Glitches

Here’s a question just in from Oracle-L that demonstrates the pain of assuming things work consistently when sometimes Oracle development hasn’t quite finished a bug fix or enhancement. Here’s the problem – which starts from the “scott.emp” table (which I’m not going to create in the code below):

rem
rem     Script:         fbi_fetch_first_bug.sql
rem     Author:         Jonathan Lewis
rem     Dated:          June 2019
rem 

-- create and populate EMP table from SCOTT demo schema

create index e_sort1 on emp (job, hiredate);
create index e_low_sort1 on emp (lower(job), hiredate);

set serveroutput off
alter session set statistics_level = all;
set linesize 156
set pagesize 60

select * from emp where job='CLERK'         order by hiredate fetch first 2 rows only; 
select * from table(dbms_xplan.display_cursor(null,null,'cost allstats last outline alias'));

select * from emp where lower(job)='clerk' order by hiredate fetch first 2 rows only; 
select * from table(dbms_xplan.display_cursor(null,null,'cost allstats last outline alias'));

Both queries use the 12c “fetch first” feature to select two rows from the table. We have an index on (job, hiredate) and a similar index on (lower(job), hiredate), and given the similarity of the queries and the respective indexes (get the first two rows by hiredate where job/lower(job) is ‘CLERK’/’clerk’) we might expect to see the same execution plan in both cases with the only change being the choice of index used. But here are the plans:

select * from emp where job='CLERK'         order by hiredate fetch
first 2 rows only

Plan hash value: 92281638

----------------------------------------------------------------------------------------------------------------
| Id  | Operation                     | Name    | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers |
----------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT              |         |      1 |        |     2 (100)|      2 |00:00:00.01 |       4 |
|*  1 |  VIEW                         |         |      1 |      2 |     2   (0)|      2 |00:00:00.01 |       4 |
|*  2 |   WINDOW NOSORT STOPKEY       |         |      1 |      3 |     2   (0)|      2 |00:00:00.01 |       4 |
|   3 |    TABLE ACCESS BY INDEX ROWID| EMP     |      1 |      3 |     2   (0)|      3 |00:00:00.01 |       4 |
|*  4 |     INDEX RANGE SCAN          | E_SORT1 |      1 |      3 |     1   (0)|      3 |00:00:00.01 |       2 |
----------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter("from$_subquery$_002"."rowlimit_$$_rownumber"<=2)
   2 - filter(ROW_NUMBER() OVER ( ORDER BY "EMP"."HIREDATE")<=2)
   4 - access("JOB"='CLERK')


select * from emp where lower(job)='clerk' order by hiredate fetch
first 2 rows only

Plan hash value: 4254915479

-------------------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                             | Name        | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
-------------------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                      |             |      1 |        |     1 (100)|      2 |00:00:00.01 |       2 |       |       |          |
|*  1 |  VIEW                                 |             |      1 |      2 |     1   (0)|      2 |00:00:00.01 |       2 |       |       |          |
|*  2 |   WINDOW SORT PUSHED RANK             |             |      1 |      1 |     1   (0)|      2 |00:00:00.01 |       2 |  2048 |  2048 | 2048  (0)|
|   3 |    TABLE ACCESS BY INDEX ROWID BATCHED| EMP         |      1 |      1 |     1   (0)|      4 |00:00:00.01 |       2 |       |       |          |
|*  4 |     INDEX RANGE SCAN                  | E_LOW_SORT1 |      1 |      1 |     1   (0)|      4 |00:00:00.01 |       1 |       |       |          |
-------------------------------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter("from$_subquery$_002"."rowlimit_$$_rownumber"<=2)
   2 - filter(ROW_NUMBER() OVER ( ORDER BY "EMP"."HIREDATE")<=2)
   4 - access("EMP"."SYS_NC00009$"='clerk')

As you can see, with the “normal” index Oracle is able to walk the index “knowing” that the data is appearing in order, and stopping as soon as possible (almost) – reporting the WINDOW operation as “WINDOW NOSORT STOPKEY”. On the other hand with the function-based index Oracle retrieves all the data by index, sorts it, then applies the ranking requirement – reporting the WINDOW operation as “WINDOW SORT PUSHED RANK”.

Clearly it’s not going to make a lot of difference to performance in this tiny case, but there is a threat that the whole data set for ‘clerk’ will be accessed – and that’s the first performance threat, with the additional threat that the optimizer might decide that a full tablescan would be more efficient than the index range scan.

Can we fix it ?

Yes, Bob, we can. The problem harks back to a limitation that probably got fixed some time between 10g and 11g – here are two, simpler, queries against the emp table and the two new indexes, each with the resulting execution plan when run under Oracle 10.2.0.5:

select ename from emp where       job  = 'CLERK' order by hiredate;
select ename from emp where lower(job) = 'clerk' order by hiredate;

---------------------------------------------------------------------------------------
| Id  | Operation                   | Name    | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT            |         |     3 |    66 |     2   (0)| 00:00:01 |
|   1 |  TABLE ACCESS BY INDEX ROWID| EMP     |     3 |    66 |     2   (0)| 00:00:01 |
|*  2 |   INDEX RANGE SCAN          | E_SORT1 |     3 |       |     1   (0)| 00:00:01 |
---------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - access("JOB"='CLERK')


--------------------------------------------------------------------------------------------
| Id  | Operation                    | Name        | Rows  | Bytes | Cost (%CPU)| Time     |
--------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |             |     3 |    66 |     3  (34)| 00:00:01 |
|   1 |  SORT ORDER BY               |             |     3 |    66 |     3  (34)| 00:00:01 |
|   2 |   TABLE ACCESS BY INDEX ROWID| EMP         |     3 |    66 |     2   (0)| 00:00:01 |
|*  3 |    INDEX RANGE SCAN          | E_LOW_SORT1 |     3 |       |     1   (0)| 00:00:01 |
--------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access(LOWER("JOB")='clerk')

The redundant SORT ORDER BY is present in 10g even for a simple index range scan. By 11.2.0.4 the optimizer was able to get rid of the redundant step, but clearly there’s a little gap in the code relating to the over() clause that hasn’t acquired the correction – even in 18.3.0.0 (or 19.2 according to a test on https://livesql.oracle.com).

To fix the 10g problem you just had to include the first column of the index in the order by clause: the result doesn’t change, of course, because you’re simply prefixing the required columns with a column which holds the single value you were probing the index for but suddenly the optimizer realises that it can do a NOSORT operation – so the “obvious” guess was to do the same for this “first fetch” example:

select * from emp where lower(job)='clerk' order by lower(job), hiredate fetch first 2 rows only;

--------------------------------------------------------------------------------------------------------------------
| Id  | Operation                     | Name        | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers |
--------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT              |             |      1 |        |     3 (100)|      2 |00:00:00.01 |       4 |
|*  1 |  VIEW                         |             |      1 |      2 |     3  (34)|      2 |00:00:00.01 |       4 |
|*  2 |   WINDOW NOSORT STOPKEY       |             |      1 |      1 |     3  (34)|      2 |00:00:00.01 |       4 |
|   3 |    TABLE ACCESS BY INDEX ROWID| EMP         |      1 |      1 |     2   (0)|      3 |00:00:00.01 |       4 |
|*  4 |     INDEX RANGE SCAN          | E_LOW_SORT1 |      1 |      1 |     1   (0)|      3 |00:00:00.01 |       2 |
--------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter("from$_subquery$_002"."rowlimit_$$_rownumber"<=2)
   2 - filter(ROW_NUMBER() OVER ( ORDER BY "EMP"."SYS_NC00009$","EMP"."HIREDATE")<=2)
   4 - access("EMP"."SYS_NC00009$"='clerk')

It’s just one of those silly little details where you can waste a HUGE amount of time (in a complex case) because it never crossed your mind that something that clearly ought to work might need testing for a specific use case – and I’ve lost count of the number of times I’ve been caught out by this type of “not quite finished” anomaly.

Footnote

If you follow the URL to the Oracle-L thread you’ll see that Tanel Poder has supplied a couple of MoS Document Ids discussing the issue and warning of other bugs with virtual column / FBI translation, and has shown an alternative workaround that takes advantage of a hidden parameter.

 

Timestamp Oddity

[Editorial note: this is something I started writing in 2013, managed to complete in 2017, and still failed to publish. It should have been a follow-on to another posting on the oddities of timestamp manipulation.]

Just as national language support used to be, timestamps and time-related columns are still a bit of a puzzle to the Oracle world – so much so that OEM could cripple a system if it was allowed to do the check for “failed logins over the last 30 minutes”. And, just like NLS, it’s one of those things that you use so rarely that you keep forgetting what went wrong the last time you used it. Here’s one little oddity that I reminded myself about recently:

rem
rem     Script:         timestamp_anomaly.sql
rem     Author:         Jonathan Lewis
rem     Dated:          April 2013
rem
create table t1 (
        ts_tz   timestamp(9) with time zone,
        ts_ltz  timestamp(9) with local time zone
);

insert into t1 values(systimestamp, systimestamp);
commit;

alter table t1 add constraint ts_ltz_uk unique (ts_ltz);
alter table t1 add constraint ts_tz_uk  unique (ts_tz);

Nothing terribly difficult – just a table with two variants on the timestamp data type and a unique constraint on both: except for one problem. Watch what happens as I create the unique constraints:

SQL> alter table t1 add constraint ts_ltz_uk unique (ts_ltz);

Table altered.

SQL> alter table t1 add constraint ts_tz_uk  unique (ts_tz);
alter table t1 add constraint ts_tz_uk  unique (ts_tz)
                                        *
ERROR at line 1:
ORA-02329: column of datatype TIME/TIMESTAMP WITH TIME ZONE cannot be unique or a primary key

Slightly unexpected – unless you’ve memorized the manuals, of course, which I hadn’t. I wonder if you can create a unique index on timestamp with time zone:

SQL> create unique index ts_tz_uk on t1(ts_tz);

Index created.

You can’t have a unique constraint, but you CAN create a unique index! How curious – did that really happen ?

SQL> select index_name, column_name from user_ind_columns where table_name = 'T1';

INDEX_NAME           COLUMN_NAME
-------------------- --------------------
TS_LTZ_UK            TS_LTZ
TS_TZ_UK             SYS_NC00003$

The index is on a column called SYS_NC00003$ – which looks suspiciously like one of those “function-based-index” things:

SQL> select * from user_ind_expressions where table_name = 'T1';

INDEX_NAME           TABLE_NAME           COLUMN_EXPRESSION                        COLUMN_POSITION
-------------------- -------------------- ---------------------------------------- ---------------
TS_TZ_UK             T1                   SYS_EXTRACT_UTC("TS_TZ")                               1

Oracle has silently invoked the sys_extract_utc() function on our (free-floating) timestamp column to normalize it to UTC. This is really not very friendly but it does make sense, of course – it would be rather expensive to enforce uniqueness if there were (at least) 24 different ways of storing the same absolute value – and 24 is a conservative estimate.

 

 

Descending bug

Following on from Monday’s posting about reading execution plans and related information, I noticed a question on the ODC database forum asking about the difference between “in ({list of values})” and a list of “column = {constant}” predicates connected by OR. The answer to the question is that there’s essentially no difference as you would be able to see from the Predicate Information of an execution plan:

SELECT  c1, c2, c3, c4, c5, c6, c7, c8..  
FROM    TAB1  
WHERE   STS IN ( 'A', 'B')  
AND     cnt < '4'  
AND     dt < sysdate  
and     rownum <=1;  
  
---------------------------------------------------------------------------------------------------------  
| Id  | Operation                     | Name                    | Rows  | Bytes | Cost (%CPU)| Time     |  
---------------------------------------------------------------------------------------------------------  
|   0 | SELECT STATEMENT              |                         |     1 |  1847 | 33399   (1)| 00:03:14 |  
|*  1 |  COUNT STOPKEY                |                         |       |       |            |          |  
|   2 |   INLIST ITERATOR             |                         |       |       |            |          |  
|*  3 |    TABLE ACCESS BY INDEX ROWID| TAB1                    |   114K|   201M| 33399   (1)| 00:03:14 |  
|*  4 |     INDEX RANGE SCAN          | TAB1_STS_IDX            |   114K|       |   723   (1)| 00:00:05 |  
---------------------------------------------------------------------------------------------------------  
  
Predicate Information (identified by operation id):  
---------------------------------------------------  
   1 - filter(ROWNUM<=1)  
   3 - filter("cnt"<'4' AND "dt"<SYSDATE@!)  
   4 - access("STS"='A' OR "STS"='B')  

Note how the predicate section tells you that the original “sts in ( ‘A’, ‘B’ )” has been transformed into “sts = ‘A’ or sts = ‘B'”.

A further point I made about IN-lists in Monday’s post was that as one step in the transformation Oracle would start by sorting the list and eliminating duplicates. It suddenly occurred to me to wonder whether Oracle would sort the list in descending order if the only relevant index were defined to start with a descending column. Naturally I had to test the idea, so here’s a suitable script to prepare some data:

rem
rem     Script:         descending_bug_06.sql
rem     Author:         Jonathan Lewis
rem     Dated:          Aug 2018
rem     Purpose:
rem
rem     Last tested
rem             18.3.0.0        Crashes
rem             12.2.0.1        Crashes
rem             12.1.0.2        Crashes
rem             11.2.0.4        Bad Plan
rem

create table t1
nologging
pctfree 95 pctused 5
as
with generator as (
        select 
                rownum id
        from dual 
        connect by 
                level <= 1e4 -- > comment to avoid WordPress format issue
)
select
        rownum                          id,
        lpad(rownum,10,'0')             v1,
        case mod(rownum,1000)
                when 0 then 'A'
                when 3 then 'B'
                when 6 then 'C'
                       else 'D'
        end                             sts,
        case mod(rownum,1000)
                when 0 then '1'
                when 3 then '2'
                when 6 then '3'
                       else '4'
        end                             cnt,
        lpad('x',100,'x')               padding
from
        generator       v1,
        generator       v2
where
        rownum <= 1e5 -- > comment to avoid WordPress format issue
;

create index t1_i1a on t1(sts) nologging;
create index t1_i1d on t1(sts desc) nologging;

begin
        dbms_stats.gather_table_stats(
                ownname     => null,
                tabname     => 'T1',
                method_opt  => 'for all columns size skewonly'
        );
end;
/

There is one oddity in this script – if you’ve declared every column in an index as DESC you’ve made a mistake and none of the columns should have been declared as DESC. The feature is relevant only if you want a mixture of ascending and descending columns in a single index. By making every column descending the only thing you’ve achieved is to make the index one byte per column larger for every row in the index – while introducing the opportunity of hitting various limitations and bugs.

An important detail of the script is that I’ve gathered stats after creating the objects. It’s important to do this for my demo, even in 19.3, because (a) creating the “descending” index will result in a hidden virtual column being created to represent each descending column and I want to make sure I have stats on the virtual column in my example and (b) the “stats on creation” code doesn’t generate histograms and I want a (frequency) histogram on columns sts and the hidden, virtual, descending version of the column.

After generating the data and checking that I have the correct histograms for sts and sys_nc00006$ (the relevant hidden column) I can then run the following test:

set serveroutput off
alter session set statistics_level = all;

alter index t1_i1d invisible;

select  sts, count(*)
from    t1
where   sts in ('B','C')
group by
        sts
;

select * from table(dbms_xplan.display_cursor(null,null,'allstats last'));

alter index t1_i1d   visible;
alter index t1_i1a invisible;

select  sts, count(*)
from    t1
where   sts in ('B','C')
group by
        sts
;

select * from table(dbms_xplan.display_cursor(null,null,'allstats last'));

alter session set statistics_level = typical;
set serveroutput on

The code makes one index invisible then runs a query that should use an inlist iterator; then it switches indexes making the invisible one visible and vice versa and repeats the query. I’ve enabled rowsource execution statistics and pulled the execution plans from memory to make sure I don’t get fooled by any odd glitches that might exist within “explain plan”. Here are the results from 11.2.0.4 – normal index, then descending index – with a little cosmetic cleaning:

S   COUNT(*)
- ----------
B        100
C        100

SQL_ID  f20u42pmw1z6w, child number 0
-------------------------------------
select sts, count(*) from t1 where sts in ('B','C') group by  sts

-----------------------------------------------------------------------------------------
| Id  | Operation            | Name   | Starts | E-Rows | A-Rows |   A-Time   | Buffers |
-----------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT     |        |      1 |        |      2 |00:00:00.01 |       5 |
|   1 |  SORT GROUP BY NOSORT|        |      1 |      2 |      2 |00:00:00.01 |       5 |
|   2 |   INLIST ITERATOR    |        |      1 |        |    200 |00:00:00.01 |       5 |
|*  3 |    INDEX RANGE SCAN  | T1_I1A |      2 |    178 |    200 |00:00:00.01 |       5 |
-----------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access(("STS"='B' OR "STS"='C'))


Index altered.
Index altered.


S   COUNT(*)
- ----------
C        100
B        100


SQL_ID  f20u42pmw1z6w, child number 0
-------------------------------------
select sts, count(*) from t1 where sts in ('B','C') group by  sts

-----------------------------------------------------------------------------------------
| Id  | Operation            | Name   | Starts | E-Rows | A-Rows |   A-Time   | Buffers |
-----------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT     |        |      1 |        |      2 |00:00:00.02 |     198 |
|   1 |  SORT GROUP BY NOSORT|        |      1 |      2 |      2 |00:00:00.02 |     198 |
|*  2 |   INDEX FULL SCAN    | T1_I1D |      1 |   1000 |    200 |00:00:00.02 |     198 |
-----------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter((SYS_OP_UNDESCEND("T1"."SYS_NC00006$")='B' OR
              SYS_OP_UNDESCEND("T1"."SYS_NC00006$")='C'))

As expected we see counts of 100 for ‘B’s and ‘C’s, and we also see that the “sort group by nosort” operation with the descending index has produced the results in descending order. The problem though is that the optimizer has decided to use an “index full scan” on the descending index and the estimate of the rows returned is terribly wrong (and seems to be the common “5% guess” used once for each target value), and the number of buffer visits is huge compared to the result from the normal index – Oracle really did walk every leaf block in the index to get this result. The predicate section also looks rather silly – why hasn’t the optimizer produced predicates more like: “sys_nc00006$ = sys_op_descend(‘B’)” ?

In passing you’ll notice that the estimated rows in the plan using the normal index is a little low. This is the result of Oracle using a small sample (ca. 5,500 rows) in 11g to gather histogram stats. 12c will do better for a frequency histogram with the fast (“approximate_ndv”) algorithm it uses for a 100% (auto) sample size.

So 11g doesn’t do very well but we’ve got 12.1.0.2, 12.2.0.1, and (in the last couple of weeks) 18.3 to play with. Here’s the result from 12.1.0.2 and 12.2.0.1 for the query that should use the descending index:

select  sts, count(*)
*
ERROR at line 1:
ORA-00600: internal error code, arguments: [qernsRowP], [1], [], [], [], [], [], [], [], [], [], []


SQL_ID  f20u42pmw1z6w, child number 0
-------------------------------------
select sts, count(*) from t1 where sts in ('B','C') group by  sts

-----------------------------------------------------------------------------------------
| Id  | Operation            | Name   | Starts | E-Rows | A-Rows |   A-Time   | Buffers |
-----------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT     |        |      1 |        |      0 |00:00:00.01 |       0 |
|   1 |  SORT GROUP BY NOSORT|        |      1 |      2 |      0 |00:00:00.01 |       0 |
|   2 |   INLIST ITERATOR    |        |      1 |        |    101 |00:00:00.03 |       5 |
|*  3 |    INDEX RANGE SCAN  | T1_I1D |      2 |    200 |    101 |00:00:00.01 |       5 |
-----------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access(("T1"."SYS_NC00006$"=SYS_OP_DESCEND('B') OR
              "T1"."SYS_NC00006$"=SYS_OP_DESCEND('C')))
       filter((SYS_OP_UNDESCEND("T1"."SYS_NC00006$")='B' OR
              SYS_OP_UNDESCEND("T1"."SYS_NC00006$")='C'))

The query crashed! The plan, however, did look appropriate – the optimizer picked an inlist iterator, picked an index range scan, got the correct estimate of rows (index entries), and did better with the predicate section (though having used a sensible predicate for the access predciate it then used the bizarre version as the filter predicate). Judging from the A-rows column the query seems to have crashed at roughly the point where the optimizer was switching from the range scan for the first iteration into the range scan for the second iteration.

And then there’s Oracle 18.3 – which does the same as the 12c versions :(

To make sure that my silly “single column so it shouldn’t be declared descending” index was the sole cause of the problem I repeated the tests using a two-column index on (sts, cnt).

Conclusion:

Descending indexes or (to be more accurate) indexes with descending columns can still produce problems even in the very latest version of Oracle.

Footnote

Oracle MoS has the wonderful “ORA-600/ORA-7445/ORA-700 Error Look-up Tool (Doc ID 153788.1)” (which doesn’t yet allow you to choose 18.3 as a version) so I used this to do a look up for ORA-00600 errors with first paremeter qernsRowP in 12.2.0.1 and got the following suggestion from doc ID 285913.1: “set event:10119 to disable no-sort fetch and then reparse the failing SQL.” The example suggested setting the event to level 12, and this solved the problem for all three failing versions – but the suggestion came with a warning: “Setting this event at system level may impact the performance of database.” The execution plan (taken, in this case, from 18.2) may explain the warning:

 

S   COUNT(*)
- ----------
B	 100
C	 100

SQL_ID	f20u42pmw1z6w, child number 0
-------------------------------------
select sts, count(*) from t1 where sts in ('B','C') group by  sts

------------------------------------------------------------------------------------------------------------------
| Id  | Operation          | Name   | Starts | E-Rows | A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |	    |	   1 |	      |      2 |00:00:00.01 |	    4 |       |       | 	 |
|   1 |  HASH GROUP BY	   |	    |	   1 |	    2 |      2 |00:00:00.01 |	    4 |  1558K|  1558K|  659K (0)|
|   2 |   INLIST ITERATOR  |	    |	   1 |	      |    200 |00:00:00.01 |	    4 |       |       | 	 |
|*  3 |    INDEX RANGE SCAN| T1_I1D |	   2 |	  200 |    200 |00:00:00.01 |	    4 |       |       | 	 |
------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access(("T1"."SYS_NC00006$"=SYS_OP_DESCEND('B') OR "T1"."SYS_NC00006$"=SYS_OP_DESCEND('C')))
       filter((SYS_OP_UNDESCEND("T1"."SYS_NC00006$")='B' OR SYS_OP_UNDESCEND("T1"."SYS_NC00006$")='C'))

The plan has changed from using a “sort group by nosort” – which effectively means just keeping a running count as you go – to a real “hash group by” which means you have to do the hashing arithmetic for every value (though maybe there’s a deterministic trick that means Oracle won’t do the arithmetic if the next value to be hashed is the same as the previous value) and the actual memory used (659K) does seem a little extreme for counting two distinct values.

Update Aug 2019

The bug is still present in 19.3

 

 

Upgrades

One of my maxims for Oracle performance is: “Don’t try to be too clever”. Apart from the obvious reason that no-one else may be able to understand how to modify your code if the requirements change at a future date, there’s always the possibility that an Oracle upgrade will mean some clever trick you implemented will simply stop working.

While searching for information about a possible Oracle bug recently I noticed the following fix control (v$system_fix_control) in 12.2.0.1:

     BUGNO OPTIMIZE SQL_FEATURE                        DESCRIPTION                                                             VALUE
---------- -------- ---------------------------------- ---------------------------------------------------------------- ------------
  18385778          QKSFM_CARDINALITY_18385778         avoid virtual col usage if FI is unusable or invisible 

Maybe that’s just invalidated an idea I published 12 years ago.

I haven’t researched the bug or any underlying SR, but I can think of valid argument both for and against the fix as described.

 

 

FBIs don’t exist

This is a reprint (of a reprint) of a note I wrote more than 11 years ago on my old website. I’ve decided to republish it on the blog simply because one day I’ll probably decide to stop paying for the website given how old all the material is and this article makes an important point about the need (at least some of the time) for accuracy in the words you use to describe things.

—————————————————————————-

There’s no such thing as a function-based index.

Well, okay, that’s what the manuals call them but it would be so much better if they were called “indexes with virtual columns” – because that’s what they are and that’s a name that would eliminate confusion.

To demonstrate what I mean, ask yourself this question: “Can the rule based optimizer use a function-based index ?”. The answer is ‘Yes’, as the following code fragment demonstrates:

rem
rem     Script:         fbi_rule.sql
rem     Author:         Jonathan Lewis
rem     Dated:          Jan 2005
rem

create table t1 as
select
         rownum                         id,
         dbms_random.value(0,500)       n1,
         rpad('x',10)                   small_vc,
         rpad('x',100)                  padding
from
         all_objects
where
         rownum <= 3000
;
 
create index t1_i1 on t1(id, trunc(n1));
 
set autotrace traceonly explain
 
select
         /*+ rule */
         small_vc
from
         t1
where    id = 55
and      trunc(n1) between 1 and 10
;


set autotrace off
 
Execution Plan
----------------------------------------------------------
0      SELECT STATEMENT Optimizer=HINT: RULE
1   0    TABLE ACCESS (BY INDEX ROWID) OF 'T1'
2   1      INDEX (RANGE SCAN) OF 'T1_I1' (NON-UNIQUE)

Last time I asked an audience if the rule-based optimizer (RBO) could use a function-based index, most of them thought the answer was ‘No’. Even the Oracle manuals make the same mistake – for example in the 10g Release 2 Application Developers Guide p5-8, one of the restrictions on function-based indexes is “Only cost based optimization can use function-based indexes”.

If I had asked the audience “Can the rule-based optimizer use an index which includes a virtual column ?” I wonder how many of them would have paused for thought, then asked themselves what would happen if the index started with “ordinary” columns and the “function-based” bit was later on in the index.

The manuals should, of course, state: “The rule-based optimizer cannot take advantage of any virtual columns in an index, or of any columns that follow the first virtual column”. Given a correct name and a correct description of functionality you can then conclude that if the first column is a virtual column the rule-based optimizer won’t use the index.

I’m not suggesting, by the way, that you should be using the rule-based optimizer, or even that this specific example of functionality is going to be particularly beneficial to many people (RBO still uses the “trunc(n1)” as a filter predicate after reaching the table rather than as an access predicate – or even filter predicate – on the index); but it does demonstrate how easy it is for the wrong name, or terminology, to distract people from the truth.

And here’s another thought for Oracle Corporation. Since it seems to be easy to implement virtual columns (there is a hidden entry for each such column in the data dictionary, and the text of the function defining the column appears as the default value), why should they exist only in indexes? Why can’t we have virtual columns which aren’t indexed, so that we can collect statistics on a virtual column and give the optimizer some information about the data distribution of some commonly used expression that we don’t actually want to build an index on.

(Update Jan 2007 – this is likely to happen in 11g according to ‘sneak preview’ presentations made by Oracle at OW2006.

P.S. There really are function-based indexes in Oracle. But Oracle Corp. calls them domain indexes (or co-operative indexes) and tells you that the things you build them with are operators, not functions … which actually makes them operator-based indexes!

—————————————————————————-

 Footnote (May 2018)

I’ve updated the reference to the 10g manuals (chapter 5 page 8) to include a URL, but the URL is for 11gR2 since the only 10g manual I could find online was the full pdf download.  It’s  interesting to note what restrictions on the use of “function-based” indexes are reported in this manual, and I’m not sure that all of them were true at the time, and I’m fairly sure that some of them must be false by now, which is why it’s always good to have test scripts that you can run as you upgrade.

There is an interesting variation over time for this example:

• In 9.2.0.8 and 10.2.0.5 the predicate on trunc(n1) is a filter predicate on the table
• In 11.1.0.7 the predicate trunc(n1) became an access predicate in the index
• In 11.2.0.4 the optimizer (finally) declined to use the index under the rule hint (but introduced a strange side effect … more about that later)

Execution plan from 11.1.0.7

Execution Plan
----------------------------------------------------------
Plan hash value: 1429545322

---------------------------------------------
| Id  | Operation                   | Name  |
---------------------------------------------
|   0 | SELECT STATEMENT            |       |
|   1 |  TABLE ACCESS BY INDEX ROWID| T1    |
|*  2 |   INDEX RANGE SCAN          | T1_I1 |
---------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - access("ID"=55 AND TRUNC("N1")>=1 AND TRUNC("N1")<=10)

Note
-----
   - rule based optimizer used (consider using cbo)

In passing – the change in the execution plan from 10g to 11.1 to 11.2 does mean that anyone still using the rule-based optimizer could find that an upgrade makes a difference to rule-based execution plans.

As well as ignoring the index, 11.2.0.4 did something else that was new. I happened to have a second index on the table defined as (n1, trunc(id)); this had no impact on the execution plan for all the previous versions of Oracle, apart from switching to a full tablescan 11.2.0.4 also introduced an extra predicate:

PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------------------
Plan hash value: 3617692013

----------------------------------
| Id  | Operation         | Name |
----------------------------------
|   0 | SELECT STATEMENT  |      |
|*  1 |  TABLE ACCESS FULL| T1   |
----------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter(TRUNC("N1")<=10 AND TRUNC("N1")>=1 AND
              TRUNC("ID")=TRUNC(55) AND "ID"=55)

Note
-----
   - rule based optimizer used (consider using cbo)

Some piece of code somewhere must have been looking at the second “function-based index” – or, at least, it’s virtual column definition – to be able to generate that trunc(id) = trunc(55) predicate. This was a detail introduced in 11.2.0.2, affected by fix control 9263333: “generate transitive predicates for virtual column expressions”. It’s possible that a change like this could result in changes in execution plan due to the extra predicates – even under rule-based optimisation.

FBI Limitation

A recent question on the ODC (OTN) database forum prompted me to point out that the optimizer doesn’t consider function-based indexes on remote tables in distributed joins. I then spent 20 minutes trying to find the blog note where I had demonstrated this effect, or an entry in the manuals reporting the limitation – but I couldn’t find anything, so I’ve written a quick demo which I’ve run on 12.2.0.1 to show the effect. First, the SQL to create a couple of tables and a couple of indexes:

rem
rem     Script:         fbi_limitation.sql
rem     Author:         Jonathan Lewis
rem     Dated:          May 2018
rem

-- create public database link orcl@loopback using 'orcl'; 
define m_target = orcl@loopback

create table t1
segment creation immediate
nologging
as
with generator as (
        select
                rownum id
        from dual
        connect by
                level <= 1e4 -- > comment to avoid WordPress format issue
)
select
        rownum                          id,
        rownum                          n1,
        lpad(rownum,10,'0')             v1,
        lpad('x',100,'x')               padding
from
        generator       v1,
        generator       v2
where
        rownum <= 1e6 -- > comment to avoid WordPress format issue
;

create table t2
nologging
as
select * from t1
;

alter table t1 add constraint t1_pk primary key(id);
alter table t2 add constraint t2_pk primary key(id);
create unique index t2_f1 on t2(id+1);

begin
        dbms_stats.gather_table_stats(
                ownname     => user,
                tabname     => 'T1',
                cascade     => true,
                method_opt  => 'for all columns size 1'
        );

        dbms_stats.gather_table_stats(
                ownname     => user,
                tabname     => 'T2',
                cascade     => true,
                method_opt  => 'for all columns size 1'
        );
end;
/

The code is very simple, it creates a couple of identical tables with an id column that will produce an index with a very good clustering_factor. You’ll notice that I’ve (previously) created a public database link that is (in my case) a loopback to the current database and the code defines a variable that I can use as a substitution variable later on. If you want to do further tests with this model you’ll need to make some changes in these two lines.

So now I’m going to execute a query that should result in the optimizer choosing a nested loop between the tables – but I have two versions of the query, one which treats t2 as the local table it really is, and one that pretends (through the loopback) that t2 is remote.

set serveroutput off

select
        t1.v1, t2.v1
from
        t1,
        t2
--      t2@orcl@loopback
where
        t2.id+1 = t1.id
and     t1.n1 between 101 and 110
;


select * from table(dbms_xplan.display_cursor);

select
        t1.v1, t2.v1
from
        t1,
--      t2
        t2@orcl@loopback
where
        t2.id+1 = t1.id
and     t1.n1 between 101 and 110
;

select * from table(dbms_xplan.display_cursor);

Here are the two execution plans, pulled from memory – including the “remote” section in the distributed case:

SQL_ID  fthq1tqthq8js, child number 0
-------------------------------------
select  t1.v1, t2.v1 from  t1,  t2 -- t2@orcl@loopback where  t2.id+1 =
t1.id and t1.n1 between 101 and 110

Plan hash value: 1798294492

--------------------------------------------------------------------------------------
| Id  | Operation                    | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
--------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |       |       |       |  2347 (100)|          |
|   1 |  NESTED LOOPS                |       |    11 |   407 |  2347   (3)| 00:00:01 |
|*  2 |   TABLE ACCESS FULL          | T1    |    11 |   231 |  2325   (4)| 00:00:01 |
|   3 |   TABLE ACCESS BY INDEX ROWID| T2    |     1 |    16 |     2   (0)| 00:00:01 |
|*  4 |    INDEX UNIQUE SCAN         | T2_F1 |     1 |       |     1   (0)| 00:00:01 |
--------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------

   2 - filter(("T1"."N1"<=110 AND "T1"."N1">=101))
   4 - access("T2"."SYS_NC00005$"="T1"."ID")

Note
-----
   - this is an adaptive plan




SQL_ID  ftnmywddff1bb, child number 0
-------------------------------------
select  t1.v1, t2.v1 from  t1, -- t2  t2@orcl@loopback where  t2.id+1 =
t1.id and t1.n1 between 101 and 110

Plan hash value: 1770389500

-------------------------------------------------------------------------------------------
| Id  | Operation          | Name | Rows  | Bytes | Cost (%CPU)| Time     | Inst   |IN-OUT|
-------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |      |       |       |  4663 (100)|          |        |      |
|*  1 |  HASH JOIN         |      |    11 |   616 |  4663   (4)| 00:00:01 |        |      |
|*  2 |   TABLE ACCESS FULL| T1   |    11 |   231 |  2325   (4)| 00:00:01 |        |      |
|   3 |   REMOTE           | T2   |  1000K|    33M|  2319   (3)| 00:00:01 | ORCL@~ | R->S |
-------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - access("T1"."ID"="T2"."ID"+1)
   2 - filter(("T1"."N1"<=110 AND "T1"."N1">=101))

Remote SQL Information (identified by operation id):
----------------------------------------------------
   3 - SELECT "ID","V1" FROM "T2" "T2" (accessing 'ORCL@LOOPBACK' )

Both plans show that the optimizer has estimated the number of rows that would be retrieved from t1 correctly (very nearly); but while the fully local query does a nested loop join using the high-precision, very efficient function-based index (reporting the internal supporting column referenced in the predicate section) the distributed query seems to have no idea about the remote function-based index and select all the required rows from the remote table and does a hash join.

Footnote:

Another reason for changes in execution plan when you test fully local and then run distributed is due to the optimizer ignoring remote histograms, as demonstrated in a much older blog note (though still true in 12.2.0.1).

Addendum

After finishing this note, I discovered that I had written a similar note about reverse key indexes nearly five years ago. Arguably a reverse key is just a special case of a function-based index – except it’s not labelled as such in user_tab_cols, and doesn’t depend on a system-generated hidden column.

 

Adding Indexes

The following question came up on the OTN database forum recently:

We have below table with columns,

Table T1
Columns:
-----------
Col_1, Col_2, Col_3, Col_4, Col_5, Col_6, Col_7, Col_8, Col_9, Col_10, Col_11, Col_12, Col_13, Col_14, Col_15

on which below indexes are created.

XXTEST_Col_1    Col_1
XXTEST_Col_2    Col_2
XXTEST_Col_3    Col_3
XXTEST_Col_5    Col_5
XXTEST_Col_6    Col_6
XXTEST_Col_7    Col_7
XXTEST_Col_8    Col_8
XXTEST_Col_8    (Col_4, Col_10, Col_11)

I have requirement to update table T1 as below and it’s taking really long. [JPL: I’m assuming that the naming of the second xxtest_col_8 index is a trivial error introduced while the OP was concealing names.)

UPDATE T1
SET Col_3 = x_value,
    Col_6 = y_value
where Col_4='N'
and Col_3 IS NULL;

To improve performance, created below index and it improved the performance of the update.

 
New Index:  XXTEST_Col_4    (Col_4, Col_3)

My question is it good idea to create the above index with already existing indexes? Is there any other better way to handle this?

There is insufficient information to answer these questions properly but if I had been given this problem there are several things I’d want to check before I came up with any answers. (In the absence of complete information the default answers are “probably not”, “probably”).

What Happened

The first question, of course, is “why did the update take a really long time?”, and the second is “why did the performance improve after the index creation?”.

There are (generally) two execution paths for the update – full tablescan or index access through the index starting with col_4. Whichever path Oracle took would the update have been faster (and fast enough) if the alternative path had been taken ?  (I have to include the “generally” caveat since it’s just possible that with silly settings for a couple of parameters and statistics the optimizer could have been doing an amazingly stupid “index full scan” on any index that contained a column declared as not null.)

Then consider the fact that the update is changing two columns that are indexed – and one of those columns (col_3) is changing from null to a value, so it’s going to increase the size of the index. Is the slow performance due to the time spent maintaining the indexes, and is the improved performance due to the fact that there are no rows left to update because the first run of the test updated all of them ?

It does seem reasonably likely that Oracle would use the new index for the update and that the better precision would be the cause of the improved performance; but a side effect of the index creation is that Oracle would now have some statistics about the combination of (col_4, col_3) that wasn’t previously available. Perhaps gathering stats on the column group (col_4, col_3) would have been sufficient to result in a change in execution path that would have given the required performance without introducing the extra  maintenance costs.

What’s the requirement

Before deciding whether or not an index should be used to improve the performance of a statement it’s important to have some idea of the cost/benefit/risk analysis. This often boils down to “how much data should change, how often does it happen, what could possibly go wrong”.

As far as “what could possibly go wrong” – by adding the index (col_4, col_3) we introduce 4 groups of risk:

• any SQL currently using the col_3 index might change plans
• any SQL currently using the (col_4, col_10, col_11) index might change plans
• any query that has a predicate “col_3 is null” might change plans because that predicate could be resolved in the index if col_4 had been declared not null,
• any query with equality predicates on both col_3 and col_4 might change plans because the new index supplies some statistics about the combination

We don’t have enough information to evaluate the degree of risk – and we haven’t been given any figures about the volume of data that changes or the frequency with which the update runs, so we can’t really say much beyond raising the warning flags.

What does the data look like.

Despite my closing comment above, we have two little clues about the data. First is that one of the predicates is col_4 = ‘N’ – to me this always raises the suspicion that we’re looking at a column which is a Yes/No, or Yes/No/NULL column; secondly the OP has marked the following response as “Correct Answer”:

Depends on your data.
Check count of both queries

select count(*) from T1 where Col_4='N' and Col_3 IS NULL;
select count(*) from T1 where Col_4='N';

If there is a significant difference in count, and delete is executed frequently, it might be good to have

If we assume that this means (a) there is a significant difference in the counts and (b) the delete IS executed frequently then we we have some (though not really enough) useful information about what the data looks like and how it is handled. Perhaps over a short period of time a small volume of new data appears where col_3 is null and there is a scheduled mechanism that looks for that data and modifies it.

If that’s the case then an index that gives use high precision when we’re searching for a (very) small amount of data from a very large table would be very helpful, while an index that starts with the first of our search columns (col_4 is the leading column of an existing index) might actually be a liability if we do a large range scan, visit the table for lots of rows, and then discard them because they fail our second predicate.

However, if this IS the case then it’s not a good idea to create a large index that is constantly subject to change – especially if the update from NULL to something else results in updates that scatter the col_3 values randomly around the index. (The NULL rows will be well packed in the index, and may be in a small collection of recent table blocks; but the update will delete one index entry and we have no idea about the impact that inserting the replacement could have.) So, if we’re also allowed to modify the code, let’s create an index that is as small as possible, won’t be used accidentally by any other code, and minimises the workload when updated. Here’s the code to create the index and one option for gathering stats on the resulting hidden column, and the modified update code:

create index xxtext_fbi1 on t1(
        case when col_4='N' and col_3 is null then 0 end
)
;

begin
        dbms_stats.gather_table_stats(
                user,
                't1',
                method_opt=>'for columns (case when col_4=''N'' and col_3 is null then 0 end) size 1'
        );
end;
/

update t1 set
        col_3 = {x_value},
        col_6 = {y_value}
where
        case when col_4='N' and col_3 is null then 0 end = 0
;

This index will contain entries for just those rows that we need to update, and when we update them the index entries will simply disappear. The index is the smallest that we could have, with the minimum workload imposed, and no risk of being hi-jacked by any other process.

As a variation on a theme, and depending on version, we could create a virtual column with this definition (making it invisible in 12c) and creating an index on the virtual column. For preference I think I’d use the FBI in 11g, and the invisible virtual column in 12c.

alter table t1
add (
        wanted_flag invisible
        generated always as (
                case when col_4='N' and col_3 is null then 0 end
        ) virtual
)
;

begin
        dbms_stats.gather_table_stats(
                user,
                't1',
                method_opt=>'for columns wanted_flag size 1'
        );
end;
/

create index xxtest_virt1 on t1(wanted_flag);

update t1 set
        col_3 = {x_value},
        col_6 = {y_value}
where
        wanted_flag = 0
;

The thread raised a couple of other worthwhile points – one post pointed out that if they were going to create a new index they would want to make it as useful for as many other processes as possible: this, of course, is a different view on the cost/benefit/risk assessment, especially (I would say) if there is no option to create a very small, low-cost, index that is guaranteeably safe. The point also came up that there are two or three variations on how one could produce an expression that produced index entries only for the rows where col_4 = ‘N’ and col_3 is null; and a point about further possibilities if there were a few different values for col_4 that resulted in similar queries.

NLS Mess

The Oracle database has all sorts of little details built into it to help it deal with multi-national companies, but since they’re not commonly used you can find all sorts of odd “buggy” bits of behaviour when you start to look closely. I have to put “buggy” in quotes because some of the reported oddities are the inevitable consequences of (for example) how multi-byte character sets have to work; but some of the oddities look as if they simply wouldn’t be there if the programmer writing the relevant bit of code had remembered that they also had to cater for some NLS feature.

Here’s an example of the type of unexpected behaviour that can appear. There probably are some bugs in the area I’m going to demonstrate but, at first glance, I thought I was looking at an acceptable limitation imposed by a generic requirement. The example came from AskTom. which is why the data set isn’t my usual “t1” generation (and the formatting and capitalisation isn’t according to my usual standards).

The problem involves Case Insensitive indexing.

rem
rem     Script:         nls_sort_anomaly.sql
rem     Author:         Jonathan Lewis
rem     Dated:          Dec 2015
rem     Purpose:
rem
rem     Last tested
rem             19.3.0.0
rem             12.2.0.1
rem             12.1.0.2
rem             11.2.0.4


ALTER session SET nls_sort=binary_ci;
ALTER session SET nls_comp=linguistic;

CREATE TABLE log_data(
  account_id NUMBER,
  log_type NUMBER,
  sys_name VARCHAR2(30),
  log_time TIMESTAMP,
  msg varchar2(4000)
)
nologging
;

insert /*+ append */ into log_data(
  account_id,
  log_type,
  sys_name,
  log_time,
  msg
)
select
        5,
        2,
        dbms_random.string('a',1),
        sysdate + dbms_random.value,
        rpad('x',200)
from
        dual
connect by
        level <= 26000 -- > comment to avoid wordpress format issue
;

create index log_date on log_data (
        account_id, 
        log_type, 
--      sys_name,
        NLSSORT(sys_name,'NLS_SORT=BINARY_CI'),
        log_time
)
nologging
;
  
rem     ======================================================================
rem     Need to gather stats AFTER index creation because of the hidden column
rem     ======================================================================
  
begin
        dbms_stats.gather_table_stats(
                ownname          => user,
                tabname          =>'LOG_DATA',
                method_opt       => 'for all columns size 1'
        );
end;
/

And here’s the query I want to optimize:

SELECT 
        *
FROM
  (
    SELECT
        sys_name, log_time,  substr(msg,1,40) msg
    FROM log_data
    WHERE
      account_id=5
      AND log_type=2
      AND sys_name='a'
    ORDER BY
      log_time  desc
  )
WHERE
  rownum <= 10
;

The requirement of the query is that we see the ten most recent entries for a given combination of account_id, log_type and sys_name (ignoring case in sys_name). The orginal table has tens of millions of rows, of course, with many combinations, and some of the combinations have a very large number of entries hence the desire to find an access path that gets just the 10 rows we want without getting all the rows for a combination and sorting them before returning the ten.

Normally we would just create an index that started with the 3 columns used in the equality and ending with the column in the order by clause, and that would be enough for the optimizer to see the option for a “sort order by nosort” operation to get the required data through an index range scan; so that’s the index the code sample creates, except that since we’ve enabled case insensitive sorting we need to use a function-based index to hold the case-insensitive version of sys_name.

Here’s the execution plan we would get if we DIDN’T use the nlssort() function in the index – I’ve run the query in 11.2.0.4 and pulled the plan from memory with rowsource execution stats enabled:

---------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                      | Name     | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
---------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT               |          |      1 |        |   605 (100)|     10 |00:00:00.02 |    1065 |       |       |          |
|*  1 |  COUNT STOPKEY                 |          |      1 |        |            |     10 |00:00:00.02 |    1065 |       |       |          |
|   2 |   VIEW                         |          |      1 |    500 |   605   (1)|     10 |00:00:00.02 |    1065 |       |       |          |
|*  3 |    SORT ORDER BY STOPKEY       |          |      1 |    500 |   605   (1)|     10 |00:00:00.02 |    1065 |  2048 |  2048 | 2048  (0)|
|   4 |     TABLE ACCESS BY INDEX ROWID| LOG_DATA |      1 |    500 |   603   (1)|    966 |00:00:00.01 |    1065 |       |       |          |
|*  5 |      INDEX RANGE SCAN          | LOG_DATE |      1 |    500 |   103   (3)|    966 |00:00:00.01 |     100 |       |       |          |
---------------------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter(ROWNUM<=10)
   3 - filter(ROWNUM<=10)
   5 - access("ACCOUNT_ID"=5 AND "LOG_TYPE"=2)
       filter(NLSSORT("SYS_NAME",'nls_sort=''BINARY_CI''')=HEXTORAW('6100') )

Notice particularly the filter predicate at operation 5: that’s the thing we need to get into the index before we can avoid picking up excess data and sorting it. Notice also in the A-Rows column that we acquired 966 rows from the table before sorting and discarding all but 10 of them at operation 3.

Notice especially how important it is to look at the predicate section of an execution plan to gain a full understanding of what’s happening.

So here’s the execution plan we get by default with the function-based index in place (edited to use .le. for “less than or equal to” in the Predicate information):

----------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                       | Name     | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
----------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                |          |      1 |        |    13 (100)|     10 |00:00:00.01 |     969 |       |       |          |
|*  1 |  COUNT STOPKEY                  |          |      1 |        |            |     10 |00:00:00.01 |     969 |       |       |          |
|   2 |   VIEW                          |          |      1 |     11 |    13   (0)|     10 |00:00:00.01 |     969 |       |       |          |
|*  3 |    SORT ORDER BY STOPKEY        |          |      1 |     11 |    13   (0)|     10 |00:00:00.01 |     969 |  2048 |  2048 | 2048  (0)|
|   4 |     TABLE ACCESS BY INDEX ROWID | LOG_DATA |      1 |   1000 |    13   (0)|    966 |00:00:00.01 |     969 |       |       |          |
|*  5 |      INDEX RANGE SCAN DESCENDING| LOG_DATE |      1 |     11 |     2   (0)|    966 |00:00:00.01 |       5 |       |       |          |
----------------------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter(ROWNUM .le. 10)
   3 - filter(ROWNUM .le. 10)
   5 - access("ACCOUNT_ID"=5 AND "LOG_TYPE"=2 AND "LOG_DATA"."SYS_NC00006$"=HEXTORAW('6100') )

It didn’t work ! (Check the A-Rows at operations 4 and 5, and the sort that we didn’t want at operation 3 where the data is finally reduced to 10 rows.

But there’s something odd going on here – look at the predicate section: our three predicates are all access predicates for the index range scan descending. We are doing exactly what we want to do with the index, but we’re not stopping after the 10 rows that we need, we’re getting all of them (in the order we want) and then doing a trivial sort and discard. Look at the Cost column – the cost at operation 4 is exactly what we might expect for the 10 rows we want to see, and the E-rows at line 5 is clearly based on our “first 10 rows” requirement.

This raises two questions:

1. What’s gone wrong ?
2. Can we work around the problem ?

The answer to (1) is, I think, that there’s a bug in the code. Looking at the 10053 trace file I can see the optimizer correctly handling the arithmetic of the virtual column (the sys_nc000006$) representing the function in the index and then getting to the point where it goes into a code section relating to “Recost for ORDER BY”, and brings back the original function as a filter predicate – I think that in the recosting it may be losing track of the fact that sys_nc000006$ and nlssort(sys_name, ‘nls_sort=binary_ci’) are the same thing and therefore can’t apply the rule about “Equality on 1st N columns, order by on the remainder”.

There are several answers to (2).

Workarounds

The honest hack

The first one is simply to fall back to the old (probably version 7, possibly version 8) requirement for getting the “sort order by nosort” operation – put all the index columns into the order by clause. Unfortunately the optimizer then did a tablescan rather than an index range scan because my data set was so small, so I had to hack the system stats temporarily to make the tablescan very expensive:

begin
        dbms_stats.set_system_stats('MBRC',2);
        dbms_stats.set_system_stats('MREADTIM',20); 
        dbms_stats.set_system_stats('SREADTIM',5);
        dbms_stats.set_system_stats('CPUSPEED',1000); 
end;
/

... order by account_id desc, log_type desc, sys_name desc, lot_time desc

Unfortunately the optimizer still went wrong – it did an ASCENDING index range scan sorting all the data. I actually had to hint the code to use the index in descending order to get the following execution plan:

------------------------------------------------------------------------------------------------------------------
| Id  | Operation                      | Name     | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers |
------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT               |          |      1 |        |  1215 (100)|     10 |00:00:00.01 |      13 |
|*  1 |  COUNT STOPKEY                 |          |      1 |        |            |     10 |00:00:00.01 |      13 |
|   2 |   VIEW                         |          |      1 |   1000 |  1215   (1)|     10 |00:00:00.01 |      13 |
|   3 |    TABLE ACCESS BY INDEX ROWID | LOG_DATA |      1 |   1000 |  1006   (1)|     10 |00:00:00.01 |      13 |
|*  4 |     INDEX RANGE SCAN DESCENDING| LOG_DATE |      1 |   1000 |     5   (0)|     10 |00:00:00.01 |       3 |
------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter(ROWNUM .le. 10)
   4 - access("ACCOUNT_ID"=5 AND "LOG_TYPE"=2 AND "LOG_DATA"."SYS_NC00006$"=HEXTORAW('6100') )

The A-Rows tells us we’ve accessed the minimum data set, and the absence of the SORT ORDER BY STOPKEY operation tells us that we’ve avoided doing the sort. Notice, though that the cost is the cost that would have been appropriate if we have accessed all 1,000 rows that matched the equality predicates. This is an example of a plan that you couldn’t really trust if all you had done was an “explain plan” rather than running the query and checking the rowsource execution stats. If you ignore the A-Rows it looks as if the plan WOULD get all the data in order and only eliminate the redundant rows at operation 1.

The silly surprise

The original author of the problem came up with this one. Put in two predicates which, between them are equivalent to the original requirement:

where ...
and     sys_name >= 'a'
and     sys_name <= 'a'

Clearly this is totally silly – the optimizer can fold this pair of predicates into the single predicate “sys_name = ‘a'”, so it shouldn’t make any difference. But here’s the execution plan:

------------------------------------------------------------------------------------------------------------------
| Id  | Operation                      | Name     | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers |
------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT               |          |      1 |        |    13 (100)|     10 |00:00:00.01 |      13 |
|*  1 |  COUNT STOPKEY                 |          |      1 |        |            |     10 |00:00:00.01 |      13 |
|   2 |   VIEW                         |          |      1 |     11 |    13   (0)|     10 |00:00:00.01 |      13 |
|   3 |    TABLE ACCESS BY INDEX ROWID | LOG_DATA |      1 |   1000 |    13   (0)|     10 |00:00:00.01 |      13 |
|*  4 |     INDEX RANGE SCAN DESCENDING| LOG_DATE |      1 |     11 |     2   (0)|     10 |00:00:00.01 |       3 |
------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter(ROWNUM .le. 10)
   4 - access("ACCOUNT_ID"=5 AND "LOG_TYPE"=2 AND "LOG_DATA"."SYS_NC00006$"=HEXTORAW('6100') )

Yes, it’s (structurally) exactly the same plan, with exactly the same predicate section except that (a) it gets there without being hinted, (b) the Cost column looks appropriate all down the line, and (c) the E-Rows value for the VIEW operator would have helped us appreciate that the correct elimination was (probably) going to happen if all we had done was the Explain Plan.

The dirty hack

I know the name of the hidden column that’s causing the problem, and I know how to generate the value it has to be – so let’s give Oracle exactly what it needs to see rather than allowing its internal transformation to rewrite the SQL:

...
AND sys_nc00006$ = nlssort('a','nls_sort=binary_ci')
...


------------------------------------------------------------------------------------------------------------------
| Id  | Operation                      | Name     | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers |
------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT               |          |      1 |        |    13 (100)|     10 |00:00:00.01 |      13 |
|*  1 |  COUNT STOPKEY                 |          |      1 |        |            |     10 |00:00:00.01 |      13 |
|   2 |   VIEW                         |          |      1 |     11 |    13   (0)|     10 |00:00:00.01 |      13 |
|   3 |    TABLE ACCESS BY INDEX ROWID | LOG_DATA |      1 |   1000 |    13   (0)|     10 |00:00:00.01 |      13 |
|*  4 |     INDEX RANGE SCAN DESCENDING| LOG_DATE |      1 |     11 |     2   (0)|     10 |00:00:00.01 |       3 |
------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter(ROWNUM .le. 10)
   4 - access("ACCOUNT_ID"=5 AND "LOG_TYPE"=2 AND "SYS_NC00006$"=HEXTORAW('6100') )

We get exactly the plan we need – and the silly thing about this example is that it’s a case where we get the plan we want by EXPLICITLY transforming the SQL to reproduce the transformation that Oracle had done IMPLICITLY and then messed up !

Final Choice

Of the three options – the dirty hack is definitely a no-no in production; the “double the predicate” trock is undesirable because it may depend in some unexpected way on a particular optimizer bug or on some statistical detail that could change; so I’d choose the hinted path with the (nominally) redundant columns.

One final point about this solution, we actually needed to include only the sys_name in the order by clause to use the descending range scan and early stop – which is basically another indication that it’s something about the function-based column that is breaking the normal code path.

Update Oct 2019

Nothing has changed in 19c.

Index Usage – 4

Here’s a thought that came to me while I was writing up a note about identifying redundant indexes a few minutes ago. Sometimes you end up supporting applications with unexpected duplication of data and indexes and need to find ways to reduce overheads. Here’s some code modelling a scenario that I’ve seen more often than I like (actually, just once would be more often than I’d like):

rem
rem     Script:         trunc_date_opt_3.sql
rem     Author:         Jonathan Lewis
rem     Dated:          Aug 2015
rem     Purpose:
rem
rem     Last tested
rem             12.2.0.1
rem             12.1.0.2
rem             11.2.0.4
rem

create table t1
nologging
as
with generator as (
        select  --+ materialize
                rownum id
        from dual
        connect by
                level <= 1e5
)
select
        rownum                                          id,
        trunc(sysdate,'MM') + (rownum-1)/1440           date_time,
        trunc(sysdate,'MM') + trunc((rownum-1)/1440)    date_only,
        rpad('x',100)                                   padding
from
        generator       v1,
        generator       v2
where
        rownum <= 1e5     --> fudge to bypass WordPress format anomaly
;

begin 
        dbms_stats.gather_table_stats(
                ownname          => user,
                tabname          => 'T1',
                method_opt       => 'for all columns size 1'
        );
end;
/

I’ve got a table holding one row per minute since the start of the month; there’s a column which holds the date and time accurate to the minute, and another column which is supposed to hold just the date part. Is it possible to create a single index that allows Oracle to handles queries relatively efficiently whether they refer to date_time or date_only ? As a starting step could we get an index range scan on the same index for both of the following queries:

select
        max(id)
from
        t1
where
        date_only between sysdate-1 and sysdate
;


select
        max(id)
from
        t1
where
        date_time between sysdate-1 and sysdate
;

As Bob the Builder likes to say: “yes we can”.

There are a few lines of SQL between the table creation and the stats gathering that I didn’t show you. The first creates the constraint that describes the relationship between date_time and date_only – one is the truncated version of the other; the second defines the index we need, and the third (unfortunately) has to be there to declare the date_time column as a mandatory column:

alter table t1
        add constraint t1_trunc_date
        check(
                  date_only = trunc(date_time)
              and (   (date_only is null and date_time is null)
                   or (date_only is not null and date_time is not null)
              )
        )
;

create index t1_i1 on t1(trunc(date_time)) nologging;

alter table t1 modify (date_time not null);

(Given the requirement for date_time to be not null to get my indexing strategy to work, we could simplify the t1_trunc_date constraint to just (date_only = trunc(date_time)) if we declared date_only to be not null as well).

With the extra lines of SQL included here are the resulting execution plans for the two queries (running on 11.2.0.4, but you get the same plans on 12.1.0.2):

=======================================
date_only between sysdate-1 and sysdate
=======================================

---------------------------------------------------------------------------------------
| Id  | Operation                     | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT              |       |     1 |    21 |    92   (2)| 00:00:01 |
|   1 |  SORT AGGREGATE               |       |     1 |    21 |            |          |
|*  2 |   FILTER                      |       |       |       |            |          |
|*  3 |    TABLE ACCESS BY INDEX ROWID| T1    |  4306 | 90426 |    92   (2)| 00:00:01 |
|*  4 |     INDEX RANGE SCAN          | T1_I1 |  4306 |       |    13   (0)| 00:00:01 |
---------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter(SYSDATE@!>=SYSDATE@!-1)
   3 - filter("DATE_ONLY"<=SYSDATE@! AND "DATE_ONLY">=SYSDATE@!-1)
   4 - access(TRUNC(INTERNAL_FUNCTION("DATE_TIME"))>=SYSDATE@!-1 AND
              TRUNC(INTERNAL_FUNCTION("DATE_TIME"))<=SYSDATE@!)

=======================================
date_time between sysdate-1 and sysdate
=======================================

---------------------------------------------------------------------------------------
| Id  | Operation                     | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT              |       |     1 |    21 |    92   (2)| 00:00:01 |
|   1 |  SORT AGGREGATE               |       |     1 |    21 |            |          |
|*  2 |   FILTER                      |       |       |       |            |          |
|*  3 |    TABLE ACCESS BY INDEX ROWID| T1    |  1442 | 30282 |    92   (2)| 00:00:01 |
|*  4 |     INDEX RANGE SCAN          | T1_I1 |  4306 |       |    13   (0)| 00:00:01 |
---------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter(SYSDATE@!>=SYSDATE@!-1)
   3 - filter("DATE_TIME"=SYSDATE@!-1)
   4 - access(TRUNC(INTERNAL_FUNCTION("DATE_TIME"))>=TRUNC(SYSDATE@!-1) AND
              TRUNC(INTERNAL_FUNCTION("DATE_TIME"))<=TRUNC(SYSDATE@!))

The optimizer has managed to generate extra predicates in both cases by applying transitive closure to the critical constraint to produce queries that can be addressed (with some inefficiencies) through the single index.

Within limits, therefore, I can reduce two indexes to a single index. The strategy isn’t ideal but it may be appropriate in a few special cases. There are several problems that should be considered carefully:

• The date_time column has to be declared not null for this optimization strategy to appear – that’s going to limit its applicability.
• You may have more complex code where the transformation simply can’t be made to appear.
• The introduction of the trunc() function may change the optimizer’s arithmetic in ways that cause plans to change for the worse
• (Most important) The index range scan is always a multiple of 24 hours, with the excess data discarded after you reach the table. If you have lots of time-based queries for short time intervals (e.g. less than 8 hours) then the extra work done may outweigh the benefit of reducing the number of indexes – especially if all the excess table visits turn into randomly scattered single block reads.

Despite these drawbacks you may decide that you have a case where the strategy is “good enough” to help you reduce the workload on your system at some critical times during the day or night.

 

Index Usage – 2

I’ve been a little slow in the follow-up to my previous posting on possibly redundant indexes. Before going into the slightly more complex stuff, there’s another peripheral point (but a very important one) that’s worth raising about how clever the optimizer can be. Here’s some code for 11.2.0.4 to demonstrate the point:

create table t1
nologging
as
with generator as (
	select	--+ materialize
		rownum id 
	from dual 
	connect by 
		level <= 1e4
)
select
	rownum					id,
	trunc(sysdate,'MM') + (rownum-1)/1440	date_time,
	rpad('x',100)				padding
from
	generator	v1,
	generator	v2
where
	rownum <= 1e5
;


alter table t1 
add (
        date_only
	generated always as (trunc(date_time)) virtual 
)
;

begin
	dbms_stats.gather_table_stats(
		ownname		 => user,
		tabname		 =>'T1',
		method_opt	 => 'for all columns size 1'
	);
end;
/

create index t1_i1 on t1(date_only) nologging;

So, in a two-step process, I’ve got an indexed virtual column that holds the value of the date_time column truncated to just the date. Would you expect the optimizer to use the index to execute the following query efficiently:

select
        max(id)
from
        t1
where
        date_time between sysdate-1 and sysdate
;

Note that the query references the real date_time column not the virtual column date_only, and it’s not using the expression that defines the index – yet the plan reads as follows:

-----------------------------------------------------------------------------------------------
| Id  | Operation                             | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
-----------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                      |       |     1 |    21 |    86   (2)| 00:00:01 |
|   1 |  SORT AGGREGATE                       |       |     1 |    21 |            |          |
|*  2 |   FILTER                              |       |       |       |            |          |
|*  3 |    TABLE ACCESS BY INDEX ROWID BATCHED| T1    |  1442 | 30282 |    86   (2)| 00:00:01 |
|*  4 |     INDEX RANGE SCAN                  | T1_I1 |  4306 |       |    13   (0)| 00:00:01 |
-----------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter(SYSDATE@!>=SYSDATE@!-1)
   3 - filter("DATE_TIME"<=SYSDATE@! AND "DATE_TIME">=SYSDATE@!-1)
   4 - access("T1"."DATE_ONLY">=TRUNC(SYSDATE@!-1) AND
              "T1"."DATE_ONLY"<=TRUNC(SYSDATE@!))

It’s a little odd that even though the optimizer in the newer versions of Oracle treats many simple expressions on sysdate as constants it still checks (operation 2) that “sysdate >= sysdate – 1” but perhaps that’s just a case of a piece of generic code that isn’t worth the risk or effort of changing.

The key point, of course, is that Oracle has managed to generate some extra predicates that allow it to use the “wrong” index to get a first approximation of the result set fairly efficiently, and then used the original predicate to reduce the approximation down to the correct result set.

If you want a quick sanity check on the access predicates used for operation 4:

• If date_time >= sysdate-1, then trunc(date_time) >= trunc(sysdate-1)
• If date_time <= sysdate, then trunc(date_time) <= trunc(sysdate)

This style of predicate manipulation also works numeric data types, but I think its greatest benefit (or convenience) is likely to come from date data types where the data has been created with a time component but there are frequent “date-only” queries. The days of creating two indexes as a workaround for handling generated code that wants to deal with both date_time and trunc(date_time) predicates should be numbered.

Footnote:

This enhancement probably appeared in 11.2.0.2, and I first saw it described in October 2013 in this blog note by Mohamed Houri; but 12c offers a delightful little enhancement – here’s what my table looks like in the 12c version of the code:

SQL> desc t1
 Name                          Null?    Type
 ----------------------------- -------- --------------------
 ID                                     NUMBER
 DATE_TIME                              DATE
 PADDING                                VARCHAR2(100)

SQL> 

Where’s the virtual column ? The 12c version of my code had a slightly different definition for it:

alter table t1  
add (
        date_only
        invisible
        generated always as (trunc(date_time)) virtual
)
;

The transformation still works even when the virtual column is invisible. So (subject to searching for anomalies, boundary conditions and bugs) it looks as if you can change the table definition, and get the benefits of two indexes for the price of one without the application realising that anything has changed.

Easy – Oops.

A question came up on OTN today asking for suggestions on how to enforce uniqueness on a pair of columns only when the second column was not null. There’s an easy and obvious solution – but I decided to clone the OP’s example and check that I’d typed my definition up correctly before posting it; and the result came as a bit of a surprise. Here’s a demo script (not using the OP’s table):

create table t1  
(  
        col1    int not null,
        col2    varchar2(1)
);  

create unique index t1_i1 on t1( 
--      case col2 when null then cast(null as int) else col1 end,
--      case when col2 is null then cast(null as int) else col1 end,
        case when col2 is not null then col1 end,
        col2
)
;

insert into t1 values(1,null);
insert into t1 values(1,null);
insert into t1 values(1,'x');
insert into t1 values(1,'y');
insert into t1 values(1,'y');

commit;

column ind1_is   format a5
column ind1_when format 9999

set null N/A

select
        case when col2 is null then cast (null as int) else col1 end    ind1_is,
        case col2 when null then cast (null as int)  else col1 end      ind1_when
from 
        t1
;

The strategy is simple: you create a unique function-based index with two columns; the first column of the index is defined to show the first column of the table if the second column of the table is not null, the second column of the index is simply the second column of the table. So if the second column of the table is null, both columns in the index are null and there is no entry in the index; but if the second column of the table is not null then the index copies both columns from the table and a uniqueness test applies.

Based on the requirement and definition you would expect the first 4 of my insert statements to succeed and the last one to fail. The index will then have two entries, corresponding to my 3rd and 4th insertions.

I’ve actually shown three ways to use the case statement to produce the first column of the index. The last version is the cleanest but the first option is the one I first thought of – it’s virtually a literal translation the original requirement. The trouble with my first definition is that the index acquired an entry it should not have got so the second insert raised a “duplicate key” error; the error didn’t appear when I switched the syntax of the case statement to the second version.

That’s why the closing query of the demo is there – when you run it the two values reported should be the same as each other for all four rows in the table – but they’re not. This is what I got on 11.2.0.4:

IND1_IS IND1_WHEN
------- ---------
N/A             1
N/A             1
      1         1
      1         1

I’m glad I did a quick test before suggesting my original answer.

Anyone who has other versions of Oracle available is welcome to repeat the test and report back which versions they finding working correctly (or not).

Update (the next day)

It’s not a bug (see note 2 below from Jason Bucata), this is the way it’s supposed to work.  “case col2 when null” evaluates the expression “col2=null”, which doesn’t equate to TRUE, so the “else” value is used.

 

 

 

Tweaking

The following question came up on OTN recently:

Which one gives better performance? Could please explain.

1) nvl( my_column, ‘N’) <> ‘Y’

2) nvl( my_column, ‘N’) = ‘N’

It’s a question that can lead to a good 20 minute discussion – if you were in some sort of development environment and had a fairly free hand to do whatever you wanted.

The most direct answer is that you could expect the performance to be the same whichever option you chose – but the results might be different, of course, unless you had a constraint on my_column that ensured that it would hold only null, ‘Y’, or ‘N’.  (Reminder:  the constraint check (my_column in (‘Y’,’N’) will allow nulls in the column).

On the other hand, if you create a function-based index on nvl(my_column,’N’) then the second option would give the optimizer the opportunity to use the index – which could make it the more efficient option if a sufficiently small quantity of the data from a large table matched the predicate. Of course in this case you would need a histogram on the hidden column definition supporting the index so that the optimizer could detect the data skew.

But if a function-based index is a step in the right direction it’s worth thinking about how to pick the best function-based index.

create index t1_i1 on t1(case my_column when 'Y' then null else 'N' end);

execute dbms_stats.gather_table_stats(user,'t1',method_opt=>'for all columns size 1');

select * from t1 where case my_column when 'Y' then null else 'N' end = 'N';

Provided you can change the queries that use the original predicate, you can create a function-based index that is the smallest possible index to assist your query, and you won’t need a histogram to allow the optimizer to get the correct cardinality since there will be just one distinct value in the index (and the underlying column).

It’s possible to go a little further – what if the typical query that started this discussion was really something like:

select  *
from    t1
where   nvl(my_column, 'N') = 'N'
and     other_column = {some value}

If the nvl() predicate always returned a small fraction of the data we might engineer an index to handle the requirement of the entire where clause:

create index t1_i2 on t1(case my_column when 'Y' then null else other_column end);
execute dbms_stats.gather_table_stats(user,'t1',method_opt=>'for all hidden columns size 254');

In this case you might need a histogram on the hidden column definition supporting the index, which is why I’ve changed my method_opt. We’ve constructed an index that is the smallest possible index that will satisfy the query as precisely as possible while giving the optimizer the best chance of producing accurate cardinality estimates. [Update: until the appearance of Kirill’s comment (#1) I forgot to point out that once again you have to change the original SQL from using the nvl() expression to using a case expression that matches the index.]

Footnote

It would be easy to fall into the trap of playing around with this type of tweaking every time you hit a performance problem. Bear in mind, though, that even indexes which are as small and accurately targeted as possible can introduce various types of overhead (contention, cost of gathering stats, instability of histogram collection); so always try to think through the potential costs as well as the benefits of such an approach – is it really worth the effort.

It’s also worth noting that things don’t always work the way you expect.

Finally, of course, you might choose to create “proper” virtual columns in 11g so that you can refer to them by name, rather than handling them indirectly as you have to when they are “undocumented” hidden columns generated by function-based indexes.

 

Min/Max

One of my most-repeated observations about trouble-shooting Oracle is that things break when you start combining features. Here’s an example that demonstrates the point.

It’s possible to create “descending” indexes – or indexes with descending columns, as I prefer to call them, and there’s a special “min/max range scan” optimizer operation for a particular kind of index usage – demonstrated in the following code fragment (running under 11.2.0.4, and reporting the rowsource execution statistics):

Auto Sample Size

In the past I have enthused mightily about the benefits of the approximate NDV mechanism and the benefit of using auto_sample_size to collect statistics in 11g; however, as so often happens with Oracle features, there’s a down-side or boundary condition, or edge case. I’ve already picked this up once as an addendum to an earlier blog note on virtual stats, which linked to an article on OTN describing how the time taken to collect stats on a table increased dramatically after the addition of an index – where the index had this definition:

create bitmap index i_s_rmp_eval_csc_msg_actions on
    s_rmp_evaluation_csc_message (
        decode(instr(xml_message_text,' '),0,0,1)
    )
;

As you might guess from the column name, this is an index based on an XML column, which is stored as a CLOB.

In a similar vein, I showed you a few days ago an old example I had of indexing a CLOB column with a call to dbms_lob.getlength(). Both index examples suffer from the same problem – to support the index Oracle creates a hidden (virtual) column on the table that can be used to hold statistics about the values of the function; actual calculated values for the function call are stored in the index but not on the table itself – but it’s important that the optimizer has the statistics about the non-existent column values.

FBI Skip Scan

A recent posting on the OTN database forum highlighted a bug (or defect, or limitation) in the way that the optimizer handles index skip scans with “function-based” indexes – it doesn’t do them. The defect has probably been around for a long time and demonstrates a common problem with testing Oracle – it’s very easy for errors in the slightly unusual cases to be missed; it also demonstrates a general principle that it can take some time for a (small) new feature to be applied consistently across the board.

The index definitions in the original posting included expressions like substr(nls_lower(colX), 1, 25), and it’s possible for all sorts of unexpected effects to appear when your code starts running into NLS  settings, so I’ve created a much simpler example. Here’s my table definition, with three index definitions: