Nasser Naini

Service account roles used by Terraform to manage resources

When working with Google Cloud Platform (GCP) resources such as google_compute_network and google_compute_disk in Terraform, you need to assign specific roles to the service account that Terraform uses to ensure it has the necessary permissions. Here are the roles typically required for managing these resources:

For google_compute_network

Managing VPC networks (google_compute_network) generally requires permissions to create, modify, and delete network resources. The following roles are commonly used:

1. Compute Network Admin (roles/compute.networkAdmin):
   • This role includes permissions to create, modify, and delete networks and subnets.
   • It’s suitable for managing most aspects of networking in GCP, including firewalls, routes, and VPNs.
2. Project Editor (roles/editor):
   • This broader role includes permissions to modify almost all resources in a project.
   • It’s often used in development environments but might grant more permissions than necessary for production environments.

For google_compute_disk

Managing disks (google_compute_disk) typically requires permissions for disk creation, attachment, detachment, and deletion. The following roles are relevant:

1. Compute Storage Admin (roles/compute.storageAdmin):
   • This role grants permissions to create, modify, and delete disk resources.
   • It’s suitable for managing persistent disks and snapshots.
2. Project Editor (roles/editor):
   • Like with network management, this role provides broad access to modify resources within the project, including compute disks.

Best Practices for Role Assignment

• Principle of Least Privilege: Assign only the roles that provide the minimum necessary permissions to perform the intended tasks. For example, if your Terraform script only needs to manage networks, the Compute Network Admin role would be sufficient.
• Custom Roles: For more granular control, consider creating custom roles in GCP with precisely the permissions you need. This approach is particularly useful in tightly controlled environments or when adhering to specific compliance requirements.

Using the Roles in Terraform

When configuring your Terraform provider, you don’t specify these roles directly in the Terraform script. Instead, you assign these roles to the service account that Terraform uses, either through the Google Cloud Console or using the gcloud command-line tool.

After the service account is set up with the necessary roles, you use the service account’s credentials in your Terraform provider configuration:

provider "google" {
  credentials = file("<PATH_TO_SERVICE_ACCOUNT_KEY>.json")
  project     = "<YOUR_PROJECT_ID>"
  region      = "<YOUR_REGION>"
  zone        = "<YOUR_ZONE>"
}

Replace <PATH_TO_SERVICE_ACCOUNT_KEY>.json, <YOUR_PROJECT_ID>, <YOUR_REGION>, and <YOUR_ZONE> with your actual file path and project details.

Comparing AWS Aurora and GCP Spanner

Similarities:

1. High Availability and Durability:
   • Both services are designed for high availability and durability. They automatically replicate data across multiple nodes to ensure data safety and high availability.
   • They both offer multi-region deployments for enhanced availability and global distribution.
2. Scalability:
   • Both Spanner and Aurora provide scalability, but in different ways. Spanner offers horizontal scaling and can span across multiple regions, while Aurora provides vertical scaling and read replicas for horizontal read scaling.
3. Fully Managed:
   • Both are fully managed database services, reducing the overhead of manual database administration tasks like hardware provisioning, patching, configuration, or backups.
4. Strong Consistency:
   • Google Cloud Spanner offers strong consistency in reads and writes globally, which is one of its unique features. Aurora, particularly in its Aurora Global Databases configuration, offers strong consistency within a region and eventual consistency across regions.

Differences:

1. Global Scale:
   • Spanner is designed for global scale with built-in horizontal scaling and synchronous replication across regions, which is unique compared to traditional relational databases.
   • Aurora, while highly scalable within a region and offering read replicas across regions, doesn’t inherently provide the same level of global horizontal scaling for writes.
2. Database Model:
   • Spanner combines features of both relational and NoSQL databases. It provides SQL querying capabilities with ACID transactions over a globally-distributed architecture.
   • Aurora is more of a traditional relational database, compatible with MySQL and PostgreSQL, and focuses more on improving performance and reliability of these standard RDBMS models.
3. Pricing Model:
   • The pricing models of Aurora and Spanner also differ, with Spanner charging for node hours, storage, and network usage, while Aurora charges for instance hours, I/O rate, and provisioned storage.

Conclusion:

When choosing between Amazon Aurora and Google Cloud Spanner, your decision will depend on your specific use case, global distribution needs, consistency requirements, and existing cloud infrastructure. Aurora is often chosen for applications that require a traditional relational database with high performance and scalability within AWS ecosystem, while Spanner is selected for applications needing a globally distributed, highly scalable database with strong consistency across regions.