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This chapter discusses Oracle automatic SQL tuning features.

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This chapter contains the following sections:

See Also:
Oracle Database 2 Day DBA for information on monitoring and tuning SQL statements

12.1 Automatic SQL Tuning Overview

Automatic SQL Tuning is a new capability of the query optimizer that automates the entire SQL tuning process. Using the newly enhanced query optimizer to tune SQL statements, the automatic process replaces manual SQL tuning, which is a complex, repetitive, and time-consuming function. The Automatic SQL Tuning features are exposed to the user with the SQL Tuning Advisor.

This section covers the following topics:

12.1.1 Query Optimizer Modes

The enhanced query optimizer has two modes:

12.1.1.1 Normal mode

In normal mode, the optimizer compiles the SQL and generates an execution plan. The normal mode of the optimizer generates a reasonable execution plan for the vast majority of SQL statements. Under normal mode the optimizer operates with very strict time constraints, usually a fraction of a second, during which it must find a good execution plan.

12.1.1.2 Tuning mode

In tuning mode, the optimizer performs additional analysis to check whether the execution plan produced under normal mode can be further improved. The output of the query optimizer is not an execution plan, but a series of actions, along with their rationale and expected benefit for producing a significantly superior plan. When called under the tuning mode, the optimizer is referred to as the Automatic Tuning Optimizer. The tuning performed by the Automatic Tuning Optimizer is called Automatic SQL Tuning.

Under tuning mode, the optimizer can take several minutes to tune a single statement. It is both time and resource intensive to invoke the Automatic Tuning Optimizer every time a query has to be hard-parsed. The Automatic Tuning Optimizer is meant to be used for complex and high-load SQL statements that have non-trivial impact on the entire system. The Automatic Database Diagnostic Monitor (ADDM) proactively identifies high-load SQL statements which are good candidates for Automatic SQL Tuning. See Chapter 6, 'Automatic Performance Diagnostics'.

12.1.2 Types of Tuning Analysis

Automatic SQL Tuning includes four types of tuning analysis:

12.1.2.1 Statistics Analysis

The query optimizer relies on object statistics to generate execution plans. If these statistics are stale or missing, the optimizer does not have the necessary information it needs and can generate poor execution plans. The Automatic Tuning Optimizer checks each query object for missing or stale statistics, and produces two types of output:

Recommendations to gather relevant statistics for objects with stale or no statistics.
Because optimizer statistics are automatically collected and refreshed, this problem may be encountered only when automatic optimizer statistics collection has been turned off. See 'Automatic Statistics Gathering'.
Auxiliary information in the form of statistics for objects with no statistics, and statistic adjustment factor for objects with stale statistics.

This auxiliary information is stored in an object called a SQL Profile.

12.1.2.2 SQL Profiling

The query optimizer can sometimes produce inaccurate estimates about an attribute of a statement due to lack of information, leading to poor execution plans. Traditionally, users have corrected this problem by manually adding hints to the application code to guide the optimizer into making correct decisions. For packaged applications, changing application code is not an option and the only alternative available is to log a bug with the application vendor and wait for a fix.

Automatic SQL Tuning deals with this problem with its SQL Profiling capability. The Automatic Tuning Optimizer creates a profile of the SQL statement called a SQL Profile, consisting of auxiliary statistics specific to that statement. The query optimizer under normal mode makes estimates about cardinality, selectivity, and cost that can sometimes be off by a significant amount resulting in poor execution plans. SQL Profile addresses this problem by collecting additional information using sampling and partial execution techniques to verify and, if necessary, adjust these estimates.

During SQL Profiling, the Automatic Tuning Optimizer also uses execution history information of the SQL statement to appropriately set optimizer parameter settings, such as changing the OPTIMIZER_MODE initialization parameter setting from ALL_ROWS to FIRST_ROWS for that SQL statement.

The output of this type of analysis is a recommendation to accept the SQL Profile. A SQL Profile, once accepted, is stored persistently in the data dictionary. Note that the SQL Profile is specific to a particular query. If accepted, the optimizer under normal mode uses the information in the SQL Profile in conjunction with regular database statistics when generating an execution plan. The availability of the additional information makes it possible to produce well-tuned plans for corresponding SQL statement without requiring any change to the application code.

The scope of a SQL Profile can be controlled by the CATEGORY profile attribute. This attribute determines which user sessions can apply the profile. You can view the CATEGORY attribute for a SQL Profile in CATEGORY column of the DBA_SQL_PROFILES view. By default, all profiles are created in the DEFAULT category. This means that all user sessions where the SQLTUNE_CATEGORY initialization parameter is set to DEFAULT can use the profile.

By altering the category of a SQL profile, you can determine which sessions are affected by the creation of a profile. For example, by setting the category of a SQL Profile to DEV, only those users sessions where the SQLTUNE_CATEGORY initialization parameter is set to DEV can use the profile. All other sessions do not have access to the SQL Profile and execution plans for SQL statements are not impacted by the SQL profile. This technique enables you to test a SQL Profile in a restricted environment before making it available to other user sessions.

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