Type: Counter
Stability: STABLE
Labels:
- result: success or error
- apiversion: Resource API version
- kind: Resource kind
- member_cluster: Target cluster name
- operationResult: Type of update performed
Description: Number of resource update operations to member clusters.

Example query:

# Resource update error rate by kind
sum by (kind) (
  rate(karmada_update_resource_to_cluster{result="error"}[5m])
)

karmada_delete_resource_from_cluster

Component: karmada-controller-manager, karmada-agent
Type: Counter
Stability: STABLE
Labels:
- result: success or error
- apiversion: Resource API version
- kind: Resource kind
- member_cluster: Target cluster name
Description: Number of resource deletion operations from member clusters. Errors indicate issues removing resources during cleanup or migration.

Failover and Eviction Metrics

These metrics track cluster failure handling and resource eviction.

karmada_eviction_queue_depth

Component: karmada-controller-manager
Type: Gauge
Stability: STABLE
Labels:
- name: Queue name
Description: Current depth of the eviction queue. High or growing values indicate failover processing backlog.

Example query:

# Eviction queue depth
karmada_eviction_queue_depth

# Queue growth rate (positive = backlog)
deriv(karmada_eviction_queue_depth[5m])

karmada_eviction_kind_total

Component: karmada-controller-manager
Type: Gauge
Stability: STABLE
Labels:
- member_cluster: Source cluster name
- resource_kind: Resource kind
Description: Number of resources in eviction queue by kind and source cluster. Use to identify which resource types are being evicted.

Example query:

# Resources by kind across all clusters
sum by (resource_kind) (karmada_eviction_kind_total)

karmada_eviction_processing_latency_seconds

Component: karmada-controller-manager
Type: Histogram
Stability: STABLE
Labels:
- name: Queue name
Description: Latency for processing an eviction task (moving resource from failed cluster to healthy cluster). High latency delays failover.
Buckets: Exponential from 0.001s to ~32s

Example query:

# P95 eviction processing latency
histogram_quantile(0.95, rate(karmada_eviction_processing_latency_seconds_bucket[5m]))

karmada_eviction_processing_total

Component: karmada-controller-manager
Type: Counter
Stability: STABLE
Labels:
- name: Queue name
- result: success or error
Description: Total evictions processed. High error rate indicates issues rescheduling workloads during cluster failures.

Example query:

# Eviction success rate
rate(karmada_eviction_processing_total{result="success"}[5m])
  /
rate(karmada_eviction_processing_total[5m])

Autoscaling Metrics

These metrics track FederatedHPA and CronFederatedHPA controllers.

karmada_federatedhpa_process_duration_seconds

Component: karmada-controller-manager
Type: Histogram
Stability: STABLE
Labels:
- result: success or error
Description: Duration to process a FederatedHPA object (one reconciliation loop). High latency delays scaling decisions.
Buckets: Exponential from 0.01s to ~40s

Example query:

# P95 FederatedHPA processing latency
histogram_quantile(0.95, rate(karmada_federatedhpa_process_duration_seconds_bucket[5m]))

karmada_federatedhpa_pull_metrics_duration_seconds

Component: karmada-controller-manager
Type: Histogram
Stability: STABLE
Labels:
- result: success or error
- metricType: Metric type
Description: Duration for FederatedHPA to pull metrics from member clusters. High latency or errors impact scaling responsiveness.
Buckets: Exponential from 0.01s to ~40s

Example query:

# P95 metrics pull latency by type
histogram_quantile(0.95, rate(karmada_federatedhpa_pull_metrics_duration_seconds_bucket[5m])) by (metricType)

karmada_cronfederatedhpa_process_duration_seconds

Component: karmada-controller-manager
Type: Histogram
Stability: STABLE
Labels:
- result: success or error
Description: Duration to process a CronFederatedHPA object.
Buckets: Exponential from 0.001s to ~4s

karmada_cronfederatedhpa_rule_process_duration_seconds

Component: karmada-controller-manager
Type: Histogram
Stability: STABLE
Labels:
- result: success or error
Description: Duration to process a single CronFederatedHPA rule. Use to identify slow or problematic cron rules.
Buckets: Exponential from 0.001s to ~4s

HPA Controller Metrics

These metrics are from the horizontal pod autoscaler controller (based on Kubernetes upstream).

horizontal_pod_autoscaler_controller_reconciliations_total

Component: karmada-controller-manager
Type: Counter
Stability: ALPHA
Labels:
- action: scale_up, scale_down, or none
- error: spec, internal, or none
Description: Number of HPA reconciliations. action indicates scaling decision, error indicates failure type. Spec errors are configuration issues, internal errors are runtime failures.

horizontal_pod_autoscaler_controller_reconciliation_duration_seconds

Component: karmada-controller-manager
Type: Histogram
Stability: ALPHA
Labels:
- action: scale_up, scale_down, or none
- error: spec, internal, or none
Description: Time for one HPA reconciliation loop. Critical for understanding HPA responsiveness.
Buckets: Exponential from 0.001s to ~32s

horizontal_pod_autoscaler_controller_metric_computation_total

Component: karmada-controller-manager
Type: Counter
Stability: ALPHA
Labels:
- action: Scaling action
- error: Error type
- metric_type: Resource, Pods, Object, External, or ContainerResource
Description: Number of metric computations. Use to identify problematic metric types.

horizontal_pod_autoscaler_controller_metric_computation_duration_seconds

Component: karmada-controller-manager
Type: Histogram
Stability: ALPHA
Labels:
- action: Scaling action
- error: Error type
- metric_type: Metric type
Description: Time to compute one metric. Slow metrics delay scaling.
Buckets: Exponential from 0.001s to ~32s

Example query:

# P95 metric computation latency by type
histogram_quantile(0.95, rate(horizontal_pod_autoscaler_controller_metric_computation_duration_seconds_bucket[5m])) by (metric_type)

Controller Workqueue Metrics

Standard controller-runtime workqueue metrics available on all Karmada controllers.

workqueue_adds_total

Component: All controllers
Type: Counter
Stability: STABLE
Labels:
- name: Controller/queue name
Description: Total items added to the workqueue. Rate indicates incoming work load.

Example query:

# Workqueue add rate
rate(workqueue_adds_total[5m]) by (name)

workqueue_depth

Component: All controllers
Type: Gauge
Stability: STABLE
Labels:
- name: Controller/queue name
Description: Current workqueue depth. High values indicate controller cannot keep up. Values >100 warrant investigation.

Example query:

# Controllers with deep queues
workqueue_depth > 100

workqueue_queue_duration_seconds

Component: All controllers
Type: Histogram
Stability: STABLE
Labels:
- name: Controller/queue name
Description: How long items stay in queue before processing. High P95/P99 indicates controller saturation.
Buckets: Exponential from 1e-8s to ~1000s

Example query:

# P95 queue wait time by controller
histogram_quantile(0.95, rate(workqueue_queue_duration_seconds_bucket[5m])) by (name)

workqueue_work_duration_seconds

Component: All controllers
Type: Histogram
Stability: STABLE
Labels:
- name: Controller/queue name
Description: How long processing one item takes. Identifies slow reconciliation loops.
Buckets: Exponential from 1e-8s to ~1000s

Example query:

# P95 reconciliation duration by controller
histogram_quantile(0.95, rate(workqueue_work_duration_seconds_bucket[5m])) by (name)

workqueue_retries_total

Component: All controllers
Type: Counter
Stability: STABLE
Labels:
- name: Controller/queue name
Description: Total retries processed by the workqueue. High retry rate indicates transient failures or bugs. Retry rate >10% of adds rate is concerning.

Example query:

# Retry ratio (should be <10%)
rate(workqueue_retries_total[5m]) / rate(workqueue_adds_total[5m])

workqueue_unfinished_work_seconds

Component: All controllers
Type: Gauge
Stability: STABLE
Labels:
- name: Controller/queue name
Description: How long the oldest unfinished work item has been in progress. High values indicate stuck reconciliations.

Example query:

# Controllers with stuck items (>60 seconds)
workqueue_unfinished_work_seconds > 60

workqueue_longest_running_processor_seconds

Component: All controllers
Type: Gauge
Stability: STABLE
Labels:
- name: Controller/queue name
Description: How long the longest running processor has been running. Identifies stuck worker goroutines.

Webhook Metrics

These metrics track admission webhook operations.

controller_runtime_webhook_requests_total

Component: karmada-webhook
Type: Counter
Stability: STABLE
Labels:
- webhook: Webhook name
- code: HTTP status code
Description: Total requests processed by each webhook. Non-2xx codes indicate webhook failures that can block API operations. Critical to monitor.

Example query:

# Webhook error rate
sum(rate(controller_runtime_webhook_requests_total{code!~"2.."}[5m]))
  /
sum(rate(controller_runtime_webhook_requests_total[5m]))

# Failing webhooks by name
sum by (webhook) (
  rate(controller_runtime_webhook_requests_total{code!~"2.."}[5m])
)

Pool Metrics

These metrics track object pool usage for reducing memory allocations.

pool_get_operation_total

Component: karmada-controller-manager, karmada-agent
Type: Counter
Stability: STABLE
Labels:
- name: Pool name
- from: pool (cache hit) or new (cache miss)
Description: Total get operations from object pools. from=pool indicates cache hit, from=new indicates new allocation. High miss rate may indicate undersized pools.

pool_put_operation_total

Component: karmada-controller-manager, karmada-agent
Type: Counter
Stability: STABLE
Labels:
- name: Pool name
- to: pool (returned to pool) or drop (pool full)
Description: Total put operations to object pools. to=drop indicates object dropped because pool is full.

Build Information

This metric provides version and build information for all components.

karmada_build_info

Component: All components
Type: Gauge
Stability: STABLE
Labels:
- git_version: Version string
- git_commit: Full commit hash
- git_short_commit: Short commit hash
- git_tree_state: Git tree state
- build_date: Build date
- go_version: Go version
- compiler: Compiler used
- platform: Platform (OS/arch)
Description: Constant gauge (value=1) with build metadata as labels. Essential for tracking component versions and correlating issues with releases.

Example queries:

# List all component versions
karmada_build_info

# Count components by version
count by (git_version) (karmada_build_info)

# Check for version mismatches
count(count by (git_version) (karmada_build_info)) > 1

Component Metrics Summary

Quick reference for which component emits which metrics:

karmada-controller-manager

All cluster_* metrics
All resource_*, policy_*, binding_*, work_* propagation metrics
All *_resource_to_cluster / *_resource_from_cluster metrics
All eviction_* metrics
All federatedhpa_* and cronfederatedhpa_* metrics
All horizontal_pod_autoscaler_* metrics
pool_* metrics
workqueue_* metrics
karmada_build_info

karmada-scheduler

All karmada_scheduler_* metrics (except estimator)
scheduler_pending_bindings
workqueue_* metrics
karmada_build_info

karmada-scheduler-estimator

All karmada_scheduler_estimator_* metrics
karmada_build_info

karmada-agent

Subset of cluster_* metrics (for local cluster only)
work_sync_workload_duration_seconds
*_resource_to_cluster / *_resource_from_cluster metrics
pool_* metrics
workqueue_* metrics
karmada_build_info

karmada-webhook

controller_runtime_webhook_requests_total
karmada_build_info

karmada-descheduler

workqueue_* metrics
karmada_build_info

Querying Best Practices

Rate Calculations

Use appropriate time windows for rate() and increase():

# Good: 5-minute rate for dashboards
rate(karmada_scheduler_schedule_attempts_total[5m])

# Good: 1-minute rate for alerting (faster response)
rate(karmada_scheduler_schedule_attempts_total[1m])

# Bad: Too short, susceptible to noise
rate(karmada_scheduler_schedule_attempts_total[30s])

Histogram Quantiles

Use appropriate quantiles for different purposes:

# P50: Median latency (typical case)
histogram_quantile(0.50, rate(karmada_scheduler_e2e_scheduling_duration_seconds_bucket[5m]))

# P95: Good for understanding user experience
histogram_quantile(0.95, rate(karmada_scheduler_e2e_scheduling_duration_seconds_bucket[5m]))

# P99: Captures tail latency (worst case)
histogram_quantile(0.99, rate(karmada_scheduler_e2e_scheduling_duration_seconds_bucket[5m]))

Error Rates

Calculate error rates as ratio of errors to total:

# Scheduling error rate
rate(karmada_scheduler_schedule_attempts_total{result="error"}[5m])
  /
rate(karmada_scheduler_schedule_attempts_total[5m])

# Workqueue retry rate
rate(workqueue_retries_total[5m])
  /
rate(workqueue_adds_total[5m])

Resource Utilization

Calculate utilization as percentage:

# CPU utilization by cluster
(cluster_cpu_allocated_number / cluster_cpu_allocatable_number) * 100

# Memory utilization
(cluster_memory_allocated_bytes / cluster_memory_allocatable_bytes) * 100

Label Cardinality

Be mindful of label cardinality in queries:

member_cluster: Bounded by number of clusters (typically <100)
kind, apiversion: Bounded by resource types used
name (workqueue): Bounded by number of controllers (~20-30)
plugin, webhook: Bounded by installed plugins/webhooks

High-cardinality labels like namespace or individual resource names are intentionally excluded.

Deprecated Labels

The following labels are deprecated and will be removed in v1.18:

Old Label	New Label	Affected Metrics	Deprecated In
`cluster`	`member_cluster`	All cluster metrics	v1.16
`cluster_name`	`member_cluster`	All cluster metrics	v1.16

Migration: Update all queries, dashboards, and alerts to use member_cluster.

Karmada Observability Guide - Complete monitoring setup and best practices
PromQL Query Reference - Ready-to-use queries
Working with Prometheus - Prometheus setup guide

Karmada Metrics Architecture​

Metric Conventions​

Naming​

Types​

Stability Levels​

Metrics by Category​

Scheduler Metrics​

karmada_scheduler_schedule_attempts_total​

karmada_scheduler_e2e_scheduling_duration_seconds​

karmada_scheduler_scheduling_algorithm_duration_seconds​

karmada_scheduler_queue_incoming_bindings_total​

karmada_scheduler_framework_extension_point_duration_seconds​

karmada_scheduler_plugin_execution_duration_seconds​

scheduler_pending_bindings​

Scheduler Estimator Metrics​

karmada_scheduler_estimator_estimating_request_total​

karmada_scheduler_estimator_estimating_algorithm_duration_seconds​

karmada_scheduler_estimator_estimating_plugin_extension_point_duration_seconds​

karmada_scheduler_estimator_estimating_plugin_execution_duration_seconds​

Cluster Health Metrics​

cluster_ready_state​

cluster_node_number​

cluster_ready_node_number​

cluster_memory_allocatable_bytes​

cluster_cpu_allocatable_number​

cluster_pod_allocatable_number​

cluster_memory_allocated_bytes​

cluster_cpu_allocated_number​

cluster_pod_allocated_number​

cluster_sync_status_duration_seconds​

Resource Propagation Metrics​

resource_match_policy_duration_seconds​

resource_apply_policy_duration_seconds​

karmada_policy_apply_attempts_total​

policy_preemption_total​

karmada_binding_sync_work_duration_seconds​

karmada_work_sync_workload_duration_seconds​

karmada_create_resource_to_cluster​

karmada_update_resource_to_cluster​

karmada_delete_resource_from_cluster​

Failover and Eviction Metrics​

karmada_eviction_queue_depth​

karmada_eviction_kind_total​

karmada_eviction_processing_latency_seconds​

karmada_eviction_processing_total​

Autoscaling Metrics​

karmada_federatedhpa_process_duration_seconds​

karmada_federatedhpa_pull_metrics_duration_seconds​

karmada_cronfederatedhpa_process_duration_seconds​

karmada_cronfederatedhpa_rule_process_duration_seconds​

HPA Controller Metrics​

horizontal_pod_autoscaler_controller_reconciliations_total​

horizontal_pod_autoscaler_controller_reconciliation_duration_seconds​

horizontal_pod_autoscaler_controller_metric_computation_total​

horizontal_pod_autoscaler_controller_metric_computation_duration_seconds​

Controller Workqueue Metrics​

workqueue_adds_total​

workqueue_depth​

workqueue_queue_duration_seconds​

workqueue_work_duration_seconds​

workqueue_retries_total​

workqueue_unfinished_work_seconds​

workqueue_longest_running_processor_seconds​

Webhook Metrics​

controller_runtime_webhook_requests_total​

Pool Metrics​

pool_get_operation_total​

pool_put_operation_total​

Build Information​

karmada_build_info​

Component Metrics Summary​

karmada-controller-manager​

karmada-scheduler​

karmada-scheduler-estimator​

karmada-agent​

karmada-webhook​

karmada-descheduler​

Querying Best Practices​

Rate Calculations​

Histogram Quantiles​

Karmada Metrics Architecture

Metric Conventions

Naming

Types

Stability Levels

Metrics by Category

Scheduler Metrics

karmada_scheduler_schedule_attempts_total

karmada_scheduler_e2e_scheduling_duration_seconds

karmada_scheduler_scheduling_algorithm_duration_seconds

karmada_scheduler_queue_incoming_bindings_total

karmada_scheduler_framework_extension_point_duration_seconds

karmada_scheduler_plugin_execution_duration_seconds

scheduler_pending_bindings

Scheduler Estimator Metrics

karmada_scheduler_estimator_estimating_request_total

karmada_scheduler_estimator_estimating_algorithm_duration_seconds

karmada_scheduler_estimator_estimating_plugin_extension_point_duration_seconds

karmada_scheduler_estimator_estimating_plugin_execution_duration_seconds

Cluster Health Metrics

cluster_ready_state

cluster_node_number

cluster_ready_node_number

cluster_memory_allocatable_bytes

cluster_cpu_allocatable_number

cluster_pod_allocatable_number

cluster_memory_allocated_bytes

cluster_cpu_allocated_number

cluster_pod_allocated_number

cluster_sync_status_duration_seconds

Resource Propagation Metrics

resource_match_policy_duration_seconds

resource_apply_policy_duration_seconds

karmada_policy_apply_attempts_total

policy_preemption_total

karmada_binding_sync_work_duration_seconds

karmada_work_sync_workload_duration_seconds

karmada_create_resource_to_cluster

karmada_update_resource_to_cluster

karmada_delete_resource_from_cluster

Failover and Eviction Metrics

karmada_eviction_queue_depth

karmada_eviction_kind_total

karmada_eviction_processing_latency_seconds

karmada_eviction_processing_total

Autoscaling Metrics

karmada_federatedhpa_process_duration_seconds

karmada_federatedhpa_pull_metrics_duration_seconds

karmada_cronfederatedhpa_process_duration_seconds

karmada_cronfederatedhpa_rule_process_duration_seconds

HPA Controller Metrics

horizontal_pod_autoscaler_controller_reconciliations_total

horizontal_pod_autoscaler_controller_reconciliation_duration_seconds

horizontal_pod_autoscaler_controller_metric_computation_total

horizontal_pod_autoscaler_controller_metric_computation_duration_seconds

Controller Workqueue Metrics

workqueue_adds_total

workqueue_depth

workqueue_queue_duration_seconds

workqueue_work_duration_seconds

workqueue_retries_total

workqueue_unfinished_work_seconds

workqueue_longest_running_processor_seconds

Webhook Metrics

controller_runtime_webhook_requests_total

Pool Metrics

pool_get_operation_total

pool_put_operation_total

Build Information

karmada_build_info

Component Metrics Summary

karmada-controller-manager

karmada-scheduler

karmada-scheduler-estimator

karmada-agent

karmada-webhook

karmada-descheduler

Querying Best Practices

Rate Calculations

Histogram Quantiles