AWR Reports

Introduction
Differences between AWR and Statspack
Workload Repository Reports
AWR Snapshots and Baselines
Quick Summary on AWR Sections
Reading the AWR Report
        - Header Section:
                  - Cache Sizes
                  - Load Profile
                  - Instance Efficiency
                  - Shared Pool Statistics
                  - Top 5 Timed Foreground Events
        - Common Waits Events  *******
        - Time Model Statistics
        - Operating System Statistics
        - Foreground Wait Class and Foreground Wait Events
                  - Foreground Wait Class
                  - Foreground Wait Events
                  - Background Wait Events
                  - Service Statistics
        - SQL Information Section
        - Generate Execution Plan for given SQL statement
        - Instance Activity Stats Section
        - I/O Stats Section
                  - Tablespace IO Stats
                  - File IO Stats
        - Buffer Pool Statistics Section
        - Advisory Statistics Section
                  - Instance Recovery Stats
                  - Buffer Pool Advisory
        - PGA Reports
                  - PGA Aggr Target Stats
                  - PGA Aggr Target Histogram
                  - PGA Memory Advisory
        - Shared Pool Advisory
                  - SGA Target Advisory
        - Buffer Waits Statistics
        - Enqueue Statistics
        - UNDO Statistics Section
        - Latch Statistics
        - Segments by Logical Reads and Segments by Physical Reads
                  - Segments by Logical Reads
                  - Segments by Physical Reads
                  - Segments by Physical Read Requests
                  - Segments by UnOptimized Reads
                  - Segments by Direct Physical Reads
                  - Segments by Physical Writes
                  - Segments by Physical Write Requests
                  - Segments by Direct Physical Writes
                  - Segments by Table Scans
                  - Segments by DB Blocks Changes
                  - Segments by Row Lock Waits
                  - Segments by ITL Waits
                  - Segments by Buffer Busy Waits
Retrieve SQL and Execution Plan from AWR Snapshots
Get Data from ASH
Moving AWR Information
Links with AWR Analyzer


Introduction
AWR periodically gathers and stores system activity and workload data which is then analyzed by ADDM. Every layer of Oracle is equipped with instrumentation that gathers information on workload which will then be used to make self-managing decisions. AWR is the place where this data is stored. AWR looks periodically at the system performance (by default every 60 minutes) and stores the information found (by default up to 7 days). AWR runs by default and Oracle states that it does not add a noticeable level of overhead. A new background server process (MMON) takes snapshots of the in-memory database statistics (much like STATSPACK) and stores this information in the repository. MMON also provides Oracle with a server initiated alert feature, which notifies database administrators of potential problems (out of space, max extents reached, performance thresholds, etc.). The information is stored in the SYSAUX tablespace. This information is the basis for all self-management decisions.

To access Automatic Workload Repository through Oracle Enterprise Manager Database Control:
    On the Administration page, select the Workload Repository link under Workload. From the Automatic Workload Repository page, you can manage snapshots or modify AWR settings.
          o To manage snapshots, click the link next to Snapshots or Preserved Snapshot Sets. On the Snapshots or Preserved Snapshot Sets pages, you can:
                + View information about snapshots or preserved snapshot sets (baselines).
                + Perform a variety of tasks through the pull-down Actions menu, including creating additional snapshots, preserved snapshot sets from an existing range of snapshots, or an ADDM task to perform analysis on a range of snapshots or a set of preserved snapshots.
          o To modify AWR settings, click the Edit button. On the Edit Settings page, you can set the Snapshot Retention period and Snapshot Collection interval.


Most informative sections of the report
I find the following sections most useful:
- Summary
- Top 5 timed events
- Top SQL (by elapsed time, by gets, sometimes by reads)

When viewing AWR report, always check corresponding ADDM report for actionable recommendations. ADDM is a self diagnostic engine designed from the experience of Oracle’s best tuning experts. Analyzes AWR data automatically after an AWR snapshot. Makes specific performance recommendations.


Both the snapshot frequency and retention time can be modified by the user. To see the present settings, you could use:
select snap_interval, retention from dba_hist_wr_control;

SNAP_INTERVAL       RETENTION
------------------- -------------------
+00000 01:00:00.0   +00007 00:00:00.0

or
select dbms_stats.get_stats_history_availability from dual;
select dbms_stats.get_stats_history_retention from dual;

This SQL shows that the snapshots are taken every hour and the collections are retained for 7 days

If you want to extend that retention period you can execute:
execute dbms_workload_repository.modify_snapshot_settings(
      interval => 60,        -- In Minutes. Current value retained if NULL.
      retention => 43200);   -- In Minutes (= 30 Days). Current value retained if NULL

In this example the retention period is specified as 30 days (43200 min) and the interval between each snapshot is 60 min.


Differences between AWR and STATSPACK report
1)Statspack snapshot purges must be scheduled manually. When the Statspack tablespace runs out of space, Statspack quits working. AWR snapshots are purged automatically by MMON every night. MMON, by default, tries to keep one week's worth of AWR snapshots available. If AWR detects that the SYSAUX tablespace is in danger of running out of space, it will free space in SYSAUX by automatically deleting the oldest set of snapshots. If this occurs, AWR will initiate a server-generated alert to notify administrators of the out-of-space error condition.

2)The AWR repository holds all of the statistics available in STATSPACK as well as some additional statistics which are not.

3)STATSPACK does not store the Active Session History (ASH) statistics which are available in the AWR dba_hist_active_sess_history view.

4)STATSPACK does not store history for new metric statistics introduced in Oracle. The key AWR views are: dba_hist_sysmetric_history and dba_hist_sysmetric_summary.

5)The AWR also contains views such as dba_hist_service_stat , dba_hist_service_wait_class and dba_hist_service_name , which store history for performance cumulative statistics tracked for specific services.

6)The latest version of STATSPACK included with Oracle contains a set of specific tables, which track history of statistics that reflect the performance of the Oracle Streams feature. These tables are stats$streams_capture , stats$streams_apply_sum , stats$buffered_subscribers , stats$rule_set , stats$propagation_sender , stats$propagation_receiver and stats$buffered_queues . The AWR does not contain the specific tables that reflect Oracle Streams activity; therefore, if a DBA relies heavily on the Oracle Streams feature, it would be useful to monitor its performance using STATSPACK utility.

7)Statspack snapshots must be run by an external scheduler (dbms_jobs, CRON, etc.). AWR snapshots are scheduled every 60 minutes by default.

8)ADDM captures a much greater depth and breadth of statistics than Statspack does. During snapshot processing, MMON transfers an in-memory version of the statistics to the permanent statistics tables.




Workload Repository Reports
Oracle provide two main scripts to produce workload repository reports. They are similar in format to the statspack reports and give the option of HTML or plain text formats. The two reports give essential the same output but the awrrpti.sql allows you to select a single instance. The reports can be generated as follows:
    @$ORACLE_HOME/rdbms/admin/awrrpt.sql
    @$ORACLE_HOME/rdbms/admin/awrrpti.sql

There are other scripts too, here is the full list:

REPORT NAME SQL Script
Automatic Workload Repository Report awrrpt.sql
Automatic Database Diagnostics Monitor Report addmrpt.sql
ASH Report ashrpt.sql
AWR Diff Periods Report awrddrpt.sql
AWR Single SQL Statement Report awrsqrpt.sql
AWR Global Report awrgrpt.sql
AWR Global Diff Report awrgdrpt.sql
  

The scripts prompt you to enter the report format (html or text), the start snapshot id, the end snapshot id and the report filename. This script looks like Statspack; it shows all the AWR snapshots available and asks for two specific ones as interval boundaries.



AWR Snapshots and Baselines
You can create a snapshot manually using:
EXEC dbms_workload_repository.create_snapshot;

You can see what snapshots are currently in the AWR by using the DBA_HIST_SNAPSHOT view as seen in this example:

SELECT snap_id, to_char(begin_interval_time,'dd/MON/yy hh24:mi') Begin_Interval,
      
to_char(end_interval_time,'dd/MON/yy hh24:mi') End_Interval
FROM dba_hist_snapshot
ORDER BY 1;

   SNAP_ID BEGIN_INTERVAL  END_INTERVAL
---------- --------------- ---------------
       954 30/NOV/05 03:01 30/NOV/05 04:00
       955 30/NOV/05 04:00 30/NOV/05 05:00
       956 30/NOV/05 05:00 30/NOV/05 06:00
       957 30/NOV/05 06:00 30/NOV/05 07:00
       958 30/NOV/05 07:00 30/NOV/05 08:00
       959 30/NOV/05 08:00 30/NOV/05 09:00


Each snapshot is assigned a unique snapshot ID that is reflected in the SNAP_ID column. The END_INTERVAL_TIME column displays the time that the actual snapshot was taken.

Sometimes you might want to drop snapshots manually. The dbms_workload_repository.drop_snapshot_range procedure can be used to remove a range of snapshots from the AWR. This procedure takes two parameters, low_snap_id and high_snap_id, as seen in this example:

EXEC dbms_workload_repository.drop_snapshot_range(low_snap_id=>1107, high_snap_id=>1108);

The following workload repository views are available:
    * V$ACTIVE_SESSION_HISTORY - Displays the active session history (ASH) sampled every second.
    * V$METRIC - Displays metric information.
    * V$METRICNAME - Displays the metrics associated with each metric group.
    * V$METRIC_HISTORY - Displays historical metrics.
    * V$METRICGROUP - Displays all metrics groups.
    * DBA_HIST_ACTIVE_SESS_HISTORY - Displays the history contents of the active session history.
    * DBA_HIST_BASELINE - Displays baseline information.
    * DBA_HIST_DATABASE_INSTANCE - Displays database environment information.
    * DBA_HIST_SNAPSHOT - Displays snapshot information.
    * DBA_HIST_SQL_PLAN - Displays SQL execution plans.
    * DBA_HIST_WR_CONTROL - Displays AWR settings.

Finally , you can use the following query to identify the occupants of the SYSAUX Tablespace

select substr(occupant_name,1,40), space_usage_kbytes
   from v$sysaux_occupants;


AWR Automated Snapshots

Oracle uses a scheduled job, GATHER_STATS_JOB, to collect AWR statistics. This job is created, and enabled automatically, when you create a new Oracle database. To see this job, use the DBA_SCHEDULER_JOBS view as seen in this example:

SELECT a.job_name, a.enabled, c.window_name, c.schedule_name, c.start_date, c.repeat_interval
FROM dba_scheduler_jobs a, dba_scheduler_wingroup_members b, dba_scheduler_windows c
WHERE job_name='GATHER_STATS_JOB'
  And a.schedule_name=b.window_group_name
  And b.window_name=c.window_name;

You can disable this job using the dbms_scheduler.disable procedure as seen in this example:
Exec dbms_scheduler.disable('GATHER_STATS_JOB');

And you can enable the job using the dbms_scheduler.enable procedure as seen in this example:
Exec dbms_scheduler.enable('GATHER_STATS_JOB');


AWR Baselines
It is frequently a good idea to create a baseline in the AWR. A baseline is defined as a range of snapshots that can be used to compare to other pairs of snapshots. The Oracle database server will exempt the snapshots assigned to a specific baseline from the automated purge routine. Thus, the main purpose of a baseline is to preserve typical runtime statistics in the AWR repository, allowing you to run the AWR snapshot reports on the preserved baseline snapshots at any time and compare them to recent snapshots contained in the AWR. This allows you to compare current performance (and configuration) to established baseline performance, which can assist in determining database performance problems.

Creating baselines
You can use the create_baseline procedure contained in the dbms_workload_repository stored PL/SQL package to create a baseline as seen in this example:
EXEC dbms_workload_repository.create_baseline (start_snap_id=>1109, end_snap_id=>1111, baseline_name=>'EOM Baseline');

Baselines can be seen using the DBA_HIST_BASELINE view as seen in the following example:
SELECT baseline_id, baseline_name, start_snap_id, end_snap_id
FROM dba_hist_baseline;

BASELINE_ID BASELINE_NAME   START_SNAP_ID END_SNAP_ID
----------- --------------- ------------- -----------
          1 EOM Baseline             1109        1111

In this case, the column BASELINE_ID identifies each individual baseline that has been defined. The name assigned to the baseline is listed, as are the beginning and ending snapshot IDs.

Removing baselines
The pair of snapshots associated with a baseline are retained until the baseline is explicitly deleted. You can remove a baseline using the dbms_workload_repository.drop_baseline procedure as seen in this example that drops the “EOM Baseline” that we just created.
EXEC dbms_workload_repository.drop_baseline (baseline_name=>'EOM Baseline', Cascade=>FALSE);

Note that the cascade parameter will cause all associated snapshots to be removed if it is set to TRUE; otherwise, the snapshots will be cleaned up automatically by the AWR automated processes.



Quick Summary on AWR Sections
This section contains detailed guidance for evaluating each section of an AWR report.
Report Summary Section:
This gives an overall summary of the instance during the snapshot period, and it contains important aggregate summary information.
- Cache Sizes: This shows the size of each SGA region after AMM has changed them.  This information can be compared to the original init.ora parameters at the end of the AWR report.
- Load Profile: This section shows important rates expressed in units of per second and transactions per second.
- Instance Efficiency Percentages: With a target of 100%, these are high-level ratios for activity in the SGA.
- Shared Pool Statistics: This is a good summary of changes to the shared pool during the snapshot period.
- Top 5 Timed Events: This is the most important section in the AWR report.  It shows the top wait events and can quickly show the overall database bottleneck.

Wait Events Statistics Section
This section shows a breakdown of the main wait events in the database including foreground and background database wait events as well as time model, operating system, service, and wait classes statistics.
- Time Model Statistics: Time mode statistics report how database-processing time is spent. This section contains detailed timing information on particular components participating in database processing.
- Wait Class: 
- Wait Events: This AWR report section provides more detailed wait event information for foreground user processes which includes Top 5 wait events and many other wait events that occurred during the snapshot interval.
- Background Wait Events: This section is relevant to the background process wait events.
- Operating System Statistics: The stress on the Oracle server is important, and this section shows the main external resources including I/O, CPU, memory, and network usage.
- Service Statistics: The service statistics section gives information about how particular services configured in the database are operating.
- Service Wait Class Stats: 

SQL Statistics Section
This section displays top SQL, ordered by important SQL execution metrics.
- SQL Ordered by Elapsed Time: Includes SQL statements that took significant execution time during processing.
- SQL Ordered by CPU Time: Includes SQL statements that consumed significant CPU time during its processing.
- SQL Ordered by Gets: These SQLs performed a high number of logical reads while retrieving data.
- SQL Ordered by Reads: These SQLs performed a high number of physical disk reads while retrieving data.
- SQL Ordered by Executions: 
- SQL Ordered by Parse Calls: These SQLs experienced a high number of reparsing operations.
- SQL Ordered by Sharable Memory: Includes SQL statements cursors which consumed a large amount of SGA shared pool memory.
- SQL Ordered by Version Count: These SQLs have a large number of versions in shared pool for some reason.
- Complete List of SQL Text: 

Instance Activity Stats

This section contains statistical information describing how the database operated during the snapshot period.
- Instance Activity Stats - Absolute Values: This section contains statistics that have absolute values not derived from end and start snapshots.
- Instance Activity Stats - Thread Activity: This report section reports a log switch activity statistic.

I/O Stats Section

This section shows the all important I/O activity for the instance and shows I/O activity by tablespace, data file, and includes buffer pool statistics.
- Tablespace IO Stats 
- File IO Stats 

Buffer Pool Statistics Section

Advisory Statistics Section
This section show details of the advisories for the buffer, shared pool, PGA and Java pool.
- Instance Recovery Stats: 
- Buffer Pool Advisory: 
- PGA Aggr Summary: PGA Aggr Target Stats; PGA Aggr Target Histogram; and PGA Memory Advisory. 
- Shared Pool Advisory: 
- SGA Target Advisory
- Stream Spool Advisory
- Java Pool Advisory 

Wait Statistics Section
- Buffer Wait Statistics: 
This important section shows buffer cache waits statistics.
- Enqueue Activity: This important section shows how enqueue operates in the database. Enqueues are special internal structures which provide concurrent access to various database resources.

Undo Statistics Section
- Undo Segment Summary: This section gives a summary about how undo segments are used by the database.
- Undo Segment Stats: This section shows detailed history information about undo segment activity.

Latch Statistics Section:
This section shows details about latch statistics. Latches are a lightweight serialization mechanism that is used to single-thread access to internal Oracle structures.
- Latch Activity
- Latch Sleep Breakdown
- Latch Miss Sources
- Parent Latch Statistics
- Child Latch Statistics

Segment Statistics Section:
This report section provides details about hot segments using the following criteria:
- Segments by Logical Reads: Includes top segments which experienced high number of logical reads.
- Segments by Physical Reads: Includes top segments which experienced high number of disk physical reads.
- Segments by Row Lock Waits: Includes segments that had a large number of row locks on their data.
- Segments by ITL Waits: Includes segments that had a large contention for Interested Transaction List (ITL). The contention for ITL can be reduced by increasing INITRANS storage parameter of the table.
- Segments by Buffer Busy Waits: These segments have the largest number of buffer waits caused by their data blocks.

Dictionary Cache Stats Section
This section exposes details about how the data dictionary cache is operating.

Library Cache Section
Includes library cache statistics describing how shared library objects are managed by Oracle.

Memory Statistics Section
- Process Memory Summary
- SGA Memory Summary: This section provides summary information about various SGA regions.
- SGA Breakdown difference: 

Streams Statistics Section
- Streams CPU/IO Usage
- Streams Capture
- Streams Apply
- Buffered Queues
- Buffered Subscribers
- Rule Set


Reading the AWR Report

The main sections in an AWR report include:

AWR Report Header:
This section shows basic information about the report like when the snapshot was taken, for how long, Cache Sizes at the beginning and end of the Snapshot, etc.

WORKLOAD REPOSITORY report for

DB Name DB Id Instance Inst num Startup Time Release RAC
FGUARD 750434027 FGUARD 1 03-Jul-13 21:07 11.2.0.2.0 NO
Host Name Platform CPUs Cores Sockets Memory (GB)
atl-frauddb-04.fiservipo.com Linux x86 64-bit 16 8 2 11.72

Snap Id Snap Time Sessions Cursors/Session
Begin Snap: 20813 08-Jul-13 00:00:19 267 3.1
End Snap: 20854 09-Jul-13 15:54:14 278 3.6
Elapsed:   2,393.91 (mins)    
DB Time:   4,689.46 (mins)    


Cache Sizes


Begin End

Buffer Cache: 1,520M 1,344M Std Block Size: 8K
Shared Pool Size: 1,120M 1,296M Log Buffer: 8,632K

Elasped Time: It represents the snapshot window or the time between the two snapshots.
DB TIME: Represents the activity on the database.

If DB TIME is Greater than Elapsed Time then it means that database has high workload.



Load Profile:
The load profile provides an at-a-glance look at some specific operational statistics. You can compare these statistics with a baseline snapshot report to determine if database activity is different. Values for these statistics are presented in two formats. The first is the value per second (for example, how much redo was generated per second) and the second is the value per transaction (for example, 1,024 bytes of redo were generated per transaction).


Per Second Per Transaction Per Exec Per Call
DB Time(s): 2.0 0.9 0.02 0.02
DB CPU(s): 0.5 0.2 0.01 0.01
Redo size: 25,972.2 12,131.8    
Logical reads: 9,444.6 4,411.6    
Block changes: 144.7 67.6    
Physical reads: 8,671.9
4,050.7    
Physical writes: 2,641.5 1,233.9    
User calls: 83.9 39.2    
Parses: 30.7 14.3    
Hard parses: 0.4 0.2    
W/A MB processed: 4.6 2.1    
Logons: 2.5 1.2    
Executes: 88.6 41.4    
Rollbacks: 0.0 0.0    
Transactions: 2.1      

Where:

DB time(s): It's the amount of time oracle has spent performing database user calls. Note it does not include background processes.
DB CPU(s): It's the amount of CPU time spent on user calls. As DB time, it does not include background process. The value is in microseconds
Redo size: This is the amount of DML happening in the DB. If you see an increase here then more DML statements are taking place (meaning your users are doing more INSERTs, UPDATEs, and DELETEs than before). For example, the table below shows that an average transaction generates about 12,000 of redo data along with around 26,000 redo per second.
Logical reads: This is calculated as Consistent Gets + DB Block Gets =  Logical Reads
Block Changes: The number of blocks modified during the sample interval. If you see an increase here then more DML statements are taking place (meaning your users are doing more INSERTs, UPDATEs, and DELETEs than before).
Physical reads: The number of requests for a block that caused a physical I/O.
Physical writes: Number of physical writes performed
User calls: Indicates how many user calls have occurred during the snapshot period. This value can give you some indication if usage has increased.
Parses: The total of all parses; both hard and soft.
Hard Parses: Those parses requiring a completely new parse of the SQL statement.  A ‘hard parse’ rate of greater than 100 per second indicates there is a very high amount of hard parsing on the system. High hard parse rates cause serious performance issues, and must be investigated. A high hard parse rate is usually accompanied by latch contention on the shared pool and library cache latches. Check whether waits for ‘latch free’ appear in the top-5 wait events, and if so, examine the latching sections of the report. Of course, we want a low number here. Possible reasons for excessive hard parses may be a small shared pool or may be that bind variables are not being used.
Soft Parses: Not listed but derived by subtracting the hard parses from parses.  A soft parse reuses a previous hard parse and hence consumes far fewer resources. A high soft parse rate could be anywhere in the rate of 300 or more per second. Unnecessary soft parses also limit application scalability; optimally a SQL statement should be soft-parsed once per session, and executed many times.
Sorts: Number of sorts occurring in the database
Logons: No of logons during the interval
Executes: how many statements we are executing per second / transaction
Transactions: How many transactions per second we process

The per-second statistics show you the changes in throughput (i.e. whether the instance is performing more work per second). For example:
• a significant increase in ‘redo size’, ‘block changes’ and ‘pct of blocks changed per read’ would indicate the instance is performing more inserts/updates/deletes.
• an increase in the ‘redo size’ without an increase in the number of ‘transactions per second’ would indicate a changing transaction profile.
Similarly, looking at the per-transaction statistics allows you to identify changes in the application characteristics by comparing these to the corresponding statistics from the baseline report.

Additionally, the load profile section provides the percentage of blocks that were changed per read, the percentage of recursive calls that occurred, the percentage of transactions that were rolled back and the number of rows sorted per sort operation.

In this example
• Comparing the number of Physical reads per second to the number of Physical writes per second shows the physical read to physical write ratio is very high. Typical OLTP systems have a read-to-write ratio of 10:1 or 5:1
• This system is busy, with 84 User calls per second.


Instance Efficiency Percentages (Target 100%)
These statistics include several buffer related ratios including the buffer hit percentage and the library hit percentage. Also, shared pool memory usage statistics are included in this section.
Buffer Nowait %: 100.00 Redo NoWait %: 100.00
Buffer Hit %: 99.32 In-memory Sort %: 100.00
Library Hit %: 98.94 Soft Parse %: 97.25
Execute to Parse %: 75.00 Latch Hit %: 98.78
Parse CPU to Parse Elapsd %: 22.99 % Non-Parse CPU: 99.93

Buffer Nowait Ratio. This is the percentage of time that the instance made a call to get a buffer (all buffer types are included here) and that buffer was made available immediately (meaning it didn't have to wait for the buffer...hence "Buffer Nowait"). If the ratio is low, then could be a (hot) block(s) being contended for that should be found in the Buffer Wait Section.. If the ratio is low, check the Buffer Wait Statistics section of the report for more detail on which type of block is being contended for.
Buffer Hit Ratio. (also known as the buffer-cache hit ratio) Ideally more than 95 percent. It shows the % of times a particular block was found in buffer cache insted of performing a physical I/O (reading from disk).
Although historically known as one of the most important statistics to evaluate, this ratio can sometimes be misleading. A low buffer hit ratio does not necessarily mean the cache is too small; it may be that potentially valid full-table scans are artificially reducing what is otherwise a good ratio. Similarly, a high buffer hit ratio (say, 99 percent) normally indicates that the cache is adequately sized, but this assumption may not always be valid. For example, frequently executed SQL statements that repeatedly refer to a small number of buffers via indexed lookups can create a misleadingly high buffer hit ratio. When these buffers are read, they are placed at the most recently used (MRU) end of the buffer cache; iterative access to these buffers can artificially inflate the buffer hit ratio. This inflation makes tuning the buffer cache a challenge. Sometimes you can identify a too-small buffer cache by the appearance of the write complete waits event, which indicates that hot blocks (that is, blocks that are still being modified) are aging out of the cache while they are still needed; check the Wait Events list for evidence of this event. If the number is negative, the BUFFER_CACHE is too small and the data is bein aged out before it can be used.
Library Hit Ratio. This ratio, also known as the library-cache hit ratio, gives the percentage of pin requests that result in pin hits. A pin hit occurs when the SQL or PL/SQL code to be executed is already in the library cache and is valid to execute. If the "Library Hit ratio" is low, it could be indicative of a shared pool that is too small (SQL is prematurely pushed out of the shared pool), or just as likely, that the system did not make correct use of bind variables in the application. If the soft parse ratio is also low, check whether there's a parsing issue. A lower ratio could also indicate that bind variables are not used or some other issue is causing SQL not to be reused (in which case a smaller shared pool may only be a band-aid that will potentially fix a library latch problem which may result).
Execute to Parse. If value is negative, it means that the number of parses is larger than the number of executions. Another cause for a negative execute to parse ratio is if the shared pool is too small and queries are aging out of the shared pool and need to be reparsed.  This is another form of thrashing which also degrades performance tremendously. So, if you run some SQL and it has to be parsed every time you execute it (because no plan exists for this statement) then your percentage would be 0%. The more times that your SQL statement can reuse an existing plan the higher your Execute to Parse ratio is. This is very BAD!! One way to increase your parse ratio is to use bind variables.
Parse CPU to Parse Elapsd %: Generally, this is a measure of how available your CPU cycles were for SQL parsing. If this is low, you may see "latch free" as one of your top wait events.
Redo Nowait Ratio.
This ratio indicates the amount of redo entries generated for which there was space available in the redo log. The instance didn't have to wait to use the redo log if this is 100%
The redo-log space-request statistic is incremented when an Oracle process attempts to write a redo-log entry but there is not sufficient space remaining in the online redo log. Thus, a value close to 100 percent for the redo nowait ratio indicates minimal time spent waiting for redo logs to become available, either because the logs are not filling up very often or because the database is able to switch to a new log quickly whenever the current log fills up.
If your alert log shows that you are switching logs frequently (that is, more than once every 15 minutes), you may be able to reduce the amount of switching by increasing the size of the online redo logs. If the log switches are not frequent, check the disks on which the redo logs reside to see why the switches are not happening quickly. If these disks are not overloaded, they may be slow, which means you could put the files on faster disks.
In-Memory Sort Ratio. This ratio gives the percentage of sorts that were performed in memory, rather than requiring a disk-sort segment to complete the sort. Optimally, in an OLTP environment, this ratio should be high. Setting the PGA_AGGREGATE_TARGET (or SORT_AREA_SIZE) initialization parameter effectively will eliminate this problem, as a minimum you pretend to have this one in 95%
Soft Parse Ratio. This ratio gives the percentage of parses that were soft, as opposed to hard. A soft parse occurs when a session attempts to execute a SQL statement and a usable version of the statement is already in the shared pool. In other words, all data (such as the optimizer execution plan) pertaining to the statement in the shared pool is equally applicable to the statement currently being issued. A hard parse, on the other hand, occurs when the current SQL statement is either not in the shared pool or not there in a shareable form. An example of the latter case would be when the SQL statement in the shared pool is textually identical to the current statement but the tables referred to in the two statements resolve to physically different tables.
Hard parsing is an expensive operation and should be kept to a minimum in an OLTP environment. The aim is to parse once, execute many times.
Ideally, the soft parse ratio should be greater than 95 percent. When the soft parse ratio falls much below 80 percent, investigate whether you can share SQL by using bind variables or force cursor sharing by using the init.ora parameter cursor_sharing.
Before you jump to any conclusions about your soft parse ratio, however, be sure to compare it against the actual hard and soft parse rates shown in the Load Profile. If the rates are low (for example, 1 parse per second), parsing may not be a significant issue in your system. Another useful standard of comparison is the proportion of parse time that was not CPU-related, given by the following ratio:
(parse time CPU) / (parse time elapsed)

A low value for this ratio could mean that the non-CPU-related parse time was spent waiting for latches, which might indicate a parsing or latching problem. To investigate further, look at the shared-pool and library-cache latches in the Latch sections of the report for indications of contention on these latches.
Latch Hit Ratio. This is the ratio of the total number of latch misses to the number of latch gets for all latches. A low value for this ratio indicates a latching problem, whereas a high value is generally good. However, as the data is rolled up over all latches, a high latch hit ratio can artificially mask a low get rate on a specific latch. Cross-check this value with the Top 5 Wait Events to see if latch free is in the list, and refer to the Latch sections of the report. Latch Hit % of less than 99 percent is usually a big problem.

Also check the "Shared Pool Statistics", if the "End" value is in the high 95%-100% range ,this is a indication that the shared pool needs to be increased (especially if the "Begin" value is much smaller)



Example: Evaluating the Instance Efficiency Percentages Section

Instance Efficiency Percentages (Target 100%)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
            Buffer Nowait %:   99.99       Redo NoWait %:  100.00
            Buffer  Hit   %:   95.57    In-memory Sort %:   97.55
            Library Hit   %:   99.89        Soft Parse %:   99.72
         Execute to Parse %:   88.75         Latch Hit %:   99.11
Parse CPU to Parse Elapsd %:   52.66     % Non-Parse CPU:   99.99

Interpreting the ratios in this section can be slightly more complex than it may seem at first glance. While high values for the ratios are generally good (indicating high efficiency), such values can be misleading your system may be doing something efficiently that it would be better off not doing at all. Similarly, low values aren't always bad. For example, a low in-memory sort ratio (indicating a low percentage of sorts performed in memory) would not necessarily be a cause for concern in a decision- support system (DSS) environment, where user response time is less critical than in an online transaction processing (OLTP) environment.
Basically, you need to keep in mind the characteristics of your application - whether it is query-intensive or update-intensive, whether it involves lots of sorting, and so on - when you're evaluating the Instance Efficiency Percentages.

The following ratios should be above 90% in a database.
Buffer Nowait
Buffer  Hit  
Library Hit
Redo NoWait
In-memory Sort
Soft Parse
Latch Hit
Non-Parse CPU

The execute to parse ratio should be very high in a ideal database.
The execute to parse ratio is basically a measure between the number of times a sql is executed versus the number of times it is parsed.
The ratio will move higher as the number of executes go up, while the number of parses either go down or remain the same.
The ratio will be close to zero if the number of executes and parses are almost equal.
The ratio will be negative executes are lower but the parses are higher.



Another Sample Analysis
Instance Efficiency Percentages (Target 100%)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
            Buffer Nowait %:   98.56       Redo NoWait %:  100.00
            Buffer  Hit   %:   99.96    In-memory Sort %:   99.84
            Library Hit   %:   99.99        Soft Parse %:  100.00 (A)
         Execute to Parse %:    0.10 (A)     Latch Hit %:   99.37
Parse CPU to Parse Elapsd %:   58.19 (A) % Non-Parse CPU:   99.84

 Shared Pool Statistics        Begin   End
                               ------  ------
             Memory Usage %:   28.80   29.04 (B)
    % SQL with executions>1:   75.91   76.03
  % Memory for SQL w/exec>1:   83.65   84.09

Observations:
• The 100% soft parse ratio (A) indicates the system is not hard-parsing. However the system is soft parsing a lot, rather than only re-binding and re-executing the same cursors, as the Execute to Parse % is very low (A). Also, the CPU time used for parsing (A) is only 58% of the total elapsed parse time (see Parse CPU to Parse Elapsd). This may also imply some resource contention during parsing (possibly related to the latch free event?).
• There seems to be a lot of unused memory in the shared pool (only 29% is used) (B). If there is insufficient memory allocated to other areas of the database (or OS), this memory could be redeployed

***Please see the following NOTES on shared pool issues
[NOTE:146599.1] Diagnosing and Resolving Error ORA-04031
[NOTE:62143.1] Understanding and Tuning the Shared Pool
[NOTE:105813.1] SCRIPT TO SUGGEST MINIMUM SHARED POOL SIZE




Shared Pool Statistics


Begin End
Memory Usage %: 73.86 75.42
% SQL with executions>1: 92.61 93.44
% Memory for SQL w/exec>1: 94.33 94.98

 Shared Pool Statistics        Begin   End
                               ------  ------
             Memory Usage %:   42.07   43.53
    % SQL with executions>1:   73.79   75.08
  % Memory for SQL w/exec>1:   76.93   77.64

Memory Usage % = It's the shared pool usage. So here we have use 73.86 per cent of our shared pool and out of that almost 94 percent is being re-used. If Memory Usage % is too large like 90 % it could mean that your shared pool is tool small and if the percent is in 50 for example then this could mean that you shared pool is too large
% SQL with executions>1
= Shows % of SQLs executed more than 1 time. The % should be very near to value 100. If we get a low number here, then the DB is not using shared SQL statements. May be because bind variables are not being used.
% memory for SQL w/exec>1
: From the memory space allocated to cursors, shows which % has been used by cursors more than 1.

Top 5 Timed Foreground Events
This section provides insight into what events the Oracle database is spending most of it's time on (see wait events). Each wait event is listed, along with the number of waits, the time waited (in seconds), the average wait per event (in microseconds) and the associated wait class. This is one of the most important sections of the report.

Event Waits Time(s) Avg wait (ms) % DB time Wait Class
direct path write temp 12,218,833 193,657 16 68.83 User I/O
DB CPU   70,609   25.09  
log file sync 247,094 6,933 28 2.46 Commit
db file sequential read 221,301 5,813 26 2.07 User I/O
direct path read 6,773,719 4,442 1 1.58 User I/O

It's critical to look into this section. If you turn off the statistic parameter, then the Time(s) wont appear. Wait analysis should be done with respect to Time(s) as there could be million of waits but if that happens for a second or so then who cares. Therefore, time is very important component.
When you are trying to eliminate bottlenecks on your system, your report's Top 5 Timed Events section is the first place to look and you should use the HIGHEST WAIT TIMES to guide the investigation.

As you will see, you have several different types of waits, so let's discuss the most common waits on the next section.




Common WAIT EVENTS

If you want a quick instance wide wait event status, showing which events are the biggest contributors to total wait time, you can use the following query :

select event, total_waits,time_waited from V$system_event
  where event NOT IN
  ('pmon timer', 'smon timer', 'rdbms ipc reply', 'parallel deque wait',
  'virtual circuit', '%SQL*Net%', 'client message', 'NULL event')
order by time_waited desc;

EVENT                              TOTAL_WAITS         TIME_WAITED
------------------------          -------------       -------------
db file sequential read              35051309            15965640
latch free                            1373973             1913357
db file scattered read                2958367             1840810
enqueue                                  2837              370871
buffer busy waits                      444743              252664
log file parallel write                146221              123435


1. DB File Scattered Read.
That generally happens during a full scan of a table or Fast Full Index Scans.
As full table scans are pulled into memory, they rarely fall into contiguous buffers but instead are scattered throughout the buffer cache. A large number here indicates that your table may have missing indexes, statistics are not updated or your indexes are not used. Although it may be more efficient in your situation to perform a full table scan than an index scan, check to ensure that full table scans are necessary when you see these waits. Try to cache small tables to avoid reading them in over and over again, since a full table scan is put at the cold end of the LRU (Least Recently Used) list. You can use the report to help identify the query in question and fix it.
The init.ora parameter db_file_multiblock_read_count specifies the maximum numbers of blocks read in that way. Typically, this parameter should have values of 4-16 independent of the size of the database but with higher values needed with smaller Oracle block sizes. If you have a high wait time for this event, you either need to reduce the cost of I/O, e.g. by getting faster disks or by distributing your I/O load better, or you need to reduce the amount of full table scans by tuning SQL statements. The appearance of the‘db file scattered read’ and ‘db file sequential read’events may not necessarily indicate a problem, as IO is a normal activity on a healthy instance. However, they can indicate problems if any of the following circumstances are true:
• The data-access method is bad (that is, the SQL statements are poorly tuned), resulting in unnecessary or inefficient IO operations
• The IO system is overloaded and performing poorly
• The IO system is under-configured for the load
• IO operations are taking too long

If this Wait Event is a significant portion of Wait Time then a number of approaches are possible:
o Find which SQL statements perform Full Table or Fast Full Index scans and tune them to make sure these scans are necessary and not the result of a suboptimal plan.
- The view V$SQL_PLAN view can help:
For Full Table scans:
select sql_text from v$sqltext t, v$sql_plan p
  where t.hash_value=p.hash_value
    and p.operation='TABLE ACCESS'

    and p.options='FULL'
  order by p.hash_value, t.piece;

For Fast Full Index scans:
select sql_text from v$sqltext t, v$sql_plan p
  where t.hash_value=p.hash_value
    and p.operation='INDEX'

    and p.options='FULL SCAN'
  order by p.hash_value, t.piece;

o In cases where such multiblock scans occur from optimal execution plans it is possible to tune the size of multiblock I/Os issued by Oracle by setting the instance parameter DB_FILE_MULTIBLOCK_READ_COUNT so that:
DB_BLOCK_SIZE x DB_FILE_MULTIBLOCK_READ_COUNT = max_io_size of system
Query tuning should be used to optimize online SQL to use indexes.

2. DB File Sequential Read.
Is the wait that comes from the physical side of the database. It related to memory starvation and non selective index use. Sequential read is an index read followed by table read because it is doing index lookups which tells exactly which block to go to.
This could indicate poor joining order of tables or un-selective indexes in your SQL or
waiting for writes to TEMP space (direct loads, Parallel DML (PDML) such as parallel updates. It could mean that a lot of index reads/scans are going on. Depending on the problem it may help to tune PGA_AGGREGATE_TARGET and/or DB_CACHE_SIZE.
The sequential read event identifies Oracle reading blocks sequentially, i.e. one after each other. It is normal for this number to be large for a high-transaction, well-tuned system, but it can indicate problems in some circumstances. You should correlate this wait statistic with other known issues within the report, such as inefficient SQL. Check to ensure that index scans are necessary, and check join orders for multiple table joins. The DB_CACHE_SIZE will also be a determining factor in how often these waits show up. Problematic hash-area joins should show up in the PGA memory, but they're also memory hogs that could cause high wait numbers for sequential reads. They can also show up as direct path read/write waits. These circumstances are usually interrelated. When they occur in conjunction with the appearance of the 'db file scattered read' and 'db file sequential read' in the Top 5 Wait Events section, first you should examine the SQL Ordered by Physical Reads section of the report, to see if it might be helpful to tune the statements with the highest resource usage.
It could be because the indexes are fragmented. If that is the case, rebuilding the index will compact it and will produce to visit less blocks.
Then, to determine whether there is a potential I/O bottleneck, examine the OS I/O statistics for corresponding symptoms. Also look at the average time per read in the Tablespace and File I/O sections of the report. If many I/O-related events appear high in the Wait Events list, re-examine the host hardware for disk bottlenecks and check the host-hardware statistics for indications that a disk reconfiguration may be of benefit.
Block reads are fairly inevitable so the aim should be to minimize unnecessary I/O. I/O for sequential reads can be reduced by tuning SQL calls that result in full table scans and using the partitioning option for large tables.

3. Free Buffer Waits.
When a session needs a free buffer and cannot find one, it will post the database writer process asking it to flush dirty blocks (No place to put a new block). Waits in this category may indicate that you need to increase the DB_BUFFER_CACHE, if all your SQL is tuned. Free buffer waits could also indicate that unselective SQL is causing data to flood the buffer cache with index blocks, leaving none for this particular statement that is waiting for the system to process. This normally indicates that there is a substantial amount of DML (insert/update/delete) being done and that the Database Writer (DBWR) is not writing quickly enough; the buffer cache could be full of multiple versions of the same buffer, causing great inefficiency. To address this, you may want to consider accelerating incremental checkpointing, using more DBWR processes, or increasing the number of physical disks. To investigate if this is an I/O problem, look at the report I/O Statistics. Increase the DB_CACHE_SIZE; shorten the checkpoint; tune the code to get less dirty blocks, faster I/O, use multiple DBWR’s.

4. Buffer Busy Waits. A buffer busy wait happens when multiple processes concurrently want to modify the same block in the buffer cache. This typically happens during massive parallel inserts if your tables do not have free lists and it can happen if you have too few rollback segments. Buffer busy waits should not be greater than 1 percent. Check the Buffer Wait Statistics section (or V$WAITSTAT) to find out if the wait is on a segment header. If this is the case, increase the freelist groups or increase the pctused to pctfree gap. If the wait is on an undo header, you can address this by adding rollback segments; if it's on an undo block, you need to reduce the data density on the table driving this consistent read or increase the DB_CACHE_SIZE. If the wait is on a data block, you can move data to another block to avoid this hot block, increase the freelists on the table, or use Locally Managed Tablespaces (LMTs). If it's on an index block, you should rebuild the index, partition the index, or use a reverse key index. To prevent buffer busy waits related to data blocks, you can also use a smaller block size: fewer records fall within a single block in this case, so it's not as "hot." When a DML (insert/update/ delete) occurs, Oracle writes information into the block, including all users who are "interested" in the state of the block (Interested Transaction List, ITL). To decrease waits in this area, you can increase the initrans, which will create the space in the block to allow multiple ITL slots. You can also increase the pctfree on the table where this block exists (this writes the ITL information up to the number specified by maxtrans, when there are not enough slots built with the initrans that is specified). Buffer busy waits can be reduced by using reverse-key indexes for busy indexes and by partitioning busy tables.
Buffer Busy Wait on Segment Header – Add freelists (if inserts) or freelist groups (esp. RAC). Use ASSM.
Buffer Busy Wait on Data Block – Separate ‘hot’ data; potentially use reverse key indexes; fix queries to reduce the blocks popularity, use smaller blocks, I/O, Increase initrans and/or maxtrans (this one’s debatable). Reduce records per block
Buffer Busy Wait on Undo Header – Add rollback segments or increase size of segment area (auto undo) 
Buffer Busy Wait on Undo block – Commit more (not too much) Larger rollback segments/area. Try to fix the SQL.


5. Latch Free
.
Latches are low-level queuing mechanisms (they're accurately referred to as mutual exclusion mechanisms) used to protect shared memory structures in the system global area (SGA). Latches are like locks on memory that are very quickly obtained and released. Latches are used to prevent concurrent access to a shared memory structure. If the latch is not available, a latch free miss is recorded. Most latch problems are related to the failure to use bind variables (library cache latch), redo generation issues (redo allocation latch), buffer cache contention issues (cache buffers LRU chain), and hot blocks in the buffer cache (cache buffers chain). There are also latch waits related to bugs; check MetaLink for bug reports if you suspect this is the case. When latch miss ratios are greater than 0.5 percent, you should investigate the issue. If latch free waits are in the Top 5 Wait Events or high in the complete Wait Events list, look at the latch-specific sections of the report to see which latches are contended for.

6. Enqueue. An enqueue is a lock that protects a shared resource. Locks protect shared resources, such as data in a record, to prevent two people from updating the same data at the same time application, e.g. when a select for update is executed.. An enqueue includes a queuing mechanism, which is FIFO (first in, first out). Note that Oracle's latching mechanism is not FIFO. Enqueue waits usually point to the ST enqueue, the HW enqueue, the TX4 enqueue, and the TM enqueue. The ST enqueue is used for space management and allocation for dictionary-managed tablespaces. Use LMTs, or try to preallocate extents or at least make the next extent larger for problematic dictionary-managed tablespaces. HW enqueues are used with the high-water mark of a segment; manually allocating the extents can circumvent this wait. TX4s are the most common enqueue waits. TX4 enqueue waits are usually the result of one of three issues. The first issue is duplicates in a unique index; you need to commit/rollback to free the enqueue. The second is multiple updates to the same bitmap index fragment. Since a single bitmap fragment may contain multiple rowids, you need to issue a commit or rollback to free the enqueue when multiple users are trying to update the same fragment. The third and most likely issue is when multiple users are updating the same block. If there are no free ITL slots, a block-level lock could occur. You can easily avoid this scenario by increasing the initrans and/or maxtrans to allow multiple ITL slots and/or by increasing the pctfree on the table. Finally, TM enqueues occur during DML to prevent DDL to the affected object. If you have foreign keys, be sure to index them to avoid this general locking issue.
Enqueue - ST Use LMT’s or pre-allocate large extents
Enqueue - HW Pre-allocate extents above HW (high water mark.)
Enqueue – TX Increase initrans and/or maxtrans (TX4) on (transaction) the table or index.  Fix locking issues if TX6.  Bitmap (TX4) & Duplicates in Index (TX4).
Enqueue - TM Index foreign keys; Check application (trans. mgmt.) locking of tables.  DML Locks.

7. Log Buffer Space
Look at increasing log buffer size. This wait occurs because you are writing the log buffer faster than LGWR can write it to the redo logs, or because log switches are too slow. To address this problem, increase the size of the redo log files, or increase the size of the log buffer, or get faster disks to write to. You might even consider using solid-state disks, for their high speed.

The session is waiting for space in the log buffer. (Space becomes available only after LGWR has written the current contents of the log buffer to disk.) This typically happens when applications generate redo faster than LGWR can write it to disk.

8. Log File Switch
log file switch (checkpoint incomplete): May indicate excessive db files or slow IO subsystem
log file switch (archiving needed):  Indicates archive files are written too slowly
log file switch completion: May need more log files per
May indicate excessive db files or slow IO subsystem. All commit requests are waiting for "logfile switch (archiving needed)" or "logfile switch (chkpt. Incomplete)." Ensure that the archive disk is not full or slow. DBWR may be too slow because of I/O. You may need to add more or larger redo logs, and you may potentially need to add database writers if the DBWR is the problem.

9. Log File Sync
Could indicate excessive commits. A Log File Sync happens each time a commit (or rollback) takes place. If there are a lot of waits in this area then you may want to examine your application to see if you are committing too frequently (or at least more than you need to). When a user commits or rolls back data, the LGWR flushes the session's redo from the log buffer to the redo logs. The log file sync process must wait for this to successfully complete. To reduce wait events here, try to commit more records (try to commit a batch of 50 instead of one at a time, use BULKS, , for example). Put redo logs on a faster disk, or alternate redo logs on different physical disks (with no other DB Files, ASM, etc) to reduce the archiving effect on LGWR. Don't use RAID 5, since it is very slow for applications that write a lot; potentially consider using file system direct I/O or raw devices, which are very fast at writing information. The associated event, ‘log buffer parallel write’ is used by the redo log writer process, and it will indicate if your actual problem is with the log file I/O. Large wait times for this event can also be caused by having too few CPU resources available for the redolog writer process.

10. Idle Event. There are several idle wait events listed after the output; you can ignore them. Idle events are generally listed at the bottom of each section and include such things as SQL*Net message to/from client and other background-related timings. Idle events are listed in the stats$idle_event table.

11. global cache cr request: (OPS) This wait event shows the amount of time that an instance has waited for a requested data block for a consistent read and the transferred block has not yet arrived at the requesting instance. See Note 157766.1 'Sessions Wait Forever for 'global cache cr request' Wait Event in OPS or RAC'. In some cases the 'global cache cr request' wait event may be perfectly normal if large buffer caches are used and the same data is being accessed concurrently on multiple instances.  In a perfectly tuned, non-OPS/RAC database, I/O wait events would be the top wait events but since we are avoiding I/O's with RAC and OPS the 'global cache cr request' wait event often takes the place of I/O wait events.

12. library cache pin: Library cache latch contention may be caused by not using bind variables. It is due to excessive parsing of SQL statement.
The session wants to pin an object in memory in the library cache for examination, ensuring no other processes can update the object at the same time. This happens when you are compiling or parsing a PL/SQL object or a view.

13. CPU time
This is not really a wait event (hence, the new name), but rather the sum of the CPU used by this session, or the amount of CPU time used during the snapshot window. In a heavily loaded system, if the CPU time event is the biggest event, that could point to some CPU-intensive processing (for example, forcing the use of an index when a full scan should have been used), which could be the cause of the bottleneck. When CPU Other is a significant component of total Response Time the next step is to find the SQL statements that access the most blocks. Block accesses are also known as Buffer Gets and Logical I/Os. The report lists such SQL statements in section SQL ordered by Gets.

14. DB File Parallel Read  If you are doing a lot of partition activity then expect to see that wait even. it could be a table or index partition. This Wait Event is used when Oracle performs in parallel reads from multiple datafiles to non-contiguous buffers in memory (PGA or Buffer Cache). This is done during recovery operations or when buffer prefetching is being used as an optimization i.e. instead of performing multiple single-block reads. If this wait is an important component of Wait Time, follow the same guidelines as 'db file sequential read'.
This may occur during recovery or during regular activity when a session batches many single block I/O requests together and issues them in parallel.

15. PX qref latch  Can often mean that the Producers are producing data quicker than the Consumers can consume it. Maybe we could increase parallel_execution_message_size to try to eliminate some of these waits or we might decrease the degree of parallelism. If the system workload is high consider to decrease the degree of parallelism. If you have DEFAULT parallelism on your object  you can decrease the value of PARALLEL_THREADS_PER_CPU.  Have in mind  DEFAULT degree = PARALLEL_THREADS_PER_CPU * #CPU's 

16. Log File Parallel Write. It occurs when waiting for writes of REDO records to the REDO log files to complete. The wait occurs in log writer (LGWR) as part of normal activity of copying records from the REDO log buffer to the current online log. The actual wait time is the time taken for all the outstanding I/O requests to complete. Even though the writes may be issued in parallel, LGWR needs to wait for the last I/O to be on disk before the parallel write is considered complete. Hence the wait time depends on the time it takes the OS to complete all requests.
Log file parallel write waits can be reduced by moving log files to the faster disks and/or separate disks where there will be less contention.

17. SQL*Net more data to client
This means the instance is sending a lot of data to the client. You can decrease this time by having the client bring back less data. Maybe the application doesn't need to bring back as much data as it is.

18. SQL*Net message to client
The “SQL*Net message to client” Oracle metric indicates the server (foreground process) is sending a message to the client, and it can be used to identify throughput issues over a network, especially distributed databases with slow database links. The SQL*Net more data to client event happens when Oracle writes multiple data buffers (sized per SDU) in a single logical network call.

19. enq: TX - row lock contention:
Oracle keeps data consistency with the help of locking mechanism. When a particular row is being modified by the process, either through Update/ Delete or Insert operation, oracle tries to acquire lock on that row. Only when the process has acquired lock the process can modify the row otherwise the process waits for the lock. This wait situation triggers this event. The lock is released whenever a COMMIT is issued by the process which has acquired lock for the row. Once the lock is released, processes waiting on this event can acquire lock on the row and perform DML operation

20. direct Path writes: You wont see them unless you are doing some appends or data loads. The session has issued asynchronous I/O requests that bypass the buffer cache and is waiting for them to complete. These wait events often involve temporary segments, sorting activity, parallel query or hash joins.


21. direct Path reads / direct path writes: Could happen if you are doing a lot of parallel query activity. The session has issued asynchronous I/O requests that bypass the buffer cache and is waiting for them to complete. These wait events often involve temporary segments, sorting activity, parallel query or hash joins. Usually sorting to Temp. Can also be parallel query. Could also be insert append, etc Adjust PGA_AGGREGATE_TARGET to fix it.

22. write complete waits: The session is waiting for a requested buffer to be written to disk; the buffer cannot be used while it is being written.


23. direct path read temp or direct path write temp: This wait event shows Temp file activity (sort,hashes,temp tables, bitmap) check pga parameter or sort area or hash area parameters. You might want to increase them


24. Undo segment extension: The session is waiting for an undo segment to be extended or shrunk. If excessive, tune undo


25. wait for a undo record: Usually only during recovery of large transactions, look at turning off parallel undo recovery.


26. Control File Parallel Write: The session has issued multiple I/O requests in parallel to write blocks to all control files, and is waiting for all of the writes to complete.


27. Control File Sequential Read: The session is waiting for blocks to be read from a control file.


28. DB File Parallel Write: The process, typically DBWR, has issued multiple I/O requests in parallel to write dirty blocks from the buffer cache to disk and is waiting for all requests to complete.


29. Library Cache load lock: The session is waiting for the opportunity to load an object or a piece of an object into the library cache. (Only one process can load an object or a piece of an object at a time.)


30. log file sequential read: The session is waiting for blocks to be read from the online redo log into memory. This primarily occurs at instance startup and when the ARCH process archives filled online redo logs.
 

Time Model Statistics
Oracle Database time model related statistics are presented next. The time model allows you to see a summary of where the database is spending it's time. The report will present the various time related statistic (such as DB CPU) and how much total time was spent in the mode of operation represented by that statistic. Here is an example of the time model statistic report:

Statistic Name Time (s) % of DB Time
sql execute elapsed time 19,640.87 95.41
DB CPU 17,767.20 86.31
parse time elapsed 73.75 0.36
hard parse elapsed time 38.35 0.19
PL/SQL execution elapsed time 32.04 0.16
hard parse (sharing criteria) elapsed time 6.98 0.03
connection management call elapsed time 4.25 0.02
repeated bind elapsed time 3.43 0.02
PL/SQL compilation elapsed time 3.04 0.01
hard parse (bind mismatch) elapsed time 1.62 0.01
sequence load elapsed time 0.74 0.00
failed parse elapsed time 0.04 0.00
DB time 20,586.08  
background elapsed time 859.22  
background cpu time 68.05

If parsing time is very high, or if hard parsing is significant, you must investigate it further. You should not expect the % of DB Time to add up to 100% because there is overlap among statistics.
Generally you want SQL processing time high, parsing and other stuff low. Time related statistics presents the various operations which are consuming most of the database time.
If SQL time>>DB CPU time then probably have IO issues. 
If Hard parses or parsing time is very high then further investigation should be done to resolve the problem. .

Example

Statistic Name

Time (s)

% of DB Time

sql execute elapsed time

12,416.14

86.45

DB CPU

9,223.70

64.22

parse time elapsed

935.61

6.51

hard parse elapsed time

884.73

6.16

failed parse elapsed time

821.39

5.72

PL/SQL execution elapsed time

153.51

1.07

hard parse (sharing criteria) elapsed time

25.96

0.18

connection management call elapsed time

14.00

0.10

hard parse (bind mismatch) elapsed time

4.74

0.03

PL/SQL compilation elapsed time

1.20

0.01

repeated bind elapsed time

0.22

0.00

sequence load elapsed time

0.11

0.00

DB time

14,362.96

 

background elapsed time

731.00

 

background cpu time

72.00

 


In the above example, 9,223.70 seconds CPU time was used for all user sessions. This was just under 65% of database resources. 
In total there was 14363 seconds database time used. 
The total wait event time can be calculated as 14363 – 9223.70 = 5139.3 seconds. The lion share of database time (86.45%) was spent on executing sql which is a good sign. The total parse time was 935.61 seconds of which 884.73 seconds was hard parsing. The rest of statistics is tiny in this case


Operating System Statistics

This part of the report provides some basic insight into OS performance, and OS configuration too. This report may vary depending on the OS platform that your database is running on. Here is an example from a Linux system:
Statistic Value End Value
BUSY_TIME 1,831,850  
IDLE_TIME 26,901,106  
IOWAIT_TIME 226,948  
NICE_TIME 8  
SYS_TIME 212,021  
USER_TIME 1,596,003  
LOAD 1 1
RSRC_MGR_CPU_WAIT_TIME 0  
VM_IN_BYTES 1,560,961,024  
VM_OUT_BYTES 475,336,945,664  
PHYSICAL_MEMORY_BYTES 12,582,432,768  
NUM_CPUS 16  
NUM_CPU_CORES 8  
NUM_CPU_SOCKETS 2  
GLOBAL_RECEIVE_SIZE_MAX 4,194,304  
GLOBAL_SEND_SIZE_MAX 1,048,576  
TCP_RECEIVE_SIZE_DEFAULT 87,380  
TCP_RECEIVE_SIZE_MAX 174,760  
TCP_RECEIVE_SIZE_MIN 4,096  
TCP_SEND_SIZE_DEFAULT 16,384  
TCP_SEND_SIZE_MAX 131,072  
TCP_SEND_SIZE_MIN 4,096  

In this example output, for example, we have 16 CPU's on the box.

Operating System Statistics - Detail
Snap Time Load %busy %user %sys %idle %iowait
12-Jul 13:00:59 0.99          
12-Jul 14:00:03 2.67 6.47 5.63 0.77 93.53 0.56
12-Jul 15:00:08 2.45 11.67 10.22 1.30 88.33 1.47
12-Jul 16:00:12 2.88 11.93 10.43 1.35 88.07 1.45
12-Jul 17:00:16 0.74 1.61 1.37 0.21 98.39 0.46
12-Jul 18:00:21 0.80 0.19 0.13 0.06 99.81 0.01



Foreground Wait Class and Foreground Wait Events  
Closely associated with the time model section of the report are the Foreground wait class and Foreground wait event statistics sections. 
Within Oracle, the duration of a large number of operations  (e.g. Writing to disk or to the control file) is metered. These are known as wait events, because each of these operations requires the system to wait for the event to complete. 
Thus, the execution of some database operation (e.g. a SQL query) will have a number of wait events associated with it. We can try to determine which wait events are causing us problems by looking at the wait classes and the wait event reports generated from AWR.
Wait classes define "buckets" that allow for summation of various wait times. Each wait event is assigned to one of these buckets (for example System I/O or User I/O). These buckets allow one to quickly determine which subsystem is likely suspect in performance problems  (e.g. the network, or the cluster).

Foreground Wait Class
Wait Class Waits %Time -outs Total Wait Time (s) Avg wait (ms) %DB time
DB CPU     17,767   86.31
User I/O 6,536,576 0 3,500 1 17.00
Commit 169,635 0 666 4 3.24
Other 350,080 21 140 0 0.68
Concurrency 78,002 0 58 1 0.28
Network 1,755,547 0 2 0 0.01
Application 579 0 1 2 0.00
System I/O 584 0 0 0 0.00
Configuration 1 0 0 0 0.00


Here is an example of the wait class report section:

          -------------------------------------------------------------

Foreground Wait Class                               DB/Inst: A109/a1092  Snaps: 2009-2010
-> s  - second
-> cs - centisecond -     100th of a second
-> ms - millisecond -    1000th of a second
-> us - microsecond - 1000000th of a second
-> ordered by wait time desc, waits desc
                                                                  Avg
                                       %Time       Total Wait    wait     Waits
Wait Class                      Waits  -outs         Time (s)    (ms)      /txn
-------------------- ---------------- ------ ---------------- ------- ---------
System I/O                      8,142     .0               25       3      10.9
Other                         439,596   99.6                3       0     589.3
User I/O                          112     .0                0       3       0.2
Cluster                           443     .0                0       0       0.6
Concurrency                       216     .0                0       0       0.3
Commit                             16     .0                0       2       0.0
Network                         3,526     .0                0       0       4.7
Application                        13     .0                0       0       0.0
          -------------------------------------------------------------


In this report the system I/O wait class has the largest number of waits (total of 25 seconds) and an average wait of 3 milliseconds.



Foreground Wait Events
Wait events are normal occurrences, but if a particular sub-system is having a problem performing (e.g. the disk sub-system) this fact will appear in the form of one or more wait events with an excessive duration. 
The wait event report then provides some insight into the detailed wait events. Here is an example of the wait event report (we have eliminated some of the bulk of this report, because it can get quite long). Note that this section is sorted by wait time (listed in microseconds).
Event Waits %Time -outs Total Wait Time (s) Avg wait (ms) Waits /txn % DB time
direct path write temp 1,837,854 0 2,267 1 10.53 11.01
direct path read 2,838,190 0 930 0 16.26 4.52
log file sync 169,635 0 666 4 0.97 3.24
db file sequential read 13,222 0 143 11 0.08 0.69
direct path read temp 1,837,007 0 131 0 10.53 0.64
PX Deq: Slave Session Stats 131,555 0 107 1 0.75 0.52
db file scattered read 8,448 0 26 3 0.05 0.13
kksfbc child completion 441 100 22 51 0.00 0.11
latch: shared pool 4,849 0 16 3 0.03 0.08
library cache: mutex X 67,703 0 14 0 0.39 0.07
library cache lock 346 0 12 35 0.00 0.06
cursor: pin S wait on X 582 0 9 16 0.00 0.05
latch free 9,647 0 7 1 0.06 0.03
os thread startup 116 0 4 32 0.00 0.02
SQL*Net message to client 1,739,132 0 2 0 9.97 0.01
cursor: mutex S 1,666 0 2 1 0.01 0.01
latch: row cache objects 1,658 0 1 1 0.01 0.01
read by other session 92 0 1 12 0.00 0.01
db file parallel read 344 0 1 3 0.00 0.00
PX Deq: Signal ACK EXT 65,787 0 1 0 0.38 0.00
PX Deq: Signal ACK RSG 65,787 0 1 0 0.38 0.00
enq: PS - contention 758 0 1 1 0.00 0.00
enq: RO - fast object reuse 40 0 1 13 0.00 0.00
Disk file operations I/O 1,386 0 0 0 0.01 0.00
enq: KO - fast object checkpoint 539 0 0 1 0.00 0.00
PX qref latch 964 100 0 0 0.01 0.00
latch: parallel query alloc buffer 836 0 0 0 0.00 0.00
latch: cache buffers chains 174 0 0 1 0.00 0.00
SQL*Net more data to client 16,415 0 0 0 0.09 0.00
enq: TX - index contention 5 0 0 37 0.00 0.00
library cache load lock 1 0 0 139 0.00 0.00
asynch descriptor resize 71,974 100 0 0 0.41 0.00
PX Deq: Table Q Get Keys 486 0 0 0 0.00 0.00
reliable message 577 0 0 0 0.00 0.00
buffer busy waits 676 0 0 0 0.00 0.00
cursor: pin S 189 0 0 0 0.00 0.00
row cache lock 17 0 0 2 0.00 0.00
direct path sync 15 0 0 2 0.00 0.00
latch: cache buffer handles 1 0 0 29 0.00 0.00
utl_file I/O 18 0 0 1 0.00 0.00
PX Deq: Table Q qref 1,160 0 0 0 0.01 0.00
wait list latch free 13 0 0 1 0.00 0.00
latch: object queue header operation 32 0 0 0 0.00 0.00
control file sequential read 584 0 0 0 0.00 0.00
SQL*Net message from client 1,739,106 0 260,904 150 9.97  
jobq slave wait 41,892 100 20,964 500 0.24  
PX Deq: Execution Msg 746,687 0 1,612 2 4.28  
PX Deq: Table Q Normal 1,057,627 0 387 0 6.06  
PX Deq Credit: send blkd 128,373 0 266 2 0.74  
PX Deq: Execute Reply 710,735 0 51 0 4.07  
PX Deq: Parse Reply 65,790 0 13 0 0.38  
PX Deq: Join ACK 65,790 0 4 0 0.38  
PX Deq Credit: need buffer 1,783 0 3 1 0.01  
PX Deq: Table Q Sample 1,275 0 1 0 0.01  

Example
Foreground Wait Events 
Avg %Time Total Wait wait Waits Event Waits -outs Time (s) (ms) /txn ---------------------------- -------------- ------ ----------- ------- --------- control file parallel write 1,220 .0 18 15 1.6 control file sequential read 6,508 .0 6 1 8.7 CGS wait for IPC msg 422,253 100.0 1 0 566.0 change tracking file synchro 60 .0 1 13 0.1 db file parallel write 291 .0 0 1 0.4 db file sequential read 90 .0 0 4 0.1 reliable message 136 .0 0 1 0.2 log file parallel write 106 .0 0 2 0.1 lms flush message acks 1 .0 0 60 0.0 gc current block 2-way 200 .0 0 0 0.3 change tracking file synchro 59 .0 0 1 0.1

In this example our control file parallel write waits (which occurs during writes to the control file) are taking up 18 seconds total, with an average wait of 15 milliseconds per wait. 
Additionally we can see that we have 1.6 waits per transaction (or 15ms * 1.6 per transaction = 24ms).


Background Wait Events

Background wait events are those not associated with a client process. They indicate waits encountered by system and non-system processes. Examples of background system processes are LGWR and DBWR.  An example of a non-system background process would be a parallel query slave.  Note that it is possible for a wait event to appear in both the foreground and background wait events statistics, for examples the enqueue and latch free events.  The idle wait events appear at the bottom of both sections and can generally safely be ignored. Typically these type of events keep record of the time while the client is connected to the database but not requests are being made to the server. Usually not a big contributor.
Event Waits %Time -outs Total Wait Time (s) Avg wait (ms) Waits /txn % bg time
log file parallel write 189,549 0 615 3 1.09 71.53
db file async I/O submit 28,334 0 37 1 0.16 4.27
os thread startup 711 0 25 35 0.00 2.88
control file parallel write 32,030 0 16 1 0.18 1.88
db file sequential read 899 0 14 15 0.01 1.57
latch: shared pool 7,111 0 9 1 0.04 1.09
latch: call allocation 15,537 0 9 1 0.09 1.07
latch free 8,264 0 5 1 0.05 0.63
ARCH wait on ATTACH 259 0 3 11 0.00 0.34
Log archive I/O 726 0 2 3 0.00 0.24
row cache lock 2 0 1 746 0.00 0.17
control file sequential read 83,303 0 1 0 0.48 0.12
db file parallel read 30 0 1 17 0.00 0.06
Disk file operations I/O 1,214 0 0 0 0.01 0.05
log file sequential read 838 0 0 0 0.00 0.05
direct path sync 5 0 0 31 0.00 0.02
ADR block file read 80 0 0 2 0.00 0.02
log file sync 11 0 0 11 0.00 0.01
enq: PR - contention 3 0 0 23 0.00 0.01
latch: cache buffers chains 1 0 0 46 0.00 0.01
db file single write 264 0 0 0 0.00 0.00
latch: parallel query alloc buffer 63 0 0 1 0.00 0.00
LGWR wait for redo copy 521 0 0 0 0.00 0.00
Data file init write 20 0 0 1 0.00 0.00
latch: row cache objects 1 0 0 15 0.00 0.00
latch: session allocation 27 0 0 1 0.00 0.00
db file scattered read 3 0 0 4 0.00 0.00
reliable message 50 0 0 0 0.00 0.00
direct path write 14 0 0 1 0.00 0.00
asynch descriptor resize 1,442 100 0 0 0.01 0.00
wait list latch free 10 0 0 1 0.00 0.00
rdbms ipc reply 57 0 0 0 0.00 0.00
log file single write 40 0 0 0 0.00 0.00
ADR block file write 25 0 0 0 0.00 0.00
ADR file lock 30 0 0 0 0.00 0.00
library cache: mutex X 2 0 0 1 0.00 0.00
SQL*Net message to client 587 0 0 0 0.00 0.00
rdbms ipc message 323,540 38 356,760 1103 1.85  
PX Idle Wait 65,885 0 115,165 1748 0.38  
DIAG idle wait 35,863 100 35,900 1001 0.21  
Space Manager: slave idle wait 5,626 97 27,710 4925 0.03  
pmon timer 5,984 100 17,959 3001 0.03  
Streams AQ: qmn slave idle wait 642 0 17,953 27964 0.00  
Streams AQ: qmn coordinator idle wait 1,282 50 17,953 14004 0.01  
shared server idle wait 598 100 17,946 30010 0.00  
dispatcher timer 299 100 17,942 60007 0.00  
smon timer 113 33 17,719 156808 0.00  
SQL*Net message from client 795 0 1 1 0.00  
class slave wait 83 0 0 0 0.00  


Service Statistics
A service is a grouping of processes. Users may be grouped in SYS$USER. Application logins (single user) may be grouped with that user name

Service Name DB Time (s) DB CPU (s) Physical Reads (K) Logical Reads (K)
FGUARD.fiservipo.com 19,903 17,555 413,742 386,056
SYS$USERS 683 212 1,733 6,954
FGUARDXDB 0 0 0 0
SYS$BACKGROUND 0 0 3 134





SQL Information Section

Next in the report we find several different reports that present SQL statements that might be improved by tuning. Any SQL statement appears in the top 5 statements in two or more areas below, then it is a prime candidate for tuning. The sections are:

Let try to see what these mean.

SQL Ordered by Elapsed Time
Total Elapsed Time = CPU Time + Wait Time. If a SQL statement appears in the total elapsed time area of the report this means its CPU time plus any other wait times made it pop to the top of the pile. Excessive Elapsed Time could be due to excessive CPU usage or excessive wait times.
This is the area that you need to examine and probably the one that will be reported by the users or application support. From a consumer perspective, the finer details don’t matter. The application is slow. Full stop.
In conjunction with excessive Elapsed time check to see if this piece of SQL is also a high consumer under Total CPU Time. It is normally the case. Otherwise check the wait times and Total Disk Reads. They can either indicate issues with wait times (slow disks, latch gets etc) or too much Physical IO associated with tables scans or sub-optimal indexes.  This section is a gate opener and often you will need to examine other sections.

SQL Ordered by CPU Time
When a statement appears in the Total CPU Time area this indicates it used excessive CPU cycles during its processing. Excessive CPU processing time can be caused by sorting, excessive function usage or long parse times. Indicators that you should be looking at this section for SQL tuning candidates include high CPU percentages in the service section for the service associated with this SQL (a hint, if the SQL is uppercase it probably comes from a user or application; if it is lowercase it usually comes from the internal or background processes). To reduce total CPU time, reduce sorting by using composite indexes that can cover sorting and use bind variables to reduce parse times.

SQL Ordered by Buffer Gets
Total buffer gets mean a SQL statement is reading a lot of data from the db block buffers. Generally speaking buffer gets (AKA logical IO or LIO) are OK, except when they become excessive. The old saying that you reduce the logical IO, because then the physical IO (disk read) will take care of itself holds true. LIO may  have incurred a PIO in order to get the block into the buffer in the first place. Reducing buffer gets is very important and should not be underestimated. To get a block from db block buffers, we have to latch it (i.e. in order to prevent someone from modifying the data structures we are currently reading from the buffer). Although latches are less persistent than locks, a latch is still a serialization device. Serialization devices inhibit scalability, the more you use them, the less concurrency you get. Therefore in most cases optimal buffer gets can result in improved performance. Also note that by lowering buffer gets you will require less CPU usage and less latching. |Thus to reduce excessive buffer gets, optimize SQL to use appropriate indexes and reduce full table scans. You can also look at improving the indexing strategy and consider deploying partitioning (licensed).

SQL Ordered by Disk Reads
High total disk reads mean a SQL statement is reading a lot of data from disks rather than being able to access that data from the db block buffers. High physical reads after a server reboot are expected as the cache is cold and data is fetched from the disk. However, disk reads (or physical reads) are undesirable in an OLTP system, especially when they become excessive. Excessive disk reads do cause performance issues. The usual norm is to increase the db buffer cache to allow more buffers and reduce ageing . Total disk reads are typified by high physical reads, a low buffer cache hit ratio, with high IO wait times. Higher wait times for Disk IO can be associated with a variety of reasons (busy or over saturated SAN, slower underlying storage, low capacity in HBC and other hardware causes). Statistics on IO section in AWR, plus the Operating System diagnostic tools as simple as iostatcan help in identifying these issues. To reduce excessive disk reads, consider partitioning, use indexes and look at optimizing SQL to avoid excessive full table scans.

SQL Ordered by Executions
High total executions need to be reviewed to see if they are genuine executions or loops in SQL code. I have also seen situations where autosys jobs fire duplicate codes erroneously. In general statements with high numbers of executions usually are being properly reused. However, there is always a chance of unnecessary loop in PL/SQL, Java or C#. Statements with high number of executions, high number of logical and or physical reads are candidates for review to be sure they are not being executed multiple times when a single execution would serve. If the database has excessive physical and logical reads or excessive IO wait times, then look at the SQL statements that show excessive executions and show high physical and logical reads.

Parse Calls
Whenever a statement is issued by a user or process, regardless of whether it is in the SQL pool it undergoes a parse.  As explained under Parsing, the parse can be a hard parse or a soft parse. Excessive parse calls usually go with excessive executions. If the statement is using what are known as unsafe bind variables then the statement will be reparsed each time. If the header parse ratios are low look here and in the version count areas.

SQL Ordered by Memory
Sharable Memory refers to Shared Pool memory area in SGA , hence this particular section in AWR Report states about the SQL STATEMENT CURSORS which consumed the maximum amount of the Shared Pool for their execution.
In general high values for Sharable Memory doesn’t necessary imply there is an issue It simply means that:
    - These SQL statements are big or complex and Oracle has to keep lots of information about these statements OR
    - big number of child cursors exist for those parent cursors
    - combination of 1 & 2
In case of point 2, it may be due to poor coding such as bind variables mismatch, security mismatch  or overly large SQL statements that join many tables. In a DSS or  DW environment large complex statements are normal. In an OLTP database large or complex statements are usually the result of over-normalization of the database design, attempts to use an OLTP system as a DW or simply poor coding techniques. Usually large statements will result in excessive parsing, recursion, and large CPU usage.

SQL Ordered by Version Count
High version counts are usually due to multiple identical-schema databases, unsafe bind variables, or Oracle bugs.


The SQL that is stored in the shared pool SQL area (Library cache) is reported in this section in different ways:
. SQL ordered by Buffer Gets
. SQL ordered by Physical Reads
. SQL ordered by Executions
. SQL ordered by Parse Calls

- SQL ordered by Gets:
This section reports the contents of the SQL area ordered by the number of buffer gets and can be used to identify the most CPU Heavy SQL.
- Many DBAs feel that if the data is already contained within the buffer cache the query should be efficient.  This could not be further from the truth.  Retrieving more data than needed, even from the buffer cache, requires CPU cycles and interprocess IO. Generally speaking, the cost of physical I/O is not 10,000 times more expensive.  It actually is in the neighborhood of 67 times and actually almost zero if the data is stored in the UNIX buffer cache.
- The statements of interest are those with a large number of gets per execution especially if the number of executions is high.
- High buffer gets generally correlates with heavy CPU usage

- SQL ordered by Reads:
This section reports the contents of the SQL area ordered by the number of reads from the data files and can be used to identify SQL causing IO bottlenecks which consume the following resources.
- CPU time needed to fetch unnecessary data.
- File IO resources to fetch unnecessary data.
- Buffer resources to hold unnecessary data.
- Additional CPU time to process the query once the data is retrieved into the buffer.

- SQL ordered by Executions:
This section reports the contents of the SQL area ordered by the number of query executions. It is primarily useful in identifying the most frequently used SQL within the database so that they can be monitored for efficiency.  Generally speaking, a small performance increase on a frequently used query provides greater gains than a moderate performance increase on an infrequently used query. Possible reasons for high Reads per Exec are use of unselective indexes require large numbers of blocks to be fetched where such blocks are not cached well in the buffer cache, index fragmentation, large Clustering Factor in index etc.

- SQL ordered by Parse Calls:
This section shows the number of times a statement was parsed as compared to the number of times it was executed.  One to one parse/executions may indicate that:
- Bind variables are not being used.
  The shared pool may be too small and the parse is not being retained long enough for multiple executions.
- cursor_sharing is set to exact (this should NOT be changed without considerable testing on the part of the client).



Generate Execution Plan for given SQL statement

If you have identified one or more problematic SQL statement, you may want to check the execution plan. Remember the "Old Hash Value" from the report above (1279400914), then execute the scrip to generate the execution plan.

sqlplus perfstat/perfstat
SQL> @?/rdbms/admin/sprepsql.sql
Enter the Hash Value, in this example: 1279400914

SQL Text
~~~~~~~~
create table test as select * from all_objects

Known Optimizer Plan(s) for this Old Hash Value
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Shows all known Optimizer Plans for this database instance, and the Snap Id's
they were first found in the shared pool.  A Plan Hash Value will appear
multiple times if the cost has changed
-> ordered by Snap Id


  First        First          Plan
 Snap Id     Snap Time     Hash Value        Cost
--------- --------------- ------------ ----------
        6 14 Nov 04 11:26   1386862634        52


Plans in shared pool between Begin and End Snap Ids
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Shows the Execution Plans found in the shared pool between the begin and end
snapshots specified.  The values for Rows, Bytes and Cost shown below are those
which existed at the time the first-ever snapshot captured this plan - these
values often change over time, and so may not be indicative of current values
-> Rows indicates Cardinality, PHV is Plan Hash Value
-> ordered by Plan Hash Value


--------------------------------------------------------------------------------
| Operation                      | PHV/Object Name     |  Rows | Bytes|   Cost |
--------------------------------------------------------------------------------
|CREATE TABLE STATEMENT          |----- 1386862634 ----|       |      |     52 |
|LOAD AS SELECT                  |                     |       |      |        |
| VIEW                           |                     |     1K|  216K|     44 |
|  FILTER                        |                     |       |      |        |
|   HASH JOIN                    |                     |     1K|  151K|     38 |
|    TABLE ACCESS FULL           |USER$                |    29 |  464 |      2 |
|    TABLE ACCESS FULL           |OBJ$                 |     3K|  249K|     35 |
|   TABLE ACCESS BY INDEX ROWID  |IND$                 |     1 |    7 |      2 |
|    INDEX UNIQUE SCAN           |I_IND1               |     1 |      |      1 |
|   NESTED LOOPS                 |                     |     5 |  115 |     16 |
|    INDEX RANGE SCAN            |I_OBJAUTH1           |     1 |   10 |      2 |
|    FIXED TABLE FULL            |X$KZSRO              |     5 |   65 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   FIXED TABLE FULL             |X$KZSPR              |     1 |   26 |     14 |
|   VIEW                         |                     |     1 |   13 |      2 |
|    FAST DUAL                   |                     |     1 |      |      2 |
--------------------------------------------------------------------------------



Instance Activity Stats Section


This section provides us with a number of various statistics (such as, how many DBWR Checkpoints occurred, or how many consistent gets occurred during the snapshot). These are the statistics that the summary information is derived from. A list of the statistics maintained by the RDBMS kernel can be found in Appendix C of the Oracle Reference manual for the version being utilized.
Here is a partial example of the report: 

Instance Activity Stats for DB: PHS2  Instance: phs2  Snaps: 100 -104
Statistic                                    Total   per Second    per Trans
--------------------------------- ---------------- ------------ ------------
CPU used by this session                    84,161         23.4      3,825.5
CPU used when call started                 196,346         54.5      8,924.8
CR blocks created                              709          0.2         32.2
DBWR buffers scanned                             0          0.0          0.0
DBWR checkpoint buffers written                245          0.1         11.1
DBWR checkpoints                                33          0.0          1.5
DBWR cross instance writes                      93          0.0          4.2
DBWR free buffers found                          0          0.0          0.0
....
....
branch node splits                             7,162            0.1        0.0

consistent gets                       12,931,850,777      152,858.8    3,969.5
current blocks converted for CR               75,709            0.9        0.0
db block changes                         343,632,442        4,061.9      105.5
db block gets                            390,323,754        4,613.8      119.8
hot buffers moved to head of LRU         197,262,394        2,331.7       60.6
leaf node 90-10 splits                        26,429            0.3        0.0
leaf node splits                             840,436            9.9        0.3
logons cumulative                             21,369            0.3        0.0
physical reads                           504,643,275        5,965.1      154.9
physical writes                           49,724,268          587.8       15.3
session logical reads                 13,322,170,917      157,472.5    4,089.4
sorts (disk)                                   4,132            0.1        0.0
sorts (memory)                             7,938,085           93.8        2.4
sorts (rows)                             906,207,041       10,711.7      278.2
table fetch continued row                 25,506,365          301.5        7.8
table scans (long tables)                        111            0.0        0.0
table scans (short tables)                 1,543,085           18.2        0.5

Instance Activity Terminology

Session Logical Reads = All reads cached in memory.  Includes both consistent gets and also the db block gets.
Consistent Gets = These are the reads of a block that are in the cache.  They are NOT to be confused with consistent read (cr) version of a block in the buffer cache (usually the current version is read).
Db block gets = These are block gotten to be changed.  MUST be the CURRENT block and not a cr block.
Db block changes = These are the db block gets (above) that were actually changed.
Physical Reads = Blocks not read from the cache.  Either from disk, disk cache or O/S cache; there are also physical reads direct which bypass cache using Parallel Query (not in hit ratios).

Of particular interest are the following statistics.
- CPU USED BY THIS SESSION, PARSE TIME CPU or RECURSIVE CPU USAGE:  These numbers are useful to diagnose CPU saturation on the system (usually a query tuning issue). The formula to calculate the CPU usage breakdown is:
Service (CPU) Time = other CPU + parse time CPU
Other CPU = "CPU used by this session" - parse time CPU
Some releases do not correctly store this data and can show huge numbers. 

recursive cpu usage =  This component can be high if large amounts of PL/SQL are being processed. It is outside the scope of this document to go into detail with this, but you will need to identify your complete set of PL/SQL, including stored procedures, finding the ones with the highest CPU load and optimize these. If most work done in PL/SQL is procedural processing (rather than executing SQL), a high recursive cpu usage can actually indicate a potential tuning effort.

parse time cpu= Parsing SQL statements is a heavy operation, that should be avoided by reusing SQL statements as much as possible. In precompiler programs, unnecessary parting of implicit SQL statements can be avoided by increasing the cursor cache (MAXOPENCURSORS parameter) and by reusing cursors. In programs using Oracle Call Interface, you need to write the code, so that it re-executes (in stead of reparse) cursors with frequently executed SQL statements. The v$sql view contains PARSE_CALLS and EXECUTIONS columns, that can be used to identify SQL, that is parsed often or is only executed once per parse.

other cpu= The source of other cpu is primarily handling of buffers in the buffer cache. It can generally be assumed, that the CPU time spent by a SQL statement is approximately proportional to the number of buffer gets for that SQL statements, hence, you should identify and sort SQL statements by buffer gets in v$sql. In your report, look at the part ‘SQL ordered by Gets for DB’. Start tuning SQL statements from the top of this list. In Oracle, the v$sql view contain a column, CPU_TIME, which directly shows the cpu time associated with executing the SQL statement.

- DBWR BUFFERS SCANNED:  the number of buffers looked at when scanning the lru portion of the buffer cache for dirty buffers to make clean. Divide by "dbwr lru scans" to find the average number of buffers scanned. This count includes both dirty and clean buffers. The average buffers scanned may be different from the average scan depth due to write batches filling up before a scan is complete. Note that this includes scans for reasons other than make free buffer requests.
- DBWR CHECKPOINTS: the number of checkpoints messages that were sent to DBWR and not necessarily the total number of actual checkpoints that took place.  During a checkpoint there is a slight decrease in performance since data blocks are being written to disk and that causes I/O. If the number of checkpoints is reduced, the performance of normal database operations improve but recovery after instance failure is slower.
- DBWR TIMEOUTS: the number of timeouts when DBWR had been idle since the last timeout.  These are the times that DBWR looked for buffers to idle write.
- DIRTY BUFFERS INSPECTED: the number of times a foreground encountered a dirty buffer which had aged out through the lru queue, when foreground is looking for a buffer to reuse. This should be zero if DBWR is keeping up with foregrounds.
- FREE BUFFER INSPECTED: the number of buffers skipped over from the end of the LRU queue in order to find a free buffer.  The difference between this and "dirty buffers inspected" is the number of buffers that could not be used because they were busy or needed to be written after rapid aging out. They may have a user, a waiter, or being read/written.
- RECURSIVE CALLS:  Recursive calls occur because of cache misses and segment extension. In general if recursive calls is greater than 30 per process, the data dictionary cache should be optimized and segments should be rebuilt with storage clauses that have few large extents.  Segments include tables, indexes, rollback segment, and temporary segments.
NOTE: PL/SQL can generate extra recursive calls which may be unavoidable.
- REDO BUFFER ALLOCATION RETRIES: total number of retries necessary to allocate space in the redo buffer.  Retries are needed because either the redo writer has gotten behind, or because an event  (such as log switch) is occurring
- REDO LOG SPACE REQUESTS:  indicates how many times a user process waited for space in the redo log buffer.  Try increasing the init.ora parameter LOG_BUFFER so that zero Redo Log Space Requests are made.
- REDO WASTAGE: Number of bytes "wasted" because redo blocks needed to be written before they are completely full.   Early writing may be needed to commit transactions, to be able to write a database buffer, or to switch logs
- SUMMED DIRTY QUEUE LENGTH: the sum of the lruw queue length after every write request completes. (divide by write requests to get average queue length after write completion)
- TABLE FETCH BY ROWID: the number of rows that were accessed by a rowid.  This includes rows that were accessed using an index and rows that were accessed using the statement where rowid = 'xxxxxxxx.xxxx.xxxx'.
- TABLE FETCH BY CONTINUED ROW: indicates the number of rows that are chained to another block. In some cases (i.e. tables with long columns) this is unavoidable, but the ANALYZE table command should be used to further investigate the chaining, and where possible, should be eliminated by rebuilding the table.
- Table Scans (long tables) is the total number of full table scans performed on tables with more than 5 database blocks.  If the number of full table scans is high the application should be tuned to effectively use Oracle indexes. Indexes, if they exist, should be used on long tables if less than 10-20% (depending on parameter settings and CPU count) of the rows from the table are returned. If this is not the case, check the db_file_multiblock_read_count parameter setting. It may be too high.  You may also need to tweak optimizer_index_caching and optimizer_index_cost_adj.
- Table Scans (short tables) is the number of full table scans performed on tables with less than 5 database blocks.  It is optimal to perform full table scans on short tables rather than using indexes.




I/O Stats Section

The IO Stats stats report provides information on tablespace IO performance. From this report you can determine if the tablespace or datafiles are suffering from sub-standard performance in terms of IO response from the disk sub-system.
IO Activity Input/Output (IO) statistics for the instance are listed in the following sections/formats:

- Tablespace I/O Stats for DB: Ordered by total IO per tablespace.
- File I/O Stats for DB: Ordered alphabetically by tablespace, filename.

If the statistic "Buffer Waits" for a tablespace is greater than 1000, you may want to consider tablespace reorganization in order to spread tables within it across another tablespaces.

Note that Oracle considers average read times of greater than 20 ms unacceptable.  If a datafile consistently has average read times of 20 ms or greater then:
- The queries against the contents of the owning tablespace should be examined and tuned so that less data is retrieved.
- If the tablespace contains indexes, another option is to compress the indexes so that they require less space and hence, less IO.
- The contents of that datafile should be redistributed across several disks/logical volumes to more easily accommodate the load.
- If the disk layout seems optimal, check the disk controller layout.  It may be that the datafiles need to be distributed across more disk sets.

Tablespace IO Stats
Tablespace Reads Av Reads/s Av Rd(ms) Av Blks/Rd Writes Av Writes/s Buffer Waits Av Buf Wt(ms)
TEMPFG 1,839,383 102 0.00 31.00 1,837,948 102 74 1.62
FG_DATA 2,791,647 155 0.01 125.85 9,618 1 12 0.00
FG_DATA_ARCH 48,174 3 0.14 127.34 1 0 0 0.00
FG_INDX 16,194 1 3.51 60.39 4,647 0 23 0.00
SYSAUX 1,331 0 29.00 1.02 7,867 0 18 43.33
SYSTEM 4,491 0 8.38 1.30 2,154 0 278 0.97
UNDOTBS1 0 0 0.00 0.00 4,450 0 381 0.03


If the tablespace IO report seems to indicate a tablespace has IO problems, we can then use the file IO stat report allows us to drill into the datafiles of the tablespace in question and determine what the problem might be.


File IO Stats
Tablespace Filename Reads Av Reads/s Av Rd(ms) Av Blks/Rd Writes Av Writes/s Buffer Waits Av Buf Wt(ms)
FG_DATA /oradata/FGUARD/fg_data01.dbf 332,276 18 0.02 125.95 1,537 0 5 0.00
FG_DATA /oradata/FGUARD/fg_data02.dbf 332,928 19 0.02 125.87 575 0 0 0.00
FG_DATA /oradata/FGUARD/fg_data03.dbf 524,409 29 0.01 126.72 2,145 0 0 0.00
FG_DATA /oradata/FGUARD/fg_data04.dbf 421,718 23 0.02 124.68 995 0 1 0.00
FG_DATA /oradata/FGUARD/fg_data05.dbf 323,349 18 0.02 124.58 709 0 0 0.00
FG_DATA /oradata/FGUARD/fg_data06.dbf 332,548 19 0.02 125.68 1,338 0 0 0.00
FG_DATA /oradata/FGUARD/fg_data07.dbf 331,332 18 0.01 126.07 1,027 0 3 0.00
FG_DATA /oradata/FGUARD/fg_data08.dbf 193,087 11 0.00 127.85 1,292 0 3 0.00
FG_DATA_ARCH /oradata/FGUARD/fg_data_arch01.dbf 1,993 0 0.55 126.71 0 0 0 0.00
FG_DATA_ARCH /oradata/FGUARD/fg_data_arch02.dbf 15,772 1 0.19 127.35 1 0 0 0.00
FG_DATA_ARCH /oradata/FGUARD/fg_data_arch03.dbf 16,163 1 0.09 127.37 0 0 0 0.00
FG_DATA_ARCH /oradata/FGUARD/fg_data_arch04.dbf 14,246 1 0.10 127.37 0 0 0 0.00
FG_INDX /oradata/FGUARD/fg_indx01.dbf 2,908 0 3.84 39.20 358 0 1 0.00
FG_INDX /oradata/FGUARD/fg_indx02.dbf 1,204 0 10.97 71.13 1,130 0 5 0.00
FG_INDX /oradata/FGUARD/fg_indx03.dbf 7,588 0 0.93 62.00 1,132 0 6 0.00
FG_INDX /oradata/FGUARD/fg_indx04.dbf 1,733 0 5.79 64.18 829 0 2 0.00
FG_INDX /oradata/FGUARD/fg_indx05.dbf 1,714 0 7.06 65.41 927 0 3 0.00
FG_INDX /oradata/FGUARD/fg_indx06.dbf 1,047 0 3.07 80.76 271 0 6 0.00
SYSAUX /oradata/FGUARD/sysaux01.dbf 382 0 30.63 1.05 5,087 0 8 0.00
SYSAUX /oradata/FGUARD/sysaux02.dbf 949 0 28.35 1.00 2,780 0 10 78.00
SYSTEM /oradata/FGUARD/system01.dbf 4,465 0 8.32 1.30 2,001 0 269 0.93
SYSTEM /oradata/FGUARD/system02.dbf 26 0 19.23 1.00 153 0 9 2.22
TEMPFG /oradata/FGUARD/TEMPFG01.dbf 1,839,383 102 0.00 31.00 1,837,948 102 74 1.62
UNDOTBS1 /oradata/FGUARD/undotbs01.dbf 0 0     4,450 0 381 0.03



Buffer Pool Statistics Section

The buffer pool statistics report follows. It provides a summary of the buffer pool configuration and usage statistics.

The buffer statistics are comprised of two sections:

- Buffer Pool Statistics:  This section can have multiple entries if multiple buffer pools are allocated. A baseline of the database's buffer pool statistics should be available to compare with the current report buffer pool statistics.  A change in that pattern unaccounted for by a change in workload should be a cause for concern. Also check the Buffer Pool Advisory to identify if increasing that parameter (db_cache_size) would help to reduce Physical Reads.

P Number of Buffers Pool Hit% Buffer Gets Physical Reads Physical Writes Free Buff Wait Writ Comp Wait Buffer Busy Waits
D 183,303 99 34,022,316 230,167 85,445 0 0 786

- Checkpoint Activity: 


Advisory Statistics Section

In this section, we receive several recommendations on changes that we can perform and how that will affect the overall performance of the DB.

Instance Recovery Stats

The instance recovery stats report provides information related to instance recovery. By analyzing this report, you can determine roughly how long your database would have required to perform crash recovery during the reporting period. Here is an example of this report:


Targt MTTR (s) Estd MTTR (s) Recovery Estd IOs Actual RedoBlks Target RedoBlks Log Sz RedoBlks Log Ckpt Timeout RedoBlks Log Ckpt Interval RedoBlks Opt Log Sz(M) Estd RAC Avail Time
B 0 17 3186 40426 88123 995328 88123      
E 0 16 3068 34271 35669 995328 35669      


Buffer Pool Advisory

The buffer pool advisory report answers the question, how big should you make your database buffer cache.
It provides an extrapolation of the benefit or detriment that would result if you added or removed memory from the database buffer cache. These estimates are based on the current size of the buffer cache and the number of logical and physical IO's encountered during the reporting point. This report can be very helpful in "rightsizing" your buffer cache.
Here is an example of the output of this report:

P Size for Est (M) Size Factor Buffers (thousands) Est Phys Read Factor Estimated Phys Reads (thousands) Est Phys Read Time Est %DBtime for Rds
D 144 0.10 18 1.15 4,378 1 1922.00
D 288 0.19 35 1.10 4,180 1 1388.00
D 432 0.29 53 1.04 3,976 1 835.00
D 576 0.39 71 1.03 3,917 1 676.00
D 720 0.48 89 1.02 3,886 1 593.00
D 864 0.58 106 1.01 3,864 1 534.00
D 1,008 0.68 124 1.01 3,850 1 495.00
D 1,152 0.77 142 1.01 3,841 1 470.00
D 1,296 0.87 160 1.00 3,829 1 439.00
D 1,440 0.97 177 1.00 3,818 1 409.00
D 1,488 1.00 183 1.00 3,815 1