| Oracle9i Database Performance Tuning Guide and Reference Release 2 (9.2) Part Number A96533-02 |
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| Oracle9i Database Performance Tuning Guide and Reference Release 2 (9.2) Part Number A96533-02 |
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This chapter discusses SQL processing, optimization methods, and how the optimizer chooses a specific plan to execute SQL.
The chapter contains the following sections:
SQL processing uses the following main components to execute a SQL query:
Figure 1-1 illustrates SQL processing.
The optimizer determines the most efficient way to execute a SQL statement 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.
A SQL statement can be executed in many different ways, including the following:
The output from the optimizer is a plan that describes an optimum method of execution. The Oracle server provides the cost-based (CBO) and rule-based (RBO) optimization. In general, use the cost-based approach. Oracle Corporation is continually improving the CBO and new features require CBO.
You can influence the optimizer's choices by setting the optimizer approach and goal, and by gathering representative statistics for the CBO. The optimizer goal is either throughput or response time. See "Choosing an Optimizer Approach and Goal".
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:
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The following features require use of the CBO:
SAMPLE clauses in a SELECT statementFor any SQL statement processed by Oracle, the optimizer performs the operations listed in Table 1-1.
| Operation | Description |
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Evaluation of expressions and conditions |
The optimizer first evaluates expressions and conditions containing constants as fully as possible. See "How the Optimizer Performs Operations". |
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Statement transformation |
For complex statements involving, for example, correlated subqueries or views, the optimizer might transform the original statement into an equivalent join statement. See "How the Optimizer Transforms SQL Statements". |
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Choice of optimizer approaches |
The optimizer chooses either a cost-based or rule-based approach and determines the goal of optimization. See "Choosing an Optimizer Approach and Goal". |
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Choice of access paths |
For each table accessed by the statement, the optimizer chooses one or more of the available access paths to obtain table data. See "Understanding Access Paths for the CBO". |
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Choice of join orders |
For a join statement that joins more than two tables, the optimizer chooses which pair of tables is joined first, and then which table is joined to the result, and so on. See "How the CBO Chooses Execution Plans for Join Types". |
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Choice of join methods |
For any join statement, the optimizer chooses an operation to use to perform the join. See "How the CBO Chooses the Join Method". |
By default, the goal of the CBO is the best throughput. This means that it chooses the least amount of resources necessary to process all rows accessed by the statement. Oracle can also optimize a statement with the goal of best response time. This means that it uses the least amount of resources necessary to process the first row accessed by a SQL statement.
The execution plan produced by the optimizer can vary depending on the optimizer's goal. Optimizing for best throughput is more likely to result in a full table scan rather than an index scan, or a sort merge join rather than a nested loop join. Optimizing for best response time usually results in an index scan or a nested loop join.
For example, suppose you have a join statement that can be executed with either a nested loops operation or a sort-merge operation. The sort-merge operation might return the entire query result faster, while the nested loops operation might return the first row faster. If your goal is to improve throughput, then the optimizer is more likely to choose a sort merge join. If your goal is to improve response time, then the optimizer is more likely to choose a nested loop join.
Choose a goal for the optimizer based on the needs of your application:
The optimizer's behavior when choosing an optimization approach and goal for a SQL statement is affected by the following factors:
The OPTIMIZER_MODE initialization parameter establishes the default behavior for choosing an optimization approach for the instance. The possible values and description are listed in Table 1-2.
You can change the goal of the CBO for all SQL statements in a session by changing the parameter value in initialization file or by the ALTER SESSION SET OPTIMIZER_MODE statement. For example:
OPTIMIZER_MODE = FIRST_ROWS_1
ALTER SESSION SET OPTIMIZER_MODE = FIRST_ROWS_1;
If the optimizer uses the cost-based approach for a SQL statement, and if some tables accessed by the statement have no statistics, then the optimizer uses internal information, such as the number of data blocks allocated to these tables, to estimate other statistics for these tables.
To specify the goal of the CBO for an individual SQL statement, use one of the hints in the following list. Any of these hints in an individual SQL statement can override the OPTIMIZER_MODE initialization parameter for that SQL statement.
FIRST_ROWS(n), where n equals any positive integerFIRST_ROWSALL_ROWSCHOOSERULE
| See Also:
Chapter 5, "Optimizer Hints" for information on how to use hints |
The statistics used by the CBO are stored in the data dictionary. You can collect exact or estimated statistics about physical storage characteristics and data distribution in these schema objects by using the DBMS_STATS package or the ANALYZE statement.
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Note: Oracle Corporation strongly recommends that you use the However, you must use the |
To maintain the effectiveness of the CBO, you must have statistics that are representative of the data. For table columns that contain values with large variations in number of duplicates, called skewed data, you should collect histograms.
The resulting statistics provide the CBO with information about data uniqueness and distribution. Using this information, the CBO is able to compute plan costs with a high degree of accuracy. This enables the CBO to choose the best execution plan based on the least cost.
The CBO can optimize a SQL statement for fast response when the parameter OPTIMIZER_MODE is set to FIRST_ROWS_n, where n is 1, 10, 100, or 1000, or FIRST_ROWS. A hint FIRST_ROWS(n), where n is any positive integer, or FIRST_ROWS can be used to optimize an individual SQL statement for fast response.
Fast-response optimization is suitable for online users, such as those using Oracle Forms or Web access. Typically, online users are interested in seeing the first few rows and seldom look at the entire query result, especially when the result size is large. For such users, it makes sense to optimize the query to produce the first few rows as quickly as possible, even if the time to produce the entire query result is not minimized.
With fast-response optimization, the CBO generates a plan with the lowest cost to produce the first row or the first few rows. The CBO employs two different fast-response optimizations, referred to here as the old and new methods. The old method is used with the FIRST_ROWS hint or parameter value. With the old method, the CBO uses a mixture of costs and rules to produce a plan. It is retained for backward compatibility reasons.
The new method is totally based on costs, and it is sensitive to the value of n. With small values of n, the CBO tends to generate plans that consist of nested loop joins with index lookups. With large values of n, the CBO tends to generate plans that consist of hash joins and full table scans.
The value of n should be chosen based on the online user requirement and depends specifically on how the result is displayed to the user. Generally, Oracle Forms users see the result one row at a time and they are typically interested in seeing the first few screens. Other online users see the result one group of rows at a time.
With the fast-response method, the CBO explores different plans and computes the cost to produce the first n rows for each. It picks the plan that produces the first n rows at lowest cost. Remember that with fast-response optimization, a plan that produces the first n rows at lowest cost might not be the optimal plan to produce the entire result. If the requirement is to obtain the entire result of a query, then fast-response optimization should not be used. Instead use the ALL_ROWS parameter value or hint.
The CBO determines which execution plan is most efficient by considering available access paths and by factoring in information based on statistics for the schema objects (tables or indexes) accessed by the SQL statement. The CBO also considers hints, which are optimization suggestions placed in a comment in the statement.
| See Also:
Chapter 5, "Optimizer Hints" for detailed information on hints |
The CBO performs the following steps:
The cost is an estimated value proportional to the expected resource use needed to execute the statement with a particular plan. The optimizer calculates the cost of access paths and join orders based on the estimated computer resources, which includes I/O, CPU, and memory.
Serial plans with higher costs take more time to execute than those with smaller costs. When using a parallel plan, however, resource use is not directly related to elapsed time.
The CBO consists of the following three main components:
CBO components are illustrated in Figure 1-2.
The input to the query transformer is a parsed query, which is represented by a set of query blocks. The query blocks are nested or interrelated to each other. The form of the query determines how the query blocks are interrelated to each other. The main objective of the query transformer is to determine if it is advantageous to change the form of the query so that it enables generation of a better query plan. Four different query transformation techniques are employed by the query transformer:
Any combination of these transformations can be applied to a given query.
Each view referenced in a query is expanded by the parser into a separate query block. The query block essentially represents the view definition, and therefore the result of a view. One option for the optimizer is to analyze the view query block separately and generate a view subplan. The optimizer then processes the rest of the query by using the view subplan in the generation of an overall query plan. This technique usually leads to a suboptimal query plan, because the view is optimized separately from rest of the query.
The query transformer then removes the potentially suboptimal plan by merging the view query block into the query block that contains the view. Most types of views are merged. When a view is merged, the query block representing the view is merged into the containing query block. Generating a subplan is no longer necessary, because the view query block is eliminated.
For those views that are not merged, the query transformer can push the relevant predicates from the containing query block into the view query block. This technique improves the subplan of the nonmerged view, because the pushed-in predicates can be used either to access indexes or to act as filters.
Like a view, a subquery is represented by a separate query block. Because a subquery is nested within the main query or another subquery, the plan generator is constrained in trying out different possible plans before it finds a plan with the lowest cost. For this reason, the query plan produced might not be the optimal one. The restrictions due to the nesting of subqueries can be removed by unnesting the subqueries and converting them into joins. Most subqueries are unnested by the query transformer. For those subqueries that are not unnested, separate subplans are generated. To improve execution speed of the overall query plan, the subplans are ordered in an efficient manner.
A materialized view is like a query with a result that is materialized and stored in a table. When a user query is found compatible with the query associated with a materialized view, the user query can be rewritten in terms of the materialized view. This technique improves the execution of the user query, because most of the query result has been precomputed. The query transformer looks for any materialized views that are compatible with the user query and selects one or more materialized views to rewrite the user query. The use of materialized views to rewrite a query is cost-based. That is, the query is not rewritten if the plan generated without the materialized views has a lower cost than the plan generated with the materialized views.
See Also:
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The estimator generates three different types of measures:
These measures are related to each other, and one is derived from another. The end goal of the estimator is to estimate the overall cost of a given plan. If statistics are available, then the estimator uses them to compute the measures. The statistics improve the degree of accuracy of the measures.
The first measure, selectivity, represents a fraction of rows from a row set. The row set can be a base table, a view, or the result of a join or a GROUP BY operator. The selectivity is tied to a query predicate, such as last_name = 'Smith', or a combination of predicates, such as last_name = 'Smith' AND job_type = 'Clerk'. A predicate acts as a filter that filters a certain number of rows from a row set. Therefore, the selectivity of a predicate indicates how many rows from a row set will pass the predicate test. Selectivity lies in a value range from 0.0 to 1.0. A selectivity of 0.0 means that no rows will be selected from a row set, and a selectivity of 1.0 means that all rows will be selected.
The estimator uses an internal default value for selectivity, if no statistics are available. Different internal defaults are used, depending on the predicate type. For example, the internal default for an equality predicate (last_name = 'Smith') is lower than the internal default for a range predicate (last_name > 'Smith'). The estimator makes this assumption because an equality predicate is expected to return a smaller fraction of rows than a range predicate.
When statistics are available, the estimator uses them to estimate selectivity. For example, for an equality predicate (last_name = 'Smith'), selectivity is set to the reciprocal of the number n of distinct values of last_name, because the query selects rows that all contain one out of n distinct values. If a histogram is available on the last_name column, then the estimator uses it instead of the number of distinct values. The histogram captures the distribution of different values in a column, so it yields better selectivity estimates. Having histograms on columns that contain skewed data (in other words, values with large variations in number of duplicates) greatly helps the CBO generate good selectivity estimates.
Cardinality represents the number of rows in a row set. Here, the row set can be a base table, a view, or the result of a join or GROUP BY operator.
GROUP BY operator is applied. The effect of the GROUP BY operator is to decrease the number of rows in a row set. The group cardinality depends on the distinct cardinality of each of the grouping columns and on the number of rows in the row set. For an illustration of group cardinality, see Example 1-1.If a row set of 100 rows is grouped by colx, which has a distinct cardinality of 30, then the group cardinality is 30.
However, suppose the same row set of 100 rows is grouped by colx and coly, which have distinct cardinalities of 30 and 60, respectively. In this case, the group cardinality lies between the maximum of the distinct cardinalities of colx and coly, and the lower of the product of the distinct cardinalities of colx and coly, and the number of rows in the row set.
Group cardinality in this example can be represented by the following formula:
group cardinality lies between max ( dist. card. colx , dist. card. coly ) and min ( (dist. card. colx * dist. card. coly) , num rows in row set )
Substituting the numbers from the example, the group cardinality is between the maximum of (30 and 60) and the minimum of (30*60 and 100). In other words, the group cardinality is between 60 and 100.
The cost represents units of work or resource used. The CBO uses disk I/O, CPU usage, and memory usage as units of work. So, the cost used by the CBO represents an estimate of the number of disk I/Os and the amount of CPU and memory used in performing an operation. The operation can be scanning a table, accessing rows from a table by using an index, joining two tables together, or sorting a row set. The cost of a query plan is the number of work units that are expected to be incurred when the query is executed and its result produced.
The access path determines the number of units of work required to get data from a base table. The access path can be a table scan, a fast full index scan, or an index scan. During table scan or fast full index scan, multiple blocks are read from the disk in a single I/O operation. Therefore, the cost of a table scan or a fast full index scan depends on the number of blocks to be scanned and the multiblock read count value. The cost of an index scan depends on the levels in the B-tree, the number of index leaf blocks to be scanned, and the number of rows to be fetched using the rowid in the index keys. The cost of fetching rows using rowids depends on the index clustering factor.
Although the clustering factor is a property of the index, the clustering factor actually relates to the spread of similar indexed column values within data blocks in the table. A lower clustering factor indicates that the individual rows are concentrated within fewer blocks in the table. Conversely, a high clustering factor indicates that the individual rows are scattered more randomly across blocks in the table. Therefore, a high clustering factor means that it costs more to use a range scan to fetch rows by rowid, because more blocks in the table need to be visited to return the data. Example 1-2 shows how the clustering factor can affect cost.
Assume the following situation:
col1 (in tab1).c1 column currently stores the values A, B, and C.Case 1: The index clustering factor is low for the rows as they are arranged in the following diagram.
Block 1 Block 2 Block 3 ------- ------- -------- A A A B B B C C C
This is because the rows that have the same indexed column values for c1 are located within the same physical blocks in the table. The cost of using a range scan to return all of the rows that have the value A is low, because only one block in the table needs to be read.
Case 2: If the same rows in the table are rearranged so that the index values are scattered across the table blocks (rather than colocated), then the index clustering factor is higher.
Block 1 Block 2 Block 3 ------- ------- -------- A B C A B C A B C
This is because all three blocks in the table must be read in order to retrieve all rows with the value A in col1.
The join cost represents the combination of the individual access costs of the two row sets being joined. In a join, one row set is called inner, and the other is called outer.
cost = outer access cost + (inner access cost * outer cardinality)
cost = outer access cost + inner access cost + sort costs (if sort is used)
Each row from the outer row set is hashed to probe matching rows in the hash partition. The next portion of the inner row set is then hashed into memory, followed by a probe from the outer row set. This process is repeated until all partitions of the inner row set are exhausted.
cost = (outer access cost * # of hash partitions) + inner access cost
| See Also:
"Understanding Joins" for more information on joins |
The main function of the plan generator is to try out different possible plans for a given query and pick the one that has the lowest cost. Many different plans are possible because of the various combinations of different access paths, join methods, and join orders that can be used to access and process data in different ways and produce the same result.
A join order is the order in which different join items, such as tables, are accessed and joined together. For example, in a join order of t1, t2, and t3, table t1 is accessed first. Next, t2 is accessed, and its data is joined to t1 data to produce a join of t1 and t2. Finally, t3 is accessed, and its data is joined to the result of the join between t1 and t2.
The plan for a query is established by first generating subplans for each of the nested subqueries and nonmerged views. Each nested subquery or nonmerged view is represented by a separate query block. The query blocks are optimized separately in a bottom-up order. That is, the innermost query block is optimized first, and a subplan is generated for it. The outermost query block, which represents the entire query, is optimized last.
The plan generator explores various plans for a query block by trying out different access paths, join methods, and join orders. The number of possible plans for a query block is proportional to the number of join items in the FROM clause. This number rises exponentially with the number of join items.
The plan generator uses an internal cutoff to reduce the number of plans it tries when finding the one with the lowest cost. The cutoff is based on the cost of the current best plan. If the current best cost is large, then the plan generator tries harder (in other words, explores more alternate plans) to find a better plan with lower cost. If the current best cost is small, then the plan generator ends the search swiftly, because further cost improvement will not be significant.
The cutoff works well if the plan generator starts with an initial join order that produces a plan with cost close to optimal. Finding a good initial join order is a difficult problem. The plan generator uses a simple heuristic for the initial join order. It orders the join items by their effective cardinalities. The join item with the smallest effective cardinality goes first, and the join item with the largest effective cardinality goes last.
To execute a SQL statement, Oracle might need 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 path for each table that the statement accesses and an ordering of the tables (the join order) with the appropriate join method.
You can examine the execution plan chosen by the optimizer for a SQL statement by using the EXPLAIN PLAN statement. When the statement is issued, the optimizer chooses an execution plan and then inserts data describing the plan into a database table. Simply issue the EXPLAIN PLAN statement and then query the output table.
These are the basics of using the EXPLAIN PLAN statement:
UTLXPLAN.SQL to create a sample output table called PLAN_TABLE in your schema. See "Creating the PLAN_TABLE Output Table".Example 1-3 uses EXPLAIN PLAN to examine a SQL statement that selects the employee_id, job_title, salary, and department_name for the employees whose IDs are less than 103.
EXPLAIN PLAN FOR SELECT e.employee_id, j.job_title, e.salary, d.department_name FROM employees e, jobs j, departments d WHERE e.employee_id < 103 AND e.job_id = j.job_id AND e.department_id = d.department_id;
The resulting output table in Example 1-4 shows the execution plan chosen by the optimizer to execute the SQL statement in the example:
----------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| ----------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 3 | 189 | 10 (10)| | 1 | NESTED LOOPS | | 3 | 189 | 10 (10)| | 2 | NESTED LOOPS | | 3 | 141 | 7 (15)| |* 3 | TABLE ACCESS FULL | EMPLOYEES | 3 | 60 | 4 (25)| | 4 | TABLE ACCESS BY INDEX ROWID| JOBS | 19 | 513 | 2 (50)| |* 5 | INDEX UNIQUE SCAN | JOB_ID_PK | 1 | | | | 6 | TABLE ACCESS BY INDEX ROWID | DEPARTMENTS | 27 | 432 | 2 (50)| |* 7 | INDEX UNIQUE SCAN | DEPT_ID_PK | 1 | | | ----------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 3 - filter("E"."EMPLOYEE_ID"<103) 5 - access("E"."JOB_ID"="J"."JOB_ID") 7 - access("E"."DEPARTMENT_ID"="D"."DEPARTMENT_ID")
Oracle provides additional graphical tools for displaying EXPLAIN PLAN output. Figure 6-1, "Oracle SQL Analyze" is an example of the SQL statement displayed in Oracle SQL Analyze. Figure 1-3 shows a graphical EXPLAIN PLAN for the SQL statement in Example 1-3 which has been generated in an Oracle Enterprise Manager SQL Scratchpad window.
Text description of the illustration scratchpad1.gif
Note that the execution steps in Figure 1-3 are identified by the order of execution, rather than an ID as in Example 1-4.
| See Also:
For more information about Oracle Enterprise Manager and its optional applications, see Oracle Enterprise Manager Concepts Guide, Oracle Enterprise Manager Administrator's Guide, and Database Tuning with the Oracle Tuning Pack. |
Each row in the output table corresponds to a single step in the execution plan. Note that the step Ids with asterisks are listed in the Predicate Information section.
Each step of the execution plan returns a set of rows that either is used by the next step or, in the last step, is returned to the user or application issuing the SQL statement. A set of rows returned by a step is called a row set.
The numbering of the step Ids reflects the order in which they are displayed in response to the EXPLAIN PLAN statement. Each step of the execution plan either retrieves rows from the database or accepts rows from one or more row sources as input.
employees table.job_id in JOB_ID_PK index and finds the rowids of the associated rows in the jobs table.jobs table.department_id in DEPT_ID_PK index and finds the rowids of the associated rows in the departments table.departments table.job_id in the jobs and employees tables, accepting row sources from Steps 3 and 4, joining each row from Step 3 source to its corresponding row in Step 4, and returning the resulting rows to Step 2.See Also:
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The steps of the execution plan are not performed in the order in which they are numbered in Example 1-3. Oracle first performs the steps that appear indented most to the right in the EXPLAIN PLAN output. In Figure 1-3, the steps are numbered in the order that they are performed.The rows returned by each step become the row sources of its parent step. Oracle then performs the parent steps.
Oracle performs the following steps in Example 1-4 to execute the statement in Example 1-3:
If a parent step requires only a single row from its child step before it can be executed, then Oracle performs the parent step 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.
Statement execution can cascade up the tree, possibly to encompass the rest of the execution plan. Oracle performs the parent step and all cascaded steps once for each row 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 loop joins, and filters.
If a parent step requires all rows from its child step before it can be executed, then 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.
Access paths are ways in which data is retrieved from the database. In general, index access paths should be used for statements that retrieve a small subset of table rows, while full scans are more efficient when accessing a large portion of the table. Online transaction processing (OLTP) applications, which consist of short-running SQL statements with high selectivity, often are characterized by the use of index access paths. Decision support systems, on the other hand, tend to use partitioned tables and perform full scans of the relevant partitions.
This section describes the data access paths that can be used to locate and retrieve any row in any table.
This type of scan reads all rows from a table and filters out those that do not meet the selection criteria. During a full table scan, all blocks in the table that are under the high water mark are scanned. Each row is examined to determine whether it satisfies the statement's WHERE clause.
When Oracle performs a full table scan, the blocks are read sequentially. Because the blocks are adjacent, I/O calls larger than a single block can be used to speed up the process. The size of the read calls range from one block to the number of blocks indicated by the initialization parameter DB_FILE_MULTIBLOCK_READ_COUNT. Using multiblock reads means a full table scan can be performed very efficiently. Each block is read only once.
Example 1-4, "EXPLAIN PLAN Output" contains an example of a full table scan on the employees table.
Full table scans are cheaper than index range scans when accessing a large fraction of the blocks in a table. This is because full table scans can use larger I/O calls, and making fewer large I/O calls is cheaper than making many smaller calls.
The optimizer uses a full table scan in any of the following cases:
If the query is unable to use any existing indexes, then it uses a full table scan. For example, if there is a function used on the indexed column in the query, the optimizer is unable to use the index and instead uses a full table scan as in Example 1-5.
SELECT last_name, first_name FROM employees WHERE UPPER(last_name) LIKE :b1
If you need to use the index for case-independent searches, then either do not permit mixed-case data in the search columns or create a function-based index, such as UPPER(last_name), on the search column. See "Using Function-based Indexes".
If the optimizer thinks that the query will access most of the blocks in the table, then it uses a full table scan, even though indexes might be available.
If a table contains less than DB_FILE_MULTIBLOCK_READ_COUNT blocks under the high water mark, which can be read in a single I/O call, then a full table scan might be cheaper than an index range scan, regardless of the fraction of tables being accessed or indexes present.
A high degree of parallelism for a table skews the optimizer toward full table scans over range scans. Examine the DEGREE column in ALL_TABLES for the table to determine the degree of parallelism.
Use the hint FULL(table alias) if you want to force the use of a full table scan. For more information on the FULL hint, see "FULL".
Example 1-6 shows a query that uses an index range scan. Example 1-7 shows the same query using the FULL hint to force a full table scan.
SELECT employee_id, last_name FROM employees WHERE last_name LIKE :b1;
--------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| --------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 5 | 95 | 3 (34)| | 1 | TABLE ACCESS BY INDEX ROWID| EMPLOYEES | 5 | 95 | 3 (34)| |* 2 | INDEX RANGE SCAN | EMP_NAME_IX | 2 | | 3 (34)| --------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 2 - access("EMPLOYEES"."LAST_NAME" LIKE :Z) filter("EMPLOYEES"."LAST_NAME" LIKE :Z)
Example 1-7 shows the Example 1-6 query using the FULL hint to force a full table scan.
SELECT /*+ FULL(e) */ employee_id, last_name FROM employees e WHERE last_name LIKE :b1;
------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| ------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 5 | 95 | 4 (25)| |* 1 | TABLE ACCESS FULL | EMPLOYEES | 5 | 95 | 4 (25)| ------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter("E"."LAST_NAME" LIKE :Z)
Oracle does I/O by blocks. Therefore, the optimizer's decision to use full table scans is influenced by the percentage of blocks accessed, not rows. This is called the index clustering factor. If blocks contain single rows, then rows accessed and blocks accessed are the same.
However, most tables have multiple rows in each block. Consequently, the desired number of rows could be clustered together in a few blocks, or they could be spread out over a larger number of blocks.
| See Also:
"Estimator" for more information on the index clustering factor |
The data dictionary keeps track of the blocks that have been populated with rows. The high water mark is used as the end marker during a full table scan. The high water mark is stored in DBA_TABLES.BLOCKS. It is reset when the table is dropped or truncated.
For example, consider a table that had a large number of rows in the past. Most of the rows have been deleted, and now most of the blocks under the high water mark are empty. A full table scan on this table exhibits poor performance because all the blocks under the high water mark are scanned.
When a full table scan is required, response time can be improved by using multiple parallel execution servers for scanning the table. Parallel queries are used generally in low-concurrency data warehousing environments, because of the potential resource usage.
The rowid of a row specifies the datafile and data block containing the row and the location of the row in that block. Locating a row by specifying its rowid is the fastest way to retrieve a single row, because the exact location of the row in the database is specified.
To access a table by rowid, Oracle first obtains the rowids of the selected rows, either from the statement's WHERE clause or through an index scan of one or more of the table's indexes. Oracle then locates each selected row in the table based on its rowid.
In Example 1-4, "EXPLAIN PLAN Output", an index scan is performed the jobs and departments tables. The rowids retrieved are used to return the row data.
This is generally the second step after retrieving the rowid from an index. The table access might be required for any columns in the statement not present in the index.
Access by rowid does not need to follow every index scan. If the index contains all the columns needed for the statement, then table access by rowid might not occur.
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Note: Rowids are an internal Oracle representation of where data is stored. They can change between versions. Accessing data based on position is not recommended, because rows can move around due to row migration and chaining and also after export and import. Foreign keys should be based on primary keys. For more information on rowids, see Oracle9i Application Developer's Guide - Fundamentals. |
In this method, a row is retrieved by traversing the index, using the indexed column values specified by the statement. An index scan retrieves data from an index based on the value of one or more columns in the index. To perform an index scan, Oracle searches the index for the indexed column values accessed by the statement. If the statement accesses only columns of the index, then Oracle reads the indexed column values directly from the index, rather than from the table.
The index contains not only the indexed value, but also the rowids of rows in the table having that value. Therefore, if the statement accesses other columns in addition to the indexed columns, then Oracle can find the rows in the table by using either a table access by rowid or a cluster scan.
An index scan can be one of the following types:
This scan returns, at most, a single rowid. Oracle performs a unique scan if a statement contains a UNIQUE or a PRIMARY KEY constraint that guarantees that only a single row is accessed.
In Example 1-4, "EXPLAIN PLAN Output", an index scan is performed on the jobs and departments tables, using the job_id_pk and dept_id_pk indexes respectively.
This access path is used when all columns of a unique (B-tree) index are specified with equality conditions.
| See Also:
Oracle9i Database Concepts for more details on index structures and for detailed information on how a B-tree is searched |
In general, you should not need to use a hint to do a unique scan. There might be cases where the table is across a database link and being accessed from a local table, or where the table is small enough for the optimizer to prefer a full table scan.
The hint INDEX(alias index_name) specifies the index to use, but not an access path (range scan or unique scan). For more information on the INDEX hint, see "INDEX".
An index range scan is a common operation for accessing selective data. It can be bounded (bounded on both sides) or unbounded (on one or both sides). Data is returned in the ascending order of index columns. Multiple rows with identical values are sorted in ascending order by rowid.
If data must be sorted by order, then use the ORDER BY clause, and do not rely on an index. If an index can be used to satisfy an ORDER BY clause, then the optimizer uses this option and avoids a sort.
In Example 1-8, the order has been imported from a legacy system, and you are querying the order by the reference used in the legacy system. Assume this reference is the order_date.
SELECT order_status, order_id FROM orders WHERE order_date = :b1;
--------------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| --------------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | 20 | 3 (34)| | 1 | TABLE ACCESS BY INDEX ROWID| ORDERS | 1 | 20 | 3 (34)| |* 2 | INDEX RANGE SCAN | ORD_ORDER_DATE_IX | 1 | | 2 (50)| --------------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 2 - access("ORDERS"."ORDER_DATE"=:Z)
This should be a highly selective query, and you should see the query using the index on the column to retrieve the desired rows. The data returned is sorted in ascending order by the rowids for the order_date. Because the index column order_date is identical for the selected rows here, the data is sorted by rowid.
The optimizer uses a range scan when it finds one or more leading columns of an index specified in conditions, such as the following:
col1 = :b1col1 < :b1col1 > :b1AND combination of the preceding conditions for leading columns in the indexcol1 like '%ASD' Wild-card searches should not be in a leading position. The condition col1 like '%ASD' does not result in a range scan.Range scans can use unique or nonunique indexes. Range scans avoid sorting when index columns constitute the ORDER BY/GROUP BY clause.
A hint might be required if the optimizer chooses some other index or uses a full table scan. The hint INDEX(table_alias index_name) specifies the index to use. For more information on the INDEX hint, see "INDEX".
Suppose that order_id has a skewed distribution. The column has histograms, so the optimizer knows about the distribution. However, with a bind variable, the optimizer does not know the value and could choose a full table scan. You have two options:
Example 1-9 shows a query before using the INDEX hint.
SELECT l.line_item_id, order_id, l.unit_price * l.quantity FROM order_items l WHERE l.order_id = :b1;
-------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| -------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 6 | 90 | 4 (25)| |* 1 | TABLE ACCESS FULL | ORDER_ITEMS | 6 | 90 | 4 (25)| -------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - filter("L"."ORDER_ID"=TO_NUMBER(:Z))
Example 1-10 shows the Example 1-9 query using the INDEX hint.
SELECT /*+ INDEX(l item_order_ix) */ l.line_item_id, order_id, l.unit_price * l.quantity FROM order_items l WHERE l.order_id = :b1;
----------------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| ----------------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 6 | 90 | 10 (10)| | 1 | TABLE ACCESS BY INDEX ROWID| ORDER_ITEMS | 6 | 90 | 10 (10)| |* 2 | INDEX RANGE SCAN | ITEM_ORDER_IX | 6 | | 2 (50)| ----------------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 2 - access("L"."ORDER_ID"=TO_NUMBER(:Z))
An index range scan descending is identical to an index range scan, except that the data is returned in descending order. Indexes, by default, are stored in ascending order. Usually, this scan is used when ordering data in a descending order to return the most recent data first, or when seeking a value less than a specified value.
Example 1-11 uses a two-column unique index on order_id, line_item_id.
SELECT line_item_id, order_id FROM order_items WHERE order_id < :b1 ORDER BY order_id DESC;
------------------------------------------------------------------------------------ | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| ------------------------------------------------------------------------------------ | 0 | SELECT STATEMENT | | 33 | 231 | 3 (34)| | 1 | TABLE ACCESS BY INDEX ROWID | ORDER_ITEMS | 33 | 231 | 3 (34)| |* 2 | INDEX RANGE SCAN DESCENDING| ITEM_ORDER_IX | 6 | | 3 (34)| ------------------------------------------------------------------------------------ Predicate Information (identified by operation id): --------------------------------------------------- 2 - access("ORDER_ITEMS"."ORDER_ID"<TO_NUMBER(:Z)) filter("ORDER_ITEMS"."ORDER_ID"<TO_NUMBER(:Z))
The data is sorted in descending order by the order_id, line_item_id, rowid of the selected rows. However, because there is only one row for each order_id, line_item_id (item_order_ix is a unique index on the two columns), the rows are sorted by order_id, line_item_id.
The optimizer uses index range scan descending when an order by descending clause can be satisfied by an index.
The hint INDEX_DESC(table_alias index_name) is used for this access path. For more information on the INDEX_DESC hint, see "INDEX_DESC".
Index skip scans improve index scans by nonprefix columns. Often, scanning index blocks is faster than scanning table data blocks.
Skip scanning lets a composite index be split logically into smaller subindexes. In skip scanning, the initial column of the composite index is not specified in the query. In other words, it is skipped.
The number of logical subindexes is determined by the number of distinct values in the initial column. Skip scanning is advantageous if there are few distinct values in the leading column of the composite index and many distinct values in the nonleading key of the index.
Consider, for example, a table employees (sex, employee_id, address) with a composite index on (sex, employee_id). Splitting this composite index would result in two logical subindexes, one for M and one for F.
For this example, suppose you have the following index data:
(`F',98) (`F',100) (`F',102) (`F',104) (`M',101) (`M',103) (`M',105)
The index is split logically into the following two subindexes:
F.M.
The column sex is skipped in the following query:
SELECT * FROM employees WHERE employee_id = 101;
A complete scan of the index is not performed, but the subindex with the value F is searched first, followed by a search of the subindex with the value M.
A full scan is available if a predicate references one of the columns in the index. The predicate does not need to be an index driver. A full scan is also available when there is no predicate, if both the following conditions are met:
A full scan can be used to eliminate a sort operation, because the data is ordered by the index key. It reads the blocks singly.
Fast full index scans are an alternative to a full table scan when the index contains all the columns that are needed for the query, and at least one column in the index key has the NOT NULL constraint. A fast full scan accesses the data in the index itself, without accessing the table. It cannot be used to eliminate a sort operation, because the data is not ordered by the index key. It reads the entire index using multiblock reads, unlike a full index scan, and can be parallelized.
Fast full scan is available only with the CBO. You can specify it with the initialization parameter OPTIMIZER_FEATURES_ENABLE or the INDEX_FFS hint. Fast full index scans cannot be performed against bitmap indexes.
A fast full scan is faster than a normal full index scan in that it can use multiblock I/O and can be parallelized just like a table scan.
The fast full scan has a special index hint, INDEX_FFS, which has the same format and arguments as the regular INDEX hint. For more information on the INDEX_FFS hint, see "INDEX_FFS".
Fast full index scans have the following restrictions:
NOT NULL constraint.An index join is a hash join of several indexes that together contain all the table columns that are referenced in the query. If an index join is used, then no table access is needed, because all the relevant column values can be retrieved from the indexes. An index join cannot be used to eliminate a sort operation. The index join is available only with the CBO.
You can specify an index join with the initialization parameter OPTIMIZER_FEATURES_ENABLE or the INDEX_JOIN hint. For more information on the INDEX_JOIN hint, see "INDEX_JOIN".
A bitmap join uses a bitmap for key values and a mapping function that converts each bit position to a rowid. Bitmaps can efficiently merge indexes that correspond to several conditions in a WHERE clause, using Boolean operations to resolve AND and OR conditions.
Bitmap access is available only with the CBO.
|
Note: Bitmap indexes and bitmap join indexes are available only if you have purchased the Oracle9i Enterprise Edition. |
| See Also:
Oracle9i Data Warehousing Guide for more information about bitmap indexes |
A cluster scan is used to retrieve, from a table stored in an indexed cluster, all rows that have the same cluster key value. In an indexed cluster, all rows with the same cluster key value are stored in the same data block. To perform a cluster scan, Oracle first obtains the rowid of one of the selected rows by scanning the cluster index. Oracle then locates the rows based on this rowid.
A hash scan is used to locate rows in a hash cluster, based on a hash value. In a hash cluster, all rows with the same hash value are stored in the same data block. To perform a hash scan, Oracle first obtains the hash value by applying a hash function to a cluster key value specified by the statement. Oracle then scans the data blocks containing rows with that hash value.
A sample table scan retrieves a random sample of data from a table. This access path is used when a statement's FROM clause includes the SAMPLE clause or the SAMPLE BLOCK clause. To perform a sample table scan when sampling by rows (the SAMPLE clause), Oracle reads a specified percentage of rows in the table. To perform a sample table scan when sampling by blocks (the SAMPLE BLOCK clause), Oracle reads a specified percentage of table blocks.
Oracle does not support sample table scans when the query involves a join or a remote table. However, you can perform an equivalent operation by using a CREATE TABLE AS SELECT query to materialize a sample of an underlying table. You then rewrite the original query to refer to the newly created table sample. Additional queries can be written to materialize samples for other tables. Sample table scans require the CBO.
Example 1-13 uses a sample table scan to access 1% of the employees table, sampling by blocks.
SELECT * FROM employees SAMPLE BLOCK (1);
The EXPLAIN PLAN output for this statement might look like this:
------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| ------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 1 | 68 | 3 (34)| | 1 | TABLE ACCESS SAMPLE | EMPLOYEES | 1 | 68 | 3 (34)| -------------------------------------------------------------------------
The CBO chooses an access path based on the following factors:
To choose an access path, the optimizer first determines which access paths are available by examining the conditions in the statement's WHERE clause and its FROM clause for the SAMPLE or SAMPLE BLOCK clause. The optimizer then generates a set of possible execution plans using available access paths and estimates the cost of each plan, using the statistics for the index, columns, and tables accessible to the statement. Finally, the optimizer chooses the execution plan with the lowest estimated cost.
When choosing an access path, the CBO is influenced by the following:
The optimizer's choice among available access paths can be overridden with hints, except when the statement's FROM clause contains SAMPLE or SAMPLE BLOCK.
| See Also:
Chapter 5, "Optimizer Hints" for information about hints in SQL statements |
For example, if a table has not been analyzed since it was created, and if it has less than DB_FILE_MULTIBLOCK_READ_COUNT blocks under the high water mark, then the optimizer thinks that the table is small and uses a full table scan. Review the LAST_ANALYZED and BLOCKS columns in the ALL_TABLES table to see what the statistics reflect.
This section discusses how the optimizer chooses an access path.
In Example 1-14 the query uses an equality condition in its WHERE clause to select all employees named Jackson.
SELECT * FROM employees WHERE last_name = 'JACKSON';
If the last_name column is a unique or primary key, then the optimizer determines that there is only one employee named Jackson, and the query returns only one row. In this case, the query is very selective, and the optimizer is most likely to access the table using a unique scan on the index that enforces the unique or primary key.
Consider again the query in Example 1-14. If the last_name column is not a unique or primary key, then the optimizer can use the following statistics to estimate the query's selectivity:
USER_TAB_COLUMNS.NUM_DISTINCT, which is the number of values for each column in the tableUSER_TABLES.NUM_ROWS, which is the number of rows in each tableBy dividing the number of rows in the employees table by the number of distinct values in the last_name column, the optimizer estimates what percentage of employees have the same name. By assuming that the last_name values are distributed uniformly, the optimizer uses this percentage as the estimated selectivity of the query.
The following query selects all employees with employee ID numbers less than 7500:
SELECT * FROM employees WHERE employee_id < 7500;
To estimate the selectivity of the query, the optimizer uses the boundary value of 7500 in the WHERE clause condition and the values of the HIGH_VALUE and LOW_VALUE statistics for the employee_id column, if available. These statistics can be found in the USER_TAB_COL_STATISTICS view or the USER_TAB_COLUMNS view. The optimizer assumes that employee_id values are distributed evenly in the range between the lowest value and highest value. The optimizer then determines what percentage of this range is less than the value 7500 and uses this value as the estimated selectivity of the query.
The following query uses a bind variable rather than a literal value for the boundary value in the WHERE clause condition:
SELECT * FROM employees WHERE employee_id < :e1;
The optimizer does not know the value of the bind variable e1. The value of e1 might be different each time the query is executed. For this reason, the optimizer cannot use the means described in the previous example to determine selectivity of this query. In this case, the optimizer heuristically guesses a small value for the selectivity, using an internal default value. The optimizer makes this assumption whenever a bind variable is used as a boundary value in a condition with one of the following operators: <, >, <=, or >=.
The optimizer's treatment of bind variables can cause it to choose different execution plans for SQL statements that differ only in the use of bind variables rather than constants. For example, the optimizer might choose different execution plans for an embedded SQL statement with a bind variable in an Oracle precompiler program and the same SQL statement with a constant in SQL*Plus.
The following query uses two bind variables as boundary values in the condition with the BETWEEN operator:
SELECT * FROM employees WHERE employee_id BETWEEN :low_e AND :high_e;
The optimizer rewrites the BETWEEN condition as the following two conditions:
employee_id >= :low_e employee_id <= :high_e
The optimizer heuristically estimates a small selectivity (an internal default value) for indexed columns in order to favor the use of the index.
The following query uses the BETWEEN operator to select all employees with employee ID numbers between 7500 and 7800:
SELECT * FROM employees WHERE employee_id BETWEEN 7500 AND 7800;
To determine the selectivity of this query, the optimizer rewrites the WHERE clause condition into the following two conditions:
employee_id >= 7500 employee_id <= 7800
The optimizer estimates the individual selectivity of each condition, using the means described in Case 4. The optimizer then uses these selectivities (S1 and S2) and the absolute value function (ABS) to estimate the selectivity (S) of the BETWEEN condition, as follows:
S = ABS(S1 + S2 - 1)
Joins are statements that retrieve data from more than one table. A join is characterized by multiple tables in the FROM clause, and the relationship between the tables is defined through the existence of a join condition in the WHERE clause.
This section discusses:
| See Also:
A discussion of joins in the Oracle9i SQL Reference |
To choose an execution plan for a join statement, the optimizer must make these interrelated decisions:
As for simple statements, the optimizer must choose an access path to retrieve data from each table in the join statement.
To join each pair of row sources, Oracle must perform a join operation. Join methods include nested loop, sort merge, cartesian, and hash joins.
To execute a statement that joins more than two tables, Oracle joins two of the tables and then joins the resulting row source to the next table. This process is continued until all tables are joined into the result.
The optimizer estimates the cost of each join method and chooses the method with the least cost. If a join returns many rows, then the optimizer considers the following three factors:
cost= access cost of A + (access cost of B * number of rows from A)
cost= (access cost of A * number of hash partitions of B) + access cost of B
cost= access cost of A + access cost of B +(sort cost of A + sort cost of B)
When the data is presorted, the sort costs are both zero.
The following considerations apply to both the cost-based and rule-based approaches:
UNIQUE and PRIMARY KEY constraints on the tables. If such a situation exists, then the optimizer places these tables first in the join order. The optimizer then optimizes the join of the remaining set of tables.With the CBO, the optimizer generates a set of execution plans, according to possible join orders, join methods, and available access paths. The optimizer then estimates the cost of each plan and chooses the one with the lowest cost. The optimizer estimates costs in the following ways:
The optimizer also considers other factors when determining the cost of each operation. For example:
SORT_AREA_SIZE.
DB_FILE_MULTIBLOCK_READ_COUNT.With the CBO, the optimizer's choice of join orders can be overridden with the ORDERED hint. If the ORDERED hint specifies a join order that violates the rule for an outer join, then the optimizer ignores the hint and chooses the order. Also, you can override the optimizer's choice of join method with hints.
| See Also:
Chapter 5, "Optimizer Hints" for more information about optimizer hints |
An anti-join returns rows from the left side of the predicate for which there are no corresponding rows on the right side of the predicate. That is, it returns rows that fail to match (NOT IN) the subquery on the right side. For example, an anti-join can select a list of employees who are not in a particular set of departments:
SELECT * FROM employees WHERE department_id NOT IN (SELECT department_id FROM departments WHERE location_id = 1700);
The optimizer uses a nested loops algorithm for NOT IN subqueries by default. However, if the MERGE_AJ, HASH_AJ, or NL_AJ hint is used and various required conditions are met, the NOT IN uncorrelated subquery can be changed into a sort merge or hash antijoin.
A semi-join returns rows that match an EXISTS subquery without duplicating rows from the left side of the predicate when multiple rows on the right side satisfy the criteria of the subquery. For example:
SELECT * FROM departments WHERE EXISTS (SELECT * FROM employees WHERE departments.department_id = employees.department_id AND employees.salary > 2500);
In this query, only one row needs to be returned from departments, even though many rows in employees might match the subquery. If there is no index on the salary column in employees, then a semi-join can be used to improve query performance.
The optimizer uses a nested loops algorithm by default for IN or EXISTS subqueries that cannot be merged with the containing query. However, if the MERGE_SJ, HASH_SJ, or NL_SJ hint is used and various required conditions are met, the subquery can be changed into a sort merge or hash semi-join.
| See Also:
Chapter 5, "Optimizer Hints" for information about optimizer hints |
|
Note: Semi-join transformation cannot be done if the subquery is on an |
Some data warehouses are designed around a star schema, which includes a large fact table and several small dimension (lookup) tables. The fact table stores primary information. Each dimension table stores information about an attribute in the fact table.
A star query is a join between a fact table and a number of lookup tables. Each lookup table is joined by its primary keys to the corresponding foreign keys of the fact table, but the lookup tables are not joined to each other.
The CBO recognizes star queries and generates efficient execution plans for them. Star queries are not recognized by the RBO.
A typical fact table contains keys and measures. For example, a simple fact table might contain the measure Sales, and the keys Time, Product, and Market. In this case there would be corresponding dimension tables for Time, Product, and Market. The Product dimension table, for example, typically contains information about each product number that appears in the fact table.
A star join uses a join of foreign keys in a fact table to the corresponding primary keys in dimension tables. The fact table normally has a concatenated index on the foreign key columns to facilitate this type of join, or it has a separate bitmap index on each foreign key column.
| See Also:
Oracle9i Data Warehousing Guide for more information about tuning star queries |
Nested loop joins are useful when small subsets of data are being joined and if the join condition is an efficient way of accessing the second table.
It is very important to ensure that the inner table is driven from (dependent on) the outer table. If the inner table's access path is independent of the outer table, then the same rows are retrieved for every iteration of the outer loop, degrading performance considerably. In such cases, hash joins joining the two independent row sources perform better.
A nested loop join involves the following steps:
NESTED LOOPSouter_loopinner_loop
This section discusses the outer and inner loops for the following nested loops in the query in Example 1-3.
... | 2 | NESTED LOOPS | | 3 | 141 | 7 (15)| |* 3 | TABLE ACCESS FULL | EMPLOYEES | 3 | 60 | 4 (25)| | 4 | TABLE ACCESS BY INDEX ROWID| JOBS | 19 | 513 | 2 (50)| |* 5 | INDEX UNIQUE SCAN | JOB_ID_PK | 1 | | | ...
In this example, the outer loop retrieves all the rows of the employees table. For every employee retrieved by the outer loop, the inner loop retrieves the associated row in the jobs table.
In the execution plan in Example 1-4, the outer loop and the equivalent statement are as follows:
3 | TABLE ACCESS FULL | EMPLOYEES SELECT e.employee_id, e.salary FROM employees e WHERE e.employee_id < 103
The execution plan in Example 1-4 shows the inner loop being iterated for every row fetched from the outer loop, as follows:
4 | TABLE ACCESS BY INDEX ROWID| JOBS 5 | INDEX UNIQUE SCAN | JOB_ID_PK SELECT j.job_title FROM jobs j WHERE e.job_id = j.job_id
The optimizer uses nested loop joins when joining small number of rows, with a good driving condition between the two tables. You drive from the outer loop to the inner loop, so the order of tables in the execution plan is important.
The outer loop is the driving row source. It produces a set of rows for driving the join condition. The row source can be a table accessed using an index scan or a full table scan. Also, the rows can be produced from any other operation. For example, the output from a nested loop join can be used as a row source for another nested loop join.
The inner loop is iterated for every row returned from the outer loop, ideally by an index scan. If the access path for the inner loop is not dependent on the outer loop, then you can end up with a Cartesian product; for every iteration of the outer loop, the inner loop produces the same set of rows. Therefore, you should use other join methods when two independent row sources are joined together.
If the optimizer is choosing to use some other join method, you can use the USE_NL(table1 table2) hint, where table1 and table2 are the aliases of the tables being joined.
For some SQL examples, the data is small enough for the optimizer to prefer full table scans and use hash joins. This is the case for the SQL example shown in Example 1-17, "Hash Joins". However, you can add a USE_NL hint that changes the join method to nested loop. For more information on the USE_NL hint, see "USE_NL".
The outer loop of a nested loop can be a nested loop itself. You can nest two or more outer loops together to join as many tables as needed. Each loop is a data access method, as follows:
SELECT STATEMENT NESTED LOOP 3 NESTED LOOP 2 (OUTER LOOP 3.1) NESTED LOOP 1 (OUTER LOOP 2.1) OUTER LOOP 1.1 - #1 INNER LOOP 1.2 - #2 INNER LOOP 2.2 - #3 INNER LOOP 3.2 - #4
Hash joins are used for joining large data sets. The optimizer uses the smaller of two tables or data sources to build a hash table on the join key in memory. It then scans the larger table, probing the hash table to find the joined rows.
This method is best used when the smaller table fits in available memory. The cost is then limited to a single read pass over the data for the two tables.
However, if the hash table grows too big to fit into the memory, then the optimizer breaks it up into different partitions. As the partitions exceed allocated memory, parts are written to temporary segments on disk. Larger temporary extent sizes lead to improved I/O when writing the partitions to disk; the recommended temporary extent is about 1 MB. Temporary extent size is specified by INITIAL and NEXT for permanent tablespaces and by UNIFORM SIZE for temporary tablespaces.
After the hash table is complete, the following processes occur:
When the hash table build is complete, it is possible that an entire hash table partition is resident in memory. Then, you do not need to build the corresponding partition for the second (larger) table. When that table is scanned, rows that hash to the resident hash table partition can be joined and returned immediately.
Each hash table partition is then read into memory, and the following processes occur:
This process is repeated for the rest of the partitions. The cost can increase to two read passes over the data and one write pass over the data.
If the hash table does not fit in the memory, it is possible that parts of it may need to be swapped in and out, depending on the rows retrieved from the second table. Performance for this scenario can be extremely poor.
The optimizer uses a hash join to join two tables if they are joined using an equijoin and if either of the following conditions are true:
In Example 1-17, the table orders is used to build the hash table, and order_items is the larger table, which is scanned later.
SELECT o.customer_id, l.unit_price * l.quantity FROM orders o ,order_items l WHERE l.order_id = o.order_id;
-------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| -------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 665 | 13300 | 8 (25)| |* 1 | HASH JOIN | | 665 | 13300 | 8 (25)| | 2 | TABLE ACCESS FULL | ORDERS | 105 | 840 | 4 (25)| | 3 | TABLE ACCESS FULL | ORDER_ITEMS | 665 | 7980 | 4 (25)| -------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 1 - access("L"."ORDER_ID"="O"."ORDER_ID")
Apply the USE_HASH hint to advise the optimizer to use a hash join when joining two tables together. If you are having trouble getting the optimizer to use hash joins, investigate the values for the HASH_AREA_SIZE and HASH_JOIN_ENABLED parameters.
For more information on the USE_HASH hint, see "USE_HASH".
Sort merge joins can be used to join rows from two independent sources. Hash joins generally perform better than sort merge joins. On the other hand, sort merge joins can perform better than hash joins if both of the following conditions exist:
However, if a sort merge join involves choosing a slower access method (an index scan as opposed to a full table scan), then the benefit of using a sort merge might be lost.
Sort merge joins are useful when the join condition between two tables is an inequality condition (but not a nonequality) like <, <=, >, or >=. Sort merge joins perform better than nested loop joins for large data sets. You cannot use hash joins unless there is an equality condition.
In a merge join, there is no concept of a driving table. The join consists of two steps:
If the input is already sorted by the join column, then a sort join operation is not performed for that row source.
The optimizer can choose a sort merge join over a hash join for joining large amounts of data if any of the following conditions are true:
OPTIMIZER_MODE is set to RULE.HASH_JOIN_ENABLED is false.HASH_AREA_SIZE and SORT_AREA_SIZE.
To advise the optimizer to use a sort merge join, apply the USE_MERGE hint. You might also need to give hints to force an access path.
There are situations where it is better to override the optimize with the USE_MERGE hint. For example, the optimizer can choose a full scan on a table and avoid a sort operation in a query. However, there is an increased cost because a large table is accessed through an index and single block reads, as opposed to faster access through a full table scan.
For more information on the USE_MERGE hint, see "USE_MERGE".
A Cartesian join is used when one or more of the tables does not have any join conditions to any other tables in the statement. The optimizer joins every row from one data source with every row from the other data source, creating the Cartesian product of the two sets.
The optimizer uses Cartesian joins when it is asked to join two tables with no join conditions. In some cases, a common filter condition between the two tables could be picked up by the optimizer as a possible join condition. This is even more dangerous, because the joins are not flagged in the execution plan as being a Cartesian product.
Cartesian joins generally result from poorly written SQL. You should specify join criteria and avoid Cartesian products. In some situations, a query can involve a large number of tables and an extra table is contained in the FROM clause, but not in the WHERE clause. With such queries, a DISTINCT clause can weed out multiple rows. However, while the DISTINCT clause can be used to remove the extra tuples generated by the Cartesian product, the performance can be severely degraded.
In a SQL query where the inner table of a nested loop operation is not driven from the outer table, but from an independent row source, then the rows accessed can be the same as in a Cartesian product. Because the join condition is present but is applied after accessing the table, the result is not a Cartesian product. However, the cost of accessing the table (rows accessed) is about the same.
Applying the ORDERED hint, causes the optimizer uses a Cartesian join. By specifying a table before its join table is specified, the optimizer does a Cartesian join. For more information on the ORDERED hint, see "ORDERED".
An outer join extends the result of a simple join. An outer join returns all rows that satisfy the join condition and also returns some or all of those rows from one table for which no rows from the other satisfy the join condition.
This operation is used when an outer join is used between two tables. The outer join returns the outer (preserved) table rows, even when there are no corresponding rows in the inner (optional) table.
In a regular outer join, the optimizer chooses the order of tables (driving and driven) based on the cost. However, in a nested loop outer join, the order of tables is determined by the join condition. The outer table, with rows that are being preserved, is used to drive to the inner table.
The optimizer uses nested loop joins to process an outer join in the following circumstances:
For an example of a nested loop outer join, you can add the USE_NL hint to Example 1-18 to ensure that a nested loop is used. For example:
SELECT /*+ USE_NL(c o) */ cust_last_name, sum(nvl2(o.customer_id,0,1)) "Count"
The optimizer uses hash joins for processing an outer join if the data volume is high enough to make the hash join method efficient or if it is not possible to drive from the outer table to inner table.
Like an outer join, the order of tables is not determined by the cost, but by the join condition. The outer table (with preserved rows) is used to build the hash table, and the inner table is used to probe the hash table.
Example 1-18 shows a typical hash join outer join query. In this example, all the customers with credit limits greater than 1000 are queried. An outer join is needed so that you do not miss the customers who do not have any orders.
SELECT cust_last_name, sum(nvl2(o.customer_id,0,1)) "Count" FROM customers c, orders o WHERE c.credit_limit > 1000 AND c.customer_id = o.customer_id(+) GROUP BY cust_last_name;
------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| ------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 168 | 3192 | 11 (28)| | 1 | SORT GROUP BY | | 168 | 3192 | 11 (28)| |* 2 | HASH JOIN OUTER | | 260 | 4940 | 10 (20)| |* 3 | TABLE ACCESS FULL | CUSTOMERS | 260 | 3900 | 6 (17)| |* 4 | TABLE ACCESS FULL | ORDERS | 105 | 420 | 4 (25)| ------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 2 - access("C"."CUSTOMER_ID"="O"."CUSTOMER_ID"(+)) 3 - filter("C"."CREDIT_LIMIT">1000) 4 - filter("O"."CUSTOMER_ID"(+)>0)
The query looks for customers which satisfy various conditions. An outer join returns NULL for the inner table columns along with the outer (preserved) table rows when it does not find any corresponding rows in the inner table. This operation finds all the customers rows that do not have any orders rows.
In this case, the outer join condition is the following:
customers.customer_id = orders.customer_id(+)
The components of this condition represent the following:
customers.orders.customers rows, including those rows without a corresponding row in orders.customers.orders.You could use a NOT EXISTS subquery to return the rows. However, because you are querying all the rows in the table, the hash join performs better (unless the NOT EXISTS subquery is not nested).
In Example 1-19, the outer join is to a multitable view. The optimizer cannot drive into the view like in a normal join or push the predicates, so it builds the entire row set of the view.
SELECT c.cust_last_name, sum(revenue) FROM customers c, v_orders o WHERE c.credit_limit > 2000 AND o.customer_id(+) = c.customer_id GROUP BY c.cust_last_name;
---------------------------------------------------------------------------- | Id | Operation | Name | Rows | Bytes | Cost (%CPU)| ---------------------------------------------------------------------------- | 0 | SELECT STATEMENT | | 144 | 4608 | 16 (32)| | 1 | SORT GROUP BY | | 144 | 4608 | 16 (32)| |* 2 | HASH JOIN OUTER | | 663 | 21216 | 15 (27)| |* 3 | TABLE ACCESS FULL | CUSTOMERS | 195 | 2925 | 6 (17)| | 4 | VIEW | V_ORDERS | 665 | 11305 | | | 5 | SORT GROUP BY | | 665 | 15960 | 9 (34)| |* 6 | HASH JOIN | | 665 | 15960 | 8 (25)| |* 7 | TABLE ACCESS FULL| ORDERS | 105 | 840 | 4 (25)| | 8 | TABLE ACCESS FULL| ORDER_ITEMS | 665 | 10640 | 4 (25)| ---------------------------------------------------------------------------- Predicate Information (identified by operation id): --------------------------------------------------- 2 - access("O"."CUSTOMER_ID"(+)="C"."CUSTOMER_ID") 3 - filter("C"."CREDIT_LIMIT">2000) 6 - access("O"."ORDER_ID"="L"."ORDER_ID") 7 - filter("O"."CUSTOMER_ID">0)
The view definition is as follows:
CREATE OR REPLACE view v_orders AS SELECT l.product_id, SUM(l.quantity*unit_price) revenue, o.order_id, o.customer_id FROM orders o, order_items l WHERE o.order_id = l.order_id GROUP BY l.product_id, o.order_id, o.customer_id;
When an outer join cannot drive from the outer (preserved) table to the inner (optional) table, it cannot use a hash join or nested loop joins. Then it uses the sort merge outer join for performing the join operation.
The optimizer uses sort merge for an outer join if a nested loop join is inefficient. A nested loop join can be inefficient because of data volumes; or if, because of sorts already required by other operations, the optimizer finds it is cheaper to use a sort merge over a hash join.
A full outer join acts like a combination of the left and right outer joins. In addition to the inner join, rows from both tables that have not been returned in the result of the inner join are preserved and extended with nulls. In other words, full outer joins let you join tables together, yet still show rows that do not have corresponding rows in the joined tables.
The query in Example 1-20 retrieves all departments and all employees in each department, but also includes:
SELECT d.department_id, e.employee_id FROM employees e FULL OUTER JOIN departments d ON e.department_id = d.department_id ORDER BY d.department_id;
The statement produces the following output:
DEPARTMENT_ID EMPLOYEE_ID ------------- ----------- 10 200 20 201 20 202 30 114 30 115 30 116 ... 270 280 178 207 125 rows selected.
This section contains some of the parameters specific to the optimizer. The following sections are especially useful when tuning Oracle applications.
You enable optimizer features by setting the OPTIMIZER_FEATURES_ENABLE initialization parameter.
The OPTIMIZER_FEATURES_ENABLE parameter acts as an umbrella parameter for the CBO. This parameter can be used to enable a series of CBO-related features, depending on the release. It accepts one of a list of valid string values corresponding to the release numbers, such as 8.0.4, 8.1.5, and so on. For example, the following statement enables the use of the optimizer features in Oracle9i, Release 2 (9.2).
OPTIMIZER_FEATURES_ENABLE=9.2.0;
This statement causes the Release 2 (9.2) optimizer features to be used in generating query plans. For example, you can use ALL_ROWS or FIRST_ROWS optimizer mode for recursive user SQL generated by PL/SQL procedures. Prior to Oracle8i Release 8.1.6, only RULE or CHOOSE optimizer mode was used for such recursive SQL, and when the user explicitly set the OPTIMIZER_MODE parameter to FIRST_ROWS or ALL_ROWS, a CHOOSE mode was used instead.
The OPTIMIZER_FEATURES_ENABLE parameter was introduced in Oracle8, release 8.0.4. The main goal was to allow customers the ability to upgrade the Oracle server, yet preserve the old behavior of the CBO after the upgrade. For example, when you upgrade the Oracle server from release 8.1.5 to release 8.1.6, the default value of the OPTIMIZER_FEATURES_ENABLE parameter changes from 8.1.5 to 8.1.6. This upgrade results in the CBO enabling optimization features based on 8.1.6, as opposed to 8.1.5.
For plan stability or backward compatibility reasons, you might not want the query plans to change because of new optimizer features in a new release. In such a case, you can set the OPTIMIZER_FEATURES_ENABLE parameter to an earlier version. For example, to preserve the behavior of the CBO to release 8.1.5, set the parameter as follows:
OPTIMIZER_FEATURES_ENABLE=8.1.5;
This statement disables all new optimizer features that were added in releases following release 8.1.5.
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Note: If you upgrade to a new release and you want to enable the features available with that release, then you do not need to explicitly set the |
Oracle Corporation does not recommend explicitly setting the OPTIMIZER_FEATURES_ENABLE parameter to an earlier release. Instead, execution plan or query performance issues should be resolved on a case-by-case basis.
Table 1-3 describes some of the optimizer features that are enabled when you set the OPTIMIZER_FEATURES_ENABLE parameter to each of the following release values.
The CBO peeks at the values of user-defined bind variables on the first invocation of a cursor. This feature lets the optimizer determine the selectivity of any WHERE clause condition, as well as if literals have been used instead of bind variables. On subsequent invocations of the cursor, no peeking takes place, and the cursor is shared, based on the standard cursor-sharing criteria, even if subsequent invocations use different bind values.
When bind variables are used in a statement, it is assumed that cursor sharing is intended and that different invocations are supposed to use the same execution plan. If different invocations of the cursor would significantly benefit from different execution plans, then bind variables may have been used inappropriately in the SQL statement.
This section lists some initialization parameters that can be used to control the behavior of the cost-based optimizer. These parameters can be used to enable various optimizer features in order to improve the performance of SQL execution.
This parameter converts literal values in SQL statements to bind variables. Converting the values improves cursor sharing and can affect the execution plans of SQL statements. The optimizer generates the execution plan based on the presence of the bind variables and not the actual literal values.
This parameter specifies the number of blocks that are read in a single I/O during a full table scan or index fast full scan. The optimizer uses the value of DB_FILE_MULTIBLOCK_READ_COUNT to cost full table scans and index fast full scans. Larger values result in a cheaper cost for full table scans and can result in the optimizer choosing a full table scan over an index scan.
This parameter specifies the amount of memory (in bytes) to be used for hash joins. The CBO uses this parameter to cost a hash join operation. Larger values for HASH_AREA_SIZE reduce the cost of hash joins.
This parameter can be used to enable or disable the use of hash joins as a join method chosen by the optimizer. When set to true, the optimizer considers hash joins as a possible join method. The CBO chooses a hash join if the cost is better than other join methods, such as nested loops or sort merge joins.
This parameter controls the costing of an index probe in conjunction with a nested loop. The range of values 0 to 100 for OPTIMIZER_INDEX_CACHING indicates percentage of index blocks in the buffer cache, which modifies the optimizer's assumptions about index caching for nested loops and IN-list iterators. A value of 100 infers that 100% of the index blocks are likely to be found in the buffer cache and the optimizer adjusts the cost of an index probe or nested loop accordingly. Use caution when using this parameter because execution plans can change in favor of index caching.
This parameter can be used to adjust the cost of index probes. The range of values is 1 to 10000. The default value is 100, which means that indexes are evaluated as an access path based on the normal costing model. A value of 10 means that the cost of an index access path is one-tenth the normal cost of an index access path.
This parameter controls the maximum number of permutations that the CBO considers when generating execution plans for SQL statements with joins. The range of values is 4 to 80000. A value of 80000 corresponds to no limit. Setting this parameter to a value less than 1000 normally ensures parse times of a few seconds or less.
The OPTIMIZER_MAX_PERMUTATIONS parameter can be used to reduce parse times for complex SQL statements that join a large number of tables. However, reducing its value can result in the optimizer missing an optimal join permutation.
This initialization parameter sets the mode of the optimizer at instance startup. The possible values are RULE, CHOOSE, ALL_ROWS, FIRST_ROWS_n, and FIRST_ROWS. For description of these parameter values, see "OPTIMIZER_MODE Initialization Parameter".
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Note: Oracle Corporation strongly advises the use of cost-based optimization. Rule-based optimization will be deprecated in a future release. |
This parameter enables the partition view pruning feature. If set to true, then the CBO scans only the required partitions, based on the view predicates or filters.
This parameter enables the query rewrite feature, which works in conjunction with materialized views. If set to true, then the CBO considers query rewrites, using materialized views to satisfy the original query. This parameter also controls whether or not function-based indexes are used.
This parameter specifies the amount of memory (in bytes) that will be used to perform sorts. If a sort operation is performed, and if the amount of data to be sorted exceeds the value of SORT_AREA_SIZE, then data beyond the value of SORT_AREA_SIZE is written to the temporary tablespace. The CBO uses the value of SORT_AREA_SIZE to cost sort operations including sort merge joins. Larger values for SORT_AREA_SIZE result in cheaper CBO costs for sort operations.
This parameter, if set to true, enables the CBO to cost a star transformation for star queries. The star transformation combines the bitmap indexes on the various fact table columns rather than using a Cartesian approach.
| See Also:
Oracle9i Database Reference for complete information about each parameter |
The extensible optimizer is part of the CBO. It allows the authors of user-defined functions and domain indexes to control the three main components that the CBO uses to select an execution plan: statistics, selectivity, and cost evaluation.
The extensible optimizer lets you:
DBMS_STATS package or the ANALYZE statement to invoke user-defined statistics collection and deletion functions
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Note: Oracle Corporation strongly recommends that you use the However, you must use the |
| See Also:
Oracle9i Data Cartridge Developer's Guide for details about the extensible optimizer |
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.
The selectivity of a predicate in a SQL statement is used to estimate the cost of a particular access path; 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, standalone 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.
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 might underestimate the cost of a user-defined function that invokes PL/SQL, uses recursive SQL, accesses a BFILE, or is CPU-intensive.
You can define costs for domain indexes and user-defined standalone functions, package functions, and type methods. 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.
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