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Dash0

Dash0

What's Changed

Full Changelog: https://github.com/dash0hq/dash0-operator/compare/0.142.0...0.143.0

The old assistant has been replaced by an autonomous production AI built on a new execution runtime, with continuous environment scanning, full-loop investigation, and pull request generation against your codebase.

Key capabilities

  • Continuous environment scanning — surfaces failing services, infrastructure pressure, and deployment correlations
  • Integrated contextual experience across Dash0 features
  • Parallel capabilities including environment scanning, Q&A, multi-signal correlation, and asset generation
  • Sandboxed execution runtime — similar in architecture to Claude Code
  • Full-loop incident response — root cause identification to commit level and PR drafting
  • Validated dashboards and alerts against live telemetry
  • Native integrations: GitHub, Linear, Confluence, SQL, Bash, and MCP servers
  • Service-centric chat interface redesign
  • Task-based credit pricing model

What's Changed

Full Changelog: https://github.com/dash0hq/dash0-operator/compare/0.141.0...0.142.0

Dash0 now supports web event annotations on time-series charts, letting you correlate spikes and dips in your time-series charts with what real users were doing.

Real User Monitoring (RUM) web events can be overlaid on time-series dashboard widgets alongside existing annotation sources such as logs, Kubernetes events, and service events.

Features:

  • Apply filters consistent with the Web Events Explorer interface
  • View matching events as markers on chart timelines
  • Clustered markers collapse into grouped pills by event type, maintaining visual distinction between event categories
  • Chart legends include a dedicated "Web events" row with separate event counts
  • Each marker displays a tooltip with event attributes and provides one-click navigation to the Web Events Explorer with filters and selections preserved, enabling rapid navigation from dashboard observations to detailed session analysis

Dash0 expanded its Kubernetes monitoring capabilities by introducing support for new OpenTelemetry semantic conventions and improving the k8sobjectsreceiver integration.

New Resource Types

The OpenTelemetry K8s SIG introduced new semantic conventions for multiple Kubernetes resource types. Dash0 added support for replication controllers, horizontal pod autoscalers, persistent volumes and claims, and resource quotas through new resource types:

  • k8s.replicationcontroller
  • k8s.service
  • k8s.persistentvolume
  • k8s.persistentvolumeclaim
  • k8s.hpa
  • k8s.resourcequota

A generic k8s.object resource type was also added for Kubernetes resources lacking existing semantic conventions, including CRDs.

k8sobjectsreceiver Enhancements

The k8sobjectsreceiver generates log records for Kubernetes API state changes. Dash0 improved this by extracting resource attributes from log bodies — such as pod names, UIDs, and node information — to better associate logs with their corresponding resources.

Key improvements include:

  • Extraction of k8s.* attributes from log message bodies for proper resource association
  • Replacement of placeholder fields with semantically meaningful k8s.object designations
  • Support for dashboard annotations based on Kubernetes resource changes
  • Normalization of Kubernetes event formats between different receivers
  • UTC timestamp standardization for the k8seventsreceiver

These enhancements allow users to overlay Kubernetes resource modifications against telemetry data for improved observability.

What's Changed

Full Changelog: https://github.com/dash0hq/dash0-operator/compare/0.140.0...0.141.0

When troubleshooting slow or failing database queries, knowing which query ran is the first thing you need, but finding it buried in the Attributes tab is painful. Now it's front and center in the span sidebar, syntax-highlighted, with prepared statement parameters already filled in.

Making database queries easy to find

A dedicated query widget was added to the span sidebar. If a span is a database span — carrying either the db.query.text or legacy db.statement attribute — the query appears prominently with syntax highlighting. No more hunting through tabs.

Query parameters for prepared statements

Several OpenTelemetry instrumentations for Java, .NET, and Python collect prepared statement parameters as db.query.parameter.<id_or_key> span attributes. These values are interpolated directly into the query so users can read it as it actually ran.

The interpolated parameters are interactive: filter them in or out, inspect individual values, copy to clipboard, or pin as a span table column.

Enabling query parameters with the Dash0 Operator for Kubernetes

Starting with Operator v0.141.0, the Dash0 Operator supports capturing query parameters via instrumentWorkloads.captureSqlQueryParameters: true in the Dash0Monitoring resource. This enables parameter collection for injected Java and .NET instrumentations. Python requires code-level configuration; other languages can use custom instrumentations.

Not using the Dash0 Operator, or libraries that collect database query parameters?

The Agent Skills (v1.3.0) help configure parameter collection for Java, .NET, and Python apps, plus custom instrumentations for languages lacking built-in support.

What's Changed

Full Changelog: https://github.com/dash0hq/dash0-operator/compare/0.139.0...0.140.0

What's Changed

Full Changelog: https://github.com/dash0hq/dash0-operator/compare/0.138.1...0.139.0

What's Changed

Full Changelog: https://github.com/dash0hq/dash0-operator/compare/0.137.0...0.138.0

When you spot something unusual in a chart — a sudden spike in a logs chart, a latency outlier, an error rate climbing on one service — the next question is always the same: which records actually caused this? Dash0 now gives you a direct way of answering this question. Select a time range or on a chart, click a pie segment, or click a stat value, and an action bar appears with Explore and Triage actions that take you straight into the matching signal explorer with filters and time scope already applied.

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

Exploring data behind a chart used to mean hopping between a dashboard, the spans explorer, the logs explorer, and a metric drilldown with manual filter copying. The new feature collapses this workflow into a single action.

Try It

Open any dashboard and select a time range on a chart to access Explore or Triage actions. Documentation

What's Changed

Full Changelog: https://github.com/dash0hq/dash0-operator/compare/0.136.0...0.137.0

The Integrations Hub now includes 101 curated check (alerting) rules covering technologies like Kubernetes, Vercel, AWS RDS, Istio, Argo CD, Cilium, the OpenTelemetry Collector, and many more.

Setting up alerting for a new technology has always been the slow part of getting full coverage. Rather than manually configuring PromQL expressions, thresholds, and severity levels from scratch, these pre-built rules handle that automatically.

Key capabilities:

  • Browse rules publicly at dash0.com/hub before signup
  • One-click installation from in-app integration pages
  • Pre-configured PromQL expressions with sensible thresholds and severity levels
  • Transparent documentation showing what each rule does and its required metrics and dependencies
  • Visibility into which metrics are already flowing to your organization
  • Rules are disabled by default to prevent immediate alert spam
  • Full customization available after installation — edit, route, and adjust thresholds as needed

The Hub is designed as a curated starting point, not a black box.

Bug Fixes

  • spam-filters: Fix spam filter FilterCriteria format in fixtures and tests to match the Dash0 API schema (#134) The filter criteria now use the correct flat format with operator and value fields instead of the nested stringValue format.

Dash0 now supports managing spam filters through Infrastructure as Code tooling. Teams can define and manage spam filters — which drop unwanted logs, spans, and metrics before they reach storage by matching on structured attribute conditions — via three methods:

Dash0 CLI (experimental, requires -X flag)

dash0 spam-filters create -X --dataset default -f drop-health-checks.yaml
dash0 spam-filters list -X --dataset default
dash0 spam-filters get -X --dataset default <id>
dash0 spam-filters update -X --dataset default <id> -f drop-health-checks.yaml
dash0 spam-filters delete -X --dataset default <id>

Terraform Provider

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

Kubernetes Operator

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"

The Spam Filters UI was also updated to support naming filters and downloading their YAML configuration for use across all three provisioning methods. Additional spam filter capabilities are expected in the coming weeks.

New Components

  • spam-filters: Add spam-filters command with list, get, create, update, and delete subcommands (#132) Manage dataset-scoped spam filters via dash0 spam-filters <subcommand>. Supports file-based input (-f), dry-run validation, and all standard output formats (table, wide, json, yaml, csv).
Last Checked
1h ago
Domain
dash0.com
Tracking since Jun 25, 2024