Automatic tuning

THIS TOPIC APPLIES TO:yesSQL Server (starting with 2017)noAzure SQL DatabasenoAzure SQL Data Warehouse noParallel Data Warehouse

Automatic tuning is a database feature that provides insight into potential query performance problems, recommend solutions, and automatically fix identified problems.

Automatic tuning in SQL Server 2017, notifies you whenever a potential performance issue is detected, and lets you apply corrective actions, or lets the Database Engine automatically fix performance problems. Automatic tuning in SQL Server 2017 enables you to identify and fix performance issues caused by SQL plan choice regressions.

What is plan choice regression?

SQL Server Database Engine may use different SQL plans to execute the Transact-SQL queries. Query plans depend on the statistics, indexes, and other factors. The optimal plan that should be used to execute some Transact-SQL query might be changed over time. In some cases, the new plan might not be better than the previous one, and the new plan might cause a performance regression.

SQL plan choice regression

In order to prevent unexpected performance issues, users must periodically monitor system and look for the queries that regressed. If any plan regressed, user should find some previous good plan and force it instead of the current one using sp_query_store_force_plan procedure. The best practice would be to force last known good plan because older plans might be invalid due to statistic or index changes. The user who forces the last known good plan should monitor performance of the query that is executed using the forced plan and verify that forced plan works as expected. Depending on the results of monitoring and analysis, plan should be forced or user should find some other way to optimize the query. Manually forced plans should not be forced forever, because the Database Engine should be able to apply optimal plans. The user or DBA should eventually unforce the plan using sp_query_store_unforce_plan procedure, and let the Database Engine find the optimal plan.

SQL Server provides all necessary views and procedures required to monitor performance and fix problems in Query Store. However, continuous monitoring and fixing performance issues might be a tedious process.

Database Engine in SQL Server 2017 provides information about regressed plans and recommended corrective actions. Additionally, Database Engine enables you to fully automate this process and let Database Engine fix any problem found related to the plan changes.

How to detect plan choice regression?

In SQL Server 2017, the Database Engine detects and shows potential plan choice regressions and the recommended actions that should be applied in the sys.dm_db_tuning_recommendations (Transact-SQL) view. The view shows information about the problem, the importance of the issue, and details such as the identified query, the id of the regressed plan, the id of the plan that was used as baseline for comparison, and the Transact-SQL statement that can be executed to fix the problem.

type description datetime score details
FORCE_LAST_GOOD_PLAN CPU time changed from 4 ms to 14 ms 3/17/2017 83 queryId recommendedPlanId regressedPlanId T-SQL
FORCE_LAST_GOOD_PLAN CPU time changed from 37 ms to 84 ms 3/16/2017 26 queryId recommendedPlanId regressedPlanId T-SQL

Some columns from this view are described in the following list:

  • Type of the recommended action - FORCE_LAST_GOOD_PLAN.
  • Description that contains information why Database Engine thinks that this plan change is a potential performance regression.
  • Datetime when the potential regression is detected.
  • Score of this recommendation.
  • Details about the issues such as id of the detected plan, id of the regressed plan, id of the plan that should be forced to fix the issue, Transact-SQL script that might be applied to fix the issue, etc. Details are stored in JSON format.

Use the following query to obtain a script that fixes the issue and additional information about the estimated gain:

SELECT reason, score,
      script = JSON_VALUE(details, '$.implementationDetails.script'),
      planForceDetails.*,
      estimated_gain = (regressedPlanExecutionCount+recommendedPlanExecutionCount)
                  *(regressedPlanCpuTimeAverage-recommendedPlanCpuTimeAverage)/1000000,
      error_prone = IIF(regressedPlanErrorCount>recommendedPlanErrorCount, 'YES','NO')
FROM sys.dm_db_tuning_recommendations
  CROSS APPLY OPENJSON (Details, '$.planForceDetails')
    WITH (  [query_id] int '$.queryId',
            [current plan_id] int '$.regressedPlanId',
            [recommended plan_id] int '$.recommendedPlanId',

            regressedPlanErrorCount int,
            recommendedPlanErrorCount int,

            regressedPlanExecutionCount int,
            regressedPlanCpuTimeAverage float,
            recommendedPlanExecutionCount int,
            recommendedPlanCpuTimeAverage float

          ) as planForceDetails;

Here is the result set.

reason score script query_id current plan_id recommended plan_id estimated_gain error_prone
CPU time changed from 3 ms to 46 ms 36 EXEC sp_query_store_force_plan 12, 17; 12 28 17 11.59 0

estimated\_gain represents the estimated number of seconds that would be saved if the recommended plan would be executed instead of the current plan. The recommended plan should be forced instead of the current plan if the gain is greater than 10 seconds. If there are more errors (for example, time-outs or aborted executions) in the current plan than in the recommended plan, the column error\_prone would be set to the value YES. Error prone plan is another reason why the recommended plan should be forced instead of the current one.

Automatic plan choice correction

In addition to detection, the Database Engine can automatically switch to the last known good plan whenever the regression is detected.

SQL plan choice correction

When the Database Engine applies a recommendation, it automatically monitors the performance of the forced plan. The forced plan will be retained until a recompile (for example, on next statistics or schema change) if it is better than the regressed plan. If the forced plan is not better than the regressed plan, the new plan will be unforced and the Database Engine will compile a new plan.

The user can enable automatic tuning per database and specify that last good plan should be forced whenever some plan change regression is detected. Automatic tuning is enabled using the following command:

ALTER DATABASE current
SET AUTOMATIC_TUNING ( FORCE_LAST_GOOD_PLAN = ON ); 

Once you turn-on this option, Database Engine will automatically force any recommendation where the gain is higher than 10 seconds, or the number of errors in the new plan is higher than the number of errors in the recommended plan, and verify that the forced plan is better than the current one.

The status of the automatic tuning option is shown in the following view:

SELECT name, desired_state_desc, actual_state_desc, reason_desc
FROM sys.database_automatic_tuning_options;

Here is the result set.

name desired_state_desc actual_state_desc reason_desc
FORCE_LAST_GOOD_PLAN ON OFF QUERY_STORE_OFF

FORCE_LAST_GOOD_PLAN option might be in OFF state even if the user specified ON. The option might be disabled if Query Store is disabled or in read-only mode. Column actual_state_desc gives information about the current state of automatic tuning option, and column reason_desc gives information why is actual state different that desired state. Values in reason_desc column are shown in the following table:

reason reason_desc description
2 DISABLED Option is disabled by system.
11 QUERY_STORE_OFF Query Store is turned off.
12 QUERY_STORE_READ_ONLY Query Store is in read-only mode.
13 NOT_SUPPORTED Available only in Enterprise Edition of SQL Server.

See Also

ALTER DATABASE SET AUTOMATIC_TUNING (Transact-SQL)
sys.database_automatic_tuning_options (Transact-SQL)
sys.dm_db_tuning_recommendations (Transact-SQL)
sp_query_store_force_plan (Transact-SQL)
sp_query_store_unforce_plan (Transact-SQL)
sys.database_query_store_options (Transact-SQL)
JSON functions