1. Managed MongoDB for Real-time AI Inference Data


    Managing a MongoDB instance for real-time AI inference data typically involves provisioning a managed MongoDB resource on a cloud provider, setting up the instance to handle the expected load, and configuring security and access settings. MongoDB is a popular NoSQL database that is well-suited for handling unstructured data often used in AI and machine learning scenarios.

    To implement a managed MongoDB instance using Pulumi, I will choose MongoDB Atlas because it provides a fully-managed MongoDB service that simplifies the setup and operational aspects of running a MongoDB cluster, which is ideal for applications that require real-time data analysis, such as AI inference engines.

    Below is a Pulumi program that creates a new project and cluster within MongoDB Atlas. For the purpose of AI inference, you would likely want to choose an instance size and configuration that can support the expected workload. You would also set up the network access, database user, and IP whitelisting to ensure secure access to the database.

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Replace these variables with appropriate values atlas_public_key = 'your-public-key' atlas_private_key = 'your-private-key' organization_id = 'your-org-id' # Configure the MongoDB Atlas provider atlas_provider = mongodbatlas.Provider("atlas_provider", public_key=atlas_public_key, private_key=atlas_private_key) # Create a new project project = mongodbatlas.Project("project", org_id=organization_id, name="ai-inference-project", opts=pulumi.ResourceOptions(provider=atlas_provider)) # Create a MongoDB cluster within the project # The instance size, pitEnabled, diskSizeGb, and other parameters will need to be set # according to the specific requirements of the AI inference workloads. cluster = mongodbatlas.Cluster("cluster", project_id=project.id, name="ai-inference-cluster", cluster_type="REPLICASET", replication_factor=3, provider_backup_enabled=True, provider_instance_size_name="M40", # Choose an instance size appropriate for the workload provider_disk_type_name="P1", # Choose disk types based on IOPS and throughput needs provider_encrypt_ebs_volume=True, provider_region_name="US_EAST_1", # Choose the region that is closest to your users opts=pulumi.ResourceOptions(provider=atlas_provider)) # Export the cluster ID and connection string for later use pulumi.export('cluster_id', cluster.id) pulumi.export('connection_strings', cluster.connection_strings)

    This program can be executed using the Pulumi CLI after setting up the Pulumi Python environment and MongoDB Atlas credentials. The pulumi.export lines at the end will output the cluster_id and connection_strings, which are important for connecting your applications to the newly created MongoDB Atlas cluster. You can further expand on this program by adding resources such as network peering, database users, and IAM roles as needed for your specific application and infrastructure requirements.

    Please make sure to replace atlas_public_key, atlas_private_key, and organization_id with your actual MongoDB Atlas credentials and organization ID. It's important to note that handling secrets like API keys should be done securely — often using a secrets manager or Pulumi's built-in secrets handling.

    Additionally, when considering production scenarios, be sure to tune the cluster settings like provider_instance_size_name, replication_factor, and provider_disk_type_name, to match your performance and reliability requirements. MongoDB Atlas provides a variety of instance sizes and configurations to handle different workloads, and you need to choose an instance that meets your expected load and offers the necessary computing resources for real-time AI inference.