1. Large Scale Social Network Analysis with AWS Neptune


    Social network analysis often requires handling large, complex, and highly connected datasets. AWS Neptune is a managed graph database service designed to store and navigate such datasets. With Neptune, you can build and run applications that work with highly connected datasets with ease, making it suitable for social network analysis.

    Below is a Pulumi Python program that sets up AWS Neptune for large-scale social network analysis. We will create a Neptune cluster, a cluster instance, a subnet group (to define the subnets within your VPC where the Neptune instances will be located), and finally, set up a security group to allow ingress on the default port that Neptune uses.

    import pulumi import pulumi_aws as aws # Create a Neptune Cluster # For production, consider setting enable_cloudwatch_logs_exports to audit or enable fine-grained access control with iam_database_authentication_enabled. neptune_cluster = aws.neptune.Cluster("neptuneCluster", iam_database_authentication_enabled=True, # Enable IAM database authentication. engine="neptune", # The engine type - Neptune for a graph database. cluster_identifier="my-neptune-cluster", # Identifier for the cluster, should be unique. skip_final_snapshot=True, # Skip creating a final snapshot on deletion - not recommended for production. apply_immediately=True, # Apply changes immediately; for production, it is often better to set to 'False'. backup_retention_period=7, # Change as per your retention requirements. preferred_backup_window="07:00-09:00", # Set your preferred backup window within a 24-hour day. ) # Create a Neptune Cluster Instance # The instance_size decides the capacity and performance characteristics. neptune_cluster_instance = aws.neptune.ClusterInstance("neptuneClusterInstance", cluster_identifier=neptune_cluster.id, # Reference to the cluster created above. instance_class="db.r5.large", # Decide the instance class based on your need (cpu, memory, network performance). engine="neptune", # The engine type - Neptune for a graph database. apply_immediately=True, # Similar to above, controls the application of modifications. ) # Create a Neptune Subnet Group # Define which subnets inside your VPC should be used for the Neptune instances. neptune_subnet_group = aws.neptune.SubnetGroup("neptuneSubnetGroup", subnet_ids=["subnet-abc123", "subnet-def456"], # Replace with your actual subnet IDs from your VPC. tags={ "Name": "my-neptune-subnet-group", } ) # Create a Security Group # We define a simple security group to allow inbound traffic on port 8182 (default for Neptune). neptune_security_group = aws.ec2.SecurityGroup("neptuneSecurityGroup", description="Allow inbound traffic for Neptune", ingress=[ { "from_port": 8182, # Neptune Port "to_port": 8182, "protocol": "tcp", "cidr_blocks": [""], # WARNING: this allows access from any IP, adjust to your needs } ], egress=[ { "from_port": 0, "to_port": 0, "protocol": "-1", # Allow all outbound traffic "cidr_blocks": [""], }, ] ) # Export the Neptune cluster endpoint, which will be used to connect to your graph database pulumi.export("neptune_cluster_endpoint", neptune_cluster.endpoint)

    Explanation of resources:

    • Neptune Cluster: This is the core resource for AWS Neptune, which manages multiple instances of data replicas. We have configured it with IAM database authentication and enabled backups.

    • Neptune Cluster Instance: Each Neptune cluster needs instances; they handle the data processing and storage. We've defined one instance, but you can add more based on your load requirements.

    • Neptune Subnet Group: This resource groups together the subnets within your Virtual Private Cloud (VPC) that the Neptune cluster can use to provide failover support and redundancy.

    • Security Group: The security group acts like a firewall for the Neptune instances. We've allowed inbound traffic on the Neptune port (8182) to enable connections to the database.

    Points to note for production:

    • Always set skip_final_snapshot to False to ensure that you have a backup before you delete a Neptune cluster.
    • The apply_immediately=True flag ensures that changes to the instance or cluster are applied right away, which can be useful in development but might not be ideal in a production environment where changes should be applied within a maintenance window.
    • Make sure to replace the dummy subnet_ids with your actual AWS subnet IDs.
    • IMPORTANT: Security! The above code uses for allowed CIDR blocks. In a real-world scenario, you would restrict this to the IPs that need access to Neptune.
    • IAM roles and further access controls might be needed depending on your security and compliance requirements.

    You would need to apply your actual configurations, particularly for subnets, VPC, and security groups. Moreover, consider adding high availability and backup options for a production environment.