What query languages does Cloud Monitoring support for dashboards?

Cloud Monitoring supports MQL (Monitoring Query Language), PromQL (Prometheus Query Language), and the legacy Monitoring filter syntax. MQL is Google's native language with powerful operations like group_by, align, and outer_join. PromQL is ideal if your team already uses Prometheus. Both languages can query the same metric data — choose based on your team's familiarity. The builder generates queries in your preferred language.

Can I version-control and deploy dashboards programmatically?

Yes. Dashboards are JSON resources that can be created and updated via the Cloud Monitoring API, gcloud CLI, or Terraform google_monitoring_dashboard resources. Export a dashboard's JSON from the console, store it in version control, and deploy it across projects using your CI/CD pipeline. This approach ensures consistent observability across development, staging, and production environments.

GCP Cloud Monitoring Dashboard Builder

MonitoringGCP

Build Cloud Monitoring dashboard JSON with mosaic layouts, XY charts, scorecards, and template filters.

Cloud Monitoring Dashboards Configuration

Build Cloud Monitoring dashboard JSON with mosaic layouts, XY charts, scorecards, and template filters.

Required Fields

displayNamemosaicLayout.tiles

{
  "displayName": "Production GKE Cluster Dashboard",
  "mosaicLayout": {
    "columns": 12,
    "tiles": [
      {
        "width": 6,
        "height": 4,
        "widget": {
          "title": "CPU Utilization by Node",
          "xyChart": {
            "dataSets": [
              {
                "timeSeriesQuery": {
                  "timeSeriesFilter": {
                    "filter": "resource.type=\"k8s_node\" AND metric.type=\"kubernetes.io/node/cpu/allocatable_utilization\"",
                    "aggregation": {
                      "alignmentPeriod": "300s",
                      "perSeriesAligner": "ALIGN_MEAN",
                      "crossSeriesReducer": "REDUCE_MEAN",
                      "groupByFields": ["resource.label.node_name"]
                    }
                  }
                },
                "plotType": "LINE",
                "legendTemplate": "${resource.labels.node_name}"
              }
            ],
            "yAxis": {
              "label": "Utilization",
              "scale": "LINEAR"
            },
            "timeshiftDuration": "0s"
          }
        }
      },
      {
        "xPos": 6,
        "width": 6,
        "height": 4,
        "widget": {
          "title": "Memory Usage by Node",
          "xyChart": {
            "dataSets": [
              {
                "timeSeriesQuery": {
                  "timeSeriesFilter": {
                    "filter": "resource.type=\"k8s_node\" AND metric.type=\"kubernetes.io/node/memory/allocatable_utilization\"",
                    "aggregation": {
                      "alignmentPeriod": "300s",
                      "perSeriesAligner": "ALIGN_MEAN",
                      "crossSeriesReducer": "REDUCE_MEAN",
                      "groupByFields": ["resource.label.node_name"]
                    }
                  }
                },
                "plotType": "LINE"
              }
            ]
          }
        }
      },
      {
        "yPos": 4,
        "width": 4,
        "height": 4,
        "widget": {
          "title": "Pod Restart Count",
          "scorecard": {
            "timeSeriesQuery": {
              "timeSeriesFilter": {
                "filter": "resource.type=\"k8s_container\" AND metric.type=\"kubernetes.io/container/restart_count\"",
                "aggregation": {
                  "alignmentPeriod": "3600s",
                  "perSeriesAligner": "ALIGN_RATE",
                  "crossSeriesReducer": "REDUCE_SUM"
                }
              }
            },
            "thresholds": [
              {
                "value": 5,
                "color": "YELLOW",
                "direction": "ABOVE",
                "label": "Warning"
              },
              {
                "value": 20,
                "color": "RED",
                "direction": "ABOVE",
                "label": "Critical"
              }
            ]
          }
        }
      },
      {
        "xPos": 4,
        "yPos": 4,
        "width": 8,
        "height": 4,
        "widget": {
          "title": "Request Latency (p50/p95/p99)",
          "xyChart": {
            "dataSets": [
              {
                "timeSeriesQuery": {
                  "timeSeriesFilter": {
                    "filter": "resource.type=\"k8s_container\" AND metric.type=\"custom.googleapis.com/http/request_latency\"",
                    "aggregation": {
                      "alignmentPeriod": "60s",
                      "perSeriesAligner": "ALIGN_PERCENTILE_99"
                    }
                  }
                },
                "plotType": "LINE",
                "legendTemplate": "p99"
              }
            ]
          }
        }
      }
    ]
  },
  "dashboardFilters": [
    {
      "filterType": "RESOURCE_LABEL",
      "labelKey": "cluster_name",
      "templateVariable": "cluster"
    },
    {
      "filterType": "RESOURCE_LABEL",
      "labelKey": "namespace_name",
      "templateVariable": "namespace"
    }
  ],
  "labels": {
    "team": "sre",
    "env": "production"
  }
}

Generated Output

Output will appear here...

About This Tool

Cloud Monitoring dashboards let you visualize metrics, logs, and traces from GCP services, custom applications, and third-party integrations in a single pane. Dashboards are defined as JSON configurations and can be version-controlled and deployed programmatically. This builder helps you create dashboard definitions with widget layouts including line charts, scorecards, heatmaps, alert charts, and log panels, along with MQL or PromQL metric queries, generating the JSON or Terraform configuration for deployment.

When to Use This Tool

•Building a service health dashboard that displays latency percentiles, error rates, request counts, and SLO burn rate for Cloud Run services
•Creating an infrastructure overview dashboard with CPU utilization, memory usage, disk IOPS, and network throughput across Compute Engine fleets
•Designing a GKE cluster dashboard with pod status, node resource utilization, and container restart counts using Kubernetes metrics
•Assembling a cost monitoring dashboard that tracks BigQuery slot usage, Cloud Storage request counts, and network egress volumes

Frequently Asked Questions

What query languages does Cloud Monitoring support for dashboards?: Cloud Monitoring supports MQL (Monitoring Query Language), PromQL (Prometheus Query Language), and the legacy Monitoring filter syntax. MQL is Google's native language with powerful operations like group_by, align, and outer_join. PromQL is ideal if your team already uses Prometheus. Both languages can query the same metric data — choose based on your team's familiarity. The builder generates queries in your preferred language.
Can I version-control and deploy dashboards programmatically?: Yes. Dashboards are JSON resources that can be created and updated via the Cloud Monitoring API, gcloud CLI, or Terraform google_monitoring_dashboard resources. Export a dashboard's JSON from the console, store it in version control, and deploy it across projects using your CI/CD pipeline. This approach ensures consistent observability across development, staging, and production environments.

Related Learning Guides

GCP Network Security Guide24 min read

Disclaimer: This tool runs entirely in your browser. No data is sent to our servers. Always verify outputs before using them in production. AWS, Azure, and GCP are trademarks of their respective owners.

GCP Cloud Monitoring Dashboard Builder

MonitoringGCP

Build Cloud Monitoring dashboard JSON with mosaic layouts, XY charts, scorecards, and template filters.

Cloud Monitoring Dashboards Configuration

Build Cloud Monitoring dashboard JSON with mosaic layouts, XY charts, scorecards, and template filters.

Required Fields

displayNamemosaicLayout.tiles

Generated Output

Output will appear here...

About This Tool

When to Use This Tool

•Building a service health dashboard that displays latency percentiles, error rates, request counts, and SLO burn rate for Cloud Run services
•Creating an infrastructure overview dashboard with CPU utilization, memory usage, disk IOPS, and network throughput across Compute Engine fleets
•Designing a GKE cluster dashboard with pod status, node resource utilization, and container restart counts using Kubernetes metrics
•Assembling a cost monitoring dashboard that tracks BigQuery slot usage, Cloud Storage request counts, and network egress volumes

Frequently Asked Questions

What query languages does Cloud Monitoring support for dashboards?: Cloud Monitoring supports MQL (Monitoring Query Language), PromQL (Prometheus Query Language), and the legacy Monitoring filter syntax. MQL is Google's native language with powerful operations like group_by, align, and outer_join. PromQL is ideal if your team already uses Prometheus. Both languages can query the same metric data — choose based on your team's familiarity. The builder generates queries in your preferred language.
Can I version-control and deploy dashboards programmatically?: Yes. Dashboards are JSON resources that can be created and updated via the Cloud Monitoring API, gcloud CLI, or Terraform google_monitoring_dashboard resources. Export a dashboard's JSON from the console, store it in version control, and deploy it across projects using your CI/CD pipeline. This approach ensures consistent observability across development, staging, and production environments.

Related Learning Guides

GCP Network Security Guide24 min read