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The Cluster Metrics page provides real-time visualization of your cluster’s resource usage. Connect a Prometheus data source to enable comprehensive metrics monitoring.

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Overview

Ankra’s metrics visualization helps you understand your cluster’s health and resource consumption at a glance. View CPU usage, memory consumption, network throughput, disk I/O, and pod restart patterns, all from a single dashboard.

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Accessing Cluster Metrics

  1. Navigate to your cluster from the sidebar.
  2. Click Metrics in the cluster navigation.
  3. View real-time charts and summary cards.
Metrics require a configured Prometheus data source. See Prometheus Integration or Cluster Settings to configure your data source.

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Summary Cards

At the top of the metrics page, summary cards show current resource utilization:
CardDescription
CPU UsageCurrent CPU utilization vs total capacity
Memory UsageCurrent memory consumption vs total capacity
Network RateCombined inbound and outbound network throughput
Pod StatusRunning pods vs total pod count
These cards provide instant visibility into cluster health without scrolling through detailed charts.

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Metrics Charts

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CPU Usage

Visualize CPU consumption across your cluster nodes over time. What it shows:
  • Per-node CPU usage lines
  • Total cluster CPU trend
  • Usage patterns over the selected time range
Use cases:
  • Identify nodes with consistently high CPU usage
  • Spot CPU spikes that correlate with deployments or traffic
  • Plan capacity based on usage trends

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Memory Usage

Track memory consumption across your cluster. What it shows:
  • Per-node memory usage
  • Memory pressure indicators
  • Historical memory trends
Use cases:
  • Detect memory leaks in applications
  • Identify nodes approaching memory limits
  • Plan memory allocation for new workloads

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Network I/O

Monitor network traffic flowing in and out of your cluster. What it shows:
  • Receive: Inbound network traffic per node
  • Transmit: Outbound network traffic per node
  • Throughput rates in bytes/second
Use cases:
  • Identify network-intensive workloads
  • Detect unusual traffic patterns
  • Monitor data transfer costs

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Disk I/O

Track disk read and write operations across nodes. What it shows:
  • Disk read throughput
  • Disk write throughput
  • I/O patterns per node
Use cases:
  • Identify storage bottlenecks
  • Monitor database disk activity
  • Plan storage capacity

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Pod Restarts

Monitor pod restart frequency to detect stability issues. What it shows:
  • Pod restart counts over time
  • Restart patterns by namespace or workload
  • Correlation with other events
Use cases:
  • Detect crashlooping pods
  • Identify unstable deployments
  • Troubleshoot OOMKilled containers

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Time Range Selection

Control the time window for metrics data:
RangeUse Case
Last 15 minutesReal-time monitoring
Last hourRecent activity review
Last 6 hoursShift-based monitoring
Last 24 hoursDaily patterns
Last 7 daysWeekly trends
CustomSpecific time windows

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Changing Time Range

  1. Click the Time Range picker in the top-right corner
  2. Select a preset range or define a custom window
  3. Charts automatically update to show the selected period

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Auto-Refresh

Keep metrics up-to-date with automatic refresh:
IntervalDescription
OffManual refresh only
10 secondsNear real-time updates
30 secondsBalanced refresh rate
1 minuteLow-overhead monitoring
5 minutesBackground monitoring

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Manual Refresh

Click the Refresh button at any time to fetch the latest data immediately.

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Prometheus Configuration

Metrics require a connected Prometheus instance.

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Setting Up Prometheus

1

Install Prometheus

Deploy Prometheus to your cluster using a stack with the kube-prometheus-stack add-on, or connect to an existing Prometheus instance.
2

Configure Data Source

Go to cluster Settings → Metrics and enter your Prometheus URL.
3

Test Connection

Verify the connection is successful before saving.
4

View Metrics

Navigate to the Metrics page to see your cluster data.

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Default Prometheus URL

If using kube-prometheus-stack deployed via Ankra: 
http://kube-prometheus-stack-prometheus.prometheus.svc:9090

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Troubleshooting

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”Prometheus Not Configured”

Cause: No Prometheus data source has been set up. Solution:
  1. Go to cluster Settings → Metrics
  2. Configure your Prometheus URL
  3. Return to the Metrics page

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”Unable to Load Metrics”

Cause: Connection to Prometheus failed. Solutions:
  • Verify Prometheus is running in your cluster
  • Check the Prometheus URL is correct
  • Ensure network connectivity between the Ankra agent and Prometheus
  • Review Prometheus service account permissions

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Missing Data for Some Metrics

Cause: Prometheus may not be scraping all required metrics. Solutions:
  • Verify node-exporter is deployed for node metrics
  • Check kube-state-metrics is running for Kubernetes metrics
  • Review Prometheus scrape configurations

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Best Practices

Use appropriate time ranges: For real-time debugging, use 15-minute windows. For capacity planning, use 7-day views.
Set up alerts: Combine metrics visualization with Alerts to get notified when thresholds are exceeded.
Monitor during deployments: Watch metrics during rollouts to catch performance regressions early.
Correlate with events: Use the time range selector to align metrics with known incidents or changes.

Prometheus Integration

Set up Prometheus for your cluster.

Alerts

Configure alerts based on metrics.

Cluster Settings

Configure metrics data sources.

AI Troubleshooting

Use AI to analyze performance issues.