Adaptive Query Processing and Automatic Tuning - Introduction - 1

SQL Server 2017 and Azure SQL Database are being touted as the self learning, artificial intelligent database. One of the main reasons for the same is due to release of the following features

 1) Adaptive Query Processing

2) Automatic Tuning

Adaptive Query Processing is SQL Server's first attempt using artificial intelligence to fix poor performing queries due to incorrect cardinality estimates. Query optimizer produces better plans using "Adaptive Query Processing" which is enabled by default when one is on SQL 2017's compatibility mode (CE 140). Queries can benefit via Adaptive Query Processing in the following scenarios.

1) Adaptive Join -> Query optimizer makes a intelligent choice between Nested Loop and Hash Join in runtime

2) Memory Grant Feedback -> Query optimizer adjusts the memory granted to a query depending upon the previous run's execution results

3) Interleaved Execution -> Portion of the query is executed while generating the query plan to come with the estimates. Applies currently for Multi Statement Table Valued Functions alone

 The above scenarios and operators are likely to expand in upcoming releases or patches

 Other aspect of auto driven query tuning is "Automatic Tuning" feature. Automatic Query tuning helps in the following scenarios

 Automatic Tuning:

1) Force Last Good Plan: Attempts to fix query regression by detecting any sudden change in query performance due to plan change and forcing the last good performing plan. one can turn it on using the command below

ALTER DATABASE DBName SET AUTOMATIC_TUNING ( FORCE_LAST_GOOD_PLAN = ON );

2) Automatic Index creation and deletion: Feature available only in Azure SQL Database. By studying the workload, the database engine automatically deletes and creates indexes!!!

This is just a introduction post. As usual,will be writing a series of posts to cover the individual sections in detail.