How to use this image

All examples in this guide use the public image. If you've mirrored the repository for your own use (for example, to your Docker Hub namespace), update your commands to reference the mirrored image instead of the public one.

For example:

• Public image: dhi.io/<repository>:<tag>
• Mirrored image: <your-namespace>/dhi-<repository>:<tag>

For the examples, you must first use docker login dhi.io to authenticate to the registry to pull the images.

This guide provides practical examples for using the OpenTelemetry Collector Hardened Image to collect, process, and export telemetry data (traces, metrics, and logs).

What's included in this OpenTelemetry Collector image

This Docker Hardened OpenTelemetry Collector image includes:

• The otelcol binary (core distribution) or otelcol-contrib binary (contrib distribution) built from the official OpenTelemetry Collector releases.
• A default configuration installed at /etc/otelcol/config.yaml (core) or /etc/otelcol-contrib/config.yaml (contrib).
• The entrypoint is the otelcol binary at /usr/local/bin/otelcol (or /usr/local/bin/otelcol-contrib for contrib); the default command loads the configuration from the installed config file.

Start an OpenTelemetry Collector container

docker run -d --name otel-collector \
    -p 4317:4317 \
    -p 4318:4318 \
    -p 8888:8888 \
    -p 13133:13133 \
    dhi.io/opentelemetry-collector:<tag>

Common use cases

Run with Docker Compose

cat <<EOF > docker-compose.yaml
services:
  otel-collector:
    image: dhi.io/opentelemetry-collector:<tag>
    ports:
      - "4317:4317"
      - "4318:4318"
      - "8888:8888"
      - "13133:13133"
EOF

Start the collector:

docker compose up -d

Run with Docker Compose (Custom Configuration)

The default configuration binds the health check extension to localhost:13133, which is not accessible from outside the container. To expose health checks externally, use a custom configuration.

Create otel-config.yaml:

cat <<EOF > otel-config.yaml
receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318

processors:
  batch:

exporters:
  debug:
    verbosity: detailed

extensions:
  health_check:
    endpoint: 0.0.0.0:13133

service:
  extensions: [health_check]
  telemetry:
    metrics:
      readers:
        - pull:
            exporter:
              prometheus:
                host: 0.0.0.0
                port: 8888
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [debug]
    metrics:
      receivers: [otlp]
      processors: [batch]
      exporters: [debug]
EOF

Create docker-compose.yaml:

cat <<EOF > docker-compose.yaml
services:
  otel-collector:
    image: dhi.io/opentelemetry-collector:<tag>
    ports:
      - "4317:4317"
      - "4318:4318"
      - "8888:8888"
      - "13133:13133"
    volumes:
      - ./otel-config.yaml:/etc/otelcol/config.yaml:ro
EOF

Start the collector:

docker compose up -d

Verify the health check endpoint:

curl http://localhost:13133/
{"status":"Server available","upSince":"2026-01-26T07:26:00.886986636Z","uptime":"5.162434461s"}

Run with Docker Compose (Full Stack with Jaeger)

To test the OpenTelemetry Collector with a tracing backend, use this complete Docker Compose configuration:

Create otel-config.yaml:

cat <<EOF > otel-config.yaml
receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318

processors:
  batch:

exporters:
  debug:
    verbosity: detailed
  otlp/jaeger:
    endpoint: jaeger:4317
    tls:
      insecure: true

extensions:
  health_check:
    endpoint: 0.0.0.0:13133

service:
  extensions: [health_check]
  telemetry:
    metrics:
      readers:
        - pull:
            exporter:
              prometheus:
                host: 0.0.0.0
                port: 8888
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch]
      exporters: [debug, otlp/jaeger]
    metrics:
      receivers: [otlp]
      processors: [batch]
      exporters: [debug]
EOF

Create docker-compose.yaml:

cat <<EOF > docker-compose.yml
services:
  otel-collector:
    image: dhi.io/opentelemetry-collector:<tag>
    ports:
      - "4317:4317"
      - "4318:4318"
      - "8888:8888"
      - "13133:13133"
    volumes:
      - ./otel-config.yaml:/etc/otelcol/config.yaml:ro
    depends_on:
      - jaeger
    restart: unless-stopped

  jaeger:
    image: jaegertracing/all-in-one:latest
    ports:
      - "16686:16686"
    environment:
      - COLLECTOR_OTLP_ENABLED=true
EOF

Start the stack:

docker compose up -d

Verify the collector is running:

curl http://localhost:13133/
{"status":"Server available","upSince":"...","uptime":"..."}

Send a test trace:

curl -X POST http://localhost:4318/v1/traces \
    -H "Content-Type: application/json" \
    -d '{
      "resourceSpans": [{
        "resource": {
          "attributes": [{
            "key": "service.name",
            "value": {"stringValue": "test-service"}
          }]
        },
        "scopeSpans": [{
          "spans": [{
            "traceId": "5B8EFFF798038103D269B633813FC60C",
            "spanId": "EEE19B7EC3C1B174",
            "name": "test-span",
            "kind": 1,
            "startTimeUnixNano": "1704067200000000000",
            "endTimeUnixNano": "1704067201000000000"
          }]
        }]
      }]
    }'

Access the Jaeger UI at http://localhost:16686 to view the trace.

Use OpenTelemetry Collector in Kubernetes

To use the OpenTelemetry Collector hardened image in Kubernetes, set up authentication and update your Kubernetes deployment.

cat <<EOF > otel-collector.yaml
apiVersion: apps/v1
kind: Deployment
metadata:
  name: otel-collector
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: otel-collector
  template:
    metadata:
      labels:
        app: otel-collector
    spec:
      containers:
        - name: otel-collector
          image: dhi.io/opentelemetry-collector:<tag>
          ports:
            - containerPort: 4317
              name: otlp-grpc
            - containerPort: 4318
              name: otlp-http
            - containerPort: 8888
              name: metrics
            - containerPort: 13133
              name: health
      imagePullSecrets:
        - name: <your-registry-secret>
---
apiVersion: v1
kind: Service
metadata:
  name: otel-collector
  namespace: default
spec:
  ports:
    - port: 4317
      targetPort: 4317
      name: otlp-grpc
    - port: 4318
      targetPort: 4318
      name: otlp-http
    - port: 8888
      targetPort: 8888
      name: metrics
  selector:
    app: otel-collector
EOF

Then apply the manifest to your Kubernetes cluster:

kubectl apply -n default -f otel-collector.yaml

Verify the deployment:

$ kubectl get pods -n default
NAME                              READY   STATUS    RESTARTS   AGE
otel-collector-6959756cc4-bbkp9   1/1     Running   0          38s

Access the metrics:

$ kubectl port-forward -n default deployment/otel-collector 8888:8888
$ curl http://localhost:8888/metrics | head -10

For examples of how to configure the OpenTelemetry Collector itself, see the OpenTelemetry Collector documentation⁠.

Non-hardened images vs Docker Hardened Images

Key differences

Why no shell or package manager?

Docker Hardened Images prioritize security through minimalism:

• Reduced attack surface: Fewer binaries mean fewer potential vulnerabilities
• Immutable infrastructure: Runtime containers shouldn't be modified after deployment
• Compliance ready: Meets strict security requirements for regulated environments

The hardened images intended for runtime don't contain a shell nor any tools for debugging. Common debugging methods for applications built with Docker Hardened Images include:

• Docker Debug⁠ to attach to containers
• Docker's Image Mount feature to mount debugging tools
• Ecosystem-specific debugging approaches

Docker Debug provides a shell, common debugging tools, and lets you install other tools in an ephemeral, writable layer that only exists during the debugging session.

For example, you can use Docker Debug:

$ docker debug otel-collector

Or mount debugging tools with the image mount feature:

$ docker run --rm -it --pid container:otel-collector \
    --mount=type=image,source=dhi.io/busybox:<tag>,destination=/dbg,ro \
    dhi.io/opentelemetry-collector:<tag> /dbg/bin/sh

Image variants

Docker Hardened Images come in different variants depending on their intended use. Image variants are identified by their tag.

The OpenTelemetry Collector image provides runtime, dev, and FIPS variants. Runtime variants are designed to run your application in production. These images are intended to be used either directly or as the FROM image in the final stage of a multi-stage build. These images typically:

• Run as a nonroot user
• Do not include a shell or a package manager
• Contain only the minimal set of libraries needed to run the app

The OpenTelemetry Collector is available in two distributions:

• Core distribution: Uses the otelcol binary with config at /etc/otelcol/config.yaml
• Contrib distribution: Uses the otelcol-contrib binary with additional receivers, exporters, and processors; config at /etc/otelcol-contrib/config.yaml

To view the image variants and get more information about them, select the Tags tab for this repository, and then select a tag.

FIPS variants

FIPS variants include fips in the variant name and tag. These variants use cryptographic modules that have been validated under FIPS 140, a U.S. government standard for secure cryptographic operations. Docker Hardened OpenTelemetry Collector images include FIPS-compliant variants for environments requiring Federal Information Processing Standards compliance.

Steps to verify FIPS:

# Compare image sizes (FIPS variants are larger due to FIPS crypto libraries)
$ docker images | grep opentelemetry-collector

# Verify FIPS compliance using image labels
$ docker inspect dhi.io/opentelemetry-collector:<tag>-fips \
    --format '{{index .Config.Labels "com.docker.dhi.compliance"}}'
fips,stig,cis

Runtime requirements specific to FIPS:

• FIPS mode enforces stricter cryptographic standards
• Use FIPS variants when connecting to backends with FIPS-compliant TLS
• Required for deployments in US government or regulated environments
• Only FIPS-approved cryptographic algorithms are available for TLS connections

Migrate to a Docker Hardened Image

To migrate your application to a Docker Hardened Image, you must update your Dockerfile. At minimum, you must update the base image in your existing Dockerfile to a Docker Hardened Image. This and a few other common changes are listed in the following table of migration notes:

The following steps outline the general migration process.

1. Find hardened images for your app.

   A hardened image may have several variants. Inspect the image tags and find the image variant that meets your needs.

2. Update the base image in your Dockerfile.

   Update the base image in your application's Dockerfile to the hardened image you found in the previous step. For framework images, this is typically going to be an image tagged as dev because it has the tools needed to install packages and dependencies.

3. For multi-stage Dockerfiles, update the runtime image in your Dockerfile.

   To ensure that your final image is as minimal as possible, you should use a multi-stage build. All stages in your Dockerfile should use a hardened image. While intermediary stages will typically use images tagged as dev, your final runtime stage should use a non-dev image variant.

4. Install additional packages

   Docker Hardened Images contain minimal packages in order to reduce the potential attack surface. You may need to install additional packages in your Dockerfile. Inspect the image variants to identify which packages are already installed.

   Only images tagged as dev typically have package managers. You should use a multi-stage Dockerfile to install the packages. Install the packages in the build stage that uses a dev image. Then, if needed, copy any necessary artifacts to the runtime stage that uses a non-dev image.

   For Alpine-based images, you can use apk to install packages. For Debian-based images, you can use apt-get to install packages.

Troubleshoot migration

General debugging

The hardened images intended for runtime don't contain a shell nor any tools for debugging. The recommended method for debugging applications built with Docker Hardened Images is to use Docker Debug⁠ to attach to these containers. Docker Debug provides a shell, common debugging tools, and lets you install other tools in an ephemeral, writable layer that only exists during the debugging session.

Permissions

By default image variants intended for runtime, run as the nonroot user. Ensure that necessary files and directories are accessible to the nonroot user. You may need to copy files to different directories or change permissions so your application running as the nonroot user can access them.

Privileged ports

Non-dev hardened images run as a nonroot user by default. As a result, applications in these images can't bind to privileged ports (below 1024) when running in Kubernetes or in Docker Engine versions older than 20.10.

No shell

By default, image variants intended for runtime don't contain a shell. Use dev images in build stages to run shell commands and then copy any necessary artifacts into the runtime stage. In addition, use Docker Debug to debug containers with no shell.

Entry point

Docker Hardened Images may have different entry points than images such as Docker Official Images. Use docker inspect to inspect entry points for Docker Hardened Images and update your Dockerfile if necessary.