Release 8.1.5
A67775-01
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| Oracle8i Tuning Release 8.1.5 A67775-01 |
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This chapter explains how to avoid I/O bottlenecks that could prevent Oracle from performing at its maximum potential. This chapter covers the following topics:
This section introduces I/O performance issues. It covers:
The performance of many software applications is inherently limited by disk input/output (I/O). Often, CPU activity must be suspended while I/O activity completes. Such an application is said to be "I/O bound". Oracle is designed so that performance need not be limited by I/O.
Tuning I/O can enhance performance if a disk containing database files is operating at its capacity. However, tuning I/O cannot help performance in "CPU bound" cases--or cases in which your computer's CPUs are operating at their capacity.
It is important to tune I/O after following the recommendations presented in Chapter 19, "Tuning Memory Allocation". That chapter explains how to allocate memory so as to reduce I/O to a minimum. After reaching this minimum, follow the instructions in this chapter to achieve more efficient I/O performance.
When designing a new system, you should analyze I/O needs from the top down, determining what resources you will require in order to achieve the desired performance.
For an existing system, you should approach I/O tuning from the bottom up:
This section explains how to determine your system's I/O requirements.
Begin by figuring out the number of reads and writes involved in each transaction, and distinguishing the objects against which each operation is performed.
In an OLTP application, for example, each transaction might involve:
One transaction in this example thus requires 2 reads and 1 write, all to different objects.
In this example, the designer might specify that 100 tps would constitute an acceptable level of performance. To achieve this, the system must be able to perform 300 I/Os per second:
To do this, ascertain the number of I/Os that each disk can perform per second. This numbers depends on three factors:
In general, disk speed tends to have the following characteristics:
| Disk Speed: | File System | Raw Devices |
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Reads per second |
fast |
slow |
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Writes per second |
slow |
fast |
Table 20-2 Disk I/O Analysis Worksheet
| Disk Speed: | File System | Raw Devices |
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Reads per second |
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Writes per second |
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Table 20-3 Sample Disk I/O Analysis
| Disk Speed: | File System | Raw Devices |
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Reads per second |
50 |
45 |
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Writes per second |
20 |
50 |
Table 20-4 Disk I/O Requirements Worksheet
| Object | If Stored on File System | If Stored on Raw Devices | ||||
| R/W Needed per Sec. | Disk R/W Capabil. per Sec. | Disks Needed | R/W Needed per Sec. | Disk R/W Capabil. per Sec. | Disks Needed | |
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A |
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B |
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C |
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Disks Req'd |
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Table 20-5 shows the values from this example:
This section explains how to determine whether your application will run best by:
Use the following approach to design file storage:
These steps are described in detail under the following headings.
Evaluate your application to determine how often it requires each type of I/O operation. Table 20-6 shows the types of read and write operations performed by each of the background processes, by foreground processes, and by parallel execution servers.
| Operation | Process | |||||||
| LGWR | DBWn | ARCH | SMON | PMON | CKPT | Fore-ground | PQ Processes | |
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Sequential Read |
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X |
X |
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X |
X |
X |
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Sequential Write |
X |
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X |
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X |
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Random Read |
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X |
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X |
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Random Write |
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X |
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In this discussion, a sample application might involve 50% random reads, 25% sequential reads, and 25% random writes.
This section illustrates relative performance of read/write operations by a particular test system. On raw devices, reads and writes are done on the character level; on block devices, these operations are done on the block level. (Many concurrent processes may generate overhead due to head and arm movement of the disk drives.)
Table 20-7 and Figure 20-1 show speed of sequential read in milliseconds per I/O, for each of the three disk layout options on a test system.
Doing research like this helps determine the correct stripe size. In this example, it takes at most 5.3 milliseconds to read 16KB. If your data were in chunks of 256KB, you could stripe the data over 16 disks (as described) and maintain this low read time.
By contrast, if all your data were on one disk, read time would be 80 milliseconds. Thus the test results show that on this particular set of disks, things look quite different from what might be expected: it is sometimes beneficial to have a smaller stripe size, depending on the size of the I/O.
Table 20-8 and Figure 20-2 show speed of sequential write in milliseconds per I/O, for each of the three disk layout options on the test system.
Table 20-9 and Figure 20-3 show speed of random read in milliseconds per I/O, for each of the three disk layout options on the test system.
Table 20-10 and Figure 20-4 show speed of random write in milliseconds per I/O, for each of the three disk layout options on the test system.
Knowing the types of operation that predominate in your application and the speed with which your system can process the corresponding I/Os, you can choose the disk layout that will maximize performance.
For example, with the sample application and test system described previously, the UNIX file system would be a good choice. With random reads predominating (50% of all I/O operations), 8KB would be a good block size. Raw devices with UNIX file systems provide comparable performance of random reads at this block size. Furthermore, the UNIX file system in this example processes sequential reads (25% of all I/O operations) almost twice as fast as raw devices, given an 8KB block size.
Table data in the database is stored in data blocks. This section describes how to allocate space within data blocks for best performance. With single block I/O (random read), retrieve all desired data from a single block in one read for best performance. How you store the data determines whether this performance objective will be achieved. It depends on two factors: storage of the rows, and block size.
The operating system I/O size should be equal to or greater than the database block size. Sequential read performance will improve if operating system I/O size is twice or three times the database block size (as in the example in "Testing the Performance of Your Disks"). This assumes that the operating system can buffer the I/O so that the next block will be read from that particular buffer.
Figure 20-5 illustrates the suitability of various block sizes to online transaction processing (OLTP) or decision support (DSS) applications.
This section describes advantages and disadvantages of different block sizes.
The number of I/Os a disk can perform depends on whether the operations involve reading or writing to objects stored on raw devices or on the file system. This affects the number of disks you must use to achieve the desired level of performance.
This section describes two tasks to perform if you suspect a problem with I/O usage:
Oracle compiles file I/O statistics that reflect disk access to database files. These statistics report only the I/O utilization of Oracle sessions--yet every process affects the available I/O resources. Tuning non-Oracle factors can thus improve performance.
Use operating system monitoring tools to determine what processes are running on the system as a whole, and to monitor disk access to all files. Remember that disks holding datafiles and redo log files may also hold files that are not related to Oracle. Try to reduce any heavy access to disks that contain database files. Access to non-Oracle files can be monitored only through operating system facilities rather than through the V$FILESTAT view.
Tools such as sar -d on many UNIX systems enable you to examine the iostat I/O statistics for your entire system. (Some UNIX-based platforms have an iostat command.) On NT systems, use Performance Monitor.
This section identifies the views and processes that provide Oracle I/O statistics, and shows how to check statistics using V$FILESTAT.
Table 20-12 shows dynamic performance views to check for I/O statistics relating to Oracle database files, log files, archive files, and control files.
Table 20-13 lists processes whose statistics reflect I/O throughput for the different Oracle file types.
| File | Process | |||||||
| LGWR | DBWn | ARCH | SMON | PMON | CKPT | Fore-ground | PQ Process | |
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Database Files |
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X |
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X |
X |
X |
X |
X |
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Log Files |
X |
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Archive Files |
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X |
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Control Files |
X |
X |
X |
X |
X |
X |
X |
X |
V$SYSTEM_EVENT, for example, shows the total number of I/Os and average duration, by type of I/O. You can thus determine which types of I/O are too slow. If there are Oracle-related I/O problems, tune them. But if your process is not consuming the available I/O resources, then some other process is. Go back to the system to identify the process that is using up so much I/O, and determine why. Then tune this process.
Examine disk access to database files through the dynamic performance view V$FILESTAT. This view shows the following information for database I/O (but not for log file I/O):
By default, this view is available only to the user SYS and to users granted SELECT ANY TABLE system privilege, such as SYSTEM. The following column values reflect the number of disk accesses for each datafile:
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PHYWRTS |
The number of writes to each database file. |
Use the following query to monitor these values over some period of time while your application is running:
SELECT name, phyrds, phywrts FROM v$datafile df, v$filestat fs WHERE df.file# = fs.file#;
This query also retrieves the name of each datafile from the dynamic performance view V$DATAFILE. Sample output might look like this:
NAME PHYRDS PHYWRTS -------------------------------------------- ---------- ---------- /oracle/ora70/dbs/ora_system.dbf 7679 2735 /oracle/ora70/dbs/ora_temp.dbf 32 546
The PHYRDS and PHYWRTS columns of V$FILESTAT can also be obtained through SNMP.
The total I/O for a single disk is the sum of PHYRDS and PHYWRTS for all the database files managed by the Oracle instance on that disk. Determine this value for each of your disks. Also determine the rate at which I/O occurs for each disk by dividing the total I/O by the interval of time over which the statistics were collected.
The rest of this chapter describes various techniques of solving I/O problems:
This section describes how to reduce disk contention.
Disk contention occurs when multiple processes try to access the same disk simultaneously. Most disks have limits on both the number of accesses and the amount of data they can transfer per second. When these limits are reached, processes may have to wait to access the disk.
In general, consider the statistics in the V$FILESTAT view and your operating system facilities. Consult your hardware documentation to determine the limits on the capacity of your disks. Any disks operating at or near full capacity are potential sites for disk contention. For example, 40 or more I/Os per second is excessive for most disks on VMS or UNIX operating systems.
To reduce the activity on an overloaded disk, move one or more of its heavily accessed files to a less active disk. Apply this principle to each of your disks until they all have roughly the same amount of I/O. This is referred to as distributing I/O.
Oracle processes constantly access datafiles and redo log files. If these files are on common disks, there is potential for disk contention. Place each datafile on a separate disk. Multiple processes can then access different files concurrently without disk contention.
Place each set of redo log files on a separate disk with no other activity. Redo log files are written by the Log Writer process (LGWR) when a transaction is committed. Information in a redo log file is written sequentially. This sequential writing can take place much faster if there is no concurrent activity on the same disk. Dedicating a separate disk to redo log files usually ensures that LGWR runs smoothly with no further tuning attention. Performance bottlenecks related to LGWR are rare. For information on tuning LGWR, see the section "Detecting Contention for Redo Log Buffer Latches".
Dedicating separate disks and mirroring redo log files are important safety precautions. Dedicating separate disks to datafiles and redo log files ensures that the datafiles and the redo log files cannot both be lost in a single disk failure. Mirroring redo log files ensures that a redo log file cannot be lost in a single disk failure.
Striping, or spreading a large table's data across separate datafiles on separate disks, can also help to reduce contention. This strategy is fully discussed in the section "Striping Disks".
It is not necessary to separate a frequently used table from its index. During the course of a transaction, the index is read first, and then the table is read. Because these I/Os occur sequentially, the table and index can be stored on the same disk without contention.
If possible, eliminate I/O unrelated to Oracle on disks that contain database files. This measure is especially helpful in optimizing access to redo log files. Not only does this reduce disk contention, it also allows you to monitor all activity on such disks through the dynamic performance table V$FILESTAT.
This section describes:
"Striping" divides a large table's data into small portions and stores these portions in separate datafiles on separate disks. This permits multiple processes to access different portions of the table concurrently without disk contention. Striping is particularly helpful in optimizing random access to tables with many rows. Striping can either be done manually (described below), or through operating system striping utilities.
Benchmark tuners in the past tried hard to ensure that the I/O load was evenly balanced across the available devices. Currently, operating systems are providing the ability to stripe a heavily used container file across many physical devices. However, such techniques are productive only where the load redistribution eliminates or reduces some form of queue.
If I/O queues exist or are suspected, then load distribution across the available devices is a natural tuning step. Where larger numbers of physical drives are available, consider dedicating two drives to carrying redo logs (two because redo logs should always be mirrored either by the operating system or using Oracle redo log group features). Because redo logs are written serially, drives dedicated to redo log activity normally require limited head movement. This significantly accelerates log writing.
When archiving, it is beneficial to use extra disks so that LGWR and ARCH do not compete for the same read/write head. This is achieved by placing logs on alternating drives.
Mirroring can also be a cause of I/O bottlenecks. The process of writing to each mirror is normally done in parallel, and does not cause a bottleneck. However, if each mirror is striped differently, then the I/O is not completed until the slowest mirror member is finished. To avoid I/O problems, stripe using the same number of disks for the destination database, or the copy, as you used for the source database.
For example, if you have 160KB of data striped over 8 disks, but the data is mirrored onto only one disk, then regardless of how quickly the data is processed on the 8 disks, the I/O is not completed until 160KB has been written onto the mirror disk. It might thus take 20.48 milliseconds to write the database, but 137 milliseconds to write the mirror.
To stripe disks manually, you need to relate an object's storage requirements to its I/O requirements.
For example, if an object requires 5GB in Oracle storage space, you need one 5GB disk or two 4GB disks to accommodate it. On the other hand, if the system is configured with 1GB or 2GB disks, the object may require 5 or 3 disks, respectively.
For example, if the storage requirement is 5 disks (1GB each), and the I/O requirement is 2 disks, then your application requires the higher value: 5 disks.
CREATE TABLESPACE stripedtabspace DATAFILE 'file_on_disk_1' SIZE 1GB, 'file_on_disk_2' SIZE 1GB, 'file_on_disk_3' SIZE 1GB, 'file_on_disk_4' SIZE 1GB, 'file_on_disk_5' SIZE 1GB;
Also specify the size of the table extents in the STORAGE clause. Store each extent in a separate datafile. The table extents should be slightly smaller than the datafiles in the tablespace to allow for overhead. For example, when preparing for datafiles of 1GB (1024MB), you can set the table extents to be 1023MB:
CREATE TABLE stripedtab ( col_1 NUMBER(2), col_2 VARCHAR2(10) ) TABLESPACE stripedtabspace STORAGE ( INITIAL 1023MB NEXT 1023MB MINEXTENTS 5 PCTINCREASE 0 );
(Alternatively, you can stripe a table by entering an ALTER TABLE ALLOCATE EXTENT statement, with a DATAFILE 'size' SIZE clause.)
These steps result in the creation of table STRIPEDTAB. STRIPEDTAB has 5 initial extents, each of size 1023MB. Each extent takes up one of the datafiles named in the DATAFILE clause of the CREATE TABLESPACE statement. Each of these files is on a separate disk. The 5 extents are all allocated immediately, because MINEXTENTS is 5.
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See Also:
Oracle8i SQL Reference for more information on MINEXTENTS and the other storage parameters. |
As an alternative to striping disks manually, use operating system striping software, such as an LVM (logical volume manager), to stripe disks. With striping software, the concern is choosing the right stripe size. This depends on the Oracle block size and disk access method.
In striping, uniform access to the data is assumed. If the stripe size is too large, can a hot spot may appear on one disk or on a small number of disks. Avoid this by reducing the stripe size, thus spreading the data over more disks.
Consider an example in which 100 rows of fixed size are evenly distributed over 5 disks, with each disk containing 20 sequential rows. If you application only requires access to rows 35 through 55, then only 2 disks must perform the I/O. At this rate, the system cannot achieve the desired level of performance.
Correct this problem by spreading rows 35 through 55 across more disks. In the current example, if there were two rows per block, then we could place rows 35 and 36 on the same disk, and rows 37 and 38 on a different disk. Taking this approach, we could spread the data over all the disks and I/O throughput would improve.
Redundant arrays of inexpensive disks (RAID) can offer significant advantages in their failure resilience features. They also permit striping to be achieved quite easily, but do not appear to provide any significant performance advantage. In fact, they may impose a higher cost in I/O overhead.
In some instances, performance can be improved by not using the full features of RAID technology. In other cases, RAID technology's resilience to single component failure may justify its cost in terms of performance.
When you create an object such as a table or rollback segment, Oracle allocates space in the database for the data. This space is called a segment. If subsequent database operations cause the data volume to increase and exceed the space allocated, Oracle extends the segment. Dynamic extension then reduces performance.
This section discusses:
Dynamic extension causes Oracle to execute SQL statements in addition to those SQL statements issued by user processes. These SQL statements are called recursive calls because Oracle issues these statements itself. Recursive calls are also generated by these activities:
Examine the RECURSIVE CALLS statistic through the dynamic performance view V$SYSSTAT. By default, this view is available only to user SYS and to users granted the SELECT ANY TABLE system privilege, such as SYSTEM. Use the following query to monitor this statistic over a period of time:
SELECT name, valueFROM v$sysstat WHERE NAME = 'recursive calls';
Oracle responds with something similar to:
NAME VALUE ------------------------------------------------------- ---------- recursive calls 626681
If Oracle continues to make excessive recursive calls while your application is running, determine whether these recursive calls are due to an activity, other than dynamic extension, that generates recursive calls. If you determine that the recursive calls are caused by dynamic extension, reduce this extension by allocating larger extents.
Follow these steps to avoid dynamic extension:
Larger extents tend to benefit performance for these reasons:
However, since large extents require more contiguous blocks, Oracle may have difficulty finding enough contiguous space to store them. To determine whether to allocate only a few large extents or many small extents, evaluate the benefits and drawbacks of each in consideration of plans for the growth and use of your objects.
Automatically re-sizable datafiles can also cause problems with dynamic extension. Avoid using the automatic extension. Instead, manually allocate more space to a datafile during times when the system is relatively inactive.
Even though an object may have unlimited extents, this does not mean that having a large number of small extents is acceptable. For optimal performance you may decide to reduce the number of extents.
Extent maps list all extents for a particular segment. The number of extents per Oracle block depends on operating system block size and platform. Although an extent is a data structure inside Oracle, the size of this data structure depends on the platform. Accordingly, this affects the number of extents Oracle can store in a single operating system block. Typically, this value is as follows:
| Block Size (KB) | Max. Number of Extents |
|
2 |
121 |
|
4 |
255 |
|
8 |
504 |
|
16 |
1032 |
|
32 |
2070 |
For optimal performance, you should be able to read the extent map with a single I/O. Performance degrades if multiple I/Os are necessary for a full table scan to get the extent map.
Avoid dynamic extension in dictionary-mapped tablespaces. For dictionary-mapped tablespaces, do not let the number of extents exceed 1,000. If extent allocation is local, do not have more than 2,000 extents. Having too many extents reduces performance when dropping or truncating tables.
The optimal choice in most situations is to enable AUTOEXTEND. You can also use a proven value for allocating extents if you are sure the value provides optimal performance.
This section explains various ramifications of using multiple extents.
The size of rollback segments can affect performance. Rollback segment size is determined by the rollback segment's storage parameter values. Your rollback segments must be large enough to hold the rollback entries for your transactions. As with other objects, you should avoid dynamic space management in rollback segments.
Use the SET TRANSACTION statement to assign transactions to rollback segments of the appropriate size based on the recommendations in the following sections. If you do not explicitly assign a transaction to a rollback segment, Oracle automatically assigns it to a rollback segment.
For example, the following statement assigns the current transaction to the rollback segment OLTP_13:
SET TRANSACTION USE ROLLBACK SEGMENT oltp_13
Also monitor the shrinking, or dynamic deallocation, of rollback segments based on the OPTIMAL storage parameter. For information on choosing values for this parameter, monitoring rollback segment shrinking, and adjusting the OPTIMAL parameter, please see the Oracle8i Administrator's Guide.
Assign large rollback segments to transactions that modify data that is concurrently selected by long queries. Such queries may require access to rollback segments to reconstruct a read-consistent version of the modified data. The rollback segments must be large enough to hold all the rollback entries for the data while the query is running.
Assign large rollback segments to transactions that modify large amounts of data. A large rollback segment can improve the performance of such a transaction, because the transaction generates large rollback entries. If a rollback entry does not fit into a rollback segment, Oracle extends the segment. Dynamic extension reduces performance and should be avoided whenever possible.
OLTP applications are characterized by frequent concurrent transactions, each of which modifies a small amount of data. Assign OLTP transactions to small rollback segments, provided that their data is not concurrently queried. Small rollback segments are more likely to remain stored in the buffer cache where they can be accessed quickly. A typical OLTP rollback segment might have 2 extents, each approximately 10 kilobytes in size. To best avoid contention, create many rollback segments and assign each transaction to its own rollback segment.
If an UPDATE statement increases the amount of data in a row so that the row no longer fits in its data block, Oracle tries to find another block with enough free space to hold the entire row. If such a block is available, Oracle moves the entire row to the new block. This is called migrating a row. If the row is too large to fit into any available block, Oracle splits the row into multiple pieces and stores each piece in a separate block. This is called chaining a row. Rows can also be chained when they are inserted.
Dynamic space management, especially migration and chaining, is detrimental to performance:
Identify migrated and chained rows in a table or cluster using the ANALYZE statement with the LIST CHAINED ROWS option. This statement collects information about each migrated or chained row and places this information into a specified output table.
The definition of a sample output table named CHAINED_ROWS appears in a SQL script available on your distribution medium. The common name of this script is UTLCHAIN.SQL, although its exact name and location varies depending on your platform. Your output table must have the same column names, datatypes, and sizes as the CHAINED_ROWS table.
You can also detect migrated or chained rows by checking the TABLE FETCH CONTINUED ROW column in V$SYSSTAT. Increase PCTFREE to avoid migrated rows. If you leave more free space available in the block, the row will have room to grow. You can also reorganize or re-create tables and indexes with high deletion rates.
To reduce migrated and chained rows in an existing table, follow these steps:
ANALYZE TABLE order_hist LIST CHAINED ROWS;
SELECT * FROM chained_rows WHERE table_name = 'ORDER_HIST'; OWNER_NAME TABLE_NAME CLUST... HEAD_ROWID TIMESTAMP ---------- ---------- -----... ------------------ --------- SCOTT ORDER_HIST ... AAAAluAAHAAAAA1AAA 04-MAR-96 SCOTT ORDER_HIST ... AAAAluAAHAAAAA1AAB 04-MAR-96 SCOTT ORDER_HIST ... AAAAluAAHAAAAA1AAC 04-MAR-96
The output lists all rows that are either migrated or chained.
CREATE TABLE int_order_hist AS SELECT * FROM order_hist WHERE ROWID IN (SELECT head_rowid FROM chained_rows WHERE table_name = 'ORDER_HIST');
DELETE FROM order_hist WHERE ROWID IN (SELECT head_rowid FROM chained_rows WHERE table_name = 'ORDER_HIST');
INSERT INTO order_hist SELECT * FROM int_order_hist;
DROP TABLE int_order_history;
DELETE FROM chained_rows WHERE table_name = 'ORDER_HIST';
Retrieval of migrated rows is resource intensive; therefore, all tables subject to UPDATE should have their distributed free space set to allow enough space within the block for the likely update.
The SQL.BSQ file runs when you issue the CREATE DATABASE statement. This file contains the table definitions that make up the Oracle server. The views you use as a DBA are based on these tables. Oracle recommends that you strictly limit modifications to SQL.BSQ.
There is a trade-off between performance and memory usage. For best performance, most sorts should occur in memory; sorts written to disk adversely affect performance. If the sort area size is too large, too much memory may be used. If the sort area size is too small, sorts may have to be written to disk which, as, mentioned, can severely degrade performance.
This section describes:
The default sort area size is adequate to hold all the data for most sorts. However, if your application often performs large sorts on data that does not fit into the sort area, you may want to increase the sort area size. Large sorts can be caused by any SQL statement that performs a sort on many rows.
Oracle collects statistics that reflect sort activity and stores them in the dynamic performance view V$SYSSTAT. By default, this view is available only to the user SYS and to users granted the SELECT ANY TABLE system privilege. These statistics reflect sort behavior:
Use the following query to monitor these statistics over time:
SELECT name, value FROM v$sysstat WHERE name IN ('sorts (memory)', 'sorts (disk)');
The output of this query might look like this:
NAME VALUE ------------------------------------------------------- ---------- sorts(memory) 965 sorts(disk) 8
The information in V$SYSSTAT can also be obtained through the SNMP (Simple Network Management Protocol).
SORT_AREA_SIZE is a dynamically modifiable initialization parameter that specifies the maximum amount of memory to use for each sort. If a significant number of sorts require disk I/O to temporary segments, your application's performance may benefit from increasing the size of the sort area. In this case, increase the value of SORT_AREA_SIZE.
The maximum value of this parameter depends on your operating system. You need to determine how large a SORT_AREA_SIZE makes sense. If you set SORT_AREA_SIZE to an adequately large value, most sorts should not have to go to disk (unless, for example, you are sorting a 10-gigabyte table).
As mentioned, increasing sort area size decreases the chances that sorts go to disk. Therefore, with a larger sort area, most sorts