7 Memory Configuration and Use

7.1.4.1 Viewing Information About Dynamic Resize Operations

The following views provide information about dynamic resize operations:

• V$MEMORY_CURRENT_RESIZE_OPS displays information about memory resize operations (both automatic and manual) which are currently in progress.

• V$MEMORY_DYNAMIC_COMPONENTS displays information about the current sizes of all dynamically tuned memory components, including the total sizes of the SGA and instance PGA.

• V$MEMORY_RESIZE_OPS displays information about the last 800 completed memory resize operations (both automatic and manual). This does not include in-progress operations.

• V$MEMORY_TARGET_ADVICE displays tuning advice for the MEMORY_TARGET initialization parameter.

• V$SGA_CURRENT_RESIZE_OPS displays information about SGA resize operations that are currently in progress. An operation can be a grow or a shrink of a dynamic SGA component.

• V$SGA_RESIZE_OPS displays information about the last 800 completed SGA resize operations. This does not include any operations currently in progress.

• V$SGA_DYNAMIC_COMPONENTS displays information about the dynamic components in SGA. This view summarizes information based on all completed SGA resize operations since startup.

• V$SGA_DYNAMIC_FREE_MEMORY displays information about the amount of SGA memory available for future dynamic SGA resize operations.

  See Also:

  • Oracle Database Concepts for more information about dynamic SGA

  • Oracle Database Reference for detailed column information for these views

7.1.6.1 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 operating system utilities to examine the operating system, to identify whether there is a lot of paging on your system. If so, then the total system memory may 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.

7.1.6.2 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.

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

SHOW SGA

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

7.1.6.3 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.

7.2.2.1 Buffer Cache Advisory Statistics

You can use several statistics to examine buffer cache activity, including the following:

• V$DB_CACHE_ADVICE

• Buffer cache hit ratio

7.2.2.2 Using V$DB_CACHE_ADVICE

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
   FROM V$DB_CACHE_ADVICE
   WHERE name          = 'DEFAULT'
     AND block_size    = (SELECT value FROM V$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.

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

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

Examining Figure 7-1 leads to the following observations:

• The benefit from increasing buffers from point A to point B is considerably higher than from point B to point C.

• The decrease in the physical I/O between points A and B and points B and C is not smooth, as indicated by the dotted line in the graph.

7.2.2.3 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.

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.

SELECT NAME, VALUE
  FROM V$SYSSTAT
WHERE NAME IN ('db block gets from cache', 'consistent gets from cache', 'physical reads cache');

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

7.2.3.1 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.

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).

7.2.3.2 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.

7.2.4.1 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.

7.2.4.2 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.

7.2.4.3 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.

7.3.1.1 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.

7.3.1.2 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 sharable 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.

7.3.1.3 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.

7.3.1.4 Result Cache Concepts

Systems with large amounts of memory can take advantage of the result cache to improve response times of repetitive queries.

The result cache stores the results of SQL queries and PL/SQL functions in an area called Result Cache Memory in the shared pool. When these queries and functions are executed repeatedly, the results are retrieved directly from the cache memory. This results in a faster response time. The cached results stored become invalid when data in the dependent database objects is modified. The use of the result cache is database-wide decision. Result cache itself is instance specific and can be sized differently on different instances. To disable the result cache in a cluster, you must explicitly set the RESULT_CACHE_MAX_SIZE initialization parameter to 0 during every instance startup.

The Result Cache Memory pool consists of the SQL Query Result Cache, which stores the results of SQL queries, and the PL/SQL Function Result Cache, which stores the values returned by PL/SQL functions. For details of using the result cache in PL/SQL functions, refer to Using the Cross-Session PL/SQL Function Result Cache in the Oracle Database PL/SQL Language Reference.

SQL Query Result Cache Concepts

SQL query results can be cached for reuse. For a read-consistent snapshot to be reusable, one of the following must be true:

• The read-consistent snapshot used to build the result retrieves the most current committed state of the data.

• The query points to an explicit point in time using flashback query.

The use of the SQL query result cache can be controlled by setting the RESULT_CACHE_MODE initialization parameter.

RESULT_CACHE_MODE

The RESULT_CACHE_MODE initialization parameter determines the SQL query result cache behavior. The possible initialization parameter values are MANUAL and FORCE.

When set to MANUAL, you must specify, by using the result_cache hint, that a particular result is using the cache. If the result is not available in the cache, then the query will be executed and the result will be stored in the cache. Subsequent executions of the exact same statement, including the result cache hint, will be served out of the cache.

When set to FORCE, all results use the cache, if possible. You can use the no_result_cache hint to bypass the cache when using the FORCE mode.

A least-recently used algorithm is used to age out cached results. Query results that are bigger than the available space in the result cache will not be cached.

The ResultCache Operator

To store the result of a query in the result cache when RESULT_CACHE_MODE is set to MANUAL, you must include the result_cache hint in your query. For example:

select /*+ result_cache */ deptno, avg(sal) 
from emp 
group by deptno;

This hint introduces the ResultCache operator into the execution plan for the query. When you execute the query, the ResultCache operator will look up the result cache memory to check if the result for the query already exists in the cache. If it exists, then the result is retrieved directly out of the cache. If it does not yet exist in the cache, then the query is executed. The result is returned as output, and is also stored in the result cache memory.

If the RESULT_CACHE_MODE is set to FORCE, and you do not want to include the result of a query to the result cache, then you must use the no_result_cache hint in your query.

Restrictions on Using the SQL Query Result Cache

You cannot cache results when you use the following database objects or functions in your SQL query:

• Dictionary and temporary tables

• Sequence CURRVAL and NEXTVAL pseudo columns

• SQL functions current_date, current_timestamp, local_timestamp, userenv/sys_context (with non-constant variables), sys_guid, sysdate, and sys_timestamp

• Non-deterministic PL/SQL functions

Cached results are parameterized with the parameter values if any of the following constructs are used in the query:

• Bind variables.

• The following SQL functions: dbtimezone, sessiontimezone, userenv/sys_context (with constant variables), uid, and user.

• NLS parameters.

Parameterized cache results can be reused if the query is equivalent and the parameter values are the same.

A query result based on a read-consistent snapshot of data that is older than the latest committed version of the data will not be cached. If any of the tables used to build a cached result has been modified in an ongoing transaction in the current session then the result is never cached.

Adding the RESULT_CACHE hint to inline views disables optimizations between the outer query and the inline view in order to maximize the reusability of the cached result. Subqueries cannot be cached.

7.3.2.1 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:

• Use bind variables rather than literals in SQL statements whenever possible. For example, the following two statements cannot use the same shared area because they do not match character for character:

  SELECT employee_id FROM employees WHERE department_id = 10;
  SELECT employee_id FROM employees WHERE department_id = 20;
  

  By replacing the literals with a bind variable, only one SQL statement would result, which could be executed twice:

  SELECT employee_id FROM employees WHERE department_id = :dept_id;
  

  Note:

  For existing applications where rewriting the code to use bind variables is impractical, it is possible to use the CURSOR_SHARING initialization parameter to avoid some of the hard parse overhead. For more information see section "CURSOR_SHARING for Existing Applications".

• Avoid application designs that result in large numbers of users issuing dynamic, unshared SQL statements. Typically, the majority of data required by most users can be satisfied using preset queries. Use dynamic SQL where such functionality is required.

• Be sure that users of the application do not change the optimization approach and goal for their individual sessions.

• Establish the following policies for application developers:

  • Standardize naming conventions for bind variables and spacing conventions for SQL statements and PL/SQL blocks.

  • Consider using stored procedures whenever possible. Multiple users issuing the same stored procedure use the same shared PL/SQL area automatically. Because stored procedures are stored in a parsed form, their use reduces runtime parsing.

• For SQL statements which are identical but are not being shared, you can query V$SQL_SHARED_CURSOR to determine why the cursors are not shared. This would include optimizer settings and bind variable mismatches.

7.3.2.2 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.

7.3.2.3 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.

7.3.2.4 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.

7.3.2.5 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.

7.3.2.6 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.

7.3.2.7 Use of Result Cache

OLTP applications can benefit significantly from the use of the result cache. The benefits highly depend on the application. Consider the use of the PL/SQL function result cache and the SQL query result cache when evaluating whether your application can benefit from the result cache.

7.3.3.1 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.

7.3.3.2 V$LIBRARYCACHE

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:

• SQL AREA

• TABLE/PROCEDURE

• BODY

• TRIGGER

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

To examine each namespace individually, use the following query:

SELECT NAMESPACE, PINS, PINHITS, RELOADS, INVALIDATIONS
  FROM V$LIBRARYCACHE
 ORDER BY NAMESPACE;

The output of this query could look like the following:

NAMESPACE             PINS    PINHITS    RELOADS INVALIDATIONS
--------------- ---------- ---------- ---------- -------------
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:

SUM(PINHITS)/SUM(PINS)
----------------------
            .999466248

Examining the returned data leads to the following observations:

• For the SQL AREA namespace, there were 21,536,413 executions.

• 11,204 of the executions resulted in a library cache miss, requiring Oracle to implicitly reparse a statement or block or reload an object definition because it aged out of the library cache (that is, a RELOAD).

• SQL statements were invalidated two times, again causing library cache misses.

• The hit percentage is about 99.94%. This means that only .06% of executions resulted in reparsing.

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:

SELECT * FROM V$SGASTAT 
 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.

7.3.3.3 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, and 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.

7.3.3.4 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.

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
  FROM V$ROWCACHE
 WHERE gets > 0
 GROUP BY parameter;

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

PARAMETER              SUM(GETS) SUM(GETMISSES) PCT_SUCC_GETS      UPDATES
--------------------- ---------- -------------- ------------- ------------
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:

• There are large numbers of misses and updates for used extents, free extents, and segments. This implies that the instance had a significant amount of dynamic space extension.

• Based on the percentage of successful gets, and comparing that statistic with the actual number of gets, the shared pool is large enough to store dictionary cache data adequately.

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:

SELECT (SUM(GETS - GETMISSES - FIXED)) / SUM(GETS) "ROW CACHE" FROM V$ROWCACHE;

7.3.3.5 Shared Pool: Result Cache Statistics

The DBMS_RESULT_CACHE package provides statistics, information, and operators that enable you to manage memory allocation for the result cache.

You can use the DBMS_RESULT_CACHE package to perform various operations such as bypassing the cache, retrieving statistics on the cache memory usage, and flushing the cache. For example, to view the memory allocation statistics, use the following SQL procedure:

SQL> set serveroutput on
SQL> execute dbms_result_cache.memory_report

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

R e s u l t C a c h e M e m o r y R e p o r t
[Parameters]
Block Size = 1024 bytes
Maximum Cache Size = 950272 bytes (928 blocks)
Maximum Result Size = 47104 bytes (46 blocks)
[Memory]
Total Memory = 46340 bytes [0.048% of the Shared Pool]
... Fixed Memory = 10696 bytes [0.011% of the Shared Pool]
... State Object Pool = 2852 bytes [0.003% of the Shared Pool]
... Cache Memory = 32792 bytes (32 blocks) [0.034% of the Shared Pool]
....... Unused Memory = 30 blocks
....... Used Memory = 2 blocks
........... Dependencies = 1 blocks
 
........... Results = 1 blocks
............... SQL = 1 blocks
 
PL/SQL procedure successfully completed. 

To remove all existing results and clear the cache memory, use the command:

SQL>execute dbms_result_cache.flush

For detailed information on the DBMS_RESULT_CACHE package, refer to Oracle Database PL/SQL Packages and Types Reference.

Accessing Information on the Result Cache

Table 7-3 lists the views that provide information and statistics on the result cache memory. The views show the aggregated statistics for the SQL query result cache and the PL/SQL function result cache:

Use the following query to monitor the statistics in the V$RESULT_CACHE_STATISTICS view:

column name format a20
select name, value from v$result_cache_statistics;

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

NAME                          VALUE
--------------------     ----------
Block Size (Bytes)             1024
Block Count Maximum           3136
Block Count Current             32
Result Size Maximum (Blocks)   156
Create Count Success             2
Create Count Failure             0
Find Count                       0
Invalidation Count               0
Delete Count Invalid             0
Delete Count Valid               0

A system that makes good use of the SQL query result cache should show relatively low values for Create Count Failure and Delete Count Valid, while showing relatively high values for Find Count.

For more details on these views, refer to Oracle Database Reference.

7.3.4.1 Increasing Memory Allocation

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

7.3.4.2 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.

7.3.5.1 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.

SELECT SUM(VALUE) || ' BYTES' "TOTAL MEMORY FOR ALL SESSIONS"
    FROM V$SESSTAT, V$STATNAME
    WHERE NAME = 'session uga memory'
    AND V$SESSTAT.STATISTIC# = V$STATNAME.STATISTIC#;

SELECT SUM(VALUE) || ' BYTES' "TOTAL MAX MEM FOR ALL SESSIONS"
    FROM V$SESSTAT, V$STATNAME
    WHERE NAME = 'session uga memory max'
    AND V$SESSTAT.STATISTIC# = V$STATNAME.STATISTIC#;

TOTAL MEMORY FOR ALL SESSIONS
-----------------------------
157125 BYTES

TOTAL MAX MEM FOR ALL SESSIONS
------------------------------
417381 BYTES