1. Automated Access Control for MongoDB in Hybrid Architectures


    To set up automated access control for MongoDB in hybrid architectures, you can use infrastructure as code with Pulumi to create and manage your MongoDB deployments and their access controls seamlessly. For this purpose, you can deploy MongoDB Atlas resources using the pulumi_mongodbatlas plugin or MongoDB resources on Azure using the pulumi_azure_native plugin, depending on where your MongoDB instance resides.

    For this example, let's assume you want to implement access control for a MongoDB Atlas cluster, as MongoDB Atlas is a fully-managed service suitable for hybrid architectures where some resources may be in the cloud and others on-premises. You will use MongoDB Atlas's role-based access control (RBAC) to define user roles with specific permissions.

    Below is a detailed program that sets up a MongoDB Atlas project, a cluster within that project, and enables database auditing to track access. This ensures a good level of security and control over who can access the MongoDB data and how they can interact with it.

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Create a new MongoDB Atlas project where your cluster will be deployed. project = mongodbatlas.Project("myProject", org_id="your_org_id", # Replace with your MongoDB Atlas organization ID. name="access-controlled-project") # Create a MongoDB Atlas cluster within the project you just created. # Note that this is just an example and will need valid inputs for your account. cluster = mongodbatlas.Cluster("myCluster", project_id=project.id, name="myAccessControlledCluster", cluster_type="REPLICASET", replication_factor=3, provider_backup_enabled=True, provider_instance_size_name="M10", provider_name="AWS", provider_region_name="US_EAST_1", provider_disk_iops=100, provider_encrypt_ebs_volume=False, mongo_db_major_version="4.2", auto_scaling_disk_gb_enabled=True) # Enable MongoDB Atlas Database Auditing to track data access and changes. # This step is critical for maintaining access logs and tracking. auditing = mongodbatlas.Auditing("myAuditing", project_id=project.id, audit_filter='{ "atype": { "$in": ["authenticate", "createCollection", "dropCollection", "insert", "update", "delete"] }, "result": "0" }', enabled=True) # Use MongoDB Atlas's RBAC to define a user role with specific permissions. # Below is an example that you should replace with actual user details and relevant permissions. user_role_definition = mongodbatlas.CustomDbRole("myCustomRole", project_id=project.id, role_name="readWriteWithAudit", actions=[mongodbatlas.CustomDbRoleActionArgs( action="INSERT", resources=[mongodbatlas.CustomDbRoleResourceArgs( db="your_db_name", collection="your_collection_name" )] )]) # Export the cluster ID and auditing ID as stack outputs. pulumi.export('cluster_id', cluster.id) pulumi.export('auditing_id', auditing.id)

    In this program, we start by importing the pulumi and pulumi_mongodbatlas modules that enable us to interact with the MongoDB Atlas API through Pulumi.

    We then create a project within MongoDB Atlas using the mongodbatlas.Project resource. We replace 'your_org_id' with our actual MongoDB Atlas organization ID.

    Next, we deploy a MongoDB Atlas cluster within the project. The mongodbatlas.Cluster resource held within the cluster variable represents the MongoDB deployment. We specify various parameters such as the cluster type, replication factor, provider details, etc., which should be tailored to match your requirements and account details.

    We also establish database auditing for our project using the mongodbatlas.Auditing resource. This is an important step to track security-related operations within the cluster, such as authentication and data modifications.

    Finally, we define a custom database role using the mongodbatlas.CustomDbRole resource. This role is set to have certain permissions on a specific database and collection – in this case, allowing insert operations. You need to replace 'your_db_name' and 'your_collection_name' with the names of your database and collection, respectively.

    To deploy this configuration, save the code to a file (e.g., main.py), and simply run pulumi up from the same directory after setting up your Pulumi account and configuring the MongoDB Atlas provider. The program will interact with the MongoDB Atlas service to provision and configure the resources as defined.

    Lastly, the pulumi.export commands at the end of the script output the cluster and auditing IDs. This is useful for integrating the Pulumi stack outputs with other services or for reference purposes in your infrastructure's monitoring tools.