Kerry Osborne's Oracle Blog

In the constant battle to provide consistent performance, Oracle took a giant step backwards with the 9i version by introducing an “Enhancement” called Bind Variable Peeking. I’ll explain what I mean in a minute, but first a bit of history.

When Oracle introduced histograms in 8i, they provided a mechanism for the optimizer to recognize that the values in a column were not distributed evenly. That is, in a table with 100 rows and 10 distinct values, the default assumption the optimizer would make, in the absence of a histogram, would be that no matter which value you picked – you would always get 100/10 or 10 rows back. Histograms let the optimizer know if that was not the case. The classic example would be 100 records with 2 distinct values where one value, say “Y”, occurred 99 times and the other value, say “N”, occurred only 1 time.  So without a histogram the optimizer would always assume that whether you requested records with a “Y” or an “N”, you would get half the records back (100/2 = 50). Therefore you always want to do a full table scan as opposed to using an index on the column. A histogram, assuming it was accurate (we’ll come back to that later), would let the optimizer know that the distribution was not normal (i.e. not spread out evenly – also commonly called skewed) and that a “Y” would get basically the whole table, while an “N” would get only 1%. This would allow the optimizer to pick an appropriate plan regardless of which value was specified in the Where Clause.

So let’s consider the implications of that. Would that improve the response time for the query where the value was “Y”. The answer is no. In this simple case, the default costing algorithm is close enough and produces the same plan that the histogram produces. The full table scan takes just as long whether the optimizer thought it was getting 50 rows or 99 rows. But what about the case where we specified the value of “N”. In this case, with a histogram we would pick up the index on that column and presumably get a much better response time than the full table scan. This is an important point. Generally speaking it is only the outliers, the exceptional cases if you will, where the histogram really makes a difference.

So at first glance, all appeared well with the world. But there was a fly in the ointment. You had to use literals in your SQL statements for the optimizer to be able use the histograms. So you had to write your statements like this:

SELECT XYZ FROM TABLE1 WHERE COLUMN1 = ‘Y’;

SELECT XYZ FROM TABLE1 WHERE COLUMN1 = ‘N’;

Not a problem in our simple example, because you only have two possibilities. But consider a statement with 2 or 3 skewed columns, each with a couple of hundred distinct values. The possible combinations could quickly grow into the millions. Not a good thing for the shared pool.

Enter our star: Bind Variable Peeking, a new feature introduced in 9i that was added to allow the optimizer to peek at the value of bind variables and then use a histogram to pick an appropriate plan, just like it would do with literals. The problem with the new feature was that it only looked at the variables once, when the statement was parsed. So let’s make our simple example a little more realistic by assuming we have a 10 million row table where 99% have a value of “Y” and 1% have a value of “N”. So in our example, if the first time the statement was executed it was passed a “Y”, the full table scan plan would be locked in and it would be used until the statement had to be re-parsed, even if the value “N” was passed to it in subsequent executions.

So let’s consider the implication of that. When you get the full table scan plan (because you passed a “Y” the first time) it behaves the same way no matter what which value you pass subsequently. Always a full table scan, always the same amount of work and the same basic elapsed time. From a user standpoint that seems reasonable. The performance is consistent. (this is the way it would work without a histogram by the way) On the other hand, if the index plan gets picked because the parse occurs with a value of “N”, the executions where the value is “N” will be even faster than they were before, but the execution with a value of “Y” will be incredibly slow. This is not at all what the user expects. They expect the response time to be about the same every time they execute a piece of code. And this is the problem with bind variable peeking. It’s basically just Russian Roulette. It just depends on what value you happen to pass the statement when it’s parsed (which could be any execution by the way).

So is Bind Variable Peeking a feature or a bug? Well technically it’s not a bug because it works the way it’s designed. I just happen to believe that it was not a good decision to implement it that way. But what other choices did the optimizer development group have?

• They could have evaluated the bind variables and re-parsed  for every execution of every statement using bind variables. This would eliminate the advantage of having bind variables in the first place and would never work for high transaction systems. So it was basically not an option.
• They could have just said no, and made us use literals in order to get the benefit of histograms (probably not a bad option in retrospect – the fact that they added _optim_peek_user_binds probably means that they decided later to give us that option via setting this hidden parameter).
• They could have implemented a system where they could identify statements that might benefit from different plans based on the values of bind variables. Then peek at those variables for every execution of those “bind sensitive” statements (sound familiar? – that’s what they finally did in 11g with Adaptive Cursor Sharing).

So why is it such a pervasive problem? And I do believe it is a pervasive problem with 10g in particular. A couple of reasons come to mind:

1. We’ve been taught to always use bind variables. It’s a best practice which allows SQL statements to be shared, thus eliminating a great deal of work/contention. Using bind variable is an absolute necessity when building scalable high transaction rate systems. (of course that doesn’t mean that you can’t bend the rule occasionally)
2. 10g changed it’s default stats gathering method to automatically gather histograms. So in a typical 10g database there are a huge number of histograms, many of them inappropriate (i.e. on columns that don’t have significantly skewed distributions) and many of them created with very small sample sizes causing the histograms to be less than accurate. Note that 11g appears to be better on both counts – that is to say, 11g seems to create fewer inappropriate histograms and seems to create much more accurate histograms with small sample sizes. But the jury is still out on 11g stats gathering as it has not been widely adopted at this point in time.
3. In my humble opinion, Bind Variable Peeking is not that well understood. When I talk to people about the issue, they usually have heard of it and have a basic idea what the problem is, but their behavior (in terms of the code they write and how they manage their databases) indicates that they don’t really have a good handle on the issue.

So what’s the best way to deal with this issue? Well recognizing that you have a problem is the first step to recovery, so being able to identify that you have a problem with plan stability is an appropriate first step. Direct queries against the Statspack or AWR tables are probably the best way to identify the issue. I’ve posted a couple of scripts that I find useful for this purpose previously – (unstable_plans.sql, awr_plan_stats.sql, awr_plan_change.sql). What you’re looking for is statements that flip flop back and forth between 2 or more plans. Note that there are other reasons for statements to change plans, but Bind Variable Peeking is the number one suspect. Here’s an example of their usage:

SQL> @unstable_plans
SQL> break on plan_hash_value on startup_time skip 1
SQL> select * from (
  2  select sql_id, sum(execs), min(avg_etime) min_etime, max(avg_etime) max_etime, stddev_etime/min(avg_etime) norm_stddev
  3  from (
  4  select sql_id, plan_hash_value, execs, avg_etime,
  5  stddev(avg_etime) over (partition by sql_id) stddev_etime
  6  from (
  7  select sql_id, plan_hash_value,
  8  sum(nvl(executions_delta,0)) execs,
  9  (sum(elapsed_time_delta)/decode(sum(nvl(executions_delta,0)),0,1,sum(executions_delta))/1000000) avg_etime
 10  -- sum((buffer_gets_delta/decode(nvl(buffer_gets_delta,0),0,1,executions_delta))) avg_lio
 11  from DBA_HIST_SQLSTAT S, DBA_HIST_SNAPSHOT SS
 12  where ss.snap_id = S.snap_id
 13  and ss.instance_number = S.instance_number
 14  and executions_delta > 0
 15  and elapsed_time_delta > 0
 16  group by sql_id, plan_hash_value
 17  )
 18  )
 19  group by sql_id, stddev_etime
 20  )
 21  where norm_stddev > nvl(to_number('&min_stddev'),2)
 22  and max_etime > nvl(to_number('&min_etime'),.1)
 23  order by norm_stddev
 24  /
Enter value for min_stddev:
Enter value for min_etime:

SQL_ID        SUM(EXECS)   MIN_ETIME   MAX_ETIME   NORM_STDDEV
------------- ---------- ----------- ----------- -------------
1tn90bbpyjshq         20         .06         .24        2.2039
0qa98gcnnza7h         16       20.62      156.72        4.6669
7vgmvmy8vvb9s        170         .04         .39        6.3705
32whwm2babwpt        196         .02         .26        8.1444
5jjx6dhb68d5v         51         .03         .47        9.3888
71y370j6428cb        155         .01         .38       19.7416
66gs90fyynks7        163         .02         .55       21.1603
b0cxc52zmwaxs        197         .02         .68       23.6470
31a13pnjps7j3        196         .02        1.03       35.1301
7k6zct1sya530        197         .53       49.88       65.2909

10 rows selected.

SQL> @find_sql
SQL> select sql_id, child_number, plan_hash_value plan_hash, executions execs,
  2  (elapsed_time/1000000)/decode(nvl(executions,0),0,1,executions) avg_etime,
  3  buffer_gets/decode(nvl(executions,0),0,1,executions) avg_lio,
  4  sql_text
  5  from v$sql s
  6  where upper(sql_text) like upper(nvl('&sql_text',sql_text))
  7  and sql_text not like '%from v$sql where sql_text like nvl(%'
  8  and sql_id like nvl('&sql_id',sql_id)
  9  order by 1, 2, 3
 10  /
Enter value for sql_text:
Enter value for sql_id: 0qa98gcnnza7h

SQL_ID         CHILD  PLAN_HASH        EXECS     AVG_ETIME      AVG_LIO SQL_TEXT
------------- ------ ---------- ------------ ------------- ------------ ------------------------------------------------------------
0qa98gcnnza7h      0  568322376            3          9.02      173,807 select avg(pk_col) from kso.skew where col1 > 0

SQL> @awr_plan_stats
SQL> break on plan_hash_value on startup_time skip 1
SQL> select sql_id, plan_hash_value, sum(execs) execs, sum(etime) etime, sum(etime)/sum(execs) avg_etime, sum(lio)/sum(execs) avg_lio
  2  from (
  3  select ss.snap_id, ss.instance_number node, begin_interval_time, sql_id, plan_hash_value,
  4  nvl(executions_delta,0) execs,
  5  elapsed_time_delta/1000000 etime,
  6  (elapsed_time_delta/decode(nvl(executions_delta,0),0,1,executions_delta))/1000000 avg_etime,
  7  buffer_gets_delta lio,
  8  (buffer_gets_delta/decode(nvl(buffer_gets_delta,0),0,1,executions_delta)) avg_lio
  9  from DBA_HIST_SQLSTAT S, DBA_HIST_SNAPSHOT SS
 10  where sql_id = nvl('&sql_id','4dqs2k5tynk61')
 11  and ss.snap_id = S.snap_id
 12  and ss.instance_number = S.instance_number
 13  and executions_delta > 0
 14  )
 15  group by sql_id, plan_hash_value
 16  order by 5
 17  /
Enter value for sql_id: 0qa98gcnnza7h

SQL_ID        PLAN_HASH_VALUE        EXECS          ETIME    AVG_ETIME        AVG_LIO
------------- --------------- ------------ -------------- ------------ --------------
0qa98gcnnza7h       568322376           14          288.7       20.620      172,547.4
0qa98gcnnza7h      3723858078            2          313.4      156.715   28,901,466.0

SQL> @awr_plan_change
SQL> break on plan_hash_value on startup_time skip 1
SQL> select ss.snap_id, ss.instance_number node, begin_interval_time, sql_id, plan_hash_value,
  2  nvl(executions_delta,0) execs,
  3  (elapsed_time_delta/decode(nvl(executions_delta,0),0,1,executions_delta))/1000000 avg_etime,
  4  (buffer_gets_delta/decode(nvl(buffer_gets_delta,0),0,1,executions_delta)) avg_lio
  5  from DBA_HIST_SQLSTAT S, DBA_HIST_SNAPSHOT SS
  6  where sql_id = nvl('&sql_id','4dqs2k5tynk61')
  7  and ss.snap_id = S.snap_id
  8  and ss.instance_number = S.instance_number
  9  and executions_delta > 0
 10  order by 1, 2, 3
 11  /
Enter value for sql_id: 0qa98gcnnza7h

   SNAP_ID   NODE BEGIN_INTERVAL_TIME            SQL_ID        PLAN_HASH_VALUE        EXECS    AVG_ETIME        AVG_LIO
---------- ------ ------------------------------ ------------- --------------- ------------ ------------ --------------
     21857      1 20-MAR-09 04.00.08.872 PM      0qa98gcnnza7h       568322376            1       31.528      173,854.0
     22027      1 27-MAR-09 05.00.08.006 PM      0qa98gcnnza7h                            1      139.141      156,807.0
     22030      1 27-MAR-09 08.00.15.380 PM      0qa98gcnnza7h                            3       12.451      173,731.0
     22031      1 27-MAR-09 08.50.04.757 PM      0qa98gcnnza7h                            2        8.771      173,731.0
     22032      1 27-MAR-09 08.50.47.031 PM      0qa98gcnnza7h      3723858078            1      215.876   28,901,466.0
     22033      1 27-MAR-09 08.57.37.614 PM      0qa98gcnnza7h       568322376            2        9.804      173,731.0
     22034      1 27-MAR-09 08.59.12.432 PM      0qa98gcnnza7h      3723858078            1       97.554   28,901,466.0
     22034      1 27-MAR-09 08.59.12.432 PM      0qa98gcnnza7h       568322376            2        8.222      173,731.5
     22035      1 27-MAR-09 09.12.00.422 PM      0qa98gcnnza7h                            3        9.023      173,807.3

9 rows selected.

So back to the question, what’s the best way to deal with the issue. In general, the best way to eliminate Bind Variable Peeking is as follows:

1. Only create histograms on skewed columns.
2. Use literals in where clauses on columns where you have histograms and want to use them. Note that it’s not necessary to use literals for every possible value of a skewed column. There may be only a few outlier values that result in significantly different plans. With a little extra code you can use literals for those values and bind variables for the rest of the values that don’t matter.
3. If you can’t modify the code, consider turning off Bind Variable Peeking by setting the _OPTIM_PEEK_USER_BINDS parameter to false. You won’t get the absolute best performance for every possible statement, but you will get much more consistent performance, which is, in my opinion, more important than getting the absolute best performance. Keep in mind that this is a hidden parameter and so should be carefully tested and probably discussed with Oracle support prior to implementing it in any production system.
4. You can also consider stronger methods of forcing the optimizer’s hand such as Outlines (see my previous posts on Unstable Plans and on Outlines). This option provides a quick method of locking in a single plan, but it’s not fool proof. Even with outlines, there is some possibility that the plan can change. Also note that this option is only palatable in situations where you have a relatively small number of problem SQL statements.
5. Upgrade to 11g and let Adaptive Cursor Sharing take care of all your problems for you (don’t bet on it working without a little effort – I’ll try to do a post on that soon).

In summary, using literals with histograms on columns with skewed data distributions are really the only effective way to deal with the issue and still retain the ability for the optimizer to choose the absolute best execution plans. However, if circumstances prevent this approach, there are other techniques that can be applied. These should be considered temporary fixes, but may work well while a longer term solution is contemplated. From a philosophical stand point, I strongly believe that consistency is more important than absolute speed. So when a choice must be made, I would always favor slightly reduced but consistent performance over anything that didn’t provide that consistency.

Your comments are always welcome. Please let me know what you think.

I did a little talk at the Dallas Area Users Group this afternoon. The talk was about 11g stuff. Here’s a link to the presentation materials.

DOUG 11g presentation materials

It’s a zip file with the power point and several text files with examples. Also, I promised to upload Randy Johnson’s slides from our presentation several months ago at an Oracle Tech day. His material included info on new features of RMAN and compression in 11g. I’ll add that here as soon as I get it from him.

Please let me know if you have any questions or comments.

Karen Morton just posted a great paper on her blog about statistics gathering. The paper is titled “Managing Statistics for Optimal Query Performance“. I was excited to see it because I think gathering stats is one of the most important and least well understood aspects of managing an Oracle environment. I must admit that I was expecting a recommended method or framework for gathering stats, but actually the paper is really more about how the statistics are used along with general guidelines for gathering them, rather than a direct recommendation on how to gather them. Nevertheless, it is one of the best papers I’ve seen on the subject. She’s going to present the paper at the Hotsos Symposium to be held in Dallas the week of March 9th. I’m going to be there and am really looking forward to hearing what she has to say on the subject.

By the way, I can’t recommend this conference highly enough. If you really want to understand how Oracle works, this is the place to be. You should know that I am not generally a fan of formal training classes. I have often been disappointed because I felt like my time would have been better spent researching the subject matter myself. On the other hand, I have found a lot of value in working closely on a project with someone who knows the subject matter well, kind of like a mentor. But generally speaking, the formal classes have been less satisfying, except in the rare case where you get the great instructor that wrote the class. This symposium format on the other hand allows you to listen to a collection of really knowledgeable Oracle people packed into a short period of time. I have been to the Hotsos Symposiums for several years in a row and I always come away with pages of notes on things I want to investigate further. And the participants are, generally speaking, a collection of very bright Oracle people. So even the conversations between the presentations are often very interesting. Finally, they run two presentations at a time which allows you to pick the presentation that is most interesting. I have often found it hard to choose (don’t tell anyone, but I have on more than one occasion listened to the first half of one and then the second half of the other). So like I said, I find it to be a very productive few days.

But I digress, Karen’s paper is pretty long (24 pages) but it covers a vast amount of ground. There are a number of one liners that could be expanded into full papers. In the paper she discusses a number of topics including dealing with short comings of the optimizer in 10g. One of those issues is bind variable peeking (probably my least favorite optimizer feature, quirk, bug, … what ever you want to call it). I must say that I think it has caused far more problems than it solved, and I frankly don’t know what they were thinking when they put that feature in. I wrote a little about a way to get around it using outlines here. By the way, this reminds me of a cartoon I drew 20 years ago that looked very similar to this one (that I lifted off of Steve Karam’s blog)

Of course as Karen points out, the right way to deal with bind variable peeking issues it is to understand your data and use literals where they are appropriate, keeping in mind the number of additional statements that will need to be parsed and dealt with in the shared pool. She also points out that code could be written to selectively use literals for specific values, giving you a mix of literals and bind variables for the same statement. This approach should allow you to minimize the impact on the shared pool while still providing the optimizer with the data it needs to make good decisions (this is a great idea but I’ve never seen anyone actually do it). And of course she points out that 11g has a much better mechanism for dealing with this whole issue.

Another idea that really got me thinking was the use of dynamic sampling . Karen clearly shows one of the advantages of dynamic sampling in the case of correlated predicates (i.e. the optimizer assumes a query where car_model = ‘Mustang’ and car_make = ‘Ford’ are independent, when clearly they are not). She shows how dynamic sampling can be very useful in conjunction with normal statistics in this situation. (rats, now I’m going to have to go play around with that a bit – so much to do, so little time)

Finally, she discusses some of the statistics gathering options and differences in 9i, 10g, and 11g. The automatic creation of histograms is one of the main differences between 9i and 10g and she discusses this issue, but doesn’t go into to much detail on it. I must admit that I think 10g’s default setup does a very poor job when it comes to histograms. This is the one area I would have liked to see address a little more fully, but at 24 pages already I can understand why she had to draw the line somewhere. Anyway, by default 10g creates histograms on columns based on several factors including their use in where clauses. Unfortunately, histograms often get created on columns where their usefulness is questionable at best and they regularly get created with a very small sample sizes. The small sample sizes often result in significant inaccuracies. I personally think that allowing the gather stats job to automatically create histograms in 10g is really bad idea.

Anyway, this is a paper that is well worth reading in my opinion. Typical Hotsos Symposium fare!

Recently we ran into a database with a (or some) incomplete transactions that had not been able to rollback due to a file in the undo tablespace that had been deleted. And as luck would have it, the log files were some how lost as well (so we couldn’t just recover the undo file). So the database was up and running, but the undo file was missing and any time you hit one of the records that needed info from the missing undo file to rebuild a consistent version, it would fail with a ORA-00376  error (file not found). Technically that is not a corrupt block, just an uncommitted record that has been written, with missing undo. Make sense? Anyway, we narrowed it down to a couple of sub-partitions and were able to export the data from all the other sub-partitions. To get the data out of the affected sub-partitions we decided to use an approached based on the old rowid, which contained file_id,block_id, and row_num.

Toon Koppelaars did an excellent write up on this basic approach here. But it was written a while back and unfortunately the rowid was changed somewhere around the Oracle 8i time frame making it a little more difficult. However, in their infinite wisdom, the developers at Oracle added the DBMS_ROWID package which allows us to work around the issue.

So here’s the psuedo code for what we did:

Get Object Name (object_name)
Get Max Rows Per Block (max_rows_per_block)
Get List of Blocks by Extent for Object

For Each Extent
  For Each Block 
    For row in 1 to max_rows_per_block
      insert into saved_table select * from object_name 
        where rowid = dbms_rowid.rowid_create(file,block,row);
    End Row Loop
  End Block Loop
End Extent Loop

The actual script we used is a little more complicated. It actually created a Bad Blocks table as well so we’d know how many blocks were skipped and it had some error checking. I later embellished it a bit to make the object name dynamic (which was considerably more work than I thought it would be). Here’s the actual script I ended up with: save_u.sql. NOTE: I am not the worlds greatest pl/sql guy, so if you have any suggestions, let me know! But it seems to do the job. You may also find this script (obj_blocks.sql) useful for getting a list of all the blocks mapped to a specific object. And here’s a couple of scripts to create functions that might come in handy: create_new_rowid.sql (creates function new_rowid which returns the new format rowid if you give it the obj#, file#, block#, and row#) and create_old_rowid.sql (which returns the old format rowid if given the new format id). Here’s a quick example of the scripts in use:

> sqlplus / as sysdba

SQL*Plus: Release 10.2.0.3.0 - Production on Thu Feb 12 11:35:14 2009

Copyright (c) 1982, 2006, Oracle.  All Rights Reserved.


Connected to:
Oracle Database 10g Enterprise Edition Release 10.2.0.3.0 - Production
With the Partitioning, Real Application Clusters, OLAP and Data Mining options

SQL> @obj
Enter value for owner: MF
Enter value for name: BROKE%
Enter value for type: 

OWNER           OBJECT_NAME                    OBJECT_TYPE         STATUS  T
--------------- ------------------------------ ------------------- ------- -
MF              BROKE_NO_DEPENDENCIES          TABLE               VALID   N
MF              BROKE_YES_DEPENDENCIES         TABLE               VALID   N

SQL> set echo on
SQL> @save_u
Enter value for owner_name: mf
Enter value for table_name: BROKE_NO_DEPENDENCIES

WARNING: This script may issue a DROP TABLE command. Do not execute it unless you have read through it
and are comfortable you know what it does.

Ready? (hit ctl-C to quit)  
Enter value for owner_name: mf
Enter value for table_name: BROKE_NO_DEPENDENCIES    


Saved 18800 records in BROKE_NO_DEPENDENCIES_SAVED.
35 bad records in BROKE_NO_DEPENDENCIES_BAD.

PL/SQL procedure successfully completed.

SQL> set echo on
SQL> @create_old_rowid
SQL> create or replace function old_rowid (p_rowid rowid)
  2  return varchar as
  3  
  4    rowid_type NUMBER;
  5    object_id NUMBER;
  6    fileno NUMBER;
  7    blockno   NUMBER;
  8    rowno  NUMBER;
  9  
 10  BEGIN
 11  
 12     dbms_rowid.rowid_info(p_rowid, rowid_type, object_id, fileno, blockno, rowno);
 13  /*
 14     dbms_output.put_line('Row Typ-' || TO_CHAR(rowid_type));
 15     dbms_output.put_line('Obj No-' || TO_CHAR(object_id));
 16     dbms_output.put_line('RFNO-' || TO_CHAR(fileno));
 17     dbms_output.put_line('Block No-' || TO_CHAR(blockno));
 18     dbms_output.put_line('Row No-' || TO_CHAR(rowno));
 19  */
 20  return(to_char(fileno)||'.'||to_char(blockno)||'.'||to_char(rowno));
 21  
 22  END;
 23  /

Function created.

SQL> @create_new_rowid
SQL> create or replace function new_rowid (p_object_id number, p_old_rowid varchar)
  2  return varchar as
  3  
  4    new_rowid varchar2(30);
  5    fileno NUMBER;
  6    blockno   NUMBER;
  7    rowno  NUMBER;
  8  
  9  BEGIN
 10  
 11    fileno := substr(p_old_rowid,1,instr(p_old_rowid,'.')-1);
 12    blockno := substr(p_old_rowid,instr(p_old_rowid,'.')+1,instr(p_old_rowid,'.',1,2)-instr(p_old_rowid,'.'));
 13    rowno := substr(p_old_rowid,instr(p_old_rowid,'.',1,2)+1,100);
 14    new_rowid := DBMS_ROWID.ROWID_CREATE(1, p_object_id, fileno , blockno , rowno);
 15  
 16    return(new_rowid);
 17  
 18  END;
 19  /

Function created.

SQL> select rowid,old_rowid(rowid) old_rowid from mf.BROKE_NO_DEPENDENCIES where rownum < 5
SQL> /

ROWID              OLD_ROWID
------------------ ------------------------------
AAACYmAAEAAAAAMAAt 4.12.45
AAACYmAAEAAAACIAAt 4.136.45
AAACYmAAEAAAAAMAAF 4.12.5
AAACYmAAEAAAACIAAF 4.136.5

One other thing to keep in mind, as Toon mentioned, it may be possible to retrieve data from an index, even if the underlying data block is messed up. Selecting only the values of the indexed columns allows Oracle to completely bypass the data blocks. So, if for example, you found a set of blocks were inaccessible, you may be able to construct statements that would retrieve at least some of the data from the indexes like so:

select /*+ index (messed_up_object  mess_up_object_pk) */ indexed_column1, indexed_column2 
from messed_up_object 
where rowid in (select dbms_rowid.rowid_create(1,object,file,block,row) from bad_rows_table);

Your comments are always welcomed!

One of my favorite holiday treats was my MeeMaw’s fudge brownies. Note: I did a brief poll (only 5 people so not statistically significant) but nevertheless, 100% of the people I surveyed had a grandparent that they called either MeeMaw or PopPa. And 40% had both a MeeMaw and a PopPa. Of course all 5 of the pollees were native Texans. Anyway, here’s what my MeeMaw’s fudge brownies looked like.

Oracle has a long history of baking fudge as well.

So here’s a little Oracle Fudge for you!

11gR1 has 4 parameters with the word fudge in them.

_nested_loop_fudge
_parallelism_cost_fudge_factor
_px_broadcast_fudge_factor
_query_rewrite_fudge

These four “fudge” parameters have been around with the same default values since at least 8.1.7. Maybe the elves will fix these in version 12.

And in keeping with the holiday theme, I’m reminded of the song “My Favorite Things” (often sung at Christmas) that goes:

“blah, blah, blah, blah, blah, blah,
These are a few of my favorite things”
(think Julie Andrews in Sound of Music)

Anyway, here’s a few of my favorite parameters (and my interpretation of what they mean):

db_cache_advice – If you turn this one on, Oracle will tell you what to do with your money.
db_cache_size – And this one will tell you how much money you have.
db_ultra_safe – Oddly enough, this one defaults to OFF. Seems like you’d want your database to be “Ultra Safe”.
ifile – Looks like someone from Apple slipped this one in (you know – iPod, iPhone, iMac, etc…).
large_pool_size – Just how big is your pool?
skip_unusable_indexes – Defaults to TRUE. I guess if you want, you can tell Oracle to use those unusable indexes.
_addm_skiprules – Yeah, rules suck!
_ash_size – Do these pants make my butt look big?
_backup_max_gap_size – How big does the doorway have to be to get your butt through it?
_asm_disk_repair_time – Uh oh, time to repair those disks.
_avoid_prepare – Why get ready ahead of time.
_awr_disabled_flush_tables – Not sure but it sounds stinky.
_awr_sql_child_limit – Population control?
_bloom_pruning_enabled – Trim the roses!
_bwr_for_flushed_pi – Not sure what this one does, but flushing pie seems like such a waste.
_cvw_enable_weak_checking – I’d prefer strong checking please! Get that weak stuff out of here!
_db_aging_cool_count – I used to be cool, I think.
_db_aging_hot_criteria – ???
_db_block_bad_write_check – I hope our database is not writing bad checks!
_db_block_check_for_debug – I think a developer from Chicago named this one (and it should be: _db_block_check_for_the_bug).
_db_cache_crx_check – I don’t know what a crx check is, but cashing any kind of check should be good, right?
_db_large_dirty_queue – Just like in the laundry room at home.
_db_row_overlap_checking – Do your rows overlap? Perhaps we should check that.
_disable_fast_aggregation – Why would anyone use this, “No thanks, I want really slow aggregation”
_dtree_pruning_enabled – Trim D-Tree too while yer at it!
_extended_pruning_enabled – Cut ’em way back!
_disable_recoverable_recovery – Hmmmm???? I guess if you don’t want your recovery to be recoverable you can set this one.
_dummy_instance – I’ve thought this many times (it’s basically the same as the _stupid_database parameter).
_dispatcher_rate_scale – How much are we paying that dispatcher anyway?
_fairness_threshold – My queries should always run faster than everyone else’s, I think that’s fair.
_flashback_fuzzy_barrier – Fuzzy Wuzzy was a bear, Fuzzy Wuzzy had no hair, Fuzzy Wuzzy wasn’t very fuzzy was he.
_gc_defer_time – I’ve wanted to do this many times in the past.
_ges_dd_debug – Sounds like a speech impediment, b,b,but maybe not.
_hard_protection – Well if it was easy, everyone would be doing it.
_imr_avoid_double_voting – They needed this in Florida during the 2004 election.
_in_memory_undo – Same as the _forget parameter.
_kdli_STOP_dba – Keep the DBA from messing up the system.
_kdli_delay_flushes – Don’t flush until a specified threshold is reached.
_kdlwp_flush_threshold – The amount of poo that triggers flushing (see _kdli_delay_flushes).
_kdli_memory_protect – Same as the _dont_forget parameter (i.e. the opposite of the _forget parameter).
_kdli_squeeze – One of my favorite bands.
_kebm_nstrikes – The number of strikes before you are out (defaults to 3 – no joke).
_kebm_suspension_time – How long before convicted felons can return to playing football.
_kill_enqueue_blocker – What the defensive linemen try to do on every play.
_kill_java_threads_on_eoc – I always turn this one on, because anything that kills java threads is OK in my book.
_kfm_disable_set_fence – Good fences make good neighbors.
_kse_signature_limit – The cash advance limit on your credit card.
_kse_snap_ring_size
_ksi_clientlocks_enabled – Can be used on clients when they won’t follow your advice.
_lm_better_ddvictim – Not sure what this one does, but it has the word victim in it, scary!
_lm_master_weight – Set this to get control of your diet.
_lm_tx_delta – Ah the Texas delta, I think it’s some where near Galveston.
_max_exponential_sleep – The older I get, the longer the naps.
_memory_sanity_check – Do I seem crazy to you?
_mv_generalized_oj_refresh_opt – In general, orange juice is refreshing!
_olapi_memory_operation_history_retention – Same as the _dont_forget parameter (see _kdli_memory_protect).
_olap_wrap_errors – When you cut the wrapping paper too short and it won’t go around the present and you have to cut a little strip to cover the gap.
_optimizer_ignore_hints – No matter what you hear in there, no matter how cruelly I beg you, no matter how terribly I may scream, don’t open that door.
_optimizer_random_plan – This one is self evident and has defaulted to TRUE since the CBO first came out.
_optimizer_squ_bottomup – Bottoms Up!
_parallel_fake_class_pct – “The higher you hold your pinky, the fancier you are.” – Patrick from Sponge Bob.
_parallel_syspls_obey_force – The parameter that allows you to get the sysadmins (syspls) to do what you tell them.
_pct_refresh_double_count_prevented – This would have been useful in Florida during the 2004 election.
_pdml_gim_staggered – Gim must have had too much to drink.
_pred_move_around – If your dad was in the air force and you moved from base to base while growing up, setting this parameter will make you feel right at home.
_px_no_stealing – This parameter is set to TRUE by default and it’s actually against the law to change it.
_px_nss_planb – Use this parameter if plan A doesn’t work.
_shrunk_aggs_enabled – I don’t like shrunk aggs, I like the big-uns, wif bacun!
_spin_count – Have you ever seen fans at a baseball game put their heads on the end of a bat and turn circles and then run? You get the idea.
_two_pass_reverse_polish_enabled – I don’t think this one is politically correct.
_use_best_fit – One size fits all does not fit all.
_write_clones – Send a letter to your siblings.
_ultrafast_latch_statistics – Anything that is ultrafast has got to be fantastic!
_xsolapi_densify_cubes – Densify??? I think they made that word up.
_xsolapi_optimize_suppression – Allows us to to keep the common man down as effectively as possible.
_xsolapi_stringify_order_levels – Stringify??? I think they made that word up too!
_xsolapi_use_models – It will make our advertising look better!

Here a few that allow you to turn on (or off) special checks:

_disable_acid_check – My acid is just fine thanks.
_disable_cpu_check – Yep, this machine has got at least one.
_disable_health_check – I don’t need no stinking annual checkup!
_disable_image_check – And I really don’t care how I look!

Here’s a few parameters dealing with death:

_px_execution_services_enabled – Apparently we can set up a special service for executions.
_xsolapi_share_executors – And if you have a lot of killing to do, it’s wasteful to not share executors.
_imr_splitbrain_res_wait – Sounds like it would hurt (if not kill).
_ksv_spawn_control_all – Creates zombies.
_cgs_zombie_member_kill_wait – Specifies how long to wait before slaying zombies!
_imr_evicted_member_kill – Boy I hope I don’t get evicted!
_ksu_diag_kill_time – Killing Time!
_ksuitm_dont_kill_dumper – The rumor is that this one was named by an animal rights activist and it was supposed to be “Don’t Kill Thumper”.
_lm_rcvr_hang_kill – Death by hanging!
_ksv_pool_hang_kill_to – If hanging doesn’t work, drown them in the pool!
_hang_detection – Turn this on so we’ll know when anybody is getting hung.

And just in case the 2000+ parameters in 11g aren’t enough, they have a few spares:

_first_spare_parameter
_second_spare_parameter
_third_spare_parameter
_fourth_spare_parameter
_fifth_spare_parameter
_sixth_spare_parameter
_seventh_spare_parameter

 Anyway, that’s it for now. I hope you have a happy holiday and a …