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/datahub-gms:<tag>
• Mirrored image: <your-namespace>/dhi-datahub-gms:<tag>

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

What's included in this datahub-gms image

This Docker Hardened DataHub GMS image packages the General Metadata Service — the central backend service of the DataHub⁠ open-source AI data catalog. DataHub is an enterprise-grade metadata platform that enables discovery, governance, and observability across your entire data ecosystem, originally created at LinkedIn and now maintained by the DataHub Project and Acryl Data.

• datahub-gms: The General Metadata Service provides a unified REST and GraphQL API for ingesting and querying metadata across datasets, dashboards, pipelines, ML models, and data assets. It is a Spring Boot application built on OpenJDK 17, packaged as a WAR and run via an embedded servlet container. This DHI variant also bundles two optional Java agents — the OpenTelemetry javaagent and the JMX Prometheus javaagent — that are activated via environment variables at runtime.

DataHub GMS is one component in a larger DataHub deployment. A complete deployment requires Apache Kafka (for event-driven metadata propagation), Elasticsearch or OpenSearch (for search and indexing), and optionally Neo4j (for graph-based lineage traversal — if omitted, the Elasticsearch graph backend is used instead).

Run the datahub-gms container

DataHub GMS is designed to run as part of a complete DataHub stack and requires its dependent services to be reachable before it starts. The start script uses dockerize to wait for Kafka, Elasticsearch (or OpenSearch), and optionally Neo4j to become available before launching the JVM.

To display help information for the start script:

docker run --rm --entrypoint /bin/bash dhi.io/datahub-gms:<tag> \
  /datahub/datahub-gms/scripts/start.sh --help

To verify the bundled Java runtime version:

docker run --rm --entrypoint java dhi.io/datahub-gms:<tag> -version

Deploy DataHub GMS with Docker Compose

The following example shows a minimal DataHub GMS configuration alongside Kafka, Zookeeper, and Elasticsearch. This is intended to illustrate the environment variable wiring required for GMS — it is not a production-ready deployment. Real-world usage requires schema registry, MySQL or another RDBMS for Ebean persistence, and potentially additional DataHub components (frontend, ingestion, etc.).

services:
  zookeeper:
    image: confluentinc/cp-zookeeper:7.6.0
    environment:
      ZOOKEEPER_CLIENT_PORT: 2181

  kafka:
    image: confluentinc/cp-kafka:7.6.0
    depends_on:
      - zookeeper
    environment:
      KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
      KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:9092
      KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1

  elasticsearch:
    image: elasticsearch:8.13.0
    environment:
      - discovery.type=single-node
      - xpack.security.enabled=false
    ports:
      - "9200:9200"

  datahub-gms:
    image: dhi.io/datahub-gms:<tag>
    ports:
      - "8080:8080"
    depends_on:
      - kafka
      - elasticsearch
    environment:
      EBEAN_DATASOURCE_USERNAME: datahub
      EBEAN_DATASOURCE_PASSWORD: datahub
      EBEAN_DATASOURCE_HOST: mysql:3306
      EBEAN_DATASOURCE_URL: "jdbc:mysql://mysql:3306/datahub?verifyServerCertificate=false&useSSL=true&useUnicode=yes&characterEncoding=UTF-8"
      EBEAN_DATASOURCE_DRIVER: com.mysql.jdbc.Driver
      KAFKA_BOOTSTRAP_SERVER: kafka:9092
      KAFKA_SCHEMAREGISTRY_URL: http://schema-registry:8081
      ELASTICSEARCH_HOST: elasticsearch
      ELASTICSEARCH_PORT: 9200
      GRAPH_SERVICE_IMPL: elasticsearch
      ENTITY_SERVICE_IMPL: ebean
      JAVA_OPTS: "-Xms512m -Xmx2g"

Environment variables

Key environment variables for configuring DataHub GMS:

Health check

The GMS health endpoint is available at GET /<DATAHUB_GMS_BASE_PATH>health on port 8080. When DATAHUB_GMS_BASE_PATH is unset (the default), the URL is:

http://localhost:8080/health

Because the runtime image does not include a shell or curl, use wget for health checks:

wget -qO- http://localhost:8080/health

In a Compose file, use the binary-native form:

healthcheck:
  test: ["CMD", "wget", "-qO-", "http://localhost:8080/health"]
  interval: 30s
  timeout: 10s
  retries: 5
  start_period: 60s

Deploy DataHub with the upstream Helm chart

For production Kubernetes deployments, use the upstream DataHub Helm chart, which deploys the full DataHub stack (GMS, frontend, ingestion, and all dependencies). To substitute the Docker Hardened GMS image, override the image reference in your values.yaml:

datahub-gms:
  image:
    repository: dhi.io/datahub-gms
    tag: <tag>

The upstream Helm chart is available at: https://artifacthub.io/packages/helm/datahub/datahub⁠

Enable observability agents

The two bundled Java agents are off by default and are activated at container start time via environment variables:

OpenTelemetry tracing (agent at /datahub/datahub-gms/lib/opentelemetry-javaagent.jar):

environment:
  ENABLE_OTEL: "true"
  OTEL_EXPORTER_OTLP_ENDPOINT: http://otel-collector:4318

JMX Prometheus metrics (agent at /datahub/datahub-gms/lib/jmx_prometheus_javaagent.jar, scraped on port 4318):

environment:
  ENABLE_PROMETHEUS: "true"

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.

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.