1. MongoDB Atlas for Real-Time AI Feature Stores


    To set up MongoDB Atlas as a real-time AI feature store, we can use Pulumi's MongoDB Atlas provider. Pulumi allows you to declare infrastructure using code in a simple and predictable way. By using Pulumi to manage MongoDB Atlas resources, you can easily deploy and manage your clusters, users, and other resources as part of your infrastructure.

    Below, you will find a Pulumi program written in Python that creates a MongoDB Atlas project and an Atlas cluster which is suitable for a real-time AI feature store application. It provisions resources requiring properties, such as the backing provider name, MongoDB major version, and instance size name, which are important for performance considerations in real-time applications.

    The mongodbatlas.Project resource is used to create a new project under the organization associated with the given API key. Within this project, we'll create a mongodbatlas.Cluster which is the actual MongoDB database cluster. The cluster can be configured according to the needs of your application, but for this example, we'll set up a basic cluster with predefined properties.

    Before you run this program, make sure you have set up the Pulumi MongoDB Atlas provider and the required credentials as per the Pulumi MongoDB Atlas setup.

    Here's the Pulumi Python program:

    import pulumi import pulumi_mongodbatlas as mongodbatlas # Create a new project project = mongodbatlas.Project("ai-feature-store-project", name="ai-feature-store", orgId="your-mongodb-atlas-organization-id") # Create a MongoDB cluster suitable for real-time AI feature store cluster = mongodbatlas.Cluster("ai-feature-store-cluster", projectId=project.id, providerName="AWS", # Using AWS as the cloud provider for Atlas providerRegionName="US_EAST_1", mongoDbMajorVersion="4.4", # Define the version of MongoDB providerInstanceSizeName="M10", # This is the smallest dedicated paid tier clusterType="REPLICASET", diskSizeGb=10, # Size of the storage in gigabytes backupEnabled=True, # Enable cloud provider snapshots for backups autoScalingDiskGbEnabled=True) # Enable disk auto-scaling # Exports pulumi.export("projectId", project.id) pulumi.export("clusterName", cluster.name) pulumi.export("clusterConnectionString", cluster.connectionStrings.apply(lambda cs: cs["standardSrv"]))

    Make sure to replace "your-mongodb-atlas-organization-id" with the actual Organization ID where you want the project to be created.

    This program defines two resources:

    1. mongodbatlas.Project: This is the project under which all other resources like clusters are grouped. It's analogous to a namespace that helps you manage all your MongoDB Atlas resources together.

    2. mongodbatlas.Cluster: This is the MongoDB cluster resource representing your database. We used the M10 instance size which is suitable for small-scale applications, but you should select a size that matches your workload requirements. We also enabled backups for data safety and disk auto-scaling to automatically manage disk size based on the workload.

    The program exports the project ID, the cluster name, and the connection string for the MongoDB cluster. The connection string is necessary for your applications to connect to the database. It is exposed using Pulumi Outputs, which are a way to retrieve and work with asynchronous values.

    To execute this program, you will need to run the following commands with Pulumi after installing the CLI:

    1. pulumi stack init [stack-name] to create a new stack for your project.
    2. pulumi up to deploy your infrastructure.

    Please be aware of the costs associated with the resources you create in MongoDB Atlas as per their pricing models, and make sure to remove the resources when not needed using pulumi destroy to avoid incurring unnecessary charges.