Best Practices for Confluent Terraform Provider

Managing Confluent Cloud infrastructure efficiently poses challenges due to the complexities involved in deploying and maintaining various components like environments, clusters, topics, and authorizations. Without proper tooling and practices, teams struggle with manual configuration errors, lack of consistency, and potential security risks.

The Confluent Terraform Provider serves as a crucial tool for deploying Confluent Cloud infrastructure, offering a streamlined approach to managing various elements such as environments, Apache Kafka® clusters, Kafka topics, and additional resources within Confluent Cloud. 

By leveraging the Confluent Terraform Provider effectively for Confluent Cloud resource provisioning, and implementing best practices for Terraform and DevOps processes, you can enhance workflow automation and resource management.

Segregate and organize state files

To mitigate risk and enhance manageability, it's essential to isolate and organize state files when deploying Confluent Cloud resources using Terraform. Conventional approaches often employ a single state file for all project resources, which poses a significant risk due to potential configuration mistakes impacting all resources. A recommended best practice is to utilize multiple state files, particularly for distinct parts of your infrastructure. By logically segregating resources and assigning each its own state file in the backend, changes to one resource will remain isolated. Furthermore, employing different state files for various environments enhances control, enforces least privilege, and minimizes conflicts. In the context of Confluent Cloud deployments, this segregation can be achieved by structuring state store names to incorporate environment, cluster, and resource-specific identifiers. 

For example, in scenarios where organizations are organized by domains, separate state stores can be created for each environment, resource type, and domain.

terraform {
  backend "azurerm" {
    resource_group_name  = "terraform-rg"
    storage_account_name = "terraformsa"
    container_name       = "terraformstate"
    key                  = "developmentEnv.kafkaClusterName.teamName.topics.tfstate"
  }
}

Use modules for resources deployments

Terraform configurations can become complex as infrastructure scales. Modules offer a solution by encapsulating related resources. They abstract infrastructure design into reusable components. In Confluent Cloud deployments, a modular approach is recommended. Specific modules for each resource type enhance manageability. See this GitHub repository for examples.

Split Confluent Cloud resources into environment-specific subdirectories

Organize Confluent Cloud Terraform configurations into environment-specific subdirectories for effective management, which includes two main directories: modules and environments. Modules house resource-specific configurations, while environments contain configurations for different environments (dev, qa, prod). See this GitHub repository for examples. This approach enhances modularity and reusability, ensuring streamlined deployment across Confluent Cloud environments. The following is an example directory structure for organizing Confluent Cloud Terraform configurations:

-- CONFLUENCE CLOUD-DIRECTORY/
   -- modules/
      -- <resource-name>/
         -- main.tf
         -- variables.tf
         -- outputs.tf
         -- provider.tf
         -- README
      -- ...other modules…

   -- environments/
      -- dev/
         –- topics/
           -- backend.tf
           -- main.tf
           -- variables.tf
           -- outputs.tf
           -- …additional environment-specific configurations…
         –- clusters/
           -- backend.tf
           -- main.tf
           -- variables.tf
           -- outputs.tf
           -- …additional environment-specific configurations…
         -- ...other resources…      
      -- qa/
         –- topics/
           -- backend.tf
           -- main.tf
           -- variables.tf
           -- outputs.tf
           -- …additional environment-specific configurations…
         –- clusters/
           -- backend.tf
           -- main.tf
           -- variables.tf
           -- outputs.tf
           -- …additional environment-specific configurations…
         -- ...other resources…       
      -- prod/
         –- topics/
           -- backend.tf
           -- main.tf
           -- variables.tf
           -- outputs.tf
           -- …additional environment-specific configurations…
         –- clusters/
           -- backend.tf
           -- main.tf

Iterate over maps and objects

Enhance your Confluent Cloud Terraform deployments by efficiently iterating over maps or objects instead of manually handling multiple module or resource instances. Utilizing Terraform's for_each meta-argument empowers dynamic creation of resource instances based on predefined configurations.

This feature enables the generation of multiple module or resource blocks, accommodating maps or sets of strings. This approach proves particularly beneficial when deploying various configured resources in Confluent Cloud, such as topics, RBAC roles, and ACLs.

For instance, when deploying Kafka topics using the Confluent Terraform Provider, you can dynamically configure each topic using a map structure:

resource "confluent_kafka_topic" "contoso_test" {
  kafka_cluster {
    id = data.confluent_kafka_cluster.cluster_contoso.id
  }
  for_each = var.topics

  topic_name       = each.key
  partitions_count = each.value.partitions_count

  rest_endpoint = data.confluent_kafka_cluster.cluster_contoso.rest_endpoint
  config = {
    "cleanup.policy" = each.value.cleanup_policy
    # Additional configuration options
  }
}

Utilize the lifecycle block

Leverage the lifecycle block in Confluent Cloud Terraform deployments for enhanced instance protection. Use prevent_destroy=true to safeguard critical instances from accidental deletion. Exercise caution with prevent_destroy to avoid restrictions on configuration changes and Terraform destroy commands. Enable/disable prevent_destroy based on environment needs for optimal resource management. 

Avoid hard-coded values

Enhance Confluent Cloud Terraform configurations by avoiding hard-coded values. Define variables for flexibility and utilize data sources for dynamic attribute fetching. The following example leverages variables and data sources for environments and Kafka cluster IDs:

data "confluent_environment" "env_contoso" {
  id = var.env_contoso_id
}

data "confluent_kafka_cluster" "cluster_contoso" {
  id = var.cluster_contoso_id
  environment {
    id = data.confluent_environment.env_contoso_id
  }
}

resource "confluent_kafka_topic" "example_topic" {
  kafka_cluster {
    id = data.confluent_kafka_cluster.cluster_contoso.id
  }
  ...
}

Manage a single Kafka cluster in the same Terraform workspace

Managing a single Kafka cluster within the same Terraform workspace offers streamlined resource provisioning and maintenance, simplifying key rotation processes while enhancing security by safeguarding sensitive credentials. The following example demonstrates topic creation, leveraging Kafka API keys necessary to interact with Kafka clusters in Confluent Cloud, as defined in the provider configuration:

provider "confluent" {
  kafka_id            = var.kafka_id 
  kafka_rest_endpoint = var.kafka_rest_endpoint
  kafka_api_key       = var.kafka_api_key
  kafka_api_secret    = var.kafka_api_secret
}

resource "confluent_kafka_topic" "orders" {
  topic_name         = "orders"

  lifecycle {
    prevent_destroy = true
  }
}

Secrets management strategy

When deploying Confluent Cloud resources via Terraform, it's crucial to implement a robust secrets management strategy. Secure API keys/secrets in DevOps pipeline to ensure a strong secrets management strategy when deploying Confluent Cloud resources via Terraform. Protect sensitive data like API keys/secrets by avoiding plaintext storage in version control systems. Implement secure secret stores such as HashiCorp Vault or Azure Key Vault for enhanced access control and security.

Additionally, ensure robust security measures for managing API keys and secrets generated with the confluent_api_key resource. Avoid storing sensitive data in plaintext within Terraform state files, especially with remote backends. Utilize encryption-enabled backends and implement IAM policies to prevent unauthorized access. Consider employing a Key Management Service for secure API key and secret management, ensuring encrypted protection. Find a detailed example of this approach using AWS Secret Manager and API key rotation here.

Enforce policy on resource configuration

Enhance resource configuration in Confluent Cloud with enforced policies using Confluent OPA Policies. This library provides prescriptive Sentinel policies that can be used to establish well managed Terraform configuration for Confluent resources. Utilize validation blocks within variables to restrict values, ensuring adherence to specified criteria and informative error messages. The Confluent OPA Policies for Terraform cover various aspects such as API key ownership, RBAC roles, resource approval, cloud provider constraints, permitted connectors, and topic properties regulation. By integrating these policies with variable validations, you establish a robust governance framework, enhancing security and compliance across infrastructure.

Ensure consistent naming conventions

Ensuring consistent naming conventions is vital for effective Terraform code management. While various conventions exist, it's crucial to select one that aligns with your team's preferences and apply it consistently across projects. Embracing a naming convention that employs underscores to separate multiple words consistently is recommended. This approach aligns configuration objects with resource types, data source types, and predefined values, enhancing clarity and maintaining consistency throughout your Terraform codebase.

Additional Terraform best practices

Adhering to general Terraform best practices ensures efficient and reliable infrastructure management. Here are some additional recommendations to consider:

• Don't configure providers or backends in shared modules: Avoid configuring providers or backends in shared modules (define the minimum required provider versions). Instead, define these configurations in root modules for better control and consistency.

• Store variables in .tfvars files: For shared modules, specify the minimum required provider versions in a required_providers block and store variables in a .tfvars file. In root modules, provide variables using a terraform.tfvars file to maintain consistency.

• Avoid manual Terraform state modifications: Manual modifications to the Terraform state file can lead to corruption and significant infrastructure issues. It's crucial to avoid manual changes and let Terraform manage the state automatically. If the state is out of sync, Terraform may destroy or change your existing resources. After you rule out configuration errors, review your state. Ensure your configuration is in sync by refreshing, importing, or replacing resources.

• Implement remote state management: Use remote state management, such as HashiCorp Terraform Cloud Remote State Management, to enhance collaboration and reliability. Storing state files remotely ensures consistency and mitigates the risk of local state file corruption. It is recommended to utilize remote state stores with high availability backends to ensure replication and backup across different zones or regions. This approach enhances resilience by safeguarding against potential data loss and ensures accessibility during outages.

• Follow a plan: Always generate a plan first for Terraform executions, saving it to an output file. Execute the plan only after receiving approval from a peer or the infrastructure owner.

• Use DevOps pipelines: Execute terraform plan and terraform apply using automated tooling.

• Regularly review version pins: While version pinning ensures stability, it may hinder the incorporation of bug fixes and improvements. Regularly review version pins for Terraform, providers, and modules to stay updated with the latest features and fixes.

• Mark sensitive outputs: Instead of manually encrypting sensitive values, utilize Terraform's built-in support for sensitive state management. Ensure that sensitive values exported as outputs are appropriately marked as sensitive.

Conclusion

By implementing best practices for deploying Confluent Cloud resources using the Confluent Terraform Provider and embracing Terraform and DevOps processes, organizations can achieve enhanced consistency, reduced manual errors, and improved security measures. This holistic approach ultimately leads to optimized management of Confluent Cloud infrastructure, ensuring smooth operations and increased efficiency in resource provisioning and management.