1. Managed RDS for Multi-Tenant AI Platform Databases


    Creating a managed RDS instance suitable for multi-tenant databases, like for an AI platform, is a common cloud infrastructure task. By leveraging a managed RDS instance, you can make sure that your databases are reliable, scalable, and secure.

    In Pulumi, this task involves several resources:

    1. RDS Instance: Represents the managed database itself.
    2. DB Subnet Group: A collection of subnets within a VPC that you can assign to the RDS instance, ensuring the RDS instance has network connectivity.
    3. Security Group: Defines the firewall rules to allow traffic to and from the RDS instance.
    4. Parameter Group: Manages the runtime configuration of the RDS instance.
    5. IAM Role Association (Optional): Grants the RDS service specific permissions using IAM roles.

    For a multi-tenant platform, efficiency and isolation can be key. You should make sure to set up different schemas or databases within the RDS instance for each tenant, but that kind of setup is done at the application level and isn’t handled directly by infrastructure as code tools like Pulumi.

    Here's a Pulumi program that outlines the essential steps for setting up a managed RDS instance for a multi-tenant AI platform:

    import pulumi import pulumi_aws as aws # VPC and Security Group are prerequisites # Assume that we have an existing VPC and subnets for the RDS Instance. # You would typically obtain these from your infrastructure configuration: vpc = aws.ec2.Vpc.get("existing-vpc", id="vpc-id") subnets = aws.ec2.get_subnet_ids(vpc_id=vpc.id) # Create a DB subnet group for the RDS instance db_subnet_group = aws.rds.SubnetGroup("dbSubnetGroup", subnet_ids=subnets.ids, tags={ "Name": "MyDbSubnetGroup" }) # Create a security group for the RDS instance rds_security_group = aws.ec2.SecurityGroup("rdsSecurityGroup", vpc_id=vpc.id, description="Allow access to RDS instance", ingress=[{ "protocol": "tcp", "from_port": 5432, # Assuming PostgreSQL, adjust the port for your DB engine "to_port": 5432, "cidr_blocks": [""], # Adjust the CIDR block based on your security requirements }], egress=[{ "protocol": "-1", "from_port": 0, "to_port": 0, "cidr_blocks": [""], }]) # Create a DB instance rds_instance = aws.rds.Instance("rdsInstance", allocated_storage=20, engine="postgres", # Adjust the engine for your specific database engine_version="13.3", # Set this to your preferred version instance_class="db.t3.micro", name="mydb", username="user", password="password", # You should ideally use the Pulumi config or secret manager to handle the password parameter_group_name=aws.rds.ParameterGroup("default.postgres13.3").name, db_subnet_group_name=db_subnet_group.name, vpc_security_group_ids=[rds_security_group.id], skip_final_snapshot=True) # Export the RDS instance endpoint to access it from your application pulumi.export("rds_endpoint", rds_instance.endpoint)

    This program does the following:

    1. Retrieves an existing VPC and its subnet IDs.
    2. Creates an RDS Subnet Group, which allows us to specify in which subnets our RDS Instance will live, offering high availability.
    3. Defines a security group to control access to the RDS instance. In this example, it's open to the world on the PostgreSQL default port. You'll want to restrict it to a more suitable CIDR block.
    4. Provisions an RDS instance with PostgreSQL as the database engine, using db.t3.micro as the instance class for this example. The database name, username, and password are specified as well.
    5. Exports the endpoint of the RDS instance so that it can be used to connect to the database once it is ready.

    A few important notes:

    • Security: The password should not be hardcoded as shown. You should use Pulumi's secret management to store it securely. Similarly, the security group rules should be updated to reflect real-world CIDR blocks and not be open to the world.
    • High Availability: This example uses the minimum configurations for demonstration. For production, you should consider multi-AZ deployments, instance sizing, and backup settings according to your needs.
    • Database Engine: You can select a different database engine such as MySQL, MariaDB, Oracle, or Microsoft SQL Server according to your requirements.
    • Costs: Be aware of the costs associated with the AWS resources you are provisioning.

    Remember to configure Pulumi with necessary AWS credentials and settings, as well as adjust the resource parameters to fit the actual requirements of your multi-tenant AI platform.