1. Graph-based Recommendation Systems with AWS Neptune


    To set up a graph-based recommendation system using AWS Neptune, you need to create a Neptune cluster, which is the core component of Neptune that holds your graph database. Neptune is purpose-built for storing billions of relationships and querying the graph with milliseconds latency.

    Below is a Python program using Pulumi that provisions an AWS Neptune cluster, along with the necessary infrastructure such as a Cluster Instance, a Subnet Group for the cluster that spans multiple availability zones, and security group rules to control the access.

    Before you delve into the code, ensure you have the following prerequisites covered:

    • Pulumi CLI installed and configured with AWS credentials.
    • AWS CLI installed and configured with the same AWS credentials if you want to use the CLI to manage resources.
    import pulumi import pulumi_aws as aws # Create a new security group for the Neptune cluster neptune_sg = aws.ec2.SecurityGroup('neptune-sg', description='Enable Neptune access', ingress=[ # Typically you should restrict the ingress to a minimal set of IPs aws.ec2.SecurityGroupIngressArgs( description='Allow Neptune access from within the VPC', from_port=8182, # The default port for Neptune to_port=8182, protocol='tcp', cidr_blocks=['your.vpc.cidr.block/16'], # Replace 'your.vpc.cidr.block/16' with your VPC CIDR ), ], egress=[ # Allow all outgoing traffic aws.ec2.SecurityGroupEgressArgs( from_port=0, to_port=0, protocol='-1', cidr_blocks=[''], ), ]) # Create a Subnet Group for the Neptune cluster # This group should span at least two Availability Zones for high availability. neptune_subnet_group = aws.neptune.SubnetGroup('neptune-subnet-group', description='Neptune subnet group', subnet_ids=['subnet-id-1', 'subnet-id-2']) # Replace with the actual subnet IDs # Create a new Neptune cluster neptune_cluster = aws.neptune.Cluster('neptune-cluster', apply_immediately=True, backup_retention_period=7, # Backups are retained for 7 days cluster_identifier="neptune-cluster-example", engine='neptune', skip_final_snapshot=True, # Skip final snapshot before deletion (for production set to False) vpc_security_group_ids=[neptune_sg.id], iam_database_authentication_enabled=True, # Enable IAM database authentication neptune_subnet_group_name=neptune_subnet_group.name) # Create a cluster instance which is the running database where you can submit your queries neptune_cluster_instance = aws.neptune.ClusterInstance('neptune-instance', apply_immediately=True, cluster_identifier=neptune_cluster.cluster_identifier, engine='neptune', instance_class='db.r4.large', # Choose an appropriate instance class neptune_subnet_group_name=neptune_subnet_group.name) # Export the Neptune cluster endpoint to be used by your applications pulumi.export('neptune_cluster_endpoint', neptune_cluster.endpoint)

    This program sets up a Neptune graph database in AWS using Pulumi. Here's what each part does:

    1. A security group neptune_sg is defined to control access to the Neptune database. Modify the cidr_blocks to match the IP range from which you will access the database.

    2. A Neptune subnet group neptune_subnet_group is defined, which groups together the subnets where the cluster can live. Ensure you use at least two different subnets in separate availability zones for high availability.

    3. The actual Neptune cluster neptune_cluster is defined, with some important parameters such as retention period for backups, IAM database authentication enabled, and linking to the security group we created.

    4. A cluster instance neptune_cluster_instance is then created which represents the running database servers. The instance size is defined by instance_class; choose the size based on your workload.

    5. Finally, we export the cluster endpoint as neptune_cluster_endpoint, which is the URL your application uses to connect to the Neptune database.

    Please replace placeholders like your.vpc.cidr.block/16, subnet-id-1, and subnet-id-2 with the actual values from your environment.

    This setup lacks details like proper error handling, logging, monitoring, and fine-grained access control, which should be added before deploying this in a production environment.