Oracle8i Concepts
Release 2 (8.1.6)

Part Number A76965-01





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The Optimizer

This chapter introduces the Oracle optimizer. It includes:

Introduction to Optimization

Optimization is the process of choosing the most efficient way to execute a SQL statement. This is an important step in the processing of any data manipulation language (DML) statement: SELECT, INSERT, UPDATE, or DELETE. Many different ways to execute a SQL statement often exist, for example, by varying the order in which tables or indexes are accessed. The procedure Oracle uses to execute a statement can greatly affect how quickly the statement executes.

The optimizer calculates the most efficient way to execute a SQL statement. The optimizer evaluates many factors to select among alternative access paths. It can use a cost-based or rule-based approach.


The optimizer may not make the same decisions from one version of Oracle to the next. In more recent versions, the optimizer may make different decisions based on better, more sophisticated information available to it.  

You can influence the optimizer's choices by setting the optimizer approach and goal and by gathering statistics for cost-based optimization. Sometimes the application designer, who has more information about a particular application's data than is available to the optimizer, can choose a more effective way to execute a SQL statement. The application designer can use hints in SQL statements to specify how the statement should be executed.

See Also:


Execution Plans

To execute a DML statement, Oracle may have to perform many steps. Each of these steps either retrieves rows of data physically from the database or prepares them in some way for the user issuing the statement. The combination of the steps Oracle uses to execute a statement is called an execution plan. An execution plan includes an access method for each table that the statement accesses and the join order of the tables.

Figure 21-1 shows a graphical representation of the execution plan for the following SQL statement, which selects the name, job, salary, and department name for all employees whose salaries do not fall into a recommended salary range:

SELECT ename, job, sal, dname 
    FROM emp, dept 
  WHERE emp.deptno = dept.deptno 
      (SELECT * 
           FROM salgrade 
         WHERE emp.sal BETWEEN losal AND hisal); 

Figure 21-1 An Execution Plan

Steps of Execution Plan

Each step of the execution plan returns a set of rows that either are used by the next step or, in the last step, are returned to the user or application issuing the SQL statement. A set of rows returned by a step is called a row source.

Figure 21-1 is a hierarchical diagram showing the flow of row sources from one step to another. The numbering of the steps reflects the order in which they are displayed in response to the EXPLAIN PLAN statement (described in the next section). This generally is not the order in which the steps are executed. Each step of the execution plan either retrieves rows from the database or accepts rows from one or more row sources as input:

The EXPLAIN PLAN statement

You can examine the execution plan chosen by the optimizer for a SQL statement by using the EXPLAIN PLAN statement, which causes the optimizer to choose the execution plan and then inserts data describing the plan into a database table.

For example, the following output table is such a description for the statement examined in the previous section:

1       FILTER 
2         NESTED LOOPS 
3           TABLE ACCESS  FULL           EMP 
4           TABLE ACCESS  BY ROWID       DEPT 
5             INDEX       UNIQUE SCAN    PK_DEPTNO 
6           TABLE ACCESS  FULL           SALGRADE 

Each box in Figure 21-1 and each row in the output table corresponds to a single step in the execution plan. For each row in the listing, the value in the ID column is the value shown in the corresponding box in Figure 21-1.

You can obtain such a listing by using the EXPLAIN PLAN statement and then querying the output table.

See Also:

Oracle8i Designing and Tuning for Performance for information on how to use EXPLAIN PLAN 

Execution Order

The steps of the execution plan are not performed in the order in which they are numbered. Rather, Oracle first performs the steps that appear as leaf nodes in the tree-structured graphical representation of the execution plan (Steps 3, 5, and 6 in Figure 21-1). The rows returned by each step become the row sources of its parent step. Then Oracle performs the parent steps.

To execute the statement for Figure 21-1, for example, Oracle performs the steps in this order:

Note that Oracle performs Steps 5, 4, 2, 6, and 1 once for each row returned by Step 3. If a parent step requires only a single row from its child step before it can be executed, Oracle performs the parent step (and possibly the rest of the execution plan) as soon as a single row has been returned from the child step. If the parent of that parent step also can be activated by the return of a single row, then it is executed as well.

Thus the execution can cascade up the tree. Oracle performs the parent step and all cascaded steps once for each row in turn retrieved by the child step. The parent steps that are triggered for each row returned by a child step include table accesses, index accesses, nested loops joins, and filters.

If a parent step requires all rows from its child step before it can be executed, Oracle cannot perform the parent step until all rows have been returned from the child step. Such parent steps include sorts, sort-merge joins, and aggregate functions.

Optimizer Plan Stability

After carefully tuning an application, you might want to ensure that the optimizer generates the same execution plan whenever the same SQL statements are executed. Plan stability allows you to maintain the same execution plans for the same SQL statements, regardless of changes to the database. Changes to the database include:

The CREATE OUTLINE statement creates a stored outline, which contains a set of attributes that the optimizer uses to create an execution plan. Stored outlines can also be created automatically by setting the system parameter CREATE_STORED_OUTLINES to TRUE.

The system parameter USE_STORED_OUTLINES can be set to TRUE, FALSE, or a category name to indicate whether to make use of existing stored outlines for queries that are being executed. The OUTLN_PKG package provides procedures used for managing stored outlines.

Implementing plan stability creates a new schema called OUTLN, which is created with DBA privileges. The database administrator should change the password for the OUTLN schema just as for the SYS and SYSTEM schemas.

See Also:


Cost-Based Optimization

Using the cost-based approach, the optimizer determines which execution plan is most efficient by considering available access paths and factoring in information based on statistics for the schema objects (tables or indexes) accessed by the SQL statement. The cost-based approach also considers hints, which are optimization suggestions placed in a Comment in the statement.

Conceptually, the cost-based approach consists of these steps:

  1. The optimizer generates a set of potential execution plans for the SQL statement based on its available access paths and hints.

  2. The optimizer estimates the cost of each execution plan based on statistics in the data dictionary for the data distribution and storage characteristics of the tables, indexes, and partitions accessed by the statement.

    The cost is an estimated value proportional to the expected resource use needed to execute the statement with a particular execution plan. The optimizer calculates the cost of each possible access method and join order based on the estimated computer resources, including (but not limited to) I/O, CPU time, and memory, that are required to execute the statement using the plan.

    Serial execution plans with greater costs take more time to execute than those with smaller costs. When using a parallel execution plan, however, resource use is not directly related to elapsed time.

  3. The optimizer compares the costs of the execution plans and chooses the one with the smallest cost.

Goal of the Cost-Based Approach

By default, the goal of the cost-based approach is the best throughput, or minimal resource use necessary to process all rows accessed by the statement.

Oracle can also optimize a statement with the goal of best response time, or minimal resource use necessary to process the first row accessed by a SQL statement.

For parallel execution of a SQL statement, the optimizer can choose to minimize elapsed time at the expense of resource consumption. The initialization parameter OPTIMIZER_PERCENT_PARALLEL specifies how much the optimizer attempts to parallelize execution.

See Also:

Oracle8i Designing and Tuning for Performance for information about using the OPTIMIZER_PERCENT_PARALLEL parameter and for information about how the optimizer chooses an optimization approach and goal  

Statistics for Cost-Based Optimization

The cost-based approach uses statistics to calculate the selectivity of predicates and estimate the cost of each execution plan. Selectivity is the fraction of rows in a table that the SQL statement's predicate chooses. The optimizer uses the selectivity of a predicate to estimate the cost of a particular access method and to determine the optimal join order.

Statistics quantify the data distribution and storage characteristics of tables, columns, indexes, and partitions. The optimizer uses these statistics to estimate how much I/O, CPU time, and memory are required to execute a SQL statement using a particular execution plan. The statistics are stored in the data dictionary, and they can be exported from one database and imported into another. For example, you can transfer production statistics to a test system to simulate the real environment, even though the test system may only have small samples of data.

You must gather statistics on a regular basis to provide the optimizer with information about schema objects. New statistics should be gathered after a schema object's data or structure are modified in ways that make the previous statistics inaccurate. For example, after loading a significant number of rows into a table, you should collect new statistics on the number of rows. After updating data in a table, you do not need to collect new statistics on the number of rows but you might need new statistics on the average row length.

See Also:

"Gathering Statistics" 

Histograms for Cost-Based Optimization

Cost-based optimization uses data value histograms to get accurate estimates of the distribution of column data. A histogram partitions the values in the column into bands, so that all column values in a band fall within the same range. Histograms provide improved selectivity estimates in the presence of data skew, resulting in optimal execution plans with nonuniform data distributions. Ranges in a histogram are called buckets.

One of the fundamental capabilities of cost-based optimization is determining the selectivity of predicates that appear in queries. Selectivity estimates are used to decide when to use an index and the order in which to join tables. Most attribute domains (a table's columns) are not uniformly distributed.

Cost-based optimization uses height-balanced histograms on specified attributes to describe the distributions of nonuniform domains. In a height-balanced histogram, the column values are divided into bands so that each band contains approximately the same number of values. The useful information that the histogram provides, then, is where in the range of values the endpoints fall.

Comparing Height-balanced and Width-balanced Histograms

Oracle uses height-balanced histograms as opposed to width-balanced histograms. The two types of histograms differ in the following ways:

Consider a column C with values between 1 and 100 and a histogram with 10 buckets. If the data in C is uniformly distributed, this histogram would look like this, where the numbers are the endpoint values:

The number of rows in each bucket is one tenth the total number of rows in the table. Four-tenths of the rows have values between 60 and 100 in this example of uniform distribution.

If the data is not uniformly distributed, the histogram might look like this:

In this case, most of the rows have the value 5 for the column. In this example, only 1/10 of the rows have values between 60 and 100.

Suppose that the values in a single column of a 1000-row table range between 1 and 100, and suppose that you want a 10-bucket histogram. In a width-balanced histogram, the buckets would be of equal width (1-10, 11-20, 21-30, and so on) and each bucket would count the number of rows that fall into that bucket's range. In a height-balanced histogram, each bucket has the same height (in this case 100 rows) and the endpoints for each bucket are determined by the density of the distinct values in the column.

The advantage of the height-balanced approach is clear when the data is highly skewed. Suppose that 800 rows of a 1000-row table have the value 5, and the remaining 200 rows are evenly distributed between 1 and 100. A width-balanced histogram would have 820 rows in the bucket labeled 1-10 and approximately 20 rows in each of the other buckets. The height-based histogram would have one bucket labeled 1-5, seven buckets labeled 5-5, one bucket labeled 5-50, and one bucket labeled 50-100.

If you want to know how many rows in the table contain the value 5, the height-balanced histogram shows that approximately 80% of the rows contain this value. However, the width-balanced histogram does not provide a mechanism for differentiating between the value 5 and the value 6. You would compute only 8% of the rows contain the value 5 in a width-balanced histogram. Therefore height-based histograms are more appropriate for determining the selectivity of column values.

When to Use Histograms

Histograms can affect performance and should be used only when they substantially improve query plans. In general, you should create histograms on columns that are frequently used in WHERE clauses of queries and have a highly skewed data distribution. For many applications, it is appropriate to create histograms for all indexed columns because indexed columns typically are the columns most often used in WHERE clauses.

Histograms are persistent objects, so there is a maintenance and space cost for using them. You should compute histograms only for columns that you know have highly skewed data distribution. For uniformly distributed data, cost-based optimization can make fairly accurate guesses about the cost of executing a particular statement without the use of histograms.

Histograms, like all other optimizer statistics, are static. They are useful only when they reflect the current data distribution of a given column. The data in the column can change as long as the distribution remains constant. If the data distribution of a column changes frequently, you must recompute its histogram frequently.

Histograms are not useful for columns with the following characteristics:

You generate histograms by using the DBMS_STATS package or the ANALYZE statement. You can generate histograms for columns of a table or partition. Histogram statistics are not collected in parallel.

You can view histogram information with the following data dictionary views:

Statistics for Partitioned Schema Objects

Partitioned schema objects may contain multiple sets of statistics. They can have statistics which refer to the following:

Unless the query predicate narrows the query to a single partition, the optimizer uses the global statistics. Since most queries are not likely to be this restrictive, it is most important to have accurate global statistics. Intuitively, it may seem that generating global statistics from partition-level statistics should be straightforward; however, this is only true for some of the statistics. For example, it is very difficult to figure out the number of distinct values for a column from the number of distinct values found in each partition because of the possible overlap in values. Therefore, actually gathering global statistics with the DBMS_STATS package is highly recommended, rather than calculating them with the ANALYZE statement.


Oracle currently does not gather global histogram statistics.  

See Also:

"The ANALYZE Statement" 

Gathering Statistics

This section describes the different methods you can use to gather statistics.

The DBMS_STATS Package

The PL/SQL package DBMS_STATS enables you to generate and manage statistics for cost-based optimization. You can use this package to gather, modify, view, and delete statistics. You can also use this package to store sets of statistics.

The DBMS_STATS package can gather statistics on indexes, tables, columns, and partitions, as well as statistics on all schema objects in a schema or database. It does not gather cluster statistics--you can use DBMS_STATS to gather statistics on the individual tables instead of the whole cluster.

The statistics-gathering operations can run either serially or in parallel. Whenever possible, DBMS_STATS calls a parallel query to gather statistics with the specified degree of parallelism; otherwise, it calls a serial query or the ANALYZE statement. Index statistics are not gathered in parallel.

The statistics can be computed exactly or estimated from a random sampling of rows or blocks.

For partitioned tables and indexes, DBMS_STATS can gather separate statistics for each partition as well as global statistics for the entire table or index. Similarly, for composite partitioning DBMS_STATS can gather separate statistics for subpartitions, partitions, and the entire table or index. Depending on the SQL statement being optimized, the optimizer may choose to use either the partition (or subpartition) statistics or the global statistics.

DBMS_STATS gathers statistics only for cost-based optimization. It does not gather other statistics. For example, the table statistics gathered by DBMS_STATS include the following:

The table statistics gathered by DBMS_STATS do not include the following:

The COMPUTE STATISTICS Clause for Indexes

Oracle can gather some statistics automatically while creating or rebuilding a B-tree or bitmap index. The COMPUTE STATISTICS clause of CREATE INDEX or ALTER INDEX ... REBUILD enables this gathering of statistics.

The statistics that Oracle gathers for the COMPUTE STATISTICS clause depend on whether the index is partitioned or nonpartitioned:

To ensure correctness of the statistics Oracle always uses base tables when creating an index with the COMPUTE STATISTICS clause, even if another index is available that could be used to create the index.

See Also:

Oracle8i SQL Reference for details about the COMPUTE STATISTICS clause of the CREATE INDEX and ALTER INDEX statements 

The ANALYZE Statement

The ANALYZE statement can also generate statistics for cost-based optimization. Using ANALYZE for this purpose is not recommended because of various restrictions, for example:

ANALYZE can gather additional information that is not used by the optimizer, such as information about chained rows and the structural integrity of indexes, tables, and clusters. DBMS_STATS does not gather this information.

See Also:

Oracle8i SQL Reference for detailed information about the ANALYZE statement 

Exact and Estimated Statistics

The statistics gathered by the DBMS_STATS package or ANALYZE statement can be exact or estimated. (The COMPUTE STATISTICS clause for creating or rebuilding indexes always gathers exact statistics.)

To compute exact statistics, Oracle must read all of the data in the index, table, partition, or schema. Some statistics are always computed exactly, such as the number of data blocks currently containing data in a table or the depth of an index from its root block to its leaf blocks.

To estimate statistics, Oracle selects a random sample of data. You can specify the sampling percentage and whether sampling should be based on rows or blocks.

Row sampling reads rows without regard to their physical placement on disk. This method provides the most random data for estimates, but it can result in reading more data than necessary. For example, in the worst case a row sample might select one row from each block, requiring a full scan of the table or index.

Block sampling reads a random sample of blocks and uses all of the rows in those blocks for estimates. This method reduces the amount of I/O activity for a given sample size, but it can reduce the randomness of the sample if rows are not randomly distributed on disk. Block sampling is not available for index statistics.

Managing Statistics

This section describes statistics tables and lists the views that display information about statistics stored in the data dictionary.

Statistics Tables

The DBMS_STATS package enables you to store statistics in a statistics table. You can transfer the statistics for a column, table, index, or schema into a statistics table and subsequently restore those statistics to the data dictionary. The optimizer does not use statistics that are stored in a statistics table.

Statistics tables enable you to experiment with different sets of statistics. For example, you can back up a set of statistics before you delete them, modify them, or generate new statistics. You can then compare the performance of SQL statements optimized with different sets of statistics, and if the statistics stored in a table give the best performance, you can restore them to the data dictionary.

A statistics table can keep multiple distinct sets of statistics, or you can create multiple statistics tables to store distinct sets of statistics separately.

Viewing Statistics

You can use the DBMS_STATS package to view the statistics stored in the data dictionary or in a statistics table.

You can also query these data dictionary views for statistics in the data dictionary:

When to Use the Cost-Based Approach

In general, you should use the cost-based approach for all new applications; the rule-based approach is provided for applications that were written before cost-based optimization was available. Cost-based optimization can be used for both relational data and object types.

The following features can only use cost-based optimization:

Extensible Optimization

Extensible optimization allows the authors of user-defined functions and domain indexes to control the three main components that cost-based optimization uses to select an execution plan: statistics, selectivity, and cost evaluation.

Extensible optimization allows you to:

User-Defined Statistics

You can define statistics collection functions for domain indexes, individual columns of a table, and user-defined datatypes.

Whenever a domain index is analyzed to gather statistics, Oracle calls the associated statistics collection function. Whenever a column of a table is analyzed, Oracle collects the standard statistics for that column and calls any associated statistics collection function. If a statistics collection function exists for a datatype, then Oracle calls it for each column that has that datatype in the table being analyzed.

User-Defined Selectivity

The selectivity of a predicate in a SQL statement is used to estimate the cost of a particular access method; it is also used to determine the optimal join order. The optimizer cannot compute an accurate selectivity for predicates that contain user-defined operators because it does not have any information about these operators.

You can define selectivity functions for predicates containing user-defined operators, stand-alone functions, package functions, or type methods. The optimizer calls the user-defined selectivity function whenever it encounters a predicate that contains the operator, function, or method in one of the following relations with a constant: <, <=, =, >=, >, or LIKE.

User-Defined Costs

The optimizer cannot compute an accurate estimate of the cost of a domain index because it does not know the internal storage structure of the index. Also, the optimizer may underestimate the cost of a user-defined function with any of the following characteristics:

You can define costs for the following:

These user-defined costs can be in the form of default costs that the optimizer simply looks up or they can be full-fledged cost functions that the optimizer calls to compute the cost.

Rule-Based Optimization

Using the rule-based approach, the optimizer chooses an execution plan based on the access paths available and the ranks of these access paths. You can use rule-based optimization to access both relational data and object types.

Oracle's ranking of the access paths is heuristic. If there is more than one way to execute a SQL statement, the rule-based approach always uses the operation with the lower rank. Usually, operations of lower rank execute faster than those associated with constructs of higher rank.


Rule-based optimization is not available for some advanced features of Oracle8i.  

See Also:


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