Prerequisites

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.

Start a redis-exporter instance

Run the following command and replace <tag> with the image variant you want to run.

# First, start a Redis instance
$ docker run -d \
  --name redis-server \
  -p 6379:6379 \
  dhi.io/redis:<tag>

# Then start the redis-exporter
$ docker run -d \
  --name redis-exporter \
  -p 9121:9121 \
  dhi.io/redis-exporter:<tag> \
  --redis.addr=redis://redis-server:6379

# Verify it's working
$ curl http://localhost:9121/health

# Clean up after testing
$ docker rm -f redis-server redis-exporter

Common redis-exporter use cases

Single Redis instance monitoring

Monitor a single Redis instance with default settings:

# First, start a Redis instance
$ docker run -d \
  --name redis-server \
  -p 6379:6379 \
  dhi.io/redis:<tag>

# Then start the exporter to monitor it
$ docker run -d \
  --name redis-exporter \
  -p 9121:9121 \
  dhi.io/redis-exporter:<tag> \
  --redis.addr=redis://redis-server:6379

Access metrics at http://localhost:9121/metrics for Prometheus scraping.

Clean up after testing:

$ docker rm -f redis-server redis-exporter

Multi-target monitoring

Run in multi-target mode to scrape multiple Redis instances via the /scrape endpoint:

# Start exporter with empty redis.addr for multi-target mode
$ docker run -d \
  --name redis-exporter-multi \
  -p 9123:9121 \
  dhi.io/redis-exporter:<tag> \
  --redis.addr=

Then scrape different Redis instances dynamically:

$ curl http://localhost:9123/scrape?target=redis://redis-server-1:6379
$ curl http://localhost:9123/scrape?target=redis://redis-server-2:6380

Clean up after testing:

$ docker rm -f redis-exporter-multi

Environment variable configuration

Configure using environment variables instead of command flags:

# Start Redis instance with unique name and port
$ docker run -d \
  --name redis-server-env \
  -p 6380:6379 \
  dhi.io/redis:<tag>

# Start exporter with environment variables
$ docker run -d \
  --name redis-exporter-env \
  -p 9122:9121 \
  -e REDIS_ADDR=redis://redis-server-env:6379 \
  -e REDIS_EXPORTER_WEB_LISTEN_ADDRESS=0.0.0.0:9121 \
  -e REDIS_EXPORTER_WEB_TELEMETRY_PATH=/metrics \
  dhi.io/redis-exporter:<tag>

# Access metrics
$ curl http://localhost:9122/metrics

# Clean up after testing
$ docker rm -f redis-server-env redis-exporter-env

Custom metrics configuration

Expose metrics on custom port/path and monitor specific keys:

# Start Redis instance with unique name and port
$ docker run -d \
  --name redis-server-custom \
  -p 6381:6379 \
  dhi.io/redis:<tag>

# Start exporter with custom configuration
$ docker run -d \
  --name redis-exporter-custom \
  -p 8080:8080 \
  dhi.io/redis-exporter:<tag> \
  --redis.addr=redis://redis-server-custom:6379 \
  --web.listen-address=0.0.0.0:8080 \
  --web.telemetry-path=/custom-metrics \
  --namespace=custom_redis \
  --include-system-metrics \
  --check-single-keys=db0=user:count,db0=queue:tasks

# Access custom metrics
$ curl http://localhost:8080/custom-metrics

# Clean up after testing
$ docker rm -f redis-server-custom redis-exporter-custom

Docker Compose deployment

Complete monitoring stack with Redis and Redis Exporter:

services:
  redis:
    image: dhi.io/redis:<tag>  # Use :8 for Redis
    container_name: redis-server
    ports:
      - "6379:6379"
    volumes:
      - redis-data:/data
    restart: unless-stopped

  redis-exporter:
    image: dhi.io/redis-exporter:<tag>  # Use :1.80.1 for exporter
    container_name: redis-exporter
    ports:
      - "9121:9121"
    command:
      - --redis.addr=redis://redis:6379
      - --include-system-metrics
    depends_on:
      - redis
    restart: unless-stopped

volumes:
  redis-data:

Integration with Prometheus

Configure Prometheus to scrape Redis Exporter metrics:

First, create prometheus.yml in your current directory:

global:
  scrape_interval: 15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'redis'
    static_configs:
      - targets: ['redis-exporter:9121']

Then create docker-compose.yml:

services:
  redis:
    image: dhi.io/redis:<tag>  # Use :8 for Redis
    container_name: redis-server

  redis-exporter:
    image: dhi.io/redis-exporter:<tag>  # Use :1.80.1 for exporter
    container_name: redis-exporter
    command: --redis.addr=redis://redis:6379
    depends_on:
      - redis

  prometheus:
    image: prom/prometheus:latest
    container_name: prometheus
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml
    command:
      - '--config.file=/etc/prometheus/prometheus.yml'
    depends_on:
      - redis-exporter

Non-hardened images vs Docker Hardened Images

Key differences

Why no shell or package manager?

Docker Hardened Images prioritize security through minimalism:

• Reduced attack surface: Fewer binaries mean fewer potential vulnerabilities
• Immutable infrastructure: Runtime containers shouldn't be modified after deployment
• Compliance ready: Meets strict security requirements for regulated environments

The hardened images intended for runtime don't contain a shell nor any tools for debugging. Common debugging methods for applications built with Docker Hardened Images include:

• Docker Debug⁠ to attach to containers
• Docker's Image Mount feature to mount debugging tools
• Ecosystem-specific debugging approaches

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.

For example, you can use Docker Debug:

$ docker debug <container-name>

or mount debugging tools with the Image Mount feature:

$ docker run --rm -it --pid container:my-container \
  --mount=type=image,source=dhi.io/busybox,destination=/dbg,ro \
  dhi.io/redis-exporter:<tag> /dbg/bin/sh

Image variants

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

Available image tags for redis-exporter:

Tag selection guidance:

• Use dhi.io/redis-exporter:1.80.1 for standard production deployments
• Use dhi.io/redis-exporter:1.80.1-fips for FIPS-compliant environments
• Use major version tags (like :1) for automatic minor updates (not recommended for production)

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

FIPS variants include fips in the variant name and tag. For Redis Exporter DHI, only runtime variants are available. These variants use cryptographic modules that have been validated under FIPS 140, a U.S. government standard for secure cryptographic operations.

Runtime requirements specific to FIPS:

• FIPS mode restricts cryptographic operations to FIPS-validated algorithms
• Usage of non-compliant operations (like MD5) will fail
• Larger image size due to FIPS-validated cryptographic libraries

Steps to verify FIPS:

# Check if FIPS variant is being used
$ docker inspect dhi.io/redis-exporter:<tag> | grep fips

# Verify FIPS mode in running container
$ docker logs <container-name>

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.

Troubleshoot 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.

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.