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.

What's included in this datahub-upgrade image

The DataHub Upgrade image is a run-to-completion batch container that applies schema migrations, rebuilds search and graph indices, and emits upgrade readiness signals for a running DataHub deployment. It packages the datahub-upgrade.jar Spring Boot application (Java 21). The entrypoint waits for upstream datastores before the JVM starts, and the image also ships a kubectl binary for Kubernetes-side operations and opt-in OpenTelemetry and JMX Prometheus Java agents.

The entrypoint is /datahub/datahub-upgrade/scripts/start.sh, which reads environment variables to determine which datastores to wait for and which optional agents to activate, then starts the upgrade job. Pass the job name with -u <JobName> to select which upgrade job to run. Full documentation is available at https://docs.datahub.com⁠.

Run the datahub-upgrade container

The upgrade job requires connectivity to Elasticsearch and at least one primary datastore. The following example runs the standard SystemUpdate job, which is the job executed during routine DataHub version upgrades:

docker run --rm \
  -e ELASTICSEARCH_HOST=elasticsearch \
  -e ELASTICSEARCH_PORT=9200 \
  -e EBEAN_DATASOURCE_HOST=mysql:3306 \
  -e EBEAN_DATASOURCE_USERNAME=datahub \
  -e EBEAN_DATASOURCE_PASSWORD=datahub \
  -e EBEAN_DATASOURCE_URL="jdbc:mysql://mysql:3306/datahub?verifyServerCertificate=false&useSSL=true&useUnicode=yes&characterEncoding=UTF-8" \
  -e KAFKA_BOOTSTRAP_SERVER=broker:29092 \
  -e SCHEMA_REGISTRY_URL=http://schema-registry:8081 \
  dhi.io/datahub-upgrade:<tag> -u SystemUpdate

To rebuild Elasticsearch indices from the primary metadata store (for example, after an index mapping change or cluster recovery):

docker run --rm \
  -e ELASTICSEARCH_HOST=elasticsearch \
  -e ELASTICSEARCH_PORT=9200 \
  -e EBEAN_DATASOURCE_HOST=mysql:3306 \
  -e EBEAN_DATASOURCE_USERNAME=datahub \
  -e EBEAN_DATASOURCE_PASSWORD=datahub \
  -e EBEAN_DATASOURCE_URL="jdbc:mysql://mysql:3306/datahub?verifyServerCertificate=false&useSSL=true&useUnicode=yes&characterEncoding=UTF-8" \
  dhi.io/datahub-upgrade:<tag> -u RestoreIndices

To display the help for the upgrade tool directly (bypassing the entrypoint script):

docker run --rm --entrypoint java \
  dhi.io/datahub-upgrade:<tag> \
  -jar /datahub/datahub-upgrade/bin/datahub-upgrade.jar --help

Supported upgrade jobs

Select the job by passing -u <JobName> as the command argument:

Environment variables

The entrypoint script reads the following environment variables before starting the JVM:

Deploy with Helm (recommended for production)

In production, datahub-upgrade is almost always executed through the official DataHub Helm chart as a pre-install and pre-upgrade hook, not as a standalone docker run command. The Helm chart runs datahub-upgrade as the datahubSystemUpdate job (template charts/datahub/templates/datahub-upgrade/datahub-system-update-job.yml⁠), which reads its image from .Values.datahubSystemUpdate.image. To use the Docker Hardened Image, override that block in your values.yaml:

datahubSystemUpdate:
  image:
    repository: dhi.io/datahub-upgrade
    tag: "<tag>"

Install or upgrade the chart:

helm repo add datahub https://helm.datahubproject.io/
helm upgrade --install datahub datahub/datahub \
  --namespace datahub \
  --values values.yaml

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

Run with Docker Compose

The following Docker Compose snippet shows a minimal setup that runs SystemUpdate as a one-shot service alongside Elasticsearch and MySQL. For a complete DataHub Compose stack, see the upstream repository.

services:
  datahub-upgrade:
    image: dhi.io/datahub-upgrade:<tag>
    command: ["-u", "SystemUpdate"]
    depends_on:
      elasticsearch:
        condition: service_healthy
      mysql:
        condition: service_healthy
    environment:
      ELASTICSEARCH_HOST: elasticsearch
      ELASTICSEARCH_PORT: "9200"
      EBEAN_DATASOURCE_HOST: "mysql:3306"
      EBEAN_DATASOURCE_USERNAME: datahub
      EBEAN_DATASOURCE_PASSWORD: datahub
      EBEAN_DATASOURCE_URL: "jdbc:mysql://mysql:3306/datahub?verifyServerCertificate=false&useSSL=true&useUnicode=yes&characterEncoding=UTF-8"
      KAFKA_BOOTSTRAP_SERVER: broker:29092
      SCHEMA_REGISTRY_URL: http://schema-registry:8081
    restart: "no"

  elasticsearch:
    image: elasticsearch:7.11.1
    environment:
      discovery.type: single-node
    healthcheck:
      test: ["CMD-SHELL", "curl -sf http://localhost:9200/_cluster/health || exit 1"]
      interval: 10s
      timeout: 5s
      retries: 10

  mysql:
    image: mysql:8.2
    environment:
      MYSQL_ROOT_PASSWORD: datahub
      MYSQL_DATABASE: datahub
      MYSQL_USER: datahub
      MYSQL_PASSWORD: datahub
    healthcheck:
      test: ["CMD", "mysqladmin", "ping", "-h", "localhost"]
      interval: 10s
      timeout: 5s
      retries: 10

FIPS variant

The FIPS variant (dhi.io/datahub-upgrade:<tag>-fips) enables FIPS 140-validated cryptography for the Java workload:

• The image installs the FIPS-validated Temurin JRE from the eclipse-temurin-21-jre-fips Debian package.
• BouncyCastle FIPS jars (bc-fips, bctls-fips, bcutil-fips, bcpkix-fips, bc-rng-jent) are bundled under /usr/lib/bouncycastle/ and wired in at JVM bootstrap via JDK_JAVA_OPTIONS=@/datahub/datahub-upgrade/scripts/datahub-fips.properties. The properties file prepends the BouncyCastle jars to the boot classpath (-Xbootclasspath/a:), enables org.bouncycastle.fips.approved_only=true, and sets the JVM trust store to the BCFKS store shipped by the FIPS Temurin package (/usr/lib/bouncycastle/cacerts.bcfks). The net effect is that all Java TLS and JCE operations — JDBC, Kafka client TLS, Elasticsearch HTTPS — go through BouncyCastle FIPS rather than the default SunJCE provider.
• The environment variable DATAHUB_FIPS=true is set automatically in this variant so operators can branch on it at runtime without inspecting image labels.

To run the FIPS variant:

docker run --rm \
  -e ELASTICSEARCH_HOST=elasticsearch \
  -e ELASTICSEARCH_PORT=9200 \
  -e EBEAN_DATASOURCE_HOST=mysql:3306 \
  -e EBEAN_DATASOURCE_USERNAME=datahub \
  -e EBEAN_DATASOURCE_PASSWORD=datahub \
  -e EBEAN_DATASOURCE_URL="jdbc:mysql://mysql:3306/datahub?verifyServerCertificate=false&useSSL=true&useUnicode=yes&characterEncoding=UTF-8" \
  dhi.io/datahub-upgrade:<tag>-fips -u SystemUpdate

No application-level configuration changes are required to switch from the standard runtime variant to the FIPS variant — the cryptographic substitution is transparent to the upgrade jobs.

Image variants

Docker Hardened Images come in different variants depending on their intended use.

• 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 the 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 variant 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.

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.