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.

Prerequisites

• A Kubernetes cluster and kubectl.
• A reachable log backend for your chosen destination (Elasticsearch, S3, Kafka, CloudWatch, etc.).
• Read access to the node log paths the DaemonSet tails (/var/log and the container runtime log dir). See Running as the non-root user⁠.

What's included in this image

This image is the Kubernetes DaemonSet build of Fluentd. It tails container and node logs, enriches each record with Kubernetes metadata, and forwards records to a single output destination. The destination is encoded in the tag flavor:

For example, dhi.io/fluentd-kubernetes-daemonset:1.19-elasticsearch8 ships to Elasticsearch 8. Every flavor includes the shared Kubernetes plugins (metadata filter, systemd, Prometheus, concat, grok, multi-format parser) plus the output plugin for that destination.

Configuration lives in /etc/fluent (fluent.conf includes systemd.conf, prometheus.conf, kubernetes.conf, and conf.d/*.conf). The custom parsers are in /etc/fluent/plugins. The runtime listens on 24231/tcp for Prometheus metrics.

Deploy as a DaemonSet

The image runs as the non-root nonroot user (uid 65532) and reads its destination settings from environment variables. A minimal Elasticsearch DaemonSet:

apiVersion: v1
kind: ServiceAccount
metadata: { name: fluentd, namespace: kube-system }
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRole
metadata: { name: fluentd }
rules:
  - apiGroups: [""]
    resources: ["pods", "namespaces"]
    verbs: ["get", "list", "watch"]
---
apiVersion: rbac.authorization.k8s.io/v1
kind: ClusterRoleBinding
metadata: { name: fluentd }
roleRef: { apiGroup: rbac.authorization.k8s.io, kind: ClusterRole, name: fluentd }
subjects:
  - { kind: ServiceAccount, name: fluentd, namespace: kube-system }
---
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: fluentd
  namespace: kube-system
spec:
  selector:
    matchLabels: { app: fluentd }
  template:
    metadata:
      labels: { app: fluentd }
    spec:
      serviceAccountName: fluentd
      securityContext:
        runAsUser: 65532
        runAsGroup: 65532
        fsGroup: 65532
      containers:
        - name: fluentd
          image: dhi.io/fluentd-kubernetes-daemonset:1.19-elasticsearch8
          env:
            - { name: FLUENT_ELASTICSEARCH_HOST, value: elasticsearch.logging.svc }
            - { name: FLUENT_ELASTICSEARCH_PORT, value: "9200" }
            - { name: FLUENT_ELASTICSEARCH_SCHEME, value: https }
          ports:
            - { name: metrics, containerPort: 24231 }
          volumeMounts:
            - { name: varlog, mountPath: /var/log, readOnly: true }
            - { name: varlibdockercontainers, mountPath: /var/lib/docker/containers, readOnly: true }
            - { name: state, mountPath: /fluentd/log }
      volumes:
        - { name: varlog, hostPath: { path: /var/log } }
        - { name: varlibdockercontainers, hostPath: { path: /var/lib/docker/containers } }
        - { name: state, emptyDir: {} }

The ServiceAccount + ClusterRole above give the Kubernetes metadata filter the get/list/watch access to pods and namespaces it needs; the API connection uses the in-cluster credentials automatically (KUBERNETES_SERVICE_HOST/KUBERNETES_SERVICE_PORT are injected by Kubernetes). To also collect logs from control-plane nodes, add a tolerations block to the pod spec that tolerates the control-plane taints.

The default config also enables the systemd input (journald). If you are not collecting node journald logs, set FLUENTD_SYSTEMD_CONF=disable to silence the "No such file or directory" warnings; to collect them, mount the host journal (e.g. /var/log/journal or /run/log/journal) read-only and add the systemd-journal group via securityContext.supplementalGroups.

Running as the non-root user

Upstream fluent/fluentd-kubernetes-daemonset runs as root so it can both read host logs and write its state files under /var/log. This image runs as nonroot (uid 65532) and does not add a -root or -compat flavor, so a deployment must arrange two things:

• Read access to the node logs. The collector tails /var/log/containers/*.log (symlinks into the container runtime log dir). Mount host /var/log and /var/lib/docker/containers (or the containerd equivalent) read-only. On most distributions these files are group-readable; if your nodes make them root-only, add the owning group via securityContext.supplementalGroups (note: fsGroup does not apply to hostPath volumes). Confirm with ls -l /var/log/containers inside the pod.
• A writable state directory. fluentd writes in_tail position files, journald cursors, and (for s3/gcs/logzio/ azureblob) disk buffers to /fluentd/log. The image creates /fluentd/log owned by uid 65532, so it starts out of the box, but that is container-ephemeral — mount a writable volume there (an emptyDir as above, or a per-node hostPath you own) so read positions and buffered events survive pod restarts. Do not point state back under the read-only /var/log mount.

This is the key difference from running upstream as root: writes go to /fluentd/log, not /var/log.

Configure the destination

Each flavor reads FLUENT_<DESTINATION>_* environment variables (the same names upstream uses). Examples:

• Elasticsearch: FLUENT_ELASTICSEARCH_HOST, FLUENT_ELASTICSEARCH_PORT, FLUENT_ELASTICSEARCH_SCHEME, FLUENT_ELASTICSEARCH_USER, FLUENT_ELASTICSEARCH_PASSWORD.
• S3: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, S3_BUCKET_NAME, S3_BUCKET_REGION (or an IRSA role).
• Kafka: FLUENT_KAFKA_BROKERS, plus the topic/format settings in fluent.conf. SASL PLAIN/SCRAM/OAUTHBEARER and GSSAPI/Kerberos are supported (for GSSAPI, mount your krb5.conf and keytab).

To disable the systemd or Prometheus inputs, set FLUENTD_SYSTEMD_CONF=disable or FLUENTD_PROMETHEUS_CONF=disable. Add your own snippets by mounting files into /etc/fluent/conf.d/.

Add plugins with the dev variant

The runtime image ships only the plugins for its flavor. To add another plugin, build on the -dev variant, which includes the Ruby build toolchain needed for gems with native extensions:

FROM dhi.io/fluentd-kubernetes-daemonset:1.19-elasticsearch8-dev
RUN gem install --no-document fluent-plugin-<name>

Migration notes

• Config and state paths. Configuration lives under /etc/fluent (not upstream's /fluentd/etc); custom plugins go in /etc/fluent/plugins and config snippets in /etc/fluent/conf.d; GEM_HOME is /usr/local/bundle. State files (position files, journald cursors, disk buffers) are written to /fluentd/log, not /var/log — see Running as the non-root user⁠. Environment-driven configuration (the FLUENT_* variables) is unchanged.
• Plugin load errors.uninitialized constant usually means the plugin isn't shipped in this flavor; add it on the -dev variant. The Elasticsearch/OpenSearch simple sniffer is registered automatically by the entrypoint.
• Debugging on Kubernetes. The runtime keeps bash only for the entrypoint; there is no package manager and most inspection tools are absent. Use kubectl debug -it <pod> --image=dhi.io/fluentd-kubernetes-daemonset:1.19-<flavor>-dev for an ephemeral debug container, or Docker Debug outside Kubernetes.
• jemalloc. jemalloc is built in but not preloaded by default, matching upstream's daemonset (the systemd input plugin + jemalloc combination has a known crash bug). Opt in by setting LD_PRELOAD=/usr/lib/libjemalloc/libjemalloc.so.2.
• logentries. Upstream comments its plugin out of the Gemfile; this image ships a working logentries flavor with fluent-plugin-logentries, but that plugin is unmaintained upstream (treat it as best-effort).
• Parquet on S3. The optional columnify helper (used only for store_as parquet) is not bundled; the default gzip/text/json store formats work out of the box. Add columnify on the dev variant if you need Parquet output.

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

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.