As a result, you'll experience a significant negative effect on query performance. Storage throttling occurs when the combined IOPS or throughput generated by applications, services, and serverless SQL pool workloads exceeds the limits of the storage account. Multiple applications and services might access your storage account. If they aren't in the same region, there will be increased latency for the data's network transfer between the remote region and the endpoint's region. Storage accounts and endpoints provisioned during workspace creation are located in the same region.įor optimal performance, if you access other storage accounts with serverless SQL pool, make sure they're in the same region. To minimize latency, colocate your Azure Storage account or Azure Cosmos DB analytic storage and your serverless SQL pool endpoint. Colocate your storage and serverless SQL pool Here are best practices for storage and content layout in serverless SQL pool. Use Azure Data Studio or SQL Server Management Studio to read a large amount of data.

Azure Synapse Studio is a web tool that uses the HTTPS protocol to transfer data.

Don't use Azure Synapse Studio to return a large amount of data.Make sure you don't have network bandwidth issues while reading a large amount of data.If you're reading data from your on-premises application, make sure the Azure Synapse workspace is in the region that's close to your location.Placing a client application and the Azure Synapse workspace in different regions could cause bigger latency and slower streaming of results. If needed, create the separate workspaces that are paired with your client applications. If you're using applications such as Power BI or Azure Analysis Service, make sure they're in the same region where you placed your Azure Synapse workspace. Colocate a client application with the Azure Synapse workspace.Make sure your client application is connected to the closest possible Azure Synapse workspace with the optimal connection. Serverless SQL pools can't provide an interactive experience in Power BI Direct Query mode if you're using complex queries or processing a large amount of data.Ĭlient applications and network connections Consider caching the results on the client side by using Power BI import mode or Azure Analysis Services, and periodically refresh them.If you're filtering results by string column, try to use a BIN2_UTF8 collation.Azure Synapse Studio is a web tool that isn't designed for large result sets. If you're returning a large number of results, make sure you're using SQL Server Management Studio or Azure Data Studio and not Azure Synapse Studio.Try to optimize storage layout by using partitioning and keeping your files in the range between 100 MB and 10 GB.Storage examples include Azure Data Lake Storage and Azure Cosmos DB. Make sure the storage and serverless SQL pool are in the same region.Client application examples include Power BI Desktop, SQL Server Management Studio, and Azure Data Studio. If you're using client applications outside Azure, make sure you're using serverless SQL pool in a region that's close to your client computer.Make sure your client applications are collocated with serverless SQL pool.Everything related to reading files from storage might affect query performance. All files that the queries target are external to serverless SQL pool. It doesn't have local storage or ingestion capabilities. Serverless SQL pool allows you to query files in your Azure Storage accounts. If you're working with a dedicated SQL pool, see Best practices for dedicated SQL pools for specific guidance. Serverless SQL pool is a resource in Azure Synapse Analytics. In this article, you'll find a collection of best practices for using serverless SQL pool.

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