Cross-Region Data Queries for AI Workflows using MongoDB Atlas Federated Database Instances

Question

Pulumi · Accepted Answer

To set up a cross-region data query environment for AI workflows using MongoDB Atlas, you'll need to leverage MongoDB Atlas's federated database instances. With Pulumi, you can automate the provisioning and management of your MongoDB Atlas resources, including federated database clusters that allow you to query data across multiple regions.

Here's what we'll do with Pulumi:

1. Create a MongoDB Atlas project.
2. Configure a MongoDB Atlas cluster with cross-region replication to ensure high availability and data locality.
3. Set up the required network peering for cross-region communication if necessary.
4. Integrate with third-party services for AI workflows if needed.

Below is a Python program using Pulumi to create these resources. Comments in the code will guide you through what each section is doing.

```python
import pulumi
import pulumi_mongodbatlas as mongodbatlas

# Replace these variables with your own desired settings
atlas_public_key = 'your_atlas_public_key'
atlas_private_key = 'your_atlas_private_key'
organization_id = 'your_organization_id'
project_name = 'your_project_name'

# Configure MongoDB Atlas provider with your public and private keys
atlas_provider = mongodbatlas.Provider("provider", public_key=atlas_public_key, private_key=atlas_private_key)

# Create a new project in MongoDB Atlas
project = mongodbatlas.Project("project", 
    name=project_name,
    org_id=organization_id,
    __opts__=pulumi.ResourceOptions(provider=atlas_provider))

# Define your cluster's replication specs for cross-region deployment
replication_specs = [{
    "region_configs": [
        {
            "region_name": "US_EAST_1", 
            "electable_nodes": 3, 
            "priority": 7,
            "analytics_nodes": 0,
            "readonly_nodes": 0,
        },
        {
            "region_name": "EU_WEST_1", 
            "electable_nodes": 2, 
            "priority": 5,
            "analytics_nodes": 0,
            "readonly_nodes": 0,
        }
    ]
}]

# Create a MongoDB Atlas cluster with the specified replication specs
cluster = mongodbatlas.Cluster("cluster",
    project_id=project.id,
    cluster_type="REPLICASET",
    replication_specs=replication_specs,
    provider_name="AWS",
    provider_region_name="US_EAST_1",
    mongo_db_major_version="4.4",
    provider_instance_size_name="M10",
    provider_backup_enabled=True,
    __opts__=pulumi.ResourceOptions(depends_on=[project]))

# Export the MongoDB Atlas cluster's connection strings for your applications to use
pulumi.export('cluster_connection_strings', cluster.connection_strings)
```

In this program:

- We set up the Pulumi provider to communicate with MongoDB Atlas using the `atlas_public_key` and `atlas_private_key`. Be sure to replace `'your_atlas_public_key'` and `'your_atlas_private_key'` with your actual Atlas API keys.
- We create a new project inside MongoDB Atlas with the specified organization ID and project name. The organization ID should be the one pertaining to your MongoDB Atlas account, and you can name your project as you prefer.
- We define the `replication_specs` with two regions. You can adjust these regions and the number of nodes per region based on your needs.
- We create a cluster in the project, specifying that it should be a replica set and configuring the primary region and other settings, including the MongoDB version and instance size.
- Finally, we export the cluster's connection strings so you can connect to the cluster from your application code.

Once this code is executed with Pulumi CLI, it will provision the specified MongoDB Atlas resources, ready to handle cross-region queries for your AI workflows.