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Oracle System Optimization

The process of identifying and eliminating bottlenecks of the system performance is commonly referred as system optimization or tuning. System-level Oracle tuning is the first step of the overall database performance optimization. This whitepaper explores main steps of this process.

Optimize the server hardware. It is extremely important to constantly optimize the disk and network subsystems to increase the performance in terms of the input/output time, network packet size and dispatching frequency. Unix/Linux OS provide the following tools for monitoring performance of hardware components:

Adjust optimizer statistics. Oracle model of the execution plan's cost relies on statistics collected by the optimizer on the host, the database and objects involved in a query. Optimizer statistics include the following entries:

Those statistics must be always collected and stored carefully to allow the optimizer to learn more about the data and to make better execution plans.

Also, it is reasonable to adjust the following optimizer parameters:

Optimize the instance. Key to succeed this step is to know the performance baseline, it helps to identify performance issues and analyze the related statistics. The baseline information should contain the following: application statistics (transaction volumes, response time), database statistics, operating system statistics, disk I/O and network statistics.

These settings can affect the performance of Oracle instance: db_block_size, db_cache_size, db_file_multiblock_read_count, and cpu_count.

Use diagnostic software and follow its recommendations. For example, SQL Access Advisor identifies performance problems caused by missing indexes and materialized views. In this case just follow the recommendations - optimize your SQL workload by adding required indexes, especially function-based indexes.

Distinguish problems from symptoms

The most common mistake in performance tuning is to mix the symptoms of a problem and the actual problem itself. Most statistics gathered by performance analyzers identify the symptoms, not the problem. For example:

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