1. DocumentDB for Session Storage in ML Recommendation Systems


    In this program, you'll be creating AWS DocumentDB cluster, which is an AWS service compatible with MongoDB, commonly used for cases like session storage in Machine Learning (ML) recommendation systems. The DocumentDB cluster will provide a managed, scalable, and secure database for storing session data. We will use the Pulumi AWS package, which provides convenient access to AWS services, including DocumentDB.

    Here's what we'll do:

    1. Create a VPC and subnets: The DocumentDB cluster must reside within an AWS Virtual Private Cloud (VPC) and be associated with subnets.

    2. Create a DocumentDB Subnet Group: Define a subnet group for your DocumentDB cluster. DocumentDB uses this subnet group to choose a subnet and IP addresses within that subnet to associate with your instances.

    3. Set up a DocumentDB Cluster: Instantiate a cluster where your data will reside.

    4. Define a DocumentDB Instance: Specify an instance within the cluster to handle the database operations. You can have multiple instances for increased throughput and fault tolerance.

    We will provide simple configurations for each of these resources, without diving into more complex aspects like security groups or maintenance windows, to keep it straightforward.

    import pulumi import pulumi_aws as aws # Create a VPC for our DocumentDB cluster to live in. vpc = aws.ec2.Vpc("vpc", cidr_block="", enable_dns_hostnames=True, enable_dns_support=True, tags={ "Name": "pulumi-docdb-vpc", }) # Create subnets for the VPC. We'll need at least two subnets in two different Availability Zones. subnet_az1 = aws.ec2.Subnet("subnetAz1", vpc_id=vpc.id, cidr_block="", availability_zone="us-west-2a", tags={ "Name": "pulumi-docdb-subnet-az1", }) subnet_az2 = aws.ec2.Subnet("subnetAz2", vpc_id=vpc.id, cidr_block="", availability_zone="us-west-2b", tags={ "Name": "pulumi-docdb-subnet-az2", }) # Create a subnet group for the DocumentDB cluster. docdb_subnet_group = aws.docdb.SubnetGroup("docdbSubnetGroup", subnet_ids=[subnet_az1.id, subnet_az2.id], tags={ "Name": "pulumi-docdb-subnet-group", }) # Define a DocumentDB cluster. docdb_cluster = aws.docdb.Cluster("docdbCluster", master_username="masteruser", master_password="masterpassword123", skip_final_snapshot=True, db_subnet_group_name=docdb_subnet_group.name, tags={ "Name": "pulumi-docdb-cluster", }) # Define a DocumentDB instance in the cluster. docdb_instance = aws.docdb.ClusterInstance("docdbInstance", cluster_identifier=docdb_cluster.cluster_identifier, instance_class="db.r5.large", engine="docdb", tags={ "Name": "pulumi-docdb-instance", }) # Export the DocumentDB cluster endpoint to be easily viewed or used by other services. pulumi.export('docdb_endpoint', docdb_cluster.endpoint)

    Let's explain that in detail:

    • We've created a VPC within the CIDR block, which is a common private IP range. This VPC provides an isolated network for our DocumentDB cluster.

    • We added two subnets, subnetAz1 and subnetAz2, each in separate Availability Zones (AZs). AWS recommends that you create a subnet in at least two Availability Zones for high availability.

    • We've then created a DocumentDB Subnet Group docdbSubnetGroup using the two subnets. This group ensures that our DocumentDB instances will span multiple AZs for failover support.

    • We instantiated a DocumentDB cluster docdbCluster with a master username and password. Note that in a production scenario, you would want to manage the master password outside of your version control, using secure secrets management like AWS Secrets Manager.

    • A DocumentDB cluster instance docdbInstance was created. Here, you can choose the instance size you need (we've used db.r5.large for this example).

    • The endpoint of the DocumentDB cluster is exported as an output using pulumi.export. This value will be outputted when you run pulumi up and it's the endpoint your application will use to connect to the DocumentDB cluster.

    Before running this Pulumi program, make sure to replace masterpassword123 with a secure password and manage it properly. For actual deployments, consider adding additional security measures like VPC security groups, encryption in transit, and encryption at rest to protect your data.

    This program sets up the infrastructure for your ML recommendation system's session storage but does not include the actual data modeling or ML components. Those would be a part of your application code which interacts with the DocumentDB through the provided endpoint.