otel-collector

Expert guidance for configuring and deploying the OpenTelemetry Collector. Use when setting up a Collector pipeline, configuring receivers, exporters, or processors, deploying a Collector to Kubernetes or Docker, or forwarding telemetry to Dash0. Triggers on requests involving collector, pipeline, OTLP receiver, exporter, or Dash0 collector setup.

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Install skill "otel-collector" with this command: npx skills add dash0hq/agent-skills/dash0hq-agent-skills-otel-collector

OpenTelemetry Collector configuration guide

Expert guidance for configuring and deploying the OpenTelemetry Collector to receive, process, and export telemetry.

Rules

RuleDescription
receiversReceivers — OTLP, Prometheus, filelog, hostmetrics
exportersExporters — OTLP/gRPC to Dash0, debug, authentication
processorsProcessors — memory limiter, resource detection, ordering, sending queue
pipelinesPipelines — service section, per-signal configuration, connectors
deploymentDeployment — agent vs gateway patterns, deployment method selection
dash0-operatorDash0 Kubernetes Operator — automated instrumentation, Collector management, Dash0 export
collector-helm-chartCollector Helm chart — presets, modes, image selection
opentelemetry-operatorOpenTelemetry Operator — Collector CRD, auto-instrumentation, sidecar
raw-manifestsRaw Kubernetes manifests — DaemonSet, Deployment, RBAC, Docker Compose
samplingSampling — head, tail, load balancing
red-metricsRED metrics — span-derived request rate, error rate, duration histograms

Key principles

  • Processor ordering matters. Place memory_limiter first in every pipeline. Use the exporter's sending_queue with file_storage instead of the batch processor. Incorrect ordering causes memory exhaustion or data loss.
  • One pipeline per signal type. Define separate pipelines for traces, metrics, and logs. Mixing signals in a single pipeline breaks processing and causes runtime errors.
  • Every declared component must appear in a pipeline. The Collector rejects configurations that declare receivers, processors, or exporters not referenced by any pipeline.
  • Consistent resource enrichment across pipelines. Apply processors that enrich resource attributes like resourcedetection and k8sattributes to every signal pipeline (traces, metrics, and logs), not just one. If one pipeline enriches telemetry with k8s.namespace.name or host.name but another does not, correlation between signals is compromised by incomplete metadata.
  • Memory safety is non-negotiable. Always configure memory_limiter in production. Without it, a burst of telemetry can cause the Collector to OOM and crash.

Quick reference

What do you need?Rule
Accept OTLP telemetry from applicationsreceivers
Scrape Prometheus endpointsreceivers
Collect log files or host metricsreceivers
Send telemetry to Dash0exporters
Configure retry, queue, or compressionexporters
Set processor orderingprocessors
Add Kubernetes or cloud metadataprocessors
Wire receivers → processors → exporterspipelines
Complete working configurationpipelines
Validate the pipeline with the debug exportercollector-helm-chart, opentelemetry-operator, raw-manifests, or dash0-operator
Deploy as DaemonSet or Deploymentraw-manifests
Deploy with Helmcollector-helm-chart
Deploy with the OTel Operatoropentelemetry-operator
Deploy with the Dash0 Operatordash0-operator
Auto-instrument applications in Kubernetesopentelemetry-operator or dash0-operator
Local development with Docker Composeraw-manifests
Reduce trace volumesampling
Keep errors and slow traces, drop the restsampling
Generate RED metrics from tracesred-metrics

Official documentation

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