Collector Helm chart

The OpenTelemetry Collector Helm chart deploys the Collector as a DaemonSet, Deployment, or StatefulSet with a single helm install command. Use it instead of raw manifests for production Kubernetes deployments.

Installation

helm repo add open-telemetry https://open-telemetry.github.io/opentelemetry-helm-charts
helm install otel-collector open-telemetry/opentelemetry-collector \
  --namespace otel --create-namespace \
  --set mode=daemonset

Mode

The mode value is required and determines the Kubernetes workload type.

Agent configuration (DaemonSet)

Deploy as an agent to collect node-level telemetry and receive OTLP from local applications.

# values-agent.yaml
mode: daemonset

image:
  repository: ghcr.io/open-telemetry/opentelemetry-collector-releases/opentelemetry-collector-k8s

command:
  name: otelcol-k8s

presets:
  logsCollection:
    enabled: true
  kubernetesAttributes:
    enabled: true
  hostMetrics:
    enabled: true

resources:
  limits:
    cpu: 500m
    memory: 512Mi

config:
  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: 410
      spike_limit_mib: 100

  exporters:
    otlp:
      endpoint: <OTLP_ENDPOINT>
      headers:
        Authorization: "Bearer ${env:DASH0_AUTH_TOKEN}"
      sending_queue:
        enabled: true
        queue_size: 5000
        storage: file_storage

  extensions:
    health_check:
      endpoint: 0.0.0.0:13133
    file_storage:
      directory: /var/lib/otelcol/queue

  service:
    extensions: [health_check, file_storage]
    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]

extraEnvsFrom:
  - secretRef:
      name: dash0-credentials

Install with:

helm install otel-agent open-telemetry/opentelemetry-collector \
  --namespace otel --create-namespace \
  -f values-agent.yaml

Presets

Presets inject both the collector configuration and the required Kubernetes plumbing (volumes, RBAC rules, volume mounts) automatically. You cannot remove preset-injected config via .Values.config — if you need to override preset behaviour, disable the preset and configure the component manually.

Gateway configuration (Deployment)

Deploy as a gateway for centralized processing, enrichment, and export.

# values-gateway.yaml
mode: deployment

image:
  repository: ghcr.io/open-telemetry/opentelemetry-collector-releases/opentelemetry-collector-k8s

command:
  name: otelcol-k8s

replicaCount: 2

autoscaling:
  enabled: true
  minReplicas: 2
  maxReplicas: 10
  targetCPUUtilizationPercentage: 70

presets:
  kubernetesAttributes:
    enabled: true

resources:
  limits:
    cpu: "2"
    memory: 2Gi

config:
  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: 1638
      spike_limit_mib: 400
    resourcedetection:
      detectors: [env, system]
      timeout: 5s
      override: false
    resource:
      attributes:
        - key: k8s.cluster.name
          value: "<CLUSTER_NAME>"
          action: upsert

  exporters:
    otlp:
      endpoint: <OTLP_ENDPOINT>
      headers:
        Authorization: "Bearer ${env:DASH0_AUTH_TOKEN}"
      compression: gzip
      sending_queue:
        enabled: true
        num_consumers: 10
        queue_size: 5000
        storage: file_storage

  extensions:
    health_check:
      endpoint: 0.0.0.0:13133
    file_storage:
      directory: /var/lib/otelcol/queue

  service:
    extensions: [health_check, file_storage]
    pipelines:
      traces:
        receivers: [otlp]
        processors: [memory_limiter, resourcedetection, resource]
        exporters: [otlp]
      metrics:
        receivers: [otlp]
        processors: [memory_limiter, resourcedetection, resource]
        exporters: [otlp]
      logs:
        receivers: [otlp]
        processors: [memory_limiter, resourcedetection, resource]
        exporters: [otlp]

extraEnvsFrom:
  - secretRef:
      name: dash0-credentials

Image selection

Use the otelcol-k8s distribution for Kubernetes deployments. It includes the Kubernetes-specific components (k8sattributes, kubeletstats, k8s_cluster) that are not in the core distribution.

Pin the image tag to a specific version in production (e.g., tag: "0.120.0"). Do not use latest.

Memory management

The chart sets GOMEMLIMIT to 80 percent of the memory limit by default (useGOMEMLIMIT: true). Set memory_limiter.limit_mib to 80 percent of the container memory limit in the collector config.

Validating the setup with the debug exporter

Add the debug exporter to the Helm values to verify that the pipeline processes telemetry correctly before sending it to a production backend.

# values-debug.yaml (merge with your agent or gateway values)
config:
  exporters:
    debug:
      verbosity: detailed

  service:
    pipelines:
      traces:
        exporters: [otlp, debug]
      metrics:
        exporters: [otlp, debug]
      logs:
        exporters: [otlp, debug]

Upgrade the release with the debug values:

helm upgrade otel-collector open-telemetry/opentelemetry-collector \
  --namespace otel \
  -f values-agent.yaml \
  -f values-debug.yaml

Inspect the output:

kubectl logs -n otel -l app.kubernetes.io/name=opentelemetry-collector --tail=100 -f

What to check

Run through this checklist after adding the debug exporter:

1. Resource attributes are present. Verify that resourcedetection and k8sattributes added the expected attributes (e.g., host.name, k8s.namespace.name, k8s.deployment.name). If attributes are missing, check detector configuration and RBAC permissions.
2. Resource attributes are consistent across signals. Compare the resource attributes on a trace, a metric, and a log record from the same service. All three must carry the same set of resource attributes. A mismatch means a processor is missing from one of the pipelines.
3. Filters drop the right data. Confirm that outdated or unwanted metrics no longer appear in the output. If a metric that should be dropped still shows up, check the filter processor's instrumentation_scope.name condition.
4. Metric names and units match stable semantic conventions. Verify that the metrics reaching the exporter use the expected names (e.g., http.server.request.duration, not http.server.duration) and units (e.g., s, not ms).
5. Spans have expected attributes and parent-child relationships. Check that business attributes set in application code (e.g., order.id) appear on spans, and that CLIENT spans are children of SERVER spans (not root spans).
6. Dash0 dataset header is set. If the organization uses multiple datasets, verify the Dash0-Dataset header in the Helm values. The debug exporter shows telemetry data, not outgoing headers — check the values file directly. See Dash0 dataset routing.

Remove the debug exporter before deploying to production

The debug exporter serializes every telemetry item to stdout. In production this wastes CPU and I/O, and risks logging sensitive attribute values. Remove the debug values overlay and upgrade the release once validation is complete.

See debug exporter for verbosity levels and configuration.

Dash0 dataset routing

If the Dash0 organization uses multiple datasets, add the Dash0-Dataset header to the OTLP exporter to route telemetry to the correct dataset.

config:
  exporters:
    otlp:
      endpoint: <OTLP_ENDPOINT>
      headers:
        Authorization: "Bearer ${env:DASH0_AUTH_TOKEN}"
        Dash0-Dataset: "my-dataset"

A missing or incorrect Dash0-Dataset header causes telemetry to land in the default dataset. The debug exporter shows telemetry data, not outgoing headers — verify the header in the Helm values directly.

Anti-patterns

• Missing mode value. The chart requires mode to be set explicitly. Omitting it causes a deployment failure.
• Overriding preset config in .Values.config. Preset-injected components cannot be removed via the config overlay. Disable the preset and configure the component manually instead.
• Using the core image with Kubernetes presets. The core distribution does not include k8sattributes, kubeletstats, or k8s_cluster. Use the otelcol-k8s or contrib image.

References

• OpenTelemetry Collector Helm chart
• Helm chart values
• Deployment patterns