Scalable Databases for AI Workloads with OpenStack Cinder

Question

Pulumi · Accepted Answer

OpenStack Cinder is the block storage service for the OpenStack cloud computing platform. It allows users to create and manage a service that provides persistent block storage to run instances. It's important to note that managing OpenStack through Pulumi would typically involve interacting with the OpenStack APIs through the `openstack` provider; however, an OpenStack provider for Pulumi is not currently available.

For scalable databases that can handle AI workloads, we'd ideally look to a managed service that provides high performance and automatically handles things like replication, backups, and scaling. Since we cannot provide a direct integration with OpenStack Cinder using Pulumi, we can explore creating scalable databases with another cloud provider or consider setting up a scalable, distributed database system like Cassandra that can run on VM instances which are backed by Cinder volumes.

Given the available Pulumi Registry Results, we have providers like `azure-native`, `alicloud`, and `aws` that support creating scalable databases. Here, we can show you an example using AWS, which provides managed database services that can scale according to demand, though not using OpenStack Cinder.

For this example, we will use Amazon Aurora, a MySQL and PostgreSQL-compatible relational database built for the cloud that combines the performance and availability of traditional enterprise databases with the simplicity and cost-effectiveness of open source databases.

With Pulumi's `aws` package, one could create an Aurora database cluster and enable Auto Scaling to manage the workload demand automatically. Below is a Python program using Pulumi that sets up a scalable Aurora database cluster:

```python
import pulumi
import pulumi_aws as aws

# Create an Aurora DB cluster
db_cluster = aws.rds.Cluster("aiWorkloadsCluster",
    engine=aws.rds.EngineType.AURORA_MYSQL,
    engine_version="5.7.mysql_aurora.2.03.2",
    database_name="aiworkloadsdb",
    master_username="admin",
    master_password="secret-password",
    skip_final_snapshot=True)

# Define the Aurora DB instance size to populate the cluster
db_instance = aws.rds.ClusterInstance("aiWorkloadsClusterInstance",
    cluster_identifier=db_cluster.cluster_identifier,
    instance_class=aws.rds.InstanceType.R5_LARGE,
    engine=aws.rds.EngineType.AURORA_MYSQL,
    engine_version="5.7.mysql_aurora.2.03.2",
    publicly_accessible=True)

# Set up Auto Scaling for the Aurora cluster to manage read replicas
scaling_policy = aws.appautoscaling.Target("aiWorkloadsReadReplicaAutoScaling",
    max_capacity=5,
    min_capacity=1,
    resource_id=pulumi.Output.concat("cluster:", db_cluster.cluster_identifier),
    scalable_dimension="rds:cluster:ReadReplicaCount",
    service_namespace="rds")

# Define the scaling policy for the read replicas 
scaling_policy_rule = aws.appautoscaling.Policy("aiWorkloadsReadReplicaAutoScalingPolicy",
    policy_type="TargetTrackingScaling",
    resource_id=scaling_policy.resource_id,
    scalable_dimension=scaling_policy.scalable_dimension,
    service_namespace=scaling_policy.service_namespace,
    target_tracking_scaling_policy_configuration=aws.appautoscaling.PolicyTargetTrackingScalingPolicyConfigurationArgs(
        target_value=70.0,
        scale_in_cooldown=300,
        scale_out_cooldown=300,
        predefined_metric_specification=aws.appautoscaling.PolicyPredefinedMetricSpecificationArgs(
            predefined_metric_type="RDSReaderAverageCPUUtilization"
        )
    ))

# Export the cluster endpoint, which can be used to connect to the database
pulumi.export('db_cluster_endpoint', db_cluster.endpoint)
```

In this program:
- We create an Amazon Aurora database cluster with `aws.rds.Cluster` for AI workloads.
- Add one instance to the database cluster with `aws.rds.ClusterInstance`.
- Implement a scaling policy using `aws.appautoscaling.Target` to enable the database to scale based on demand.
- The auto scaling policy `aws.appautoscaling.Policy` is defined to track CPU utilization and adjust the number of read replicas.

Remember to replace `master_password` with a secure password and manage it outside of your version control system, possibly using secret management tools like AWS Secrets Manager in conjunction with Pulumi's secret management.

This is a fundamental setup without considering data migration, security (VPC, security groups, IAM roles), monitoring, or deep customization for AI workloads, which would typically be part of a production deployment. The scalability of this setup is mostly in terms of read throughput by adjusting the number of read replicas based on the CPU utilization.

This setup should be adaptable to most AI workloads that require relational database capabilities. If the workloads differ or require non-relational databases, AWS offers other database services such as DynamoDB for NoSQL or the ElasticSearch Service for search workloads, which are also manageable via Pulumi.

Finally, keep in mind that without a direct Pulumi OpenStack provider, managing OpenStack resources will have to be done through other means. If you'd like to suggest updating Pulumi with an OpenStack provider, Pulumi's [GitHub Issues](https://github.com/pulumi/pulumi/issues) is a good place to start.