OpenTelemetry Collector configuration guide

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

Rules

Rule	Description
[receivers](./rules/receivers.md)	Receivers — OTLP, Prometheus, filelog, hostmetrics
[exporters](./rules/exporters.md)	Exporters — OTLP/gRPC to Dash0, debug, authentication
[processors](./rules/processors.md)	Processors — memory limiter, resource detection, ordering, sending queue
[pipelines](./rules/pipelines.md)	Pipelines — service section, per-signal configuration, connectors
[deployment](./rules/deployment.md)	Deployment — agent vs gateway patterns, deployment method selection
[dash0-operator](./rules/deployment/dash0-operator.md)	Dash0 Kubernetes Operator — automated instrumentation, Collector management, Dash0 export
[collector-helm-chart](./rules/deployment/collector-helm-chart.md)	Collector Helm chart — presets, modes, image selection
[opentelemetry-operator](./rules/deployment/opentelemetry-operator.md)	OpenTelemetry Operator — Collector CRD, auto-instrumentation, sidecar
[raw-manifests](./rules/deployment/raw-manifests.md)	Raw Kubernetes manifests — DaemonSet, Deployment, RBAC, Docker Compose
[sampling](./rules/sampling.md)	Sampling — head, tail, load balancing
[red-metrics](./rules/red-metrics.md)	RED metrics — span-derived request rate, error rate, duration histograms
[custom-distributions](./rules/custom-distributions.md)	Custom distributions — building a stripped-down Collector binary with OCB

Key principles

Processor ordering matters.

Place memorylimiter first in every pipeline. Use the exporter's sendingqueue 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 start

Minimal working configuration: OTLP receiver → memory limiter → OTLP/gRPC exporter to Dash0.

receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318

processors:
  memory_limiter:
    check_interval: 1s
    limit_mib: 400
    spike_limit_mib: 100

exporters:
  otlp:
    endpoint: ingress.eu-west-1.aws.dash0.com:4317
    headers:
      Authorization: "Bearer ${env:DASH0_TOKEN}"
    sending_queue:
      enabled: true
      storage: file_storage

service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [memory_limiter]
      exporters: [otlp]
    metrics:
      receivers: [otlp]
      processors: [memory_limiter]
      exporters: [otlp]
    logs:
      receivers: [otlp]
      processors: [memory_limiter]
      exporters: [otlp]

See [exporters](./rules/exporters.md) for full authentication and queue configuration, and [processors](./rules/processors.md) for adding resource detection.

Configuration workflow

Write config — define receivers, processors, and exporters; wire them in service.pipelines.
Validate locally — run otelcol validate --config=config.yaml to catch structural errors before deployment.
Deploy — choose a deployment method from the [deployment](./rules/deployment.md) rule (Helm, Operator, raw manifests, or Docker Compose).
Verify — add the debug exporter to a pipeline temporarily and inspect stdout to confirm telemetry is flowing; then remove it before going to production.

Quick reference

What do you need?	Rule
Accept OTLP telemetry from applications	[receivers](./rules/receivers.md)
Scrape Prometheus endpoints	[receivers](./rules/receivers.md)
Collect log files or host metrics	[receivers](./rules/receivers.md)
Send telemetry to Dash0	[exporters](./rules/exporters.md)
Configure retry, queue, or compression	[exporters](./rules/exporters.md)
Set processor ordering	[processors](./rules/processors.md)
Add Kubernetes or cloud metadata	[processors](./rules/processors.md)
Wire receivers → processors → exporters	[pipelines](./rules/pipelines.md)
Complete working configuration	[pipelines](./rules/pipelines.md)
Validate the pipeline with the debug exporter	[collector-helm-chart](./rules/deployment/collector-helm-chart.md), [opentelemetry-operator](./rules/deployment/opentelemetry-operator.md), [raw-manifests](./rules/deployment/raw-manifests.md), or [dash0-operator](./rules/deployment/dash0-operator.md)
Deploy as DaemonSet or Deployment	[raw-manifests](./rules/deployment/raw-manifests.md)
Deploy with Helm	[collector-helm-chart](./rules/deployment/collector-helm-chart.md)
Deploy with the OTel Operator	[opentelemetry-operator](./rules/deployment/opentelemetry-operator.md)
Deploy with the Dash0 Operator	[dash0-operator](./rules/deployment/dash0-operator.md)
Auto-instrument applications in Kubernetes	[opentelemetry-operator](./rules/deployment/opentelemetry-operator.md) or [dash0-operator](./rules/deployment/dash0-operator.md)
Local development with Docker Compose	[raw-manifests](./rules/deployment/raw-manifests.md)
Reduce trace volume	[sampling](./rules/sampling.md)
Keep errors and slow traces, drop the rest	[sampling](./rules/sampling.md)
Redact sensitive data in the pipeline	[processors](./rules/processors.md#sensitive-data-redaction)
Generate RED metrics from traces	[red-metrics](./rules/red-metrics.md)
Build a custom Collector binary	[custom-distributions](./rules/custom-distributions.md)

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