11/30/2022 0 Comments Query processing in dbmsPrior to SQL Server 2017, the behavior of Query Optimizer was not optimal it bound to select the lowest cost query plan for execution, despite poor initial assumptions in estimating inaccurate cardinality, which led to bad query performance. We will discuss exactly how SQL Server achieves this in rest of the section. This improvement leads to a better and improved execution of query, known as adaptive query processing. The accuracy of cardinality drives a better quality of query execution plan and more accurate allocation of memory, CPU to the query. Cardinality represents the estimated number of rows returned on execution of a query. Also, cardinality estimates play a vital role to decide the quality of the query plan. In this process, query performance depends on the overall quality of the query plan, join order, operation order and selection of physical algorithm like nested loop join or hash join, etc. Please see Understanding a SQL Server Query Execution Plan for more information. This end to end process (query plan generation, and submitting the plan for processing) once started, has to run to completion. Once the query execution plan is ready and available to execute, SQL Server storage engines get the query plan and execute it based on the actual query plan and return the data set. Based on the cost of the query plan, optimizer selects the lowest-cost query plan. The SQL Server query optimizer first generates a set of feasible query plans for a query or batch of T-SQL code submitted by the database engine to query optimizer. Prior to SQL Server 2017, query processing was a uniform process with a certain set of steps. This improved query plan quality is Adaptive Query Processing. In SQL Server 2017, Microsoft went one step further and worked on improving the query plan quality. You can read more about Query Store in Monitor Query Performance Using Query Store in SQL Server. Any point in time these plans can be reviewed, and the user can use plan forcing to enforce the query processor to select and use a specific query plan for execution. Query store is a feature to persist query execution plans and analyze the history of query plans to identify the queries that can cause performance bottlenecks. Query Store monitors query performance and provides methods to choose better query plans for execution. In SQL Server 2016, Microsoft introduced Query Store. Every so often, Microsoft includes new feature and functionality in SQL Server to improve query performance to help their users. Query performance is always a subject of research and innovation. This method is available in SQL Server (starting with SQL Server 2017 (14.x)) and Azure SQL Database. Adaptive query processing is the latest, improved query processing feature introduced in the SQL Server database engine.
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