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/spark-dependencies:<tag>
• Mirrored image: <your-namespace>/dhi-spark-dependencies:<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 spark-dependencies image

This image contains a single storage-backend-specific shaded jar (all dependencies bundled) that runs an Apache Spark job to analyze Jaeger spans and compute service dependency links. Which jar is included is fixed by the image flavor (part of the tag):

Unlike most Jaeger images, STORAGE does not need to be set — each flavor's jar already targets exactly one backend.

Run the spark-dependencies container

This is a batch job: the container runs to completion and exits, rather than staying up as a service. By default it processes all spans from the current UTC day; pass a YYYY-mm-dd date as the first argument (or set the DATE environment variable) to process a different day.

Replace <tag> with the flavor and version you want to use (see What's included⁠).

Cassandra

docker run --rm \
  -e CASSANDRA_CONTACT_POINTS=cassandra-host1,cassandra-host2 \
  dhi.io/spark-dependencies:<tag>-cassandra

Elasticsearch

docker run --rm \
  -e ES_NODES=http://elasticsearch:9200 \
  dhi.io/spark-dependencies:<tag>-elasticsearch9

OpenSearch

docker run --rm \
  -e OS_NODES=http://opensearch:9200 \
  dhi.io/spark-dependencies:<tag>-opensearch

Process a specific day

docker run --rm \
  -e CASSANDRA_CONTACT_POINTS=cassandra-host1 \
  dhi.io/spark-dependencies:<tag>-cassandra \
  2026-08-01

Environment variables

Common to every flavor:

Cassandra flavor:

Elasticsearch flavors:

OpenSearch flavor:

See the Jaeger Spark Dependencies README⁠ for the complete list of configuration variables.

Non-hardened images vs. Docker Hardened Images

Upstream's own image (jaegertracing/spark-dependencies on Docker Hub, last pushed in 2021) differs from this image in several ways:

• Entrypoint path: upstream's entrypoint.sh lives at /entrypoint.sh; this image installs it at /usr/local/bin/entrypoint.sh, matching Docker Hardened Images convention.
• Jar layout: upstream builds one image per VARIANT build-arg and copies a single app.jar to /app/app.jar. This image's package stages all five backend jars under /usr/share/spark-dependencies/spark-dependencies-<flavor>.jar, and each flavor image sets JAR_PATH to its own jar at build time instead of a runtime build-arg.
• Launch mechanism: upstream's entrypoint runs java -cp $JAR_PATH $MAIN_CLASS, picking $MAIN_CLASS at container start via shell logic keyed on $VARIANT_TYPE. Each of this image's flavor jars already carries the correct Main-Class in its manifest, so the entrypoint runs java -jar $JAR_PATH directly - no variant-selection logic needed at runtime.
• User: upstream runs as USER 185 and its entrypoint calls a patch_uid() function that appends an /etc/passwd entry for whatever arbitrary uid OpenShift's default restricted SCC assigns at runtime - Hadoop's UserGroupInformation needs a resolvable passwd entry for the running uid, or it can fail to determine the current user. This image runs as the standard Docker Hardened Images nonroot uid 65532 by default, which has a valid /etc/passwd entry baked in. That said, OpenShift's default restricted SCC overrides a container's uid with a randomly-assigned one regardless of what the image declares, so the same gap upstream's patch_uid() works around can still show up here too; this entrypoint has no equivalent patch, and writing /etc/passwd at container start isn't reliable on a read-only root filesystem anyway. If you deploy under the default restricted SCC, either grant the ServiceAccount the anyuid SCC (this image already runs as a fixed, known nonroot uid, so anyuid doesn't lower its security posture) or set HADOOP_USER_NAME explicitly so Hadoop doesn't need to resolve the uid via /etc/passwd.

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.

Exception for this image: the runtime variant does include bash - see No shell⁠ below for why.

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.

Exception for this image: the runtime variant does include bash, since the entrypoint script needs shell logic to forward the JAVA_OPTS environment variable and select the flavor's jar path at container start - neither is expressible as a pure exec-form (ENTRYPOINT ["java", ...]) entrypoint without dropping JAVA_OPTS support.

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.