1. Capturing Azure SQL Database Logs for Performance Tuning


    To capture Azure SQL Database logs for performance tuning, we need to create resources that allow us to monitor and store the metrics and logs, which will then help us analyze the performance and address any inefficiencies or issues. Azure provides various services for monitoring, but for the purpose of this exercise, we will focus on setting up an Azure SQL Database with auditing enabled to track database events and then send those logs to Azure Monitor Logs where we can query and analyze them.

    Here's what we'll do:

    1. Create an Azure SQL Server: This will be the primary resource that will host our Azure SQL Database instances.
    2. Create an Azure SQL Database: This is where the data will be stored and for which we want to capture and analyze the performance logs.
    3. Enable Auditing on the Azure SQL Database: Auditing tracks database events and writes them to an audit log in your Azure storage account, Log Analytics workspace, or Event Hubs.
    4. Set up an Azure Log Analytics Workspace: This is where our logs will be collected and where we can perform the analysis.
    5. (Optional) Set up additional tools or integrate with existing systems for more in-depth performance tuning.

    Let's write a Pulumi program in Python to implement the above.

    import pulumi import pulumi_azure_native as azure_native # Create a resource group for all our resources. resource_group = azure_native.resources.ResourceGroup("resource_group") # Create an Azure SQL Server that will host the SQL Database. sql_server = azure_native.sql.Server("sqlServer", resource_group_name=resource_group.name, location=resource_group.location, version="12.0", # Version of the SQL Server. administrator_login="your_admin_login", administrator_login_password="your_admin_password") # Create an Azure SQL Database on the SQL Server we just created. sql_database = azure_native.sql.Database("sqlDatabase", resource_group_name=resource_group.name, server_name=sql_server.name, location=resource_group.location, sku=azure_native.sql.SkuArgs( name="S0", tier="Standard", ) ) # Enable Auditing on the Azure SQL Database to capture performance logs. auditing_settings = azure_native.sql.DatabaseBlobAuditingPolicy("auditingSettings", resource_group_name=resource_group.name, server_name=sql_server.name, database_name=sql_database.name, state="Enabled", audit_actions_and_groups=[ "SUCCESSFUL_DATABASE_AUTHENTICATION_GROUP", "FAILED_DATABASE_AUTHENTICATION_GROUP", "BATCH_COMPLETED_GROUP", ], is_storage_secondary_key_in_use=False, is_azure_monitor_target_enabled=True, retention_days=7) # Number of days to keep the logs. # Create an Azure Log Analytics Workspace to analyze the performance logs. log_analytics_workspace = azure_native.operationalinsights.Workspace("analyticsWorkspace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.operationalinsights.WorkspaceSkuArgs( name="PerGB2018", ) ) # Configure the database to send logs to the Log Analytics Workspace. log_analytics_solution = azure_native.insights.Solution("analyticsSolution", resource_group_name=resource_group.name, location=resource_group.location, solution_name="SQLAnalytics", publisher="Microsoft", product="OMSGallery/SQLAssessment", workspace_resource_id=log_analytics_workspace.id) pulumi.export('sql_server_name', sql_server.name) pulumi.export('sql_database_name', sql_database.name) pulumi.export('log_analytics_workspace_name', log_analytics_workspace.name)

    In the above program, we start by creating a resource group that will be the container of all our cloud resources. We then create an Azure SQL Server and within this server, we create an Azure SQL Database instance. With the database in place, we enable Auditing on it. This will ensure that all the loggable actions in the database, like successful or failed logins and completed batch operations, are written and stored for analysis. The DatabaseBlobAuditingPolicy resource configures the auditing settings of the Azure SQL Database.

    Next, we set up an Azure Log Analytics Workspace, which is a service provided by Azure for log data aggregation and analysis. This workspace will receive logs from our SQL Database and allow us to run queries and analysis on the data to gain insights into the performance of our database.

    Finally, we export the names as stack outputs for easy access and use in subsequent commands or scripts.

    Make sure to replace placeholders like 'your_admin_login' and 'your_admin_password' with secure credentials of your choice. Also, note that any Pulumi program requires appropriate Azure credentials to create and manage resources on Azure.

    Now, when you run this Pulumi program, it will set up the infrastructure so that you can capture SQL Database logs and query them for performance tuning using Azure's native tools.