Automated AI Workload Scaling through MongoDB Atlas Access

Question

Pulumi · Accepted Answer

To create an automated AI workload scaling solution with MongoDB Atlas on Pulumi, you need to set up a MongoDB Atlas cluster with autoscaling capabilities. Here's how you can accomplish this using Pulumi in Python.

### Preparing the Pulumi Program

Before you start writing your Pulumi program, make sure you have the following prerequisites:

1. MongoDB Atlas account: You will need an account on MongoDB Atlas. Ensure you have access to your organization and project within MongoDB Atlas where you want to deploy your cluster.
2. Pulumi account: Sign up for a Pulumi account and set up the Pulumi CLI on your machine.
3. Install the Pulumi MongoDB Atlas provider: You can do this by running `pip install pulumi_mongodbatlas`.
4. Configure Pulumi to use your MongoDB Atlas credentials: Store your MongoDB Atlas public and private API keys in a place where Pulumi can access them, typically using `pulumi config set`.

### Writing the Pulumi Program

Below is a Pulumi program in Python that creates an autoscaling MongoDB Atlas cluster. The autoscaling feature automatically adjusts compute and storage resources to match your workload demands.

```python
import pulumi
import pulumi_mongodbatlas as mongodbatlas

# Replace these variables with your specific MongoDB Atlas Project ID and desired cluster specifications
project_id = 'your-atlas-project-id'
cluster_name = 'ai-workload-cluster'
instance_size = 'M40'  # Choose an instance size that suits your needs.
region = 'US_EAST_1'  # Select the region where you want to deploy your cluster.

# Create a MongoDB Atlas cluster with autoscaling enabled
cluster = mongodbatlas.Cluster("mongo-cluster",
    project_id=project_id,
    name=cluster_name,
    cluster_type="REPLICASET",
    replication_factor=3,
    provider_instance_size_name=instance_size,
    provider_name="AWS",
    provider_region_name=region,
    provider_backup_enabled=True,  # Enable cloud provider snapshots.
    provider_disk_iops=100,
    provider_encrypt_ebs_volume=False,
    auto_scaling_disk_gb_enabled=True,  # Enable storage autoscaling.
    auto_scaling_compute_enabled=True,  # Enable compute autoscaling.
    auto_scaling_compute_scale_down_enabled=True,
    mongo_db_major_version="4.4",
    provider_auto_scaling_compute_max_instance_size="M40",
    provider_auto_scaling_compute_min_instance_size="M10",
)

# Export the Cluster ID and Connection String for external use.
pulumi.export('cluster_id', cluster.id)
pulumi.export('cluster_connection_string', cluster.connection_strings.apply(lambda cs: cs['standard']))
```

### Explanation of the Program

- We start by importing the required Pulumi packages.
- The `project_id`, `cluster_name`, `instance_size`, and `region` are placeholder variables that you should replace with your actual MongoDB Atlas project information and desired cluster configuration.
- The `mongodbatlas.Cluster` resource is used to create a new MongoDB Atlas cluster. The properties we set configure the following:
  - `cluster_type`: The type of cluster to deploy; in this case, a replica set.
  - `replication_factor`: The number of replica set members.
  - `provider_instance_size_name`: The instance size for each member of the cluster.
  - `provider_name`: The cloud provider for the cluster, which is AWS in this example.
  - `provider_region_name`: The region where the cluster will be deployed.
  - `provider_backup_enabled`: Whether or not to enable cloud provider backups.
  - `provider_disk_iops`: The IOPS for the cluster's storage.
  - `auto_scaling_disk_gb_enabled`: Enables autoscaling for storage.
  - `auto_scaling_compute_enabled`: Enables autoscaling for compute resources (CPU and RAM).
  - `auto_scaling_compute_scale_down_enabled`: Allows the cluster to scale down compute resources when they're not needed.
  - `mongo_db_major_version`: The major version of MongoDB to use.
  - `provider_auto_scaling_compute_max_instance_size`: The maximum instance size that the cluster can autoscale to.
  - `provider_auto_scaling_compute_min_instance_size`: The minimum instance size that the cluster can autoscale down to.

Finally, we export the `cluster_id` and `cluster_connection_string`, as these can be used to connect to the cluster from application code or to integrate with other services or resources.

With autoscaling enabled, your AI workload will be able to scale compute and storage resources automatically without manual intervention, adapting to the needs of your application as it grows or experiences varying load. This ensures that you only pay for the resources you need while maintaining the performance and availability of your AI services.