Rediger

Del via

Configure a SQL Server Big Data Cluster

  • Artikkel

Applies to: SQL Server 2019 (15.x)

Important

The Microsoft SQL Server 2019 Big Data Clusters add-on will be retired. Support for SQL Server 2019 Big Data Clusters will end on February 28, 2025. All existing users of SQL Server 2019 with Software Assurance will be fully supported on the platform and the software will continue to be maintained through SQL Server cumulative updates until that time. For more information, see the announcement blog post and Big data options on the Microsoft SQL Server platform.

Configuration management enables administrators to ensure their big data cluster is always prepared for their workload needs. With this functionality, cluster administrators can alter or tune various parts of the big data cluster at deployment time or post-deployment and get deeper insight into the configurations running in their big data cluster.

Configuration management allows an administrator to enable SQL Agent, define the baseline resources for their organization's Spark jobs, or even see what settings are configurable at each scope. At deployment time, configurations can be configured through the deployment bdc.json file and post-deployment through azdata CLI.

Configuration Scopes

Big Data Clusters configuration has three scoping levels: cluster, service, and resource. The hierarchy of the settings follows in this order as well, from highest to lowest. BDC components will take the value of the setting defined at the lowest scope. If the setting is not defined at a given scope, it will inherit the value from its higher parent scope.

For example, you may want to define the default number of cores the Spark driver will use in the storage pool and Sparkhead resources. To define the default number of cores, you can do one of the following actions:

  • Specify a default cores value at the Spark service scope

  • Specify a default cores value at the storage-0 and sparkhead resource scope

In the first scenario, all lower-scoped resources of the Spark service (storage pool and Sparkhead) will inherit the default number of cores from the Spark service default value.

In the second scenario, each resource will use the value defined at its respective scope.

If the default number of cores is configured at both service and resource scope, then the resource-scoped value will override the service-scoped value since this is the lowest user configured scope for the given setting.

Next steps

For specific information about configuration, see the appropriate articles:

Reference: