Views are saved queries in Dash0 that capture filters, groupings, and visualizations for specific telemetry patterns you need to revisit regularly. Instead of reconstructing complex queries each time you investigate an issue or review system behavior, views preserve your query configuration and make it accessible with a single click. They work across all telemetry types: logs, spans, metrics, failed checks, web events, and resources.

A typical view might filter production error logs grouped by service name and pod, or show high-latency traces from a specific endpoint. Views appear in the navigation sidebar alongside dashboards, giving you quick access to frequently used queries. Unlike dashboards that compose multiple panels into a single view, a Dash0 view represents one saved query configuration for a single telemetry type.

Creating and Managing Views

You can create views directly in the Dash0 UI by building a query with filters and groupings, then saving it through the view menu. Once saved, the view appears in your navigation sidebar and can be shared with teammates through access control permissions. Views support both view-only and edit permissions, allowing you to control who can modify query configurations.

For infrastructure as code workflows, views can be exported to YAML format and managed programmatically using the Dash0 Operator for Kubernetes, Dash0 Terraform Provider, or Dash0 CLI. The Terraform provider's dash0_view resource enables you to version control view definitions alongside your infrastructure code, deploy them through CI/CD pipelines, and maintain consistent query configurations across environments.

View Types and Schema

Each view is scoped to a specific telemetry type and includes type-appropriate filters and grouping options:

logs — Saved log queries with filters on severity, message content, attributes, and grouping by service, host, or custom dimensions. Useful for recurring log analysis patterns like error investigations or audit queries.
spans — Saved trace queries filtering by operation name, duration, status code, or trace attributes. Group by service, endpoint, or span attributes to analyze request patterns and performance characteristics.
metrics — Saved metric queries with specific metric names, aggregations, and dimensional filters. These preserve visualization preferences and time range selections for frequently monitored metrics.
failed_checks — Views of alert failures with filters on check rule name, severity, or affected resources. These help teams track specific classes of failures without manually filtering the full alert history.
web_events — Saved queries for browser-side telemetry including page views, user interactions, and client-side errors. Filter by page path, user cohort, or event type.
resources — Saved views of services, pods, hosts, or other resource types with filters on resource attributes. Useful for focusing on specific subsets of your infrastructure like production Kubernetes namespaces or services owned by a particular team.

The exported YAML schema includes the view type, display metadata (name and description), filter array with key/operator/value tuples, and groupBy array specifying dimensional breakdowns. This consistent schema across view types makes it straightforward to understand and modify view definitions in code, whether you're updating filters to reflect new semantic conventions or adjusting groupings to match organizational changes.

Views

Creating and Managing Views

View Types and Schema

Further Reading