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

Part Number B10752-01
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Memory Configuration and Use

This chapter explains how to allocate memory to Oracle memory caches, and how to use those caches. Proper sizing and effective use of the Oracle memory caches greatly improves database performance.

Oracle recommends automatic memory configuration for your system using the SGA_TARGET and PGA_AGGREGATE_TARGET initialization parameters. However, you can manually adjust the memory pools on your system and that process is provided in this chapter.

This chapter contains the following sections:

Understanding Memory Allocation Issues

Oracle stores information in memory caches and on disk. Memory access is much faster than disk access. Disk access (physical I/O) take a significant amount of time, compared with memory access, typically in the order of 10 milliseconds. Physical I/O also increases the CPU resources required, because of the path length in device drivers and operating system event schedulers. For this reason, it is more efficient for data requests for frequently accessed objects to be satisfied solely by memory, rather than also requiring disk access.

A performance goal is to reduce the physical I/O overhead as much as possible, either by making it more likely that the required data is in memory or by making the process of retrieving the required data more efficient.

Oracle strongly recommends the use of automatic memory management. Before setting any memory pool sizes, review the following:

If you need to configure memory allocations, Oracle Enterprise Manager provides the Memory Advisor for updates. To access the Memory Advisor through Oracle Enterprise Manager Database Control:

Oracle Memory Caches

The main Oracle memory caches that affect performance are:

Automatic Shared Memory Management

Automatic Shared Memory Management simplifies the configuration of the SGA and is the recommended memory configuration. To use Automatic Shared Memory Management, set the SGA_TARGET initialization parameter to a nonzero value and set the STATISTICS_LEVEL initialization parameter to TYPICAL or ALL. The value of the SGA_TARGET parameter should be set to the amount of memory that you want to dedicate for the SGA. In response to the workload on the system, the automatic SGA management distributes the memory appropriately for the following memory pools:

If these automatically tuned memory pools had been set to nonzero values, those values are used as a minimum levels by Automatic Shared Memory Management. You would set minimum values if an application components needs a minimum amount of memory to function properly.

SGA_TARGET is a dynamic parameter and can be changed through Oracle Enterprise Manager or with the ALTER SYSTEM command. SGA_TARGET can be set less than or equal to the value of SGA_MAX_SIZE initialization parameter. Changes in the value of SGA_TARGET automatically resize the automatically tuned memory pools.

See Also:

If you set SGA_TARGET to 0, Automatic Shared Memory Management is disabled and you can manually size the memory pools with the DB_CACHE_SIZE, SHARED_POOL_SIZE, LARGE_POOL_SIZE, and JAVA_POOL_SIZE initialization parameters. See "Dynamically Changing Cache Sizes".

The following pools are manually sized components and are not affected by Automatic Shared Memory Management:

To manually size these memory pools, you need to set the DB_KEEP_CACHE_SIZE, DB_RECYCLE_CACHE_SIZE, DB_nK_CACHE_SIZE, STREAMS_POOL_SIZE, and LOG_BUFFER initialization parameters. The memory allocated to these pools is deducted from the total available for SGA_TARGET when Automatic Shared Memory Management computes the values of the automatically tuned memory pools.

See Also:

Dynamically Changing Cache Sizes

If the system is not using Automatic Shared Memory Management, you can choose to dynamically reconfigure the sizes of the shared pool, the large pool, the buffer cache, and the process-private memory. The following sections contain details on sizing of caches:

The size of these memory caches is configurable using initialization configuration parameters, such as DB_CACHE_ADVICE, JAVA_POOL_SIZE, LARGE_POOL_SIZE, LOG_BUFFER, and SHARED_POOL_SIZE. The values for these parameters are also dynamically configurable using the ALTER SYSTEM statement except for the log buffer pool and process-private memory, which are static after startup.

Memory for the shared pool, large pool, java pool, and buffer cache is allocated in units of granules. The granule size is 4MB if the SGA size is less than 1GB. If the SGA size is greater than 1GB, the granule size changes to 16MB. The granule size is calculated and fixed when the instance starts up. The size does not change during the lifetime of the instance.

The granule size that is currently being used for SGA can be viewed in the view V$SGA_DYNAMIC_COMPONENTS. The same granule size is used for all dynamic components in the SGA.

You can expand the total SGA size to a value equal to the SGA_MAX_SIZE parameter. If the SGA_MAX_SIZE is not set, you can decrease the size of one cache and reallocate that memory to another cache if necessary. SGA_MAX_SIZE defaults to the aggregate setting of all the components.


SGA_MAX_SIZE cannot be dynamically resized.

The maximum amount of memory usable by the instance is determined at instance startup by the initialization parameter SGA_MAX_SIZE. You can specify SGA_MAX_SIZE to be larger than the sum of all of the memory components, such as buffer cache and shared pool. Otherwise, SGA_MAX_SIZE defaults to the actual size used by those components. Setting SGA_MAX_SIZE larger than the sum of memory used by all of the components lets you dynamically increase a cache size without needing to decrease the size of another cache.

See Also:

Your operating system's documentation for information on managing dynamic SGA.

Viewing Information About Dynamic Resize Operations

The following views provide information about dynamic SGA resize operations:

Application Considerations

With memory configuration, it is important to size the cache appropriately for the application's needs. Conversely, tuning the application's use of the caches can greatly reduce resource requirements. Efficient use of the Oracle memory caches also reduces the load on related resources, such as the latches that protect the caches, the CPU, and the I/O system.

For best performance, you should consider the following:

Making changes or additions to an existing application might require resizing Oracle memory structures to meet the needs of your modified application.

If your application uses Java, you should investigate whether you need to modify the default configuration for the Java pool. See the Oracle Database Java Developer's Guide for information on Java memory usage.

Operating System Memory Use

For most operating systems, it is important to consider the following:

Reduce paging

Paging occurs when an operating system transfers memory-resident pages to disk solely to allow new pages to be loaded into memory. Many operating systems page to accommodate large amounts of information that do not fit into real memory. On most operating systems, paging reduces performance.

Use the operating system utilities to examine the operating system, to identify whether there is a lot of paging on your system. If there is, then the total memory on the system might not be large enough to hold everything for which you have allocated memory. Either increase the total memory on your system, or decrease the amount of memory allocated.

Fit the SGA into main memory

Because the purpose of the SGA is to store data in memory for fast access, the SGA should be within main memory. If pages of the SGA are swapped to disk, then the data is no longer quickly accessible. On most operating systems, the disadvantage of paging significantly outweighs the advantage of a large SGA.


The LOCK_SGA parameter can be used to lock the SGA into physical memory and prevent it from being paged out.

To see how much memory is allocated to the SGA and each of its internal structures, enter the following SQL*Plus statement:


The output of this statement will look similar to the following:

Total System Global Area  840205000 bytes
Fixed Size                   279240 bytes
Variable Size             520093696 bytes
Database Buffers          318767104 bytes
Redo Buffers                1064960 bytes

Allow adequate memory to individual users

When sizing the SGA, ensure that you allow enough memory for the individual server processes and any other programs running on the system.

See Also:

Your operating system hardware and software documentation, as well as the Oracle documentation specific to your operating system, for more information on tuning operating system memory usage

Iteration During Configuration

Configuring memory allocation involves distributing available memory to Oracle memory structures, depending on the needs of the application. The distribution of memory to Oracle structures can affect the amount of physical I/O necessary for Oracle to operate. Having a good first initial memory configuration also provides an indication of whether the I/O system is effectively configured.

It might be necessary to repeat the steps of memory allocation after the initial pass through the process. Subsequent passes let you make adjustments in earlier steps, based on changes in later steps. For example, decreasing the size of the buffer cache lets you increase the size of another memory structure, such as the shared pool.

Configuring and Using the Buffer Cache

For many types of operations, Oracle uses the buffer cache to store blocks read from disk. Oracle bypasses the buffer cache for particular operations, such as sorting and parallel reads. For operations that use the buffer cache, this section explains the following:

Using the Buffer Cache Effectively

To use the buffer cache effectively, SQL statements for the application should be tuned to avoid unnecessary resource consumption. To ensure this, verify that frequently executed SQL statements and SQL statements that perform many buffer gets have been tuned.

See Also:

Chapter 12, "SQL Tuning Overview" for information on how to do this

Sizing the Buffer Cache

When configuring a new instance, it is impossible to know the correct size for the buffer cache. Typically, a database administrator makes a first estimate for the cache size, then runs a representative workload on the instance and examines the relevant statistics to see whether the cache is under or over configured.

Buffer Cache Advisory Statistics

A number of statistics can be used to examine buffer cache activity. These include the following:


This view is populated when the DB_CACHE_ADVICE initialization parameter is set to ON. This view shows the simulated miss rates for a range of potential buffer cache sizes.

Each cache size simulated has its own row in this view, with the predicted physical I/O activity that would take place for that size. The DB_CACHE_ADVICE parameter is dynamic, so the advisory can be enabled and disabled dynamically to allow you to collect advisory data for a specific workload.

There is some overhead associated with this advisory. When the advisory is enabled, there is a small increase in CPU usage, because additional bookkeeping is required.

Oracle uses DBA-based sampling to gather cache advisory statistics. Sampling substantially reduces both CPU and memory overhead associated with bookkeeping. Sampling is not used for a buffer pool if the number of buffers in that buffer pool is small to begin with.

To use V$DB_CACHE_ADVICE, the parameter DB_CACHE_ADVICE should be set to ON, and a representative workload should be running on the instance. Allow the workload to stabilize before querying the V$DB_CACHE_ADVICE view.

The following SQL statement returns the predicted I/O requirement for the default buffer pool for various cache sizes:

COLUMN size_for_estimate          FORMAT 999,999,999,999 heading 'Cache Size (MB)'
COLUMN buffers_for_estimate       FORMAT 999,999,999 heading 'Buffers'
COLUMN estd_physical_read_factor  FORMAT 999.90 heading 'Estd Phys|Read Factor'
COLUMN estd_physical_reads        FORMAT 999,999,999 heading 'Estd Phys| Reads'

SELECT size_for_estimate, buffers_for_estimate, estd_physical_read_factor, estd_physical_reads
   WHERE name          = 'DEFAULT'
     AND block_size    = (SELECT value FROM V$PARAMETER WHERE name = 'db_block_size')
     AND advice_status = 'ON';

The following output shows that if the cache was 212 MB, rather than the current size of 304 MB, the estimated number of physical reads would increase by a factor of 1.74 or 74%. This means it would not be advisable to decrease the cache size to 212MB.

However, increasing the cache size to 334MB would potentially decrease reads by a factor of .93 or 7%. If an additional 30MB memory is available on the host machine and the SGA_MAX_SIZE setting allows the increment, it would be advisable to increase the default buffer cache pool size to 334MB.

                                Estd Phys    Estd Phys
 Cache Size (MB)      Buffers Read Factor        Reads
---------------- ------------ ----------- ------------
              30        3,802       18.70  192,317,943      10% of Current Size 
              60        7,604       12.83  131,949,536
              91       11,406        7.38   75,865,861
             121       15,208        4.97   51,111,658
             152       19,010        3.64   37,460,786
             182       22,812        2.50   25,668,196
             212       26,614        1.74   17,850,847
             243       30,416        1.33   13,720,149
             273       34,218        1.13   11,583,180
             304       38,020        1.00   10,282,475      Current Size 
             334       41,822         .93    9,515,878
             364       45,624         .87    8,909,026
             395       49,426         .83    8,495,039
             424       53,228         .79    8,116,496
             456       57,030         .76    7,824,764
             486       60,832         .74    7,563,180
             517       64,634         .71    7,311,729
             547       68,436         .69    7,104,280
             577       72,238         .67    6,895,122
             608       76,040         .66    6,739,731      200% of Current Size 

This view assists in cache sizing by providing information that predicts the number of physical reads for each potential cache size. The data also includes a physical read factor, which is a factor by which the current number of physical reads is estimated to change if the buffer cache is resized to a given value.


With Oracle, physical reads do not necessarily indicate disk reads; physical reads may well be satisfied from the file system cache.

The relationship between successfully finding a block in the cache and the size of the cache is not always a smooth distribution. When sizing the buffer pool, avoid the use of additional buffers that contribute little or nothing to the cache hit ratio. In the example illustrated in Figure 7-1, only narrow bands of increments to the cache size may be worthy of consideration.

Figure 7-1 Physical I/O and Buffer Cache Size

Text description of pfgrf041.gif follows

Text description of the illustration pfgrf041.gif

Examining Figure 7-1 leads to the following observations:

Calculating the Buffer Cache Hit Ratio

The buffer cache hit ratio calculates how often a requested block has been found in the buffer cache without requiring disk access. This ratio is computed using data selected from the dynamic performance view V$SYSSTAT. The buffer cache hit ratio can be used to verify the physical I/O as predicted by V$DB_CACHE_ADVICE.

The statistics in Table 7-1 are used to calculate the hit ratio.

Table 7-1  Statistics for Calculating the Hit Ratio
Statistic Description

consistent gets from cache

Number of times a consistent read was requested for a block from the buffer cache.

db block gets from cache

Number of times a CURRENT block was requested from the buffer cache.

physical reads cache

The total number of requests to access a data block that resulted in access to the buffer cache.

Example 7-1 has been simplified by using values selected directly from the V$SYSSTAT table, rather than over an interval. It is best to calculate the delta of these statistics over an interval while your application is running, then use them to determine the hit ratio.

See Also:

Chapter 6, "Automatic Performance Diagnostics" for more information on collecting statistics over an interval

Example 7-1 Calculating the Buffer Cache Hit Ratio

WHERE NAME IN ('db block gets from cache', 'consistent gets from cache', 
'physical reads cache');

Using the values in the output of the query, calculate the hit ratio for the buffer cache with the following formula:

1 - (('physical reads cache') / ('consistent gets from cache' + 'db block gets from cache')

See Also:

Oracle Database Reference for information on the V$SYSSTAT view

Interpreting and Using the Buffer Cache Advisory Statistics

There are many factors to examine before considering whether to increase or decrease the buffer cache size. For example, you should examine V$DB_CACHE_ADVICE data and the buffer cache hit ratio.

A low cache hit ratio does not imply that increasing the size of the cache would be beneficial for performance. A good cache hit ratio could wrongly indicate that the cache is adequately sized for the workload.

To interpret the buffer cache hit ratio, you should consider the following:

Increasing Memory Allocated to the Buffer Cache

As a general rule, investigate increasing the size of the cache if the cache hit ratio is low and your application has been tuned to avoid performing full table scans.

To increase cache size, first set the DB_CACHE_ADVICE initialization parameter to ON, and let the cache statistics stabilize. Examine the advisory data in the V$DB_CACHE_ADVICE view to determine the next increment required to significantly decrease the amount of physical I/O performed. If it is possible to allocate the required extra memory to the buffer cache without causing the host operating system to page, then allocate this memory. To increase the amount of memory allocated to the buffer cache, increase the value of the DB_CACHE_SIZE initialization parameter.

If required, resize the buffer pools dynamically, rather than shutting down the instance to perform this change.


When the cache is resized significantly (greater than 20 percent), the old cache advisory value is discarded and the cache advisory is set to the new size. Otherwise, the old cache advisory value is adjusted to the new size by the interpolation of existing values.

The DB_CACHE_SIZE parameter specifies the size of the default cache for the database's standard block size. To create and use tablespaces with block sizes different than the database's standard block sizes (such as to support transportable tablespaces), you must configure a separate cache for each block size used. The DB_nK_CACHE_SIZE parameter can be used to configure the nonstandard block size needed (where n is 2, 4, 8, 16 or 32 and n is not the standard block size).


The process of choosing a cache size is the same, regardless of whether the cache is the default standard block size cache, the KEEP or RECYCLE cache, or a nonstandard block size cache.

See Also:

Oracle Database Reference and Oracle Database Administrator's Guide for more information on using the DB_nK_CACHE_SIZE parameters

Reducing Memory Allocated to the Buffer Cache

If the cache hit ratio is high, then the cache is probably large enough to hold the most frequently accessed data. Check V$DB_CACHE_ADVICE data to see whether decreasing the cache size significantly causes the number of physical I/Os to increase. If not, and if you require memory for another memory structure, then you might be able to reduce the cache size and still maintain good performance. To make the buffer cache smaller, reduce the size of the cache by changing the value for the parameter DB_CACHE_SIZE.

Considering Multiple Buffer Pools

A single default buffer pool is generally adequate for most systems. However, users with detailed knowledge of an application's buffer pool might benefit from configuring multiple buffer pools.

With segments that have atypical access patterns, store blocks from those segments in two different buffer pools: the KEEP pool and the RECYCLE pool. A segment's access pattern may be atypical if it is constantly accessed (that is, hot) or infrequently accessed (for example, a large segment accessed by a batch job only once a day).

Multiple buffer pools let you address these differences. You can use a KEEP buffer pool to maintain frequently accessed segments in the buffer cache, and a RECYCLE buffer pool to prevent objects from consuming unnecessary space in the cache. When an object is associated with a cache, all blocks from that object are placed in that cache. Oracle maintains a DEFAULT buffer pool for objects that have not been assigned to a specific buffer pool. The default buffer pool is of size DB_CACHE_SIZE. Each buffer pool uses the same LRU replacement policy (for example, if the KEEP pool is not large enough to store all of the segments allocated to it, then the oldest blocks age out of the cache).

By allocating objects to appropriate buffer pools, you can:

Random Access to Large Segments

A problem can occur with an LRU aging method when a very large segment is accessed with a large or unbounded index range scan. Here, very large means large compared to the size of the cache. Any single segment that accounts for a substantial portion (more than 10%) of nonsequential physical reads can be considered very large. Random reads to a large segment can cause buffers that contain data for other segments to be aged out of the cache. The large segment ends up consuming a large percentage of the cache, but it does not benefit from the cache.

Very frequently accessed segments are not affected by large segment reads because their buffers are warmed frequently enough that they do not age out of the cache. However, the problem affects warm segments that are not accessed frequently enough to survive the buffer aging caused by the large segment reads. There are three options for solving this problem:

  1. If the object accessed is an index, find out whether the index is selective. If not, tune the SQL statement to use a more selective index.
  2. If the SQL statement is tuned, you can move the large segment into a separate RECYCLE cache so that it does not affect the other segments. The RECYCLE cache should be smaller than the DEFAULT buffer pool, and it should reuse buffers more quickly than the DEFAULT buffer pool.
  3. Alternatively, you can move the small warm segments into a separate KEEP cache that is not used at all for large segments. The KEEP cache can be sized to minimize misses in the cache. You can make the response times for specific queries more predictable by putting the segments accessed by the queries in the KEEP cache to ensure that they do not age out.

Oracle Real Application Cluster Instances

You can create multiple buffer pools for each database instance. The same set of buffer pools need not be defined for each instance of the database. Among instances, the buffer pools can be different sizes or not defined at all. Tune each instance according to the application requirements for that instance.

Using Multiple Buffer Pools

To define a default buffer pool for an object, use the BUFFER_POOL keyword of the STORAGE clause. This clause is valid for CREATE and ALTER TABLE, CLUSTER, and INDEX SQL statements. After a buffer pool has been specified, all subsequent blocks read for the object are placed in that pool.

If a buffer pool is defined for a partitioned table or index, then each partition of the object inherits the buffer pool from the table or index definition, unless you override it with a specific buffer pool.

When the buffer pool of an object is changed using the ALTER statement, all buffers currently containing blocks of the altered segment remain in the buffer pool they were in before the ALTER statement. Newly loaded blocks and any blocks that have aged out and are reloaded go into the new buffer pool.

See Also:

Oracle Database SQL Reference for information on specifying BUFFER_POOL in the STORAGE clause

Buffer Pool Data in V$DB_CACHE_ADVICE

V$DB_CACHE_ADVICE can be used to size all pools configured on an instance. Make the initial cache size estimate, run the representative workload, then simply query the V$DB_CACHE_ADVICE view for the pool you want to use.

For example, to query data from the KEEP pool:

    WHERE NAME          = 'KEEP'

Buffer Pool Hit Ratios

The data in V$SYSSTAT reflects the logical and physical reads for all buffer pools within one set of statistics. To determine the hit ratio for the buffer pools individually, query the V$BUFFER_POOL_STATISTICS view. This view maintains statistics for each pool on the number of logical reads and writes.

The buffer pool hit ratio can be determined using the following formula:

1 - (physical_reads/(db_block_gets + consistent_gets))

The ratio can be calculated with the following query:


See Also:

Oracle Database Reference for information on the V$BUFFER_POOL_STATISTICS view

Determining Which Segments Have Many Buffers in the Pool

The V$BH view shows the data object ID of all blocks that currently reside in the SGA. To determine which segments have many buffers in the pool, you can use one of the two methods described in this section. You can either look at the buffer cache usage pattern for all segments (Method 1) or examine the usage pattern of a specific segment, (Method 2).

Method 1

The following query counts the number of blocks for all segments that reside in the buffer cache at that point in time. Depending on buffer cache size, this might require a lot of sort space.


      AND o.OWNER         != 'SYS'

OBJECT_NAME                              NUMBER_OF_BLOCKS
---------------------------------------- ----------------
OA_PREF_UNIQ_KEY                                        1
SYS_C002651                                             1
DS_PERSON                                              78
OM_EXT_HEADER                                         701
OM_SHELL                                            1,765
OM_HEADER                                           5,826
OM_INSTANCE                                        12,644
Method 2

Use the following steps to determine the percentage of the cache used by an individual object at a given point in time:

  1. Find the Oracle internal object number of the segment by entering the following query:
     WHERE OBJECT_NAME = UPPER('segment_name'); 

    Because two objects can have the same name (if they are different types of objects), use the OBJECT_TYPE column to identify the object of interest.

  2. Find the number of buffers in the buffer cache for SEGMENT_NAME:
      FROM V$BH
     WHERE OBJD = data_object_id_value; 

    where data_object_id_value is from step 1.

  3. Find the number of buffers in the instance:
  4. Calculate the ratio of buffers to total buffers to obtain the percentage of the cache currently used by SEGMENT_NAME:
    % cache used by segment_name = [buffers(Step2)/total buffers(Step3)] 


    This technique works only for a single segment. You must run the query for each partition for a partitioned object.


If there are certain segments in your application that are referenced frequently, then store the blocks from those segments in a separate cache called the KEEP buffer pool. Memory is allocated to the KEEP buffer pool by setting the parameter DB_KEEP_CACHE_SIZE to the required size. The memory for the KEEP pool is not a subset of the default pool. Typical segments that can be kept are small reference tables that are used frequently. Application developers and DBAs can determine which tables are candidates.

You can check the number of blocks from candidate tables by querying V$BH, as described in "Determining Which Segments Have Many Buffers in the Pool".


The NOCACHE clause has no effect on a table in the KEEP cache.

The goal of the KEEP buffer pool is to retain objects in memory, thus avoiding I/O operations. The size of the KEEP buffer pool, therefore, depends on the objects that you want to keep in the buffer cache. You can compute an approximate size for the KEEP buffer pool by adding together the blocks used by all objects assigned to this pool. If you gather statistics on the segments, you can query DBA_TABLES.BLOCKS and DBA_TABLES.EMPTY_BLOCKS to determine the number of blocks used.

Calculate the hit ratio by taking two snapshots of system performance at different times, using the previous query. Subtract the more recent values for physical reads, block gets, and consistent gets from the older values, and use the results to compute the hit ratio.

A buffer pool hit ratio of 100% might not be optimal. Often, you can decrease the size of your KEEP buffer pool and still maintain a sufficiently high hit ratio. Allocate blocks removed from the KEEP buffer pool to other buffer pools.


If an object grows in size, then it might no longer fit in the KEEP buffer pool. You will begin to lose blocks out of the cache.

Each object kept in memory results in a trade-off. It is beneficial to keep frequently-accessed blocks in the cache, but retaining infrequently-used blocks results in less space for other, more active blocks.


It is possible to configure a RECYCLE buffer pool for blocks belonging to those segments that you do not want to remain in memory. The RECYCLE pool is good for segments that are scanned rarely or are not referenced frequently. If an application accesses the blocks of a very large object in a random fashion, then there is little chance of reusing a block stored in the buffer pool before it is aged out. This is true regardless of the size of the buffer pool (given the constraint of the amount of available physical memory). Consequently, the object's blocks need not be cached; those cache buffers can be allocated to other objects.

Memory is allocated to the RECYCLE buffer pool by setting the parameter DB_RECYCLE_CACHE_SIZE to the required size. This memory for the RECYCLE buffer pool is not a subset of the default pool.

Do not discard blocks from memory too quickly. If the buffer pool is too small, then blocks can age out of the cache before the transaction or SQL statement has completed execution. For example, an application might select a value from a table, use the value to process some data, and then update the record. If the block is removed from the cache after the SELECT statement, then it must be read from disk again to perform the update. The block should be retained for the duration of the user transaction.

Configuring and Using the Shared Pool and Large Pool

Oracle uses the shared pool to cache many different types of data. Cached data includes the textual and executable forms of PL/SQL blocks and SQL statements, dictionary cache data, and other data.

Proper use and sizing of the shared pool can reduce resource consumption in at least four ways:

  1. Parse overhead is avoided if the SQL statement is already in the shared pool. This saves CPU resources on the host and elapsed time for the end user.
  2. Latching resource usage is significantly reduced, which results in greater scalability.
  3. Shared pool memory requirements are reduced, because all applications use the same pool of SQL statements and dictionary resources.
  4. I/O resources are saved, because dictionary elements that are in the shared pool do not require disk access.

This section covers the following:

Shared Pool Concepts

The main components of the shared pool are the library cache and the dictionary cache. The library cache stores the executable (parsed or compiled) form of recently referenced SQL and PL/SQL code. The dictionary cache stores data referenced from the data dictionary. Many of the caches in the shared pool automatically increase or decrease in size, as needed, including the library cache and the dictionary cache. Old entries are aged out of these caches to accommodate new entries when the shared pool does not have free space.

A cache miss on the data dictionary cache or library cache is more expensive than a miss on the buffer cache. For this reason, the shared pool should be sized to ensure that frequently used data is cached.

A number of features make large memory allocations in the shared pool: for example, the shared server, parallel query, or Recovery Manager. Oracle recommends segregating the SGA memory used by these features by configuring a distinct memory area, called the large pool.

See Also:

"Using the Large Pool" for more information on configuring the large pool

Allocation of memory from the shared pool is performed in chunks. This allows large objects (over 5k) to be loaded into the cache without requiring a single contiguous area, hence reducing the possibility of running out of enough contiguous memory due to fragmentation.

Infrequently, Java, PL/SQL, or SQL cursors may make allocations out of the shared pool that are larger than 5k. To allow these allocations to occur most efficiently, Oracle segregates a small amount of the shared pool. This memory is used if the shared pool does not have enough space. The segregated area of the shared pool is called the reserved pool.

See Also:

"Configuring the Reserved Pool" for more information on the reserved area of the shared pool

Dictionary Cache Concepts

Information stored in the data dictionary cache includes usernames, segment information, profile data, tablespace information, and sequence numbers. The dictionary cache also stores descriptive information, or metadata, about schema objects. Oracle uses this metadata when parsing SQL cursors or during the compilation of PL/SQL programs.

Library Cache Concepts

The library cache holds executable forms of SQL cursors, PL/SQL programs, and Java classes. This section focuses on tuning as it relates to cursors, PL/SQL programs, and Java classes. These are collectively referred to as application code.

When application code is run, Oracle attempts to reuse existing code if it has been executed previously and can be shared. If the parsed representation of the statement does exist in the library cache and it can be shared, then Oracle reuses the existing code. This is known as a soft parse, or a library cache hit. If Oracle is unable to use existing code, then a new executable version of the application code must be built. This is known as a hard parse, or a library cache miss. See "SQL Sharing Criteria" for details on when a SQL and PL/SQL statements can be shared.

Library cache misses can occur on either the parse step or the execute step when processing a SQL statement. When an application makes a parse call for a SQL statement, if the parsed representation of the statement does not already exist in the library cache, then Oracle parses the statement and stores the parsed form in the shared pool. This is a hard parse. You might be able to reduce library cache misses on parse calls by ensuring that all shareable SQL statements are in the shared pool whenever possible.

If an application makes an execute call for a SQL statement, and if the executable portion of the previously built SQL statement has been aged out (that is, deallocated) from the library cache to make room for another statement, then Oracle implicitly reparses the statement, creating a new shared SQL area for it, and executes it. This also results in a hard parse. Usually, you can reduce library cache misses on execution calls by allocating more memory to the library cache.

In order to perform a hard parse, Oracle uses more resources than during a soft parse. Resources used for a soft parse include CPU and library cache latch gets. Resources required for a hard parse include additional CPU, library cache latch gets, and shared pool latch gets. See "SQL Execution Efficiency" for a discussion of hard and soft parsing.

SQL Sharing Criteria

Oracle automatically determines whether a SQL statement or PL/SQL block being issued is identical to another statement currently in the shared pool.

Oracle performs the following steps for the comparison:

  1. The text of the statement issued is compared to existing statements in the shared pool.
  2. The text of the statement is hashed. If there is no matching hash value, then the SQL statement does not currently exist in the shared pool, and a hard parse is performed.
  3. If there is a matching hash value for an existing SQL statement in the shared pool, then Oracle compares the text of the matched statement to the text of the statement hashed to see if they are identical. The text of the SQL statements or PL/SQL blocks must be identical, character for character, including spaces, case, and comments. For example, the following statements cannot use the same shared SQL area:
    SELECT * FROM employees;
    SELECT * FROM Employees;
    SELECT *  FROM employees;

    Usually, SQL statements that differ only in literals cannot use the same shared SQL area. For example, the following SQL statements do not resolve to the same SQL area:

    SELECT count(1) FROM employees WHERE manager_id = 121;
    SELECT count(1) FROM employees WHERE manager_id = 247;

    The only exception to this rule is when the parameter CURSOR_SHARING has been set to SIMILAR or FORCE. Similar statements can share SQL areas when the CURSOR_SHARING parameter is set to SIMILAR or FORCE. The costs and benefits involved in using CURSOR_SHARING are explained later in this section.

    See Also:

    Oracle Database Reference for more information on the CURSOR_SHARING parameter

  4. The objects referenced in the issued statement are compared to the referenced objects of all existing statements in the shared pool to ensure that they are identical.

    References to schema objects in the SQL statements or PL/SQL blocks must resolve to the same object in the same schema. For example, if two users each issue the following SQL statement:

    SELECT * FROM employees;

    and they each have their own employees table, then this statement is not considered identical, because the statement references different tables for each user.

  5. Bind variables in the SQL statements must match in name, datatype, and length.

    For example, the following statements cannot use the same shared SQL area, because the bind variable names differ:

    SELECT * FROM employees WHERE department_id = :department_id;
    SELECT * FROM employees WHERE department_id = :dept_id;

    Many Oracle products, such as Oracle Forms and the precompilers, convert the SQL before passing statements to the database. Characters are uniformly changed to uppercase, white space is compressed, and bind variables are renamed so that a consistent set of SQL statements is produced.

  6. The session's environment must be identical. For example, SQL statements must be optimized using the same optimization goal.

Using the Shared Pool Effectively

An important purpose of the shared pool is to cache the executable versions of SQL and PL/SQL statements. This allows multiple executions of the same SQL or PL/SQL code to be performed without the resources required for a hard parse, which results in significant reductions in CPU, memory, and latch usage.

The shared pool is also able to support unshared SQL in data warehousing applications, which execute low-concurrency, high-resource SQL statements. In this situation, using unshared SQL with literal values is recommended. Using literal values rather than bind variables allows the optimizer to make good column selectivity estimates, thus providing an optimal data access plan.

See Also:

Oracle Data Warehousing Guide

In an OLTP system, there are a number of ways to ensure efficient use of the shared pool and related resources. Discuss the following items with application developers and agree on strategies to ensure that the shared pool is used effectively:

Efficient use of the shared pool in high-concurrency OLTP systems significantly reduces the probability of parse-related application scalability issues.

Shared Cursors

Reuse of shared SQL for multiple users running the same application, avoids hard parsing. Soft parses provide a significant reduction in the use of resources such as the shared pool and library cache latches. To share cursors, do the following:

Single-User Logon and Qualified Table Reference

Large OLTP systems where users log in to the database as their own user ID can benefit from explicitly qualifying the segment owner, rather than using public synonyms. This significantly reduces the number of entries in the dictionary cache. For example:

SELECT employee_id FROM hr.employees WHERE department_id = :dept_id;

An alternative to qualifying table names is to connect to the database through a single user ID, rather than individual user IDs. User-level validation can take place locally on the middle tier. Reducing the number of distinct userIDs also reduces the load on the dictionary cache.

Use of PL/SQL

Using stored PL/SQL packages can overcome many of the scalability issues for systems with thousands of users, each with individual user sign-on and public synonyms. This is because a package is executed as the owner, rather than the caller, which reduces the dictionary cache load considerably.


Oracle Corporation encourages the use of definer-rights packages to overcome scalability issues. The benefits of reduced dictionary cache load are not as obvious with invoker-rights packages.

Avoid Performing DDL

Avoid performing DDL operations on high-usage segments during peak hours. Performing DDL on such segments often results in the dependent SQL being invalidated and hence reparsed on a later execution.

Cache Sequence Numbers

Allocating sufficient cache space for frequently updated sequence numbers significantly reduces the frequency of dictionary cache locks, which improves scalability. The CACHE keyword on the CREATE SEQUENCE or ALTER SEQUENCE statement lets you configure the number of cached entries for each sequence.

See Also:

Oracle Database SQL Reference for details on the CREATE SEQUENCE and ALTER SEQUENCE statements

Cursor Access and Management

Depending on the Oracle application tool you are using, it is possible to control how frequently your application performs parse calls.

The frequency with which your application either closes cursors or reuses existing cursors for new SQL statements affects the amount of memory used by a session and often the amount of parsing performed by that session.

An application that closes cursors or reuses cursors (for a different SQL statement), does not need as much session memory as an application that keeps cursors open. Conversely, that same application may need to perform more parse calls, using extra CPU and Oracle resources.

Cursors associated with SQL statements that are not executed frequently can be closed or reused for other statements, because the likelihood of reexecuting (and reparsing) that statement is low.

Extra parse calls are required when a cursor containing a SQL statement that will be reexecuted is closed or reused for another statement. Had the cursor remained open, it could have been reused without the overhead of issuing a parse call.

The ways in which you control cursor management depends on your application development tool. The following sections introduce the methods used for some Oracle tools.

See Also:
  • The tool-specific documentation for more information about each tool
  • Oracle Database Concepts for more information on cursors shared SQL
Reducing Parse Calls with OCI

When using Oracle Call Interface (OCI), do not close and reopen cursors that you will be reexecuting. Instead, leave the cursors open, and change the literal values in the bind variables before execution.

Avoid reusing statement handles for new SQL statements when the existing SQL statement will be reexecuted in the future.

Reducing Parse Calls with the Oracle Precompilers

When using the Oracle precompilers, you can control when cursors are closed by setting precompiler clauses. In Oracle mode, the clauses are as follows:

Oracle Corporation recommends that you not use ANSI mode, in which the values of HOLD_CURSOR and RELEASE_CURSOR are switched.

The precompiler clauses can be specified on the precompiler command line or within the precompiler program. With these clauses, you can employ different strategies for managing cursors during execution of the program.

See Also:

Your language's precompiler manual for more information on these clauses

Reducing Parse Calls with SQLJ

Prepare the statement, then reexecute the statement with the new values for the bind variables. The cursor stays open for the duration of the session.

Reducing Parse Calls with JDBC

Avoid closing cursors if they will be reexecuted, because the new literal values can be bound to the cursor for reexecution. Alternatively, JDBC provides a SQL statement cache within the JDBC client using the setStmtCacheSize() method. Using this method, JDBC creates a SQL statement cache that is local to the JDBC program.

See Also:

Oracle Database JDBC Developer's Guide and Reference for more information on using the JDBC SQL statement cache

Reducing Parse Calls with Oracle Forms

With Oracle Forms, it is possible to control some aspects of cursor management. You can exercise this control either at the trigger level, at the form level, or at run time.

Sizing the Shared Pool

When configuring a brand new instance, it is impossible to know the correct size to make the shared pool cache. Typically, a DBA makes a first estimate for the cache size, then runs a representative workload on the instance, and examines the relevant statistics to see whether the cache is under-configured or over-configured.

For most OLTP applications, shared pool size is an important factor in application performance. Shared pool size is less important for applications that issue a very limited number of discrete SQL statements, such as decision support systems (DSS).

If the shared pool is too small, then extra resources are used to manage the limited amount of available space. This consumes CPU and latching resources, and causes contention. Optimally, the shared pool should be just large enough to cache frequently accessed objects. Having a significant amount of free memory in the shared pool is a waste of memory. When examining the statistics after the database has been running, a DBA should check that none of these mistakes are in the workload.

Shared Pool: Library Cache Statistics

When sizing the shared pool, the goal is to ensure that SQL statements that will be executed multiple times are cached in the library cache, without allocating too much memory.

The statistic that shows the amount of reloading (that is, reparsing) of a previously cached SQL statement that was aged out of the cache is the RELOADS column in the V$LIBRARYCACHE view. In an application that reuses SQL effectively, on a system with an optimal shared pool size, the RELOADS statistic will have a value near zero.

The INVALIDATIONS column in V$LIBRARYCACHE view shows the number of times library cache data was invalidated and had to be reparsed. INVALIDATIONS should be near zero. This means SQL statements that could have been shared were invalidated by some operation (for example, a DDL). This statistic should be near zero on OLTP systems during peak loads.

Another key statistic is the amount of free memory in the shared pool at peak times. The amount of free memory can be queried from V$SGASTAT, looking at the free memory for the shared pool. Optimally, free memory should be as low as possible, without causing any reloads on the system.

Lastly, a broad indicator of library cache health is the library cache hit ratio. This value should be considered along with the other statistics discussed in this section and other data, such as the rate of hard parsing and whether there is any shared pool or library cache latch contention.

These statistics are discussed in more detail in the following section.


You can monitor statistics reflecting library cache activity by examining the dynamic performance view V$LIBRARYCACHE. These statistics reflect all library cache activity since the most recent instance startup.

Each row in this view contains statistics for one type of item kept in the library cache. The item described by each row is identified by the value of the NAMESPACE column. Rows with the following NAMESPACE values reflect library cache activity for SQL statements and PL/SQL blocks:

Rows with other NAMESPACE values reflect library cache activity for object definitions that Oracle uses for dependency maintenance.

See Also:

Oracle Database Reference for information about the dynamic performance V$LIBRARYCACHE view

To examine each namespace individually, use the following query:


The output of this query could look like the following:

--------------- ---------- ---------- ---------- -------------
BODY                  8870       8819          0             0
CLUSTER                393        380          0             0
INDEX                   29          0          0             0
OBJECT                   0          0          0             0
PIPE                 55265      55263          0             0
SQL AREA          21536413   21520516      11204             2
TABLE/PROCEDURE   10775684   10774401          0             0
TRIGGER               1852       1844          0             0

To calculate the library cache hit ratio, use the following formula:

Library Cache Hit Ratio = sum(pinhits) / sum(pins)

Using the library cache hit ratio formula, the cache hit ratio is the following:



These queries return data from instance startup, rather than statistics gathered during an interval; interval statistics can better pinpoint the problem.

See Also:

Chapter 6, "Automatic Performance Diagnostics" for information on how gather information over an interval

Examining the returned data leads to the following observations:

The amount of free memory in the shared pool is reported in V$SGASTAT. Report the current value from this view using the following query:

 WHERE NAME = 'free memory'
   AND POOL = 'shared pool';

The output will be similar to the following:

POOL        NAME                            BYTES
----------- -------------------------- ----------
shared pool free memory                   4928280

If free memory is always available in the shared pool, then increasing the size of the pool offers little or no benefit. However, just because the shared pool is full does not necessarily mean there is a problem. It may be indicative of a well-configured system.

Shared Pool Advisory Statistics

The amount of memory available for the library cache can drastically affect the parse rate of an Oracle instance. The shared pool advisory statistics provide a database administrator with information about library cache memory, allowing a DBA to predict how changes in the size of the shared pool can affect aging out of objects in the shared pool.

The shared pool advisory statistics track the library cache's use of shared pool memory and predict how the library cache will behave in shared pools of different sizes. Two fixed views provide the information to determine how much memory the library cache is using, how much is currently pinned, how much is on the shared pool's LRU list, as well as how much time might be lost or gained by changing the size of the shared pool.

The following views of the shared pool advisory statistics are available. These views display any data when shared pool advisory is on. These statistics reset when the advisory is turned off.


This view displays information about estimated parse time in the shared pool for different pool sizes. The sizes range from 10% of the current shared pool size or the amount of pinned library cache memory, whichever is higher, to 200% of the current shared pool size, in equal intervals. The value of the interval depends on the current size of the shared pool.


This view displays information about memory allocated to library cache memory objects in different namespaces. A memory object is an internal grouping of memory for efficient management. A library cache object may consist of one or more memory objects.


These views contain Java pool advisory statistics that track information about library cache memory used for Java and predict how changes in the size of the Java pool can affect the parse rate.

V$JAVA_POOL_ADVICE displays information about estimated parse time in the Java pool for different pool sizes. The sizes range from 10% of the current Java pool size or the amount of pinned Java library cache memory, whichever is higher, to 200% of the current Java pool size, in equal intervals. The value of the interval depends on the current size of the Java pool.

See Also:

Oracle Database Reference for information about the dynamic performance V$SHARED_POOL_ADVICE, V$LIBRARY_CACHE_MEMORY, V$JAVA_POOL_ADVICE, and V$JAVA_LIBRARY_CACHE_MEMORY view

Shared Pool: Dictionary Cache Statistics

Typically, if the shared pool is adequately sized for the library cache, it will also be adequate for the dictionary cache data.

Misses on the data dictionary cache are to be expected in some cases. On instance startup, the data dictionary cache contains no data. Therefore, any SQL statement issued is likely to result in cache misses. As more data is read into the cache, the likelihood of cache misses decreases. Eventually, the database reaches a steady state, in which the most frequently used dictionary data is in the cache. At this point, very few cache misses occur.

Each row in the V$ROWCACHE view contains statistics for a single type of data dictionary item. These statistics reflect all data dictionary activity since the most recent instance startup. The columns in the V$ROWCACHE view that reflect the use and effectiveness of the data dictionary cache are listed in Table 7-2.

Table 7-2  V$ROWCACHE Columns
Column Description


Identifies a particular data dictionary item. For each row, the value in this column is the item prefixed by dc_. For example, in the row that contains statistics for file descriptions, this column has the value dc_files.


Shows the total number of requests for information on the corresponding item. For example, in the row that contains statistics for file descriptions, this column has the total number of requests for file description data.


Shows the number of data requests which were not satisfied by the cache, requiring an I/O.


Shows the number of times data in the dictionary cache was updated.

Use the following query to monitor the statistics in the V$ROWCACHE view over a period of time while your application is running. The derived column PCT_SUCC_GETS can be considered the item-specific hit ratio:

column parameter format a21
column pct_succ_gets format 999.9
column updates format 999,999,999

SELECT parameter
     , sum(gets)
     , sum(getmisses)
     , 100*sum(gets - getmisses) / sum(gets)  pct_succ_gets
     , sum(modifications)                     updates
 WHERE gets > 0
 GROUP BY parameter;

The output of this query will be similar to the following:

--------------------- ---------- -------------- ------------- ------------
dc_database_links             81              1          98.8            0
dc_free_extents            44876          20301          54.8       40,453
dc_global_oids                42              9          78.6            0
dc_histogram_defs           9419            651          93.1            0
dc_object_ids              29854            239          99.2           52
dc_objects                 33600            590          98.2           53
dc_profiles                19001              1         100.0            0
dc_rollback_segments       47244             16         100.0           19
dc_segments               100467          19042          81.0       40,272
dc_sequence_grants           119             16          86.6            0
dc_sequences               26973             16          99.9       26,811
dc_synonyms                 6617            168          97.5            0
dc_tablespace_quotas         120              7          94.2           51
dc_tablespaces            581248             10         100.0            0
dc_used_extents            51418          20249          60.6       42,811
dc_user_grants             76082             18         100.0            0
dc_usernames              216860             12         100.0            0
dc_users                  376895             22         100.0            0

Examining the data returned by the sample query leads to these observations:

It is also possible to calculate an overall dictionary cache hit ratio using the following formula; however, summing up the data over all the caches will lose the finer granularity of data:


Interpreting Shared Pool Statistics

Shared pool statistics indicate adjustments that can be made. The following sections describe some of your choices.

Increasing Memory Allocation

Increasing the amount of memory for the shared pool increases the amount of memory available to both the library cache and the dictionary cache.

Allocating Additional Memory for the Library Cache

To ensure that shared SQL areas remain in the cache after their SQL statements are parsed, increase the amount of memory available to the library cache until the V$LIBRARYCACHE.RELOADS value is near zero. To increase the amount of memory available to the library cache, increase the value of the initialization parameter SHARED_POOL_SIZE. The maximum value for this parameter depends on your operating system. This measure reduces implicit reparsing of SQL statements and PL/SQL blocks on execution.

To take advantage of additional memory available for shared SQL areas, you might also need to increase the number of cursors permitted for a session. You can do this by increasing the value of the initialization parameter OPEN_CURSORS.

Allocating Additional Memory to the Data Dictionary Cache

Examine cache activity by monitoring the GETS and GETMISSES columns. For frequently accessed dictionary caches, the ratio of total GETMISSES to total GETS should be less than 10% or 15%, depending on the application.

Consider increasing the amount of memory available to the cache if all of the following are true:

Increase the amount of memory available to the data dictionary cache by increasing the value of the initialization parameter SHARED_POOL_SIZE.

Reducing Memory Allocation

If your RELOADS are near zero, and if you have a small amount of free memory in the shared pool, then the shared pool is probably large enough to hold the most frequently accessed data.

If you always have significant amounts of memory free in the shared pool, and if you would like to allocate this memory elsewhere, then you might be able to reduce the shared pool size and still maintain good performance.

To make the shared pool smaller, reduce the size of the cache by changing the value for the parameter SHARED_POOL_SIZE.

Using the Large Pool

Unlike the shared pool, the large pool does not have an LRU list. Oracle does not attempt to age objects out of the large pool.

You should consider configuring a large pool if your instance uses any of the following:

Tuning the Large Pool and Shared Pool for the Shared Server Architecture

As Oracle allocates shared pool memory for shared server session memory, the amount of shared pool memory available for the library cache and dictionary cache decreases. If you allocate this session memory from a different pool, then Oracle can use the shared pool primarily for caching shared SQL and not incur the performance overhead from shrinking the shared SQL cache.

Oracle recommends using the large pool to allocate the shared server-related User Global Area (UGA), rather that using the shared pool. This is because Oracle uses the shared pool to allocate System Global Area (SGA) memory for other purposes, such as shared SQL and PL/SQL procedures. Using the large pool instead of the shared pool decreases fragmentation of the shared pool.

To store shared server-related UGA in the large pool, specify a value for the initialization parameter LARGE_POOL_SIZE. To see which pool (shared pool or large pool) the memory for an object resides in, check the column POOL in V$SGASTAT. The large pool is not configured by default; its minimum value is 300K. If you do not configure the large pool, then Oracle uses the shared pool for shared server user session memory.

Configure the size of the large pool based on the number of simultaneously active sessions. Each application requires a different amount of memory for session information, and your configuration of the large pool or SGA should reflect the memory requirement. For example, assuming that the shared server requires 200K to 300K to store session information for each active session. If you anticipate 100 active sessions simultaneously, then configure the large pool to be 30M, or increase the shared pool accordingly if the large pool is not configured.


If a shared server architecture is used, then Oracle allocates some fixed amount of memory (about 10K) for each configured session from the shared pool, even if you have configured the large pool. The CIRCUITS initialization parameter specifies the maximum number of concurrent shared server connections that the database allows.

See Also:
Determining an Effective Setting for Shared Server UGA Storage

The exact amount of UGA Oracle uses depends on each application. To determine an effective setting for the large or shared pools, observe UGA use for a typical user and multiply this amount by the estimated number of user sessions.

Even though use of shared memory increases with shared servers, the total amount of memory use decreases. This is because there are fewer processes; therefore, Oracle uses less PGA memory with shared servers when compared to dedicated server environments.


For best performance with sorts using shared servers, set SORT_AREA_SIZE and SORT_AREA_RETAINED_SIZE to the same value. This keeps the sort result in the large pool instead of having it written to disk.

Checking System Statistics in the V$SESSTAT View

Oracle collects statistics on total memory used by a session and stores them in the dynamic performance view V$SESSTAT. Table 7-3 lists these statistics.

Table 7-3  V$SESSTAT Statistics Reflecting Memory
Statistic Description

session UGA memory

The value of this statistic is the amount of memory in bytes allocated to the session.

Session UGA memory max

The value of this statistic is the maximum amount of memory in bytes ever allocated to the session.

To find the value, query V$STATNAME. If you are using a shared server, you can use the following query to decide how much larger to make the shared pool. Issue the following queries while your application is running:

    WHERE NAME = 'session uga memory'

    WHERE NAME = 'session uga memory max'

These queries also select from the dynamic performance view V$STATNAME to obtain internal identifiers for session memory and max session memory. The results of these queries could look like the following:

157125 BYTES

417381 BYTES

The result of the first query indicates that the memory currently allocated to all sessions is 157,125 bytes. This value is the total memory with a location that depends on how the sessions are connected to Oracle. If the sessions are connected to dedicated server processes, then this memory is part of the memories of the user processes. If the sessions are connected to shared server processes, then this memory is part of the shared pool.

The result of the second query indicates that the sum of the maximum size of the memory for all sessions is 417,381 bytes. The second result is greater than the first because some sessions have deallocated memory since allocating their maximum amounts.

If you use a shared server architecture, you can use the result of either of these queries to determine how much larger to make the shared pool. The first value is likely to be a better estimate than the second unless nearly all sessions are likely to reach their maximum allocations at the same time.

Limiting Memory Use for Each User Session by Setting PRIVATE_SGA

You can set the PRIVATE_SGA resource limit to restrict the memory used by each client session from the SGA. PRIVATE_SGA defines the number of bytes of memory used from the SGA by a session. However, this parameter is used rarely, because most DBAs do not limit SGA consumption on a user-by-user basis.

See Also:

Oracle Database SQL Reference, ALTER RESOURCE COST statement, for more information about setting the PRIVATE_SGA resource limit

Reducing Memory Use with Three-Tier Connections

If you have a high number of connected users, then you can reduce memory usage by implementing three-tier connections. This by-product of using a transaction process (TP) monitor is feasible only with pure transactional models, because locks and uncommitted DMLs cannot be held between calls. A shared server environment offers the following advantages:


If you have no library cache misses, then you might be able to accelerate execution calls by setting the value of the initialization parameter CURSOR_SPACE_FOR_TIME to true. This parameter specifies whether a cursor can be deallocated from the library cache to make room for a new SQL statement. CURSOR_SPACE_FOR_TIME has the following values meanings:

Setting the value of the parameter to true saves Oracle a small amount of time and can slightly improve the performance of execution calls. This value also prevents the deallocation of cursors until associated application cursors are closed.

Do not set the value of CURSOR_SPACE_FOR_TIME to true if you have found library cache misses on execution calls. Such library cache misses indicate that the shared pool is not large enough to hold the shared SQL areas of all concurrently open cursors. If the value is true, and if the shared pool has no space for a new SQL statement, then the statement cannot be parsed, and Oracle returns an error saying that there is no more shared memory. If the value is false, and if there is no space for a new statement, then Oracle deallocates an existing cursor. Although deallocating a cursor could result in a library cache miss later (only if the cursor is reexecuted), it is preferable to an error halting your application because a SQL statement cannot be parsed.

Do not set the value of CURSOR_SPACE_FOR_TIME to true if the amount of memory available to each user for private SQL areas is scarce. This value also prevents the deallocation of private SQL areas associated with open cursors. If the private SQL areas for all concurrently open cursors fills your available memory so that there is no space for a new SQL statement, then the statement cannot be parsed. Oracle returns an error indicating that there is not enough memory.

Caching Session Cursors

If an application repeatedly issues parse calls on the same set of SQL statements, then the reopening of the session cursors can affect system performance. To minimize the impact on performance, session cursors can be stored in a session cursor cache. These cursors are those that have been closed by the application and can be reused. This feature can be particularly useful for applications that use Oracle Forms, because switching from one form to another closes all session cursors associated with the first form.

Oracle checks the library cache to determine whether more than three parse requests have been issued on a given statement. If so, then Oracle assumes that the session cursor associated with the statement should be cached and moves the cursor into the session cursor cache. Subsequent requests to parse that SQL statement by the same session then find the cursor in the session cursor cache.

To enable caching of session cursors, you must set the initialization parameter SESSION_CACHED_CURSORS. The value of this parameter is a positive integer specifying the maximum number of session cursors kept in the cache. An LRU algorithm removes entries in the session cursor cache to make room for new entries when needed.

You can also enable the session cursor cache dynamically with the statement:


To determine whether the session cursor cache is sufficiently large for your instance, you can examine the session statistic session cursor cache hits in the V$SYSSTAT view. This statistic counts the number of times a parse call found a cursor in the session cursor cache. If this statistic is a relatively low percentage of the total parse call count for the session, then consider setting SESSION_CACHED_CURSORS to a larger value.

Configuring the Reserved Pool

Although Oracle breaks down very large requests for memory into smaller chunks, on some systems there might still be a requirement to find a contiguous chunk (for example, over 5 KB) of memory. (The default minimum reserved pool allocation is 4,400 bytes.)

If there is not enough free space in the shared pool, then Oracle must search for and free enough memory to satisfy this request. This operation could conceivably hold the latch resource for detectable periods of time, causing minor disruption to other concurrent attempts at memory allocation.

Hence, Oracle internally reserves a small memory area in the shared pool that can be used if the shared pool does not have enough space. This reserved pool makes allocation of large chunks more efficient.

By default, Oracle configures a small reserved pool. This memory can be used for operations such as PL/SQL and trigger compilation or for temporary space while loading Java objects. After the memory allocated from the reserved pool is freed, it returns to the reserved pool.

You probably will not need to change the default amount of space Oracle reserves. However, if necessary, the reserved pool size can be changed by setting the SHARED_POOL_RESERVED_SIZE initialization parameter. This parameter sets aside space in the shared pool for unusually large allocations.

For large allocations, Oracle attempts to allocate space in the shared pool in the following order:

  1. From the unreserved part of the shared pool.
  2. From the reserved pool. If there is not enough space in the unreserved part of the shared pool, then Oracle checks whether the reserved pool has enough space.
  3. From memory. If there is not enough space in the unreserved and reserved parts of the shared pool, then Oracle attempts to free enough memory for the allocation. It then retries the unreserved and reserved parts of the shared pool.


The default value for SHARED_POOL_RESERVED_SIZE is 5% of the SHARED_POOL_SIZE. This means that, by default, the reserved list is configured.

If you set SHARED_POOL_RESERVED_SIZE to more than half of SHARED_POOL_SIZE, then Oracle signals an error. Oracle does not let you reserve too much memory for the reserved pool. The amount of operating system memory, however, might constrain the size of the shared pool. In general, set SHARED_POOL_RESERVED_SIZE to 10% of SHARED_POOL_SIZE. For most systems, this value is sufficient if you have already tuned the shared pool. If you increase this value, then the database takes memory from the shared pool. (This reduces the amount of unreserved shared pool memory available for smaller allocations.)

Statistics from the V$SHARED_POOL_RESERVED view help you tune these parameters. On a system with ample free memory to increase the size of the SGA, the goal is to have the value of REQUEST_MISSES equal zero. If the system is constrained for operating system memory, then the goal is to not have REQUEST_FAILURES or at least prevent this value from increasing.

If you cannot achieve these target values, then increase the value for SHARED_POOL_RESERVED_SIZE. Also, increase the value for SHARED_POOL_SIZE by the same amount, because the reserved list is taken from the shared pool.

See Also:

Oracle Database Reference for details on setting the LARGE_POOL_SIZE parameter


The reserved pool is too small when the value for REQUEST_FAILURES is more than zero and increasing. To resolve this, increase the value for the SHARED_POOL_RESERVED_SIZE and SHARED_POOL_SIZE accordingly. The settings you select for these parameters depend on your system's SGA size constraints.

Increasing the value of SHARED_POOL_RESERVED_SIZE increases the amount of memory available on the reserved list without having an effect on users who do not allocate memory from the reserved list.


Too much memory might have been allocated to the reserved list if:

If either of these conditions is true, then decrease the value for SHARED_POOL_RESERVED_SIZE.

When SHARED_POOL_SIZE is Too Small

The V$SHARED_POOL_RESERVED fixed view can also indicate when the value for SHARED_POOL_SIZE is too small. This can be the case if REQUEST_FAILURES is greater than zero and increasing.

If you have enabled the reserved list, then decrease the value for SHARED_POOL_RESERVED_SIZE. If you have not enabled the reserved list, then you could increase SHARED_POOL_SIZE.

Keeping Large Objects to Prevent Aging

After an entry has been loaded into the shared pool, it cannot be moved. Sometimes, as entries are loaded and aged, the free memory can become fragmented.

Use the PL/SQL package DBMS_SHARED_POOL to manage the shared pool. Shared SQL and PL/SQL areas age out of the shared pool according to a least recently used (LRU) algorithm, similar to database buffers. To improve performance and prevent reparsing, you might want to prevent large SQL or PL/SQL areas from aging out of the shared pool.

The DBMS_SHARED_POOL package lets you keep objects in shared memory, so that they do not age out with the normal LRU mechanism. By using the DBMS_SHARED_POOL package and by loading the SQL and PL/SQL areas before memory fragmentation occurs, the objects can be kept in memory. This ensures that memory is available, and it prevents the sudden, inexplicable slowdowns in user response time that occur when SQL and PL/SQL areas are accessed after aging out.

The DBMS_SHARED_POOL package is useful for the following:

To use the DBMS_SHARED_POOL package to pin a SQL or PL/SQL area, complete the following steps:

  1. Decide which packages or cursors to pin in memory.
  2. Start up the database.
  3. Make the call to DBMS_SHARED_POOL.KEEP to pin your objects.

    This procedure ensures that your system does not run out of shared memory before the kept objects are loaded. By pinning the objects early in the life of the instance, you prevent memory fragmentation that could result from pinning a large portion of memory in the middle of the shared pool.

    See Also:

    PL/SQL Packages and Types Reference for specific information on using DBMS_SHARED_POOL procedures

CURSOR_SHARING for Existing Applications

One of the first stages of parsing is to compare the text of the statement with existing statements in the shared pool to see if the statement can be shared. If the statement differs textually in any way, then Oracle does not share the statement.

Exceptions to this are possible when the parameter CURSOR_SHARING has been set to SIMILAR or FORCE. When this parameter is used, Oracle first checks the shared pool to see if there is an identical statement in the shared pool. If an identical statement is not found, then Oracle searches for a similar statement in the shared pool. If the similar statement is there, then the parse checks continue to verify the executable form of the cursor can be used. If the statement is not there, then a hard parse is necessary to generate the executable form of the statement.

Similar SQL Statements

Statements that are identical, except for the values of some literals, are called similar statements. Similar statements pass the textual check in the parse phase when the CURSOR_SHARING parameter is set to SIMILAR or FORCE. Textual similarity does not guarantee sharing. The new form of the SQL statement still needs to go through the remaining steps of the parse phase to ensure that the execution plan of the preexisting statement is equally applicable to the new statement.

See Also:

"SQL Sharing Criteria" for more details on the various checks performed


Setting CURSOR_SHARING to EXACT allows SQL statements to share the SQL area only when their texts match exactly. This is the default behavior. Using this setting, similar statements cannot shared; only textually exact statements can be shared.

Setting CURSOR_SHARING to either SIMILAR or FORCE allows similar statements to share SQL. The difference between SIMILAR and FORCE is that SIMILAR forces similar statements to share the SQL area without deteriorating execution plans. Setting CURSOR_SHARING to FORCE forces similar statements to share the executable SQL area, potentially deteriorating execution plans. Hence, FORCE should be used as a last resort, when the risk of suboptimal plans is outweighed by the improvements in cursor sharing.


The CURSOR_SHARING initialization parameter can solve some performance problems. It has the following values: FORCE, SIMILAR, and EXACT (default). Using this parameter provides benefit to existing applications that have many similar SQL statements.


Oracle does not recommend setting CURSOR_SHARING to FORCE in a DSS environment or if you are using complex queries. Also, star transformation is not supported with CURSOR_SHARING set to either SIMILAR or FORCE. For more information, see the "OPTIMIZER_FEATURES_ENABLE Parameter".

The optimal solution is to write sharable SQL, rather than rely on the CURSOR_SHARING parameter. This is because although CURSOR_SHARING does significantly reduce the amount of resources used by eliminating hard parses, it requires some extra work as a part of the soft parse to find a similar statement in the shared pool.


Setting CURSOR_SHARING to SIMILAR or FORCE causes an increase in the maximum lengths (as returned by DESCRIBE) of any selected expressions that contain literals (in a SELECT statement). However, the actual length of the data returned does not change.

Consider setting CURSOR_SHARING to SIMILAR or FORCE if both of the following questions are true:

  1. Are there statements in the shared pool that differ only in the values of literals?
  2. Is the response time low due to a very high number of library cache misses?


    Setting CURSOR_SHARING to FORCE or SIMILAR prevents any outlines generated with literals from being used if they were generated with CURSOR_SHARING set to EXACT.

    To use stored outlines with CURSOR_SHARING=FORCE or SIMILAR, the outlines must be generated with CURSOR_SHARING set to FORCE or SIMILAR and with the CREATE_STORED_OUTLINES parameter.

Using CURSOR_SHARING = SIMILAR (or FORCE) can significantly improve cursor sharing on some applications that have many similar statements, resulting in reduced memory usage, faster parses, and reduced latch contention.

Maintaining Connections

Large OLTP applications with middle tiers should maintain connections, rather than connecting and disconnecting for each database request. Maintaining persistent connections saves CPU resources and database resources, such as latches.

See Also:

"Operating System Statistics" for a description of important operating system statistics

Configuring and Using the Redo Log Buffer

Server processes making changes to data blocks in the buffer cache generate redo data into the log buffer. LGWR begins writing to copy entries from the redo log buffer to the online redo log if any of the following are true:

When LGWR writes redo entries from the redo log buffer to a redo log file or disk, user processes can then copy new entries over the entries in memory that have been written to disk. LGWR usually writes fast enough to ensure that space is available in the buffer for new entries, even when access to the redo log is heavy.

A larger buffer makes it more likely that there is space for new entries, and also gives LGWR the opportunity to efficiently write out redo records (too small a log buffer on a system with large updates means that LGWR is continuously flushing redo to disk so that the log buffer remains 2/3 empty).

On machines with fast processors and relatively slow disks, the processors might be filling the rest of the buffer in the time it takes the redo log writer to move a portion of the buffer to disk. A larger log buffer can temporarily mask the effect of slower disks in this situation. Alternatively, you can do one of the following:

Good usage of the redo log buffer is a simple matter of:

The size of the redo log buffer is determined by the initialization parameter LOG_BUFFER. The log buffer size cannot be modified after instance startup.

Figure 7-2 Redo Log Buffer

Text description of pfgrf040.gif follows

Text description of the illustration pfgrf040.gif

Sizing the Log Buffer

Applications that insert, modify, or delete large volumes of data usually need to change the default log buffer size. The log buffer is small compared with the total SGA size, and a modestly sized log buffer can significantly enhance throughput on systems that perform many updates.

A reasonable first estimate for such systems is to the default value, which is:

MAX(0.5M, (128K * number of cpus))

On most systems, sizing the log buffer larger than 1M does not provide any performance benefit. Increasing the log buffer size does not have any negative implications on performance or recoverability. It merely uses extra memory.

Log Buffer Statistics

The statistic REDO BUFFER ALLOCATION RETRIES reflects the number of times a user process waits for space in the redo log buffer. This statistic can be queried through the dynamic performance view V$SYSSTAT.

Use the following query to monitor these statistics over a period of time while your application is running:

 WHERE NAME = 'redo buffer allocation retries';

The value of redo buffer allocation retries should be near zero over an interval. If this value increments consistently, then processes have had to wait for space in the redo log buffer. The wait can be caused by the log buffer being too small or by checkpointing. Increase the size of the redo log buffer, if necessary, by changing the value of the initialization parameter LOG_BUFFER. The value of this parameter is expressed in bytes. Alternatively, improve the checkpointing or archiving process.

Another data source is to check whether the log buffer space wait event is not a significant factor in the wait time for the instance; if not, the log buffer size is most likely adequate.

PGA Memory Management

The Program Global Area (PGA) is a private memory region containing data and control information for a server process. Access to it is exclusive to that server process and is read and written only by the Oracle code acting on behalf of it. An example of such information is the runtime area of a cursor. Each time a cursor is executed, a new runtime area is created for that cursor in the PGA memory region of the server process executing that cursor.


Part of the runtime area can be located in the SGA when using shared servers.

For complex queries (for example, decision support queries), a big portion of the runtime area is dedicated to work areas allocated by memory intensive operators, such as the following:

A sort operator uses a work area (the sort area) to perform the in-memory sort of a set of rows. Similarly, a hash-join operator uses a work area (the hash area) to build a hash table from its left input.

The size of a work area can be controlled and tuned. Generally, bigger work areas can significantly improve the performance of a particular operator at the cost of higher memory consumption. Ideally, the size of a work area is big enough that it can accommodate the input data and auxiliary memory structures allocated by its associated SQL operator. This is known as the optimal size of a work area. When the size of the work area is smaller than optimal, the response time increases, because an extra pass is performed over part of the input data. This is known as the one-pass size of the work area. Under the one-pass threshold, when the size of a work area is far too small compared to the input data size, multiple passes over the input data are needed. This could dramatically increase the response time of the operator. This is known as the multi-pass size of the work area. For example, a serial sort operation that needs to sort 10GB of data needs a little more than 10GB to run optimal and at least 40MB to run one-pass. If this sort gets less that 40MB, then it must perform several passes over the input data.

The goal is to have most work areas running with an optimal size (for example, more than 90% or even 100% for pure OLTP systems), while a smaller fraction of them are running with a one-pass size (for example, less than 10%). Multi-pass execution should be avoided. Even for DSS systems running large sorts and hash-joins, the memory requirement for the one-pass executions is relatively small. A system configured with a reasonable amount of PGA memory should not need to perform multiple passes over the input data.

Automatic PGA memory management simplifies and improves the way PGA memory is allocated. By default, PGA memory management is enabled. In this mode, Oracle dynamically adjusts the size of the portion of the PGA memory dedicated to work areas, based on 20% of the SGA memory size. The minimum value is 10MB.


For backward compatibility, automatic PGA memory management can be disabled by setting the value of the PGA_AGGREGATE_TARGET initialization parameter to 0. When automatic PGA memory management is disabled, the maximum size of a work area can be sized with the associated _AREA_SIZE parameter, such as the SORT_AREA_SIZE initialization parameter.

See Oracle Database Reference for information on the PGA_AGGREGATE_TARGET, SORT_AREA_SIZE, HASH_AREA_SIZE, BITMAP_MERGE_AREA_SIZE and CREATE_BITMAP_AREA_SIZE initialization parameters.

Configuring Automatic PGA Memory

When running under the automatic PGA memory management mode, sizing of work areas for all sessions becomes automatic and the *_AREA_SIZE parameters are ignored by all sessions running in that mode. At any given time, the total amount of PGA memory available to active work areas in the instance is automatically derived from the PGA_AGGREGATE_TARGET initialization parameter. This amount is set to the value of PGA_AGGREGATE_TARGET minus the amount of PGA memory allocated by other components of the system (for example, PGA memory allocated by sessions). The resulting PGA memory is then assigned to individual active work areas, based on their specific memory requirements.

Under automatic PGA memory management mode, the main goal of Oracle is to honor the PGA_AGGREGATE_TARGET limit set by the DBA, by controlling dynamically the amount of PGA memory allotted to SQL work areas. At the same time, Oracle tries to maximize the performance of all the memory-intensive SQL operations, by maximizing the number of work areas that are using an optimal amount of PGA memory (cache memory). The rest of the work areas are executed in one-pass mode, unless the PGA memory limit set by the DBA with the parameter PGA_AGGREGATE_TARGET is so low that multi-pass execution is required to reduce even more the consumption of PGA memory and honor the PGA target limit.

When configuring a brand new instance, it is hard to know precisely the appropriate setting for PGA_AGGREGATE_TARGET. You can determine this setting in three stages:

  1. Make a first estimate for PGA_AGGREGATE_TARGET, based on a rule of thumb. By default, Oracle uses 20% of the SGA size. However, this initial setting may be too low for a large DSS system.
  2. Run a representative workload on the instance and monitor performance, using PGA statistics collected by Oracle, to see whether the maximum PGA size is under-configured or over-configured.
  3. Tune PGA_AGGREGATE_TARGET, using Oracle PGA advice statistics.

    See Also:

    Oracle Database Reference for information on the PGA_AGGREGATE_TARGET initialization parameter

The following sections explain this in detail:


The value of the PGA_AGGREGATE_TARGET initialization parameter (for example 100000 KB, 2500 MB, or 50 GB) should be set based on the total amount of memory available for the Oracle instance. This value can then be tuned and dynamically modified at the instance level. Example 7-2 illustrates a typical situation.

Example 7-2 Initial Setting of PGA_AGGREGATE_TARGET

Assume that an Oracle instance is configured to run on a system with 4 GB of physical memory. Part of that memory should be left for the operating system and other non-Oracle applications running on the same hardware system. You might decide to dedicate only 80% (3.2 GB) of the available memory to the Oracle instance.

You must then divide the resulting memory between the SGA and the PGA.

Good initial values for the parameter PGA_AGGREGATE_TARGET might be:

In this example, with a value of total_mem equal to 4 GB, you can initially set PGA_AGGREGATE_TARGET to 1600 MB for a DSS system and to 655 MB for an OLTP system.

Monitoring the Performance of the Automatic PGA Memory Management

Before starting the tuning process, you need to know how to monitor and interpret the key statistics collected by Oracle to help in assessing the performance of the automatic PGA memory management component. Several dynamic performance views are available for this purpose:


This view gives instance-level statistics on the PGA memory usage and the automatic PGA memory manager. For example:


The output of this query might look like the following:

NAME                                                          VALUE UNIT
-------------------------------------------------------- ---------- ------------
aggregate PGA target parameter                             41156608 bytes
aggregate PGA auto target                                  21823488 bytes
global memory bound                                         2057216 bytes
total PGA inuse                                            16899072 bytes
total PGA allocated                                        35014656 bytes
maximum PGA allocated                                     136795136 bytes
total freeable PGA memory                                    524288 bytes
PGA memory freed back to OS                              1713242112 bytes
total PGA used for auto workareas                                 0 bytes
maximum PGA used for auto workareas                         2383872 bytes
total PGA used for manual workareas                               0 bytes
maximum PGA used for manual workareas                       8470528 bytes
over allocation count                                           291
bytes processed                                          2124600320 bytes
extra bytes read/written                                   39949312 bytes
cache hit percentage                                          98.15 percent

The main statistics displayed in V$PGASTAT are as follows:

Example 7-3 Calculating Cache Hit Percentage

Consider a simple example: Four sort operations have been executed, three were small (1 MB of input data) and one was bigger (100 MB of input data). The total number of bytes processed (BP) by the four operations is 103 MB. If one of the small sorts runs one-pass, an extra pass over 1 MB of input data is performed. This 1 MB value is the number of extra bytes read/written, or EBP. The cache hit percentage is calculated by the following formula:

BP x 100 / (BP + EBP)

The cache hit percentage in this case is 99.03%, almost 100%. This value reflects the fact that only one of the small sorts had to perform an extra pass while all other sorts were able to run optimally. Hence, the cache hit percentage is almost 100%, because this extra pass over 1 MB represents a tiny overhead. On the other hand, if the big sort is the one to run one-pass, then EBP is 100 MB instead of 1 MB, and the cache hit percentage falls to 50.73%, because the extra pass has a much bigger impact.


This view has one row for each Oracle process connected to the instance. The columns PGA_USED_MEM, PGA_ALLOC_MEM, PGA_FREEABLE_MEM and PGA_MAX_MEM can be used to monitor the PGA memory usage of these processes. For example:


The output of this query might look like the following:

-------------------------------------- ------------ ------------- ---------------- -----------
PSEUDO                                            0             0                0           0
oracle@dlsun1690 (PMON)                      314540        685860                0      685860
oracle@dlsun1690 (MMAN)                      313992        685860                0      685860
oracle@dlsun1690 (DBW0)                      696720       1063112                0     1063112
oracle@dlsun1690 (LGWR)                    10835108      22967940                0    22967940
oracle@dlsun1690 (CKPT)                      352716        710376                0      710376
oracle@dlsun1690 (SMON)                      541508        948004                0     1603364
oracle@dlsun1690 (RECO)                      323688        685860                0      816932
oracle@dlsun1690 (q001)                      233508        585128                0      585128
oracle@dlsun1690 (QMNC)                      314332        685860                0      685860
oracle@dlsun1690 (MMON)                      885756       1996548           393216     1996548
oracle@dlsun1690 (MMNL)                      315068        685860                0      685860
oracle@dlsun1690 (q000)                      330872        716200            65536      716200
oracle@dlsun1690 (TNS V1-V3)                 635768        928024                0     1255704
oracle@dlsun1690 (CJQ0)                      533476       1013540                0     1144612
oracle@dlsun1690 (TNS V1-V3)                 430648        812108                0      812108


This view shows the number of work areas executed with optimal memory size, one-pass memory size, and multi-pass memory size since instance start-up. Statistics in this view are subdivided into buckets that are defined by the optimal memory requirement of the work area. Each bucket is identified by a range of optimal memory requirements specified by the values of the columns LOW_OPTIMAL_SIZE and HIGH_OPTIMAL_SIZE.

Example 7-4 and Example 7-5 show two ways of using V$SQL_WORKAREA_HISTOGRAM.

Example 7-4 Querying V$SQL_WORKAREA_HISTOGRAM: Non-empty Buckets

Consider a sort operation that requires 3 MB of memory to run optimally (cached). Statistics about the work area used by this sort are placed in the bucket defined by LOW_OPTIMAL_SIZE = 2097152 (2 MB) and HIGH_OPTIMAL_SIZE = 4194303 (4 MB minus 1 byte), because 3 MB falls within that range of optimal sizes. Statistics are segmented by work area size, because the performance impact of running a work area in optimal, one-pass or multi-pass mode depends mainly on the size of that work area.

The following query shows statistics for all non-empty buckets. Empty buckets are removed with the predicate where total_execution != 0.

       (HIGH_OPTIMAL_SIZE+1)/1024 high_kb,

The result of the query might look like the following:

------ ------- ------------------ ------------------ ----------------------
     8      16             156255                  0                      0
    16      32                150                  0                      0
    32      64                 89                  0                      0
    64     128                 13                  0                      0
   128     256                 60                  0                      0
   256     512                  8                  0                      0
   512    1024                657                  0                      0
  1024    2048                551                 16                      0
  2048    4096                538                 26                      0
  4096    8192                243                 28                      0
  8192   16384                137                 35                      0
 16384   32768                 45                107                      0
 32768   65536                  0                153                      0
 65536  131072                  0                 73                      0
131072  262144                  0                 44                      0
262144  524288                  0                 22                      0

The query result shows that, in the 1024 KB to 2048 KB bucket, 551 work areas used an optimal amount of memory, while 16 ran in one-pass mode and none ran in multi-pass mode. It also shows that all work areas under 1 MB were able to run in optimal mode.

Example 7-5 Querying V$SQL_WORKAREA_HISTOGRAM: Percent Optimal

You can also use V$SQL_WORKAREA_HISTOGRAM to find the percentage of times work areas were executed in optimal, one-pass, or multi-pass mode since start-up. This query only considers work areas of a certain size, with an optimal memory requirement of at least 64 KB.

SELECT optimal_count, round(optimal_count*100/total, 2) optimal_perc, 
       onepass_count, round(onepass_count*100/total, 2) onepass_perc,
       multipass_count, round(multipass_count*100/total, 2) multipass_perc
 (SELECT decode(sum(total_executions), 0, 1, sum(total_executions)) total,
         sum(OPTIMAL_EXECUTIONS) optimal_count,
         sum(ONEPASS_EXECUTIONS) onepass_count,
         sum(MULTIPASSES_EXECUTIONS) multipass_count
    FROM v$sql_workarea_histogram
   WHERE low_optimal_size > 64*1024);

The output of this query might look like the following:

------------- ------------ ------------- ------------ --------------- --------------
         2239        81.63           504        18.37               0              0

This result shows that 81.63% of these work areas have been able to run using an optimal amount of memory. The rest (18.37%) ran one-pass. None of them ran multi-pass. Such behavior is preferable, for the following reasons:


This view can be used to display the work areas that are active (or executing) in the instance. Small active sorts (under 64 KB) are excluded from the view. Use this view to precisely monitor the size of all active work areas and to determine if these active work areas spill to a temporary segment. Example 7-6 shows a typical query of this view:

Example 7-6 Querying V$SQL_WORKAREA_ACTIVE

SELECT to_number(decode(SID, 65535, NULL, SID)) sid,
       operation_type OPERATION,
       trunc(EXPECTED_SIZE/1024) ESIZE,
       trunc(ACTUAL_MEM_USED/1024) MEM,
       trunc(MAX_MEM_USED/1024) "MAX MEM",
       trunc(TEMPSEG_SIZE/1024) TSIZE
 ORDER BY 1,2;

The output of this query might look like the following:
--- ----------------- --------- --------- --------- ----- -------
  8   GROUP BY (SORT)       315       280       904     0
  8         HASH-JOIN      2995      2377      2430     1   20000
  9   GROUP BY (SORT)     34300     22688     22688     0
 11         HASH-JOIN     18044     54482     54482     0
 12         HASH-JOIN     18044     11406     21406     1  120000

This output shows that session 12 (column SID) is running a hash-join having its work area running in one-pass mode (PASS column). This work area is currently using 11406 KB of memory (MEM column) and has used, in the past, up to 21406 KB of PGA memory (MAX MEM column). It has also spilled to a temporary segment of size 120000 KB. Finally, the column ESIZE indicates the maximum amount of memory that the PGA memory manager expects this hash-join to use. This maximum is dynamically computed by the PGA memory manager according to workload.

When a work area is deallocated--that is, when the execution of its associated SQL operator is complete--the work area is automatically removed from the V$SQL_WORKAREA_ACTIVE view.


Oracle maintains cumulative work area statistics for each loaded cursor whose execution plan uses one or more work areas. Every time a work area is deallocated, the V$SQL_WORKAREA table is updated with execution statistics for that work area.

V$SQL_WORKAREA can be joined with V$SQL to relate a work area to a cursor. It can even be joined to V$SQL_PLAN to precisely determine which operator in the plan uses a work area.

Example 7-7 shows three typical queries on the V$SQL_WORKAREA dynamic view:

Example 7-7 Querying V$SQL_WORKAREA

The following query finds the top 10 work areas requiring most cache memory:

     ( SELECT workarea_address, operation_type, policy, estimated_optimal_size
        ORDER BY estimated_optimal_size )

The following query finds the cursors with one or more work areas that have been executed in one or even multiple passes:

col sql_text format A80 wrap 
SELECT sql_text, sum(ONEPASS_EXECUTIONS) onepass_cnt,
       sum(MULTIPASSES_EXECUTIONS) mpass_cnt 
WHERE s.address = wa.address 
GROUP BY sql_text 

Using the hash value and address of a particular cursor, the following query displays the cursor execution plan, including information about the associated work areas.

col "O/1/M" format a10
col name format a20
SELECT operation, options, object_name name,
       trunc(bytes/1024/1024) "input(MB)",
       trunc(last_memory_used/1024) last_mem,
       trunc(estimated_optimal_size/1024) optimal_mem, 
       trunc(estimated_onepass_size/1024) onepass_mem, 
       decode(optimal_executions, null, null, 
              multipasses_executions) "O/1/M"
 WHERE p.address=w.address(+) 
   AND p.hash_value=w.hash_value(+) 
   AND p.address='88BB460C'
   AND p.hash_value=3738161960; 

------------ -------- -------- --------- -------- ---------- ---------- ------
SELECT STATE                                                                  
SORT         GROUP BY               4582        8         16         16 16/0/0
HASH JOIN    SEMI                   4582     5976       5194       2187 16/0/0
TABLE ACCESS FULL     ORDERS          51                                      
TABLE ACCESS FUL      LINEITEM      1000                                      

You can get the address and hash value from the V$SQL view by specifying a pattern in the query. For example:

SELECT address, hash_value 
WHERE sql_text LIKE '%my_pattern%';


To help you tune the initialization parameter PGA_AGGREGATE_TARGET, Oracle provides two PGA advice performance views:

By examining these two views, you no longer need to use an empirical approach to tune the value of PGA_AGGREGATE_TARGET. Instead, you can use the content of these views to determine how key PGA statistics will be impacted if you change the value of PGA_AGGREGATE_TARGET.

In both views, values of PGA_AGGREGATE_TARGET used for the prediction are derived from fractions and multiples of the current value of that parameter, to assess possible higher and lower values. Values used for the prediction range from 10 MB to a maximum of 256 GB.

Oracle generates PGA advice performance views by recording the workload history and then simulating this history for different values of PGA_AGGREGATE_TARGET. The simulation process happens in the background and continuously updates the workload history to produce the simulation result. You can view the result at any time by querying V$PGA_TARGET_ADVICE or V$PGA_TARGET_ADVICE_HISTOGRAM.

To enable automatic generation of PGA advice performance views, make sure the following parameters are set:

The content of these PGA advice performance views is reset at instance start-up or when PGA_AGGREGATE_TARGET is altered.


Simulation cannot include all factors of real execution, so derived statistics might not exactly match up with real performance statistics. You should always monitor the system after changing PGA_AGGREGATE_TARGET, to verify that the new performance is what you expect.


This view predicts how the statistics cache hit percentage and over allocation count in V$PGASTAT will be impacted if you change the value of the initialization parameter PGA_AGGREGATE_TARGET. Example 7-8 shows a typical query of this view:

Example 7-8 Querying V$PGA_TARGET_ADVICE

SELECT round(PGA_TARGET_FOR_ESTIMATE/1024/1024) target_mb,
       ESTD_PGA_CACHE_HIT_PERCENTAGE cache_hit_perc,

The output of this query might look like the following:

----------  --------------  --------------------
        63              23                   367
       125              24                    30
       250              30                     3
       375              39                     0
       500              58                     0
       600              59                     0
       700              59                     0
       800              60                     0
       900              60                     0
      1000              61                     0
      1500              67                     0
      2000              76                     0
      3000              83                     0
      4000              85                     0

The result of the this query can be plotted as shown in Example 7-3:

Figure 7-3 Graphical Representation of V$PGA_TARGET_ADVICE

Text description of pfgrf212.gif follows

Text description of the illustration pfgrf212.gif

The curve shows how the PGA cache hit percentage improves as the value of PGA_AGGREGATE_TARGET increases. The shaded zone in the graph is the over allocation zone, where the value of the column ESTD_OVERALLOCATION_COUNT is nonzero. It indicates that PGA_AGGREGATE_TARGET is too small to even meet the minimum PGA memory needs. If PGA_AGGREGATE_TARGET is set within the over allocation zone, the memory manager will over-allocate memory and actual PGA memory consumed will be more than the limit you set. It is therefore meaningless to set a value of PGA_AGGREGATE_TARGET in that zone. In this particular example PGA_AGGREGATE_TARGET should be set to at least 375 MB.


Even though the theoretical maximum for the PGA cache hit percentage is 100%, there is a practical limit on the maximum size of a work area, which may prevent this theoretical maximum from being reached, even if you further increase PGA_AGGREGATE_TARGET. This should happen only in large DSS systems where the optimal memory requirement is large and might cause the value of the cache hit percentage to taper off at a lower percentage, like 90%.

Beyond the over allocation zone, the value of the PGA cache hit percentage increases rapidly. This is due to an increase in the number of work areas which run optimally or one-pass and a decrease in the number of multi-pass executions. At some point, somewhere around 500 MB in this example, there is an inflection in the curve that corresponds to the point where most (probably all) work areas can run optimally or at least one-pass. After this inflection, the cache hit percentage keeps increasing, though at a lower pace, up to the point where it starts to taper off and shows only slight improvement with increase in PGA_AGGREGATE_TARGET. In Figure 7-3, this happens when PGA_AGGREGATE_TARGET reaches 3 GB. At that point, the cache hit percentage is 83% and only improves marginally (by 2%) with one extra gigabyte of PGA memory. In this particular example, 3 GB is probably the optimal value for PGA_AGGREGATE_TARGET.

Ideally, PGA_AGGREGATE_TARGET should be set at the optimal value, or at least to the maximum value possible in the region beyond the over allocation zone. As a rule of thumb, the PGA cache hit percentage should be higher than 60%, because at 60% the system is almost processing double the number of bytes it actually needs to process in an ideal situation. Using this particular example, it makes sense to set PGA_AGGREGATE_TARGET to at least 500 MB and as close as possible to 3 GB. But the right setting for the parameter PGA_AGGREGATE_TARGET really depends on how much memory can be dedicated to the PGA component. Generally, adding PGA memory requires reducing memory for some of the SGA components, like the shared pool or the buffer cache. This is because the overall memory dedicated to the Oracle instance is often bound by the amount of physical memory available on the system. As a result, any decisions to increase PGA memory must be taken in the larger context of the available memory in the system and the performance of the various SGA components (which you monitor with shared pool advisory and buffer cache advisory statistics). If memory cannot be taken away from the SGA, you might consider adding more physical memory to the system.

See Also:

You can use the following steps as a tuning guideline in tuning PGA_AGGREGATE_TARGET:

  1. Set PGA_AGGREGATE_TARGET so there is no memory over-allocation; avoid setting it in the over-allocation zone. In Example 7-8, PGA_AGGREGATE_TARGET should be set to at least 375 MB.
  2. After eliminating over-allocations, aim at maximizing the PGA cache hit percentage, based on your response-time requirement and memory constraints. In Example 7-8, assume you have a limit X on memory you can allocate to PGA.
    • If this limit X is beyond the optimal value, then you would set PGA_AGGREGATE_TARGET to the optimal value. After this point, the incremental benefit with higher memory allocation to PGA_AGGREGATE_TARGET is very small. In Example 7-8, if you have 10 GB to dedicate to PGA, set PGA_AGGREGATE_TARGET to 3 GB, the optimal value. The remaining 7 GB is dedicated to the SGA.
    • If the limit X is less than the optimal value, then you would set PGA_AGGREGATE_TARGET to X. In Example 7-8, if you have only 2 GB to dedicate to PGA, set PGA_AGGREGATE_TARGET to 2 GB and accept a cache hit percentage of 75%.

Finally, like most statistics collected by Oracle that are cumulative since instance start-up, you can take a snapshot of the view at the beginning and at the end of a time interval. You can then derive the predicted statistics for that time interval as follows:

         estd_overalloc_count = (difference in estd_overalloc_count between the two snapshots)

                                    (difference in bytes_processed between the two snapshots)
estd_pga_cache_hit_percentage = ----------------------------------------------------------------- 
                                (difference in bytes_processed + extra_bytes_rw  between the two snapshots )


This view predicts how the statistics displayed by the performance view V$SQL_WORKAREA_HISTOGRAM will be impacted if you change the value of the initialization parameter PGA_AGGREGATE_TARGET. You can use the dynamic view V$PGA_TARGET_ADVICE_HISTOGRAM to view detailed information on the predicted number of optimal, one-pass and multi-pass work area executions for the set of PGA_AGGREGATE_TARGET values you use for the prediction.

The V$PGA_TARGET_ADVICE_HISTOGRAM view is identical to the V$SQL_WORKAREA_HISTOGRAM view, with two additional columns to represent the PGA_AGGREGATE_TARGET values used for the prediction. Therefore, any query executed against the V$SQL_WORKAREA_HISTOGRAM view can be used on this view, with an additional predicate to select the desired value of PGA_AGGREGATE_TARGET.


The following query displays the predicted content of V$SQL_WORKAREA_HISTOGRAM for a value of the initialization parameter PGA_AGGREGATE_TARGET set to twice its current value.

SELECT LOW_OPTIMAL_SIZE/1024 low_kb, (HIGH_OPTIMAL_SIZE+1)/1024 high_kb, 
       estd_optimal_executions estd_opt_cnt, 
       estd_onepass_executions estd_onepass_cnt, 
       estd_multipasses_executions estd_mpass_cnt 
  FROM v$pga_target_advice_histogram 
 WHERE pga_target_factor = 2 
   AND estd_total_executions != 0 

The output of this query might look like the following.

------   -------   ----------------   ----------------   --------------
     8        16             156107                  0                0
    16        32                148                  0                0
    32        64                 89                  0                0
    64       128                 13                  0                0
   128       256                 58                  0                0
   256       512                 10                  0                0
   512      1024                653                  0                0
  1024      2048                530                  0                0
  2048      4096                509                  0                0
  4096      8192                227                  0                0
  8192     16384                176                  0                0
 16384     32768                133                 16                0
 32768     65536                 66                103                0
 65536    131072                 15                 47                0
131072    262144                  0                 48                0
262144    524288                  0                 23                0

The output shows that increasing PGA_AGGREGATE_TARGET by a factor of 2 will allow all work areas under 16 MB to execute in optimal mode.

See Also:

Oracle Database Reference


Statistics in the V$SYSSTAT and V$SESSTAT views show the total number of work areas executed with optimal memory size, one-pass memory size, and multi-pass memory size. These statistics are cumulative since the instance or the session was started.

The following query gives the total number and the percentage of times work areas were executed in these three modes since the instance was started:

SELECT name profile, cnt, decode(total, 0, 0, round(cnt*100/total)) percentage
    FROM (SELECT name, value cnt, (sum(value) over ()) total
    WHERE name like 'workarea exec%');

The output of this query might look like the following:

PROFILE                                    CNT PERCENTAGE
----------------------------------- ---------- ----------
workarea executions - optimal             5395         95
workarea executions - onepass              284          5
workarea executions - multipass              0          0


The OLAP_PAGE_POOL_SIZE initialization parameter specifies (in bytes) the maximum size of the paging cache to be allocated to an OLAP session.

For performance reasons, it is usually preferable to configure a small OLAP paging cache and set a larger default buffer pool with DB_CACHE_SIZE. An OLAP paging cache of 4 MB is fairly typical, with 2 MB used for systems with limited memory resources.

See Also:

Oracle OLAP Application Developer's Guide