![]() ![]() The data for the query hash map is consistent, but the volume of data in the runtime statistics cache store fluctuates depending on the workload. Data is inserted into these memory buffers for new queries, updated for previously-executed queries, and while data is flushed to disk regularly, it is expected that data continuously resides in these buffers. query hash map, runtime statistics cache store). Separate from storing the Query Store in the user database, data is also held in different memory buffers (e.g. There are many production environments with a Query Store that is 10GB or less in size, but I know of Query Stores that are 200-250GB in size on disk, which typically indicates an anti-pattern with the workload. As a result of this limitation for Azure SQL DB, the amount of data that Query Store has to manage can be significantly less than what we see for an on-prem solution. There is no a limit for SQL Server 2016 or 2017. In Azure SQL Database the maximum value one can set for MAX_STORAGE_SIZE_MB is 10GB. A good example is the amount of space that you can allocate to Query Store within the user database (MAX_STORAGE_SIZE_MB). This history is relevant when people ask about solution performance and Query Store.įirst, understand that there are differences in how Query Store works in Azure SQL Database compared to on-prem. This is not atypical – it’s extremely difficult to develop software that accommodates every single workload in the world both in the cloud and on-prem. Thus, while the initial internal thresholds for Query Store were determined based upon Azure SQL Database solutions and appropriate for most on-prem systems, they were not fully suited to every variation of an on-prem solution. Much of this was due to limitations in Azure tiers at the time and the limited number (comparatively) of companies that had embraced using a cloud solution. The telemetry data was invaluable to Microsoft’s developers as they prepared Query Store for release, but the variety in size and workload that exist in on-premises solutions was not accurately represented. Query Store was made publicly available in late 2015, and included in the SQL Server 2016 release. As Query Store was implemented for more databases, the information captured was used to enhance its functionality and improve its performance. One of the features that benefited most from the insight provided by the telemetry data was Query Store, which was originally released in private preview for Azure SQL Database in early 2015. At the time of the SQL Server release, Azure SQL Database had been in existence for over two years and Microsoft had been capturing telemetry and using that data to understand how features were being used, as well as improve existing features. “Data Driven” was frequently used in Microsoft marketing materials for 2016, but it wasn’t hype it was true. One reason the SQL Server 2016 release was such a solid release was that it was data driven. You should be running the latest version CU for SQL Server 2017 and latest CU for SQL Server 2016 SP2 to get all performance-related improvements Microsoft has implemented specific to Query Store.With an ad-hoc workload there are additional factors to consider when using Query Store. An impact on system performance can be seen with ad-hoc workloads (think Entity Framework, NHibernate), but I still think it’s worth enabling.Is there a “magic number” to use to figure out Query Store performance and the increase in resource use? No, it will depend on the type of workload. ![]() Will there be an increase in resource use (CPU, memory)? Yes.The majority of workloads won’t see an impact on system performance.What people are really asking is “Does enabling Query Store affect the performance of my queries?” Where “my queries” are user queries, queries from the application, etc. I get asked this question almost every time I present on a topic related to Query Store. “What is the performance overhead of enabling Query Store?” Edit: Novem– Updated information about Query Store performance overhead can be found in this post.
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