num_index_keys

The title is the name of an Oracle hint that came into existence in Oracle 10.2.0.3 and made an appearance recently in a question on the rarely used “My Oracle Support” Community forum (you’ll need a MOS account to be able to read the original). I wouldn’t have found it but the author also emailed me the link asking if I could take a look at it.  (If you want to ask me for help – without paying me, that is – then posting a public question in the Oracle (ODC) General Database or SQL forums and emailing me a private link is the strategy most likely to get an answer, by the way.)

The question was about a very simple query using a straightforward index – with a quirky change of plan after upgrading from 10.2.0.3 to 12.2.0.1. Setting the optimizer_features_enable to ‘10.2.0.3’ in the 12.2.0.1 system re-introduced the 10g execution plan. Here’s the query:

SELECT t1.*
   FROM   DW1.t1
  WHERE   t1.C1 = '0001' 
    AND   t1.C2 IN ('P', 'F', 'C')
    AND   t1.C3 IN (
                    '18110034450001',
                    '18110034450101',
                    '18110034450201',
                    '18110034450301',
                    '18110034450401',
                    '18110034450501'
          );
 

Information supplied: t1 holds about 500 million rows at roughly 20 rows per block, the primary key index is (c1, c2, c3, c4), there are just a few values for each of c1, c2 and c4, while c3 is “nearly unique” (which, for clarity, was expanded to “the number of distinct values of c3 is virtually the same as the number of rows in the table”).

At the moment we don’t have any information about histograms and we don’t known whether or not “nearly unique” might still allow a few values of c3 to have a large number of duplicates, so that’s something we might want to follow up on later.

Here are the execution plans – the fast one (from 10g) first, then the slow (12c) plan – and you should look carefully at the predicate section of the two plans:

10g (pulled from memory with rowsource execution statistics enabled)
--------------------------------------------------------------------------------------------------------------------
| Id  | Operation                    | Name             | Starts | E-Rows | A-Rows |   A-Time   | Buffers | Reads  |
--------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |                  |      1 |        |      6 |00:00:00.01 |      58 |      5 |
|   1 |  INLIST ITERATOR             |                  |      1 |        |      6 |00:00:00.01 |      58 |      5 |
|   2 |   TABLE ACCESS BY INDEX ROWID| T1               |     18 |      5 |      6 |00:00:00.01 |      58 |      5 |
|*  3 |    INDEX RANGE SCAN          | PK_T1            |     18 |      5 |      6 |00:00:00.01 |      52 |      4 |
--------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("T1"."C1"='0001' AND (("T1"."C2"='C' OR "T1"."C2"='F' OR
              "T1"."C2"='P')) AND (("C3"='18110034450001' OR "C3"='18110034450101' OR
              "C3"='18110034450201' OR "C3"='18110034450301' OR "C3"='18110034450401' OR
              "C3"='18110034450501')))

 

12c (from explain plan)
---------------------------------------------------------------------------------------------------------
| Id  | Operation                            | Name             | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                     |                  |     1 |   359 |     7   (0)| 00:00:01 |
|   1 |  INLIST ITERATOR                     |                  |       |       |            |          |
|   2 |   TABLE ACCESS BY INDEX ROWID BATCHED| T1               |     1 |   359 |     7   (0)| 00:00:01 |
|*  3 |    INDEX RANGE SCAN                  | PK_T1            |     1 |       |     6   (0)| 00:00:01 |
---------------------------------------------------------------------------------------------------------
 
Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("T1"."C1"='0001' AND ("T1"."C2"='C' OR "T1"."C2"='F' OR
              "T1"."C2"='P'))
       filter("C3"='18110034450001' OR "C3"='18110034450101' OR
              "C3"='18110034450201' OR "C3"='18110034450301' OR
              "C3"='18110034450401' OR "C3"='18110034450501')
  

When comparing plans it’s better, of course, to present the same sources from the two systems, it’s not entirely helpful to have the generated plan from explain plan in one version and a run-time plan with stats in the other – given the choice I’d like to see the run-time from both. Despite this, I felt fairly confident that the prediction would match the run-time for 12c and that I could at least guess the “starts” figure for 12c.

The important thing to notice is the way that the access predicate in 10g has split into an access predicate followed by a filter predicate in 12c. So 12c is going to iterate three times (once for each of the values  ‘C’, ‘F’, ‘P’) and then walk a potentially huge linked list of index leaf blocks looking for 6 values of c3, while 10g is going to probe the index 18 times (3 combinations of c2 x six combinations of c3) to find “nearly unique” rows which means probably one leaf block per probe.

The 12c plan was taking minutes to run, the 10g plan was taking less than a second. The difference in execution time was probably the effect of the 12c plan ranging through (literally) thousands of index leaf blocks.

There are many bugs and anomalies relating to in-list iteration and index range scans and cardinality calculations – here’s a quick sample of v$system_fix_control in 12.2.0.1:

select optimizer_feature_enable ofe, sql_feature, bugno, description
from v$system_fix_control
where
	optimizer_feature_enable between '10.2.0.4' and '12.2.0.1'
and	(   sql_feature like '%CBO%'
	 or sql_feature like '%CARDINALITY%'
	)
and	(    lower(description) like '%list%'
	 or  lower(description) like '%iterat%'
	 or  lower(description) like '%multi%col%'
	)
order by optimizer_feature_enable, sql_feature, bugno
;

OFE        SQL_FEATURE                      BUGNO DESCRIPTION
---------- --------------------------- ---------- ----------------------------------------------------------------
10.2.0.4   QKSFM_CBO_5259048              5259048 undo unused inlist
           QKSFM_CBO_5634346              5634346 Relax equality operator restrictions for multicolumn inlists

10.2.0.5   QKSFM_CBO_7148689              7148689 Allow fix of bug 2218788 for in-list predicates

11.1.0.6   QKSFM_CBO_5139520              5139520 kkoDMcos: For PWJ on list dimension, use part/subpart bits

11.2.0.1   QKSFM_CBO_6818410              6818410 eliminate redundant inlist predicates

11.2.0.2   QKSFM_CBO_9069046              9069046 amend histogram column tracking for multicolumn stats

11.2.0.3   QKSFM_CARDINALITY_11876260    11876260 use index filter inlists with extended statistics
           QKSFM_CBO_10134677            10134677 No selectivity for transitive inlist predicate from equijoin
           QKSFM_CBO_11834739            11834739 adjust NDV for list partition key column after pruning
           QKSFM_CBO_11853331            11853331 amend index cost compare with inlists as filters
           QKSFM_CBO_12591120            12591120 check inlist out-of-range values with extended statistics

11.2.0.4   QKSFM_CARDINALITY_12828479    12828479 use dynamic sampling cardinality for multi-column join key check
           QKSFM_CARDINALITY_12864791    12864791 adjust for NULLs once for multiple inequalities on nullable colu
           QKSFM_CARDINALITY_13362020    13362020 fix selectivity for skip scan filter with multi column stats
           QKSFM_CARDINALITY_14723910    14723910 limit multi column group selectivity due to NDV of inlist column
           QKSFM_CARDINALITY_6873091      6873091 trim histograms based on in-list predicates
           QKSFM_CBO_13850256            13850256 correct estimates for transitive inlist predicate with equijoin

12.2.0.1   QKSFM_CARDINALITY_19847091    19847091 selectivity caching for inlists
           QKSFM_CARDINALITY_22533539    22533539 multi-column join sanity checks for table functions
           QKSFM_CARDINALITY_23019286    23019286 Fix cdn estimation with multi column stats on fixed data types
           QKSFM_CARDINALITY_23102649    23102649 correction to inlist element counting with constant expressions
           QKSFM_CBO_17973658            17973658 allow partition pruning due to multi-inlist iterator
           QKSFM_CBO_21057343            21057343 order predicate list
           QKSFM_CBO_22272439            22272439 correction to inlist element counting with bind variables

There are also a number of system parameters relating to inlists that are new (or have changed values) in 12.2.0.1 when compared with 10.2.0.3 – but I’m not going to go into those right now.

I was sufficiently curious about this anomaly that I emailed the OP to say I would be happy to take a look at the 10053 trace files for the query – the files probably weren’t going to be very large given that it was only a single table query – but in the end it turned out that I solved the problem before he’d had time to email them. (Warning – don’t email me a 10053 file on spec; if I want one I’ll ask for it.)

Based on the description I created an initial model of the problem – it took about 10 minutes to code:

rem     Tested on 12.2.0.1, 18.3.0.1

drop table t1 purge;

create table t1 (
	c1 varchar2(4) not null,
	c2 varchar2(1) not null,
	c3 varchar2(15) not null,
	c4 varchar2(4)  not null,
	v1 varchar2(250)
)
;

insert into t1
with g as (
	select rownum id 
	from dual
	connect by level <= 1e4 -- > hint to avoid wordpress format issue
)
select
	'0001',
	chr(65 + mod(rownum,11)),
	'18110034'||lpad(1+100*rownum,7,'0'),
	lpad(mod(rownum,9),4,'0'),
	rpad('x',250,'x')
from
	g,g
where
        rownum <= 1e5 -- > hint to avoid wordpress format issue
;


create unique index t1_i1 on t1(c1, c2, c3, c4);

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

alter session set statistics_level = all;
set serveroutput off

prompt	==========================
prompt	Default optimizer features
prompt	==========================

select
        /*+ optimizer_features_enable('12.2.0.1') */
	t1.*
FROM	t1
WHERE
	t1.c1 = '0001' 
AND	t1.c2 in ('H', 'F', 'C')
AND	t1.c3 in (
		'18110034450001',
		'18110034450101',
		'18110034450201',
		'18110034450301',
		'18110034450401',
		'18110034450501'
	)
;

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

select 
        /*+ optimizer_features_enable('10.2.0.3') */
	t1.*
FROM	t1
WHERE
	t1.c1 = '0001' 
AND	t1.c2 in ('H', 'F', 'C')
AND	t1.c3 in (
		'18110034450001',
		'18110034450101',
		'18110034450201',
		'18110034450301',
		'18110034450401',
		'18110034450501'
	)
;

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

alter session set statistics_level = all;
set serveroutput off

The two queries produced the same plan – regardless of the setting for optimizer_features_enable – it was the plan originally used by the OP’s 10g setting:

-------------------------------------------------------------------------------------------------------------
| Id  | Operation                    | Name  | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers |
-------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |       |      1 |        |    20 (100)|      0 |00:00:00.01 |      35 |
|   1 |  INLIST ITERATOR             |       |      1 |        |            |      0 |00:00:00.01 |      35 |
|   2 |   TABLE ACCESS BY INDEX ROWID| T1    |     18 |      2 |    20   (0)|      0 |00:00:00.01 |      35 |
|*  3 |    INDEX RANGE SCAN          | T1_I1 |     18 |      2 |    19   (0)|      0 |00:00:00.01 |      35 |
-------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("T1"."C1"='0001' AND (("T1"."C2"='C' OR "T1"."C2"='F' OR "T1"."C2"='H')) AND
              (("T1"."C3"='18110034450001' OR "T1"."C3"='18110034450101' OR "T1"."C3"='18110034450201' OR
              "T1"."C3"='18110034450301' OR "T1"."C3"='18110034450401' OR "T1"."C3"='18110034450501')))

There was one important difference between the 10g and the 12c plans – in 10g the cost of the table access (hence the cost of the total query) was 20; in 12c it jumped to 28 – maybe there’s a change in the arithmetic for costing the iterator, and maybe that’s sufficient to cause a problem.

Before going further it’s worth checking what the costs would look like (and, indeed, if the plan is possible in both versions) if we force Oracle into the “bad” plan. That’s where we finally get to the hint in the title of this piece. If I add the hint /*+ num_index_keys(t1 t1_i1 2) */ what’s going to happen ? (Technically I’ve included a hint to use the index, and specified the query block name to make sure Oracle doesn’t decide to switch to a tablescan):

select
        /*+
            optimizer_features_enable('12.2.0.1')
            index_rs_asc(@sel$1 t1@sel$1 (t1.c1 t1.c2 t1.c3 t1.c4))
            num_index_keys(@sel$1 t1@sel$1 t1_i1 2)
        */
        t1.*
FROM        t1
WHERE
        t1.c1 = '0001'
AND        t1.c2 in ('H', 'F', 'C')
AND        t1.c3 in (
                '18110034450001',
                '18110034450101',
                '18110034450201',
                '18110034450301',
                '18110034450401',
                '18110034450501'
        )
;

------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                            | Name  | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers | Reads  |
------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                     |       |      1 |        |   150 (100)|      0 |00:00:00.01 |     154 |      1 |
|   1 |  INLIST ITERATOR                     |       |      1 |        |            |      0 |00:00:00.01 |     154 |      1 |
|   2 |   TABLE ACCESS BY INDEX ROWID BATCHED| T1    |      3 |     18 |   150   (2)|      0 |00:00:00.01 |     154 |      1 |
|*  3 |    INDEX RANGE SCAN                  | T1_I1 |      3 |     18 |   142   (3)|      0 |00:00:00.01 |     154 |      1 |
------------------------------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("T1"."C1"='0001' AND (("T1"."C2"='C' OR "T1"."C2"='F' OR "T1"."C2"='H')))
       filter(("T1"."C3"='18110034450001' OR "T1"."C3"='18110034450101' OR "T1"."C3"='18110034450201' OR
              "T1"."C3"='18110034450301' OR "T1"."C3"='18110034450401' OR "T1"."C3"='18110034450501'))

This was the plan from 12.2.0.1 – and again the plan for 10.2.0.3 was identical except for costs which became 140 for the index range scan and 141 for the table access. At first sight it looks like 10g may be using the total selectivity of the entire query as the scaling factor for the index clustering_factor to find the table cost while 12c uses the cost of accessing the table for one iteration (rounding up) before multiplying by the number of iterations.

Having observed this detail I thought I’d do a quick test of what happened by default if I requested 145 distinct values of c3. Both versions defaulted to the access/filter path rather than the pure access path – but again there was a difference in costs. The 10g index cost was 140 with a table access cost of 158, while 12c had an index cost of 179 and a table cost of 372. So both versions switch plans at some point – do they switch at the same point ? Reader, I could not resist temptation, so I ran a test loop. With my data set the 12c version switched paths at 61 values in the in-list and 10g switched at 53 values –

Conclusion: there’s been a change in the selectivity calculations for the use of in-list iterators, which leads to a change in costs, which can lead to a change in plans; the OP was just unlucky with his data set and stats. Possibly there’s something about his data or stats that makes the switch appear with a much smaller in-list than mine.

Footnote:

When I responded to the thread on MOSC with the suggestion that the problem was in part due to statistics and might be affected by out of date stats (or a histogram on the (low-frequency) c2 column) the OP noted that stats hadn’t been gathered since some time in August – and found that the 12c path changed to the efficient (10g) one after re-gathering stats on the table.

 

Subquery Order

From time to time I’ve wanted to optimize a query by forcing Oracle to execute existence (or non-existence) subqueries in the correct order because I know which subquery will eliminate most data most efficiently, and it’s always a good idea to look for ways to eliminate early. I’ve only just discovered (which doing some tests on 18c) that Oracle 12.2.0.1 introduced the /*+ order_subq() */ hint that seems to be engineered to do exactly that.

Here’s a very simple (and completely artificial) demonstration of use.

rem
rem     Script:         122_order_subq.sql
rem     Author:         Jonathan Lewis
rem     Dated:          Sep 2018
rem

create table t1 as select * from all_objects;
create table t2 as select * from all_objects;
create table t3 as select * from all_objects;

create index t2_i1 on t2(object_id);
create index t3_i1 on t3(object_id);

prompt  =============================
prompt  order_subq(@main subq2 subq3)
prompt  =============================

explain plan for
select
        /*+
                qb_name(main)
                no_unnest(@subq2)
                no_unnest(@subq3)
                order_subq(@main subq2 subq3)
        */
        t1.object_name, t1.object_type
from
        t1
where
        exists (
                select
                        /*+ qb_name(subq2) */
                        null
                from    t2
                where   t2.object_id = t1.object_id * 5
        )
and     exists (
                select
                        /*+ qb_name(subq3) */
                        null
                from    t3
                where   t3.object_id = t1.object_id * 13
        )
;

select * from table(dbms_xplan.display(null,null,'outline'));

=============================
order_subq(@main subq2 subq3)
=============================

PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------------------
Plan hash value: 2585036931

----------------------------------------------------------------------------
| Id  | Operation          | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |       |     1 |    53 | 51090   (1)| 00:00:02 |
|*  1 |  FILTER            |       |       |       |            |          |
|   2 |   TABLE ACCESS FULL| T1    | 61765 |  3196K|   163   (4)| 00:00:01 |
|*  3 |   INDEX RANGE SCAN | T2_I1 |     1 |     5 |     1   (0)| 00:00:01 |
|*  4 |   INDEX RANGE SCAN | T3_I1 |     1 |     5 |     1   (0)| 00:00:01 |
----------------------------------------------------------------------------

Outline Data
-------------
  /*+
      BEGIN_OUTLINE_DATA
...
      ORDER_SUBQ(@"MAIN" "SUBQ2" "SUBQ3")
...
  */

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter( EXISTS (SELECT /*+ NO_UNNEST QB_NAME ("SUBQ2") */ 0 FROM
              "T2" "T2" WHERE "T2"."OBJECT_ID"=:B1*5) AND  EXISTS (SELECT /*+
              NO_UNNEST QB_NAME ("SUBQ3") */ 0 FROM "T3" "T3" WHERE
              "T3"."OBJECT_ID"=:B2*13))
   3 - access("T2"."OBJECT_ID"=:B1*5)
   4 - access("T3"."OBJECT_ID"=:B1*13)

I’ve blocked subquery unnesting for the purposes of the demo and given a query block name to the two subqueries (using a name that identifies the associated table). As you can see, the execution plan uses the subqueries as filter subqueries, operating them in the order I’ve specified in my hint. You can also see that the hint is echoed down into the Outline section of the plan.

It’s possible that this is the plan that the optimizer would have chosen without the order_subq hint, so I ought to see if I can also use the hint to make the subqueries filter in the oppostie order. I happen to know that executing the subquery against t3 is likely to eliminate more rows that executing the subquery against t2. (The “* 13” compared to the “* 5” is significant) so I really want the subqueries to be used in the opposite order anyway – so here’s what happens when I reverse the order in the hint:

prompt  =============================
prompt  order_subq(@main subq3 subq2)
prompt  =============================

explain plan for
select
        /*+
                qb_name(main)
                no_unnest(@subq2)
                no_unnest(@subq3)
                order_subq(@main subq3 subq2)
        */
        t1.object_name, t1.object_type
from
        t1
where
        exists (
                select
                        /*+ qb_name(subq2) */
                        null
                from    t2
                where   t2.object_id = t1.object_id * 5
        )
and     exists (
                select
                        /*+ qb_name(subq3) */
                        null
                from    t3
                where   t3.object_id = t1.object_id * 13
        )
;

select * from table(dbms_xplan.display(null,null,'outline'));

=============================
order_subq(@main subq2 subq3)
=============================

PLAN_TABLE_OUTPUT
------------------------------------------------------------------------------------------------------------------------------------
Plan hash value: 3585049451

----------------------------------------------------------------------------
| Id  | Operation          | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |       |     1 |    53 | 51090   (1)| 00:00:02 |
|*  1 |  FILTER            |       |       |       |            |          |
|   2 |   TABLE ACCESS FULL| T1    | 61765 |  3196K|   163   (4)| 00:00:01 |
|*  3 |   INDEX RANGE SCAN | T3_I1 |     1 |     5 |     1   (0)| 00:00:01 |
|*  4 |   INDEX RANGE SCAN | T2_I1 |     1 |     5 |     1   (0)| 00:00:01 |
----------------------------------------------------------------------------

Outline Data
-------------
  /*+
      BEGIN_OUTLINE_DATA
...
      ORDER_SUBQ(@"MAIN" "SUBQ3" "SUBQ2")
...
  */

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter( EXISTS (SELECT /*+ NO_UNNEST QB_NAME ("SUBQ3") */ 0 FROM
              "T3" "T3" WHERE "T3"."OBJECT_ID"=:B1*13) AND  EXISTS (SELECT /*+
              NO_UNNEST QB_NAME ("SUBQ2") */ 0 FROM "T2" "T2" WHERE
              "T2"."OBJECT_ID"=:B2*5))
   3 - access("T3"."OBJECT_ID"=:B1*13)
   4 - access("T2"."OBJECT_ID"=:B1*5)

With the modified hint in place the order of the filter subqueries is reversed. Notice how the Predicate section also echoes the ordering of the subqueries.

Footnote

It should be noted that the order_subq() hint doesn’t get mentioned in the 18c SQL Language Reference “Alphabetical List of Hints”. If it were then one of the little oddities that might get a mention is that the optimizer seems to ignore the hint if you disable CPU costing. (not that anyone should be doing that since 10g).

pushing predicates

I came across this odd limitation (maybe defect) with pushing predicates (join predicate push down) a few years ago that made a dramatic difference to a client query when fixed but managed to hide itself rather cunningly until you looked closely at what was going on. Searching my library for something completely different I’ve just rediscovered the model I built to demonstrate the issue so I’ve tested it against a couple of newer versions  of Oracle (including 18.1) and found that the anomaly still exists. It’s an interesting little detail about checking execution plans properly so I’ve written up the details. The critical feature of the problem is a union all view:

rem
rem	Script:		push_pred_limitation.sql
rem	Author:		Jonathan Lewis
rem	Dated:		Jan 2015
rem
rem	Last tested 
rem		18.1.0.0	via LiveSQL
rem		12.2.0.1
rem		12.1.0.2
rem		11.2.0.4
rem

create table t1
as
select	* 
from	all_objects
where	rownum <= 10000 -- > comment to avoid WordPress format issue
;

create table t2
as
select	* 
from	all_objects
where	rownum <= 10000 -- > comment to avoid WordPress format issue
;

create table t3
as
select	* 
from	all_objects
where	rownum <= 10000 -- > comment to avoid WordPress format issue
;

begin
	dbms_stats.gather_table_stats(
		ownname		 => user,
		tabname		 =>'T1',
		method_opt	 => 'for all columns size 1 for columns owner size 254'
	);
	dbms_stats.gather_table_stats(
		ownname		 => user,
		tabname		 =>'T2',
		method_opt	 => 'for all columns size 1'
	);
	dbms_stats.gather_table_stats(
		ownname		 => user,
		tabname		 =>'T3',
		method_opt	 => 'for all columns size 1'
	);
end;
/

create index t2_id on t2(object_id);
-- create index t2_id_ot on t2(object_id, object_type);

create index t3_name_type on t3(object_name, object_type);

create or replace view v1
as
select 
	/*+ qb_name(part1) */
	t2.object_id,
	t2.object_type	object_type_2,
	t3.object_type	object_type_3,
	t2.created	date_2,
	t3.created	date_3
from
	t2, t3
where
	t3.object_name = t2.object_name
union all
select
	/*+ qb_name(part2) */
	t2.object_id,
	t2.object_type	object_type_2,
	t3.object_type	object_type_3,
	t2.last_ddl_time	date_2,
	t3.last_ddl_time	date_3
from
	t2, t3
where
	t3.object_name = t2.object_name
;

Two points to note so far: first, the view is basically joining the same two tables in the same way twice but selecting different columns. It’s a close model of what the client was doing but so much simpler that it wouldn’t be hard to find a different way of getting the same result: the client’s version would have been much far harder to rewrite. Secondly, I’ve listed two possible indexes for table t2 but commented one of them out. The indexing will make a difference that I’ll describe later.

So here’s the query with execution plan (from explain plan – but pulling the plan from memory gives the same result):

select
	/*+ qb_name(main) */
	t1.object_name, t1.object_type,
	v1.object_id, v1.date_2, v1.date_3
from
	t1,
	v1
where
	v1.object_id = t1.object_id
and	v1.object_type_2 = t1.object_type
and	v1.object_type_3 = t1.object_type
and	t1.owner = 'OUTLN'
;

Plan hash value: 4123301926

---------------------------------------------------------------------------------------------------------
| Id  | Operation                                | Name         | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                         |              |     7 |   588 |    82   (2)| 00:00:01 |
|   1 |  NESTED LOOPS                            |              |     7 |   588 |    82   (2)| 00:00:01 |
|*  2 |   TABLE ACCESS FULL                      | T1           |     7 |   280 |    26   (4)| 00:00:01 |
|*  3 |   VIEW                                   | V1           |     1 |    44 |     8   (0)| 00:00:01 |
|   4 |    UNION ALL PUSHED PREDICATE            |              |       |       |            |          |
|   5 |     NESTED LOOPS                         |              |     1 |    77 |     4   (0)| 00:00:01 |
|   6 |      NESTED LOOPS                        |              |     1 |    77 |     4   (0)| 00:00:01 |
|   7 |       TABLE ACCESS BY INDEX ROWID BATCHED| T2           |     1 |    41 |     2   (0)| 00:00:01 |
|*  8 |        INDEX RANGE SCAN                  | T2_ID        |     1 |       |     1   (0)| 00:00:01 |
|*  9 |       INDEX RANGE SCAN                   | T3_NAME_TYPE |     1 |       |     1   (0)| 00:00:01 |
|  10 |      TABLE ACCESS BY INDEX ROWID         | T3           |     1 |    36 |     2   (0)| 00:00:01 |
|  11 |     NESTED LOOPS                         |              |     1 |    77 |     4   (0)| 00:00:01 |
|  12 |      NESTED LOOPS                        |              |     1 |    77 |     4   (0)| 00:00:01 |
|  13 |       TABLE ACCESS BY INDEX ROWID BATCHED| T2           |     1 |    41 |     2   (0)| 00:00:01 |
|* 14 |        INDEX RANGE SCAN                  | T2_ID        |     1 |       |     1   (0)| 00:00:01 |
|* 15 |       INDEX RANGE SCAN                   | T3_NAME_TYPE |     1 |       |     1   (0)| 00:00:01 |
|  16 |      TABLE ACCESS BY INDEX ROWID         | T3           |     1 |    36 |     2   (0)| 00:00:01 |
---------------------------------------------------------------------------------------------------------


Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter("T1"."OWNER"='OUTLN')
   3 - filter("V1"."OBJECT_TYPE_2"="T1"."OBJECT_TYPE")
   8 - access("T2"."OBJECT_ID"="T1"."OBJECT_ID")
   9 - access("T3"."OBJECT_NAME"="T2"."OBJECT_NAME" AND "T3"."OBJECT_TYPE"="T1"."OBJECT_TYPE")
  14 - access("T2"."OBJECT_ID"="T1"."OBJECT_ID")
  15 - access("T3"."OBJECT_NAME"="T2"."OBJECT_NAME" AND "T3"."OBJECT_TYPE"="T1"."OBJECT_TYPE")

The execution plan appears to be fine – we can see at operation 4 that the union all view has been access with the pushed predicate option and that the subsequent sub-plan has
used index driven nested loop joins in both branches – until we look a little more closely and examine the Predicate section of the plan. What, exactly, has been pushed ?

Look at the predicate for operation 3: “V1″.”OBJECT_TYPE_2″=”T1″.”OBJECT_TYPE”. It’s a join predicate that hasn’t been pushed into the view. On the other hand the original, and similar, join predicate v1.object_type_3 = t1.object_type has been pushed into the view, appearing at operations 9 and 15. There is a difference, of course, the object_type_3 column appears as the second column of the index on table t3.

Two questions then: (a) will the object_type_2 predicate be pushed if we add it to the relevant index on table t2, (b) is there a way to get the predicate pushed without adding it to the index. The answer to both questions is yes. First the index – re-run the test but create the alternative index on t2 and the plan changes to:

Plan hash value: 497545587

---------------------------------------------------------------------------------------------------------
| Id  | Operation                                | Name         | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                         |              |     7 |   553 |    82   (2)| 00:00:01 |
|   1 |  NESTED LOOPS                            |              |     7 |   553 |    82   (2)| 00:00:01 |
|*  2 |   TABLE ACCESS FULL                      | T1           |     7 |   280 |    26   (4)| 00:00:01 |
|   3 |   VIEW                                   | V1           |     1 |    39 |     8   (0)| 00:00:01 |
|   4 |    UNION ALL PUSHED PREDICATE            |              |       |       |            |          |
|   5 |     NESTED LOOPS                         |              |     1 |    77 |     4   (0)| 00:00:01 |
|   6 |      NESTED LOOPS                        |              |     1 |    77 |     4   (0)| 00:00:01 |
|   7 |       TABLE ACCESS BY INDEX ROWID BATCHED| T2           |     1 |    41 |     2   (0)| 00:00:01 |
|*  8 |        INDEX RANGE SCAN                  | T2_ID_OT     |     1 |       |     1   (0)| 00:00:01 |
|*  9 |       INDEX RANGE SCAN                   | T3_NAME_TYPE |     1 |       |     1   (0)| 00:00:01 |
|  10 |      TABLE ACCESS BY INDEX ROWID         | T3           |     1 |    36 |     2   (0)| 00:00:01 |
|  11 |     NESTED LOOPS                         |              |     1 |    77 |     4   (0)| 00:00:01 |
|  12 |      NESTED LOOPS                        |              |     1 |    77 |     4   (0)| 00:00:01 |
|  13 |       TABLE ACCESS BY INDEX ROWID BATCHED| T2           |     1 |    41 |     2   (0)| 00:00:01 |
|* 14 |        INDEX RANGE SCAN                  | T2_ID_OT     |     1 |       |     1   (0)| 00:00:01 |
|* 15 |       INDEX RANGE SCAN                   | T3_NAME_TYPE |     1 |       |     1   (0)| 00:00:01 |
|  16 |      TABLE ACCESS BY INDEX ROWID         | T3           |     1 |    36 |     2   (0)| 00:00:01 |
---------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter("T1"."OWNER"='OUTLN')
   8 - access("T2"."OBJECT_ID"="T1"."OBJECT_ID" AND "T2"."OBJECT_TYPE"="T1"."OBJECT_TYPE")
   9 - access("T3"."OBJECT_NAME"="T2"."OBJECT_NAME" AND "T3"."OBJECT_TYPE"="T1"."OBJECT_TYPE")
  14 - access("T2"."OBJECT_ID"="T1"."OBJECT_ID" AND "T2"."OBJECT_TYPE"="T1"."OBJECT_TYPE")
  15 - access("T3"."OBJECT_NAME"="T2"."OBJECT_NAME" AND "T3"."OBJECT_TYPE"="T1"."OBJECT_TYPE")

Notice how the predicate at operation 3 has disappeared, and the access predicate at operation 8 now includes the predicate “T2″.”OBJECT_TYPE”=”T1″.”OBJECT_TYPE”.

Alternatively, don’t mess about with the indexes – just tell Oracle to push the predicate. Normally I would just try /*+ push_pred(v1) */ as the hint to do this, but the Outline section of the original execution plan already included a push_pred() hint that looked like this: PUSH_PRED(@”MAIN” “V1″@”MAIN” 3 1), so I first copied exactly that into the SQL to see if it would make any difference. It did – I got the following plan (and the hint in the outline changed to PUSH_PRED(@”MAIN” “V1″@”MAIN” 3 2 1) so this may be a case where the plan produced by a baseline will perform better than the plan that the produced the baseline!):

Plan hash value: 4123301926

---------------------------------------------------------------------------------------------------------
| Id  | Operation                                | Name         | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                         |              |     7 |   553 |    82   (2)| 00:00:01 |
|   1 |  NESTED LOOPS                            |              |     7 |   553 |    82   (2)| 00:00:01 |
|*  2 |   TABLE ACCESS FULL                      | T1           |     7 |   280 |    26   (4)| 00:00:01 |
|   3 |   VIEW                                   | V1           |     1 |    39 |     8   (0)| 00:00:01 |
|   4 |    UNION ALL PUSHED PREDICATE            |              |       |       |            |          |
|   5 |     NESTED LOOPS                         |              |     1 |    77 |     4   (0)| 00:00:01 |
|   6 |      NESTED LOOPS                        |              |     1 |    77 |     4   (0)| 00:00:01 |
|*  7 |       TABLE ACCESS BY INDEX ROWID BATCHED| T2           |     1 |    41 |     2   (0)| 00:00:01 |
|*  8 |        INDEX RANGE SCAN                  | T2_ID        |     1 |       |     1   (0)| 00:00:01 |
|*  9 |       INDEX RANGE SCAN                   | T3_NAME_TYPE |     1 |       |     1   (0)| 00:00:01 |
|  10 |      TABLE ACCESS BY INDEX ROWID         | T3           |     1 |    36 |     2   (0)| 00:00:01 |
|  11 |     NESTED LOOPS                         |              |     1 |    77 |     4   (0)| 00:00:01 |
|  12 |      NESTED LOOPS                        |              |     1 |    77 |     4   (0)| 00:00:01 |
|* 13 |       TABLE ACCESS BY INDEX ROWID BATCHED| T2           |     1 |    41 |     2   (0)| 00:00:01 |
|* 14 |        INDEX RANGE SCAN                  | T2_ID        |     1 |       |     1   (0)| 00:00:01 |
|* 15 |       INDEX RANGE SCAN                   | T3_NAME_TYPE |     1 |       |     1   (0)| 00:00:01 |
|  16 |      TABLE ACCESS BY INDEX ROWID         | T3           |     1 |    36 |     2   (0)| 00:00:01 |
---------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter("T1"."OWNER"='TEST_USER')
   7 - filter("T2"."OBJECT_TYPE"="T1"."OBJECT_TYPE")
   8 - access("T2"."OBJECT_ID"="T1"."OBJECT_ID")
   9 - access("T3"."OBJECT_NAME"="T2"."OBJECT_NAME" AND "T3"."OBJECT_TYPE"="T1"."OBJECT_TYPE")
  13 - filter("T2"."OBJECT_TYPE"="T1"."OBJECT_TYPE")
  14 - access("T2"."OBJECT_ID"="T1"."OBJECT_ID")
  15 - access("T3"."OBJECT_NAME"="T2"."OBJECT_NAME" AND "T3"."OBJECT_TYPE"="T1"."OBJECT_TYPE")

In this case we see that the critical late-joining predicate has disappeared from operation 3 and re-appeared as a filter predicate at operation 7 In many cases you may find that the change in predicate use makes little difference to the performance – in my example the variation in run time over several executions of each query was larger than the average run time of the query; nevertheless it’s worth noting that the delayed use of the predicate could have increased the number of probes into table t3 for both branches of the union all and resulted in redundant data passing up through several layers of the call stack before being eliminated … and “eliminate early” is one of the major commandments of optimisation.

You might notice that the Plan Hash Value for the hinted execution plan is the same as for the original execution plan: the hashing algorithm doesn’t take the predicates into account (just one of many points that Randolf Geist raised in a blog post several years ago). This is one of the little details that makes it easy to miss the little changes in a plan that can make a big difference in performance.

Summary

If you have SQL that joins simple tables to set based (union all, etc.) views and you see the pushed predicate option appearing take a little time to examine the predicate section of the execution plan to see if the optimizer is pushing all the join predicates that it should and, if it isn’t, test the effects of pushing more predicates.

In many cases adding the hint /*+ push_pred(your_view_name) */ at the top of the query may be sufficient to get the predicate pushing you need, but you may need to look at the outline section of the execution plan and add a series of more complicated push_pred() and no_push_pred() hints because the push_pred hint has evolved over time to deal with increasingly complicated transformations.

 

Hacking Profiles

Saturday’s posting about setting cursor_sharing to force reminded me about one of the critical limitations of SQL Profiles (which is one of those little reason why you shouldn’t be hacking SQL Profiles as a substitute for SQL Plan Baselines). Here’s a demo (taking advantage of some code that I think Kerry Osborne published several years ago) of creating an SQL Profile from the current execution plan of a simple statement – first we create some data and find the sql_id and child_number for a simple query:

rem
rem     Script:         sql_profile_restriction.sql
rem     Author:         Jonathan Lewis
rem     Dated:          Jun 2018
rem     Purpose:
rem
rem     Last tested
rem             12.2.0.1
rem             12.1.0.2

create table t1
as
select
        rownum            n1,
        rownum            n2,
        lpad(rownum,10)   small_vc,
        rpad('x',100,'x') padding
from dual
connect by
        level <= 1e4 -- > comment to avoid WordPress format issue
;

alter system flush shared_pool;

select /*+ find this */ count(*) from t1 where n1 = 15 and n2 = 15;

column sql_id new_value m_sql_id
column child_number new_value m_child_number

select  sql_id , child_number
from    v$sql
where   sql_text like 'selec%find this%'
and     sql_text not like '%v$sql%'
;

Now I can create the SQL Profile for this query using the Kerry Osborne code:

declare
        ar_profile_hints        sys.sqlprof_attr;
        cl_sql_text clob;
begin
        select
                extractvalue(value(d), '/hint') as outline_hints
        bulk collect into 
                ar_profile_hints
        from
                xmltable(
                        '/*/outline_data/hint'
                        passing (
                                select
                                        xmltype(other_xml) as xmlval
                                from
                                        v$sql_plan
                                where
                                        sql_id = '&m_sql_id'
                                and     child_number =  &m_child_number 
                                and     other_xml is not null
                )
        ) d;

        select
                sql_fulltext
        into
                cl_sql_text
        from
                v$sql
        where
                sql_id = '&m_sql_id'
        and     child_number =  &m_child_number
        ;

        dbms_sqltune.import_sql_profile(
                sql_text        => cl_sql_text, 
                profile         => ar_profile_hints, 
                category        => 'DEFAULT',
                name            => 'PROFILE_LITERAL',
                force_match     =>  true
        );
end;
/

Note particularly that I have given the profile a simple name, put it in the DEFAULT category, and set force_match to true (which means that the profile ought to be used even if I change the literal values in the query). So now let’s check that the profile will be used as expected. First I’ll create an index that is a really good index for this query, then I’ll run the query to see if Oracle uses the index or obeys the profile; then I’ll change the query (literals) slightly and check again. I’ll also run a query that won’t be recognised as legally matching (thanks to the changed “hint”) to demonistrate that the index could have been used if the profile hadn’t been there:

alter system flush shared_pool;
set serveroutput off

prompt  =============================
prompt  Is the SQL Profile used ? Yes
prompt  =============================

select /*+ find this */ count(*) from t1 where n1 = 15 and n2 = 15;
select * from table(dbms_xplan.display_cursor);

select /*+ find this */ count(*) from t1 where n1 = 16 and n2 = 16;
select * from table(dbms_xplan.display_cursor);

select /*+ Non-match */ count(*) from t1 where n1 = 16 and n2 = 16;
select * from table(dbms_xplan.display_cursor);

Here (with a little cosmetic adjustment) are the three outputs from dbms_xplan.display_cursor():

SQL_ID  ayxnhrqzd38g3, child number 0
-------------------------------------
select /*+ find this */ count(*) from t1 where n1 = 15 and n2 = 15
---------------------------------------------------------------------------
| Id  | Operation          | Name | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |      |       |       |    24 (100)|          |
|   1 |  SORT AGGREGATE    |      |     1 |     8 |            |          |
|*  2 |   TABLE ACCESS FULL| T1   |     1 |     8 |    24   (5)| 00:00:01 |
---------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter(("N1"=15 AND "N2"=15))

Note
-----
   - SQL profile PROFILE_LITERAL used for this statement


SQL_ID  gqjb8pp35cnyp, child number 0
-------------------------------------
select /*+ find this */ count(*) from t1 where n1 = 16 and n2 = 16
---------------------------------------------------------------------------
| Id  | Operation          | Name | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |      |       |       |    24 (100)|          |
|   1 |  SORT AGGREGATE    |      |     1 |     8 |            |          |
|*  2 |   TABLE ACCESS FULL| T1   |     1 |     8 |    24   (5)| 00:00:01 |
---------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter(("N1"=16 AND "N2"=16))

Note
-----
   - SQL profile PROFILE_LITERAL used for this statement


SQL_ID  3gvaxypny9ry1, child number 0
-------------------------------------
select /*+ Non-match */ count(*) from t1 where n1 = 16 and n2 = 16
---------------------------------------------------------------------------
| Id  | Operation         | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------
|   0 | SELECT STATEMENT  |       |       |       |     1 (100)|          |
|   1 |  SORT AGGREGATE   |       |     1 |     8 |            |          |
|*  2 |   INDEX RANGE SCAN| T1_I1 |     1 |     8 |     1   (0)| 00:00:01 |
---------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - access("N1"=16 AND "N2"=16)

As you can see the SQL Profile is reported as used in the first two queries, and (visibly) seems to have been used. Then in the third query where we wouldn’t expect a match the SQL Profile is not used and we get a plan that shows the index would have been used for the other queries had the SQL Profile not been there. So far, so good – the profile behaves as everyone might expect.

Bind Variable Breaking

Now let’s repeat the entire experiment but first do a global find and replace to change every occurrence of “n2 = 16” to “n2 = :b1”. We’ll also change the name of the SQL Profile when we create it to PROFILE_MIXED, and we’ll put in a couple of lines at the top of the script to declare the variable b1 and set its value, then the final test in the script will look like this:

alter system flush shared_pool;
create index t1_i1 on t1(n1, n2);

exec :b1 := 15

select /*+ find this */ count(*) from t1 where n1 = 15 and n2 = :b1;
select * from table(dbms_xplan.display_cursor);

exec :b1 := 16

select /*+ find this */ count(*) from t1 where n1 = 16 and n2 = :b1;
select * from table(dbms_xplan.display_cursor);

And here are the execution plans from the two queries:

SQL_ID  236f82vmsvjab, child number 0
-------------------------------------
select /*+ find this */ count(*) from t1 where n1 = 15 and n2 = :b1
---------------------------------------------------------------------------
| Id  | Operation          | Name | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------
|   0 | SELECT STATEMENT   |      |       |       |    24 (100)|          |
|   1 |  SORT AGGREGATE    |      |     1 |     8 |            |          |
|*  2 |   TABLE ACCESS FULL| T1   |     1 |     8 |    24   (5)| 00:00:01 |
---------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter(("N1"=15 AND "N2"=:B1))

Note
-----
   - SQL profile PROFILE_MIXED used for this statement


SQL_ID  7nakm3tw27z3c, child number 0
-------------------------------------
select /*+ find this */ count(*) from t1 where n1 = 16 and n2 = :b1
---------------------------------------------------------------------------
| Id  | Operation         | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------
|   0 | SELECT STATEMENT  |       |       |       |     1 (100)|          |
|   1 |  SORT AGGREGATE   |       |     1 |     8 |            |          |
|*  2 |   INDEX RANGE SCAN| T1_I1 |     1 |     8 |     1   (0)| 00:00:01 |
---------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - access("N1"=16 AND "N2"=:B1)

As you can see the execution plan for the original query is still doing a full tablescan and reporting the SQL Profile as used; but we’re not using (or reporting) the SQL Profile when we change the literal values – even though a query against dba_sql_profiles will tell us that the profile has force_matching = ‘YES’.

tl;dr

(Clarified in response to Mohammed Houri’s comment below)
If you use an SQL Profile with force_match => true to “hide” the literals in a statement that includes bind variables (even if they appear only in the select list, in fact) the mechanism will not be used, and the SQL Profile will apply only to the original statement.

Update

Christian Antognini has an elegant little script that uses the dbms_sqltune.sqltext_to_signature() function to highlight this point (among others).  Bear in mind, before you run the script, that you need to be licensed to use the dbms_sqltune package to do so.

 

Conditional SQL – 5

Here’s a note that has been sitting around for more than 3 years (the draft date is Jan 2015), waiting for me to finish it off; and in that time we’ve got a new version of Oracle that changes the solution to the problem it presented. (I also managed to write “Conditional SQL –  6” in the intervening period !)

This posting started with a question on the OTN (now ODC) database forum about an execution plan used by 11.2.0.3.  Here’s a model to represent the data and the query:

rem
rem     Script:         null_plan_4.sql
rem     Author:         Jonathan Lewis
rem     Dated:          Jan 2015
rem
rem     Last tested
rem             12.2.0.1
rem             12.1.0.2
rem             11.2.0.4
rem

create table catentry as
with generator as (
        select  --+ materialize
                rownum id
        from dual
        connect by
                level <= 1e4 -- > comment here to avoid format issue
)
select
        rownum  catentry_id,
        case
                when mod(rownum-1,100) > 0 then mod(rownum-1,100)
        end     member_id,
        case
                when trunc((rownum-1)/100) > 0 then trunc((rownum-1)/100)
        end     partnumber,
        rpad('x',100)   padding
from
        generator,
        generator
where
        rownum <= 100 * 100 -- > comment here to avoid format issue
;

execute dbms_stats.gather_table_stats(user,'catentry');

create unique index cat_i0 on catentry(member_id, partnumber) compress 1;
--  create        index cat_i1 on catentry(member_id, partnumber, 0) compress 1;
--  create        index cat_i2 on catentry(partnumber, member_id, 0) compress 1;

variable b1 number
variable b2 number
variable b3 number
variable b4 number

begin
        :b1 := 22;
        :b2 := 1;
        :b3 := 44;
        :b4 := 1;
end;
/

select
        catentry_id
from
        catentry
where
        (   partnumber= :b1
         or (0 = :b2 and partnumber is null)
        )
and     (    member_id= :b3
         or (0 = :b4 and member_id is null)
        )
;

select * from table(dbms_xplan.display_cursor);

==============================================================================

------------------------------------------------------------------------------
| Id  | Operation         | Name     | Rows  | Bytes | Cost (%CPU)| Time     |
------------------------------------------------------------------------------
|   0 | SELECT STATEMENT  |          |       |       |    23 (100)|          |
|*  1 |  TABLE ACCESS FULL| CATENTRY |     1 |    10 |    23   (5)| 00:00:01 |
------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter((("PARTNUMBER"=:B1 OR ("PARTNUMBER" IS NULL AND 0=:B2))
              AND ("MEMBER_ID"=:B3 OR ("MEMBER_ID" IS NULL AND 0=:B4))))

The question this example raised was, effectively: “Why didn’t Oracle use bind peeking to work out that the best plan for this query – with these input values – was an index range scan?”

The basic answer to this question is this: “No matter how clever Oracle can be with bind peeking and executions plans it has to produce an execution plan that will give the right answer whatever the supplied values might be.”

The OP was hoping that the optimizer would see :b2 and :b4 were arriving with the value 1, infer that “0 = 1” is always false, and reduce the query predicate to “partnumber =22 and member_id = 44” to produce the following plan:

----------------------------------------------------------------------------------------
| Id  | Operation                   | Name     | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT            |          |       |       |     2 (100)|          |
|   1 |  TABLE ACCESS BY INDEX ROWID| CATENTRY |     1 |    10 |     2   (0)| 00:00:01 |
|*  2 |   INDEX UNIQUE SCAN         | CAT_I0   |     1 |       |     1   (0)| 00:00:01 |
----------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - access("PARTNUMBER"=22 AND "MEMBER_ID"=44)

But this plan could produce the wrong results if the next execution of the query supplied zeros for :b2 or :b4, so Oracle has to do something more generic. (Bear in mind that adaptive cursor sharing keeps reusing the same execution plan until it detects that the performance for some input values is bad; it doesn’t pre-emptively create new plans based on the incoming values – though in principle it might be possible for the Oracle developers to introduce code that can recognise special cases for predicates of the form “constant1 = constant2”).

If you review the SQL statement you can see that it’s clearly trying to allow the user to find data about member_ids and partnumbers where both, neither, or either value is allowed to be null: a couple of “if – then – else” conditions that should have been handled in the client code have been embedded in the code. As we have seen several times before if you can’t change the client code then you have to hope that Oracle will use some clever transformation to handle the query in sections.

We can infer from various details of the posting that the member_id and partnumber columns were both allowed to be null, so if we want to make sure that Oracle always uses an indexed access path to acquire data for this query we need to have an index which starts with those two columns and then has at least one column which is guaranteed to be non-null so, for example, we could simply drop the current index and replace it with one that has a fixed zero on the end:

create index cat_i1 on catentry(member_id, partnumber, 0) compress 1;

With my particular data set, query, and version of Oracle this didn’t make any difference to the plan. But then I thought about the data definition and realised (and checked) that the index had a terrible clustering_factor, so I dropped it and created it with the first two columns in the opposite order:

create index cat_i2 on catentry(partnumber, member_id, 0) compress 1;

Side note:
You’ll notice that I’ve replaced the original unique index with a non-unique index. This was necessary because there were many rows where both partnumber and member_id were null, so if I want to maintain the logic of the previous unique index I’ll need to add a unique constraint on (member_id, partnumber). It’s possible, of course, that in similar circumstances I might want both indexes – one for the uniqueness and to access the data using only one of the columns, the other to access the data using only the other column.

With this index in place, and unhinted, the plan I got from 11.2.0.4 changed to use concatenation with an impressive four-way split:

------------------------------------------------------------------------------------------
| Id  | Operation                     | Name     | Rows  | Bytes | Cost (%CPU)| Time     |
------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT              |          |       |       |    12 (100)|          |
|   1 |  CONCATENATION                |          |       |       |            |          |
|*  2 |   FILTER                      |          |       |       |            |          |
|   3 |    TABLE ACCESS BY INDEX ROWID| CATENTRY |     1 |    10 |     3   (0)| 00:00:01 |
|*  4 |     INDEX RANGE SCAN          | CAT_I2   |     1 |       |     2   (0)| 00:00:01 |
|*  5 |   FILTER                      |          |       |       |            |          |
|*  6 |    TABLE ACCESS BY INDEX ROWID| CATENTRY |     1 |    10 |     3   (0)| 00:00:01 |
|*  7 |     INDEX RANGE SCAN          | CAT_I2   |     1 |       |     2   (0)| 00:00:01 |
|*  8 |   FILTER                      |          |       |       |            |          |
|*  9 |    TABLE ACCESS BY INDEX ROWID| CATENTRY |     1 |    10 |     3   (0)| 00:00:01 |
|* 10 |     INDEX RANGE SCAN          | CAT_I2   |     1 |       |     2   (0)| 00:00:01 |
|* 11 |   TABLE ACCESS BY INDEX ROWID | CATENTRY |     1 |    10 |     3   (0)| 00:00:01 |
|* 12 |    INDEX RANGE SCAN           | CAT_I2   |     1 |       |     2   (0)| 00:00:01 |
------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter((0=:B2 AND 0=:B4))
   4 - access("PARTNUMBER" IS NULL AND "MEMBER_ID" IS NULL)
       filter("MEMBER_ID" IS NULL)
   5 - filter(0=:B2)
   6 - filter((LNNVL("MEMBER_ID" IS NULL) OR LNNVL(0=:B4)))
   7 - access("PARTNUMBER" IS NULL AND "MEMBER_ID"=:B3)
       filter("MEMBER_ID"=:B3)
   8 - filter(0=:B4)
   9 - filter((LNNVL("PARTNUMBER" IS NULL) OR LNNVL(0=:B2)))
  10 - access("PARTNUMBER"=:B1 AND "MEMBER_ID" IS NULL)
  11 - filter(((LNNVL("MEMBER_ID" IS NULL) OR LNNVL(0=:B4)) AND
              (LNNVL("PARTNUMBER" IS NULL) OR LNNVL(0=:B2))))
  12 - access("PARTNUMBER"=:B1 AND "MEMBER_ID"=:B3)

To execute this plan the run-time engine works as follows:

• Operation 2: If :b2 and :b4 are both zero we use the index to find the rows where member_id and partnumber are null (the filter “member_id is null” seems to be redundant)
• Operation 5: if :b2 is zero we use the index to find rows where the partnumber is null and the member_id is the supplied value (and if that’s null the access will immediately return zero rows because of the equality predicate), and discard any rows that have already been returned by operation 2
• Operation 8: if :b4 is zero we will use the index to find rows where the partnumber is the supplied value (and if the partnumber is null, that access will immediately return zero rows because of the equality predicate) and the member_id is null, and discard any rows that have already been returned by operation 2.
• Operations 11 and 12 will always run – using the index to find rows that match with equality on both the incoming member_id and partnumber, discarding any rows already returned by the previous operations, and obviously not matching any rows where either column “IS” null.

The critical feature of this plan, of course, is that we got it because we have given Oracle an efficient option to find the rows where both member_id and partnumber are null – and that allows the rest of the concatenation options to take place.

Hints and Upgrades

Interestingly, after the clue that 11g would happily use concatenation with a “good enough” index I went back to the example where I’d just added a zero to the existing index and checked to see what would happen if I added a /*+ use_concat */ hint (without any of the qualifying parameters that the hint can now use) and got the same concatenated plan. The fact that the path appeared wasn’t the interesting bit – see if you can spot what is the interesting bit:

------------------------------------------------------------------------------------------
| Id  | Operation                     | Name     | Rows  | Bytes | Cost (%CPU)| Time     |
------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT              |          |       |       |    12 (100)|          |
|   1 |  CONCATENATION                |          |       |       |            |          |
|*  2 |   FILTER                      |          |       |       |            |          |
|   3 |    TABLE ACCESS BY INDEX ROWID| CATENTRY |     1 |    10 |     3   (0)| 00:00:01 |
|*  4 |     INDEX RANGE SCAN          | CAT_I1   |     1 |       |     2   (0)| 00:00:01 |
|*  5 |   FILTER                      |          |       |       |            |          |
|*  6 |    TABLE ACCESS BY INDEX ROWID| CATENTRY |     1 |    10 |     3   (0)| 00:00:01 |
|*  7 |     INDEX RANGE SCAN          | CAT_I1   |     1 |       |     2   (0)| 00:00:01 |
|*  8 |   FILTER                      |          |       |       |            |          |
|*  9 |    TABLE ACCESS BY INDEX ROWID| CATENTRY |     1 |    10 |     3   (0)| 00:00:01 |
|* 10 |     INDEX RANGE SCAN          | CAT_I1   |     1 |       |     2   (0)| 00:00:01 |
|* 11 |   TABLE ACCESS BY INDEX ROWID | CATENTRY |     1 |    10 |     3   (0)| 00:00:01 |
|* 12 |    INDEX RANGE SCAN           | CAT_I1   |     1 |       |     2   (0)| 00:00:01 |
------------------------------------------------------------------------------------------

Check the cost, and compare it with the cost of the full tablescan. The hinted path has a lower cost than the default path. I think this may be another case of an “unknowable” range scan being ignored in favour of a known alternative.

Finally, we get to today – when I tested the code against 12.1.0.2 and 12.2.0.1. Nothing exciting happened in 12.1.0.2 – the plans were just like the 11g plans, but here’s the plan I got in 12.2 with the “bad” indexing (original column order with added zero column – index cat_i1) without any hints in the SQL:

----------------------------------------------------------------------------------------------------------
| Id  | Operation			       | Name		 | Rows  | Bytes | Cost (%CPU)| Time	 |
----------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT		       |		 |	 |	 |     9 (100)| 	 |
|   1 |  VIEW				       | VW_ORE_37A7142B |     4 |    52 |     9   (0)| 00:00:01 |
|   2 |   UNION-ALL			       |		 |	 |	 |	      | 	 |
|   3 |    TABLE ACCESS BY INDEX ROWID	       | CATENTRY	 |     1 |    10 |     2   (0)| 00:00:01 |
|*  4 |     INDEX UNIQUE SCAN		       | CAT_I1 	 |     1 |	 |     1   (0)| 00:00:01 |
|*  5 |    FILTER			       |		 |	 |	 |	      | 	 |
|   6 |     TABLE ACCESS BY INDEX ROWID BATCHED| CATENTRY	 |     1 |    10 |     2   (0)| 00:00:01 |
|*  7 |      INDEX RANGE SCAN		       | CAT_I1 	 |     1 |	 |     1   (0)| 00:00:01 |
|*  8 |    FILTER			       |		 |	 |	 |	      | 	 |
|   9 |     TABLE ACCESS BY INDEX ROWID BATCHED| CATENTRY	 |     1 |    10 |     2   (0)| 00:00:01 |
|* 10 |      INDEX RANGE SCAN		       | CAT_I1 	 |     1 |	 |     1   (0)| 00:00:01 |
|* 11 |    FILTER			       |		 |	 |	 |	      | 	 |
|  12 |     TABLE ACCESS BY INDEX ROWID BATCHED| CATENTRY	 |     1 |    10 |     3   (0)| 00:00:01 |
|* 13 |      INDEX RANGE SCAN		       | CAT_I1 	 |     1 |	 |     2   (0)| 00:00:01 |
----------------------------------------------------------------------------------------------------------

Outline Data
-------------
  /*+
      BEGIN_OUTLINE_DATA
      ...
      OR_EXPAND(@"SEL$1" (1) (2) (3) (4))
      ...
      END_OUTLINE_DATA
  */

Predicate Information (identified by operation id):
---------------------------------------------------
   4 - access("MEMBER_ID"=:B3 AND "PARTNUMBER"=:B1)
   5 - filter(0=:B4)
   7 - access("MEMBER_ID" IS NULL AND "PARTNUMBER"=:B1)
       filter(("PARTNUMBER"=:B1 AND LNNVL("MEMBER_ID"=:B3)))
   8 - filter(0=:B2)
  10 - access("MEMBER_ID"=:B3 AND "PARTNUMBER" IS NULL)
       filter(LNNVL("PARTNUMBER"=:B1))
  11 - filter((0=:B4 AND 0=:B2))
  13 - access("MEMBER_ID" IS NULL AND "PARTNUMBER" IS NULL)
       filter(("PARTNUMBER" IS NULL AND LNNVL("PARTNUMBER"=:B1) AND LNNVL("MEMBER_ID"=:B3)))

The full tablescan didn’t appear – but it wasn’t eliminated by concatenation but by the “new” 12.2  variant: “OR EXPANSION”. In this case the net effect is remarkably similar – we still have filter operations comparing :b2 and :b4 with zero, and we still have a scattering of lnnvl() function calls being used to discard rows we’ve already accessed, but the pattern is slightly different and we have a union all rather than concatenation.

This change prompted me to go back to testing with just the original index (member_id, partnumber – index cat_i0) … which took me back to the full tablescan until I added the hint /*+ or_expand */ to the query to get the following plan:

----------------------------------------------------------------------------------------------------------
| Id  | Operation			       | Name		 | Rows  | Bytes | Cost (%CPU)| Time	 |
----------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT		       |		 |	 |	 |    29 (100)| 	 |
|   1 |  VIEW				       | VW_ORE_37A7142B |     4 |    52 |    29   (4)| 00:00:01 |
|   2 |   UNION-ALL			       |		 |	 |	 |	      | 	 |
|   3 |    TABLE ACCESS BY INDEX ROWID	       | CATENTRY	 |     1 |    10 |     2   (0)| 00:00:01 |
|*  4 |     INDEX UNIQUE SCAN		       | CAT_I0 	 |     1 |	 |     1   (0)| 00:00:01 |
|*  5 |    FILTER			       |		 |	 |	 |	      | 	 |
|   6 |     TABLE ACCESS BY INDEX ROWID BATCHED| CATENTRY	 |     1 |    10 |     2   (0)| 00:00:01 |
|*  7 |      INDEX RANGE SCAN		       | CAT_I0 	 |     1 |	 |     1   (0)| 00:00:01 |
|*  8 |    FILTER			       |		 |	 |	 |	      | 	 |
|   9 |     TABLE ACCESS BY INDEX ROWID BATCHED| CATENTRY	 |     1 |    10 |     2   (0)| 00:00:01 |
|* 10 |      INDEX RANGE SCAN		       | CAT_I0 	 |     1 |	 |     1   (0)| 00:00:01 |
|* 11 |    FILTER			       |		 |	 |	 |	      | 	 |
|* 12 |     TABLE ACCESS FULL		       | CATENTRY	 |     1 |    10 |    23   (5)| 00:00:01 |
----------------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   4 - access("MEMBER_ID"=:B3 AND "PARTNUMBER"=:B1)
   5 - filter(0=:B4)
   7 - access("MEMBER_ID" IS NULL AND "PARTNUMBER"=:B1)
       filter(("PARTNUMBER"=:B1 AND LNNVL("MEMBER_ID"=:B3)))
   8 - filter(0=:B2)
  10 - access("MEMBER_ID"=:B3 AND "PARTNUMBER" IS NULL)
       filter(LNNVL("PARTNUMBER"=:B1))
  11 - filter((0=:B4 AND 0=:B2))
  12 - filter(("PARTNUMBER" IS NULL AND "MEMBER_ID" IS NULL AND LNNVL("PARTNUMBER"=:B1) AND
	      LNNVL("MEMBER_ID"=:B3)))

The plan shows “or expansion”, and highlights the most significant difference between concatenation and expansion – concatenation requires indexed access paths in all branches, or-expansion doesn’t.

At first sight this plan with its full tablescan at operation 12 might seem like a bad idea; but check operation 11, the guarding filter, the tablescan occurs only if both :b2 and :b4 are null. Perhaps that special condition is never supposed to appear, perhaps it’s going to do a lot of work whether or not you can use an index. The fact that you can now handle the original problem without adding or altering existing indexes – provided you can inject this or_expand hint – may be of significant benefit. (Of course, being able to recreate the original index with the columns in the reverse order would even avoid the necessity of worrying about the hint.)

tl;dr

Applications that push “if-then-else” decisions into the SQL and down to the optimizer are a pain in the backside; the performance problems they produce can sometimes be bypassed by the addition of extra indexes that might give you plans (possibly hinted) that report the use of the concatentation operation. In 12cR2 the optimizer has an improved strategy for damage limitation “Cost-based Or Expansion” that can produce very similar effects without the addition of extra indexes. These plans will report union all operations, referencing views with names like: VW_ORE_xxxxxxxxx.

When you next upgrade you may find a few cases where you can get rid of indexes that were only created to work around defective application coding strategies. You may also want to hunt down any code where you’ve added use_concat hints and see if they can be removed, or if they should be replaced by or_expand. since the former hint will disable the latter feature.

 

dbms_sqldiag

If you’re familiar with SQL Profiles and SQL Baselines you may also know about SQL Patches – a feature that allows you to construct hints that you can attach to SQL statements at run-time without changing the code. Oracle 12c Release 2 introduces a couple of important changes to this feature:

• It’s now official – the feature had been copied from package dbms_sqldiag_internal to package dbms_sqldiag.
• The limitation of 500 characters has been removed from the hint text – it’s now a CLOB column.

H/T to Nigel Bayliss for including this detail in his presentation to the UKOUG last week, and pointing out that it’s also available for Standard Edition.

There are a couple of other little changes as you can see below from the two extract from the 12.2 declarations of dbms_sqldiag and dbms_sqldiag_internal below:

dbms_sqldiag
------------
FUNCTION CREATE_SQL_PATCH RETURNS VARCHAR2
 Argument Name                  Type                    In/Out Default?
 ------------------------------ ----------------------- ------ --------
 SQL_TEXT                       CLOB                    IN
 HINT_TEXT                      CLOB                    IN
 NAME                           VARCHAR2                IN     DEFAULT
 DESCRIPTION                    VARCHAR2                IN     DEFAULT
 CATEGORY                       VARCHAR2                IN     DEFAULT
 VALIDATE                       BOOLEAN                 IN     DEFAULT

FUNCTION CREATE_SQL_PATCH RETURNS VARCHAR2
 Argument Name                  Type                    In/Out Default?
 ------------------------------ ----------------------- ------ --------
 SQL_ID                         VARCHAR2                IN
 HINT_TEXT                      CLOB                    IN
 NAME                           VARCHAR2                IN     DEFAULT
 DESCRIPTION                    VARCHAR2                IN     DEFAULT
 CATEGORY                       VARCHAR2                IN     DEFAULT
 VALIDATE                       BOOLEAN                 IN     DEFAULT

dbms_sqldiag_internal
---------------------
FUNCTION I_CREATE_PATCH RETURNS VARCHAR2
 Argument Name                  Type                    In/Out Default?
 ------------------------------ ----------------------- ------ --------
 SQL_ID                         VARCHAR2                IN
 HINT_TEXT                      CLOB                    IN
 CREATOR                        VARCHAR2                IN
 NAME                           VARCHAR2                IN     DEFAULT
 DESCRIPTION                    VARCHAR2                IN     DEFAULT
 CATEGORY                       VARCHAR2                IN     DEFAULT
 VALIDATE                       BOOLEAN                 IN     DEFAULT

FUNCTION I_CREATE_PATCH RETURNS VARCHAR2
 Argument Name                  Type                    In/Out Default?
 ------------------------------ ----------------------- ------ --------
 SQL_TEXT                       CLOB                    IN
 HINT_TEXT                      CLOB                    IN
 CREATOR                        VARCHAR2                IN
 NAME                           VARCHAR2                IN     DEFAULT
 DESCRIPTION                    VARCHAR2                IN     DEFAULT
 CATEGORY                       VARCHAR2                IN     DEFAULT
 VALIDATE                       BOOLEAN                 IN     DEFAULT

• The function names change from i_create_patch to create_sql_patch when exposed in dbms_sqldiag.
• There are two versions of the function – one that requires you to supply the exact SQL text, and a new version that allows you to supply an SQL ID.
• The internal function also adds a creator to the existing parameter list – and it doesn’t have a default so if you’ve already got some code to use the internal version it’s not going to work on an upgrade to 12.2 until you change it.

I was prompted to write this note by a tweet asking me if there’s any SQL available to see the contents of an SQL Profile in 11g and 12c. (I published some simple code several years ago for 10g, (before accepting – in the body of the blog, after accepting – in the linked comment) but Oracle changed the base tables in 11g). The answer is yes, probably on the Internet somewhere, but here’s some code I wrote a couple of years ago to report profiles in the more recent versions of Oracle:

rem
rem     sql_profile_baseline_11g.sql
rem     J.P.Lewis
rem     July 2010
rem

set pagesize 60
set linesize 132
set trimspool on

column hint format a70 wrap word
column signature format 999,999,999,999,999,999,999

break on signature skip 1 on opt_type skip 1 on plan_id skip 1

spool sql_profile_baseline_11g

select
        prf.signature,
        decode(
                obj_type,
                1,'Profile',
                2,'Baseline',
                3,'Patch',
                'Other'
        )       opt_type,
        prf.plan_id,
        extractvalue(value(tab),'.')    hint
from
        (
        select
                /*+ no_eliminate_oby */
                *
        from
                sqlobj$data
        where
                comp_data is not null
        order by
                signature, obj_type, plan_id
        )       prf,
        table(
                xmlsequence(
                        extract(xmltype(prf.comp_data),'/outline_data/hint')
                )
        )       tab
;

This will report the hints associated with SQL Baselines, SQL Profiles, and SQL Patches – all three store the data in the same base table. As a minor variation I also have a query that will reported a named profile/baseline/patch, but this requires a join to the sqlobj$ table. As you can see from the substitution variable near the end of the text, the script will prompt you for an object name.

set pagesize 60
set linesize 180
set trimspool on

column  plan_name format a32
column  signature format 999,999,999,999,999,999,999
column  category  format a10
column  hint format a70 wrap word

break on plan_name skip 1 on signature skip 1 on opt_type skip 1 on category skip 1 on plan_id skip 1

spool sql_profile_baseline_11g

select
        prf.plan_name,
        prf.signature,
        decode(
                obj_type,
                1,'Profile',
                2,'Baseline',
                3,'Patch',
                  'Other'
        )       opt_type,
        prf.category,
        prf.plan_id,
        extractvalue(value(hnt),'.') hint
from
        (
        select
                /*+ no_eliminate_oby */
                so.name         plan_name,
                so.signature,
                so.category,
                so.obj_type,
                so.plan_id,
                sod.comp_data
                from
                        sqlobj$         so,
                        sqlobj$data     sod
                where
                        so.name = '&m_plan_name'
                and     sod.signature = so.signature
                and     sod.category = so.category
                and     sod.obj_type = so.obj_type
                and     sod.plan_id = so.plan_id
                order by
                        signature, obj_type, plan_id
        )       prf,
        table (
                select
                        xmlsequence(
                                extract(xmltype(prf.comp_data),'/outline_data/hint')
                        )
                from
                        dual
        )       hnt
;

Lagniappe:

One of the enhancements that appeared in 12c for SQL Baselines was that the plan the baseline was supposed to produce was stored in the database so that Oracle could check that the baseline would still reproduce the expected plan before applying it the DBA could see what the baseline has been producing before Oracle stopped using it. (Currently Oracle stores the plan’s hash value, and stops using the baseline if it starts to produce a different hash value. Storing the plan as well gives the DBA a chance of working out how to reproduce the correct plan and create a new baseline to get to it.)

These plans (also generated for Profiles and Patches) are stored in the table sqlobj$plan, and the dbms_xplan package has been enhanced with three new functions to report them:

FUNCTION DISPLAY_SQL_PATCH_PLAN RETURNS DBMS_XPLAN_TYPE_TABLE
 Argument Name                  Type                    In/Out Default?
 ------------------------------ ----------------------- ------ --------
 NAME                           VARCHAR2                IN
 FORMAT                         VARCHAR2                IN     DEFAULT

FUNCTION DISPLAY_SQL_PLAN_BASELINE RETURNS DBMS_XPLAN_TYPE_TABLE
 Argument Name                  Type                    In/Out Default?
 ------------------------------ ----------------------- ------ --------
 SQL_HANDLE                     VARCHAR2                IN     DEFAULT
 PLAN_NAME                      VARCHAR2                IN     DEFAULT
 FORMAT                         VARCHAR2                IN     DEFAULT

FUNCTION DISPLAY_SQL_PROFILE_PLAN RETURNS DBMS_XPLAN_TYPE_TABLE
 Argument Name                  Type                    In/Out Default?
 ------------------------------ ----------------------- ------ --------
 NAME                           VARCHAR2                IN
 FORMAT                         VARCHAR2                IN     DEFAULT

e.g.
SQL> select * from table(dbms_xplan.display_sql_profile_plan('SYS_SQLPROF_015c9bd3bceb0000'));

PLAN_TABLE_OUTPUT
--------------------------------------------------------------------------------------------------------------------

--------------------------------------------------------------------------------
SQL text: select        t1.id, t2.id from       t1, t2 where    t1.id between 10000 and
          20000 and     t2.n1 = t1.n1 and       t2.n1 = t2.v2
--------------------------------------------------------------------------------

--------------------------------------------------------------------------------
SQL Profile Name: SYS_SQLPROF_015c9bd3bceb0000
Status:           ENABLED
Plan rows:        From dictionary
--------------------------------------------------------------------------------

Plan hash value: 3683239666

-----------------------------------------------------------------------------------------------------------------
| Id  | Operation               | Name     | Rows  | Bytes | Cost (%CPU)| Time     |    TQ  |IN-OUT| PQ Distrib |
-----------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT        |          | 10501 |   287K|   248   (4)| 00:00:01 |        |      |            |
|   1 |  PX COORDINATOR         |          |       |       |     0   (0)|          |        |      |            |
|   2 |   PX SEND QC (RANDOM)   | :TQ10002 | 10501 |   287K|   248   (4)| 00:00:01 |  Q1,02 | P->S | QC (RAND)  |
|*  3 |    HASH JOIN BUFFERED   |          | 10501 |   287K|   248   (4)| 00:00:01 |  Q1,02 | PCWP |            |
|   4 |     PX RECEIVE          |          | 10002 |    97K|   123   (3)| 00:00:01 |  Q1,02 | PCWP |            |
|   5 |      PX SEND HASH       | :TQ10000 | 10002 |    97K|   123   (3)| 00:00:01 |  Q1,00 | P->P | HASH       |
|   6 |       PX BLOCK ITERATOR |          | 10002 |    97K|   123   (3)| 00:00:01 |  Q1,00 | PCWC |            |
|*  7 |        TABLE ACCESS FULL| T1       | 10002 |    97K|   123   (3)| 00:00:01 |  Q1,00 | PCWP |            |
|   8 |     PX RECEIVE          |          |   104K|  1845K|   124   (4)| 00:00:01 |  Q1,02 | PCWP |            |
|   9 |      PX SEND HASH       | :TQ10001 |   104K|  1845K|   124   (4)| 00:00:01 |  Q1,01 | P->P | HASH       |
|  10 |       PX BLOCK ITERATOR |          |   104K|  1845K|   124   (4)| 00:00:01 |  Q1,01 | PCWC |            |
|* 11 |        TABLE ACCESS FULL| T2       |   104K|  1845K|   124   (4)| 00:00:01 |  Q1,01 | PCWP |            |
-----------------------------------------------------------------------------------------------------------------

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

   3 - access("T2"."N1"="T1"."N1")
   7 - filter("T1"."ID"=10000)
  11 - filter("T2"."N1"=TO_NUMBER("T2"."V2"))

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 2

Disclaimer – I’ve checked only the SQL_PROFILE function call on 12.2, after creating a profile to check that my old 11g report still worked in 12c.

Update Aug 2017

I have just rediscovered a note I made (though I don’t have a reference to the source) that Patch 17203284 backports the visibility of create_sql_patch to dbms_sqldiag in 12.1.0.2. The description for the patch is: Enhancements for dbms_sqldiag_internal.i_create_patch but the “Bugs resolved by this patch” link on the patch details screen leads to the “Requested bug could not be displayed” page.

[Update: Oct 2017,(and see comment below) – this patch doesn’t make public a procedure create_sql_patch, it simply adds a version of i_create_patch that takes a CLOB hint text to dbms_sqldiag_internal.]

 

Parallelism

Headline – if you don’t want to read the note – the /*+ parallel(N) */ hint doesn’t mean a query will use parallel execution, even if there are enough parallel execution server processes to make it possible. The parallel(N) hint tells the optimizer to consider the cost of using parallel execution for each path that it examines, but ultimately the optimizer will still take the lowest cost path (bar the odd few special cases) and that path could turn out to be a serial path.

The likelihood of parallelism appearing for a given query changes across versions of Oracle so you can be fooled into thinking you’re seeing bugs as you test new versions but it’s (almost certainly) the same old rule being applied in different circumstances. Here’s an example – which I’ll start off on 11.2.0.4:

create table t1
segment creation immediate
nologging
as
with generator as (
        select
                rownum id
        from dual
        connect by
                level <= 1e4
)
select
        rownum                          id,
        lpad(rownum,10,'0')             v1,
        lpad('x',100,'x')               padding
from
        generator       v1,
        generator       v2
where
        rownum <= 1e6 ; create index t1_i1 on t1(id); begin dbms_stats.gather_table_stats( ownname => user,
                tabname          =>'T1',
                method_opt       => 'for all columns size 1'
        );
end;
/

set autotrace traceonly explain

select
        count(v1)
from    t1
where   id = 10
;

select
        /*+ parallel(4) */
        count(v1)
from    t1
where   id = 10
;

select
        /*+ parallel(4) full(t1) */
        count(v1)
from    t1
where   id = 10
;

set autotrace off

I haven’t declare the index to be unique, but it clearly could be; and it’s obvious that with 1M rows and about 120M of table a parallel full scan is probably a bad idea to acquire one row (even if you’re running Exadata!). So what do we get for the three plans – I’ll skip the predicate section – when we want to collect one row.

Base plan - unhinted
--------------------------------------------------------------------------------------
| Id  | Operation                    | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
--------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |       |     1 |    16 |     4   (0)| 00:00:01 |
|   1 |  SORT AGGREGATE              |       |     1 |    16 |            |          |
|   2 |   TABLE ACCESS BY INDEX ROWID| T1    |     1 |    16 |     4   (0)| 00:00:01 |
|*  3 |    INDEX RANGE SCAN          | T1_I1 |     1 |       |     3   (0)| 00:00:01 |
--------------------------------------------------------------------------------------

Hinted parallel(4)
--------------------------------------------------------------------------------------
| Id  | Operation                    | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
--------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |       |     1 |    16 |     4   (0)| 00:00:01 |
|   1 |  SORT AGGREGATE              |       |     1 |    16 |            |          |
|   2 |   TABLE ACCESS BY INDEX ROWID| T1    |     1 |    16 |     4   (0)| 00:00:01 |
|*  3 |    INDEX RANGE SCAN          | T1_I1 |     1 |       |     3   (0)| 00:00:01 |
--------------------------------------------------------------------------------------

Hinted parallel(4) and full(t1)
----------------------------------------------------------------------------------------------------------------
| Id  | Operation              | Name     | Rows  | Bytes | Cost (%CPU)| Time     |    TQ  |IN-OUT| PQ Distrib |
----------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT       |          |     1 |    16 |   606   (2)| 00:00:02 |        |      |            |
|   1 |  SORT AGGREGATE        |          |     1 |    16 |            |          |        |      |            |
|   2 |   PX COORDINATOR       |          |       |       |            |          |        |      |            |
|   3 |    PX SEND QC (RANDOM) | :TQ10000 |     1 |    16 |            |          |  Q1,00 | P->S | QC (RAND)  |
|   4 |     SORT AGGREGATE     |          |     1 |    16 |            |          |  Q1,00 | PCWP |            |
|   5 |      PX BLOCK ITERATOR |          |     1 |    16 |   606   (2)| 00:00:02 |  Q1,00 | PCWC |            |
|*  6 |       TABLE ACCESS FULL| T1       |     1 |    16 |   606   (2)| 00:00:02 |  Q1,00 | PCWP |            |
----------------------------------------------------------------------------------------------------------------

In 11.2.0.4 the optimizer did consider the parallel hint when it appeared on its own – but it has compared the parallel(4) cost of 606 with the serial index cost of 4 and chosen the indexed access path. This is not a case of ignoring the hint, it’s an example of being fooled if you don’t know how the hint is really supposed to work.

But here’s an interesting change that appeared in 12.2 – this time just the plan with the parallel(4) hint on its own:

---------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                               | Name     | Rows  | Bytes | Cost (%CPU)| Time     |    TQ  |IN-OUT| PQ Distrib |
---------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                        |          |     1 |    16 |     4   (0)| 00:00:01 |        |      |            |
|   1 |  SORT AGGREGATE                         |          |     1 |    16 |            |          |        |      |            |
|   2 |   PX COORDINATOR                        |          |       |       |            |          |        |      |            |
|   3 |    PX SEND QC (RANDOM)                  | :TQ10001 |     1 |    16 |            |          |  Q1,01 | P->S | QC (RAND)  |
|   4 |     SORT AGGREGATE                      |          |     1 |    16 |            |          |  Q1,01 | PCWP |            |
|   5 |      TABLE ACCESS BY INDEX ROWID BATCHED| T1       |     1 |    16 |     4   (0)| 00:00:01 |  Q1,01 | PCWP |            |
|   6 |       PX RECEIVE                        |          |     1 |       |     3   (0)| 00:00:01 |  Q1,01 | PCWP |            |
|   7 |        PX SEND HASH (BLOCK ADDRESS)     | :TQ10000 |     1 |       |     3   (0)| 00:00:01 |  Q1,00 | S->P | HASH (BLOCK|
|   8 |         PX SELECTOR                     |          |       |       |            |          |  Q1,00 | SCWC |            |
|*  9 |          INDEX RANGE SCAN               | T1_I1    |     1 |       |     3   (0)| 00:00:01 |  Q1,00 | SCWP |            |
---------------------------------------------------------------------------------------------------------------------------------

You get a parallel execution plan – although it starts with a serial index range scan which is operated for the new (12c) PX Selector operator that allocates a serial operation to one of the parallel execution slaves – which, approximately, is why the indexed access cost doesn’t change in this example – rather than running it through the query coordinator (QC). The serial range scan does a hash distribution (hashed by block address of the rowids it finds to avoid collisions between parallel execution slave as they do their table accesses.

This is just one cute little trick that makes it worth looking at the upgrade to 12c – this new path is likely to be of benefit to people who had to create global (as opposed to globally partitioned) indexes on partitioned tables.

This note was prompted by a recent twitter comment by Timur Akhmadeev followed in short order by an OTN posting that added further confusion to the problem by running Siebel – which is just one of several 3rd party products that love to configure optimizer parameters with non-standard values like: optimizer_index_cost_adj = 1, or optimizer_mode = first_rows_10. (At the last update I’ve seen on the thread, there seemed to be some other reason why parallelism was being blocked.)

Footnote

In a follow-up tweet, Timue directed me to the 11.2 SQL Language Reference manual – specifically a section on the Parallel Hint, asking if this was an example of a documentation bug.

The trouble with the manuals is that sometimes they are obviously wrong, sometimes they are wrong but it’s not obvious they are wrong, sometimes they omit important information, and sometimes they are badly written and, most specfically, the writing can be ambiguous.

Here’s an extract we could consider:

For PARALLEL, if you specify integer, then that degree of parallelism will be used for the statement.

But my example above shows a “parallel({integer})” hint where we didn’t use that degree of parallelism for the statement.

However the next two sentences read as follows:

If you omit integer, then the database computes the degree of parallelism. All the access paths that can use parallelism will use the specified or computed degree of parallelism.

So what if the optimizer uses the degree of parallelism while calculating the lowest cost plan and ends up with a serial plan ? How comfortable would you feel saying that Oracle has “used the degree of parallelism for the statement”. Or would you say that the first sentence means Oracle isn’t allowed to use a serial plan even if it finds one when doing the arithmetic with the appropriate degree of parallelism.

My call is that this is one of those ambiguous cases – the manual should say something more like:

For PARALLEL, if you specify integer, then that degree of parallelism will be used by the optimizer while calculating the best execution  plan for the statement.

Even then I’m not sure that that’s a complete statement of how the hint works because when you have a full set of system statistics, or have used the dbms_resource_manager.calibrate_io mechanism to tell Oracle about the I/O capacity of the system the optimizer may do some working that says something like: “the hint says degree 64, but the stats say the maximum effective degree will be 38 so I’ll calculate using 38” (This type of thing happens with the older usage of the parallel hint with manual parallelism – I haven’t examined what happens with an automatic policy and the newer option for the hint.)

 

opt_estimate

The opt_estimate hint is one of many that shouldn’t be used in end-user code and isn’t officially documented. Nevertheless – like so many other hints – it’s a hint that is hard to ignore when you see it floating around the code generated by the Oracle software. This note is prompted by a twitter question from fellow Oak Table member Stefan Koehler asking whether the hint’s index_filter parameter worked. Checking my library I knew the answer was yes – so after a quick exchange on twitter I said I’d write up a short note about my example, and this is it.

Although the hint is not one that you should use it’s worth writing this note as a reminder of the significance to index range scans of the access predicates and filter predicates that Oracle reports in the predicate section of an execution plan.

When a query does an index range scan it’s going to walk through a (logically) consecutive set of index leaf blocks looking at each individual index entry in turn (and those index entries will be correctly “sorted” within the leaf block) to see if it should use the rowid it finds there to visit the table. For “perfect” use of an index Oracle may be able to identify the starting and ending positions it needs in the index and know that it should use every rowid in between to visit the table – there will no “wasted”examinations of index entries on the way. In a query involving a multi-column index and multiple predicates, however, Oracle might have to use predicates on the first column(s) of the index to identify the starting and ending positions, but use further predicates on later columns in the index to decide whether or not to use each index entry to visit the table.

The predicates that Oracle can use to identify the range of leaf blocks it should visit are called access predicates, and the predicates that Oracle can use to further eliminate rowids as it walks along the leaf blocks are called filter predicates.

The simplest way to demonstrate this is with a query of the form: “Index_Column1 = … and Index_Column3 = …”, and that’s what I’ll be using in my model:

rem
rem     Script:         opt_est_ind_filter.sql
rem     Author:         Jonathan Lewis
rem
rem     Last tested
rem             11.2.0.4
rem             10.2.0.5
rem

create table t1
nologging
as
with generator as (
        select
                rownum id
        from dual
        connect by
                level <= 1e4    -- > comment to bypass WordPress format issue
)
select
        rownum                          id,
        mod(rownum - 1,100)             n1,
        rownum                          n2,
        mod(rownum - 1, 100)            n3,
        lpad(rownum,10,'0')             v1,
        lpad('x',100,'x')               padding
from
        generator       v1,
        generator       v2
where
        rownum <= 1e6    -- > comment to bypass WordPress formatting issue
;

create index t1_i1 on t1(n1,n2,n3) nologging;

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

select leaf_blocks from user_indexes where index_name = 'T1_I1';

The number of leaf blocks in the index was 3,062.

I’ve defined n1 and n3 to match, and for any value between 0 and 99 there are 10,000 rows in the table where n1 and n3 hold that value. However, in the absence of a column group defined on (n1, n3), the optimizer is going to use its standard “no correlation” arithmetic to decide that there are 10,000 possible combinations of n1 and n3, and 100 rows per combination. Let’s see what this does for a simple query:

set autotrace traceonly explain

select  count(v1)
from    t1
where   n1 = 0 and n3 = 0
;

set autotrace off

--------------------------------------------------------------------------------------
| Id  | Operation                    | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
--------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |       |     1 |    17 |   134   (1)| 00:00:01 |
|   1 |  SORT AGGREGATE              |       |     1 |    17 |            |          |
|   2 |   TABLE ACCESS BY INDEX ROWID| T1    |   100 |  1700 |   134   (1)| 00:00:01 |
|*  3 |    INDEX RANGE SCAN          | T1_I1 |   100 |       |    34   (3)| 00:00:01 |
--------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("N1"=0 AND "N3"=0)
       filter("N3"=0)

The plan shows an index range scan where n3=0 is used as a filter predicate and n1=0 (with a tiny bit of extra accuracy from the n3=0) is used as the access predicate, and the optimizer has calculated that 100 rowids will be retrieved from the index and used to find 100 rows in the table.

The cost of the range scan is 34: The optimizer’s estimate is that the scale of the initial access to the index will be due to the predicate n1 = 0 which is responsible for 1% of the index – giving us 3,062/100 leaf blocks (rounded up). Added to that there will be a little extra cost for the trip down the blevel of the index and a little extra for the CPU usage.

Now let’s tell the optimizer that its cardinality estimate is out by a factor of 25 (rather than 100 we actually know it to be) in one of two different ways:

prompt  ============================
prompt  index_scan - scale_rows = 25
prompt  ============================

select
        /*+
                qb_name(main)
                index(@main t1(n1, n2, n3))
                opt_estimate(@main index_scan t1, t1_i1, scale_rows=25)
        */
        count(v1)
from    t1
where   n1 = 0 and n3 = 0
;

prompt  ==============================
prompt  index_filter - scale_rows = 25
prompt  ==============================

select
        /*+
                qb_name(main)
                index(@main t1(n1, n2, n3))
                opt_estimate(@main index_filter t1, t1_i1, scale_rows=25)
        */
        count(v1)
from    t1
where   n1 = 0 and n3 = 0
;

In both examples I’ve hinted the index to stop the optimizer from switching to a tablescan; but in the first case I’ve told Oracle that the entire index range scan has to be scaled up by a factor of 25 while in the second case I’ve told Oracle that its estimate due to the final filter has to be scaled up by a factor of 25. How does this affect the costs and cardinalities of the plans:

============================
index_scan - scale_rows = 25
============================
--------------------------------------------------------------------------------------
| Id  | Operation                    | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
--------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |       |     1 |    17 |  3285   (1)| 00:00:17 |
|   1 |  SORT AGGREGATE              |       |     1 |    17 |            |          |
|   2 |   TABLE ACCESS BY INDEX ROWID| T1    |   100 |  1700 |  3285   (1)| 00:00:17 |
|*  3 |    INDEX RANGE SCAN          | T1_I1 |  2500 |       |   782   (2)| 00:00:04 |
--------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("N1"=0 AND "N3"=0)
       filter("N3"=0)

==============================
index_filter - scale_rows = 25
==============================
--------------------------------------------------------------------------------------
| Id  | Operation                    | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
--------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |       |     1 |    17 |  2537   (1)| 00:00:13 |
|   1 |  SORT AGGREGATE              |       |     1 |    17 |            |          |
|   2 |   TABLE ACCESS BY INDEX ROWID| T1    |   100 |  1700 |  2537   (1)| 00:00:13 |
|*  3 |    INDEX RANGE SCAN          | T1_I1 |  2500 |       |    34   (3)| 00:00:01 |
--------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("N1"=0 AND "N3"=0)
       filter("N3"=0)

In both cases the cardinality estimate has gone up by a factor of 25 for the index range scan. Notice, though, that the optimizer is now suffering from cognitive dissonance – it “knows” that it’s going to get 2,500 rowids to use to visit the table, it “knows” there are no extra predicates to eliminate rows from the table when it gets there, but it also “knows” that it’s going to find only 100 rows. Messing around with opt_estimate() and cardinality() hints is difficult even for fairlyl trivial cases – like all hinting, it takes more than one or two hints to achieve the result you want.

More significantly for the purposes of this note, are the costs. When we use the index_filter parameter the optimizer still thinks it’s going to access the same number of leaf blocks and the only correction it has to make is the number of rowids it finds in those blocks – so the index range scan cost hasn’t changed (though I suppose in some cases it might change slightly due to increased CPU costs). When we use the index_scan parameter the optimizer scales up its estimate of the number of leaf blocks (hence cost), which we can see in the figures 782 / 25 = 31.28. (Without going into the trace file and checking exact details that’s close enough to the previously reported 34 for me to think it’s allowing for 25 times the number of leaf blocks plus a chunk more CPU)

Conclusion

As I said at the outset, opt_estimate() really isn’t a hint you should be playing with, but I hope that this note has helped to shed some light on the significance of access predicates and filter predicates in relation to the costs of index range scans.

Footnote

There were two significant details in the notes I had in my script. First was the frequency of the expression “it looks as if” – which is my shorthand for “I really ought to do some more tests before I publish any conclusions”; second was that my most recent testing had been on 10.2.0.5 (where the results were slightly different thanks to sampling in the statistics). Given that Stefan Koehler had mentioned 11.2.0.3 as his version I ran up an instance of 11.1.0.7 – and found that the index_filter example didn’t scale up the cardinality – so maybe his problem is a version problem.

 

Parallel First_rows()

A recent posting on OTN raised the question of whether or not the “parallel” hint and the “first_rows(n)” hint were mutually incompatible. This reminded me that from time to time other posters on OTN (copying information from various websites, perhaps) have claimed that “parallel doesn’t work with first rows” or, conversely, “first rows doesn’t work with parallel”. This is one of those funny little myths that is so old that the script I’ve got to demonstrate the misconception is dated 2003 with a first test version of 8.1.7.4.

Since I haven’t run the test on any version of Oracle newer than 9.2.0.4 I thought it was time to dust it down, modernise it slightly, and run it again. So here’s the bit that creates a sample data set:

create table t1 (
        id      number,
        v1      varchar2(10),
        padding varchar2(100),
        constraint      t_pk primary key(id) using index local
)
partition by range(id) (
        partition p1000 values less than (1000),
        partition p2000 values less than (2000),
        partition p3000 values less than (3000),
        partition p4000 values less than (4000),
        partition p5000 values less than (5000)
)
;

insert into t1
select
        rownum - 1,
        rpad(rownum-1,10),
        rpad('x',100)
from
        all_objects
where
        rownum <= 5000 -- > hint to avoid WordPress formatting issue
order by 
        dbms_random.value
;

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

Now I’m going to run a simple query, hinted in 4 different ways:

• no hints
• parallel hint only: /*+ parallel */
• first_rows(1) hint only: /*+ first_rows(1) */
• parallel and first_rows(1): /*+ parallel first_rows(1) */

Here’s the version of the query that has both hints in place:

set serveroutput off
set linesize 156
set pagesize 60
set trimspool on

select
        /*+ parallel first_rows(1) */
        v1
from
        t1
where
        id between 1500 and 2000
;

select * from table(dbms_xplan.display_cursor(null,null,'cost outline'));

I’ve actually run the query and used the display_cursor() option to pull the plan from memory – in the original (8i) script I used autotrace and the old (deprecated, backwards compatibility only) first_rows hint. To do any other tests just clone and edit. Here are the 4 outputs from the call to display_cursor() – with a little cosmetic editing:

SQL_ID  63qnzam9b8m9g, child number 0
=====================================
select  /*+ */  v1 from  t1 where  id between 1500 and 2000

Plan hash value: 277861402

-------------------------------------------------------------------------------------------------
| Id  | Operation                | Name | Rows  | Bytes | Cost (%CPU)| Time     | Pstart| Pstop |
-------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT         |      |       |       |    15 (100)|          |       |       |
|   1 |  PARTITION RANGE ITERATOR|      |   502 |  7530 |    15   (0)| 00:00:01 |     2 |     3 |
|*  2 |   TABLE ACCESS FULL      | T1   |   502 |  7530 |    15   (0)| 00:00:01 |     2 |     3 |
-------------------------------------------------------------------------------------------------

Outline Data
-------------
  /*+
      BEGIN_OUTLINE_DATA
      IGNORE_OPTIM_EMBEDDED_HINTS
      OPTIMIZER_FEATURES_ENABLE('11.2.0.4')
      DB_VERSION('11.2.0.4')
      ALL_ROWS
      OUTLINE_LEAF(@"SEL$1")
      FULL(@"SEL$1" "T1"@"SEL$1")
      END_OUTLINE_DATA
  */

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter(("ID"<=2000 AND "ID">=1500))


SQL_ID  ahary3u8q88mq, child number 1
=====================================
select  /*+ parallel */  v1 from  t1 where  id between 1500 and 2000

Plan hash value: 9959369

------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation            | Name     | Rows  | Bytes | Cost (%CPU)| Time     | Pstart| Pstop |    TQ  |IN-OUT| PQ Distrib |
------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT     |          |       |       |     8 (100)|          |       |       |        |      |            |
|   1 |  PX COORDINATOR      |          |       |       |            |          |       |       |        |      |            |
|   2 |   PX SEND QC (RANDOM)| :TQ10000 |   502 |  7530 |     8   (0)| 00:00:01 |       |       |  Q1,00 | P->S | QC (RAND)  |
|   3 |    PX BLOCK ITERATOR |          |   502 |  7530 |     8   (0)| 00:00:01 |     2 |     3 |  Q1,00 | PCWC |            |
|*  4 |     TABLE ACCESS FULL| T1       |   502 |  7530 |     8   (0)| 00:00:01 |     2 |     3 |  Q1,00 | PCWP |            |
------------------------------------------------------------------------------------------------------------------------------

Outline Data
-------------
  /*+
      BEGIN_OUTLINE_DATA
      IGNORE_OPTIM_EMBEDDED_HINTS
      OPTIMIZER_FEATURES_ENABLE('11.2.0.4')
      DB_VERSION('11.2.0.4')
      ALL_ROWS
      SHARED(2)
      OUTLINE_LEAF(@"SEL$1")
      FULL(@"SEL$1" "T1"@"SEL$1")
      END_OUTLINE_DATA
  */

Predicate Information (identified by operation id):
---------------------------------------------------
   4 - access(:Z>=:Z AND :Z<=:Z)
       filter(("ID"<=2000 AND "ID">=1500))

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 2


SQL_ID  3m6mnk9b337dd, child number 0
=====================================
select  /*+ first_rows(1) */  v1 from  t1 where  id between 1500 and
2000

Plan hash value: 1044541683

-----------------------------------------------------------------------------------------------------------
| Id  | Operation                          | Name | Rows  | Bytes | Cost (%CPU)| Time     | Pstart| Pstop |
-----------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                   |      |       |       |     6 (100)|          |       |       |
|   1 |  PARTITION RANGE ITERATOR          |      |     4 |    60 |     6   (0)| 00:00:01 |     2 |     3 |
|   2 |   TABLE ACCESS BY LOCAL INDEX ROWID| T1   |     4 |    60 |     6   (0)| 00:00:01 |     2 |     3 |
|*  3 |    INDEX RANGE SCAN                | T_PK |       |       |     2   (0)| 00:00:01 |     2 |     3 |
-----------------------------------------------------------------------------------------------------------

Outline Data
-------------
  /*+
      BEGIN_OUTLINE_DATA
      IGNORE_OPTIM_EMBEDDED_HINTS
      OPTIMIZER_FEATURES_ENABLE('11.2.0.4')
      DB_VERSION('11.2.0.4')
      FIRST_ROWS(1)
      OUTLINE_LEAF(@"SEL$1")
      INDEX_RS_ASC(@"SEL$1" "T1"@"SEL$1" ("T1"."ID"))
      END_OUTLINE_DATA
  */

Predicate Information (identified by operation id):
---------------------------------------------------
   3 - access("ID">=1500 AND "ID"<=2000) -- > needs edit to avoid WordPress formatting issue


SQL_ID  9asm7t1zbv4q8, child number 1
=====================================
select  /*+ parallel first_rows(1) */  v1 from  t1 where  id between
1500 and 2000

Plan hash value: 4229065483

----------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                            | Name     | Rows  | Bytes | Cost (%CPU)| Time     | Pstart| Pstop |    TQ  |IN-OUT| PQ Distrib |
----------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                     |          |       |       |     3 (100)|          |       |       |        |      |            |
|   1 |  PX COORDINATOR                      |          |       |       |            |          |       |       |        |      |            |
|   2 |   PX SEND QC (RANDOM)                | :TQ10000 |     4 |    60 |     3   (0)| 00:00:01 |       |       |  Q1,00 | P->S | QC (RAND)  |
|   3 |    PX PARTITION RANGE ITERATOR       |          |     4 |    60 |     3   (0)| 00:00:01 |     2 |     3 |  Q1,00 | PCWC |            |
|   4 |     TABLE ACCESS BY LOCAL INDEX ROWID| T1       |     4 |    60 |     3   (0)| 00:00:01 |     2 |     3 |  Q1,00 | PCWP |            |
|*  5 |      INDEX RANGE SCAN                | T_PK     |       |       |     1   (0)| 00:00:01 |     2 |     3 |  Q1,00 | PCWP |            |
----------------------------------------------------------------------------------------------------------------------------------------------

Outline Data
-------------
  /*+
      BEGIN_OUTLINE_DATA
      IGNORE_OPTIM_EMBEDDED_HINTS
      OPTIMIZER_FEATURES_ENABLE('11.2.0.4')
      DB_VERSION('11.2.0.4')
      FIRST_ROWS(1)
      SHARED(2)
      OUTLINE_LEAF(@"SEL$1")
      INDEX_RS_ASC(@"SEL$1" "T1"@"SEL$1" ("T1"."ID"))
      END_OUTLINE_DATA
  */

Predicate Information (identified by operation id):
---------------------------------------------------
   5 - access("ID">=1500 AND "ID"<=2000)

Note
-----
   - automatic DOP: Computed Degree of Parallelism is 2

Critically we get four different execution plans from the four different strategies – so clearly the optimizer is perfectly happy to accept the parallel and first_rows() hints simultaneously. Note, particularly, how the first_rows(1) hint when combined with the parallel hint moved us from a parallel full tablescan to a parallel index range scan.

Whether or not it’s sensible to use the hint combination in this way is a matter for careful consideration, of course, but there could be circumstances where the combination really is the best way to get the starting row(s) from a query that otherwise has to return a large amount of data.

use_nl hint

In response to a recent lamentation from Richard Foote about the degree of ignorance regarding the clustering_factor of indexes I commented on the similar level of understanding of a specific hint syntax, namely use_nl(a b) pointing out that this does not mean “do a nested loop from a to b”. My comment was underscored by a fairly prompt response asking what the hint did mean.

Surprisingly, although I’ve explained it many times over the last couple of decades (here’s one from 10 years ago), I couldn’t find an explanation on my blog though I did find a blog note where I’d made a passing comment about the equivalent misunderstanding of the use_hash(a b) syntax.

The misunderstanding is not entirely surprising since for many years the Oracle manuals seemed to suggest (in their examples) that the hint did have a multi-table meaning and it wasn’t until 10g that the manual gave an explicit statement of the single-table nature of the hint. The hint /*+ use_nl(a b) */ is a short-hand for the pair of hints /*+ use_nl(a)  use_nl(b) */ it doesn’t say anything about whether a and b should be joined, or in what order. If you want to guarantee that a and b will be joined in that order by a nested loop you will have to work a lot harder with your hints – and almost certainly need to make use of the /+ leading() */ hint.

Consider the following query (I’ll put the table creation code at the end of the article if you want to experiment):

select
	/*+ use_nl(a b) */
	a.v1, b.v1, c.v1, d.v1
from
	a, b, c, d
where
	d.n100 = 0
and	a.n100 = d.id
and	b.n100= a.n2
and	c.id = a.id
;

Only one of the tables a and b can be the first table in the final execution plan so one of them will be “the next table in the join order” at some point, so this hint will guarantee that one of the tables will be the inner table of a nested loop join. Here’s the plan I happened to get with my data, indexing, version (11.2.0.4), etc.:

---------------------------------------------------------------------------------------
| Id  | Operation                      | Name | Rows  | Bytes | Cost (%CPU)| Time     |
---------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT               |      | 20000 |  1347K| 30125   (1)| 00:00:02 |
|   1 |  HASH JOIN                     |      | 20000 |  1347K| 30125   (1)| 00:00:02 |
|   2 |   TABLE ACCESS FULL            | C    | 10000 |   146K|    26   (4)| 00:00:01 |
|   3 |   HASH JOIN                    |      | 20000 |  1054K| 30098   (1)| 00:00:02 |
|   4 |    TABLE ACCESS FULL           | D    |   100 |  1800 |    26   (4)| 00:00:01 |
|   5 |    NESTED LOOPS                |      | 20000 |   703K| 30072   (1)| 00:00:02 |
|   6 |     NESTED LOOPS               |      | 20000 |   703K| 30072   (1)| 00:00:02 |
|   7 |      TABLE ACCESS FULL         | B    | 10000 |   136K|    26   (4)| 00:00:01 |
|   8 |      INDEX RANGE SCAN          | A_I2 |     2 |       |     1   (0)| 00:00:01 |
|   9 |     TABLE ACCESS BY INDEX ROWID| A    |     2 |    44 |     3   (0)| 00:00:01 |
---------------------------------------------------------------------------------------

In this case it’s table a that ends up in a position to be the inner table of a nested loop join.

You may be wondering why there seems to be a hash join into b when we’ve hinted a nested loop join – but the join order that Oracle is using is B -> A -> D -> C with a swap_join_inputs(d) swap_join_inputs(d), so b is never “the next table in the join order”.

If you want an even more confusing (at first sight) plan here’s the plan I got if I changed the one hint to /*+ use_nl(a) */

-----------------------------------------------------------------------------
| Id  | Operation            | Name | Rows  | Bytes | Cost (%CPU)| Time     |
-----------------------------------------------------------------------------
|   0 | SELECT STATEMENT     |      | 20000 |  1347K|   105   (5)| 00:00:01 |
|   1 |  HASH JOIN           |      | 20000 |  1347K|   105   (5)| 00:00:01 |
|   2 |   TABLE ACCESS FULL  | B    | 10000 |   136K|    26   (4)| 00:00:01 |
|   3 |   HASH JOIN          |      | 10000 |   537K|    78   (4)| 00:00:01 |
|   4 |    TABLE ACCESS FULL | C    | 10000 |   146K|    26   (4)| 00:00:01 |
|   5 |    HASH JOIN         |      | 10000 |   390K|    52   (4)| 00:00:01 |
|   6 |     TABLE ACCESS FULL| D    |   100 |  1800 |    26   (4)| 00:00:01 |
|   7 |     TABLE ACCESS FULL| A    | 10000 |   214K|    26   (4)| 00:00:01 |
-----------------------------------------------------------------------------

This plan really looks as if Oracle should have done a nested loop into a but didn’t. Again appearanced are deceptive thanks to the effects of swap_join_inputs(): the join order here is A -> D -> C -> B (note that we don’t have a use_nl(b) hint in this example).

If you want a plan where the optimizer produces a nested loop join between a and b you’ll need to put in a leading() hint which places b immediately after a somewhere in the list of tables with just use_nl(b) being sufficient to enforce the join method. Here, for example, is the plan with hints /*+ leading(d a b c) use_nl(b) */ for my data set:

----------------------------------------------------------------------------------------
| Id  | Operation                     | Name   | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT              |        | 20000 |  1347K| 30164   (1)| 00:00:02 |
|   1 |  HASH JOIN                    |        | 20000 |  1347K| 30164   (1)| 00:00:02 |
|   2 |   TABLE ACCESS FULL           | C      | 10000 |   146K|    26   (4)| 00:00:01 |
|   3 |   NESTED LOOPS                |        | 20000 |  1054K| 30137   (1)| 00:00:02 |
|   4 |    NESTED LOOPS               |        |  1000K|  1054K| 30137   (1)| 00:00:02 |
|   5 |     HASH JOIN                 |        | 10000 |   390K|    52   (4)| 00:00:01 |
|   6 |      TABLE ACCESS FULL        | D      |   100 |  1800 |    26   (4)| 00:00:01 |
|   7 |      TABLE ACCESS FULL        | A      | 10000 |   214K|    26   (4)| 00:00:01 |
|   8 |     INDEX RANGE SCAN          | B_I100 |   100 |       |     1   (0)| 00:00:01 |
|   9 |    TABLE ACCESS BY INDEX ROWID| B      |     2 |    28 |   101   (0)| 00:00:01 |
----------------------------------------------------------------------------------------

Notice, yet again, Oracle has done hash join to c with a swap_join_inputs().

Creation Script:

create table a
nologging
as
with generator as (
        select 
                rownum id
        from dual 
        connect by 
                level <= 1e4
)
select
	rownum				id,
	mod(rownum,5000)		n2,
	mod(rownum,100)			n100,
	lpad(rownum,10,'0')		v1,
	lpad('x',100,'x')		padding
from
        generator       v1
;

create table b nologging as select * from a;
create table c nologging as select * from a;
create table d nologging as select * from a;

alter table a add constraint a_pk primary key(id);
alter table b add constraint b_pk primary key(id);
alter table c add constraint c_pk primary key(id);
alter table d add constraint d_pk primary key(id);

create index a_i2 on a(n2) nologging;
create index b_i2 on b(n2) nologging;
create index c_i2 on c(n2) nologging;
create index d_i2 on d(n2) nologging;

create index a_i100 on a(n100) nologging;
create index b_i100 on b(n100) nologging;
create index c_i100 on c(n100) nologging;
create index d_i100 on d(n100) nologging;

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

Hinting

A posting on the OTN database forum a few days ago demonstrated an important problem with hinting especially (though it didn’t come up in the thread)  in the face of upgrades. A simple query needed a couple of hints to produce the correct plan but a slight change to the query seemed to result in Oracle ignoring the hints. The optimizer doesn’t ignore hints, of course, but there are many reasons why it might have appeared to so I created a little demonstration of the problem – starting with the following data set:

rem
rem     Script:  OTN_DAG.sql
rem     Author:  J.P.Lewis
rem     Dated:   March 2016
rem

create table t1
nologging
as
with generator as (
        select  --+ materialize
                rownum id
        from dual
        connect by
                level <= 1e4 -- > comment to avoid wordpress format issue
)
select
        mod(rownum,200)         n1,
        mod(rownum,200)         n2,
        rpad(rownum,180)        v1
from
        generator       g1,
        generator       g2
where
        rownum <= 24000 -- > comment to avoid wordpress format issue
;

create table t2
nologging
as
with generator as (
        select  --+ materialize
                rownum id
        from dual
        connect by
                level <= 1e4 -- > comment to avoid wordpress format issue
)
select
        trunc((rownum-1)/15)    n1,
        trunc((rownum-1)/15)    n2,
        rpad(rownum,180)        v1
from    generator
where
        rownum <= 3000 -- > comment to avoid wordpress format issue
;

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

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

(Ignore the silliness of the way I’ve created the data, it’s a consequence of using my standard template).

For every row in t2 there are 8 rows in t1, so when I join t1 to t2 on n2 it would obviously be sensible for the resulting hash join to use the t2 (smaller) rowsource as the build table and the t1 rowsource as the probe table; but I’m going to pretend that the optimizer is making an error and needs to be hinted to use t1 as the build table and t2 as the probe. Here’s a query, and execution plan, from 11.2.0.4:

explain plan for
select
        /*+ leading(t1) use_hash(t2) no_swap_join_inputs(t2) */
        count(t1.n2)
from
        t1, t2
where
        t2.n2 = t1.n2
and     t1.n1 = 15
and     t2.n1 = 15
;

select * from table(dbms_xplan.display(null,null,'outline alias'));

----------------------------------------------------------------------------
| Id  | Operation           | Name | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------
|   0 | SELECT STATEMENT    |      |     1 |    16 |    97   (3)| 00:00:01 |
|   1 |  SORT AGGREGATE     |      |     1 |    16 |            |          |
|*  2 |   HASH JOIN         |      |    20 |   320 |    97   (3)| 00:00:01 |
|*  3 |    TABLE ACCESS FULL| T1   |   120 |   960 |    85   (3)| 00:00:01 |
|*  4 |    TABLE ACCESS FULL| T2   |    15 |   120 |    12   (0)| 00:00:01 |
----------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - access("T2"."N2"="T1"."N2")
   3 - filter("T1"."N1"=15)
   4 - filter("T2"."N1"=15)

As you can see, the optimizer has obeyed my hinting – the join order is t1 -> t2, I’ve used a hash join to join t2, and Oracle hasn’t swapped the join inputs despite the fact that the t1 rowsource is larger than the t2 rowsource (960 bytes vs. 120 bytes) which should have persuaded it to swap. (Technically, the leading() hint seems to block the option for swapping the first two tables anyway – see the “Special Case” section at this URL – but I’ve included the no_swap_join_inputs() hint anyway to make the point explicit.)

So now, instead of just count n2, we’ll modify the query to count the number of distinct values for n2:

explain plan for
select
        /*+ leading(t1) use_hash(t2) no_swap_join_inputs(t2) */
        count(distinct t1.n2) 
from
        t1, t2
where
        t2.n2 = t1.n2
and     t1.n1 = 15
and     t2.n1 = 15
;

select * from table(dbms_xplan.display(null,null,'outline alias'));

----------------------------------------------------------------------------------
| Id  | Operation             | Name     | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT      |          |     1 |    13 |    98   (4)| 00:00:01 |
|   1 |  SORT AGGREGATE       |          |     1 |    13 |            |          |
|   2 |   VIEW                | VW_DAG_0 |    20 |   260 |    98   (4)| 00:00:01 |
|   3 |    HASH GROUP BY      |          |    20 |   320 |    98   (4)| 00:00:01 |
|*  4 |     HASH JOIN         |          |    20 |   320 |    97   (3)| 00:00:01 |
|*  5 |      TABLE ACCESS FULL| T2       |    15 |   120 |    12   (0)| 00:00:01 |
|*  6 |      TABLE ACCESS FULL| T1       |   120 |   960 |    85   (3)| 00:00:01 |
----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   4 - access("T2"."N2"="T1"."N2")
   5 - filter("T2"."N1"=15)
   6 - filter("T1"."N1"=15)

Check operations 5 and 6 – Oracle has swapped the join inputs: t2 (the obvious choice) is now the build table. Has Oracle ignored the hint ? (Answer: No).
If you look at operation 2 you can see that Oracle has generated an internal view called VW_DAG_0 – this is an example of the “Distinct Aggregate” transformation taking place. It seems to be a pointless exercise in this case and the 10053 trace file suggests that, at present, it’s a heuristic transformation rather than a cost-based transformation (i.e. the optimizer does it because it can, not because it’s cheaper). Oracle has transformed the SQL to the following (to which I have applied a little cosmetic tidying):

SELECT  /*+ LEADING (T1) */
        COUNT(VW_DAG_0.ITEM_1) "COUNT(DISTINCTT1.N2)"
FROM    (
        SELECT  T1.N2 ITEM_1
        FROM    TEST_USER.T2 T2,TEST_USER.T1 T1
        WHERE   T2.N2=T1.N2
        AND     T1.N1=15
        AND     T2.N1=15
        GROUP BY
                T1.N2
        ) VW_DAG_0

Notice how the use_hash() and no_swap_join_input() hints have disappeared. I am slightly surprised that the leading() hint is still visible, I would have expected all three to stay or all three to disappear; regardless of that, though, the single remaining hint references an object that does not exist in the query block where the hint has been placed. The original hint has not been “ignored” it has become irrelevant. (I’ll be coming back to an odd little detail about this transformed query a little later on but for the moment I’m going to pursue the problem of making the optimizer do what we want.)

We have three strategies we could pursue at this point. We could tell the optimizer that we don’t want it to do the transformation; we could work out the query block name of the query block that holds t1 and t2 after the transformation and direct the hints into that query block; or we could tell Oracle to pretend it was using an older version of the optimizer because that Distinct Aggregate transformation didn’t appear until 11.2.0.1.

You’ll notice that I used the ‘alias’ formatting option in my call to dbms_xplan.display(). Here’s the queryblock / alias section of the resulting output:

Query Block Name / Object Alias (identified by operation id):
-------------------------------------------------------------
   1 - SEL$C33C846D
   2 - SEL$5771D262 / VW_DAG_0@SEL$C33C846D
   3 - SEL$5771D262
   5 - SEL$5771D262 / T1@SEL$1
   6 - SEL$5771D262 / T2@SEL$1

Strategy A says try adding the hint: /*+ no_transform_distinct_agg(@sel$1) */
Strategy B says try using the hints: /*+ leading(@sel$5771d262 t1@sel$1 t2@sel$1) use_hash(@sel$5771d262 t2@sel$1 no_swap_join_inputs(@sel$5771d262 t2@sel$1) */
Strategy C says try adding the hint: /*+ optimizer_features_enable(‘11.1.0.7’) */

Strategies A and C (stopping the transformation) produce the following plan:

----------------------------------------------------------------------------
| Id  | Operation           | Name | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------
|   0 | SELECT STATEMENT    |      |     1 |    16 |    98   (4)| 00:00:01 |
|   1 |  SORT GROUP BY      |      |     1 |    16 |            |          |
|*  2 |   HASH JOIN         |      |    20 |   320 |    98   (4)| 00:00:01 |
|*  3 |    TABLE ACCESS FULL| T1   |   120 |   960 |    85   (3)| 00:00:01 |
|*  4 |    TABLE ACCESS FULL| T2   |    15 |   120 |    12   (0)| 00:00:01 |
----------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - access("T2"."N2"="T1"."N2")
   3 - filter("T1"."N1"=15)
   4 - filter("T2"."N1"=15)

Strategy B (allowing the transformation but addressing the hints to the generated query block) produces this plan:

----------------------------------------------------------------------------------
| Id  | Operation             | Name     | Rows  | Bytes | Cost (%CPU)| Time     |
----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT      |          |     1 |    13 |    98   (4)| 00:00:01 |
|   1 |  SORT AGGREGATE       |          |     1 |    13 |            |          |
|   2 |   VIEW                | VW_DAG_0 |    20 |   260 |    98   (4)| 00:00:01 |
|   3 |    HASH GROUP BY      |          |    20 |   320 |    98   (4)| 00:00:01 |
|*  4 |     HASH JOIN         |          |    20 |   320 |    97   (3)| 00:00:01 |
|*  5 |      TABLE ACCESS FULL| T1       |   120 |   960 |    85   (3)| 00:00:01 |
|*  6 |      TABLE ACCESS FULL| T2       |    15 |   120 |    12   (0)| 00:00:01 |
----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   4 - access("T2"."N2"="T1"."N2")
   5 - filter("T1"."N1"=15)
   6 - filter("T2"."N1"=15)

All three Strategies have produced plans that use t1, the larger rowsource, as the build table. It’s hard to resist asking if it’s possible to claim that one of the three strategies is the best strategy; it’s hard to say but I think I’d favour using the no_transform_distinct_agg() hint because it’s precisely targetted – so avoids the brute force thuggish nature of reverting back to an old version and also avoids the (possible) fragility of needing to know a very precise query block name which (possibly) might change for some reason if the query were to be modified very slightly. The argument, of course, comes from the perspective of a friendly consultant who visits for a couple of days, gets a bit clever with your SQL, then walks away leaving you to worry about whether you understand why your SQL now works the way it does.

Upgrades

My opening comment was about the difficulty of hinting across upgrades. Imagine you had been running this count(distinct) query in Oracle 10.2.0.5 and, after some experimentation, had found that you got the path you needed by adding the hints: /*+ leading(t1 t2)  full(t1) use_hash(t2) no_swap_join_inputs(t2) full(t2) */. This is a careful and thorough piece of hinting (and it does work, of course, in 10.2.0.5).

When the big day for upgrading to 11.2 arrives (just in time for Oracle to end extended support of 10g, possibly) you find that this query changes its execution plan. This scenario is NOT a rare occurrence. I’ve said it before, and I’ll keep saying it: hinting – especially with “micro-management” hints – is undesirable in a production system. You probably haven’t done it right, and even if the hints are (broadly speaking) perfect in the current version they may be pushed out of context by a new feature in the next version.  If you’ve hinted your code you have to check every single hinted statement to make sure the hints still have the same effect on the upgrade.

This is why I produced the sound-bite (which Maria Colgan subsequently nicked): “if you can hint it, baseline it”.  If you had generated a baseline (or outline) from a query with these hints in 10g Oracle would have included the /*+ optimizer_features_enable(‘10.2.0.5’) */ hint with the functional hints and the upgrade wouldn’t have produced a different plan.

Technically, of course, you might have remembered to add the optimizer_features_enable() hint to your production code – but in many cases Oracle introduces far more hints in an SQL Baseline than you might want to put into your code; and by using the SQL Baseline approach you’ve given yourself the option to get rid of the “hidden hinting” in a future version of Oracle by dropping the baseline rather than rewriting the code and (perhaps) recompiling the application.

Inevitably there are cases where setting the optimizer_features_enable() backwards doesn’t rescue you from a new plan – there are probably a few cases where the internal code forgets to check the value and bypass some subroutines; more significantly there are cases where one version of Oracle will give you an efficient plan because of an optimizer bug and setting the version backwards won’t re-introduce that bug.

Footnote

I said I’d come back to the “unparsed” query that the optimizer generated from the original count(distinct) statement and the way it left the leading(t1) hint in place but lost the use_hash(t2) and no_swap_join_inputs(t2). I got curious about how Oracle would optimize the unparsed query if I supplied it from SQL*Plus – and this is the plan I got:

explain plan for
SELECT  /*+ LEADING (T1) */
        COUNT(VW_DAG_0.ITEM_1) "COUNT(DISTINCTT1.N2)"
FROM    (
        SELECT  T1.N2 ITEM_1
        FROM    TEST_USER.T2 T2,TEST_USER.T1 T1
        WHERE   T2.N2=T1.N2
        AND     T1.N1=15
        AND     T2.N1=15
        GROUP BY
                T1.N2
        ) VW_DAG_0
;

-----------------------------------------------------------------------------------
| Id  | Operation             | Name      | Rows  | Bytes | Cost (%CPU)| Time     |
-----------------------------------------------------------------------------------
|   0 | SELECT STATEMENT      |           |     1 |    13 |    98   (4)| 00:00:01 |
|   1 |  SORT AGGREGATE       |           |     1 |    13 |            |          |
|   2 |   VIEW                | VM_NWVW_0 |    20 |   260 |    98   (4)| 00:00:01 |
|   3 |    HASH GROUP BY      |           |    20 |   320 |    98   (4)| 00:00:01 |
|*  4 |     HASH JOIN         |           |    20 |   320 |    97   (3)| 00:00:01 |
|*  5 |      TABLE ACCESS FULL| T1        |   120 |   960 |    85   (3)| 00:00:01 |
|*  6 |      TABLE ACCESS FULL| T2        |    15 |   120 |    12   (0)| 00:00:01 |
-----------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   4 - access("T2"."N2"="T1"."N2")
   5 - filter("T1"."N1"=15)
   6 - filter("T2"."N1"=15)

Oracle has managed to do a transformation to this statement that it didn’t do when it first generated the statement – too much recursion, perhaps – and that floating leading(t1) hint has been squeezed back into action by a view-merging step in the optimization that got the hint back into a query block that actually contained t1 and t2!  At this point I feel like quoting cod-philosophy from the great Dune trilogy by Frank Herbert: “Just when you think you understand …”

 

Wrong Results

Just in – a post on the Oracle-L mailing lists asks: “Is it a bug if a query returns one answer if you hint a full tablescan and another if you hint an indexed access path?” And my answer is, I think: “Not necessarily”:

SQL> select /*+ full(pt_range)  */ n2 from pt_range where n1 = 1 and n2 = 1;

        N2
----------
         1
SQL> select /*+ index(pt_range pt_i1) */ n2 from pt_range where n1 = 1 and n2 = 1;

        N2
----------
         1
         1

The index is NOT corrupt.

The reason why I’m not sure you should call this a bug is that it is a side effect of putting the database into an incorrect state. You might have guessed from the name that the table is a (range) partitioned table, and I’ve managed to get this effect by doing a partition exchange with the “without validation” option.

create table t1 (
        n1      number(4),
        n2      number(4)
);

insert into t1
select  rownum, rownum
from    all_objects
where   rownum <= 5
;

create table pt_range (
        n1      number(4),
        n2      number(4)
)
partition by range(n1) (
        partition p10 values less than (10),
        partition p20 values less than (20)
)
;

insert into pt_range
select
        rownum, rownum
from
        all_objects
where
        rownum <= 15
;

create index pt_i1 on pt_range(n1,n2);

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

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

alter table pt_range
exchange partition p20 with table t1
including indexes
without validation
update indexes
;

The key feature (in this case) is that the query can be answered from the index without reference to the table. When I force a full tablescan Oracle does partition elimination and looks at just one partition; when I force the indexed access path Oracle doesn’t eliminate rows that belong to the wrong partition – though technically it could (because it could identify the target partition by the partition’s data_object_id which is part of the extended rowid stored in global indexes).

Here are the two execution plans (from 11.2.0.4) – notice how the index operation has no partition elimination while the table operation prunes partitions:

select /*+ full(pt_range)  */ n2 from pt_range where n1 = 1 and n2 = 1

---------------------------------------------------------------------------------------------------
| Id  | Operation              | Name     | Rows  | Bytes | Cost (%CPU)| Time     | Pstart| Pstop |
---------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT       |          |       |       |     2 (100)|          |       |       |
|   1 |  PARTITION RANGE SINGLE|          |     1 |     6 |     2   (0)| 00:00:01 |     1 |     1 |
|*  2 |   TABLE ACCESS FULL    | PT_RANGE |     1 |     6 |     2   (0)| 00:00:01 |     1 |     1 |
---------------------------------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter(("N1"=1 AND "N2"=1))


select /*+ index(pt_range pt_i1) */ n2 from pt_range where n1 = 1 and n2 = 1

--------------------------------------------------------------------------
| Id  | Operation        | Name  | Rows  | Bytes | Cost (%CPU)| Time     |
--------------------------------------------------------------------------
|   0 | SELECT STATEMENT |       |       |       |     1 (100)|          |
|*  1 |  INDEX RANGE SCAN| PT_I1 |     1 |     6 |     1   (0)| 00:00:01 |
--------------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - access("N1"=1 AND "N2"=1)

Note: If I had a query that did a table access by (global) index rowid after the index range scan it WOULD do partition elimination and visit just the one partition – never seeing the data in the wrong partition.

So is it a bug ? You told Oracle not to worry about bad data – so how can you complain if it reports bad data.

Harder question – which answer is the “right” one – the answer which shows you all the data matching the query, or the answer which shows you only the data that is in the partition it is supposed to be in ?

Connect By

I received an email a couple of days ago that was a little different from usual – although the obvious answer was “it’s the data”. A connect by query with any one of several hundred input values ran in just a few seconds, but with one specific input it was still running 4,000 seconds later using the same execution plan – was this a bug ?

There’s nothing to suggest that it should be, with skewed data anything can happen: even a single table access by exact index could take 1/100th of a second to return a result if there was only one row matching the requirement and 1,000 seconds if there were 100,000 rows in 100,000 different table blocks (and the table was VERY big). The same scaling problem could be true of any type of query – and “connect by” queries can expose you to a massive impact because their run time can increase geometrically as the recursion takes place.

So it was easy to answer the question – no it’s (probably) not a bug, check the data for that one value.

Then I decided to build a simple model. The original email had a four table join, but I just created a single table, and used a “no filtering” connect by which I had to hint. Here’s some code I ran on 11.2.0.4:

rem
rem     script: connect_by_skew.sql
rem     dated:  Feb 2016
rem     Last tested:
rem             12.1.0.2
rem

create table t1 nologging 
as
select 
        rownum id_p, 10 * rownum id
from
        all_objects
where 
        rownum <= 50000 ; execute dbms_stats.gather_table_stats(user,'t1', method_opt=>'for all columns size 1')

alter system flush shared_pool;

set serveroutput off
alter session set statistics_level = all;

select sum(ct) 
from    (
        select
                /*+ no_connect_by_filtering */
                count(id) ct
        from
                t1
        connect by
                id = 20 * prior id_p
        start with
                id_p = 1
        group by
                id
)
;

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

update t1 set id_p = 0
where   id_p = 1
;

update t1 set id_p = 1
where   id_p > 45000
;

select sum(ct) 
from    (
        select
                /*+ no_connect_by_filtering */
                count(id) ct
        from
                t1
        connect by
                id = 20 * prior id_p
        start with
                id_p = 1
        group by
                id
)
;

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

The sum() of the inline aggregate view emulates the original code – I don’t know what it was for, possibly it was a way of demonstrating the problem without producing a large output, I just copied it.

As you can see in my script every parent id (id_p) starts out unique, and if I look at the pattern of the raw data identified by the recursion from id_p = 1 (rather than looiking at the result of the actual query) this is what I’d get:

      ID_P         ID
---------- ----------
         1         10
         2         20
         4         40
         8         80
        16        160
        32        320
        64        640
       128       1280
       256       2560
       512       5120
      1024      10240
      2048      20480
      4096      40960
      8192      81920
     16384     163840
     32768     327680

When I modify the data so that I have exactly 5,000 rows with id_p = 1 the initial data generation will be 80,000 rows of data. If you want to try setting id_p = 1 for more rows make sure you do it to rows where id_p is already greater than 32768 or you’ll run into Oracle error ORA-01436: CONNECT BY loop in user data.

Here’s the execution plan, with rowsource execution stats I got for the first query (running 11.2.0.4):

-----------------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                                  | Name | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
-----------------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                           |      |      1 |        |    32 (100)|      1 |00:00:00.44 |     103 |       |       |          |
|   1 |  SORT AGGREGATE                            |      |      1 |      1 |            |      1 |00:00:00.44 |     103 |       |       |          |
|   2 |   VIEW                                     |      |      1 |      2 |    32   (7)|     16 |00:00:00.44 |     103 |       |       |          |
|   3 |    HASH GROUP BY                           |      |      1 |      2 |    32   (7)|     16 |00:00:00.44 |     103 |  1519K|  1519K| 1222K (0)|
|*  4 |     CONNECT BY NO FILTERING WITH START-WITH|      |      1 |        |            |     16 |00:00:00.44 |     103 |       |       |          |
|   5 |      TABLE ACCESS FULL                     | T1   |      1 |  50000 |    31   (4)|  50000 |00:00:00.10 |     103 |       |       |          |
-----------------------------------------------------------------------------------------------------------------------------------------------------

As you can see, this took 0.44 seconds, generated the expected 16 rows (still visible up to operation 2) which it then counted. Oracle followed the same execution plan when I set 5,000 rows to the critical value – here’s the new run-time plan:

-----------------------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                                  | Name | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
-----------------------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT                           |      |      1 |        |    32 (100)|      1 |00:05:39.25 |     103 |       |       |          |
|   1 |  SORT AGGREGATE                            |      |      1 |      1 |            |      1 |00:05:39.25 |     103 |       |       |          |
|   2 |   VIEW                                     |      |      1 |      2 |    32   (7)|   5015 |00:05:39.24 |     103 |       |       |          |
|   3 |    HASH GROUP BY                           |      |      1 |      2 |    32   (7)|   5015 |00:05:39.22 |     103 |  5312K|  2025K| 1347K (0)|
|*  4 |     CONNECT BY NO FILTERING WITH START-WITH|      |      1 |        |            |  80000 |00:05:38.56 |     103 |       |       |          |
|   5 |      TABLE ACCESS FULL                     | T1   |      1 |  50000 |    31   (4)|  50000 |00:00:00.09 |     103 |       |       |          |
-----------------------------------------------------------------------------------------------------------------------------------------------------

As expected, 80,000 rows generated (5,000 * 16), aggregated down to 5,015, then aggregated again to the one row result. Time to complete: 5 minutes 39 seconds – and it was all CPU time. It’s not entirely surprising – a single recursive descent (with startup overheads) took 0.44 seconds – presumably a fairly large fraction of that was startup, but even 0.1 seconds adds up if you do it 5,000 times.

Everybody knows that skewed data can produced extremely variable response times. With a deeper tree and more rows with the special value it wouldn’t be hard for the total run time of this query to get to the 4,000 seconds reported in the original email. (I also tried running with 10,000 rows set to 1 and the run time went up to 18 minutes – of which a large fraction was reading from the TEMPORARY tablespace because something had overflowed to disc).

Was there a solution ?

I don’t know – but I did suggest two options
a) create a histogram on the data to show that there was one particular special value; since the code seemed to include literals perhaps the optimizer would notice the special case and choose a different plan.
b) hint the code to use a different strategy – the hint would be /*+ connect_by_filtering */. Here’s the resulting execution plan:

---------------------------------------------------------------------------------------------------------------------------------------
| Id  | Operation                    | Name | Starts | E-Rows | Cost (%CPU)| A-Rows |   A-Time   | Buffers |  OMem |  1Mem | Used-Mem |
---------------------------------------------------------------------------------------------------------------------------------------
|   0 | SELECT STATEMENT             |      |      1 |        |    95 (100)|      1 |00:00:06.50 |    1751 |       |       |          |
|   1 |  SORT AGGREGATE              |      |      1 |      1 |            |      1 |00:00:06.50 |    1751 |       |       |          |
|   2 |   VIEW                       |      |      1 |      2 |    95   (6)|   5015 |00:00:06.49 |    1751 |       |       |          |
|   3 |    HASH GROUP BY             |      |      1 |      2 |    95   (6)|   5015 |00:00:06.47 |    1751 |  5312K|  2025K| 1346K (0)|
|   4 |     CONNECT BY WITH FILTERING|      |      1 |        |            |  80000 |00:00:06.30 |    1751 |   337K|   337K|  299K (0)|
|*  5 |      TABLE ACCESS FULL       | T1   |      1 |      1 |    31   (4)|   5000 |00:00:00.01 |     103 |       |       |          |
|*  6 |      HASH JOIN               |      |     16 |      1 |    63   (5)|     15 |00:00:05.98 |    1648 |  1969K|  1969K|  741K (0)|
|   7 |       CONNECT BY PUMP        |      |     16 |        |            |     16 |00:00:00.01 |       0 |       |       |          |
|   8 |       TABLE ACCESS FULL      | T1   |     16 |  50000 |    31   (4)|    800K|00:00:01.49 |    1648 |       |       |          |
---------------------------------------------------------------------------------------------------------------------------------------

We get the result in 6.5 seconds! [UPDATE: but there’s a nice explanation for that – most of the time comes from the work done gathering rowsource execution statistics; with statistics_level set back to typical the run time dropped to 0.19 seconds.]

Hinting

This is just a little example of thinking about hinting for short-term hacking requirements. It’s the answer to a question that came up on the Oracle-L listserver  a couple of months ago (Oct 2015) and is a convenient demonstration of a principle that can often (not ALWAYS) be applied as a response to the problem: “I can make this query work quickly once, how do I make it work quickly when I make it part of a join ?”

The question starts with this query, which returns “immediately” for any one segment:

SELECT DE.TABLESPACE_NAME, DE.OWNER,DE.SEGMENT_NAME,
       MAX(DE.BYTES) LARGEST_EXTENT_BYTES
FROM dba_extents DE
WHERE 1=1
  AND DE.OWNER           = <owner>
  AND DE.SEGMENT_NAME    = <segment_name>
  AND DE.segment_type    = <segment_type>
  AND DE.tablespace_name = <tablespace_name>
  AND DE.partition_name  = <max_partition_name>
GROUP BY DE.TABLESPACE_NAME, DE.OWNER, DE.SEGMENT_NAME
;

But the email then goes on to explain: “I’ve got a table of values that I need to use as a list of inputs for this query, but when I do the join it takes ages to complete; how do I make the join run quickly?”

Here’s the initial code:

WITH SEGMENT_LIST AS
(
  select * from (
   SELECT /*+ materialize cardinality(100) */
           owner, segment_name, segment_type, tablespace_name,
           MAX(partition_name) MAX_PARTITION_NAME
   FROM my_custom_table
   GROUP BY owner, segment_name, segment_type, tablespace_name
  ) where rownum < 2
)
SELECT
       DE.TABLESPACE_NAME, DE.OWNER,DE.SEGMENT_NAME,
       MAX(DE.BYTES) LARGEST_EXTENT_BYTES
FROM SEGMENT_LIST SL, dba_extents DE
WHERE 1=1
  AND DE.OWNER           = SL.OWNER
  AND DE.SEGMENT_NAME    = SL.SEGMENT_NAME
  AND DE.segment_type    = SL.segment_type
  AND DE.tablespace_name = SL.tablespace_name
  AND DE.partition_name  = SL.max_partition_name
GROUP BY DE.TABLESPACE_NAME, DE.OWNER, DE.SEGMENT_NAME

What we’ve got is a query where the user’s reference table generates a list of segments (the rownum < 2 was a temporary test) and we want the detail query to run for each segment identified. The “for each segment” gives us a clue that what we want to see is a simple nested loop join, driven by the factored subquery, with the very efficient query above running as the “second table of the nested loop”.

What I failed to notice at the time is that the /*+ materialize */ hint was in the wrong place, it should have been placed after the outer (i.e. very first) select, and it’s possible that if it had been in the right place the user would have got the plan they wanted – especially in the later versions of Oracle. As it was I suggested that we merely need to tell the optimizer to:

Visit the “tables” in the order (segment_list, dba_extents), and do a nested loop into (dba_extents), but since both segment_list and dba_extents were views we needed to stop Oracle from trying to merge them and play silly games with distinct aggregate placement, etc. by including directives that the views should not be merged, but then we might need to explain to Oracle that it would have to push the join predicate between segment_list and dba_extents inside the dba_extents view.

In other words, a list of 4 hints, as shown below:

WITH SEGMENT_LIST AS
(
  select * from (
   SELECT /*+ materialize cardinality(100) */
           owner, segment_name, segment_type, tablespace_name,
           MAX(partition_name) MAX_PARTITION_NAME
   FROM my_custom_table
   GROUP BY owner, segment_name, segment_type, tablespace_name
  ) where rownum < 2
)
SELECT /*+
        leading(sl de)
        no_merge(sl)
        no_merge(de)
        push_pred(de)
        */
       DE.TABLESPACE_NAME, DE.OWNER,DE.SEGMENT_NAME,
       MAX(DE.BYTES) LARGEST_EXTENT_BYTES
FROM SEGMENT_LIST SL, dba_extents DE
WHERE 1=1
  AND DE.OWNER           = SL.OWNER
  AND DE.SEGMENT_NAME    = SL.SEGMENT_NAME
  AND DE.segment_type    = SL.segment_type
  AND DE.tablespace_name = SL.tablespace_name
  AND DE.partition_name  = SL.max_partition_name
GROUP BY DE.TABLESPACE_NAME, DE.OWNER, DE.SEGMENT_NAME

According to a follow-up email, this was sufficient.  The OP had actually tried variations on the leading() and use_nl() hints – but without the no_merge() hint the optimizer was probably rewriting the SQL in a way that put the hints out of context. It’s worth noting that the /*+ materialize */ hint is in the wrong place – it should be after the first appearance of the SELECT keyword in the factored subquery – and that probably added to the difficulty of getting the desired execution plan.

For a production system I’d probably want to do something a little more sophisticated in terms of stability once I’d got the plan I wanted – but this looks like a DBA query used to run an ad hoc report, so perhaps this solution is good enough for the current requirement.

 

Five Hints

This is the content of a “whitepaper” I wrote for my presentation “Five Hints for Optimising SQL” at the recent DOAG conference.

Introduction

Adding hints to production code is a practice to be avoided if possible, though it’s easy to make the case for emergency patching, and hinting is also useful as the basis of a method of generating SQL Plan Baselines. However, notwithstanding (and sometimes because of) the continuing enhancements to the optimizer, there are cases where the only sensible option for dealing with a problem statement is to constrain the broad brush strategy that the optimizer can take in a way that allows it to find a reasonable execution plan in a reasonable time.

This note describes in some detail the use and effects of five of the “classic” hints that I believe are reasonable strategic options to redirect the optimizer when it doesn’t choose a path that you consider to be the most appropriate choice.

The Big Five

At the time of writing, a query against the view v$sql_hint on Oracle 12.1.0.2 reports 332 hints – but there are very few which we should really consider as safe for production code, and it’s best to view even those as nothing more than a medium-term tool to stabilise performance until the optimizer is able to do a better job with our SQL.

The handful of hints that I tend to rely on for solving problems is basically a set of what I call “structural” queries though in recent years it has become appropriate to label them as “query block” hints. These are hints that give the optimizer some idea of the shape of the best plan without trying to enforce every detail of how it should finalize the plan. The hints (with their negatives where appropriate) are:

• Unnest / no_unnest — Whether or not to unnest subqueries
• Push_subq / no_push_subq — When to handle a subquery that has not been unnested
• Merge / no_merge — Whether to use complex view merging
• Push_pred / no_push_pred — What to do with join predicates to non-merged views
• Driving_site — Where to execute a distributed query

Inevitably there are a few other hints that can be very helpful, but a key point I want to stress is that for production code I avoid what I call “micro-management” hints (such as use_nl(), index_rs_asc()) – attempts to control the optimizer’s behaviour to the last little detail; it is very easy to produce massive instability in performance once you start down the path of micro-managing your execution plans, so it’s better not to try.

The rest of this document will be devoted to describing and give examples of these hints.

The Optimizer’s Strategy

You can think of the optimizer as working on a “unit of optimization” which consists of nothing more than a simple statement of the form:

select  list of columns
from    list of tables
where   list of simple predicates

To deal with a more complex query the optimizer stitches together a small number (reduced, if it had its way, to just one) of such simple blocks. So one of the first steps taken by the optimizer aims to transform your initial query into a this simple form. Consider this example:

select
        t1.*,v1.*,t4.*
from
        t1,
        (
        select
                t2.n1, t3.n2, count(*)
        from    t2, t3
        where exists (
                select
                        null
                from    t5
                where   t5.id = t2.n1
                )
        and     t3.n1 = t2.n2
        group by t2.n1, t3.n2
        )       v1,
        t4
where
        v1.n1 = t1.n1
and     t4.n1(+) = v1.n1
;

We have an inline view consisting of a two-table join with a subquery correlated to the first table, and from our perspective we have a “simple join” of three objects – t1, v1, and t4. Before it does anything else the optimizer will try to transform this into a straight-line five-table join so that it can join all the tables in order one after the other. As part of that process it will generally attempt to eliminate subqueries in a processing known as unnesting.

Looking at the query as it has been presented author of the code may have been thinking (symbolically) of the underlying problem as:

• ( ( t1, ( ( t2, subquery t5 ), t3 ) ), t4 )

Take t1, join to it the result of applying the subquery to t2 and joining t3, then join t4.

The optimizer may decide to transform to produce the following:

• ( ( ( ( t1, t2 ), t3 ), {unnested t5} ), t4 )

Join t2 to t1, join t3 to the result, join the transformed t5 to the result, then join t4 to the result.

If I decide that the original layout demonstrates the appropriate mechanism, my target is to supply the optimizer with just enough hints to lock it into the order and strategy shown, without trying to dictate every little detail of the plan. My hints would look like this:

select
        /*+
            qb_name(main) push_pred(v1@main)
            no_merge(@inline)
            no_unnest(@subq1) push_subq(@subq1)
        */
        t1.*,v1.*,t4.*
from
        t1,
        (
        select  /*+ qb_name(inline) */
                t2.n1, t3.n2, count(*)
        from    t2, t3
        where exists (
                select  /*+ qb_name(subq1) */
                        null
                from    t5
                where   t5.id = t2.n1
                )
        and     t3.n1 = t2.n2
        group by t2.n1, t3.n2
        )       v1,
        t4
where
        v1.n1 = t1.n1
and     t4.n1(+) = v1.n1
;

I’ve labelled the three separate select clauses with a query block name (qb_name() hint), told the optimizer that the query block named “inline” should be considered as a separately optimized block (no_merge(@inline)), and the subquery inside that block called “subq1” should be treated as a filter subquery (no_unnest(@subq1)) and applied as early as possible (push_subq(@subq1)).

In some circumstances I might use one more hint to tell the optimizer to consider a single join order: t1, v1, t4 using the hint /*+ leading(t1 v1 t4) */; but in this case I’ve told the optimizer to push the join predicate v1.n1 = t1.n1 inside the view (push_pred(@inline)) – which will make the optimizer do a nested loop from table t1 to view v1, resolving the view for each row it selects from t1.

Having captured 4 of the “big 5” hints in one sample statement, I’ll now comments on each of them (and the final driving_site() hint separately).

Merge / No_merge

This pair of hints apply particularly to “complex view merging”, but can be used to “isolate” sections of a query, forcing the optimizer to break one large query into a number of smaller (hence easier) sections. I see two main uses for the hints (and particularly the no_merge option) – one is to help the optimizer get started when handling a query with a large number of table, the other is simply to block a strategy that the optimizer sometimes chooses when it is a bad move.

Consider, in the first case, a query involving 20 tables, with several subqueries. With such a long list it is very easy for the optimizer to pick a very bad starting join order and never reach a good join order; moreover, because of the multiplicative way in which the optimizer estimates selectivity it’s very easy for the optimizer to decide after a few tables that the cardinality of the join so far is so small that it doesn’t really matter which table to access next. In cases like this we might start by writing a simpler query joining the first four of five tables that we know to be the key to the whole query – once we have got the core of the query working efficiently we can “wrap” it into an inline view with a no_merge hint, and then join the rest of the tables to it, with some confidence that the optimizer will start well and that it can’t go far wrong with the remainder of the tables so, for example

select  ...
from    t1, t2, t3, ..., t20
where   {various predicates}
and     exists {correlated subquery1}
and     exists {correlated subquery2}
and     column in {non-correlated subquery}

Might become

with v1 as (
        select  /*+ no_merge cardinality(2000) */ ...
        from    t1, t2, t3, t4, t5
        where   {various predicates{
        and     exists {correlated subquery1}
)
select  ...
from    v1, t6, t7, ..., t20
where   {join conditions to v1}
and     {other join conditions}
and     exists {correlated subquery2}
and     column in {non-correlated subquery}
;

I’ve written the example up using subquery factoring; in earlier versions of Oracle the relevant piece of code would have been written as an inline view, but the “with” clause can help to tidy the SQL up and make it easier to see the logic of what’s being done – provided the practice isn’t taken to such extremes that the final query consists of large number of very small factored subqueries.

I’ve included a cardinality() hint in the factored subquery – it’s not fully documented, and it’s not commonly realised that it can be applied to a query block rather than to a table or list of tables. This query block usage is probably the safest example of using the hint – the table-related usage is badly understood and prone to mis-use.

As an example of blocking a badly selected transformation, consider the following query (where I’ve already included qb_name() hints to name the two separate query blocks):

select  /*+ qb_name(main) */
        t1.vc1, avg_val_t1
from    t1,
        (
        Select  /*+ qb_name(inline) */
                id_parent, avg(val) avg_val_t1
        from	t2
        group by
                id_parent
        ) v1
where
        t1.vc2 = 'XYZ'
and     v1.id_parent = t1.id_parent
;

There are two basic strategies the optimizer could use to optimize this query, and the choice would depend on its estimate of how much data it had to handle . Whichever choice it makes we might, depending on our better understanding of the data, want it to choose the alternative (without rewriting the query beyond hinting it).

One option is for Oracle to execute the inline view to generate the aggregate data v1 then join the result to t1; the other is to join t2 (the view’s underlying table) to t1 and then work out an aggregation of the join that would give the same result.

If I want to “join then aggregate” I would use the merge hint, if I wanted to “aggregate then join” I would use the no_merge hint. There are three different ways in which I could introduce the hint:

• In the inline view itself I could simply add the hint “merge”
• In the main query I could reference the view by view name “no_merge(v1)”
• In the main query I could reference the inline query block name “no_merge(@inline)”

Note particularly the “@” symbol that I use to point a hint at a query block; and note that this was not needed when I reference the view name. (The reference by query block name is the more modern, preferred strategy.)

Push_pred / No_push_pred

Once we start dealing with non-mergeable views and have to join to them there are two strategies that we could use for the join; the first is (nominally) to create the entire data set for the view and then use that in a merge join or hash join based on the join predicate, or we could “push a join predicate” into the view definition – in other words for each join value we could add a simple filter predicate to the view definition and derive the view result based on that predicate. For example, if we create a database view called avg_val_view with a definition matching the inline view we used in the previous example, we might see one of two possible execution plans for the following query:

select  t1.vc1, avg_val_t1
from    t1, avg_val_view
where   t1.vc2 = 'XYZ'
and     avg_val_view.id_parent = t1.id_parent
;

First – if the view is non-mergeable and we don’t push the predicate, we can see the join predicate appearing at operation 1, as we do a hash join between table t1 and the entire result set from aggregating t2. This may be sensible, but it may be very expensive to create the entire aggregate:

-------------------------------------------------------------------
| Id | Operation            | Name         | Rows  | Bytes | Cost  |
--------------------------------------------------------------------
|  0 | SELECT STATEMENT     |              |     1 |    95 |    27 |
|* 1 |  HASH JOIN           |              |     1 |    95 |    27 |
|* 2 |   TABLE ACCESS FULL  | T1           |     1 |    69 |     2 |
|  3 |   VIEW               | AVG_VAL_VIEW |    32 |   832 |    24 |
|  4 |    HASH GROUP BY     |              |    32 |   224 |    24 |
|  5 |     TABLE ACCESS FULL| T2           |  1024 |  7168 |     5 |
--------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - access("AVG_VAL_VIEW"."ID_PARENT"="T1"."ID_PARENT")
   2 - filter("T1"."VC2"='XYZ')

So we may decide to add the hint /*+ push_pred(avg_val_view) */ to the query – we have to use the view-name method since we don’t have a query block containing the view; if we were using the inline view from the previous query we could have used the “query block” format /*+ push_pred(@inline) */. The plan from pushing predicates is:

--------------------------------------------------------------------
| Id | Operation               | Name        | Rows | Bytes | Cost |
--------------------------------------------------------------------
|  0 | SELECT STATEMENT        |             |    1 |    82 |    7 |
|  1 |  NESTED LOOPS           |             |    1 |    82 |    7 |
|* 2 |   TABLE ACCESS FULL     | T1          |    1 |    69 |    2 |
|  3 |   VIEW PUSHED PREDICATE | AVG_VAL_VIEW|    1 |    13 |    5 |
|* 4 |    FILTER               |             |      |       |      |
|  5 |     SORT AGGREGATE      |             |    1 |     7 |      |
|* 6 |      TABLE ACCESS FULL  | T2          |   32 |   224 |    5 |
--------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter("T1"."VC2"='XYZ')
   4 - filter(COUNT(*)>0)
   6 - filter("ID_PARENT"="T1"."ID_PARENT")

It would actually be a bad idea in this particular case, but if we could access the rows for a given id_parent in t2 efficiently this query could be much more efficient than the previous plan because it would only aggregate the small number of rows that it was going to need at each point, with the smallest row size.

You might note that Oracle has cleverly introduced a filter as operation 4 to eliminate t1 rows where the aggregate would return a row with a zero when there was no matching data. It’s details like this that typical programmers tend to forget when trying to transform SQL by hand.

Unnest / No_unnest

The optimizer prefers joins to subqueries, and will generally try to transform a query to turn a subquery into a join – which often means a semi-join for existence/in, or an anti-join for not exists/not in). As the optimizer has improved with version many such transformations (or decisions to not transform) changed from being driven by rules to being driven by cost – and sometimes we want to override the optimizer because we know its cost calculation is bad. Most commonly we might want to write a query with a subquery – to show our intentions – but tell the optimizer to unnest the subquery: it’s much safer to take this approach rather than to rewrite the query in unnested form ourselves – I’ve seen people do the rewrite incorrectly too many times to trust a user-created rewrite. For example:

select
        /*+ qb_name(main) unnest(@subq) */
        outer.*
from
        emp outer
where   outer.sal > (
                select
                        /*+ qb_name(subq) unnest */
                        avg(inner.sal)
                from    emp inner
                where
                inner.dept_no = outer.dept_no
        )
;

I’ve show the unnest hint here, and demonstrated the two possible forms – you can either use it in the main query block hint to point it at a give query block name (@subq), or you can use it without a “parameter” in the query block you want unnested. In effect the unnest hint causes Oracle to rewrite the query as:

select
        outer.*
from
        (
        select
                dept_no, avg(sal) av_sal
        from    emp
        group by
                dept_no
        )               inner,
        emp             outer
where
        outer.dept_no = inner.dept_no
and     outer.sal > inner.av_sal
;

You’ll notice that this gives us an in-line aggregate view, so the optimizer could take (or be pushed) one more step into doing complex view merging as well, joining emp to itself before aggregating on a very messy set of columns.

Here’s the plan if we unnest:

----------------------------------------------------------------
| Id  | Operation            | Name    | Rows  | Bytes | Cost  |
----------------------------------------------------------------
|   0 | SELECT STATEMENT     |         |  1000 | 98000 |   114 |
|*  1 |  HASH JOIN           |         |  1000 | 98000 |   114 |
|   2 |   VIEW               | VW_SQ_1 |     6 |   156 |    77 |
|   3 |    HASH GROUP BY     |         |     6 |    48 |    77 |
|   4 |     TABLE ACCESS FULL| EMP     | 20000 |   156K|    36 |
|   5 |   TABLE ACCESS FULL  | EMP     | 20000 |  1406K|    36 |
----------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - access("ITEM_1"="OUTER"."DEPT_NO")
       filter("OUTER"."SAL">"AVG(INNER.SAL)")

Notice the appearance at operation 2 of a “view” names VW_SQ_1: there are a number of internal view names that appear in Oracle as it transforms queries – the fact that a view name starts with VW_ is a good clue that it’s an internal one. Note, in this particular case that the main work done in the query is the two tablescans of EMP.

Here’s the plan if we don’t unnest:

------------------------------------------------------------
| Id  | Operation           | Name | Rows  | Bytes | Cost  |
------------------------------------------------------------
|   0 | SELECT STATEMENT    |      |   167 | 12024 |   252 |
|*  1 |  FILTER             |      |       |       |       |
|   2 |   TABLE ACCESS FULL | EMP  | 20000 |  1406K|    36 |
|   3 |   SORT AGGREGATE    |      |     1 |     8 |       |
|*  4 |    TABLE ACCESS FULL| EMP  |  3333 | 26664 |    36 |
------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter("OUTER"."SAL"> (SELECT /*+ NO_UNNEST */
              AVG("INNER"."SAL") FROM "EMP" "INNER"
              WHERE "INNER"."DEPT_NO"=:B1))
   4 - filter("INNER"."DEPT_NO"=:B1)

The FILTER at operation 1 tells us that nominally the optimizer will run the subquery once for every row in the emp table, but the optimizer costing (252) tells us that it thinks that really it will execute the table scan only 7 times in total (7 * 36 = 252): once for the driving scan and six more times because there are only six departments in my emp table.  (This “how many executions” type of estimate appeared in the costing calculations relatively recently.)

Push_subq / No_push_subq

Once we can control whether or not Oracle will unnest a subquery or run it as a filter we can then choose whether the subquery should run early or late. Historically the optimizer would always leave subqueries to the very end of query operation – but recently the choice of timing acquired a costing component. “Pushing” a subquery means pushing it down the execution tree – i.e. running it earlier in the plan. To demonstrate this we need a minimum of a two-table join with subquery:

select
        /*+ leading(t1 t2) push_subq(@subq) */
        t1.v1
from    t1, t3
where   t1.n2 = 15
and     exists (
                select  --+ qb_name(subq) no_unnest push_subq
                        null
                from    t2
                where   t2.n1 = 15
                and     t2.id = t1.id
        )
and     t3.n1 = t1.n1
and     t3.n2 = 15
;

In this query I have a subquery where I’ve blocked unnesting, so it has to run as a filter subquery (in passing, I’ve use the alternative, less commonly known, format for hinting: the single-line hint/comment that starts with – – for a comment and – – + for a hint).

I’ve shown the push_subq hint (run the subquery early) in two different ways – first at the top of the query referencing the query block that I want pushed, and then in the subquery itself where it doesn’t need a parameter.

As you can see, the subquery is correlated to table t1 and I’ve told Oracle to examine only the join order t1 -> t3. The effect of the push_subq hint, therefore, is to tell Oracle to run the subquery for each row of t1 that it examines and join any survivors to t3. The alternative is for Oracle to join t1 to t3 and then run the subquery for every row in the result. Depending on the data and indexes available either option might be the more efficient.

Here are the two plans – first if I don’t push the subquery (note the FILTER operation):

--------------------------------------------------------------------
| Id | Operation                    | Name  | Rows | Bytes | Cost  |
--------------------------------------------------------------------
|  0 | SELECT STATEMENT             |       |    1 |    28 |   289 |
|* 1 |  FILTER                      |       |      |       |       |
|* 2 |   HASH JOIN                  |       |  173 |  4844 |   116 |
|* 3 |    TABLE ACCESS FULL         | T1    |  157 |  3140 |    57 |
|* 4 |    TABLE ACCESS FULL         | T3    |  157 |  1256 |    57 |
|* 5 |   TABLE ACCESS BY INDEX ROWID| T2    |    1 |     8 |     2 |
|* 6 |    INDEX UNIQUE SCAN         | T2_PK |    1 |       |     1 |
--------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - filter( EXISTS (SELECT /*+ QB_NAME ("SUBQ2") NO_UNNEST */ 0
              FROM "T2" "T2" WHERE "T2"."ID"=:B1 AND "T2"."N1"=15))
   2 - access("T3"."N1"="T1"."N1")
   3 - filter("T1"."N2"=15)
   4 - filter("T3"."N2"=15)
   5 - filter("T2"."N1"=15)
   6 - access("T2"."ID"=:B1)

Then if I push the subquery

--------------------------------------------------------------------
| Id |Operation                     | Name  | Rows | Bytes | Cost  |
--------------------------------------------------------------------
|  0 |SELECT STATEMENT              |       |    9 |   252 |   117 |
|* 1 | HASH JOIN                    |       |    9 |   252 |   115 |
|* 2 |  TABLE ACCESS FULL           | T1    |    8 |   160 |    57 |
|* 3 |   TABLE ACCESS BY INDEX ROWID| T2    |    1 |     8 |     2 |
|* 4 |    INDEX UNIQUE SCAN         | T2_PK |    1 |       |     1 |
|* 5 |  TABLE ACCESS FULL           | T3    |  157 |  1256 |    57 |
--------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   1 - access("T3"."N1"="T1"."N1")
   2 - filter("T1"."N2"=15 AND  EXISTS (SELECT /*+ QB_NAME ("SUBQ2")
              PUSH_SUBQ NO_UNNEST */ 0 FROM "T2" "T2"
              WHERE "T2"."ID"=:B1 AND "T2"."N1"=15))
   3 - filter("T2"."N1"=15)
   4 - access("T2"."ID"=:B1)
   5 - filter("T3"."N2"=15)

Notice how the access to t2 has squeezed itself between t1 and t3 and is also indented one place as a clue that it is a subordinate action on t1, but the FILTER operation visible in the previous plan has disappeared. This plan is an example of a plan that doesn’t follow the well-known “first child first / recursive descent” guideline – Oracle has hidden the FILTER operation and twisted the plan slightly out of its “tradiational” shape as a consequence.

Driving_site

The final hint is for distributed queries, and has no “negative” version. Sometimes the only way you can “tune” a distributed query is to minimise the time spent on network traffic, and this means dictating WHERE the query executes. The driving_site hint lets you make that choice. (Sometimes, having made that choice you also have to include a leading() hint to tell Oracle about the single join order you want it to consider – it’s possible for the optimizer to do some very strange things with distributed queries, especially if the instances have different NLS settings).

Consider the following query (I’ll fill in the XXXX in the hint shortly):

select  /*+ driving_site (XXXX) */
        dh.small_vc,
        da.large_vc
from
        dist_home               dh,
        dist_away@remote_db     da
where
        dh.small_vc like '1%'
and     da.id = dh.id;

This query extracts a small amount of data from a table called DIST_HOME in the local database, and joins it to some data in a table called DIST_AWAY in a remote database, producing a reasonably large number of medium-sized rows. There are basically two obvious plans:

• nested loop – for each row in dist_home, query dist_away for matching data
• hash join – create an in-memory hash table from the dist_home data, and then probe it with data from all the rows in dist_away.

The first plan will produce a large number of network round trips – so that’s not very good; the second plan will pull a very large amount of data from the remote database if the query operates at the local database (it’s only the columns we need, but it will be ALL the rows from the remote database).

Choosing the second plan but executing it at the remote database means we’ll send a small parcel of data to the remote database, do the join there to produce (we hope) a reasonable result set, then send it back to the local database. The network traffic will be minimised without causing an undesirable increase in other resource usage. To make this plan happen all I needed to do in the query was change the XXXX in the driving_site() hint to reference a table alias from a table in the remote database, in this case driving_site(da).

Here’s the execution plan:

-----------------------------------------------------------------------
| Id | Operation              | Name     | Rows | Bytes | Inst |IN-OUT|
-----------------------------------------------------------------------
|  0 | SELECT STATEMENT REMOTE|          |  216 | 48600 |      |      |
|* 1 |  HASH JOIN             |          |  216 | 48600 |      |      |
|  2 |   REMOTE               | DIST_HOME|  216 |  4320 |    ! | R->S |
|  3 |   TABLE ACCESS FULL    | DIST_AWAY| 2000 |   400K| TEST |      |
-----------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
  1 - access("A1"."ID"="A2"."ID")

Remote SQL Information (identified by operation id):
----------------------------------------------------
  2 - SELECT "ID","SMALL_VC" FROM "DIST_HOME" "A2" WHERE "SMALL_VC"
      LIKE '1%' (accessing '!')

Notice how the top line (id 0) includes the keyword REMOTE – this tells you that this is the plan from the viewpoint of the remote database/instance that will be executing it. Remember that from its viewpoint the database that we think is the local database is one that it thinks is remote – hence the REMOTE operation 2 which is addressing (our) local table DIST_HOME.

Other key points to note are the appearance of the Inst (instance) and IN-OUT columns. These tell you where each table is located – when a query executes remotely “our” database is tagged only by the name “!”.

A nice feature of the execution plan for a distributed query is that you can see how the query has been decomposed for execution at the remote site. In this case the other database will be sending our database the query at operation 2 to pull the rows it wants from small_vc so that it can do the join at its site and send the result back to us.

The thing you generally don’t want to see in more complex distributed queries is a separate query being generated for each remote table involved in the join – tables that live remotely should be joined remotely with just the join result being pulled back to the local database.

There is a special warning that goes with this hint – it isn’t valid for the select statements in “create as select” and “insert as select”. There seems to be no good reason for this limitation, but for CTAS and “insert as select” the query has to operate at the site of the table that is receiving the data. This means that you may be able to tune a naked SELECT to perform very well and then find that you can’t get the CTAS to use the same execution plan. A typical workaround to this problem is to wrap the select statement into a pipelined function and do a select from table(pipelined_function).

Conclusion

There are literally hundreds of hints available but, as a general guideline, there are only a few that are particularly useful and strategically sound. In this article I’ve listed the five hints that I’ve long considered to be the ones that are of most help and least risk. I have mentioned a couple of other hints in passing, and know that there are a couple of hints in the newer versions of Oracle that should eventually be added to the list; but the five I’ve mentioned give a sound basis to work from in understanding the benefits of using hints that shape the optimizer’s strategy for a query without trying to micro-manage it.