2 Oracle Database Performance Method

Time Model Statistics

Time model statistics are statistics that measure the time spent in the database by operation type. The most important time model statistic is database time, or DB time. Database time represents the total time spent in database calls, and is an indicator of the total instance workload. As shown in Figure 2-1, database time makes up a portion of an application's overall user response time.

Figure 2-1 DB Time in Overall User Response Time

Description of "Figure 2-1 DB Time in Overall User Response Time"

Database time is calculated by aggregating the CPU time and wait time of all user sessions not waiting for idle wait events (nonidle user sessions). For example, a user session may involve an online transaction made at a bookseller's Web site consisting of the following actions, as shown in Figure 2-2:

Figure 2-2 DB Time in User Transaction

Description of "Figure 2-2 DB Time in User Transaction"

1. Query for novels by author

   The user performs a search for novels by a particular author. This causes the application to perform a database query for novels by the author.

2. Browse results of query

   The user browses the list of novels by the author that are returned and accesses additional details, such as user reviews and inventory status, about the novels. This causes the application to perform additional database queries.

3. Add item to cart

   After browsing details on the novels, the user decides to add one of the novels to the shopping cart. This causes the application to make a database call to update the shopping cart.

4. Checkout

   The user completes the transaction by checking out using the address and payment information previously saved at the bookseller's Web site from a previous purchase. This causes the application to perform various database operations to retrieve the user's information, add a new order, update the inventory, and generate an E-mail confirmation.

For each of these actions, the user makes a request to the database, as represented by the down arrow in Figure 2-2. The CPU time spent by the database processing the request and the wait time spent waiting for the database are considered DB time, as represented by the shaded areas. Once the request is completed, the results are returned to the user, as represented by the up arrow. The space between the up and down arrows represents the total user response time for processing the request, which contains other components besides DB time, as illustrated in Figure 2-1.

The objective of database tuning is to reduce the time that users spend performing actions on the database, or reducing database time. By doing so, the overall response time of user transactions on the application can be improved.

Wait Event Statistics

Wait events are statistics that are incremented by a session to indicate that it had to wait for an event to complete before being able to continue processing. When a session has to wait while processing a user request, the AWR records the wait using one of a set of predefined wait events that are then grouped into wait classes. Wait event data reveals various symptoms of problems that might be impacting performance, such as latch contention, buffer contention, and I/O contention.

Session and System Statistics

A large number of cumulative database statistics are available on a system and session level. Some of these statistics are collected by the AWR.

Active Session History Statistics

The Active Session History (ASH) statistics are samples of session activity in the database. Active sessions are sampled every second, and are stored in a circular buffer in the system global area (SGA). Any session that is connected to the database and using CPU, or is waiting for an event that does not belong to the idle wait class, is considered an active session. By capturing only active sessions, a manageable set of data is represented with the size being directly related to the work being performed, rather than the number of sessions allowed on the system.

Using the DB time example described in "Time Model Statistics", samples of session activity are collected from the online transaction made at the bookseller's Web site, represented as vertical lines below the horizontal arrow in Figure 2-3.

Figure 2-3 Active Session History

Description of "Figure 2-3 Active Session History"

The light vertical lines represent samples of inactive session activity that are not captured in the ASH statistics. The bold vertical lines represent samples of active sessions that are captured at:

• 7:38, while novels by the author are being queried

• 7:42, while the user is browsing the query results

• 7:50, when one of the novels is added to the shopping cart

• 7:52, during the checkout process

Table 2-1 lists the ASH statistics that are collected for the active sessions, along with examples of the session ID, module, SQL ID, session state, and wait events that are sampled.

High-Load SQL Statistics

SQL statements that are consuming the most resources produce the highest load on the system, based on criteria such as elapsed time and CPU time.

Preparing the Database for Tuning

This section lists and describes the steps that need to be performed before the database can be properly tuned.

To prepare the database for tuning:

1. Get feedback from users.

   Determine the scope of the performance project and subsequent performance goals, and determine performance goals for the future. This process is key for future capacity planning.

2. Sanity-check the operating systems of all systems involved with user performance.

   Check for hardware or operating system resources that are fully utilized. List any overused resources as possible symptoms for later analysis. In addition, ensure that all hardware is functioning properly.

3. Ensure that the STATISTICS_LEVEL initialization parameter is set to TYPICAL or ALL to enable the automatic performance tuning features of Oracle Database, including the AWR and ADDM.

   The default setting for this parameter is TYPICAL.

Tuning the Database Proactively

This section lists and describes the steps required to keep the database properly tuned on a regular basis. These tuning procedures are considered proactive and should be performed as part of your daily maintenance of Oracle Database.

To tune the database proactively:

1. Review the ADDM findings, as described in Chapter 3, "Automatic Database Performance Monitoring".

   ADDM automatically detects and reports on performance problems with the database, including most of the "Common Performance Problems Found in Oracle Databases". The results are displayed as ADDM findings on the Database Home page in Enterprise Manager. Reviewing these findings enables you to quickly identify the performance problems that require your attention.

2. Implement the ADDM recommendations, as described in Chapter 3, "Automatic Database Performance Monitoring".

   ADDM automatically provides a list of recommendations for reducing the impact of the performance problem with each ADDM finding. Implementing a recommendation applies the suggested changes to improve the database performance.

3. Monitor performance problems with the database in real time, as described in Chapter 4, "Monitoring Real-Time Database Performance".

   The Database Performance page in Oracle Enterprise Manager enables you to identify and respond to real-time performance problems. By drilling down to the appropriate pages, you can identify and resolve performance problems with the database in real time, without having to wait until the next ADDM analysis.

4. Respond to performance-related alerts, as described in Chapter 5, "Monitoring Performance Alerts".

   The Database Home page in Oracle Enterprise Manager enables you to view performance-related alerts generated by the system. Typically, these alerts reveal performance problems that, once resolved, will improve the performance of your database.

5. Validate that the changes made have produced the desired effect, and verify if the perception of performance to the users has improved.

6. Repeat these steps until your performance goals are met or become impossible to achieve due to other constraints.

Tuning the Database Reactively

This section lists and describes the steps required to tune the database based on user feedback. These tuning procedure are considered reactive and should be performed periodically when performance problems are reported by the users.

To tune the database reactively:

1. Run ADDM manually to diagnose current and historical database performance when performance problems are reported by the users, as described in Chapter 6, "Manual Database Performance Monitoring".

   This is useful if you want to run ADDM before the next ADDM analysis to analyze current database performance, or to analyze historical database performance when you were not proactively monitoring the system.

2. Resolve transient performance problems, as described in Chapter 7, "Resolving Transient Performance Problems".

   The Active Session History (ASH) reports enable you to analyze transient performance problems with the database that are short-lived and do not appear in the ADDM analysis.

3. Resolve performance degradation over time, as described in Chapter 8, "Resolving Performance Degradation Over Time".

   The Automatic Workload Repository (AWR) Compare Periods reports enable you to compare database performance between two periods of time, and resolve performance degradation that may happen from one time period to another.

4. Validate that the changes made have produced the desired effect, and verify if the perception of performance to the users has improved.

5. Repeat these steps until your performance goals are met or become impossible to achieve due to other constraints.

Tuning SQL Statements

This section lists and describes the steps required to identify, tune, and optimize high-load SQL statements.

To tune SQL statements:

1. Identify high-load SQL statements, as described in Chapter 9, "Identifying High-Load SQL Statements".

   Use the ADDM findings and the Top SQL to identify high-load SQL statements that are causing the greatest contention.

2. Tune high-load SQL statements, as described in Chapter 10, "Tuning SQL Statements".

   You can improve the efficiency of high-load SQL statements by tuning them using the SQL Tuning Advisor.

3. Optimize data access paths, as described in Chapter 11, "Optimizing Data Access Paths".

   You can optimize the performance of data access paths by creating the proper set of materialized views, materialized view logs, and indexes for a given workload by using the SQL Access Advisor.

4. Repeat these steps until all high-load SQL statements are tuned for greatest efficiency.