Grafana Dashboards

PiVirt provides curated, pre-built Grafana dashboards for monitoring cluster topology, hypervisor resource saturation, VM performance, and storage pool throughput.

Dashboard Installation

Import via Grafana UI

1. Open Grafana web interface
2. Navigate to Dashboards → Import
3. Upload dashboard JSON file or enter dashboard ID

Import via Grafana API

# Import dashboard
curl -X POST http://localhost:3000/api/dashboards/db \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $GRAFANA_TOKEN" \
  -d @pivirt-cluster-overview.json

Import via Helm

values.yaml

grafana:
  sidecar:
    dashboards:
      enabled: true
      label: grafana_dashboard
      searchNamespace: ALL
  dashboardProviders:
    dashboardproviders.yaml:
      apiVersion: 1
      providers:
        - name: 'pivirt'
          orgId: 1
          folder: 'PiVirt'
          type: file
          disableDeletion: false
          editable: true
          options:
            path: /var/lib/grafana/dashboards/pivirt

Available Dashboards

1. PiVirt Cluster Overview

Dashboard ID: 12345

Panels:

• Cluster health status
• Node count and status
• VM count by status
• CPU/Memory/Disk utilization
• Network I/O
• API request rate

Variables:

• $cluster - Cluster name
• $namespace - Namespace filter
• $interval - Time interval

2. VM Performance

Dashboard ID: 12346

Panels:

• VM CPU usage (per VM)
• VM memory usage (per VM)
• VM disk I/O (read/write)
• VM network I/O (RX/TX)
• VM status timeline
• Top VMs by resource usage

Variables:

• $namespace - Namespace filter
• $vm - VM name filter
• $interval - Time interval

3. Storage Utilization

Dashboard ID: 12347

Panels:

• Storage pool capacity
• Storage pool usage
• Volume size and usage
• IOPS (read/write)
• Latency
• Replication status

Variables:

• $pool - Storage pool filter
• $volume - Volume name filter
• $interval - Time interval

4. API Performance

Dashboard ID: 12348

Panels:

• API request rate
• API error rate
• API latency (p50, p95, p99)
• API request duration histogram
• Top methods by latency
• Error rate by method

Variables:

• $method - API method filter
• $status - Status code filter
• $interval - Time interval

Dashboard JSON Examples

Cluster Overview Dashboard

{
  "dashboard": {
    "id": null,
    "uid": "pivirt-cluster-overview",
    "title": "PiVirt Cluster Overview",
    "tags": ["pivirt", "cluster"],
    "timezone": "browser",
    "panels": [
      {
        "id": 1,
        "title": "Cluster Health",
        "type": "stat",
        "targets": [
          {
            "expr": "pivirt_cluster_node_count{status=\"ready\"}",
            "legendFormat": "Ready Nodes"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "thresholds": {
              "steps": [
                {"color": "red", "value": 0},
                {"color": "yellow", "value": 2},
                {"color": "green", "value": 3}
              ]
            }
          }
        }
      },
      {
        "id": 2,
        "title": "VM Count",
        "type": "stat",
        "targets": [
          {
            "expr": "pivirt_cluster_vm_count{status=\"running\"}",
            "legendFormat": "Running VMs"
          }
        ]
      },
      {
        "id": 3,
        "title": "CPU Usage",
        "type": "gauge",
        "targets": [
          {
            "expr": "avg(pivirt_cluster_node_cpu_usage_percent)",
            "legendFormat": "Avg CPU"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "unit": "percent",
            "max": 100
          }
        }
      },
      {
        "id": 4,
        "title": "Memory Usage",
        "type": "gauge",
        "targets": [
          {
            "expr": "avg(pivirt_cluster_node_memory_usage_percent)",
            "legendFormat": "Avg Memory"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "unit": "percent",
            "max": 100
          }
        }
      },
      {
        "id": 5,
        "title": "VM Count by Status",
        "type": "piechart",
        "targets": [
          {
            "expr": "pivirt_cluster_vm_count",
            "legendFormat": "{{status}}"
          }
        ]
      },
      {
        "id": 6,
        "title": "Node CPU Usage",
        "type": "timeseries",
        "targets": [
          {
            "expr": "pivirt_cluster_node_cpu_usage_percent",
            "legendFormat": "{{node}}"
          }
        ]
      }
    ],
    "time": {
      "from": "now-1h",
      "to": "now"
    },
    "templating": {
      "list": [
        {
          "name": "cluster",
          "type": "query",
          "query": "label_values(pivirt_cluster_node_count, cluster)",
          "refresh": 1
        },
        {
          "name": "namespace",
          "type": "query",
          "query": "label_values(pivirtd_vm_count, namespace)",
          "refresh": 1
        }
      ]
    }
  }
}

VM Performance Dashboard

{
  "dashboard": {
    "id": null,
    "uid": "pivirt-vm-performance",
    "title": "VM Performance",
    "tags": ["pivirt", "vm"],
    "panels": [
      {
        "id": 1,
        "title": "VM CPU Usage",
        "type": "timeseries",
        "targets": [
          {
            "expr": "pivirtd_vm_cpu_usage_percent{namespace=~\"$namespace\", vm=~\"$vm\"}",
            "legendFormat": "{{vm}}"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "unit": "percent",
            "max": 100
          }
        }
      },
      {
        "id": 2,
        "title": "VM Memory Usage",
        "type": "timeseries",
        "targets": [
          {
            "expr": "pivirtd_vm_memory_usage_bytes{namespace=~\"$namespace\", vm=~\"$vm\"}",
            "legendFormat": "{{vm}}"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "unit": "bytes"
          }
        }
      },
      {
        "id": 3,
        "title": "VM Disk I/O",
        "type": "timeseries",
        "targets": [
          {
            "expr": "rate(pivirtd_vm_disk_read_bytes_total{namespace=~\"$namespace\", vm=~\"$vm\"}[5m])",
            "legendFormat": "{{vm}} read"
          },
          {
            "expr": "rate(pivirtd_vm_disk_write_bytes_total{namespace=~\"$namespace\", vm=~\"$vm\"}[5m])",
            "legendFormat": "{{vm}} write"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "unit": "Bps"
          }
        }
      },
      {
        "id": 4,
        "title": "VM Network I/O",
        "type": "timeseries",
        "targets": [
          {
            "expr": "rate(pivirtd_vm_network_rx_bytes_total{namespace=~\"$namespace\", vm=~\"$vm\"}[5m])",
            "legendFormat": "{{vm}} RX"
          },
          {
            "expr": "rate(pivirtd_vm_network_tx_bytes_total{namespace=~\"$namespace\", vm=~\"$vm\"}[5m])",
            "legendFormat": "{{vm}} TX"
          }
        ],
        "fieldConfig": {
          "defaults": {
            "unit": "Bps"
          }
        }
      },
      {
        "id": 5,
        "title": "Top VMs by CPU",
        "type": "bargauge",
        "targets": [
          {
            "expr": "topk(10, pivirtd_vm_cpu_usage_percent)",
            "legendFormat": "{{vm}}"
          }
        ]
      }
    ],
    "templating": {
      "list": [
        {
          "name": "namespace",
          "type": "query",
          "query": "label_values(pivirtd_vm_count, namespace)",
          "refresh": 1
        },
        {
          "name": "vm",
          "type": "query",
          "query": "label_values(pivirtd_vm_cpu_usage_percent{namespace=~\"$namespace\"}, vm)",
          "refresh": 2,
          "multi": true,
          "includeAll": true
        }
      ]
    }
  }
}

Dashboard Variables

Common Variables

{
  "templating": {
    "list": [
      {
        "name": "cluster",
        "type": "query",
        "datasource": "Prometheus",
        "query": "label_values(pivirt_cluster_node_count, cluster)",
        "refresh": 1,
        "sort": 1
      },
      {
        "name": "namespace",
        "type": "query",
        "datasource": "Prometheus",
        "query": "label_values(pivirtd_vm_count, namespace)",
        "refresh": 1,
        "sort": 1,
        "multi": true,
        "includeAll": true
      },
      {
        "name": "interval",
        "type": "interval",
        "query": "1m,5m,15m,30m,1h",
        "current": {
          "text": "5m",
          "value": "5m"
        }
      }
    ]
  }
}

Alert Integration

Dashboard Alerts

{
  "alert": {
    "name": "VM High CPU",
    "conditions": [
      {
        "evaluator": {
          "params": [90],
          "type": "gt"
        },
        "operator": {"type": "and"},
        "query": {
          "params": ["A", "5m", "now"]
        },
        "reducer": {
          "params": [],
          "type": "avg"
        },
        "type": "query"
      }
    ],
    "frequency": "1m",
    "handler": 1,
    "message": "VM CPU usage > 90%",
    "notifications": [
      {"uid": "slack-alerts"}
    ]
  }
}

Provisioning

Dashboard Provisioning

/etc/grafana/provisioning/dashboards/pivirt.yml

apiVersion: 1

providers:
  - name: 'PiVirt'
    orgId: 1
    folder: 'PiVirt'
    type: file
    disableDeletion: false
    updateIntervalSeconds: 30
    allowUiUpdates: true
    options:
      path: /var/lib/grafana/dashboards/pivirt
      foldersFromFilesStructure: true

Datasource Provisioning

/etc/grafana/provisioning/datasources/prometheus.yml

apiVersion: 1

datasources:
  - name: Prometheus
    type: prometheus
    access: proxy
    url: http://prometheus:9090
    isDefault: true
    editable: false

Best Practices

1. Use Variables

• Use template variables for filtering
• Make dashboards reusable across clusters
• Allow users to focus on specific resources

2. Organize Dashboards

• Group related panels
• Use rows for logical separation
• Keep dashboards focused

3. Set Appropriate Time Ranges

• Default to 1h for operational dashboards
• Use 24h for trend analysis
• Allow users to adjust time range

4. Use Thresholds

• Color-code panels based on thresholds
• Make critical issues immediately visible
• Use consistent color scheme

5. Document Dashboards

• Add descriptions to panels
• Document variables and filters
• Include links to related documentation

Related

• Monitoring Operations
• Prometheus Integration
• Metrics API