How to Identify Resource Intensive SQL for Tuning

Subject: How to Identify Resource Intensive SQL for Tuning
Doc ID: Note:232443.1 Type: TROUBLESHOOTING
Last Revision Date: 20-AUG-2006 Status: PUBLISHED

PURPOSE
This article provides guidance on how to identify the most resource intensive SQL statements in a database for tuning purposes.
SCOPE & APPLICATION
The techniques described here can be used when initial diagnosis of a database performance problem suggests that further investigation needs to concentrate on finding and tuning the most resource intensive SQL statements according to specific criteria, e.g.
using the most CPU
performing the most disk I/O operations
having the most executions
taking the longest time to execute (elapsed time)
The article should be of use to Database Administrators, Support Engineers, Consultants and Database Performance Analysts.
WHEN TO LOOK FOR RESOURCE INTENSIVE SQL STATEMENTS
In this section we discuss briefly the reasons that would lead us to look for the most resource intensive SQL statements while investigating a database performance problem.
Response Time Analysis shows that heaviest time consumer is CPU-related e.g. CPU Other or CPU Parse time or an I/O-related Wait Event e.g. db file sequential read or db file scattered read:
This is the method followed by tools such as Statspack.
Extensive information is gathered from the database which shows in detail in what type of activities time is being spent.
It is based on the following equation:
Response Time = Service Time + Wait Time

where Service Time is time spent on the CPU
and Wait Time is the sum of time spent on Wait Events i.e. non-idle time spent waiting for an event to complete or for a resource to become available.
Service Time is comprised of time spent on the CPU for Parsing, Recursive CPU usage (for PLSQL and recursive SQL) and CPU used for execution of SQL statements (CPU Other).

Service Time = CPU Parse + CPU Recursive + CPU Other

The above components of Service Time can be found from the following statistics:
Service Time from CPU used by this session
CPU Parse from parse time cpu
CPU Recursive from recursive cpu usage
From these, CPU Other can be calculated as follows:

CPU other = CPU used by this session - parse time cpu - recursive cpu usage

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.

Statspack lists such SQL statements in section SQL ordered by Gets.

When CPU Parse is a significant component of total Response Time the next step is to find the SQL statements that have the most parses.

Statspack lists such SQL statements in section SQL ordered by Parse Calls.
Note: this is available in Oracle9i and above.

Wait Time is the sum of time waited for non-idle Wait Events. These include I/O waits for reading blocks from disk as measured by the Wait Events db file sequential read for single-block reads and db file scattered read for multi-block reads.

When such Wait Events are found to be significant components of Response Time the next step is to find the SQL statements that read the most blocks from disk.

Statspack lists such SQL statements in section SQL ordered by Reads.

Example from Statspack: (pre-Oracle9i Release 2)
Here is an example where CPU Other was found to be a significant component of total Response Time:

Top 5 Wait Events
~~~~~~~~~~~~~~~~~ Wait % Total
Event Waits Time (cs) Wt Time
-------------------------------------------- ------------ ------------ -------
direct path read 4,232 10,827 52.01
db file scattered read 6,105 6,264 30.09
direct path write 1,992 3,268 15.70
control file parallel write 893 198 .95
db file parallel write 40 131 .63
-------------------------------------------------------------

Statistic Total per Second per Trans
--------------------------------- ---------------- ------------ ------------
CPU used by this session 358,806 130.5 12,372.6
parse time cpu 38 0.0 1.3
recursive cpu usage 186,636 67.9 6,435.7

From these figures we can obtain:
Wait Time = 10,827 x 100% / 52,01% = 20,817 cs
Service Time = 358,806 cs
Response Time = 358,806 + 20,817 = 379,623 cs
CPU Other = 358,806 - 38 - 186,636 = 172,132 cs
If we now calculate percentages for the top Response Time components:
CPU Other = 45.34%
CPU Recursive = 49.16%
direct path read = 2.85%
etc. etc.
CPU Other is a significant component of Response Time, so a possible next step is to look at the SQL ordered by Gets section.
Example from Statspack: (Oracle9i Release 2 & above)
Starting with Oracle9i Release 2, Statspack presents Service Time (obtained from the statistic CPU used by this session ) together with the top Wait Events in a section called Top 5 Timed Events, which replaces the section Top 5 Wait Events of previous releases.
Here is an example:


Top 5 Timed Events
~~~~~~~~~~~~~~~~~~ % Total
Event Waits Time (s) Ela Time
-------------------------------------------- ------------ ----------- --------
library cache lock 141 424 76.52
db file scattered read 3,367 96 17.40
CPU time 32 5.79
db file sequential read 161 1 .18
control file parallel write 245 0 .05
-------------------------------------------------------------

Statistic Total per Second per Trans
--------------------------------- ------------------ -------------- ------------
CPU used by this session 3,211 4.3 1,605.5
parse time cpu 59 0.1 29.5
recursive cpu usage 232 0.3 116.0

These figures give us directly the percentages of the Wait Events against the total Response Time so no further calculations are necessary to assess the impact of Wait Events. Service Time is presented as CPU time in this section and corresponds to the total CPU utilisation. We can drill down to the various components of Service Time as follows:
CPU Other = 3,211 - 59 - 232 = 2,920 cs
CPU Other = 2,920 / 3,211 x 5.79% = 5.26%
CPU Parse = 59 / 3,211 x 5.79% = 0.11%
CPU Recursive = 232 / 3,211 x 5.79% = 0.42%
In this example, the main performance problem was an issue related to the Library Cache.
The second most important time consumer was waiting for physical I/O due to multiblock reads (db file scattered read).
In this case a possible approach would be to look at the SQL ordered by Reads section of Statspack.

Operating System resource analysis shows that excessive CPU or Disk I/O usage is caused by one or more Oracle processes:
If Operating System utilities show that most CPU usage is due to a small number of Oracle processes then typically they will be SQL Traced and have TKPROF reports generated from their tracefiles. Analysis of the TKPROF reports will lead to the most time consuming SQL statements which will be the subject of subsequent tuning.

If CPU usage is excessive but spread out fairly evenly among all the Oracle processes on the system then typically a tool such as Statspack will be used to perform Response Time analysis. The components of Service Time will be evaluated and if this shows CPU Other as being significant the next step will be to look at the SQL performing most block accesses in the SQL by Gets section of the Statspack report.

If Disk I/O is highly utilized on the system then a Response Time analysis using Statspack can be done to confirm that the Disk I/O usage is due to Oracle processes and I/O-related Wait Events can be expected to be significant components of overall Response Time. SQL statements performing the most physical reads can then be found in the section SQL ordered by Reads .

Investigation of the performance of a batch job or other session which performs a number of SQL statements and has slow performance:
This will be done with SQL Trace and TKPROF as descrived above and the most important SQL statements in terms of time spent will be identified for further tuning.
Examination of statistics stored for SQL statements in V$ dynamic views:
Part of the information stored with a SQL statement (Shared Cursor) in the Library Cache portion of the Shared Pool are a number of statistics related to its execution. These are available through the V$SQLAREA dynamic view and can be queried to monitor the most expensive SQL statements in each category.
This is the approach used by the graphical tool SQL Analyze in the Oracle Enterprise Manager Tuning Pack.

For more information on the topics discussed in this section please refer to:
Note 190124.1 THE COE PERFORMANCE METHOD
Note 228913.1 Systemwide Tuning using STATSPACK Reports:
Note 223117.1 Tuning I/O-related waits

TOP SQL STATEMENTS IN STATSPACK REPORTS
Statspack reports generated from snapshots at level 5 (default) and above can contain SQL reporting sections for the following types of resource intensive SQL statements:
SQL ordered by Gets
containing the SQL statements with most buffer accesses.
The default threshold value is 10,000 buffer gets.
Columns displayed are:
Buffer Gets, Executions, Gets per Exec, % Total, Hash Value (8.1.7)
Buffer Gets, Executions, Gets per Exec, %Total, CPU Time (s), Elapsed Time (s), Hash Value (9.0.1 & 9.2)

SQL ordered by Reads
containing the SQL statements with most read I/Os from disk.
The default threshold value is 1,000 disk reads.
Columns displayed are:
Physical Reads, Executions, Reads per Exec, % Total, Hash Value (8.1.7)
Physical Reads, Executions, Reads per Exec, %Total, CPU Time (s), Elapsed Time (s), Hash Value (9.0.1 & 9.2)

SQL ordered by Executions
containing the SQL statements executed the most times.
The default threshold value is 100 executions.
Columns displayed are:
Executions, Rows Processed, Rows per Exec, Hash Value (8.1.7)
Executions, Rows Processed, Rows per Exec, CPU per Exec (s), Elapsed per Exec (s), Hash Value (9.0.1 & 9.2)

SQL ordered by Parse Calls (Oracle9i and above)
containing the SQL statements with most soft parse calls.
The default threshold value is 1,000 parse calls.
Columns displayed are:
Parse Calls, Executions, % Total Parses, Hash Value

SQL ordered by Sharable Memory
containing the SQL statements occupying the most Library Cache memory.
The default threshold value is 1Mb (1,048,576 bytes).
Columns displayed are:
Sharable Mem (b), Executions, % Total, Hash Value (8.1.7, 9.0.1 & 9.2)

SQL ordered by Version Count
containing the SQL statements with most versions (children cursors).
The default threshold value is 20 versions.
Columns displayed are:
Version Count, Executions, Hash Value (8.1.7, 9.0.1 & 9.2)

In the next few sections we look at examples of each type of Statspack SQL reporting section.

FINDING SQL WITH HIGH CPU OTHER TIME IN STATSPACK
Here are a few examples of SQL ordered by Gets sections from Statspack.

SQL ordered by Gets for DB: PROD Instance: prod Snaps: 8 -9
-> End Buffer Gets Threshold: 10000
-> Note that resources reported for PL/SQL includes the resources used by
all SQL statements called within the PL/SQL code. As individual SQL
statements are also reported, it is possible and valid for the summed
total % to exceed 100

Buffer Gets Executions Gets per Exec % Total Hash Value
--------------- ------------ -------------- ------- ------------
91,938,671 4,249 21,637.7 24.1 3503723562
SELECT "TEKSTI_IK", "U_VERSION", "ASIAKIR_IK", "KONTAKT_IK", "L
OMAKE_IK", "PVM", "TIEDON_LKA", "TEKSTI_VER", "TEKST_TILA", "VA
LK_AUKKO", "SUOR_PA_IK", "SUOR_PA_ID", "RESURSS_IK", "RESURSS_I

39,196,483 4,257 9,207.5 10.3 576408779
SELECT "KONTAKT_IK", "ASIAKAS_IK", "KAYNTIPVM", "KLO_JNRO", "KT
_PIKASEL", "RESURSS_ID", "SUOR_PA_IK", "SUOR_PA_ID", "TEKSTI_IK
", "KT_TEKSTI", "KONT_LAJI" FROM "TEI1000_VIEW" WHERE (kontakt_i

31,870,113 4,262 7,477.7 8.3 3583640853
SELECT "LAAKE_T_IK", "U_VERSION", "TAPAHTU_IK", "ASIAKAS_IK", "
TOT_DATIM", "LAAKE_IK", "KAUPPANIMI", "VALM_MUOTO", "VAHVUUS",
"PAKK_KOKO", "ANNOS", "INDIKAATIO", "PYSYVAIS", "VOIM_OLEVA", "S

30,567,449 4,259 7,177.1 8.0 1901268379
SELECT "LAB_TUL_IK", "U_VERSION", "TAPAHTU_IK", "ASIAKAS_IK", "
TOT_DATE", "TOT_TIME", "PALVELU_IK", "PALV_LYHEN", "SL_KOODI",
"OSAVAS_NRO", "OSAVAS_HOP", "VAST_TYYPP", "VAST_ARVO", "VAST_SIJ

Here the first SQL statement (with hash value 3503723562) alone accounts for 24.1% of all buffer gets in the instance.
The next 3 statements account for 10.3%, 8.3% and 8.0%.
All 4 statements are executed approximately the same number of times (around 4,250 executions).
The first statement has more total Gets because it fetches more buffers each time it runs (Gets/Exec is 21,637.7 compared to 9,207.5, 7,477.7 and 7,177.1).
So it is a first candidate for tuning as it has greater impact on CPU Other time than the other 3 statements.
A better execution plan for this statement resulting in fewer Gets/Exec will reduce its CPU consumption.
Here is another example:


Buffer Gets Executions Gets per Exec % Total Hash Value
--------------- ------------ -------------- ------- ------------
3,200,593 1 3,200,593.0 52.2 397365298
select c.contract_no||'/'||c.contract_version, c.owner_ag
ency_id, a.agency_name, TERRITORYPACK.getsalescont
act(a.agency_id,'SALES','N'), c.start_date, LEAST(

404,024 88,481 4.6 6.6 985453413
select cv_dist_flag from applicant
where applicant_id = :p1

178,600 3,028 59.0 2.9 3013728279
select privilege#,level from sysauth$ connect by grantee#=prior
privilege# and privilege#>0 start with (grantee#=:1 or grantee#=

The first statement (hash value 397365298) generated 52.2% of the buffer gets in the instance with just 1 execution.
It has a high number of Gets/Exec 3,200,593.0 when compared to the others.
If this statement is not just a one-time query then it is a good candidate for tuning before it starts getting used more often.
The second statement (hash value 985453413) fetches on average less than 5 buffers per execution but appears high in the list because it is executed very frequently.
Initially it is not significant enough to warrant further investigation.
If after tuning the first statement, CPU Other is still a significant component of overall Response Time and a new Statspack report shows the second statement still high on the list, then it could be looked at more closely.

Here is a similar case, this time it is from Oracle9i and we can see the new CPU & Elapsed Time columns:


CPU Elapsd
Buffer Gets Executions Gets per Exec %Total Time (s) Time (s) Hash Value
--------------- ------------ -------------- ------ -------- --------- ----------
16,177,286 1 16,177,286.0 12.1 209.00 297.91 342588312
select sf.satz_id, f1.feld, f2.feld feld20, substr(sf.fehler
,instr(sf.fehler,'geschrieben:')+13) feld30 --merkwürdigerweise
wird ab 31 Byte in eine neue Zeile geschrieben from d98_ditr_s

8,177,886 375,622 21.8 6.1 214.09 302.26 3544921891
SELECT /*+ RULE */ * from d98_schemaeintraege_view where pro
f_id = :b1 order by sortierung

The Statspack report does not always show the full text of the SQL statement. The Hash Value can be used to get this using the following query, provided the SQL statement is still in the Library Cache at the time the query is run:


SELECT sql_text
FROM v$sql_text
WHERE hash_value = '&hash_value_of_SQL'
ORDER BY piece;

FINDING SQL STATEMENTS WITH HIGH CPU PARSE TIME IN STATSPACK
If CPU Parse time is a significant component of Response Time, it can be because cursors are repeatedly opened and closed every time they are executed instead of being opened once, kept open for multiple executions and only closed when they are no longer required.
The SQL ordered by Parse Calls can help find such cursors, here is an example:


SQL ordered by Parse Calls for DB: DWI1 Instance: DWI1 Snaps: 1 -4
-> End Parse Calls Threshold: 1000

% Total
Parse Calls Executions Parses Hash Value
------------ ------------ -------- ----------
13,632,745 13,632,745 98.90 3980186470
SELECT distinct TS002.C_JOB_DEP, TS002.C_JOB FROM TS002_JO
B_DEP TS002, TS001_JOB TS001 WHERE TS001.C_JOB = TS002.C_JO
B_DEP AND TS002.C_JOB = :b1 AND TS001.C_TIP_JOB !=

11,701 27,255,840 0.08 3615375148
COMMIT

8,192 8,192 0.06 238087931
select t.schema, t.name, t.flags, q.name from system.aq$_queue_t
ables t, sys.aq$_queue_table_affinities aft, system.aq$_que
ues q where aft.table_objno = t.objno and aft.owner_instance = :
1 and q.table_objno = t.objno and q.usage = 0 and b
itand(t.flags, 4+16+32+64+128+256) = 0 for update of t.name, aft

8,192 8,192 0.06 2780709284
select q_name, state, delay, expiration, rowid, msgid, dequeue
_msgid, chain_no, local_order_no, enq_time, enq_tid, step_no,
priority, exception_qschema, exception_queue, retry_count, corri
d, time_manager_info, sender_name, sender_address, sender_prot
ocol from SYS.AQ_SRVNTFN_TABLE where time_manager_info <= :1

The first SQL statement (hash value 3980186470) has had the most parses issued against it (98.90% of all parses in the instance). It is parsed every time it is executed (Parse Calls = Executions). Due to its frequency it is a prime candidate for reducing parse calls as described above.
Note: in excessive parsing situations, it is likely that there will be symptoms such as latch free waits on the Library Cache latches and possibly the Shared Pool latch, in addition to CPU Parse time.

FINDING SQL STATEMENTS WITH HIGH DISK I/O WAITS IN STATSPACK
Identifying SQL statements responsible for most physical reads from the Statspack section SQL ordered by Reads has similar concepts as for SQL ordered by Gets.
% Total can be used to evaluate the impact of each statement.
Reads per Exec together with Executions can be used as a hint of whether the statement has a suboptimal execution plan causing many physical reads or if it is there simply because it is executed often.
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.

Here is an example:


SQL ordered by Reads for DB: PROD Instance: prod Snaps: 14 -16
-> End Disk Reads Threshold: 1000

Physical Reads Executions Reads per Exec % Total Hash Value
--------------- ------------ -------------- ------- ------------
3,633,017 48 75,687.9 28.0 3954888792
SELECT "VAR_RES_IK", "RESURSS_IK", "PVM", "ALKAEN_KLO", "PAATT_
KLO", "AJ_STATUS", "ILMOITT", "HETU", "AS_NIMI", "KASITELTY", "T
OIMIPI_IK", "SUOR_PA_IK", "SUOR_PA_ID", "AIKATYY_IK", "AIKATYY_I

1,511,867 26 58,148.7 11.6 394819206
SELECT "VAR_RES_IK", "RESURSS_IK", "PVM", "ALKAEN_KLO", "PAATT_
KLO", "AJ_STATUS", "ILMOITT", "HETU", "AS_NIMI", "KASITELTY", "T
OIMIPI_IK", "SUOR_PA_IK", "SUOR_PA_ID", "AIKATYY_IK", "AIKATYY_I

762,101 6 127,016.8 5.9 4274178025
SELECT "LAB_TUL_IK", "PALV_LYHEN", "PALV_TILA", "OSAVAS_HOP", "
VAST_ARVO", "VAST_SIJ", "MITTAYKS", "POIKKEAVA", "PALVELU_IK", "
ASIAKAS_IK", "VERIRYHMA", "VV_MAARPVM", "TOT_DATE", "TOT_TIME",

512,142 3 170,714.0 3.9 1591034069
SELECT "LAB_TUL_IK", "PALV_LYHEN", "PALV_TILA", "OSAVAS_HOP", "
VAST_ARVO", "VAST_SIJ", "MITTAYKS", "POIKKEAVA", "PALVELU_IK", "
ASIAKAS_IK", "VERIRYHMA", "VV_MAARPVM", "TOT_DATE", "TOT_TIME",

The first two SQL statements are both executed more often than the others and cause more blocks to be read in from disk each time.
Together they account for almost 40% of read I/O. They both are prime candicates for further SQL tuning.
EVALUATING SQL STATEMENTS WITH MOST EXECUTIONS IN STATSPACK
Identifying those SQL statements that execute most often in a database and tuning them can improve performance even when such statements do not consume many resources in each execution.
This is because of two reasons:

The overall resource consumption of these statements across all their executions may be significant.
Such frequently executed statements are often part of OLTP-style short transactions. Tuning them can improve the performance of the database as experienced by users entering such transactions into the system.
Here is an example of Statspack SQL ordered by Executions:

SQL ordered by Executions for DB: DIN Instance: DIN Snaps: 263 -264
-> End Executions Threshold: 100

CPU per Elap per
Executions Rows Processed Rows per Exec Exec (s) Exec (s) Hash Value
------------ --------------- ---------------- ----------- ---------- ----------
404,871 133,781 0.3 0.00 0.00 3592950473
SELECT nvl(NVL(EIGTWX.WERT,EIGTW.WERT),eig.wert) wert,
1 sortier FROM D8_EIGENTYPWERTE_TEXT EIGTWX ,D98_
EIGENTYPWERTE EIGTW ,D98_EIGENSCHAFTEN EIG WHERE EIG.ANH

324,014 324,014 1.0 0.00 0.00 293077222
SELECT /*+ INDEX (D98_SCHEMAFORMATE SCHFMT_FORM_FKT_UNTER) */
upper(funktionsname), unterformat FROM
D98_SCHEMAFORMATE WHERE formatierung = :b1

183,276 183,276 1.0 0.00 0.00 66213032
INSERT INTO D98_Batch_Variablenwerte (ausf_id, b
atch_id, var_nr, wert) VALUES (:b4,
:b3, :b2, :b1)

114,224 8,936 0.1 0.00 0.00 1604001664
SELECT termin_ist FROM d98_termine WHERE a
nhang_id=:b2 AND terminart = :b1 AND aktiv = 'J'
order by termin_ist desc

It will frequently be the case that the timing columns in this section will show 0.00 for CPU and Elapsed time, as the most frequently executing SQL statements are likely to be quite fast.
FINDING SQL STATEMENTS WITH HIGH SHARED POOL CONSUMPTION IN STATSPACK
This can help with Shared Pool and Library Cache/Shared Pool latch tuning.
Statements with many versions (multiple child cursors with the same parent cursor i.e. identical SQL text but different properties such as owning schema of objects, optimizer session settings, types & lengths of bind variables etc.) are unsharable.

This means they can consume excessive memory resources in the Shared Pool and cause performance problems related to parsing e.g. Library Cache and Shared Pool latch contention or lookup time e.g. Library Cache latch contention.

Statspack has 2 sections to help find such unsharable statements, SQL ordered by Sharable Memory and SQL ordered by Version Count:


SQL ordered by Sharable Memory for DB: DIN Instance: DIN Snaps: 263 -264
-> End Sharable Memory Threshold: 1048576

Sharable Mem (b) Executions % Total Hash Value
---------------- ------------ ------- ------------
3,445,680 1 0.1 2317124142
select /*+rule*/ decode(UPPER(:P_TITEL_SORT), 'NAME', fin_sort_ansp_name,
'FIRMA', fin_sort_ans_firma, 'KURZBEZ. FIRMA', fin_sort_ans_name,

3,386,429 76 0.1 3470710979
SELECT ID,ANHANGSTYP,OBJEKT_ID,PROF_ID,ANHANGSART,SPRACHE,AKTIV,
ANFANG,ENDE,SEITEN,ARCH_BEMERKUNG,DESKRIPTOR,EIGTW_ID,EIG_WERT,T

2,836,746 447 0.1 2274525714
SELECT ID,ANHANGSTYP,OBJEKT_ID,PROF_ID,ANHANGSART,SPRACHE,AKTIV,
ANFANG,ENDE,SEITEN,ARCH_BEMERKUNG,DESKRIPTOR,EIGTW_ID,EIG_WERT,T


SQL ordered by Version Count for DB: P97 Instance: P97 Snaps: 177 -180
-> End Version Count Threshold: 20

Version
Count Executions Hash Value
-------- ------------ ------------
26 36,228 3957083415
SELECT 1 FROM NK245_RECHTE A WHERE (NK245_NK211_ID BETWEEN :b
1 AND :b2 ) AND NK245_GEHEIMSTUFE >= :b3 AND NK245_NK209_ID = :
b4 AND NK245_NKXX_ID = :b5 AND NK245_ANZAHL > 0 AND (:b6 BETW

25 606 2916558383
UPDATE KVS.NK21_DOKUMENTE SET NK21_NK41_PDA_KUERZEL=:b1,NK21_NK4
1_PDA_NAME=:b2,NK21_STR_LEVEL=:b3,NK21_STR_NR=:b4,NK21_STR_TYP=:
b5,NK21_STRL1_NR=:b6,NK21_STRL1_TYP=:b7,NK21_STRL2_NR=:b8,NK21_S

24 1,602 1157590177
INSERT INTO NK297_NACHRICHTEN ( NK297_ID,NK297_TIMESTAMP,NK297_T
IMESTAMP_E,NK297_NK210_ABSENDER,NK297_NK270_USERNAME_ABS,NK297_T
ITEL,NK297_NTEXT,NK297_NK248_ID,NK297_NK244_ID,NK297_NK213_ID,NK

For more information on tuning the Shared Pool please refer to the article:
Note 62143.1 Understanding and Tuning the Shared Pool
TOP SQL STATEMENTS IN TKPROF REPORTS
TKPROF is a tool for producing formatted reports from SQL Trace files (also known as event 10046 tracefiles).
Each SQL (and PL/SQL) statement appearing in the tracefile has its information summarized and collected in one place in the TKPROF report.
This information includes: number of parse, execution & fetch operations, total cpu & elapsed times, buffers read in consistent (query) and current mode, blocks read from disk and row counts, Row Source operations, execution plans, library cache misses, parsing user id & optimizer mode and with TKPROF version 9.0.1 and above, summary of wait events for tracefiles generated with event 10046 at levels 8 or 12.

A powerful feature available in TKPROF is the sort option which allows for ordering the SQL statement in the report according to a number of criteria.
This enables the easy identification of the most resource-intensive SQL statements and helps target efficiently the SQL tuning process.
Here are the options available for sorting, they may be combined:


prscnt number of times parse was called
prscpu cpu time parsing
prsela elapsed time parsing
prsdsk number of disk reads during parse
prsqry number of buffers for consistent read during parse
prscu number of buffers for current read during parse
prsmis number of misses in library cache during parse
execnt number of execute was called
execpu cpu time spent executing
exeela elapsed time executing
exedsk number of disk reads during execute
exeqry number of buffers for consistent read during execute
execu number of buffers for current read during execute
exerow number of rows processed during execute
exemis number of library cache misses during execute
fchcnt number of times fetch was called
fchcpu cpu time spent fetching
fchela elapsed time fetching
fchdsk number of disk reads during fetch
fchqry number of buffers for consistent read during fetch
fchcu number of buffers for current read during fetch
fchrow number of rows fetched

The most commonly used sort options are the ones ordering according to elapsed times consumed for the execute and fetch phases: we are usually interested in tuning statements that take more time to run as time is the most important property in the tuning process.

exeela elapsed time executing
fchela fchela elapsed time fetching

It should be clear however that it is as simple to order the SQL in order to find which ones cause the most disk i/o during fetch

fchdsk number of disk reads during fetch

or according to which ones access the most buffers in consistent mode e.g. for queries this would be in the fetch phase

fchqry number of buffers for consistent read during fetch

or for DML it would be in the execute phase

exeqry number of buffers for consistent read during execute

For more information on working with TKPROF please refer to the article:
Note 32951.1 Tkprof Interpretation
TOP SQL STATEMENTS IN V$SQLAREA & V$SQL
The Oracle Server provides 3 dynamic views for querying execution statistics of all SQL statements currently cached in the Library Cache of the Shared Pool.
They are V$SQL, V$SQLAREA and V$SQLXS.

V$SQL has 1 row for each different version of a SQL statement.
This means that each child cursor has its own execution statistics.


V$SQLAREA has 1 row for each different SQL string i.e. each parent cursor.
This means that the statistics for all child cursors i.e. different versions of this cursor are grouped together. It is not based on V$SQL.
.

V$SQLXS is a simpler version of V$SQLAREA.
It is used mainly by Statspack for generating the SQL reporting sections.
It queries V$SQL with a GROUP BY.
It is defined in ORACLE_HOME/rdbms/admin/catsnmp.sql.

V$SQLAREA or V$SQLXS can be used most often to find the top few SQL statements for a specific category of statistic.
Once these are identified, V$SQL can be used to drill down to see whether different versions of each statement exhibit similar statistics or whether some particular versions stand out.

V$SQL is less resource intensive than V$SQLAREA as it avoids the GROUP BY operation and causes less Library Cache latch contention.

Here is a general form of a query on any of these views:


SELECT * FROM
(SELECT hash_value,address,substr(sql_text,1,40) sql,
[list of columns], [list of derived values]
FROM [V$SQL or V$SQLXS or V$SQLAREA]
WHERE [list of threshold conditions for columns]
ORDER BY [list of ordering columns] DESC)
WHERE rownum <= [number of top SQL statements];

Here is an example:


SELECT * FROM
(SELECT hash_value,address,substr(sql_text,1,40) sql,
buffer_gets, executions, buffer_gets/executions "Gets/Exec"
FROM V$SQLAREA
WHERE buffer_gets > 100000 AND executions > 10
ORDER BY buffer_gets DESC)
WHERE rownum <= 10;

[list of columns] = buffer_gets, executions
[list of derived values] = buffer_gets/executions
[list of threshold conditions for columns] = buffer_gets > 100000, executions > 10
[list of ordering columns] = buffer_gets
[number of top SQL statements] = 10
The following article contains a selection of ready to run Top-10 queries:

Note 235146.1 Example "Top SQL" queries from V$SQLAREA

A list of columns on which to base similar queries are:

buffer_gets
disk_reads
executions
rows_processed
sorts
parse_calls
sharable_mem
version_count
invalidations
All of these are available in all three views V$SQL, V$SQLAREA & V$SQLXS.
There are a number of other columns for queries of this kind , not so frequently used, which can be found by inspecting V$SQL and V$SQLAREA.

Note 43761.1 VIEW: "V$SQLAREA" Reference Note
Note 43762.1 VIEW: "V$SQL" Reference Note

TOP SQL STATEMENTS IN ENTEPRISE MANAGER SQL ANALYZE
SQL Analyze is a GUI tool available with the Oracle Enterprise Manager Tuning Pack that can be used to identify and can help with tuning resource intensive SQL statements.

The queries for the heaviest SQL statements are done from the Top SQL menu and a variety of search criteria are available:

Buffer Cache Hit Ratio
Buffer Gets
Buffer Gets per Execution
Buffer Gets per Row
Disk Reads
Disk Reads per Execution
Executions
Parse Calls
Parse Calls per Execution
Rows Processed
Sharable Memory
Sorts
The search can be tailored to include Top N results or All statements and can also filter out Recursive SQL.

For more information please refer to the Online Help available with the tool or the following manual:
Oracle Enterprise Manager Database Tuning with the Oracle Tuning Pack Release 9.0.1 June 2001 Part No. A86647-01

RELATED DOCUMENTS
Note 190124.1 THE COE PERFORMANCE METHOD
Note 228913.1 Systemwide Tuning using STATSPACK Reports:
Note 223117.1 Tuning I/O-related waits
Note 62143.1 Understanding and Tuning the Shared Pool
Note 43761.1 VIEW: "V$SQLAREA" Reference Note
Note 43762.1 VIEW: "V$SQL" Reference Note
Note 235146.1 Example "Top SQL" queries from V$SQLAREA
Note 32951.1 Tkprof Interpretation .

csv file and automatic ftp

create directory yangbodir as '/oracle/';
CREATE OR REPLACE PROCEDURE printcsvreport(filedir IN VARCHAR2, filename IN VARCHAR2)
AS
filehndl UTL_FILE.FILE_TYPE;
CURSOR JobCursor IS select * from test;
vJobRecord JobCursor%RowType;
BEGIN
OPEN JobCursor;
filehndl := UTL_FILE.FOPEN(filedir,filename,'w');
FETCH JobCursor INTO vJobRecord;
while JobCursor%FOUND loop
--UTL_FILE.PUT_LINE(filehndl,'Just Heading'||' Next heading');
UTL_FILE.PUTF(filehndl,'%s %s %s %s\n', vJobRecord.id||'|'||vJobRecord.name||'|'||vJobRecord.birth||'|'||vJobRecord.description);
FETCH JobCursor INTO vJobRecord;
end loop;
CLOSE JobCursor;
UTL_FILE.FCLOSE(filehndl);
EXCEPTION
WHEN UTL_FILE.INVALID_OPERATION
THEN UTL_FILE.FCLOSE(filehndl);
-- raise a user-defined exception
RAISE_APPLICATION_ERROR(-20051,'Invalid Operation');
END;
/




begin
printcsvreport('YANGBODIR','testyangbo');
end;
/

ftp 10.10.0.180 《《EOF
user username password
cd /dbms/
lcd /oracle
put testyangbo
bye
EOF