SQL Server Advanced Query Store Configuration

By Tom Nonmacher

Welcome to SQLSupport.org's latest blog post. Today, we delve into the advanced configuration of Query Store in SQL Server 2022, Azure SQL, and delve into the integration with technologies such as Microsoft Fabric, Delta Lake, OpenAI + SQL, and Databricks. As you may already know, Query Store is a powerful feature that simplifies performance troubleshooting by helping you quickly find performance differences caused by query plan changes. Let's look at how we can optimize its configuration for better performance tracking.

Firstly, it is crucial to set Query Store's operation mode correctly. The two options are "Read-Write" and "Read-Only". The former allows the capture and storage of new queries and their plans, while the latter only permits the reading of collected data. You can switch between these modes using the following T-SQL command:

ALTER DATABASE YourDatabaseName SET QUERY_STORE (OPERATION_MODE = READ_WRITE);

Next, consider adjusting the "Data Flush Interval" and "Max Size" settings. The Data Flush Interval determines how frequently Query Store data is written to disk, while Max Size controls the maximum amount of disk space that the Query Store can use. If you're using Azure SQL, it is recommended to use the default settings. However, for SQL Server 2022, you may need to tweak these settings based on your specific use case and server capacity.

With the introduction of the Microsoft Fabric, Delta Lake, OpenAI + SQL, and Databricks, you can now leverage the power of advanced analytics, AI, and machine learning to optimize your SQL Server performance. For instance, you can use OpenAI to analyze your Query Store data, identify patterns and anomalies, and make data-driven decisions to improve SQL performance.

Furthermore, by integrating with Delta Lake, you can ensure the reliability and performance of your big data workloads. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. It works well with Databricks, making it possible to analyze your Query Store data at a larger scale.

Finally, let's not forget about the integration with Microsoft Fabric. Microsoft Fabric is a distributed systems platform that makes it easy to package, deploy, and manage scalable and reliable microservices. By integrating it with SQL Server Query Store, you can monitor and manage your microservices more effectively. For example, you can track the performance of each microservice's SQL queries and use this information to troubleshoot issues.

In conclusion, the Query Store feature in SQL Server and Azure SQL provides a powerful tool for performance troubleshooting. By taking advantage of advanced configuration options and integrating with technologies like Microsoft Fabric, Delta Lake, OpenAI + SQL, and Databricks, you can optimize your SQL performance and manage your data more effectively. Stay tuned to SQLSupport.org for more tips and tricks on SQL Server and related technologies.

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