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.

About Data Prepper

This Docker Hardened Data Prepper image is a server-side data collector built for trace, log, and metric observability pipelines. Data Prepper receives telemetry from sources such as OpenTelemetry Collector, Fluent Bit, and S3, applies processors (field mapping, aggregate, grok, drop events), and routes data to sinks such as OpenSearch, S3, and Kafka. The image ships the Java 17 JRE (Eclipse Temurin), the full Data Prepper plugin set (83 plugin directories, 582 bundled JARs), and GNU gettext-base for envsubst-based pipeline config templating. A writable log directory is provided at /var/log/data-prepper. The default server configuration is at /usr/share/data-prepper/config/data-prepper-config.yaml and pipeline definitions are read from /usr/share/data-prepper/pipelines/pipelines.yaml.

Run the Data Prepper container

Data Prepper is configured through two YAML files: a server configuration file and a pipeline definitions file. The entrypoint is the wrapper script /usr/share/data-prepper/bin/data-prepper. When started with no arguments, the wrapper reads the server config from the default location and expects pipeline definitions to be present.

Start a container using the built-in defaults (self-signed SSL, basic auth admin/admin, no active pipeline inputs):

docker run -d --name data-prepper \
  -p 4900:4900 \
  dhi.io/data-prepper:<tag>

Verify the server is up by listing the running pipelines (the default config enables SSL, so pass -k to skip certificate validation for the self-signed cert):

curl -k -u admin:admin https://localhost:4900/list

The server responds with the running pipelines, for example {"pipelines":[{"name":"sample-pipeline"}]}, once it is ready. (/metrics/prometheus returns the Prometheus metrics; there is no /health endpoint.)

Mount custom configuration files

For any real workload, mount your own pipeline and server configuration files into the container.

Create a server configuration file data-prepper-config.yaml:

ssl: true
keyStoreFilePath: "/path/to/your/keystore.p12"
keyStorePassword: "your-keystore-password"
privateKeyPassword: "your-key-password"
serverPort: 4900
metricRegistries: [Prometheus]
authentication:
  http_basic:
    username: your-admin-user
    password: your-strong-password

Create a pipeline definition file pipelines.yaml (example: OTel trace pipeline to OpenSearch):

otel-trace-pipeline:
  source:
    otel_trace_source:
      port: 2021
      ssl: false
  processor:
    - trace_peer_forwarder:
  sink:
    - opensearch:
        hosts:
          - https://opensearch:9200
        username: admin
        password: your-strong-password
        insecure: false
        index_type: trace-analytics-raw

Run Data Prepper with both files mounted:

docker run -d --name data-prepper \
  -p 4900:4900 \
  -p 2021:2021 \
  -v $(pwd)/pipelines.yaml:/usr/share/data-prepper/pipelines/pipelines.yaml:ro \
  -v $(pwd)/data-prepper-config.yaml:/usr/share/data-prepper/config/data-prepper-config.yaml:ro \
  dhi.io/data-prepper:<tag>

Templated pipeline configuration with envsubst

The image ships envsubst (from gettext-base) for rendering ${VAR} placeholders in pipeline configuration files. This is the recommended pattern for injecting environment-specific values (hostnames, passwords, index prefixes) into pipeline YAML at container startup without baking secrets into the image.

Create a pipeline template file pipelines.yaml.tmpl using standard ${VAR} syntax:

log-pipeline:
  source:
    http:
      port: 2021
      ssl: false
  processor:
    - grok:
        match:
          message: ['%{COMMONAPACHELOG}']
  sink:
    - opensearch:
        hosts:
          - https://${OPENSEARCH_HOST}:9200
        username: ${OPENSEARCH_USER}
        password: ${OPENSEARCH_PASSWORD}
        index: ${INDEX_PREFIX}-logs

Use a short init step in your entrypoint wrapper or an init container to render the template before Data Prepper starts. The simplest approach with Docker Compose uses the dev variant to render the template and the runtime variant to run the result:

services:
  data-prepper-init:
    image: dhi.io/data-prepper:<tag>-dev
    environment:
      OPENSEARCH_HOST: opensearch
      OPENSEARCH_USER: admin
      OPENSEARCH_PASSWORD: ${OPENSEARCH_PASSWORD}
      INDEX_PREFIX: prod
    volumes:
      - ./pipelines.yaml.tmpl:/tmp/pipelines.yaml.tmpl:ro
      - pipeline-config:/usr/share/data-prepper/pipelines
    entrypoint: ["bash", "-c", "envsubst < /tmp/pipelines.yaml.tmpl > /usr/share/data-prepper/pipelines/pipelines.yaml"]

  data-prepper:
    image: dhi.io/data-prepper:<tag>
    depends_on:
      data-prepper-init:
        condition: service_completed_successfully
    ports:
      - "4900:4900"
      - "2021:2021"
    volumes:
      - ./data-prepper-config.yaml:/usr/share/data-prepper/config/data-prepper-config.yaml:ro
      - pipeline-config:/usr/share/data-prepper/pipelines:ro

volumes:
  pipeline-config:

Alternatively, if you are running in a single-container context, use a shell wrapper script that calls envsubst then execs the Data Prepper entrypoint:

#!/usr/bin/env bash
set -euo pipefail
envsubst < /pipelines/pipelines.yaml.tmpl > /usr/share/data-prepper/pipelines/pipelines.yaml
exec /usr/share/data-prepper/bin/data-prepper

Mount this script into the container and override the entrypoint:

docker run -d --name data-prepper \
  -p 4900:4900 \
  -e OPENSEARCH_HOST=opensearch \
  -e OPENSEARCH_USER=admin \
  -e OPENSEARCH_PASSWORD=your-strong-password \
  -e INDEX_PREFIX=prod \
  -v $(pwd)/start.sh:/start.sh:ro \
  -v $(pwd)/pipelines.yaml.tmpl:/pipelines/pipelines.yaml.tmpl:ro \
  --entrypoint /start.sh \
  dhi.io/data-prepper:<tag>-dev

Prometheus metrics

Data Prepper exposes Prometheus-format metrics on the management port 4900 when metricRegistries: [Prometheus] is set in the server configuration (the default). Scrape the metrics endpoint:

curl -k -u admin:admin https://localhost:4900/metrics/prometheus

In a Prometheus scrape configuration:

scrape_configs:
  - job_name: data-prepper
    scheme: https
    tls_config:
      insecure_skip_verify: true   # Replace with ca_file pointing to your CA in production
    basic_auth:
      username: admin
      password: your-strong-password
    static_configs:
      - targets:
          - data-prepper:4900
    metrics_path: /metrics/prometheus

Docker Compose with OpenSearch

The following example starts a single-node OpenSearch cluster and a Data Prepper instance wired to it. Replace placeholder values before use.

services:
  opensearch:
    image: dhi.io/opensearch:<tag>
    environment:
      - discovery.type=single-node
      - OPENSEARCH_INITIAL_ADMIN_PASSWORD=${OPENSEARCH_INITIAL_ADMIN_PASSWORD}
    ports:
      - "9200:9200"
    healthcheck:
      test: ["CMD-SHELL", "bash -c 'echo > /dev/tcp/localhost/9200'"]
      interval: 15s
      timeout: 5s
      retries: 10

  data-prepper:
    image: dhi.io/data-prepper:<tag>
    depends_on:
      opensearch:
        condition: service_healthy
    ports:
      - "4900:4900"
      - "2021:2021"
    volumes:
      - ./pipelines.yaml:/usr/share/data-prepper/pipelines/pipelines.yaml:ro
      - ./data-prepper-config.yaml:/usr/share/data-prepper/config/data-prepper-config.yaml:ro

Migrate from opensearchproject/data-prepper

The following table lists the key differences between the upstream opensearchproject/data-prepper image and this Docker Hardened Image.

Migration steps

1. Update the image reference in your docker run command or Compose file:

   # Before
   opensearchproject/data-prepper:<version>
   
   # After
   dhi.io/data-prepper:<tag>
   

   Copy

2. Update volume ownership. The runtime user is now UID 65532 instead of UID 1000. Any host-mounted directories that Data Prepper writes to must be writable by UID 65532:

   chown -R 65532:65532 /host/path/to/data-prepper-volumes
   

   Copy

3. Replace the bundled data-prepper-config.yaml defaults with production-grade values. At minimum, replace the self-signed keystore with a certificate signed by a trusted CA and change the admin/admin HTTP basic auth credentials.

4. Verify your pipeline configuration files are mounted at the correct paths before starting the container, since the runtime variant has no shell to interactively inspect.

Image variants

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

• 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

• Build-time variants typically include dev in the tag name and are intended for use in the first stage of a multi-stage Dockerfile. These images typically:

  • Run as the root user
  • Include a shell and package manager
  • Are used to build or compile applications

• FIPS variants include fips in the variant name and tag. They come in both runtime and build-time variants. These variants use cryptographic modules that have been validated under FIPS 140, a U.S. government standard for secure cryptographic operations. For example, usage of MD5 fails in FIPS variants.

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 of the Data Prepper image are available through DHI Select and DHI Enterprise subscriptions. To pull them, mirror the repository into your own namespace and pull from your mirror. See Mirror a DHI repository⁠.

FIPS variants replace the standard Java cryptography with the Bouncy Castle FIPS (BC FIPS) provider and pair it with the OpenSSL FIPS Provider for OS-level cryptographic operations. The following components are changed compared to the standard runtime variant:

• Java crypto provider: BC FIPS JARs (bc-fips, bctls-fips, bcutil-fips, bcpkix-fips) are symlinked into Data Prepper's lib/ directory so they appear on the classpath.
• JRE trust store: The BCFKS-format trust store at /usr/lib/bouncycastle/cacerts.bcfks replaces the standard JKS cacerts. The JVM is pointed at this store via JDK_JAVA_OPTIONS.
• OpenSSL FIPS Provider: The OpenSSL FIPS module is included for OS-level operations.
• Approved-only mode: The environment variable JDK_JAVA_OPTIONS includes -Dorg.bouncycastle.fips.approved_only=false, so the BC FIPS provider is registered but does not block non-FIPS-approved algorithms at runtime. The reason is the vendored docker-flavor data-prepper-config.yaml ships SSL enabled with a PKCS12 keystore (keystore.p12), and BC FIPS rejects non-BCFKS keystores under strict approved-only mode — the image would fail to start the server. Same constraint kafka 4.x-fips ships with.

To enable strict approved-only mode: Mount a BCFKS-format keystore and a config that sets keyStoreType: BCFKS, then override JDK_JAVA_OPTIONS (or set JAVA_TOOL_OPTIONS=-Dorg.bouncycastle.fips.approved_only=true) at docker run time. The BC FIPS provider will then refuse any non-approved algorithm — TLS 1.0/1.1, MD5 certificate signatures, RC4, etc. — and throw a runtime exception rather than silently proceeding. Permissive mode (the shipped default) still routes JVM cryptography through the BC FIPS provider; it is appropriate when full approved-algorithm isolation is not required by your compliance posture.

Verify the FIPS attestation by querying the signed FIPS attestation against your mirrored repository:

docker scout attest get \
  --predicate-type https://docker.com/dhi/fips/v0.1 \
  --predicate \
  <your-namespace>/dhi-data-prepper:<tag>-fips

FIPS variant migration notes: The FIPS runtime variant is a drop-in replacement for the standard runtime variant at the container level. No changes to your pipeline YAML or data-prepper-config.yaml are required. The same nonroot UID 65532 and the same entrypoint are used.

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.

Troubleshooting migration

The following are common issues that you may encounter during 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. To avoid issues, configure your application to listen on port 1025 or higher inside the container, even if you map it to a lower port on the host. For example, docker run -p 80:8080 my-image will work because the port inside the container is 8080, and docker run -p 80:81 my-image won't work because the port inside the container is 81.

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.