My LIO silly little benchmark

I have been working on a benchmark for LIO.  I know there are TPC and TPH transactions numbers that are published, on CPU speeds, but how much does that directly releate to LIO's, the heart of an Oracle database ?

  To help benchmark, I wrote a little pl/sql package.  This packages takes the Zip Code database, and randomly picks  some rows with a cursor (about 1 % of the table).  This package is then called by swing bench, and I put a "think time" in it for each execution of the package.

Ideally, I try to execute it up to what the Server can handle.. This was especially usefull with the benchmarking I did in a previous post on hyperthreading.

I was interested in what anyone else does ?  I try to do a LIO lookup, and compare numbers between servers.  By doing this I have a pretty good idea how many LIO's an AMD server can handle per second, an Intel server can do, and different architectures (2 socket, 4 socket, and 8 socket).. I even benchmark virutalization to see how much of an overhead is caused from the Software.

This may not be the best way (it excludes what happens with updates (redo logs etc), and how much physical I/O's affect the workload.

Any ideas would be appreciated.  I would love to come up with a nice reproducable benchark, and then maybe create a dbcapute of it, and do a dbreplay on different architectures ? Would that be more accurate.

I know many of you will say the line "well it depends on the workload", maybe the benchmarking that comes with swingbench is good enough ??

I'm just tired of reading server bencharks, and finding that for an oracle database, those benchmarks aren't very meaningful.

I would also love to do some benchmarking with Solaris X86, and RHEL/OEL on an 8 socket box.

I would also love to learn what anyone else has learned ?  I am especially interested how 8 socket intel servers compare with 2 socket. I'm seeing some pretty increadable numbers from 2 socket servers (almost 2x the speed of 8 socket).  I'm wondering if anyone else is seeing some measureable differences.

I'm starting to move to "go wide"  camp rather than go high camp for increasing server power.  The blade servers are being more, and more powerful, and you can have more memory local to the CPU. Increasing CPU sockets just increases hops to get those LIO's done, costing time, waits, latches. etc. etc.

So here is a piece of my LIO benchmark...

CREATE TABLE "KILLER" ("CC_ID" NUMBER(20, 0) NOT NULL ENABLE)  ;

/*  import 55,000 rows of distinct data */
CREATE PROCEDURE          kill_lio IS
   my_count number := 1;
   my_executions number;
   my_buffer_gets number
   my_cpu_time number;
   my_elapsed_time number;

error_code number;

BEGIN
for i in 1..10000 LOOP


select count(distinct cc_id) into my_count from kill_lio.killer;

end loop;

select executions,buffer_gets,cpu_time,elapsed_time into my_executions,my_buffer_gets,my_cpu_time,my_elapsed_time 
from sys.v_$sqlstats where sql_id='2j5tvp5rdzmym';
 
 dbms_output.put_line('exectutions:                          ' || to_char(my_executions,'999,999,999'));"
dbms_output.put_line('buffer gets:                          ' || to_char(my_buffer_gets,'999,999,999'));"
dbms_output.put_line('cpu time:                             ' || to_char(my_cpu_time,'999,999,999'));"
dbms_output.put_line('elapsed time:                         ' || to_char(my_elapsed_time,'999,999,999'));"
dbms_output.put_line('elapsed time per execution(ms)   :      ' ||to_char( my_elapsed_time/my_executions/1000,'999,999.9'));"
dbms_output.put_line('buffer_gets/second:                   ' ||to_char( my_buffer_gets/(my_elapsed_time/1000000),'999,999,999'));"

END;  -- exception handlers

and here is the output I use to compare.  I look at the average elapsed time, and buffer_gets/second to benchark systems.

executions:                                 10,000
buffer gets:                             1,190,000
cpu time:                              190,983,974
elapsed time:                          191,374,061
elapsed time per execution(ms)    :           19.1
buffer_gets/second:                          6,218

Here is the AWR report from the execution

Oracle Database Appliance

I have spent the day on an oracle call, and reading all the subsequent tweets that follow.  I think the best way to describe the appliance is that it is NOT a mini-exadata, but it is a simple rac appliance.

My impression is that it is a nice product for the small to mid market, but those us working with the bigger toys I don't see the gain.

I know, I've spent more days than I care to remember schooling the SA's on how to set up an interconnect, and ensure that all the IP's are correct.  I've worked with Storage administrators on how to present the disks, and make them available to ASM, and I've worked with networking on the ranges of IP's I need for scan, interconnect, etc. etc.  I'm sure you get the picture.

I also think that people like me that work in a big organization and have a team to handle these tasks, are probably going huh ? what is this? 

Personally, I don't see the big deal in this.. I see lots of dissadvantages.

• These Appliances cannot be clustered. What they have in them is all they will ever have in them.
• The 2 database nodes have 96g of memory, not a lot in today server sizes..
• There is no storage software like the exadata. No HCC, no offloading, no infiniband
• This is local disk in the appliance, meaning no cloning, storage virtualization, etc.
• The interconnect is 1ge, not infiniband.
• You CANNOT hook up fiber to this server, ever.
• It runs OEL, NOT redhat linux.. the differences are getting greater over time.
• This is a closed system with specific patch sets that need to be maintained to a short list of acceptable patches.

I know for a small, to midsize, the ideal of creating a new rac system in 2 hours is thing of beauty, but for bigger companies, there isn't a lot there.

Especially without the Exadata candy filling (infiniband, HCC, offloading, storage indexes).

I still think virtualiation is the direction, and this is a step in the opposite direction.  There may be a few takers, but I think companies will realize that virtualization is a better direction than a single closed appliance.

We will see.. just some thoughts.

Delphix, Solaris and ZFS

I was inspired to write a new blog posting after seeing a great presentation on Delphix.  Delphix is an extremely interesting product to help with the managing of many copies of an oracle database.  This product goes hand-in-hand with a virtualization product like VMWARE. What VMWare is servers, Delphix is to storage.

First I want to babble on a bit.. Why ? Because this is my blog.

I have used Solaris for many years and I am a big fan of Sparc/Solaris, and everything that it became over the years.  I know in the last couple of years, it has kind of fallen down, and Linux x86 has been growing leaps and bounds.  The most impressive piece of Solaris however has been ZFS.  ZFS offers snapshotting, cloning, lots of fantastic options built into the OS.  All very easy for the DBA.

I was very excited when ACFS came out (anyone remember).  It was the coolest thing going.  It looked a lot like ZFS built on top of ASM.   It had the potential to become a fantastic tool.  I was really, really, really hoping that down the road ACFS, and ZFS would combine, and replace the cooked file system with an Oracle File System that Rocked.    Well in case you didn't know ACFS has morphed into the "Cloud File System" or Cloud FS.  This is a licensed product used for the sharing of directories in a "cloud" environment.  Also, if you didn't know, ACFS won't run on the Exadata.  2 nails in the Coffin of this fantastic vision that I had in my head.

I was devastated for a while.

Why the background ? Because Delphix picked up where this vision died.  They offer the ability to do snapshots, along with deduplication, and compression to boot.

Delphix was founded by Jedidiah Yueh, who founded Avamar (dedup software that was sold to EMC),  The company also has  Karthik Rau, ad VP of Products who worked on VMware.  Lots of very bright people that were already involved in the founding technology.

Delphix makes the creation of database copies, easy, and it save space to boot.  I will post the presentation that I saw once I can link to it.

Definitely something worth checking out, and to watch for in the infrastructure Arsenal.

My quest for a performance warehouse

I have written on this before, and at openworld last year I've talked to some of the presenters about this.

My goal is to to create a performance data warehouse.  What does this mean ?

I want to take all the AWR data from all the databases in the corporate infrastructure, and create one giant repository. 

Well I just got one step closer.

I have a repository server set up already. It is just an 11.2.0.2 database with lots of space (about 3 tb), running on a Linux blade server.

My first test was to take all the AWR data from an 10g database we just retired running on HPUX. Since this database was moved to Linux (cross endian), transportable tablespaces was used, and all the AWR data was not brought over.

First step.. Get the data out of original source.

In order to do that you can use awrextr.sql in the $ORACLE_HOME/rdbms/admin directory.  I noticed (and found from other blogs) that this just calls sys.dbms_swrf_internal.awr_extract.

So 

STEPS on SOURCE.

1. Create user "awrextract" identified by xxxxx
2. grant connect ot awrextract
3. grant execute on sys.dbms_swrf_internal to awrextract
4. Login as awrextract
5. execute awr_extract procedure

exec sys.dbms_swrf_internal.awr_extract(dmpfile=>'awr_data',dmpdir=>'tmp_dir',bid=>9920,eid=>9925);

Notes

• The procedure automagically puts a .dmp on the dump file name
• You need to know the start and end snapshot you want to export
• The dmpdir needs to be a precreated directory and it has to be a cooked filesystem (non-asm).  I found that it also puts the logfile here, and that's why it has to be cooked.
• You cannot use dbms_file_transfer, especially going across endian types

 6. copy the file to my repository server

STEPS on the Destination

1.  Create schema to import the data into
2. Import the file in

DBMS_SWRF_INTERNAL.AWR_LOAD ( SCHNAME => 'AWR', dmpfile => 'awr_data', dmpdir => 'TMP_DIR');

  3.  Once imported move the awr data to sys catalogs

DBMS_SWRF_INTERNAL.MOVE_TO_AWR ( SCHNAME => 'AWR');

FINALLY

All this worked flawlessly, and then I tried to run COE_PROFILE.

IF you didn't know COE_PROFILE.sql  is part of sqlt and allows you to transfer profiles from one database to another by creating a script to build the profile.. It all works from my repository.

I will write about this again, but I am physched to finally get this off the ground.

UNYOUG meeting. Presentation on Cardinality Feedback

Well I've gotten my presentation in a pdf to take a look at.  Here it is. Short and sweet

Hyperthreading

I am am working in standing up a cluster of 2 database servers that look an aweful lot like an exadata 2-8 (only without the infiniband)..  It is a 2 node cluster of 8 socket, 8 core processrs with 256g of memory.

We started with a standard configuration and hyperthreading was turned on.. This means that Oracle sees the 64 cpu, as 128 cpus.  We were getting periodic reboots every Saturday morning.. The culprit was finally found as the analyze.. Why ??? because we have a 2 node rac, with 128 CPU's per, the degree of parallelization chosen was 512 !  With an analyze running with 512  parallel processors we were running out of memory.. The final diagnoses was to change parallel_max_servers to 64 to cut back on the parallel servers utilized. The second recommendation from Oracle was to turn off Hyperthreading.

We all said Huh ?  Hyperthreading gives you 2x the throughput because it doubles your CPU's right ???

Well lets' see.

I took my 2 node cluster and split it in 1/2  First half (A) is non-hyperthreaded, second half (B) is hyperthreaded.

I then took swingbench and threw a workload at it with lots of logical I/O.

Here is the benchmark from (A)   non-hyperthreaded

Notice we are getting 2062 transactions per minute.




 OK.. Lets see what happens with (B) hyperthreading.

Notice we are getting 1,685 transactions per minute. 

LESS wow lets look at our AWR compare of both nodes (same period) and see what the database says.

Notice  that the gets per execution match for both nodes, and the rows processed. The difference is in the exec time(ms) per exectuion, CPU time and executions.

IT is faster non-hyperthreaded.   The CPU's are doing more work with hypertrheading off, and pushing more work through.

OK... I've shown that for a non-saturating workload (under 64 active processes) on a 64 CPU box, hyperthreading off is faster.

Now let's saturate !!

First hyperthreading off.

Notice I can get the throughput up to 7,489 transactions per minute.

Now lets check the second node with hyperthreading on.

Notice we are getting 6552.. Darn !!

It looks like hypthreading isn't helping us eithor !! 

 

Now lets check the AWR for the 2 nodes.

Notice that once again the rows processed match up and the gets/execution match.

The number of executions is greater, the execution time is less, and the CPU time is way less for for hyperthreading off.

For a Logical I/O intensive workload, it looks like Hyperthreading is not as efficient.

I would love to hear any other experiences/opinions.

I just wanted to add on to this a thought as to why hypertrheading is such a big deal... Virtualization.  With 2x as many "cpus", you can fine tune the CPU a lot more.. You can carve up the server into smaller pieces.



Cardinality Feedback detailed example

Well, I have often written about cardinality feedback, and I have been striving to find out more detail on how it works, how to determine why it was used, and how to determine when it was used.

I have ran through a series of tests that answers a lot of these questions.  There is some information available, but  it isn't complete.  Here a couple of good starting points..

http://orastory.wordpress.com/2011/07/2

http://kerryosborne.oracle-guy.com/2011/07/cardinality-feedback/

http://dioncho.wordpress.com/2009/12/17/trivial-research-on-the-cardinality-feedback-on-11gr2/

They were even able to recreate some examples. Well here is my example.

First, my dataset. I used the zipcode database you can find here.  I used a sqldr script you can find here, to load a table structure you can find here.

First I loaded the data (about 74,000 rows), created a couple of indexes and ran a query.

here are the indexes

 CREATE INDEX "SYSTEM"."INDEX1" ON "SYSTEM"."TEST_DATA"
    (  "ZIPCODE" )
    TABLESPACE "SYSTEM" ;
  
 CREATE INDEX "SYSTEM"."INDEX2" ON "SYSTEM"."TEST_DATA"
    (  "CITY" )
     TABLESPACE "SYSTEM" ;
  
 CREATE INDEX "SYSTEM"."INDEX3" ON "SYSTEM"."TEST_DATA"
    (  "COUNTY" )
     TABLESPACE "SYSTEM" ;  

select /* colorme */ * from test_data where city='ROCHESTER' and county='MONROE';

There are individual indexes on both city and county, and there is a relationship between the 2 items.  This query will return 44 rows of data.

First output is the information from v$sql_shared_cursor.  Notice the column "use_feedback_stats".  I am also supplying the information from v$sqlstats for this query.  Here is the output after the first execution.

v$SQL_SHARED_CURSOR

SQL_ID        CHILD_NUMBER           USE_FEEDBACK_STATS 
------------- ---------------------- ------------------ 
33x0cazrmgrgy 0                      Y                  

V$SQLSTATS

SQL_ID        PLAN_HASH_VALUE        PARSE_CALLS            BUFFER_GETS            ROWS_PROCESSED         
------------- ---------------------- ---------------------- ---------------------- ---------------------- 
33x0cazrmgrgy 757026858              1                      551                    44                     

Notice that the column "use_feedback_stats" is set to 'Y" indicating that the query is marked for cardinality feedback to determine if the first execution is accurate enough.  I also ran a 10053 trace.  You can see from the output the query is rewritten to be executed with dynamic sampling.

SELECT /* OPT_DYN_SAMP */ /*+ ALL_ROWS IGNORE_WHERE_CLAUSE NO_PARALLEL(SAMPLESUB) opt_param('parallel_execution_enabled', 'false') NO_PARALLEL_INDEX(SAMPLESUB) NO_SQL_TUNE */ NVL(SUM(C1),0), NVL(SUM(C2),0), NVL(SUM(C3),0), NVL(SUM(C4),0) 
FROM 
(SELECT /*+ IGNORE_WHERE_CLAUSE NO_PARALLEL("TEST_DATA") FULL("TEST_DATA") NO_PARALLEL_INDEX("TEST_DATA") */ 1 AS C1, 
CASE WHEN "TEST_DATA"."CITY"='ROCHESTER' AND "TEST_DATA"."COUNTY"='MONROE' THEN 1 ELSE 0 END AS C2, 
CASE WHEN "TEST_DATA"."COUNTY"='MONROE' THEN 1 ELSE 0 END AS C3, 
CASE WHEN "TEST_DATA"."CITY"='ROCHESTER' THEN 1 ELSE 0 END AS C4 
FROM "BGRENN"."TEST_DATA" SAMPLE BLOCK (4.972376 , 1) SEED (1) "TEST_DATA") SAMPLESUB

Here is the output from explain plan

 PLAN_TABLE_OUTPUT                                                    
  
 -------------------------------------------------------------------------------------------------------------------------
   Plan hash value: 757026858                                                
   -----------------------------------------------------------------------------------------                
   | Id | Operation          | Name   | Rows | Bytes | Cost (%CPU)| Time   |                
   -----------------------------------------------------------------------------------------                
   |  0 | SELECT STATEMENT      |      |   8 | 1272 |   3  (0)| 00:00:01 |                
   |* 1 | TABLE ACCESS BY INDEX ROWID| TEST_DATA |   8 | 1272 |   3  (0)| 00:00:01 |                
   |* 2 |  INDEX RANGE SCAN     | INDEX2  |  121 |    |   1  (0)| 00:00:01 |                
   -----------------------------------------------------------------------------------------                
   Predicate Information (identified by operation id):                                   
   ---------------------------------------------------                                  
     1 - filter("COUNTY"='MONROE')                                             
     2 - access("CITY"='ROCHESTER')                                            
  
 Note                                                           
   -----                                                          
     - dynamic sampling used for this statement (level=2)                                 
  

Second output is the information from v$sql_shared_cursor (again).  Notice the column "use_feedback_stats" is set to 'N'

SQL_ID        CHILD_NUMBER           USE_FEEDBACK_STATS 
------------- ---------------------- ------------------ 
33x0cazrmgrgy 0                      Y                  
33x0cazrmgrgy 1                      N                  

SQL_ID        PLAN_HASH_VALUE        PARSE_CALLS            BUFFER_GETS            ROWS_PROCESSED         
------------- ---------------------- ---------------------- ---------------------- ---------------------- 
33x0cazrmgrgy 757026858              2                      572                    88                     

I ran a 10053 trace again (as before).  You can see in this case the results from the first execution (with 44 rows), is passed into the second execution using the hint "opt_estimate". You can also see what parts of the query are utilizing cardinality feedback to change the query.

SELECT /*+ OPT_ESTIMATE (TABLE "TEST_DATA" ROWS=44.000000 ) OPT_ESTIMATE (INDEX_SCAN "TEST_DATA" "INDEX2" MIN=69.000000 ) 
OPT_ESTIMATE (INDEX_FILTER "TEST_DATA" "INDEX2" ROWS=69.000000 ) */ 
"TEST_DATA"."ZIPCODE" "ZIPCODE",
"TEST_DATA"."LAT" "LAT",
"TEST_DATA"."CITY" "CITY",
"TEST_DATA"."STATE" "STATE",
"TEST_DATA"."COUNTY" "COUNTY",
"TEST_DATA"."TYPE" "TYPE",
"TEST_DATA"."PREFERRED" "PREFERRED",
"TEST_DATA"."WORLDREGION" "WORLDREGION",
"TEST_DATA"."COUNTRY" "COUNTRY",
"TEST_DATA"."LOCATIONTEXT" "LOCATIONTEXT",
"TEST_DATA"."LOCATION" "LOCATION",
"TEST_DATA"."POPULATION" "POPULATION",
"TEST_DATA"."HOUSINGUNITS" "HOUSINGUNITS"
,"TEST_DATA"."INCOME" "INCOME",
"TEST_DATA"."LANDAREA",
"LANDAREATEST_DATA"."WATERAREA" "WATERAREA",
"TEST_DATA"."DECOMMISIONED" "DECOMMISIONED",
"TEST_DATA"."MILITARYRESTRICTIONCODES" "MILITARYRESTRICTIONCODES" 
FROM "BGRENN"."TEST_DATA" "TEST_DATA" 
WHERE "TEST_DATA"."CITY"='ROCHESTER' AND "TEST_DATA"."COUNTY"='MONROE'

I ran this query until the v$sql_shared_cursor came back clean, and showed a "n" indicating that dynamic feedback was no longer being considered.  There was information from 2 hard parses in the 10053 file.

SQL_ID        CHILD_NUMBER           USE_FEEDBACK_STATS 
------------- ---------------------- ------------------ 
33x0cazrmgrgy 0                      Y                  
33x0cazrmgrgy 1                      N                  

SQL_ID        PLAN_HASH_VALUE        PARSE_CALLS            BUFFER_GETS            ROWS_PROCESSED         
------------- ---------------------- ---------------------- ---------------------- ---------------------- 
33x0cazrmgrgy 757026858              3                      593                    132                    

This answered my first question. How do you tell what caused the cardinality feedback ? You run a 10053, and find the OPT_ESTIMATE cardinality hints that adjust the cardinility and reparse the query.

/***************************************/

For my second test, I used the same result set, and ran statistics against the data.  After running the query from the first test, I found that cardinality was not being utilzed, so I changed my query.

select /* colorme */ * from test_data where city='ROCHESTER' and county='MONROE1' and state='NY';

This returns no rows of data, but the optimizer can't figure this out.

First execution you can see that cardinality feedback was marked for the statement, and notice the plan_hash_value of 7570268858.  In this example I've also included the output from v$SQLSTATS_PLAN_HASH. This information was added after reading the post from Lisa G.

SQL_ID        CHILD_NUMBER           USE_FEEDBACK_STATS 
------------- ---------------------- ------------------ 
88jhv4mryj58p 0                      Y                  

V$SQLSTATS

SQL_ID        PLAN_HASH_VALUE        PARSE_CALLS            BUFFER_GETS            ROWS_PROCESSED         
------------- ---------------------- ---------------------- ---------------------- ---------------------- 
88jhv4mryj58p 757026858              1                      146                    0                      

v$SQLSTATS_PLAN_HASH

SQL_ID        PLAN_HASH_VALUE        PARSE_CALLS            BUFFER_GETS            ROWS_PROCESSED         
------------- ---------------------- ---------------------- ---------------------- ---------------------- 
88jhv4mryj58p 757026858              1                      146                    0                      

Here is the output from the 10053 trace.

SELECT 
    "TEST_DATA"."ZIPCODE" "ZIPCODE",
    "TEST_DATA"."LAT" "LAT",
    "TEST_DATA"."CITY" "CITY",
    "TEST_DATA"."STATE" "STATE",
    "TEST_DATA"."COUNTY" "COUNTY",
    "TEST_DATA"."TYPE" "TYPE",
    "TEST_DATA"."PREFERRED" "PREFERRED",
    "TEST_DATA"."WORLDREGION" "WORLDREGION",
    "TEST_DATA"."COUNTRY" "COUNTRY",
    "TEST_DATA"."LOCATIONTEXT" "LOCATIONTEXT",
    "TEST_DATA"."LOCATION" "LOCATION",
    "TEST_DATA"."POPULATION" "POPULATION",
    "TEST_DATA"."HOUSINGUNITS" "HOUSINGUNITS",
    "TEST_DATA"."INCOME" "INCOME",
    "TEST_DATA"."LANDAREA",
    "LANDAREATEST_DATA"."WATERAREA" "WATERAREA",
    "TEST_DATA"."DECOMMISIONED" "DECOMMISIONED",
    "TEST_DATA"."MILITARYRESTRICTIONCODES" "MILITARYRESTRICTIONCODES" 
FROM "BGRENN"."TEST_DATA" "TEST_DATA" 
WHERE "TEST_DATA"."CITY"='ROCHESTER' AND "TEST_DATA"."COUNTY"='MONROE1'

Here is the first plan

 PLAN_TABLE_OUTPUT                                                    
  
 -------------------------------------------------------------------------------------------------------------------------
  
 Plan hash value: 757026858                                                
  
 -----------------------------------------------------------------------------------------                
  
 | Id | Operation          | Name   | Rows | Bytes | Cost (%CPU)| Time   |                
 -----------------------------------------------------------------------------------------                
 |  0 | SELECT STATEMENT      |      |   1 |  120 |   3  (0)| 00:00:01 |                
 |* 1 | TABLE ACCESS BY INDEX ROWID| TEST_DATA |   1 |  120 |   3  (0)| 00:00:01 |                
 |* 2 |  INDEX RANGE SCAN     | INDEX2  |   2 |    |   1  (0)| 00:00:01 |                
 -----------------------------------------------------------------------------------------                
 Predicate Information (identified by operation id):                                   
 ---------------------------------------------------                                   
   1 - filter("COUNTY"='MONROE1' AND "STATE"='NY')                                    
   2 - access("CITY"='ROCHESTER')                                            
  

Second execution, notice that cardinality feedback will not be used on subsequent executions.

SQL_ID        CHILD_NUMBER           USE_FEEDBACK_STATS 
------------- ---------------------- ------------------ 
88jhv4mryj58p 0                      Y                  
88jhv4mryj58p 1                      N                  

V$SQLSTATS

SQL_ID        PLAN_HASH_VALUE        PARSE_CALLS            BUFFER_GETS            ROWS_PROCESSED         
------------- ---------------------- ---------------------- ---------------------- ---------------------- 
88jhv4mryj58p 2704719303             2                      148                    0                      

v$SQLSTATS_PLAN_HASH

SQL_ID        PLAN_HASH_VALUE        PARSE_CALLS            BUFFER_GETS            ROWS_PROCESSED         
------------- ---------------------- ---------------------- ---------------------- ---------------------- 
88jhv4mryj58p 2704719303             1                      2                      0                      
88jhv4mryj58p 757026858              1                      146                    0                      

Here is the output from the 10053 trace showing the feedback from the first execution using the OPT_ESTIMATE hint again.

SELECT /*+ OPT_ESTIMATE (INDEX_SCAN "TEST_DATA" "INDEX2" MIN=69.000000 ) 
           OPT_ESTIMATE (INDEX_FILTER "TEST_DATA" "INDEX2" ROWS=69.000000 ) */ 
    "TEST_DATA"."ZIPCODE" "ZIPCODE",
    "TEST_DATA"."LAT" "LAT",
    "TEST_DATA"."CITY" "CITY",
    "TEST_DATA"."STATE" "STATE",
    "TEST_DATA"."COUNTY" "COUNTY",
    "TEST_DATA"."TYPE" "TYPE",
    "TEST_DATA"."PREFERRED" "PREFERRED",
    "TEST_DATA"."WORLDREGION" "WORLDREGION",
    "TEST_DATA"."COUNTRY" "COUNTRY",
    "TEST_DATA"."LOCATIONTEXT" "LOCATIONTEXT",
    "TEST_DATA"."LOCATION" "LOCATION",
    "TEST_DATA"."POPULATION" "POPULATION",
    "TEST_DATA"."HOUSINGUNITS" "HOUSINGUNITS",
    "TEST_DATA"."INCOME" "INCOME",
    "TEST_DATA"."LANDAREA",
    "LANDAREATEST_DATA"."WATERAREA" "WATERAREA",
    "TEST_DATA"."DECOMMISIONED" "DECOMMISIONED",
    "TEST_DATA"."MILITARYRESTRICTIONCODES" "MILITARYRESTRICTIONCODES" 
FROM "BGRENN"."TEST_DATA" "TEST_DATA" 
WHERE "TEST_DATA"."CITY"='ROCHESTER' AND "TEST_DATA"."COUNTY"='MONROE1'

But notice something else... notice the plan has value changed from 757026858 to 2704719303.  Fine right ? Cardinality feedback did it's thing, but also notice this is the output from v$sqlstats.  Huh ?? The history for executions of the plan (1 for the first plan, and 1 for the second), got summarized into the second plan.

here is the second plan

 Plan hash value: 2704719303                                                     
  
 -----------------------------------------------------------------------------------------                      
 | Id | Operation          | Name   | Rows | Bytes | Cost (%CPU)| Time   |                      
 -----------------------------------------------------------------------------------------                      
 |  0 | SELECT STATEMENT      |      |    |    |   6 (100)|     |                      
 |* 1 | TABLE ACCESS BY INDEX ROWID| TEST_DATA |   1 |  120 |   6  (0)| 00:00:01 |                      
 |* 2 |  INDEX RANGE SCAN     | INDEX3  |  38 |    |   1  (0)| 00:00:01 |                      
 -----------------------------------------------------------------------------------------                      
  
 Predicate Information (identified by operation id):                                         
 ---------------------------------------------------  
   1 - filter("CITY"='ROCHESTER')                                                  
   2 - access("COUNTY"='MONROE1')                                                  
 PLAN_TABLE_OUTPUT                                                          
 ------------------------------------------------------------------------------------------------------------------------------------
  
 Note                                                                
 -----                                                                
   - cardinality feedback used for this statement         

What does all this mean ????

1) the 10053 trace will reveal where cardinality feedback was used by finding the OPT_ESTIMATE hint.

2) the v$sql_session_cursor view will show you where cardinality feedback was used.

3) There is a new view for looking at the multiple plans created for cardinality feedback called v$SQLSTATS_PLAN_HASH.

And in looking at the DBA_HIST_SQLSTAT, I do see both plans.

SQL_ID        PLAN_HASH_VALUE        PARSE_CALLS_DELTA      BUFFER_GETS_DELTA      ROWS_PROCESSED_DELTA   
------------- ---------------------- ---------------------- ---------------------- ---------------------- 
88jhv4mryj58p 757026858              1                      146                    0                      
88jhv4mryj58p 2704719303             1                      2                      0                      

Another observation I will would make is that when you multiple columns (and you aren't using a composite index), oracle is likely to utilyze cardinality feedback to ensure the cardinality is correct.

Finally, I did try the same example with bind variables (instead of literals).  I found that Cardinality Feedback did not kick in for my example.  Bind variables must tell the optimizer to pick a "generically" better plan, and not look around.

This is all on version 11.2.0.2

I have included a query I'm using to point out where cardinality feedback has been utilized, and sorted the queries by the affect they have on performance, so I can assess the gains (or losses) associated with the use of this feature. Overall my databases have show remarkable improvement, and those fiew cases where there is a regression, the sql_id is highligted so I can create a profile.

I have to note that this query has become invaluable to identify the top queries affected by cardinality feedback.  I have used it to identify where extended stats may help, and I have been able to determine, that cardinality feedback is indeed a good thing most of the time.  A lot of this detail is lost in the AWR reports.

select  query1.sql_id,
query1.plan_hash_value phv1,
query2.plan_hash_value phv2, 
query1.executions exec1,
query2.executions exec2,
trunc((query1.avg_elapsed_time)/1000000,2) aet,
trunc(( query2.avg_elapsed_time)/1000000,2) aet_card,
trunc(( query1.avg_elapsed_time - query2.avg_elapsed_time)/1000000,2) aet_diff,
abs(trunc(( (query1.avg_elapsed_time - query2.avg_elapsed_time)* query2.executions)/1000000,2)) abs_diff,
trunc( ((query1.avg_elapsed_time - query2.avg_elapsed_time) * query2.executions)/1000000,2) tot_diff,
trunc( ((query1.avg_elapsed_time - query2.avg_elapsed_time)/ query1.avg_elapsed_time) * 100,1) card_feedback_perc_difference,query1.sql_text
  from 
(select sql_id,plan_hash_value,last_active_time,executions,(rows_processed/executions) rows_processed,
(elapsed_time/executions) avg_elapsed_time,(cpu_time/executions) avg_cpu_time,(buffer_gets/executions) avg_buffer_gets,
sql_text from V$SQLSTATS_PLAN_HASH a
where exists (select plan_hash_value  from V$SQLSTATS_PLAN_HASH b  where a.sql_id=b.sql_id and a.plan_hash_value <> b.plan_hash_value and executions > 0)
and executions > 0
order by sql_id,last_active_time) query1,
(select sql_id,plan_hash_value,last_active_time,executions,(rows_processed/executions) rows_processed,
(elapsed_time/executions) avg_elapsed_time,(cpu_time/executions) avg_cpu_time,(buffer_gets/executions) avg_buffer_gets,
sql_text from V$SQLSTATS_PLAN_HASH a
where exists (select plan_hash_value  from V$SQLSTATS_PLAN_HASH b  where a.sql_id=b.sql_id and a.plan_hash_value <> b.plan_hash_value and executions > 0)
and executions > 0
order by sql_id,last_active_time) query2
where query1.sql_id=query2.sql_id
and query1.last_active_time < query2.last_active_time 
and exists (select 1 from V$SQL_SHARED_CURSOR c where query1.sql_id=c.sql_id and c.use_feedback_stats='Y') 
--and query1.avg_elapsed_time < query2.avg_elapsed_time
order by abs_diff desc