Recent changes and improvements to Dash0

What's New

• Inline drilldown from any chart — Time-series, pie, gauge, stat, and tree-map charts now drill straight into the records behind them.
• Searchable picker for multi-time-series charts — Charts with many series open a dialog with live counts of matching records per series and full keyboard navigation.
• Per-(series × signal) drilldowns — Multi-series and multi-signal charts produce independent drilldowns, each scoped to its row's labels and its signal's filters.
• Explore and Triage modes — Read records with Explore, or surface what differentiates them with Triage. Available for spans, logs, and web events.
• Filter and time-range carry-over — Filters from PromQL and series labels and your selected time range follow you into the destination explorer with no manual setup.
  

Why This Matters

Explore data behind a chart used to mean hopping between a dashboard, the spans explorer, the logs explorer, and a metric drilldown — copying filter values and time ranges across each step. Drilldown collapses that sequence into a single click. You can spot a misbehaving series, drill straight into its underlying data, and keep visual context throughout, without any manual filter setup.

Try It

Open any dashboard, select a time range on one of your charts, and pick Explore or Triage in the action bar. For pie, gauge, stat, and tree map panels, click a segment instead.

The Drill Down from Charts documentation covers every entry point per chart type, keyboard shortcuts, the Explore vs. Triage decision, and the filter translation rules.

Kudos Are Due

The logic to decide which telemetry is used in a PromQL query has been devised together with Julius Volz, one of the founders of Prometheus and the mind behind PromLabs. If you want to learn Prometheus, we cannot recommend the self-paced courses enough, they are effectively a rite of passage at Dash0.

Setting up alerting for a new technology has always been the slow part of getting full coverage. You know which metrics matter, but you still have to write the PromQL, decide on thresholds, and pick severities. Now we do that for you.

Every integration in the Hub has a Check Rules section listing the alerts most worth running for that technology. Browse them on the public Integrations Hub at dash0.com/hub before you sign up, or open them from the in-app integration page and install with one click; already configured with sensible thresholds and severities.
Each rule card is transparent about what it does and what it needs. You can see which of its metrics are already flowing into your organization and get a clear pointer if a dependency integration isn't set up yet; added rules are also disabled by default so you don’t need to worry about them spamming you immediately before you make modifications.
Once added, these rules behave like any check rule you authored yourself; you can edit, route to notification channels, adjust thresholds. The Hub is a curated starting point, not a black box.

How it works

Every spam filter is expressed as a YAML document with kind: Dash0SpamFilterCopy. The Dash0 CLI, Terraform provider and Kubernetes operator all accept the same YAML document that you can download from the Dataset configurations:

Dash0 CLI

The spam-filtersCopy command is currently experimental and requires the --experimentalCopy (or -XCopy) flag:

spams-filters.sh
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# Create a spam filter from a YAML file
dash0 spam-filters create -X --dataset default -f drop-health-checks.yaml

# List all spam filters in a dataset
dash0 spam-filters list -X --dataset default

# Get a single spam filter as YAML
dash0 spam-filters get -X --dataset default <id>

# Update an existing spam filter
dash0 spam-filters update -X --dataset default <id> -f drop-health-checks.yaml

# Delete a spam filter
dash0 spam-filters delete -X --dataset default <id>

Terraform provider

spam-filters.tf
1234
resource "dash0_spam_filter" "drop_health_checks" {
  dataset          = "default"
  spam_filter_yaml = file("${path.module}/filters/drop-health-checks.yaml")
}

Kubernetes operator

spam-filter.yaml
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apiVersion: operator.dash0.com/v1alpha1
kind: Dash0SpamFilter
metadata:
  name: drop-health-checks
  namespace: monitoring
spec:
  contexts:
    - log
  filter:
    - key: "k8s.namespace.name"
      value:
        stringValue:
          operator: "equals"
          comparisonValue: "kube-system"

One more thing…

We have reworked the Spam Filters UI under Datasets so that you can name your spam filters (which of course works also via Infrastructure as Code), and download both the SpamFilters CRD to use with the Terraform provider for Dash0, the CLI and the Kubernetes operator.

OK, I meant two more things

With any luck, there will be another much requested capability about spam filters going live in the new few weeks. Keep your eyes peeled ;-)

How it works

Each recording rule is scoped to a dataset and defined as a YAML document that follows the PrometheusRuleCopy specification.Inside the YAML you declare one or more rule groups, each with an evaluation intervalCopy and a list of rulesCopy entries that pair a recordCopy name with a PromQL exprCopy. Dash0 evaluates these expressions on the configured cadence and writes the resulting series back into the dataset, ready for dashboards and alert conditions.

Dash0 CLI

cli-recording-rules.sh
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# Create recording rules from a YAML file
dash0 recording-rules create --dataset default -f recording-rules.yaml

# List all recording rules in a dataset
dash0 recording-rules list --dataset default

# Get a single recording rule by ID
dash0 recording-rules get --dataset default <id>

# Update an existing recording rule
dash0 recording-rules update --dataset default <id> -f recording-rules.yaml

# Delete a recording rule
dash0 recording-rules delete --dataset default <id>

Dash0 Terraform Provider

recording-rule.tf
1234
resource "dash0_recording_rule" "http_metrics" {
  dataset            = "default"
  recording_rule_yaml = file("${path.module}/rules/http-recording-rules.yaml")
}

Dash0 Operator for Kubernetes

recording-rule.yaml
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apiVersion: operator.dash0.com/v1alpha1
kind: Dash0RecordingRule
metadata:
  name: http-recording-rules
  namespace: monitoring
spec:
  dataset: default
  content: |
    apiVersion: monitoring.coreos.com/v1
    kind: PrometheusRule
    metadata:
      name: my-recording-rules
    spec:
      groups:
        - name: http_metrics
          interval: 1m
          rules:
            - record: http_requests:rate5m
              expr: rate(http_requests_total[5m])

The operator syncs the recording rule to Dash0 and reports status back on the custom resource.

Your Google Cloud infrastructure is now fully visible in Dash0 the moment you connect a project. No agents. No YAML. No per-resource configuration. This release brings Cloud Run, Pub/Sub, and Cloud Storage into Dash0 with comprehensive metrics out of the box — and broader GCP coverage is already on its way.

What's New

Zero-configuration metric collection: Connect a GCP project through a guided OAuth flow and Dash0 immediately starts collecting a default metric set across every discovered resource. Metrics flow the instant your project connects without anything to enable, or to configure.

Cloud Run monitoring: Instant visibility into Cloud Run Services and Cloud Run Jobs. Track request counts, latency, error rates, CPU, memory, and instance counts the moment you're connected.

Pub/Sub monitoring: Topics and subscriptions in a clean nested view. Catch message throughput issues, delivery latency spikes, undelivered message counts, and oldest unacked message age — without building a single dashboard first.

Cloud Storage monitoring: Request counts, data transfer volumes, and error rates at the bucket level, live from the moment your project connects.

Metric coverage: Each service type ships with Core metrics (required for the Dash0 experience), Default metrics (on automatically, covering what you would check first during an incident), and Extended metrics (available on demand via integration settings). For finer control, spam filtering applies on top of any of these.

Available in early access: Google Cloud Run Services, Google Cloud Run Jobs, Pub/Sub, Cloud Storage.

Keeping alerting configuration in sync across environments has always been tedious.You can now manage notification channels — Slack, PagerDuty, email, webhooks, and more — as code using the Dash0 CLI, Terraform provider, and Kubernetes operator.

Notification channels control where Dash0 sends alerts when check rules fire. Until now they could only be configured through the UI, which made it difficult to version-control, review, and replicate them across organizations. With IaC support, you define a channel once in a YAML file and apply it from your terminal, CI/CD pipeline, or GitOps workflow.

How it works

Each notification channel is defined as a CRD-enveloped YAML document with kind: Dash0NotificationChannelCopy.The spec.typeCopy field selects the channel type (one of 17 supported integrations), and spec.configCopy holds the type-specific settings such as webhook URLs or Slack channel names.Notification channels are organization-level resources, so no --datasetCopy flag is required.

Dash0 CLI

Since v1.9.0:

notification-channel-cli.sh
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# Create a notification channel from a YAML file
dash0 -X notification-channels create -f slack-alerts.yaml

# List all notification channels
dash0 -X notification-channels list

# Get a single notification channel as YAML
dash0 -X notification-channels get <id> -o yaml

# Update an existing notification channel
dash0 -X notification-channels update <id> -f slack-alerts-updated.yaml

# Delete a notification channel
dash0 -X notification-channels delete <id>

Terraform provider

Since v1.8.0:

notification-channel.tf
123
resource "dash0_notification_channel" "slack_alerts" {
  notification_channel_yaml = file("${path.module}/channels/slack-alerts.yaml")
}

Import existing channels with terraform import dash0_notification_channel.slack_alerts <origin>Copy.

Kubernetes operator

Since v0.136.0:

notification-channel.yaml
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apiVersion: operator.dash0.com/v1alpha1
kind: Dash0NotificationChannel
metadata:
  name: slack-alerts
  namespace: monitoring
spec:
  content: |
    kind: Dash0NotificationChannel
    metadata:
      name: Slack Alerts
    spec:
      type: slack
      config:
        url: "https://hooks.slack.com/services/T00/B00/xxxx"
      frequency: 10m

The operator syncs the channel to Dash0 and reports status back on the custom resource.

What's New

Full SQL Support: Write SQL against logs, spans, and web events. Joins, subqueries, aggregations. Functions for aggregation, string manipulation, date, array, math, logic, JSON extraction, and type conversion are supported. Because it's standard SQL, any AI tool can write queries for you — just describe what you want and paste the result in. See the full function reference in the docs.

Cross-signal joins and aggregation on arbitrary attributes unlock questions no single explorer can answer. Join spans to themselves to compute connection KPIs across service boundaries. Pull custom span attributes — query IDs, URL parameters, business-specific flags — alongside performance data to understand not just that something is slow, but which request types are responsible.

Built-in Query Templates: A library of ready-to-run query examples ships with Dash0 SQL. Pick a template, run it, and modify it from there — no blank-page problem.

Recent Queries: Dash0 SQL keeps a local history of your last 20 queries. Get back to anything you ran earlier without rewriting it from scratch.

Saved Views with Sharing: Save any query as a named view that lives in Dash0 and persists across sessions and devices. Share saved views with teammates so the whole team can build on the same go-to queries.

We've added drag-to-resize handles to table columns across Dash0's explorers. Hover over the table header row to reveal grip handles at column edges, then drag to adjust the width. Save your view to keep your preferred column sizes across sessions.

To reset column sizes, use the view reset button. If you've saved a view with custom column sizes, a "Reset column sizes" button will appear in the table Settings menu.

Available in: Trace Explorer, Log Explorer, Web Events Explorer, and Resources.

If you're using a shared web integration across multiple domains — think franchise networks, regional storefronts, or white-label deployments — you've probably wondered which domain a particular session was visiting. Until now, there was no way to tell from the Dash0 UI.

This update surfaces the page.url.domainCopy attribute in three places:

Session list sidebar

The session summary card now shows the domain alongside location and browser info, so you can identify which site a user was on at a glance.

Session detail page

When viewing a full session, the domain appears in the header for quick context as you step through page views and events.

Dashboard filters

A new Domain filter variable is available in both the Overview and Web Vitals dashboards, letting you slice metrics by domain across your entire network.

Also in this update:

• On smaller screens, the session detail header items no longer get squeezed — they now wrap gracefully instead.

How It Works

When you add Dash0 as an MCP server in your AI tool of choice, the tool will automatically open a browser window where you log in and grant consent — just like connecting any other app. Behind the scenes, Dash0 handles dynamic client registration, token exchange, and automatic refresh. You never touch a token.

For example, in Claude Code

1
claude mcp add --transport http dash0 {{endpoint_mcp}}

That's it. Claude Code will walk you through the OAuth flow on first use.

Why This Matters

• No more token management. You don't need to visit the Dash0 UI to create an auth token, copy it into your config, and remember to rotate it.
• Short-lived, auto-refreshing tokens. Access tokens expire in 15 minutes and refresh automatically - reducing the blast radius if a token is ever compromised.
• Full audit trail. Every OAuth authorization, token exchange, and revocation is tracked in your organization's audit log.
• Revocable at any time. You can review and revoke connected applications from your Dash0 settings (User Settings -> ApplicationsCopy).

What You Get

Once connected, your AI tool has access to 20+ MCP tools for querying your observability data - service catalogs, PromQL metrics, logs, traces, synthetic checks, dashboards, and more. All scoped to your user permissions and dataset access.

Traditional Tokens Still Work

If you prefer managing tokens yourself - for CI pipelines, scripts, or automated workflows - bearer token authentication remains fully supported. OAuth is an additional option, not a replacement.