1 Performance Tuning Overview

This chapter provides an introduction to performance tuning and contains the following sections:

Introduction to Performance Tuning
Introduction to Performance Tuning Features and Tools

1.1 Introduction to Performance Tuning

This guide provides information on tuning an Oracle Database system for performance. Topics discussed in this guide include:

Performance Planning
Instance Tuning
SQL Tuning

1.1.1 Performance Planning

Before starting on the instance or SQL tuning sections of this guide, make sure you have read Part II, "Performance Planning". Based on years of designing and performance experience, Oracle has designed a performance methodology. This brief section explains clear and simple activities that can dramatically improve system performance. It discusses the following topics:

1.1.2 Instance Tuning

Part III, "Optimizing Instance Performance" of this guide discusses the factors involved in the tuning and optimizing of an Oracle database instance.

When considering instance tuning, care must be taken in the initial design of the database system to avoid bottlenecks that could lead to performance problems. In addition, you need to consider:

Allocating memory to database structures
Determining I/O requirements of different parts of the database
Tuning the operating system for optimal performance of the database

After the database instance has been installed and configured, you need to monitor the database as it is running to check for performance-related problems.

1.1.2.1 Performance Principles

Performance tuning requires a different, although related, method to the initial configuration of a system. Configuring a system involves allocating resources in an ordered manner so that the initial system configuration is functional.

Tuning is driven by identifying the most significant bottleneck and making the appropriate changes to reduce or eliminate the effect of that bottleneck. Usually, tuning is performed reactively, either while the system is preproduction or after it is live.

1.1.2.2 Baselines

The most effective way to tune is to have an established performance baseline that can be used for comparison if a performance issue arises. Most database administrators (DBAs) know their system well and can easily identify peak usage periods. For example, the peak periods could be between 10.00am and 12.00pm and also between 1.30pm and 3.00pm. This could include a batch window of 12.00am midnight to 6am.

It is important to identify these peak periods at the site and install a monitoring tool that gathers performance data for those high-load times. Optimally, data gathering should be configured from when the application is in its initial trial phase during the QA cycle. Otherwise, this should be configured when the system is first in production.

Ideally, baseline data gathered should include the following:

Application statistics (transaction volumes, response time)
Database statistics
Operating system statistics
Disk I/O statistics
Network statistics

In the Automatic Workload Repository, baselines are identified by a range of snapshots that are preserved for future comparisons. See "Overview of the Automatic Workload Repository".

1.1.2.3 The Symptoms and the Problems

A common pitfall in performance tuning is to mistake the symptoms of a problem for the actual problem itself. It is important to recognize that many performance statistics indicate the symptoms, and that identifying the symptom is not sufficient data to implement a remedy. For example:

Slow physical I/O

Generally, this is caused by poorly-configured disks. However, it could also be caused by a significant amount of unnecessary physical I/O on those disks issued by poorly-tuned SQL.
Latch contention

Rarely is latch contention tunable by reconfiguring the instance. Rather, latch contention usually is resolved through application changes.
Excessive CPU usage

Excessive CPU usage usually means that there is little idle CPU on the system. This could be caused by an inadequately-sized system, by untuned SQL statements, or by inefficient application programs.

1.1.2.4 When to Tune

There are two distinct types of tuning:

Proactive Monitoring
Bottleneck Elimination

1.1.2.4.1 Proactive Monitoring

Proactive monitoring usually occurs on a regularly scheduled interval, where a number of performance statistics are examined to identify whether the system behavior and resource usage has changed. Proactive monitoring can also be considered as proactive tuning.

Usually, monitoring does not result in configuration changes to the system, unless the monitoring exposes a serious problem that is developing. In some situations, experienced performance engineers can identify potential problems through statistics alone, although accompanying performance degradation is usual.

Experimenting with or tweaking a system when there is no apparent performance degradation as a proactive action can be a dangerous activity, resulting in unnecessary performance drops. Tweaking a system should be considered reactive tuning, and the steps for reactive tuning should be followed.

Monitoring is usually part of a larger capacity planning exercise, where resource consumption is examined to see changes in the way the application is being used, and the way the application is using the database and host resources.

1.1.2.4.2 Bottleneck Elimination

Tuning usually implies fixing a performance problem. However, tuning should be part of the life cycle of an application—through the analysis, design, coding, production, and maintenance stages. Oftentimes, the tuning phase is left until the system is in production. At this time, tuning becomes a reactive fire-fighting exercise, where the most important bottleneck is identified and fixed.

Usually, the purpose for tuning is to reduce resource consumption or to reduce the elapsed time for an operation to complete. Either way, the goal is to improve the effective use of a particular resource. In general, performance problems are caused by the over-use of a particular resource. That resource is the bottleneck in the system. There are a number of distinct phases in identifying the bottleneck and the potential fixes. These are discussed in the sections that follow.

Remember that the different forms of contention are symptoms that can be fixed by making changes in the following places:

Changes in the application, or the way the application is used
Changes in Oracle
Changes in the host hardware configuration

Often, the most effective way of resolving a bottleneck is to change the application.

1.1.3 SQL Tuning

Part IV, "Optimizing SQL Statements" of this guide discusses the process of tuning and optimizing SQL statements.

Many client/server application programmers consider SQL a messaging language, because queries are issued and data is returned. However, client tools often generate inefficient SQL statements. Therefore, a good understanding of the database SQL processing engine is necessary for writing optimal SQL. This is especially true for high transaction processing systems.

Typically, SQL statements issued by OLTP applications operate on relatively few rows at a time. If an index can point to the exact rows that are required, then Oracle can construct an accurate plan to access those rows efficiently through the shortest possible path. In decision support system (DSS) environments, selectivity is less important, because they often access most of a table's rows. In such situations, full table scans are common, and indexes are not even used. This book is primarily focussed on OLTP-type applications. For detailed information on DSS and mixed environments, see the Oracle Database Data Warehousing Guide.

1.1.3.1 Query Optimizer and Execution Plans

When a SQL statement is executed on an Oracle database, the Oracle query optimizer determines the most efficient execution plan after considering many factors related to the objects referenced and the conditions specified in the query. This determination is an important step in the processing of any SQL statement and can greatly affect execution time.

During the evaluation process, the query optimizer reviews statistics gathered on the system to determine the best data access path and other considerations. You can override the execution plan of the query optimizer with hints inserted in SQL statement.

1.2 Introduction to Performance Tuning Features and Tools

Effective data collection and analysis is essential for identifying and correcting performance problems. Oracle provides a number of tools that allow a performance engineer to gather information regarding database performance. In addition to gathering data, Oracle provides tools to monitor performance, diagnose problems, and tune applications.

The Oracle gathering and monitoring features are mainly automatic, managed by an Oracle background processes. To enable automatic statistics collection and automatic performance features, the STATISTICS_LEVEL initialization parameter must be set to TYPICAL or ALL. You can administer and display the output of the gathering and tuning tools with Oracle Enterprise Manager, or with APIs and views. For ease of use and to take advantage of its numerous automated monitoring and diagnostic tools, Oracle Enterprise Manager Database Control is recommended.

1.2.1 Automatic Performance Tuning Features

The Oracle automatic performance tuning features include:

Automatic Workload Repository (AWR) collects, processes, and maintains performance statistics for problem detection and self-tuning purposes. See "Overview of the Automatic Workload Repository".
Automatic Database Diagnostic Monitor (ADDM) analyzes the information collected by the AWR for possible performance problems with the Oracle database. See "Overview of the Automatic Database Diagnostic Monitor".
SQL Tuning Advisor allows a quick and efficient technique for optimizing SQL statements without modifying any statements. See "Reactive Tuning Using the SQL Tuning Advisor".
SQLAccess Advisor provides advice on materialized views, indexes, and materialized view logs. See "Automatic SQL Tuning Features" and "Overview of the SQL Access Advisor" for information on SQLAccess Advisor.
End to End Application tracing identifies excessive workloads on the system by specific user, service, or application component. See "End to End Application Tracing".
Server-generated alerts automatically provide notifications when impending problems are detected. See Oracle Database Administrator's Guide for information about monitoring the operation of the database with server-generated alerts.
Additional advisors that can be launched from Oracle Enterprise Manager, such as memory advisors to optimize memory for an instance. The memory advisors are commonly used when automatic memory management is not set up for the database. Other advisors are used to optimize mean time to recovery (MTTR), shrinking of segments, and undo tablespace settings. See Oracle Enterprise Manager Concepts for information on advisors that are available with Oracle Enterprise Manager.

To access the advisors through Oracle Enterprise Manager Database Control:
- Click the Advisor Central link under Related Links at the bottom of the Database pages.
- On the Advisor Central page, click one of the advisor links.
Oracle Enterprise Manager Performance page displays host, instance service time, and throughput information for real time monitoring and diagnosis. The page can be set to refresh automatically in selected intervals or manually. See Oracle Enterprise Manager Concepts for information on the Performance page available with Oracle Enterprise Manager.

1.2.2 Additional Oracle Tools

This section describes additional Oracle tools that can be used for determining performance problems.

1.2.2.1 V$ Performance Views

The V$ views are the performance information sources used by all Oracle performance tuning tools. The V$ views are based on memory structures initialized at instance startup. The memory structures, and the views that represent them, are automatically maintained by Oracle throughout the life of the instance. See Chapter 10, "Instance Tuning Using Performance Views" for information diagnosing tuning problems using the V$ performance views.