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Oracle® Database Performance Tuning Guide
10g Release 1 (10.1)

Part Number B10752-01
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The Query Optimizer

This chapter discusses SQL processing, optimization methods, and how the optimizer chooses a specific plan to execute SQL.

The chapter contains the following sections:

Optimizer Operations

A SQL statement can be executed in many different ways, such as full table scans, index scans, nested loops, and hash joins. The query 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.


The optimizer might not make the same decisions from one version of Oracle to the next. In recent versions, the optimizer might make different decisions, because better information is available.

The output from the optimizer is a plan that describes an optimum method of execution. The Oracle server provides query optimization.

For any SQL statement processed by Oracle, the optimizer performs the operations listed in Table 14-1.

Table 14-1  Optimizer Operations
Operation Description

Evaluation of expressions and conditions

The optimizer first evaluates expressions and conditions containing constants as fully as possible.

Statement transformation

For complex statements involving, for example, correlated subqueries or views, the optimizer might transform the original statement into an equivalent join statement.

Choice of optimizer goals

The optimizer determines the goal of optimization. See "Choosing an Optimizer Goal".

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 Query Optimizer".

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 Query Optimizer Chooses Execution Plans for Joins".

You can influence the optimizer's choices by setting the optimizer goal, and by gathering representative statistics for the query optimizer. The optimizer goal is either throughput or response time. See "Choosing an Optimizer Goal" and "Query Optimizer Statistics in the Data Dictionary".

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:

Choosing an Optimizer Goal

By default, the goal of the query optimizer 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.

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:

OPTIMIZER_MODE Initialization Parameter

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 14-2.

Table 14-2  OPTIMIZER_MODE Parameter Values
Value Description


The optimizer uses a cost-based approach for all SQL statements in the session regardless of the presence of statistics and optimizes with a goal of best throughput (minimum resource use to complete the entire statement). This is the default value.


The optimizer uses a cost-based approach, regardless of the presence of statistics, and optimizes with a goal of best response time to return the first n number of rows; n can equal 1, 10, 100, or 1000.


The optimizer uses a mix of cost and heuristics to find a best plan for fast delivery of the first few rows.

Note: Using heuristics sometimes leads the query optimizer to generate a plan with a cost that is significantly larger than the cost of a plan without applying the heuristic. FIRST_ROWS is available for backward compatibility and plan stability; use FIRST_ROWS_n instead.


This parameter value has been desupported.


This parameter value has been desupported.

You can change the goal of the query optimizer 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:

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.

Optimizer SQL Hints for Changing the Query Optimizer Goal

To specify the goal of the query optimizer for an individual SQL statement, use one of the hints in Table 14-3. Any of these hints in an individual SQL statement can override the OPTIMIZER_MODE initialization parameter for that SQL statement.

Table 14-3 Hints for Changing the Query Optimizer Goal
Hint Description


This hint instructs Oracle to optimize an individual SQL statement with a goal of best response time to return the first n number of rows, where n equals any positive integer. The hint uses a cost-based approach for the SQL statement, regardless of the presence of statistic.


This hint explicitly chooses the cost-based approach to optimize a SQL statement with a goal of best throughput.


This hint turns CPU costing on for the SQL statement. This is the default cost model for the optimizer. The optimizer estimates the number and type of I/O operations, the number of CPU cycles the database will perform during execution of the given query, and uses system statistics to convert the number of CPU cycles and number of IOs to the estimated query execution time.


This hint turns CPU costing off for the SQL statement. The optimizer uses the I/O cost model which measures everything in single block reads and ignores CPU cost.


This hint has been desupported.


This hint has been desupported.

See Also:

Chapter 17, "Optimizer Hints" for information on how to use hints

Query Optimizer Statistics in the Data Dictionary

The statistics used by the query optimizer 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.

To maintain the effectiveness of the query optimizer, 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 query optimizer with information about data uniqueness and distribution. Using this information, the query optimizer is able to compute plan costs with a high degree of accuracy. This enables the query optimizer to choose the best execution plan based on the least cost.

If no statistics are available when using query optimization, the optimizer will do dynamic sampling depending on the setting of the OPTMIZER_DYNAMIC_SAMPLING initialization parameter. This may cause slower parse times so for best performance, the optimizer should have representative optimizer statistics.

See Also:

Enabling and Controlling Query Optimizer Features

This section contains some of the initialization parameters specific to the optimizer. The following sections are especially useful when tuning Oracle applications.

See Also:

Oracle Database Reference for information about initialization parameters

Enabling Query Optimizer Features

You enable optimizer features by setting the OPTIMIZER_FEATURES_ENABLE initialization parameter.


The OPTIMIZER_FEATURES_ENABLE parameter acts as an umbrella parameter for the query optimizer. This parameter can be used to enable a series of optimizer-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, 8.1.7, and 9.2.0. For example, the following setting enables the use of the optimizer features in generating query plans in Oracle 10g, Release 1.


The OPTIMIZER_FEATURES_ENABLE parameter was introduced with the main goal to allow customers to upgrade the Oracle server, yet preserve the old behavior of the query optimizer 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 query optimizer 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 query optimizer to release 8.1.5, set the parameter as follows:


This statement disables all new optimizer features that were added in releases following release 8.1.5.


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 OPTIMIZER_FEATURES_ENABLE initialization parameter.

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.

See Also:

Oracle Database Reference for information about optimizer features that are enabled when you set the OPTIMIZER_FEATURES_ENABLE parameter to each of the release values

Controlling the Behavior of the Query Optimizer

This section lists some initialization parameters that can be used to control the behavior of the query 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 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 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".


This parameter automatically controls the amount of memory allocated for sorts and hash joins. Larger amounts of memory allocated for sorts or hash joins reduce the optimizer cost of these operations. See "PGA Memory Management".


This parameter, if set to true, enables the query optimizer to cost a star transformation for star queries. The star transformation combines the bitmap indexes on the various fact table columns.

See Also:

Oracle Database Reference for complete information about each parameter

Understanding the Query Optimizer

The query optimizer 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 query optimizer also considers hints, which are optimization suggestions placed in a comment in the statement.

See Also:

Chapter 17, "Optimizer Hints" for detailed information on hints

The query optimizer performs the following steps:

  1. The optimizer generates a set of potential plans for the SQL statement based on available access paths and hints.
  2. The optimizer estimates the cost of each 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 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.

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

Components of the Query Optimizer

The query optimizer operations include:

Query optimizer components are illustrated in Figure 14-1.

Figure 14-1 Query Optimizer Components

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Text description of the illustration pfgrf184.gif

Transforming Queries

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. Several different query transformation techniques are employed by the query transformer, including:

Any combination of these transformations can be applied to a given query.

View Merging

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.

Predicate Pushing

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.

Subquery Unnesting

Often the performance of queries that contain subqueries can be improved 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.

Query Rewrite with Materialized Views

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.

Peeking of User-Defined Bind Variables

The query optimizer 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. Bind peeking works for a specific set of clients, not all clients.

See Also:

Oracle Data Warehousing Guide for more information on query rewrite


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.

If no statistics are available then the optimizer either uses dynamic sampling or an internal default value, depending on the value of the OPTIMIZER_DYNAMIC_SAMPLING initialization parameter. 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. See "Estimating Statistics with Dynamic Sampling".

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 query optimizer 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.


The cost represents units of work or resource used. The query optimizer uses disk I/O, CPU usage, and memory usage as units of work. So, the cost used by the query optimizer 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. See "Assessing I/O for Blocks, not Rows".

The join cost represents the combination of the individual access costs of the two row sets being joined, plus the cost of the join operation.

See Also:

"Understanding Joins" for more information on joins

Generating Plans

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 table1, table2, and table3, table table1 is accessed first. Next, table2 is accessed, and its data is joined to table1 data to produce a join of table1 and table2. Finally, table3 is accessed, and its data is joined to the result of the join between table1 and table2.

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.

Reading and Understanding Execution Plans

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.

See Also:

Overview of EXPLAIN PLAN

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:

Example 14-1 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.

Example 14-1 Using EXPLAIN PLAN

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 14-2 shows the execution plan chosen by the optimizer to execute the SQL statement in the example:

Example 14-2 EXPLAIN PLAN Output

| 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")

Steps in the Execution Plan

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.

See Also:

Chapter 19, "Using EXPLAIN PLAN"

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.

Understanding Access Paths for the Query Optimizer

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.

Full Table Scans

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. The high water mark indicates the amount of used space, or space that had been formatted to receive data. 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 14-2, "EXPLAIN PLAN Output" contains an example of a full table scan on the employees table.

Why a Full Table Scan Is Faster for Accessing Large Amounts of Data

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.

When the Optimizer Uses Full Table Scans

The optimizer uses a full table scan in any of the following cases:

Lack of Index

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.

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 for Performance".

Large Amount of Data

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.

Small Table

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.

High Degree of Parallelism

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.

Full Table Scan Hints

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".

Parallel Query Execution

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.

See Also:

Oracle Data Warehousing Guide

Rowid Scans

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 14-2, "EXPLAIN PLAN Output", an index scan is performed the jobs and departments tables. The rowids retrieved are used to return the row data.

When the Optimizer Uses Rowids

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.


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 Oracle Database Application Developer's Guide - Fundamentals.

Index Scans

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:

Assessing I/O for Blocks, not Rows

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.

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 14-3 shows how the clustering factor can affect cost.

Example 14-3 Effects of Clustering Factor on Cost

Assume the following situation:

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.

Index Unique Scans

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 14-2, "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.

When the Optimizer Uses Index Unique Scans

This access path is used when all columns of a unique (B-tree) index or an index created as a result of a primary key constraint are specified with equality conditions.

See Also:

Oracle Database Concepts for more details on index structures and for detailed information on how a B-tree is searched

Index Unique Scan Hints

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".

Index Range Scans

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 14-4, 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.

Example 14-4 Index Range Scan

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.

When the Optimizer Uses Index Range Scans

The optimizer uses a range scan when it finds one or more leading columns of an index specified in conditions, such as the following:

Range scans can use unique or nonunique indexes. Range scans avoid sorting when index columns constitute the ORDER BY/GROUP BY clause.

Index Range Scan Hints

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".

Index Range Scans Descending

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.

When the Optimizer Uses Index Range Scans Descending

The optimizer uses index range scan descending when an order by descending clause can be satisfied by an index.

Index Range Scan Descending Hints

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

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.

Example 14-5 Index Skip Scan

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:


The index is split logically into the following two subindexes:

Figure 14-2 Index Skip Scan Illustration

Text description of pfgrf197.gif follows

Text description of the illustration pfgrf197.gif

The column sex is skipped in the following query:

   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.

Full Scans

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

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.

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.

Fast Full Index Scan Hints

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".

Index Joins

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.

Index Join Hints

You can specify an index join with the INDEX_JOIN hint. For more information on the INDEX_JOIN hint, see "INDEX_JOIN".

Bitmap Indexes

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 indexes and bitmap join indexes are available only if you have purchased the Oracle Enterprise Edition.

See Also:

Oracle Data Warehousing Guide for more information about bitmap indexes

Cluster Access

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.

Hash Access

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.

Sample Table Scans

A sample table scan retrieves a random sample of data from a simple table or a complex SELECT statement, such as a statement involving joins and views. 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 with the SAMPLE clause, Oracle reads a specified percentage of rows in the table. To perform a sample table scan when sampling by blocks with the SAMPLE BLOCK clause, Oracle reads a specified percentage of table blocks.

Example 14-6 uses a sample table scan to access 1% of the employees table, sampling by blocks.

Example 14-6 Sample Table Scan

    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)|

How the Query Optimizer Chooses an Access Path

The query optimizer 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. 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 query optimizer is influenced by the following:

Understanding Joins

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. In a join, one row set is called inner, and the other is called outer.

This section discusses:

How the Query Optimizer Executes Join Statements

To choose an execution plan for a join statement, the optimizer must make these interrelated decisions:

How the Query Optimizer Chooses Execution Plans for Joins

The query optimizer considers the following when choosing an execution plan:

With the query optimizer, 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:

With the query optimizer, 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 17, "Optimizer Hints" for more information about optimizer hints

Nested Loop Joins

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.

See Also:

"Cartesian Joins"

A nested loop join involves the following steps:

  1. The optimizer determines the driving table and designates it as the outer table.
  2. The other table is designated as the inner table.
  3. For every row in the outer table, Oracle accesses all the rows in the inner table. The outer loop is for every row in outer table and the inner loop is for every row in the inner table. The outer loop appears before the inner loop in the execution plan, as follows:

Nested Loop Example

This section discusses the outer and inner loops for one of the nested loops in the query in Example 14-1.

|   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.

Outer loop

In the execution plan in Example 14-2, the outer loop and the equivalent statement are as follows:


SELECT e.employee_id, e.salary
  FROM employees e
 WHERE e.employee_id < 103
Inner loop

The execution plan in Example 14-2 shows the inner loop being iterated for every row fetched from the outer loop, as follows:

5 |     INDEX UNIQUE SCAN         | JOB_ID_PK

SELECT j.job_title
 FROM jobs j
    WHERE e.job_id = j.job_id 

When the Optimizer Uses Nested Loop Joins

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.

Nested Loop Join Hints

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 14-7, "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".

Nesting Nested Loops

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:

  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

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.

When the Optimizer Uses Hash Joins

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 14-7, the table orders is used to build the hash table, and order_items is the larger table, which is scanned later.

Example 14-7 Hash Joins

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")

Hash Join Hints

Apply the USE_HASH hint to advise the optimizer to use a hash join when joining two tables together. See "PGA Memory Management" for information on sizing of SQL work areas. For more information on the USE_HASH hint, see "USE_HASH".

Sort Merge Joins

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:

  1. Sort join operation: Both the inputs are sorted on the join key.
  2. Merge join operation: The sorted lists are merged together.

If the input is already sorted by the join column, then a sort join operation is not performed for that row source.

When the Optimizer Uses Sort Merge Joins

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:

Sort Merge Join Hints

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".

Cartesian Joins

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.

When the Optimizer Uses Cartesian Joins

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. In other cases, the optimizer may decide to generate a Cartesian product of two very small tables that are both joined to the same large table.

Cartesian Join Hints

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".

Outer Joins

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.

Nested Loop Outer Joins

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 14-8 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"

Hash Join Outer Joins

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.

The order of tables is determined by the cost. The outer table, including preserved rows, may be used to build the hash table, or it may be used to probe one.

Example 14-8 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.

Example 14-8 Hash Join Outer Joins

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:

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 14-9, 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.

Example 14-9 Outer Join to a Multitable 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;

Sort Merge Outer Joins

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:

Full Outer Joins

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 14-10 retrieves all departments and all employees in each department, but also includes:

Example 14-10 Full Outer Join

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:

------------- -----------
           10         200
           20         201
           20         202
           30         114
           30         115
           30         116

125 rows selected.