Oracle 10g Awr

Oracle Server - Enterprise Edition - Version: 10.2. Information in this document applies to any platform. Goal

RDBMS version 10g offers a new and improved tool for diagnosing Database Perfromance issues. It is the Automated WorkLoad Repository (AWR). However, there are still a number of customers using statistics package (statspack) intially introduced in RDBMS version 8.1. The goal of this document is to further assist customers/engineers when installing and using the database performance tool Statspack. During install of the RBBMS product, Oracle stores a document entitled spdoc.txt. The spdoc.txt file will be located in the following directory upon successful install of the RDBMS product 8.1.7 or higher: $ORACLE_HOME/rdbms/admin/. The StatsPack README files (spdoc.txt) include specific updated information, and history on this tool as well as platform and release specific information that will help when installing and using this product. A number of cutomers do not realize spdoc.txt is available on their systems, or would like to have it available through Oracle's Knowledge Repository for easy access. Therefore, the latest version, 10.2, spdoc.txt is published in this note. Please find below spdoc.txt for version 10.2 in it's entirety to help guide you through installation, and the most common issues you may encounter while running statspack. Information in this document will help you with all versions of RDBMS statspack product. However, Oracle still suggests you go to your $ORACLE_HOME/rdbms/admin/spdoc.txt to reference your statspack platform and version specific information on running statspack reports (i.e section 4 below). Solution

----------------------------------------------------------------------Oracle10g Server Release 10.2 Production ------------------------------------------------------------------------Copyright (C) 1993, 2005, Oracle Corporation. All rights reserved. Author: Connie Dialeris Green Contributors: Cecilia Gervasio, Graham Wood, Russell Green, Patrick Tearle, Harald Eri, Stefan Pommerenk, Vladimir Barriere

Please refer to the Oracle10g server README file in the rdbms doc directory, for copyright, disclosure, restrictions, warrant, trademark, disclaimer, and licensing information. The README file is README_RDBMS.HTM. Oracle Corporation, 500 Oracle Parkway, Redwood City, CA 94065. ------------------------------------------------------------------------Statistics Package (STATSPACK) README (spdoc.txt) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ TABLE OF CONTENTS ----------------0. Introduction and Terminology 1. Enterprise Manager (EM), Automatic Workload Repository (AWR) and Statspack 2. Statspack Configuration 2.1. Database Space Requirements 2.2. Installing the Tool 2.3. Errors during Installation 3. Gathering data - taking a snapshot 3.1. Automating Statspack Statistics Gathering 3.2. Using dbms_job 4. Running the Performance reports 4.1. Running the instance report 4.2. Running the instance report when there are multiple instances 4.3. Configuring the Instance Report 4.4. Running the SQL report 4.5. Running the SQL report when there are multiple instances 4.6. Configuring the SQL report 4.7. Gathering optimizer statistics on the PERFSTAT schema 5. Configuring the amount of data captured 5.1. Snapshot Level 5.2. Snapshot SQL thresholds 5.3. Changing the default values for Snapshot Level and SQL Thresholds 5.4. Snapshot Levels - details 5.5. Specifying a Session Id 5.6. Input Parameters for the SNAP and MODIFY_STATSPACK_PARAMETERS procedures 6. Time Units used for Performance Statistics 7. Event Timings 8. Managing and Sharing performance data 8.1. Baselining performance data 8.1.1. Input Parameters for the MAKE_BASELINE and CLEAR_BASELINE procedure and function which accept Begin and End Snap Ids 8.1.2. Input Parameters for the MAKE_BASELINE and CLEAR_BASELINE procedure and function which accept Begin and End Dates 8.2. Purging/removing unnecessary data 8.2.1. Input Parameters for the PURGE procedure and function which accept Begin Snap Id and End Snap Id 8.2.2. Input Parameters for the PURGE procedure and function

which accept Begin Date and End Date 8.2.3. Input Parameters for the PURGE procedure and function which accept a single Purge Before Date 8.2.4. Input Parameters for the PURGE procedure and function which accept the Number of Days of data to keep 8.2.5. Using sppurge.sql 8.3. Removing all data 8.4. Sharing data via export 9. New and Changed Features 9.1. Changes between 10.1 and 10.2 9.2. Changes between 9.2 and 10.1 9.3. Changes between 9.0 and 9.2 9.4. Changes between 8.1.7 and 9.0 9.5. Changes between 8.1.6 and 8.1.7 10. Compatibility and Upgrading from previous releases 10.1. Compatibility Matrix 10.1.1. Using Statspack shipped with 10.1 10.1.2. Using Statspack shipped with 10.0 10.1.3. Using Statspack shipped with 9.2 10.1.4. Using Statspack shipped with 9.0 10.1.5. Using Statspack shipped with 8.1.7 on 9i releases 10.2. Upgrading an existing Statspack schema to a newer release 10.2.1. Upgrading the Statspack schema from 10.1 to 10.2 10.2.2. Upgrading the Statspack schema from 9.2 to 10.1 10.2.3. Upgrading the Statspack schema from 9.0 to 9.2 10.2.4. Upgrading the Statspack schema from 8.1.7 to 9.0 10.2.5. Upgrading the Statspack schema from 8.1.6 to 8.1.7 10.2.6. Upgrading the Statspack schema from 8.1.6 to 9.2 10.2.7. Upgrading the Statspack schema from 8.1.6 to 9.0 10.2.8. Upgrading the Statspack schema from 8.1.7 to 9.2 11. Oracle Real Application Clusters specific considerations 11.1. Changing Instance Numbers 11.2. Cluster Specific Reports 11.3. Cluster Specific Data 12. Conflicts and differences compared to UTLBSTAT/UTLESTAT 12.1. Running BSTAT/ESTAT in conjunction to Statspack 12.2. Differences between Statspack and BSTAT/ESTAT 13. Removing the package 14. Supplied Scripts Overview 15. Limitations and Modifications 15.1. Limitations 15.2. Modifications 0. Introduction and Terminology ----------------------------------To effectively perform reactive tuning, it is vital to have an established baseline for later comparison when the system is running poorly. Without a baseline data point, it becomes very difficult to identify what a new problem is attributable to: Has the volume of transactions on the system increased? Has the transaction profile or application changed? Has the number of users increased?

Statspack fundamentally differs from the well known UTLBSTAT/UTLESTAT tuning scripts by collecting more information, and also by storing the performance statistics permanently in Oracle tables, which can later be used for reporting and analysis. The data collected can be analyzed using the report provided, which includes an 'instance health and load' summary page, high resource SQL statements, as well as the traditional wait events and initialization parameters. Statspack improves on the existing UTLBSTAT/UTLESTAT performance scripts in the following ways: - Statspack collects more data, including high resource SQL (and the optimizer execution plans for those statements) - Statspack pre-calculates many ratios useful when performance tuning, such as cache hit ratios, per transaction and per second statistics (many of these ratios must be calculated manually when using BSTAT/ESTAT) - Permanent tables owned by PERFSTAT store performance statistics; instead of creating/dropping tables each time, data is inserted into the pre-existing tables. This makes historical data comparisons easier - Statspack separates the data collection from the report generation. Data is collected when a 'snapshot' is taken; viewing the data collected is in the hands of the performance engineer when he/she runs the performance report - Data collection is easy to automate using either dbms_job or an OS utility NOTE: The term 'snapshot' is used to denote a set of statistics gathered at a single time, identified by a unique Id which includes the snapshot number (or snap_id). This term should not be confused with Oracle's Snapshot Replication technology. How does Statspack work? Statspack is a set of SQL, PL/SQL and SQL*Plus scripts which allow the collection, automation, storage and viewing of performance data. A user is automatically created by the installation script - this user, PERFSTAT, owns all objects needed by this package. This user is granted limited query-only privileges on the V$views required for performance tuning. Statspack users will become familiar with the concept of a 'snapshot'. 'snapshot' is the term used to identify a single collection of performance data. Each snapshot taken is identified by a 'snapshot id' which is a unique number generated at the time the snapshot is taken; each time a new collection is taken, a new snap_id is generated. The snap_id, along with the database identifier (dbid) and instance number (instance_number) comprise the unique key for a snapshot (using this unique combination allows storage of multiple instances of a Clustered database in the same tables).

Once snapshots are taken, it is possible to run the performance report. The performance report will prompt for the two snapshot id's the report will process. The report produced calculates the activity on the instance between the two snapshot periods specified, in a similar way to the BSTAT/ESTAT report; to compare - the first snap_id supplied can be considered the equivalent of running BSTAT; the second snap_id specified can be considered the equivalent of ESTAT. Unlike BSTAT/ESTAT which can by its nature only compare two static data points, the report can compare any two snapshots specified.

1. Enterprise Manager (EM), Automatic Workload Repository (AWR) and Statspack ---------------------------------------------------------------------------------------------Enterprise Manager -----------------Statspack allows you to capture Oracle instance-related performance data, and report on this data in a textual format. For EM managed databases in 9i, Oracle Enterprise Manager uses Statspack data and displays it graphically. Starting with 10g, Enterprise Manager instead uses data collected by the Automatic Workload Repository (AWR). AWR data is internally captured and stored by Oracle 10g databases. For more information about Oracle Enterprise Manager visit the Oracle website oracle.com --> Database --> Manageability Automatic Workload Repository and Statspack ------------------------------------------The Automatic Workload Repository (AWR) is an integrated part of the Oracle server. Its purpose is to collect server-related performance data automatically every 60 minutes (by default) when the statistics_level parameter is set to 'typical' (or 'all'). As the data is collected by the server itself, the Automated Database Diagnostic Monitor (ADDM) component of the server uses this data automatically to diagnose performance issues. DBAs and performance engineers can access the performance recommendations by using EM, or view the captured data in the AWR report, which is similar to the Statspack Instance report. To compare, Statspack is a manually installed and configured set of SQL and PL/SQL scripts which gather performance statistics. The data gathered is used by DBAs and performance engineers to manually diagnose performance problems. The AWR schema was initially based on the Statspack schema, but has since been modified. Because of this shared history, there are some similarities (e.g. concept of a snapshot, similar base tables). However, AWR is separate from Statspack. For more information on using AWR, please see the Oracle 10g Server Performance Tuning Guide. For license information regarding AWR, please see the Oracle database Licensing Information Manual.

If you are going to use AWR instead of Statspack, and you have been using Statspack at your site, it is recommended that you continue to capture Statspack data for a short time (e.g. one month) after the upgrade to 10g. This is because comparing post-upgrade Statspack data to pre-upgrade Statspack data may make diagnosing initial upgrade problems easier to detect. WARNING: If you choose to continue Statspack data collection after upgrading to 10g, and statistics_level is set to typical or all (which enables AWR collection), it is advised to stagger Statspack data collection so it does not coincide with AWR data collection (AWR data collection is by default is every hour, on the hour). Staggering data collection should be done to avoid the potential for any interference (e.g. stagger data collection by 30 minutes). Long term, typically, there is little reason to collect data through both AWR and Statspack. If you choose to use AWR instead of Statspack, you should ensure you should keep a representative set of baselined Statspack data for future reference. 2. Statspack Configuration -----------------------------2.1. Database Space Requirements The amount of database space required by the package will vary considerably based on the frequency of snapshots, the size of the database and instance, and the amount of data collected (which is configurable). It is therefore difficult to provide general storage clauses and space utilization predictions that will be accurate at each site. Space Requirements -----------------The default initial and next extent sizes are 100k, 1MB, 3MB or 5MB for all Statspack tables and indexes. To install Statspack, the minimum space requirement is approximately 100MB. However, the amount of space actually allocated will depend on the Tablespace storage characteristics of the tablespace Statspack is installed in (for example, if your minimum extent size is 10m, then the storage requirement will be considerably more than 100m). Using Locally Managed Tablespaces --------------------------------If you install the package in a locally-managed tablespace, such as SYSAUX, modifying storage clauses is not required, as the storage characteristics are automatically managed. Using Dictionary Managed Tablespaces -----------------------------------If you install the package in a dictionary-managed tablespace, Oracle suggests you monitor the space used by the objects created, and adjust the storage clauses of the segments, if required. 2.2. Installing the Tool

Installation scripts create a user called PERFSTAT, which will own all PL/SQL code and database objects created (including the STATSPACK tables, constraints and the STATSPACK package). During the installation you will be prompted for the PERFSTAT user's password and default and temporary tablespaces. The default tablespace will be used to create all Statspack objects (such as tables and indexes). Oracle recommend using the SYSAUX tablespace for the PERFSTAT user's default tablespace; the SYSAUX tablespace will be the tablespace defaulted during the installation, if no other is specified. A temporary tablespace is used for workarea activities, such as sorting (for more information on temporary tablespaces, see the Oracle10g Concepts Manual). The Statspack user's temporary tablespace will be set to the database's default temporary tablespace by the installation, if no other temporary tablespace is specified. NOTE: o A password for PERFSTAT user is mandatory and there is no default password; if a password is not specified, the installation will abort with an error indicating this is the problem. o For security reasons, keep PERFSTAT's password confidential. o Do not specify the SYSTEM tablespace for the PERFSTAT users DEFAULT or TEMPORARY tablespaces; if SYSTEM is specified the installation will terminate with an error indicating this is the problem. This is enforced as Oracle does not recommend using the SYSTEM tablespace to store statistics data, nor for workareas. Use the SYSAUX (or a TOOLS) tablespace to store the data, and your instance's TEMPORARY tablespace for workareas. o During the installation, the dbms_shared_pool PL/SQL package is created. dbms_shared_pool is used to pin the Statspack package in the shared pool dbms_job is no longer created as part of the installation, as it is already created by catproc.sql (dbms_job can be used by the DBA to schedule periodic snapshots automatically). To install the package, either change directory to the ORACLE_HOME rdbms/admin directory, or fully specify the ORACLE_HOME/rdbms/admin directory when calling the installation script, spcreate. To run the installation script, you must use SQL*Plus and connect as a user with SYSDBA privilege. e.g. Start SQL*Plus, then: on Unix: SQL> connect / as sysdba SQL> @?/rdbms/admin/spcreate

on Windows: SQL> connect / as sysdba SQL> @%ORACLE_HOME%\rdbms\admin\spcreate

The spcreate install script runs 3 other scripts - you do not need to run these - these scripts are called automatically: 1. spcusr -> creates the user and grants privileges 2. spctab -> creates the tables 3. spcpkg -> creates the package Check each of the three output files produced (spcusr.lis, spctab.lis, spcpkg.lis) by the installation to ensure no errors were encountered, before continuing on to the next step. Note that there are two ways to install Statspack - interactively (as shown above), or in 'batch' mode; batch mode is useful when you do not wish to be prompted for the PERFSTAT user's password, and default and temporary tablespaces. Batch mode installation ~~~~~~~~~~~~~~~~~~~~~~~ To install in batch mode, you must assign values to the SQL*Plus variables which specify the password and the default and temporary tablespaces before running spcreate. The variables are: perfstat_password -> for the password default_tablespace -> for the default tablespace temporary_tablespace -> for the temporary tablespace e.g. on Unix: SQL> SQL> SQL> SQL> SQL> SQL>

connect / as sysdba define default_tablespace='tools' define temporary_tablespace='temp' define perfstat_password='erg8oiw' @?/rdbms/admin/spcreate undefine perfstat_password

spcreate will no longer prompt for the above information. 2.3. Errors during installation

Specifying SYSTEM tablespace A possible error during installation is to specify the SYSTEM tablespace for the PERFSTAT user's DEFAULT or TEMPORARY tablespace. In such a situation, the installation will fail, stating the problem. To install Statspack after receiving errors during the installation To correctly install Statspack after such errors, first run the de-install script, then the install script. Both scripts must be run from SQL*Plus. e.g. Start SQL*Plus, connect as a user with SYSDBA privilege, then: SQL> @spdrop SQL> @spcreate

3. Gathering data - taking a snapshot -------------------------------------------------The simplest interactive way to take a snapshot is to login to SQL*Plus as the PERFSTAT user, and execute the procedure statspack.snap: e.g. SQL> connect perfstat/perfstat_password SQL> execute statspack.snap;

Note: In a Clustered database environment, you must connect to the instance you wish to collect data for. This will store the current values for the performance statistics in the Statspack tables, and can be used as a baseline snapshot for comparison with another snapshot taken at a later time. For better performance analysis, set the initialization parameter timed_statistics to true; this way, Statspack data collected will include important timing information. The timed_statistics parameter is also dynamically changeable using the 'alter system' command. Timing data is important and is usually required by Oracle support to diagnose performance problems. The default level of data collection is level 5. It is possible to change the amount of data captured by changing the snapshot level, and the default thresholds used by Statspack. For information on how to do this, please see the 'Configuring the amount of data captured' section of this file. Typically, in the situation where you would like to automate the gathering and reporting phases (such as during a benchmark), you may need to know the snap_id of the snapshot just taken. To take a snapshot and display the snap_id, call the statspack.snap function. Below is an example of calling the snap function using an anonymous PL/SQL block in SQL*Plus: e.g.

SQL> variable snap number; SQL> begin :snap := statspack.snap; end; 2/ PL/SQL procedure successfully completed. SQL> print snap SNAP ---------12

3.1. Automating Statspack statistics gathering To be able to make comparisons of performance from one day, week or year to the next, there must be multiple snapshots taken over a period of time. The best method to gather snapshots is to automate the collection on a regular time interval. It is possible to do this: - within the database, using the Oracle dbms_job procedure to schedule the snapshots - using Operating System utilities. On Unix systems, you could use utilities such as 'cron' or 'at'. On Windows, you could schedule a task (e.g. via Start> Programs> Accessories> System Tools> Scheduled Tasks). 3.2. Using dbms_job To use an Oracle-automated method for collecting statistics, you can use dbms_job. A sample script on how to do this is supplied in spauto.sql, which schedules a snapshot every hour, on the hour. You may wish to schedule snapshots at regular times each day to reflect your system's OLTP and/or batch peak loads. For example take snapshots at 9am, 10am, 11am, 12 midday and 6pm for the OLTP load, then a snapshot at 12 midnight and another at 6am for the batch window. In order to use dbms_job to schedule snapshots, the job_queue_processes initialization parameter must be set to a value greater than 0 for the job to run automatically. Example of setting the job_queue_processes parameter in an init.ora file: # Set to enable the job queue process to start. This allows dbms_job # to schedule automatic statistics collection using STATSPACK job_queue_processes=1 If using spauto.sql in a Clustered database environment, the spauto.sql script must be run once on each instance in the cluster. Similarly, the job_queue_processes parameter must also be set for each instance.

Changing the interval of statistics collection ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To change the interval of statistics collection use the dbms_job.interval procedure e.g. execute dbms_job.interval(1,'SYSDATE+(1/48)');

Where 'SYSDATE+(1/48)' will result in the statistics being gathered each 1/48th of a day (i.e. every 30 minutes). To force the job to run immediately, execute dbms_job.run(<job number>); To remove the auto collect job, execute dbms_job.remove(<job number>);

For more information on dbms_job, see the Supplied Packages Reference Manual. 4. Running the Performance reports ---------------------------------------Once snapshots are taken, it is possible to generate a performance report. There are two reports available - an Instance report, and a SQL report: - The Instance Report (spreport.sql and sprepins.sql) is a general instance health report, covering all aspects of instance performance. The instance report calculates and prints ratios, increases etc. for all statistics between the two snapshot periods, in a similar way to the BSTAT/ESTAT report. Note: spreport.sql calls sprepins.sql, first defaulting the dbid and instance number of the instance you are connected to. For more information on the difference between sprepins and spreport, see the 'Running the instance report when there are multiple instances' section of this document. - The SQL report (sprepsql.sql and sprsqins.sql) is a report for a specific SQL statement. The SQL report is usually run after examining the high-load SQL sections of the instance health report. The SQL report provides detailed statistics and data for a single SQL statement (as identified by the Hash Value). Note: sprepsql.sql calls sprsqins.sql, first defaulting the dbid and instance number of the instance you are connected to. For more information on the difference between sprsqins and sprepsql, see the 'Running the SQL report when there are multiple instances' section of this document.

Both reports prompt for the beginning snapshot id, the ending snapshot id, and the report name. The SQL report additionally requests the Hash Value for the SQL statement to be reported on. Note: It is not correct to specify begin and end snapshots where the begin snapshot and end snapshot were taken from different instance startups. In other words, the instance must not have been shutdown between the times that the begin and end snapshots were taken. We ask that you reference file $ORACLE_HOME/rdbms/admin/spdoc.txt, as mentioned in "Goal" section above, for details on running your specific version of statspack reports.

5. Configuring the amount of data captured ----------------------------------------------Both the snapshot level, and the thresholds specified will affect the amount of data Statspack captures. 5.1. Snapshot Level It is possible to change the amount of information gathered by the package, by specifying a different snapshot 'level'. In other words, the level chosen (or defaulted) will decide the amount of data collected. The higher the snapshot level, the more data is gathered. The default level set by the installation is level 5. For typical usage, level 5 snapshot is effective on most sites. There are certain situations when using a level 6 snapshot is beneficial, such as when taking a baseline. The events listed below are a subset of events which should prompt taking a new baseline, using level 6: - when taking the first snapshots - when a new application is installed, or an application is modified/upgraded - after gathering optimizer statistics - before and after upgrading The various levels are explained in detail 'Snapshot Levels - details' section of this document. 5.2. Snapshot SQL thresholds There are other parameters which can be configured in addition to the snapshot level. These parameters are used as thresholds when collecting data on SQL statements; data will be captured on any SQL statements that breach the specified thresholds.

Snapshot level and threshold information used by the package is stored in the stats$statspack_parameter table. 5.3. Changing the default values for Snapshot Level and SQL Thresholds If you wish to, you can change the default parameters used for taking snapshots, so that they are tailored to the instance's workload. The full list of parameters which can be passed into the modify_statspack_parameter procedure are the same as those for the snap procedure. These are listed in the 'Input Parameters for the SNAP and MODIFY_STATSPACK_PARAMETERS procedures' section of this document. Temporarily using new values -----------------------------To temporarily use a snapshot level or threshold which is different to the instance's default snapshot values, simply specify the required threshold or snapshot level when taking the snapshot. This value will only be used for immediate snapshot taken - the new value will not be saved as the default. e.g. Take a single level 6 snapshot (do not save level 6 as the default): SQL> execute statspack.snap(i_snap_level=>6);

Saving new defaults -------------------If you wish to save the new value as the instance's default, you can do this either by: o Taking a snapshot, and specifying the new defaults to be saved to the database (using statspack.snap, and using the i_modify_parameter input variable). SQL> execute statspack.snap (i_snap_level=>10, i_modify_parameter=>'true');

Setting the i_modify_parameter value to true will save the new thresholds in the stats$statspack_parameter table; these thresholds will be used for all subsequent snapshots. If the i_modify_parameter was set to false or if it were omitted, the new parameter values would not be saved. Only the snapshot taken at that point will use the specified values, any subsequent snapshots will use the preexisting values in the stats$statspack_parameter table. o Changing the defaults immediately without taking a snapshot, using the statspack.modify_statspack_parameter procedure. For example to change the

snapshot level to 10, and the SQL thresholds for buffer_gets and disk_reads, the following statement can be issued: SQL> execute statspack.modify_statspack_parameter (i_snap_level=>10, i_buffer_gets_th=>10000, i_disk_reads_th=>1000);

This procedure changes the values permanently, but does not take a snapshot. 5.4 Snapshot Levels - details Levels >= 0 General performance statistics Statistics gathered: This level and any level greater than 0 collects general performance statistics, such as: wait statistics, system events, system statistics, rollback segment data, row cache, SGA, background events, session events, lock statistics, buffer pool statistics, latch statistics, resource limit, enqueue statistics, and statistics for each of the following, if enabled: automatic undo management, buffer cache advisory data, auto PGA memory management, Cluster DB statistics. Levels >= 5 Additional data: SQL Statements This level includes all statistics gathered in the lower level(s), and additionally gathers the performance data on high resource usage SQL statements. In a level 5 snapshot (or above), note that the time required for the snapshot to complete is dependent on the shared_pool_size and on the number of SQL statements in the shared pool at the time the snapshot is taken: the larger the shared pool, the longer the time taken to complete the snapshot. SQL 'Thresholds' The SQL statements gathered by Statspack are those which exceed one of six predefined threshold parameters: - number of executions of the SQL statement (default 100) -

number of disk reads performed by the SQL statement (default 1,000) number of parse calls performed by the SQL statement (default 1,000) number of buffer gets performed by the SQL statement (default 10,000) size of sharable memory used by the SQL statement (default 1m) version count for the SQL statement (default 20)

The values of each of these threshold parameters are used when deciding which SQL statements to collect - if a SQL statement's resource usage exceeds any one of the above threshold values, it is captured during the snapshot. The SQL threshold levels used are either those stored in the table stats$statspack_parameter, or by the thresholds specified when the snapshot is taken. Levels >= 6 Additional data: SQL Plans and SQL Plan usage

This level includes all statistics gathered in the lower level(s), and additionally gathers optimizer execution plans, and plan usage data for each of the high resource usage SQL statements captured. A level 6 snapshot gathers information which is invaluable when determining whether the execution plan used for a SQL statement has changed. Therefore level 6 snapshots should be used whenever there is the possibility a plan may change, such as after large data loads, or after gathering new optimizer statistics. To capture the plan for a SQL statement, the statement must be in the shared pool at the time the snapshot is taken, and must exceed one of the SQL thresholds. To gather plans for all statements in the shared pool, you can temporarily specify the executions threshold (i_executions_th) to be zero (0) for those snapshots. For information on how to do this, see the 'Changing the default values for Snapshot Level and SQL Thresholds' section of this document. Levels >= 7 Additional data: Segment level statistics This level includes all statistics gathered in the lower level(s), and additionally gathers the performance data on highly used segments. A level 7 snapshot captures Segment-level statistics for segments which are heavily accessed or heavily contended for. Segment-level statistics captured are: - logical reads - db block changes - physical reads - physical writes - physical reads direct - physical writes direct - global cache cr blocks served * - global cache current blocks served * - buffer busy waits - ITL waits - row lock waits * Denotes the Statistic is Real Application Clusters specific. There are many uses for segment-specific statistics. Below are three examples: - The statistics relating to physical reads and writes can help you decide to modify the physical layout of some segments (or of the tablespaces they reside in). For example, to better spread the segment IO load, you can add files residing on different disks to a tablespace storing a heavily accessed segment, or you can (re)partition a segment. - High numbers of ITL waits for a specific segment may indicate a need to change segment storage attributes such as PCTFREE and/or INITRANS. - In a Real Application Clusters database, global cache statistics make it easy to spot the segments responsible for much of the cross-instance traffic. Although Statspack captures all segment statistics, it only displays the following statistics in the Instance report: - logical reads

-

physical reads buffer busy waits ITL waits row lock waits global cache cr blocks served * global cache current blocks served *

Segment statistics 'Thresholds' The segments for which statistics are gathered are those whose statistics exceed one of the following seven threshold parameters: - number of logical reads on the segment (default 10000) - number of physical reads on the segment (default 1000) - number of buffer busy waits on the segment (default 100) - number of row lock waits on the segment (default 100) - number of ITL waits on the segment (default 100) - number of global cache Consistent Read blocks served* (default 1000) - number of global cache CUrrent blocks served* (default 1000) The values of each of these thresholds are used when deciding which segments to collect statistics for. If any segment's statistic value exceeds its corresponding threshold value, all statistics for this segment are captured. The threshold levels used are either those stored in the table stats$statspack_parameter, or by the thresholds specified when the snapshot is taken. Levels >= 10 Additional statistics: Parent and Child latches This level includes all statistics gathered in the lower levels, and additionally gathers Parent and Child Latch information. Data gathered at this level can sometimes cause the snapshot to take longer to complete i.e. this level can be resource intensive, and should only be used when advised by Oracle personnel. 5.5. Specifying a Session Id If you would like to gather session statistics and wait events for a particular session (in addition to the instance statistics and wait events), it is possible to specify the session id in the call to Statspack. The statistics gathered for the session will include session statistics, session events and lock activity. The default behaviour is to not to gather session level statistics. SQL> execute statspack.snap(i_session_id=>3);

Note that in order for session statistics to be included in the report output, the session's serial number (serial#) must be the same in the begin and end snapshot. If the serial numbers differ, it means the session is not the same session, so it is not valid to generate session statistics. If the serial numbers differ, the following warning will appear (after the begin/end snapshot has been entered by the user) to signal the session statistics cannot be printed: WARNING: SESSION STATISTICS WILL NOT BE PRINTED, as session statistics

captured in begin and end snapshots are for different sessions (Begin Snap sid,serial#: 10,752, End Snap sid,serial#: 10,754). 5.6. Input Parameters for the SNAP and MODIFY_STATSPACK_PARAMETERS procedures Parameters able to be passed in to the statspack.snap and statspack.modify_statspack_parameter procedures are as follows: Range of Default Parameter Name Valid Values Value Meaning --------------------------------------------------i_snap_level 0,5,6,7,10 5 Snapshot Level i_ucomment Text Comment to be stored with Snapshot i_executions_th Integer >=0 100 SQL Threshold: number of times the statement was executed i_disk_reads_th Integer >=0 1,000 SQL Threshold: number of disk reads the statement made i_parse_calls_th Integer >=0 1,000 SQL Threshold: number of parse calls the statement made i_buffer_gets_th Integer >=0 10,000 SQL Threshold: number of buffer gets the statement made i_sharable_mem_th Integer >=0 1048576 SQL Threshold: amount of sharable memory i_version_count_th Integer >=0 20 SQL Threshold: number of versions of a SQL statement i_seg_phy_reads_th Integer >=0 1,000 Segment statistic Threshold: number of physical reads on a segment. i_seg_log_reads_th Integer >=0 1,0000 Segment statistic Threshold: number of logical reads on a segment. i_seg_buff_busy_th Integer >=0 100 Segment statistic Threshold: number of buffer busy waits for a segment. i_seg_rowlock_w_th Integer >=0 100 Segment statistic Threshold: number of row lock waits for a segment. i_seg_itl_waits_th Integer >=0 100 Segment statistic Threshold: number of ITL waits for a segment. i_seg_cr_bks_sd_th Integer >=0 1000 Segment statistic Threshold: number of Consistent Reads blocks served by the instance for the segment*. i_seg_cu_bks_sd_th Integer >=0 1000 Segment statistic Threshold: number of CUrrent blocks served by the instance for the segment*. i_session_id Valid sid 0 (no Session Id of the Oracle Session from session) to capture session granular v$session statistics for i_modify_parameter True,False False Save the parameters specified for future snapshots?

6. Time Units used for Performance Statistics -------------------------------------------------Oracle now supports capturing certain performance data with millisecond and microsecond granularity. Views which include microsecond timing include: - v$session_wait, v$system_event, v$session_event (time_waited_micro column) - v$sql, v$sqlarea (cpu_time, elapsed_time columns) - v$latch, v$latch_parent, v$latch_children (wait_time column) - v$sql_workarea, v$sql_workarea_active (active_time column) Views which include millisecond timings include: - v$enqueue_stat (cum_wait_time) Note that existing columns in other views continue to capture centi-second times. As centi-second and microsecond timing may not be appropriate for rolled up data such as that displayed by Statspack, Statspack displays most cumulative times in seconds, and average times in milliseconds (for easier comparison with Operating System monitoring utilities which often report timings in milliseconds). For clarity, the time units used are specified in the column headings of each timed column in the Statspack report. The convention used is: (s) - a second (cs) - a centisecond - which is 100th of a second (ms) - a millisecond - which is 1,000th of a second (us) - a microsecond - which is 1,000,000th of a second

7. Event Timings ----------------If timings are available, the Statspack report will order wait events by time (in the Top-5 and background and foreground Wait Events sections). If timed_statistics is false for the instance, however a subset of users or programs set timed_statistics set to true dynamically, the Statspack report output may look inconsistent, where some events have timings (those which the individual programs/users waited for), and the remaining events do not. The Top-5 section will also look unusual in this situation. Optimally, timed_statistics should be set to true at the instance level for ease of diagnosing performance problems. 8. Managing and Sharing performance data ----------------------------------------8.1. Baselining performance data It is possible to identify snapshot data worthy of keeping, which will not

be purged by the Statspack purge. This is called baselining. Once you have determined which snap Ids or times of day most represent a particular workload whose performance data you would like to keep, you can mark the data representing those times as baselines. Baselined snapshots will not be purged by the Statspack purge. If you later decide you no longer want to keep previously baselined snapshots, you can clear the baseline (clearing the baseline does not remove the data, it just identifies the data as candidates for purging). NOTE: Statspack baseline does not perform any consistency checks on the snapshots requested to be baselined (e.g. it does not check whether the specified baselines span an instance shutdown). Instead, the baseline feature merely marks Snapshot rows as worthy of keeping, while other data can be purged. New procedures and functions have been added to the Statspack package to make and clear baselines: MAKE_BASELINE, and CLEAR_BASELINE. Both of these are able to accept varying parameters (e.g. snap Ids, or dates, etc), and can be called either as a procedure, or as a function (the function returns the number of rows operated on, whereas the procedure does not). Snap Ids or Begin/End dates --------------------------The Statspack MAKE_BASELINE procedures and functions provide flexibility in the manner baselines are made or cleared. These can take various input parameters: - Begin Snap Id and End Snap Id A begin and end snap Id pair can be specified. In this case, you choose either to baseline the range of snapshots between the begin and end snapshot pair, or just the two snapshots. The default is to baseline the entire range of snapshots. - Begin Date and End Date A begin and end date pair can be specified. All snapshots which fall in the date range specified will be marked as baseline data. Similarly to the MAKE_BASELINE procedures and functions, the CLEAR_BASELINE procedures and functions accept the same arguments. Procedure or Function --------------------It is possible to call either the MAKE_BASELINE procedure, or the MAKE_BASELINE function. The only difference is the MAKE_BASELINE function returns the number of snapshots baselined, whereas the MAKE_BASELINE procedure does not. Similarly, the CLEAR_BASELINE procedure performs the same task as the CLEAR_BASELINE function, however the function returns the number of baselined snapshots which were cleared (i.e. no longer identified as baselines).

8.1.1. Input Parameters for the MAKE_BASELINE and CLEAR_BASELINE procedure and function which accept Begin and End Snap Ids This section describes the input parameters for the MAKE_BASELINE and CLEAR_BASELINE procedure and function which accept Snap Ids. The input parameters for both MAKE and CLEAR baseline are identical. The procedures/functions will either baseline (or clear the baseline for) the range of snapshots between the begin and end snap Ids identified (the default), or if i_snap_range parameter is FALSE, will only operate on the two snapshots specified. If the function is called, it will return the number of snapshots operated on. Range of Default Parameter Name Valid Values Value Meaning ------------------ ----------------- ------- ------------------------------i_begin_snap Any Valid Snap Id - SnapId to start the baseline at i_end_snap Any valid Snap Id - SnapId to end the baseline at i_snap_range TRUE/FALSE TRUE Should the range of snapshots between the begin and end snap be included? i_dbid | Any valid DBId/ Current Caters for RAC databases i_instance_number | inst number DBId/ where you may wish to baseline combination Inst # snapshots on one instance in this which were physically taken Statspack on another instance schema Example 1: To make a baseline of snaps 45 and 50 including the range of snapshots in between (and you do not wish to know the number of snapshots baselined, so call the MAKE_BASELINE procedure). Log into the PERFSTAT user in SQL*Plus, and: SQL> exec statspack.make_baseline (i_begin_snap => 45, i_end_snap => 50); Or without specifying the parameter names: SQL> exec statspack.make_baseline(45, 50); Example 2: To make a baseline of snaps 1237 and 1241 (including the range of snapshots in between), and be informed of the number of snapshots baselined (by calling the function), log into the PERFSTAT user in SQL*Plus, and: SQL> SQL> SQL> SQL>

variable num_snaps number; begin :num_snaps := statspack.make_baseline(1237, 1241); end;

SQL> / SQL> print num_snaps Example 3: To make a baseline of only snapshots 1237 and 1241 (excluding the snapshots in between), log into the PERFSTAT user in SQL*Plus, and: SQL> exec statspack.make_baseline(5, 12, false); All of the prior examples apply equally to CLEAR_BASELINE. 8.1.2. Input Parameters for the MAKE_BASELINE and CLEAR_BASELINE procedure and function which accept Begin and End Dates The input parameters for the MAKE_BASELINE and CLEAR_BASELINE procedure and function which accept begin and end dates are identical. The procedures/ functions will either baseline (or clear the baseline for) all snapshots which were taken between the begin and end dates identified. Range of Default Parameter Name Valid Values Value Meaning ------------------ ----------------- ------- ------------------------------i_begin_date Any valid date - Date to start the baseline at i_end_date Any valid date > - Date to end baseline at begin date i_dbid | Any valid DBId/ Current Caters for RAC databases i_instance_number | inst number DBId/ where you may wish to baseline combination Inst # snapshots on one instance in this which were physically taken Statspack on another instance schema Example 1: To make a baseline of snapshots taken between 12-Feb-2003 at 9am, and 12-Feb-2003 at 12 midday (and be informed of the number of snapshots affected), call the MAKE_BASELINE function. Log into the PERFSTAT user in SQL*Plus, and: SQL> variable num_snaps number; SQL> begin SQL> :num_snaps := statspack.make_baseline (to_date('12-FEB-2003 09:00','DD-MON-YYYY HH24:MI'), to_date('12-FEB-2003 12:00','DD-MON-YYYY HH24:MI')); SQL> end; SQL> / SQL> print num_snaps Example 2: To clear an existing baseline which covers the times 13-Dec-2002 at 11pm and 14-Dec-2002 at 2am (without wanting to know how many snapshots were affected), log into the PERFSTAT user in SQL*Plus, and:

SQL> exec statspack.clear_baseline (to_date('13-DEC-2002 23:00','DD-MON-YYYY HH24:MI'), to_date('14-FEB-2002 02:00','DD-MON-YYYY HH24:MI')); 8.2. Purging/removing unnecessary data It is possible to purge unnecessary data from the PERFSTAT schema using the PURGE procedures/functions. Any Baselined snapshots will not be purged. NOTE: o It is good practice to ensure you have sufficient baselined snapshots before purging data. o It is recommended you export the schema as a backup before running this script, either using your own export parameters, or those provided in spuexp.par o WARNING: It is no longer possible to rollback a requested purge operation. o The functionality which was in the sppurge.sql SQL script has been moved into the STATSPACK package. Moving the purge functionality into the STATSPACK package has allowed significantly more flexibility in how the data to be purged can be specified by the performance engineer. Purge Criteria for the STATSPACK PURGE procedures and functions --------------------------------------------------------------Data to be purged can either be specified by: - Begin Snap Id and End Snap Id A begin and end snap Id pair can be specified. In this case, you choose either to purge the range of snapshots between the begin and end snapshot pair (inclusive, which is the default), or just the two snapshots specified. The preexisting Statspack sppurge.sql SQL script has been modified to use this PURGE procedure (which purges by begin/end snap Id range). - Begin Date and End Date A begin and end date pair can be specified. All snapshots which were taken between the begin and end date will be purged. - Purge before date All snapshots which were taken before the specified date will be purged. - Number of days (N) All snapshots which were taken N or more days prior to the current date and time (i.e. SYSDATE) will be purged. Extended Purge --------------

In prior releases, Statspack identifier tables which contained SQL Text, SQL Execution plans, and Segment identifiers were not purged. It is now possible to purge the unreferenced data in these tables. This is done by requesting the 'extended purge' be performed at the same time as the normal purge. Requesting the extended purge be performed along with a normal purge is simply a matter of setting the input parameter i_extended_purge to TRUE when calling the regular purge. Purging this data may be resource intensive, so you may choose to perform an extended purge less frequently than the normal purge. Procedure or Function --------------------Each of the purge procedures has a corresponding function. The function performs the same task as the procedure, but returns the number of Snapshot rows purged (whereas the procedure does not). 8.2.1. Input Parameters for the PURGE procedure and function which accept Begin Snap Id and End Snap Id This section describes the input parameters for the PURGE procedure and function which accept Snap Ids. The input parameters for both procedure and function are identical. The procedure/function will purge all snapshots between the begin and end snap Ids identified (inclusive, which is the default), or if i_snap_range parameter is FALSE, will only purge the two snapshots specified. If i_extended_purge is TRUE, an extended purge is also performed. If the function is called, it will return the number of snapshots purged. Range of Default Parameter Name Valid Values Value Meaning ------------------ ----------------- ------- ------------------------------i_begin_snap Any Valid Snap Id - SnapId to start purging from i_end_snap Any valid Snap Id - SnapId to end purging at i_snap_range TRUE/FALSE TRUE Should the range of snapshots between the begin and end snap be included? i_extended_purge TRUE/FALSE FALSE Determines whether unused SQL Text, SQL Plans and Segment Identifiers will be purged in addition to the normal data purged i_dbid | Any valid DBId/ Current Caters for RAC databases i_instance_number | inst number DBId/ where you may wish to baseline combination Inst # snapshots on one instance in this which were physically taken Statspack on another instance schema Example 1: Purge all snapshots between the specified begin and end snap ids. Also

purge unused SQL Text, SQL Plans and Segment Identifiers, and return the number of snapshots purged. Log into the PERFSTAT user in SQL*Plus, and: SQL> variable num_snaps number; SQL> begin SQL> :num_snaps := statspack.purge ( i_begin_snap=>1237, i_end_snap=>1241 , i_extended_purge=>TRUE); SQL> end; SQL> / SQL> print num_snaps 8.2.2. Input Parameters for the PURGE procedures and functions which accept Begin Date and End Date This section describes the input parameters for the PURGE procedure and function which accept a begin date and an end date. The procedure/ function will purge all snapshots taken between the specified begin and end dates. The input parameters for both procedure and function are identical. If i_extended_purge is TRUE, an extended purge is also performed. If the function is called, it will return the number of snapshots purged. Range of Default Parameter Name Valid Values Value Meaning ------------------ ----------------- ------- ------------------------------i_begin_date Date - Date to start purging from i_end_date End date > begin - Date to end purging at date - SnapId to end the baseline at i_extended_purge TRUE/FALSE FALSE Determines whether unused SQL Text, SQL Plans and Segment Identifiers will be purged in addition to the normal data purged i_dbid | Any valid DBId/ Current Caters for RAC databases i_instance_number | inst number DBId/ where you may wish to baseline combination Inst # snapshots on one instance in this which were physically taken Statspack on another instance schema Example 1: Purge all snapshots which fall between 01-Jan-2003 and 02-Jan-2003. Also perform an extended purge. Log into the PERFSTAT user in SQL*Plus, and: SQL> exec statspack.purge (i_begin_date=>to_date('01-JAN-2003', 'DD-MON-YYYY'), i_end_date =>to_date('02-JAN-2003', 'DD-MON-YYYY'), i_extended_purge=>TRUE);

8.2.3. Input Parameters for the PURGE procedure and function which accept a single Purge Before Date This section describes the input parameters for the PURGE procedure and function which accept a single date. The procedure/function will purge all snapshots older than the date specified. If i_extended_purge is TRUE, also perform an extended purge. The input parameters for both procedure and function are identical. If the function is called, it will return the number of snapshots purged. Range of Default Parameter Name Valid Values Value Meaning ------------------ ----------------- ------- ------------------------------i_purge_before_date Date - Snapshots older than this date will be purged i_extended_purge TRUE/FALSE FALSE Determines whether unused SQL Text, SQL Plans and Segment Identifiers will be purged in addition to the normal data purged. i_dbid | Any valid DBId/ Current Caters for RAC databases i_instance_number | inst number DBId/ where you may wish to baseline combination Inst # snapshots on one instance in this which were physically taken Statspack on another instance schema Example 1: To purge data older than a specified date, without wanting to know the number of snapshots purged, log into the PERFSTAT user in SQL*Plus, and: SQL> exec statspack.purge(to_date('31-OCT-2002','DD-MON-YYYY')); 8.2.4. Input Parameters for the PURGE procedure and function which accept the Number of Days of data to keep This section describes the input parameters for the PURGE procedure and function which accept the number of days of snapshots to keep. All data older than the specified number of days will be purged. The input parameters for both procedure and function are identical. If i_extended_purge is TRUE, also perform an extended purge. If the function is called, it will return the number of snapshots purged. Range of Default Parameter Name Valid Values Value Meaning ------------------ ----------------- ------- ------------------------------i_num_days Number > 0 - Snapshots older than this number of days will be purged i_extended_purge TRUE/FALSE FALSE Determines whether unused SQL Text, SQL Plans and Segment Identifiers will be

purged in addition to the normal data purged i_dbid | Any valid DBId/ Current Caters for RAC databases i_instance_number | inst number DBId/ where you may wish to baseline combination Inst # snapshots on one instance in this which were physically taken Statspack on another instance schema Example 1: To purge data older than 31 days, without wanting to know the number of snapshots operated on, log into the PERFSTAT user in SQL*Plus, and: SQL> exec statspack.purge(31); 8.2.5. Using sppurge.sql When sppurge is run, the instance currently connected to, and the available snapshots are displayed. The DBA is then prompted for the low Snap Id and high Snap Id. All snapshots which fall within this range will be purged. WARNING: sppurge.sql has been modified to use the new Purge functionality in the STATSPACK package, therefore it is no longer possible to rollback a requested purge operation - the purge is automatically committed. e.g. Purging data - connect to PERFSTAT using SQL*Plus, then run the sppurge.sql script - sample example output appears below. SQL> connect perfstat/perfstat_password SQL> set transaction use rollback segment rbig; SQL> @sppurge Database Instance currently connected to ======================================== Instance DB Id DB Name Inst Num Name ----------- ---------- -------- ---------720559826 PERF 1 perf Snapshots for this database instance ==================================== Base- Snap Snap Id Snapshot Started line? Level Host Comment -------- --------------------- ----- ----- --------------- -------------------1 30 Feb 2000 10:00:01 6 perfhost 2 30 Feb 2000 12:00:06 Y 6 perfhost 3 01 Mar 2000 02:00:01 Y 6 perfhost

4 01 Mar 2000 06:00:01 6 perfhost WARNING ~~~~~~~ sppurge.sql deletes all snapshots ranging between the lower and upper bound Snapshot Id's specified, for the database instance you are connected to. Snapshots identified as Baseline snapshots which lie within the snapshot range will not be purged. It is NOT possible to rollback changes once the purge begins. You may wish to export this data before continuing. Specify the Lo Snap Id and Hi Snap Id range to purge ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Enter value for losnapid: 1 Using 1 for lower bound. Enter value for hisnapid: 2 Using 2 for upper bound. Deleting snapshots 1 - 2 Purge of specified Snapshot range complete. SQL> -- end of example output

Batch mode purging -----------------To purge in batch mode, you must assign values to the SQL*Plus variables which specify the low and high snapshot Ids to purge. The variables are: losnapid -> Begin Snapshot Id hisnapid -> End Snapshot Id e.g. SQL> SQL> SQL> SQL>

connect perfstat/perfstat_password define losnapid=1 define hisnapid=2 @sppurge

sppurge will no longer prompt for the above information. 8.3. Removing all data If you wish to truncate all performance data indiscriminately, it is possible to do this using sptrunc.sql This script truncates all statistics data gathered, including snapshots marked as baselines.

NOTE: It is recommended you export the schema as a backup before running this script either using your own export parameters, or those provided in spuexp.par If you run sptrunc.sql in error, the script allows you to exit before beginning the truncate operation (you do this at the 'begin_or_exit' prompt by typing in 'exit'). To truncate all data, connect to the PERFSTAT user using SQL*Plus, and run the script - sample output which truncates data is below: SQL> connect perfstat/perfstat_password SQL>