Blobfuse, Azure’s solution for mounting Blob Storage as a filesystem, is a powerful tool for streamlining cloud storage operations. But setting up Blobfuse in an automated and reusable way requires careful planning and orchestration between Packer and Terraform. In this first article of our three-part series, we’ll explore the high-level architecture of this solution, focusing on how these tools work together to set the stage for a functional and flexible deployment.

Our approach uses Packer to create a Virtual Machine (VM) image and Terraform to provision the Azure environment. These two automation components form the foundation of a robust system capable of integrating Azure Storage with Blobfuse. By understanding their interplay and the “last mile” configuration required to bridge their dependencies, we can create a scalable and efficient workflow for deploying Blobfuse-enabled VMs.

Automation Components: Packer and Terraform in Tandem

The solution relies on two critical automation tools, each fulfilling a specific role in the process.

Packer: Staging the VM Template

Packer handles the creation of a VM image by installing the necessary Blobfuse libraries and staging configuration files. These configurations include placeholders for critical details like the Azure Storage account name and container name, which aren’t available during the Packer build process. This is because Packer operates independently of the resources Terraform provisions later.

By design, the Packer image isn’t “ready to run” on its own. Instead, it acts as a template — a partially configured VM with all the foundational components in place but awaiting final adjustments that depend on Terraform’s work.

Terraform: Provisioning the Azure Environment

Terraform comes into play after the Packer image is built. It provisions the Azure infrastructure, including:

  • A Virtual Machine using the Packer-generated image.
  • A Storage Account and Blob Storage Container.
  • A User-Assigned Managed Identity for secure authentication with Azure resources.

Terraform ensures these components are created and correctly configured, but it also highlights the challenge: the dependencies between the VM and its sibling resources, such as the Storage Account and Managed Identity. These dependencies require a “last mile” configuration step to finalize the VM setup.

Tackling Co-Dependencies with “Last Mile” Configuration

Co-dependencies between the Packer image and the Terraform-provisioned resources pose a unique challenge. For example:

  • Blobfuse on the VM needs the Azure Storage account name and container name to function.
  • The Managed Identity must be assigned appropriate roles for Blobfuse to authenticate with the Storage Account.

Since these details are only known after Terraform applies, the VM cannot be fully configured at the Packer stage. Instead, the Packer template stages placeholders for these values, and a post-provisioning step is used to fill in the blanks.

This “last mile” configuration technique involves executing a script or tool on the VM after Terraform provisions the environment. The script replaces placeholders in the Blobfuse configuration file with the actual values for the Storage Account and container, completing the setup. This approach decouples the Packer image from the specific environment it runs in, making it reusable across multiple deployments.

Architectural Overview of the Solution

The overall architecture of this solution combines Packer’s templating capabilities with Terraform’s provisioning power. Key components include:

  • Azure Virtual Machine: The central piece of infrastructure, built using the Packer image and provisioned by Terraform.
  • User-Assigned Managed Identity: Configured by Terraform and assigned to the VM to enable secure authentication.
  • Azure Storage Account and Blob Storage Container: Provisioned by Terraform to store and manage data.
  • Networking Components: A Virtual Network (VNet), subnets, and any necessary security groups to enable communication between resources.

By combining these elements, we ensure a scalable, secure, and modular architecture for Blobfuse deployments.

Conclusion

The first step in building a robust automation pipeline for Blobfuse on Azure is understanding the interplay between Packer and Terraform. By leveraging Packer to create a generic VM template and Terraform to provision the environment, we can balance flexibility with automation. The “last mile” configuration bridges the gap between these tools, ensuring the VM is fully functional once deployed. In the next part of this series, we’ll dive deeper into the specifics of creating a Packer template that stages the necessary Blobfuse configuration files and executables. Stay tuned as we continue to build this solution step by step, culminating in a fully operational Blobfuse-enabled Azure Virtual Machine.