Tag: trace

Testing user calls

In the performance tuning world specially talking about Response Time Analysis (RTA), user calls is often used as a workload metric; however this instance statistic was not yet completely clear.

When this metric is incremented?, How this metric is instrumented?, Why sometimes user calls is less with the same SQL statement?; these were some questions rounding my mind.

My friend Craig Shallahamer did a great post in his blog “A Wider View” related to user calls (http://shallahamer-orapub.blogspot.mx/2010/05/understanding-user-calls.html), however looking into Tanel Poder’s blog today everything got clear.

Let’s start tracing an Oracle session using trace events ‘10046’ & ‘10051’. 

Event ‘10051’ will trace OPI calls, more information (http://blog.tanelpoder.com/2011/03/20/lobread-sql-trace-entry-in-oracle-11-2/), what are OPI calls?, well as per Oracle documentation:

Oracle Program Interface (OPI)

A networking layer responsible for responding to each of the possible messages sent by OCI. For example, an OCI request to fetch 25 rows would have an OPI response to return the 25 rows once they have been fetched.


Let’s do it!

SQL> ALTER SESSION SET EVENTS ‘10046 TRACE NAME CONTEXT FOREVER, LEVEL 12’;
ALTER SESSION SET EVENTS ‘10051 TRACE NAME CONTEXT FOREVER, LEVEL 12’;
Session altered.
SQL>
Session altered.
SQL> SELECT TRACEFILE FROM V$PROCESS WHERE ADDR =(SELECT PADDR FROM V$SESSION WHERE SID=49);
TRACEFILE
——————————————————————————–
/u01/app/oracle/diag/rdbms/rac/RAC1/trace/RAC1_ora_5393.trc

In another window open the trace file using tail –f in order to monitor the trace execution. Now let’s issue a small query:

SQL> select * from dual;
D
–
X
SQL>
Here’s the output of the trace file:

*** 2013-08-05 21:42:15.721
WAIT #3: nam=’SQL*Net message from client’ ela= 342028978 driver id=1650815232 #bytes=1 p3=0 obj#=-1 tim=1375756935721909
OPI CALL: type=105 argc= 2 cursor=  0 name=Cursor close all
CLOSE #3:c=0,e=18,dep=0,type=0,tim=1375756935722017
OPI CALL: type=94 argc=28 cursor=  0 name=V8 Bundled Exec
WAIT #1: nam=’ges message buffer allocation’ ela= 2 pool=0 request=1 allocated=0 obj#=-1 tim=1375756935722368
WAIT #1: nam=’ges message buffer allocation’ ela= 0 pool=0 request=1 allocated=0 obj#=-1 tim=1375756935722448
WAIT #1: nam=’library cache lock’ ela= 568 handle address=1287268616 lock address=1263092292 100*mode+namespace=65538 obj#=-1 tim=1375756935723089
WAIT #1: nam=’ges message buffer allocation’ ela= 1 pool=0 request=1 allocated=0 obj#=-1 tim=1375756935723137
WAIT #1: nam=’ges message buffer allocation’ ela= 2 pool=0 request=1 allocated=0 obj#=-1 tim=1375756935723205
WAIT #1: nam=’library cache pin’ ela= 473 handle address=1287268616 pin address=1263092428 100*mode+namespace=65538 obj#=-1 tim=1375756935723815
=====================
PARSING IN CURSOR #4 len=210 dep=1 uid=0 oct=3 lid=0 tim=1375756935724353 hv=864012087 ad=’4cb562e8′ sqlid=’96g93hntrzjtr’
select /*+ rule */ bucket_cnt, row_cnt, cache_cnt, null_cnt, timestamp#, sample_size, minimum, maximum, distcnt, lowval, hival, density, col#, spare1, spare2, avgcln from hist_head$ where obj#=:1 and intcol#=:2
END OF STMT
PARSE #4:c=0,e=70,p=0,cr=0,cu=0,mis=0,r=0,dep=1,og=3,plh=2239883476,tim=1375756935724351
BINDS #4:
 Bind#0
  oacdty=02 mxl=22(22) mxlc=00 mal=00 scl=00 pre=00
  oacflg=08 fl2=0001 frm=00 csi=00 siz=24 off=0
  kxsbbbfp=0040a79c  bln=22  avl=03  flg=05
  value=116
 Bind#1
  oacdty=02 mxl=22(22) mxlc=00 mal=00 scl=00 pre=00
  oacflg=08 fl2=0001 frm=00 csi=00 siz=24 off=0
  kxsbbbfp=0040a778  bln=24  avl=02  flg=05
  value=1
EXEC #4:c=0,e=89,p=0,cr=0,cu=0,mis=0,r=0,dep=1,og=3,plh=2239883476,tim=1375756935724524
FETCH #4:c=0,e=51,p=0,cr=3,cu=0,mis=0,r=1,dep=1,og=3,plh=2239883476,tim=1375756935724594
STAT #4 id=1 cnt=1 pid=0 pos=1 obj=424 op=’TABLE ACCESS BY INDEX ROWID HIST_HEAD$ (cr=3 pr=0 pw=0 time=0 us)’
STAT #4 id=2 cnt=1 pid=1 pos=1 obj=426 op=’INDEX RANGE SCAN I_HH_OBJ#_INTCOL# (cr=2 pr=0 pw=0 time=0 us)’
CLOSE #4:c=0,e=40,dep=1,type=3,tim=1375756935724660
=====================
PARSING IN CURSOR #1 len=18 dep=0 uid=0 oct=3 lid=0 tim=1375756935725402 hv=942515969 ad=’4b5c8e54′ sqlid=’a5ks9fhw2v9s1′
select * from dual
END OF STMT
PARSE #1:c=3999,e=3334,p=0,cr=3,cu=0,mis=1,r=0,dep=0,og=1,plh=272002086,tim=1375756935725401
EXEC #1:c=0,e=13,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=1,plh=272002086,tim=1375756935725457
WAIT #1: nam=’SQL*Net message to client’ ela= 2 driver id=1650815232 #bytes=1 p3=0 obj#=-1 tim=1375756935725480
WAIT #1: nam=’ges message buffer allocation’ ela= 2 pool=0 request=1 allocated=0 obj#=-1 tim=1375756935725627
WAIT #1: nam=’gc cr grant 2-way’ ela= 1035 p1=1 p2=928 p3=4 obj#=116 tim=1375756935726935
WAIT #1: nam=’db file sequential read’ ela= 634 file#=1 block#=928 blocks=1 obj#=116 tim=1375756935737381
WAIT #1: nam=’ges message buffer allocation’ ela= 2 pool=0 request=1 allocated=0 obj#=116 tim=1375756935741146
WAIT #1: nam=’gc cr grant 2-way’ ela= 470 p1=1 p2=929 p3=1 obj#=116 tim=1375756935741693
WAIT #1: nam=’db file sequential read’ ela= 1312 file#=1 block#=929 blocks=1 obj#=116 tim=1375756935743074
FETCH #1:c=1999,e=17670,p=2,cr=3,cu=0,mis=0,r=1,dep=0,og=1,plh=272002086,tim=1375756935743169
STAT #1 id=1 cnt=1 pid=0 pos=1 obj=116 op=’TABLE ACCESS FULL DUAL (cr=3 pr=2 pw=0 time=0 us cost=2 size=2 card=1)’
WAIT #1: nam=’SQL*Net message from client’ ela= 221 driver id=1650815232 #bytes=1 p3=0 obj#=116 tim=1375756935743513
OPI CALL: type= 5 argc= 2 cursor=  1 name=FETCH
FETCH #1:c=0,e=1,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=0,plh=272002086,tim=1375756935743579
WAIT #1: nam=’SQL*Net message to client’ ela= 2 driver id=1650815232 #bytes=1 p3=0 obj#=116 tim=1375756935743629
Snapper shows the below metrics for our session:

SQL> @snapper4 all 5 1 49
Sampling SID 49 with interval 5 seconds, taking 1 snapshots…
— Session Snapper v4.09 BETA – by Tanel Poder ( http://blog.tanelpoder.com ) – Enjoy the Most Advanced Oracle Troubleshooting Script on the Planet! 🙂
——————————————————————————————————————————————————————————————————————
 SID @INST, USERNAME  , TYPE, STATISTIC                                                 ,         DELTA, HDELTA/SEC,    %TIME, GRAPH       , NUM_WAITS,  WAITS/SEC,   AVERAGES
——————————————————————————————–
    49  @1, SYS       , STAT, opened cursors cumulative,             1,         .2,         ,             ,          ,           ,          1 per execution
    49  @1, SYS       , STAT, user calls,             3,        .59,         ,             ,          ,           ,          3 per execution
Now let’s create a dummy table and insert some rows:
SQL> insert into test_user (user_name) select username from dba_users;
36 rows created.

From trace file:
*** 2013-08-05 21:55:34.386
WAIT #1: nam=’SQL*Net message from client’ ela= 58636486 driver id=1650815232 #bytes=1 p3=0 obj#=-1 tim=1375757734386174
OPI CALL: type=105 argc= 2 cursor=  0 name=Cursor close all
CLOSE #1:c=0,e=15,dep=0,type=0,tim=1375757734386272
OPI CALL: type=94 argc=28 cursor=  0 name=V8 Bundled Exec
=====================
PARSING IN CURSOR #3 len=37 dep=1 uid=0 oct=3 lid=0 tim=1375757734387095 hv=1398610540 ad=’4cbf4cb0′ sqlid=’grwydz59pu6mc’
select text from view$ where rowid=:1
END OF STMT
PARSE #3:c=0,e=375,p=0,cr=0,cu=0,mis=1,r=0,dep=1,og=4,plh=0,tim=1375757734387093
BINDS #3:
 Bind#0
  oacdty=11 mxl=16(16) mxlc=00 mal=00 scl=00 pre=00
  oacflg=18 fl2=0001 frm=00 csi=00 siz=16 off=0
  kxsbbbfp=1184df90  bln=16  avl=16  flg=05
  value=00002068.0000.0001
EXEC #3:c=2000,e=54991,p=0,cr=0,cu=0,mis=1,r=0,dep=1,og=4,plh=3684871272,tim=1375757734442193
FETCH #3:c=0,e=34,p=0,cr=2,cu=0,mis=0,r=1,dep=1,og=4,plh=3684871272,tim=1375757734442283
STAT #3 id=1 cnt=1 pid=0 pos=1 obj=69 op=’TABLE ACCESS BY USER ROWID VIEW$ (cr=1 pr=0 pw=0 time=0 us cost=1 size=15 card=1)’
CLOSE #3:c=0,e=51,dep=1,type=0,tim=1375757734442357
WAIT #2: nam=’ges message buffer allocation’ ela= 4 pool=0 request=1 allocated=0 obj#=-1 tim=1375757734443204
WAIT #2: nam=’ges message buffer allocation’ ela= 2 pool=0 request=1 allocated=0 obj#=-1 tim=1375757734443382
WAIT #2: nam=’library cache lock’ ela= 549 handle address=1222331724 lock address=1220909652 100*mode+namespace=65538 obj#=-1 tim=1375757734444345
WAIT #2: nam=’KJC: Wait for msg sends to complete’ ela= 10 msg=1361877012 dest|rcvr=65536 mtype=12 obj#=-1 tim=1375757734444389
WAIT #2: nam=’ges message buffer allocation’ ela= 2 pool=0 request=1 allocated=0 obj#=-1 tim=1375757734444433
WAIT #2: nam=’ges message buffer allocation’ ela= 2 pool=0 request=1 allocated=0 obj#=-1 tim=1375757734444564
WAIT #2: nam=’library cache pin’ ela= 640 handle address=1222331724 pin address=1220909516 100*mode+namespace=65538 obj#=-1 tim=1375757734445329
WAIT #2: nam=’KJC: Wait for msg sends to complete’ ela= 9 msg=1361877012 dest|rcvr=65536 mtype=12 obj#=-1 tim=1375757734445367
=====================
PARSING IN CURSOR #6 len=210 dep=1 uid=0 oct=3 lid=0 tim=1375757734475342 hv=864012087 ad=’4cb562e8′ sqlid=’96g93hntrzjtr’
select /*+ rule */ bucket_cnt, row_cnt, cache_cnt, null_cnt, timestamp#, sample_size, minimum, maximum, distcnt, lowval, hival, density, col#, spare1, spare2, avgcln from hist_head$ where obj#=:1 and intcol#=:2
END OF STMT
PARSE #6:c=0,e=73,p=0,cr=0,cu=0,mis=0,r=0,dep=1,og=3,plh=2239883476,tim=1375757734475341
BINDS #6:
 Bind#0
  oacdty=02 mxl=22(22) mxlc=00 mal=00 scl=00 pre=00
  oacflg=08 fl2=0001 frm=00 csi=00 siz=24 off=0
  kxsbbbfp=0040210c  bln=22  avl=04  flg=05
  value=74760
 Bind#1
  oacdty=02 mxl=22(22) mxlc=00 mal=00 scl=00 pre=00
  oacflg=08 fl2=0001 frm=00 csi=00 siz=24 off=0
  kxsbbbfp=004020e8  bln=24  avl=02  flg=05
  value=1
EXEC #6:c=0,e=91,p=0,cr=0,cu=0,mis=0,r=0,dep=1,og=3,plh=2239883476,tim=1375757734475532
FETCH #6:c=0,e=15,p=0,cr=2,cu=0,mis=0,r=0,dep=1,og=3,plh=2239883476,tim=1375757734475567
STAT #6 id=1 cnt=0 pid=0 pos=1 obj=424 op=’TABLE ACCESS BY INDEX ROWID HIST_HEAD$ (cr=2 pr=0 pw=0 time=0 us)’
STAT #6 id=2 cnt=0 pid=1 pos=1 obj=426 op=’INDEX RANGE SCAN I_HH_OBJ#_INTCOL# (cr=2 pr=0 pw=0 time=0 us)’
CLOSE #6:c=0,e=1,dep=1,type=3,tim=1375757734475616
=====================
PARSING IN CURSOR #2 len=64 dep=0 uid=0 oct=2 lid=0 tim=1375757734477276 hv=1206268670 ad=’48d0a418′ sqlid=’4x5wmqj3yccry’
insert into test_user (user_name) select username from dba_users
END OF STMT
PARSE #2:c=10999,e=90955,p=0,cr=4,cu=0,mis=1,r=0,dep=0,og=1,plh=3422547789,tim=1375757734477275
WAIT #2: nam=’ges message buffer allocation’ ela= 5 pool=0 request=1 allocated=0 obj#=74760 tim=1375757734478040
WAIT #2: nam=’ges message buffer allocation’ ela= 2 pool=0 request=1 allocated=0 obj#=74760 tim=1375757734478200
WAIT #2: nam=’enq: TM – contention’ ela= 616 name|mode=1414332419 object #=74760 table/partition=0 obj#=74760 tim=1375757734478936
WAIT #2: nam=’asynch descriptor resize’ ela= 4 outstanding #aio=0 current aio limit=384 new aio limit=390 obj#=-1 tim=1375757734486405
WAIT #2: nam=’ges message buffer allocation’ ela= 2 pool=0 request=1 allocated=0 obj#=-1 tim=1375757734486564
WAIT #2: nam=’gc cr grant 2-way’ ela= 519 p1=1 p2=1976 p3=4 obj#=281 tim=1375757734487242
WAIT #2: nam=’db file sequential read’ ela= 788 file#=1 block#=1976 blocks=1 obj#=281 tim=1375757734488113
WAIT #2: nam=’ges message buffer allocation’ ela= 2 pool=0 request=1 allocated=0 obj#=281 tim=1375757734488224
WAIT #2: nam=’gc cr grant 2-way’ ela= 431 p1=1 p2=1977 p3=1 obj#=281 tim=1375757734488790
WAIT #2: nam=’db file sequential read’ ela= 889 file#=1 block#=1977 blocks=1 obj#=281 tim=1375757734489741
WAIT #2: nam=’ges message buffer allocation’ ela= 2 pool=0 request=1 allocated=0 obj#=281 tim=1375757734491203
WAIT #2: nam=’ges message buffer allocation’ ela= 2 pool=0 request=1 allocated=0 obj#=281 tim=1375757734491352
WAIT #2: nam=’enq: HW – contention’ ela= 1020 name|mode=1213661190 table space #=0 block=4282248 obj#=281 tim=1375757734492503
WAIT #2: nam=’ges message buffer allocation’ ela= 2 pool=0 request=1 allocated=0 obj#=281 tim=1375757734492596
WAIT #2: nam=’gc current grant 2-way’ ela= 422 p1=1 p2=87945 p3=33554433 obj#=74760 tim=1375757734493138
EXEC #2:c=5000,e=15461,p=2,cr=41,cu=7,mis=0,r=36,dep=0,og=1,plh=3422547789,tim=1375757734493365
STAT #2 id=1 cnt=0 pid=0 pos=1 obj=0 op=’LOAD TABLE CONVENTIONAL  (cr=41 pr=2 pw=0 time=0 us)’
STAT #2 id=2 cnt=36 pid=1 pos=1 obj=0 op=’HASH JOIN  (cr=40 pr=2 pw=0 time=35 us cost=24 size=1092 card=13)’
STAT #2 id=3 cnt=36 pid=2 pos=1 obj=0 op=’HASH JOIN  (cr=37 pr=2 pw=0 time=35 us cost=22 size=1053 card=13)’
STAT #2 id=4 cnt=36 pid=3 pos=1 obj=0 op=’HASH JOIN  (cr=28 pr=2 pw=0 time=35 us cost=17 size=1014 card=13)’
STAT #2 id=5 cnt=36 pid=4 pos=1 obj=0 op=’HASH JOIN OUTER (cr=19 pr=2 pw=0 time=140 us cost=13 size=975 card=13)’
STAT #2 id=6 cnt=36 pid=5 pos=1 obj=0 op=’HASH JOIN  (cr=16 pr=2 pw=0 time=35 us cost=10 size=598 card=13)’
STAT #2 id=7 cnt=36 pid=6 pos=1 obj=0 op=’HASH JOIN  (cr=13 pr=0 pw=0 time=0 us cost=8 size=572 card=13)’
STAT #2 id=8 cnt=2 pid=7 pos=1 obj=0 op=’MERGE JOIN CARTESIAN (cr=6 pr=0 pw=0 time=3 us cost=4 size=16 card=1)’
STAT #2 id=9 cnt=1 pid=8 pos=1 obj=280 op=’TABLE ACCESS FULL PROFILE$ (cr=3 pr=0 pw=0 time=0 us cost=2 size=8 card=1)’
STAT #2 id=10 cnt=2 pid=8 pos=2 obj=0 op=’BUFFER SORT (cr=3 pr=0 pw=0 time=1 us cost=2 size=8 card=1)’
STAT #2 id=11 cnt=2 pid=10 pos=1 obj=280 op=’TABLE ACCESS FULL PROFILE$ (cr=3 pr=0 pw=0 time=1 us cost=2 size=8 card=1)’
STAT #2 id=12 cnt=36 pid=7 pos=2 obj=22 op=’TABLE ACCESS FULL USER$ (cr=7 pr=0 pw=0 time=0 us cost=3 size=1008 card=36)’
STAT #2 id=13 cnt=2 pid=6 pos=2 obj=281 op=’TABLE ACCESS FULL PROFNAME$ (cr=3 pr=2 pw=0 time=1 us cost=2 size=2 card=1)’
STAT #2 id=14 cnt=2 pid=5 pos=2 obj=297 op=’TABLE ACCESS FULL RESOURCE_GROUP_MAPPING$ (cr=3 pr=0 pw=0 time=1 us cost=2 size=29 card=1)’
STAT #2 id=15 cnt=7 pid=4 pos=2 obj=16 op=’TABLE ACCESS FULL TS$ (cr=9 pr=0 pw=0 time=48 us cost=4 size=21 card=7)’
STAT #2 id=16 cnt=7 pid=3 pos=2 obj=16 op=’TABLE ACCESS FULL TS$ (cr=9 pr=0 pw=0 time=12 us cost=4 size=21 card=7)’
STAT #2 id=17 cnt=9 pid=2 pos=2 obj=292 op=’TABLE ACCESS FULL USER_ASTATUS_MAP (cr=3 pr=0 pw=0 time=8 us cost=2 size=27 card=9)’
WAIT #2: nam=’SQL*Net message to client’ ela= 3 driver id=1650815232 #bytes=1 p3=0 obj#=74760 tim=1375757734493823
And from Snapper:
SQL> @snapper4 all 10 1 49
Sampling SID 49 with interval 10 seconds, taking 1 snapshots…
— Session Snapper v4.09 BETA – by Tanel Poder ( http://blog.tanelpoder.com ) – Enjoy the Most Advanced Oracle Troubleshooting Script on the Planet! 🙂
——————————————————————————————————————————————————————————————————————
 SID @INST, USERNAME  , TYPE, STATISTIC                                                 ,         DELTA, HDELTA/SEC,    %TIME, GRAPH       , NUM_WAITS,  WAITS/SEC,   AVERAGES
——————————————————————————————————————————————————————————————————————
    49  @1, SYS       , STAT, opened cursors cumulative,             3,        .33,         ,             ,          ,           ,          1 per execution
    49  @1, SYS       , STAT, user calls,             2,        .22,         ,             ,          ,           ,        .67 per execution
Now let’s commit:
SQL> commit;
Commit complete.
SQL>
From trace file:
*** 2013-08-05 22:01:49.281
WAIT #2: nam=’SQL*Net message from client’ ela= 374787222 driver id=1650815232 #bytes=1 p3=0 obj#=74760 tim=1375758109281068
OPI CALL: type=105 argc= 2 cursor=  0 name=Cursor close all
CLOSE #2:c=0,e=20,dep=0,type=0,tim=1375758109281167
OPI CALL: type=94 argc=28 cursor=  0 name=V8 Bundled Exec
=====================
PARSING IN CURSOR #4 len=6 dep=0 uid=0 oct=44 lid=0 tim=1375758109281299 hv=3480936638 ad=’0′ sqlid=’23wm3kz7rps5y’
commit
END OF STMT
PARSE #4:c=0,e=91,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=0,plh=0,tim=1375758109281298
XCTEND rlbk=0, rd_only=0, tim=1375758109281345
EXEC #4:c=0,e=102,p=0,cr=0,cu=1,mis=0,r=0,dep=0,og=0,plh=0,tim=1375758109281434
WAIT #4: nam=’log file sync’ ela= 24619 buffer#=4018 sync scn=1381492 p3=0 obj#=74760 tim=1375758109306071
WAIT #4: nam=’SQL*Net message to client’ ela= 351 driver id=1650815232 #bytes=1 p3=0 obj#=74760 tim=1375758109307375
From Snapper:
SQL> @snapper4 all 6 1 49
Sampling SID 49 with interval 6 seconds, taking 1 snapshots…
— Session Snapper v4.09 BETA – by Tanel Poder ( http://blog.tanelpoder.com ) – Enjoy the Most Advanced Oracle Troubleshooting Script on the Planet! 🙂
——————————————————————————————————————————————————————————————————————
 SID @INST, USERNAME  , TYPE, STATISTIC                                                 ,         DELTA, HDELTA/SEC,    %TIME, GRAPH       , NUM_WAITS,  WAITS/SEC,   AVERAGES
——————————————————————————————————————————————————————————————————————
    49  @1, SYS       , STAT, opened cursors cumulative,             1,        .17,         ,             ,          ,           ,          ~ per execution
    49  @1, SYS       , STAT, user commits,             1,        .17,         ,             ,          ,           ,          ~ per execution
    49  @1, SYS       , STAT, user calls,             2,        .34,         ,             ,          ,           ,          ~ per execution
This is just a little example of how OPI CALL is related to user calls metric. You can play and test more scenarios of select statements to see how they are being affected by ARRAYSIZE setting in SqlPlus.
Craig posted a table containing the result of his testing which is having the same results as this test.

SQL
User Calls
Select 1 row
3
Insert
2
Commit
2
Thanks,

Alfredo

Why the execution plan changed? Case study

Overview

At some point in time the execution plan of the SQL_ID etp65bryqtd2d changed. The new execution plan drops on performance from an Avg. of 0.01 seconds per execution to an Avg. of 1.5 seconds per execution.
This increase on elapsed time per execution provoke that the application can’t process work orders fast enough, the work queue started to drastically grow leading the application team to escalate a severity 1 issue.

Analysis

Execution plan changes

The execution plan changes only when a parse is performed, this because the _optim_bind_peeking parameter is on default value “TRUE”. A hard parse is required due to cursor invalidation in the shared pool; a cursor can be invalidated for some reasons but the most happening is, object change (Index rebuild, DBMS_STATS, etc.).

Rolling Cursor Invalidation

Starting with Oracle10g cursors are marked for rolling invalidation instead of marked INVALID immediately. On the next execution of the query the Oracle server will generate a random number between 0 and the value of the _optimizer_invalidation_period parameter, which has a default value of 18000, and the cursor will remain valid for this number of seconds. Upon every following execution Oracle will check if this random selected timeout has expired. If that is the case then the cursor will be hard parsed again.
Things will change without notice! Cursor invalidation has changed in the past and the reason for this change is that previously all dependent cursors were invalidated immediately after gathering new object statistics. This massive invalidation might cause a serious degradation in performance right after statistics gathering due to a high number of hard parses. Rolling cursor invalidation spreads the invalidation of cursors out over a longer period of time thereby avoiding the performance degradation caused by hard parsing the invalidated cursors.

Reference: Metalink note:557661.1

Bind variable peeking

The query optimizer peeks at the values of user-defined bind variables on the first invocation of a cursor. This feature lets the optimizer determine the selectivity of any WHERE clause condition, as well as if literals have been used instead of bind variables. On subsequent invocations of the cursor, no peeking takes place, and the cursor is shared, based on the standard cursor-sharing criteria, even if subsequent invocations use different bind values.
In this case we can clearly see that the elapsed time per execution is not changing because of the bind peeking; when Oracle peeks a bad execution plan, all elapsed times are bad.

CBO trace event 10053

Behind the scenes, what is the CBO doing when it comes to how it comes up with an execution plan? This is where the 10053 trace event comes to play. Other tools or settings show us WHAT the CBO comes up with; the 10053 setting tells us HOW the CBO came to its decision (the final execution plan).

conn / as sysdba
oradebug setmypid
oradebug unlimit
oradebug event 10053 trace name context forever, level 1
…your statement here…
oradebug event 10053 trace name context off
oradebug tracefile_name

We have performed this CBO trace when the query was performing bad and when it was performing well. With this method we have 2 trace files to compare and find out what is making the CBO to take such bad plan.
Notice that this method will produce a trace file only in the hard parse stage. So, in order to force the CBO to hard parse the SQL we have run it with a comment hint.

variable N1 VARCHAR2(128)
exec :N1 :=’125′;
select /* comment*/…

After comparing both trace files we can clearly see when the CBO chose the good plan:
***********************
Best so far: Table#: 1  cost: 7.3522  card: 3973.0000  bytes: 218515
             Table#: 7  cost: 16.2850  card: 9.0178  bytes: 612
             Table#: 2  cost: 21.6884  card: 33.7888  bytes: 3094
             Table#: 5  cost: 48.9215  card: 3565.0298  bytes: 406410
             Table#: 4  cost: 1475.7155  card: 3565.4846  bytes: 556140
             Table#: 0  cost: 1483.2886  card: 3559.8976  bytes: 644360
             Table#: 6  cost: 2195.7103  card: 3599.4022  bytes: 770186
             Table#: 3  cost: 2197.9000  card: 3599.4022  bytes: 820572
***********************
In the other hand, the trace of the bad plan is having a huge cost:
***********************
Best so far: Table#: 1  cost: 7.3510  card: 3928.0000  bytes: 216040
             Table#: 7  cost: 16.2836  card: 8.9798  bytes: 612
             Table#: 2  cost: 21.6870  card: 32.6678  bytes: 3003
             Table#: 5  cost: 153.8561  card: 20168.2755  bytes: 2299152
             Table#: 4  cost: 8225.5481  card: 20170.8485  bytes: 3146676
             Table#: 0  cost: 8233.2391  card: 20139.2414  bytes: 3645159
             Table#: 6  cost: 12263.4248  card: 20362.7290  bytes: 4357682
             Table#: 3  cost: 12266.6610  card: 20362.7290  bytes: 4642764
***********************
In the trace files we can clearly see that cost of table#5 messed up our total cost. Why is that?
***************
Now joining: TABLE5[TL2]#5
***************
NL Join
  Outer table: Card: 32.67  Cost: 21.69  Resp: 21.69  Degree: 1  Bytes: 91
  Inner table: TABLE5  Alias: TL2
  Access Path: TableScan
Index: TABLE5_IDX3
    resc_io: 20.00  resc_cpu: 360839
    ix_sel: 3.9216e-04  ix_sel_with_filters: 3.9216e-04
    NL Join: Cost: 153.86  Resp: 153.86  Degree: 1
      Cost_io: 152.55  Cost_cpu: 18392646
      Resp_io: 152.55  Resp_cpu: 18392646
****** finished trying bitmap/domain indexes ******
  Best NL cost: 153.86
          resc: 153.86 resc_io: 152.55 resc_cpu: 18392646
          resp: 153.86 resp_io: 152.55 resp_cpu: 18392646
Join Card:  20168.28 = outer (32.67) * inner (1574304.00) * sel (3.9216e-04)
Join Card – Rounded: 20168 Computed: 20168.28
Index selectivity on TABLE5_IDX3 is very low (3.9216e-04) compared with the good plan (6.7020e-05).

Index selectivity

After looking into the Index selectivity, the question arises; why the Index selectivity is low?
B*TREE Indexes improve the performance of queries that select a small percentage of rows from a table. As a general guideline, we should create indexes on tables that are often queried for less than 15% of the table’s rows. This value may be higher in situations where all data can be retrieved from an index, or where the indexed columns can be used for joining to other tables.
The ratio of the number of distinct values in the indexed column / columns to the number of records in the table represents the selectivity of an index. The ideal selectivity is 1. Such selectivity can be reached only by unique indexes on NOT NULL columns.

Selectivity = Distinct Values / Total Number Rows

With this measure we can correlate the importance of the statistics in the objects. If we gather statistics only on the table, the total number of rows will change; leading to drastically change the Index selectivity. In the same manner, if we only gather statistics on the Index, the number of distinct values will change and the selectivity will dramatically change.
Apart of that, the accuracy of the statistics will also play an important role. The accurate the statistics, the accurate the Index selectivity.

Suggestions – How to fix this issue?

The suggestions came from the percept that Index selectivity is playing a bad game on this particular SQL_ID.
•        As table and its indexes have a close relationship on how the CBO calculates the cost, we must gather statistics on the table and its indexes in the same time. This will avoid the Index selectivity to drastically change.
•       When an index is rebuilt, we must gather statistics of the table too.
•       Review the accuracy of the statistics. In some cases AUTO_SAMPLE_SIZE is not giving a good sample.

Some test on this SQL_ID have proven that gathering statistics to the TABLE5 table and its indexes with an estimate percent of 50%, plus invalidating the cursor immediately solved the issue.
The CBO was able to pick the good execution plan because the index selectivity was good enough.

Thanks,
Alfred