1. Storing Graph Data for ML Recommendation Systems on Cosmos DB


    Azure Cosmos DB is a fully managed, globally-distributed database service that supports schema-less data that lets you build highly responsive and Always On applications. It offers a multitude of APIs, including SQL (DocumentDB), Cassandra, MongoDB, Gremlin (Graph), and Table Storage.

    For a Machine Learning (ML) recommendation system, graph databases are often a good fit because they can efficiently model and traverse relationships between data entities. Within Cosmos DB, the Gremlin API is designed for this purpose, allowing you to store graph data and execute graph traversals. Therefore, we will create an instance of Azure Cosmos DB configured to use the Gremlin API to store graph data.

    In the following Pulumi Python program, we will:

    1. Import the required Pulumi Azure Native package.
    2. Create an instance of Cosmos DB account configured for the Gremlin API.
    3. Create a Gremlin database inside the Cosmos DB account.
    4. Define a Gremlin graph where the actual graph data can be stored.
    5. Output the Gremlin endpoint and primary key for connecting applications to the graph database.
    import pulumi from pulumi_azure_native import documentdb as cosmos_db # Create a new resource group, if it's not already provided. resource_group = cosmos_db.ResourceGroup("resource_group") # Create an Azure Cosmos DB account with Gremlin (Graph) support. cosmosdb_account = cosmos_db.DatabaseAccount("cosmosDbAccount", resource_group_name=resource_group.name, location="West US", # It's possible to choose a different location. database_account_offer_type=cosmos_db.DatabaseAccountOfferType.STANDARD, capabilities=[cosmos_db.CapabilityArgs(name="EnableGremlin")] # This enables the Gremlin API (Graph) ) # Create a Gremlin database within the Cosmos DB account. gremlin_database = cosmos_db.GremlinDatabase("gremlinDatabase", resource_group_name=resource_group.name, account_name=cosmosdb_account.name, resource=cosmos_db.GremlinDatabaseResourceArgs( id="graphdb" # The ID for the Gremlin database. It's up to you to define it. ), options=cosmos_db.CreateUpdateOptionsArgs() # You can define throughput settings here if needed. ) # Define a Gremlin graph within the Gremlin database. gremlin_graph = cosmos_db.GremlinGraph("gremlinGraph", resource_group_name=resource_group.name, account_name=cosmosdb_account.name, database_name=gremlin_database.name, resource=cosmos_db.GremlinGraphResourceArgs( id="recommendationGraph" # The ID for your graph. Define as needed. ), options=cosmos_db.CreateUpdateOptionsArgs() # You can define throughput or other settings here. ) # Output the Gremlin endpoint and the primary master key for the Cosmos DB account. # These are needed to connect your application to the Cosmos DB Gremlin API. pulumi.export("endpoint", cosmosdb_account.document_endpoint) pulumi.export("primaryKey", cosmosdb_account.list_keys.apply(lambda keys: keys.primary_master_key))

    In this program, we:

    • Defined resources in a sequential manner with dependencies—Pulumi understands the dependencies between resources, which ensures they are created in the appropriate order.
    • Exported the Gremlin endpoint and primary master key. These values are sensitive and would typically be handled more securely in a production environment (e.g., using Pulumi's secret handling).
    • Used Pulumi's built-in apply function on list_keys, which performs an API call to retrieve the keys, to output the primary master key securely.

    For a recommendation system, you would typically add data to the graph to represent products, users, and interactions, which can then be queried to provide recommendations.

    Remember to configure Pulumi with the Azure credentials and install the necessary Pulumi Azure Native package using pip before running this program. Once the infrastructure is provisioned, you can use Cosmos DB SDKs in your application to interact with the Gremlin API and start storing and querying graph data.