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Query Optimization in Oracle
By: O'Reilly Media
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    2008-03-20

    Table of Contents:
  • Query Optimization in Oracle
  • Query Optimization
  • Rule-Based Optimization
  • How statistics are used

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    Query Optimization in Oracle - Rule-Based Optimization


    (Page 3 of 4 )

    Oracle has always had a query optimizer, but until Oracle7 the optimizer was only rule-based. The rule-based optimizer, as the name implies, uses a set of predefined rules as the main determinant of query optimization decisions. Since the rule-based optimizer has been desupported as of Oracle Database 10g, your interest in this topic is likely be limited to supporting old Oracle databases where this choice may have been made.

    Rule-based optimization sometimes provided better performance than the early versions of Oracle’s cost-based optimizer for specific situations. The rule-based optimizer had several weaknesses, including offering only a simplistic set of rules. The Oracle rule-based optimizer had about 20 rules and assigned a weight to each one of them. In a complex database, a query can easily involve several tables, each with several indexes and complex selection conditions and ordering. This complexity means that there were a lot of options, and the simple set of rules used by the rule-based optimizer might not differentiate the choices well enough to make the best choice.

    The rule-based optimizer assigned an optimization score to each potential execution path and then took the path with the best optimization score. Another weakness in the rule-based optimizer was resolution of optimization choices made in the event of a “tie” score. When two paths presented the same optimization score, the rule-based optimizer looked to the syntax of the SQL statement to resolve the tie. The winning execution path was based on the order in which the tables occur in the SQL statement.

    You can understand the potential impact of this type of tie-breaker by looking at a simple situation in which a small table with 10 rows, SMALLTAB, is joined to a large table with 10,000 rows, LARGETAB, as shown in Figure 4-4. If the optimizer chose to read SMALLTAB first, the Oracle database will read the 10 rows and then read LARGETAB to find the matching rows for each of the 10 rows. If the optimizer chose to read LARGETAB first, the database read 10,000 rows from LARGETAB and then read SMALLTAB 10,000 times to find the matching rows. Of course, the rows in SMALLTAB would probably be cached, reducing the impact of each probe, but you could see a dramatic difference in performance.

    Differences like this could occur with the rule-based optimizer as a result of the ordering of the table names in the query. In the previous situation the rule-based optimizer returned the same results for the query, but it used widely varying amounts of resources to retrieve those results.

    Cost-Based Optimization

    To improve the optimization of SQL statements, Oracle introduced the cost-based optimizer in Oracle7. As the name implies, the cost-based optimizer does more than simply look at a set of optimization rules; instead, it selects the execution path that requires the least number of logical I/O operations. This approach avoids the error


    Figure 4-4.   The effect of optimization choices

    discussed in the previous section. After all, the cost-based optimizer would know which table was bigger and would select the right table to begin the query, regardless of the syntax of the SQL statement.

    Oracle8 and later versions, by default, use the cost-based optimizer to identify the optimal execution plan. And, since Oracle Database 10g, the cost-based optimizer is the only supported optimizer. To properly evaluate the cost of any particular execution plan, the cost-based optimizer uses statistics about the composition of the relevant data structures. These statistics are automatically gathered by default since the Oracle Database 10g release into the Automatic Workload Repository (AWR). Among the statistics gathered in the AWR are database segment access and usage statistics, time model statistics, system and session statistics, SQL statements that produce the greatest loads, and Active Session History (ASH) statistics.

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