Dockerfile For Dataverse: Containerized Builds On RHEL/Rocky 8

by Alex Johnson 63 views

In this comprehensive guide, we'll explore how to create a Dockerfile to streamline the build process for Dataverse, particularly when running on platforms like RHEL/Rocky 8. Our production Dataverse instances currently operate on these platforms, making it essential to have a reliable and containerized build process. This approach ensures consistency, portability, and ease of deployment. By the end of this article, you'll have a clear understanding of how to set up a Dockerfile that caters to these specific requirements, while also providing a foundation that can be easily adapted for future improvements and updates.

Understanding the Need for Containerization

Before diving into the specifics of the Dockerfile, itā€™s crucial to understand why containerization is beneficial for Dataverse builds. Containerization, using tools like Docker, encapsulates an application and its dependencies into a single, standardized unit. This ensures that the application runs consistently across different environments, whether it's a development machine, a testing server, or a production instance.

Consistency and Reproducibility: One of the most significant advantages of using Docker is the consistency it provides. Building and running Dataverse within a container guarantees that the environment is always the same, regardless of the underlying infrastructure. This eliminates the ā€œit works on my machineā€ syndrome, ensuring that builds are reproducible and reliable.

Isolation: Containers provide isolation, meaning that Dataverse and its dependencies are separated from the host system. This prevents conflicts with other applications and libraries installed on the same server. It also enhances security by limiting the potential impact of vulnerabilities.

Portability: Docker containers are highly portable. They can be easily moved between different environments, whether they are on-premises servers, cloud platforms, or virtual machines. This makes it simple to deploy Dataverse across various infrastructures without worrying about compatibility issues.

Scalability: Containerization facilitates scalability. Docker containers can be easily scaled up or down to meet changing demands. This is particularly useful for Dataverse, which may experience varying levels of traffic and data processing requirements.

Simplified Deployment: Docker simplifies the deployment process. With a containerized build, you can deploy Dataverse with a single command, ensuring that all dependencies are correctly configured and that the application starts up smoothly.

Creating the Dockerfile for Dataverse

Now, let's dive into the process of creating a Dockerfile tailored for building Dataverse on RHEL/Rocky 8. This Dockerfile will serve as a blueprint for creating a Docker image that contains everything needed to build and run Dataverse.

Base Image Selection

The first step is to choose a base image. For RHEL/Rocky 8, you can use the official RHEL 8 base image or a Rocky Linux 8 image. Hereā€™s how you can specify the base image in your Dockerfile:

FROM registry.access.redhat.com/ubi8/ubi:latest
# or
FROM rockylinux:8

The FROM instruction tells Docker which base image to use as the foundation for your container. Using the official RHEL or Rocky Linux image ensures that you have a minimal, stable, and secure base to build upon.

Installing Dependencies

Next, you need to install the dependencies required to build Dataverse. This typically includes Java, Maven, Git, and other build tools. Hereā€™s an example of how to install these dependencies using the yum package manager:

RUN yum update -y && \
    yum install -y \
    java-11-openjdk-devel \
    maven \
    git \
    wget \
    unzip && \
    yum clean all

In this section:

  • yum update -y updates the package repositories and installs the latest versions of the packages.
  • yum install -y installs the necessary dependencies, such as Java, Maven, and Git.
  • yum clean all cleans up the Yum cache to reduce the size of the final image.

Setting Up the Dataverse Source Code

Now, you need to copy the Dataverse source code into the container. You can do this using the COPY instruction. Assuming your Dataverse source code is in the same directory as your Dockerfile, you can use the following:

COPY . /opt/dataverse
WORKDIR /opt/dataverse

Here, COPY . /opt/dataverse copies all files from the current directory (where the Dockerfile is located) to the /opt/dataverse directory inside the container. WORKDIR /opt/dataverse sets the working directory to /opt/dataverse, so subsequent commands will be executed in this directory.

Building Dataverse

With the dependencies installed and the source code in place, you can now build Dataverse using Maven. Add the following instruction to your Dockerfile:

RUN mvn clean install -DskipTests

This command executes the Maven build process, compiling the Dataverse source code and creating the necessary artifacts. The -DskipTests flag is used to skip the tests during the build process, which can significantly reduce the build time. You may want to remove this flag for a production build to ensure that all tests pass.

Creating a User for Dataverse

For security reasons, itā€™s best practice to run Dataverse under a non-root user. You can create a new user and group specifically for Dataverse using the following instructions:

RUN groupadd -r dataverse && \
    useradd -r -g dataverse dataverse

USER dataverse

Here, groupadd -r dataverse creates a new group named dataverse, and useradd -r -g dataverse dataverse creates a new user named dataverse and adds it to the dataverse group. USER dataverse switches the user context to the dataverse user for subsequent commands.

Defining the Entry Point

Finally, you need to define the entry point for the container. This is the command that will be executed when the container starts. For Dataverse, this typically involves starting the Glassfish application server. Hereā€™s an example:

CMD [