Resilient, Scalable SQL Database for AI Applications

Question

Pulumi · Accepted Answer

Building a resilient and scalable SQL database for AI applications often involves deploying a managed database service that offers high availability, automatic failover, and scalability. For such a setup on Azure, we would typically use Azure SQL Database or Cosmos DB with a SQL API. Azure SQL Database is a fully managed database service with built-in high availability, automated backups, and scaling capabilities. Cosmos DB, on the other hand, is globally distributed and provides multi-model support, including a SQL API for document databases.

We'll create an instance of Azure SQL Database, but since Cosmos DB might also be relevant for certain AI applications due to its low latency and global distribution, I'll include an example resource for that as well.

The Pulumi code below will create:

1. An Azure Resource Group: Groups related resources together, providing a way to monitor, control access, provision, and manage billing for collections of assets.
2. An Azure SQL Server: A logical server to host your database. This is needed before you can create an Azure SQL Database.
3. An Azure SQL Database: The actual database where you can store your data. It's set up with basic DTUs (Database Transaction Units) to start with, which you can scale according to your needs.
4. Optionally, a Cosmos DB Account and SQL Database: If you plan to leverage Cosmos DB for its unique capabilities.

Remember that this is a foundational example, and you might need additional attributes, properties, or resources depending on your specific requirements, especially surrounding networking, firewalls, and compliance.

Make sure you have Pulumi installed and configured for use with Azure. You will need appropriate Azure credentials configured where Pulumi can access them, typically via the Azure CLI.

Now, let's get to the code:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Azure resource group
resource_group = azure_native.resources.ResourceGroup("ai_sql_rg")

# Create an Azure SQL Server
sql_server = azure_native.sql.Server("ai_sql_server",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    version="12.0",  # Specify the SQL Server version
    administrator_login="sqladmin",  # Provide a username
    administrator_login_password="complex_password"  # Provide a strong password
)

# Create an Azure SQL Database on the server we just created
sql_database = azure_native.sql.Database("ai_sql_database",
    resource_group_name=resource_group.name,
    server_name=sql_server.name,
    location=resource_group.location,
    sku=azure_native.sql.SkuArgs(
        name="Standard_D2s_v3",  # Specify the performance characteristics of the SQL database
        tier="Standard",
        capacity=2,  # Represents the DTUs for the database
    ),
    max_size_bytes=2 * 1024 * 1024 * 1024  # Maximum data size (2 GB for example, you can adjust as needed)
)

# Optionally, create an Azure Cosmos DB account and SQL Database if your AI application requires it
cosmosdb_account = azure_native.documentdb.DatabaseAccount("ai_cosmosdb_account",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    database_account_offer_type="Standard",  # This defines the offer type for the Cosmos DB account
    locations=[  # An array specifying the locations where your database will be distributed
        azure_native.documentdb.LocationArgs(
            location_name=resource_group.location,
            failover_priority=0,
        )
    ]
)

cosmosdb_sql_database = azure_native.documentdb.SqlResourceSqlDatabase("ai_cosmosdb_sql_database",
    resource_group_name=resource_group.name,
    account_name=cosmosdb_account.name,
    resource=azure_native.documentdb.SqlDatabaseResourceArgs(
        id="ai-database"  # A unique identifier for the SQL database
    ),
    options=azure_native.documentdb.CreateUpdateOptionsArgs(
        throughput=400  # Set the throughput (RU/s). Adjust based on application requirements.
    )
)

# Output the connection strings for the databases
pulumi.export("sql_server_connection_string", sql_server.name.apply(
    lambda name: f"Server=tcp:{name}.database.windows.net;Database={sql_database.name};User ID=sqladmin;Password=complex_password;Encrypt=true;Connection Timeout=30;"))

pulumi.export("cosmosdb_connection_string", cosmosdb_account.name.apply(
    lambda name: f"AccountEndpoint=https://{name}.documents.azure.com:443/;AccountKey=<KEY>;"))

```

This program first creates a resource group to host all components. Then, it sets up an Azure SQL Server and an Azure SQL Database on that server. It also optionally includes resources to set up Azure Cosmos DB with SQL API if you choose to leverage Cosmos DB's capabilities.

- The `sql_server` resource sets up a logical server required to host Azure SQL databases. It includes properties for version, administrator login, and password.
- The `sql_database` resource creates a database with a specified performance level, which is represented as DTUs (Database Transaction Units).
- The `cosmosdb_account` resource creates an account for Cosmos DB which defines where and how your data is replicated.
- The `cosmosdb_sql_database` resource represents a container for your data that you can query using the SQL language.

Finally, Pulumi exports the connection strings for direct use in your application code, although in a production setting you'd want to manage credentials more securely, for example, by using Azure Key Vault.

Please replace dummy credentials such as "complex_password" with secure passwords and consider using Pulumi secrets to manage such sensitive data.

Keep in mind that to manage infrastructure on Azure, you'll need the appropriate roles and permissions set up in your Azure account. This Pulumi setup assumes those permissions are already in place.