Correlation oddity

This one’s so odd I nearly posted it as a “Quiz Night” – but decided that it would be friendlier simply to demonstrate it. Here’s a simple script to create a couple of identical tables. It’s using my standard environment but, apart from fiddling with optimizer settings, I doubt if there’s any reason why you need to worry too much about getting the environment exactly right.

create table t1
as
with generator as (
	select	--+ materialize
		rownum id 
	from dual 
	connect by 
		level <= 1000
)
select
	rownum			id,
	lpad(rownum,10,'0')	small_vc,
	rpad('x',100)		padding
from
	generator	v1,
	generator	v2
where
	rownum <= 10000
;

create table t2 as select * from t1;

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;
/

create index t2_i1 on t2(id);

All I’ve done is create a small amount of data, collected stats, and built one index. Now run the following (fairly similar) queries.

update	t1
set	small_vc = (
		select
			max(small_vc)
		from
			t2
		where	t2.id = t1.id
	)
where
	mod(id,100) = 0
;

commit;

update	t1
set	small_vc = (
		select
			small_vc
		from
			t2
		where	t2.id = t1.id
	)
where
	mod(id,100) = 0
;

commit;

All I’m doing it an update of 100 rows through a correlated subquery. For consistency you might want to recreate the tables before running each of the updates, or perhaps run the test twice with the two updates in the opposite order the second time around. I’m not trying to do anything subtle or difficult to produce an anomaly, I’m just trying to demonstrate a strange difference between these two updates.

As you might expect, both updates (in my environment) operated through a simple subquery mechansim; Oracle didn’t attempt to transform them into a some sort of flattened update join view, so the two execution plans looked like this:

Aggregate subquery plan:
-----------------------------------------------------------------------
| Id  | Operation                     | Name  | Rows  | Bytes | Cost  |
-----------------------------------------------------------------------
|   0 | UPDATE STATEMENT              |       |       |       |    27 |
|   1 |  UPDATE                       | T1    |       |       |       |
|*  2 |   TABLE ACCESS FULL           | T1    |   100 |  1500 |    27 |
|   3 |   SORT AGGREGATE              |       |     1 |    15 |       |
|   4 |    TABLE ACCESS BY INDEX ROWID| T2    |     1 |    15 |     2 |
|*  5 |     INDEX RANGE SCAN          | T2_I1 |     1 |       |     1 |
-----------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter(MOD("ID",100)=0)
   5 - access("T2"."ID"=:B1)


Simple query plan:
----------------------------------------------------------------------
| Id  | Operation                    | Name  | Rows  | Bytes | Cost  |
----------------------------------------------------------------------
|   0 | UPDATE STATEMENT             |       |       |       |    27 |
|   1 |  UPDATE                      | T1    |       |       |       |
|*  2 |   TABLE ACCESS FULL          | T1    |   100 |  1500 |    27 |
|   3 |   TABLE ACCESS BY INDEX ROWID| T2    |     1 |    15 |     2 |
|*  4 |    INDEX RANGE SCAN          | T2_I1 |     1 |       |     1 |
----------------------------------------------------------------------

Predicate Information (identified by operation id):
---------------------------------------------------
   2 - filter(MOD("ID",100)=0)
   4 - access("T2"."ID"=:B1)

There really doesn’t seem to be much difference in the mechanics. For each of the 100 rows selected from table t1, we run a subquery doing a range scan against table t2, using the index t2_i1 to find corresponding rows. In every case there is only one row match: in one update I copy back the maximum value from the list that consists of of just one row, in the other update I copy back the value from every one of the matching rows I find (all one of them); luckily the second update only ever finds one matching row for each row in the driving table, so the update completes rather than crashing with error ORA-01427: single-row subquery returns more than one row.

So – would you expect the two updates to perform differently ? I wouldn’t; but they do; here are some of the session stats:

Aggregate subquery
------------------
Name                                      Value
----                                      -----
db block gets                               208
db block changes                            407
redo entries                                108
redo size                                43,996
undo change vector size                  14,944

Simple subquery
---------------
Name                                      Value
----                                      -----
db block gets                               105
db block changes                            203
redo entries                                 63
redo size                                34,624
undo change vector size                  11,248

Obviously there were many other statistics I could have reported – consistent gets, table scan rows gotten, and so on – but all the other statistics matched very closely. What you see here is just the small number of statistics that tell us that the amount of data change was different depending on whether on not you used a (redundant) aggregation to get the value you needed to do the update. In passing, if you’re running 9i or lower, or if you’re running 10g and above with RAC, or supplemental logging enabled, or if you’re running this test in the SYS schema, you’ll see the number of redo entries at (or very close to) 202 and 101 respectively – the actual number of entries in my test is the effect of in-memory undo and private redo.

To find out what’s going on, the easiest thing to do is look at a symbolic dump of the redo log for this update which, stripped to a minimum, shows the following as the redo generated for one of the later rows updated in the batch of 100.

Aggregate subquery:
-------------------
REDO RECORD - Thread:1 RBA: 0x0005c6.00000057.0030 LEN: 0x00c8 VLD: 0x01
CHANGE #1 TYP:0 CLS:30 AFN:2 DBA:0x00807017 OBJ:4294967295 SCN:0x0000.04f86220 SEQ:  2 OP:5.1
	ktubu redo: slt: 30 rci: 99 opc: 11.1 objn: 98261 objd: 98261 tsn: 5
CHANGE #2 TYP:0 CLS: 1 AFN:5 DBA:0x01800130 OBJ:98261 SCN:0x0000.04f86192 SEQ:  2 OP:11.4
	KDO Op code: LKR row dependencies Disabled
 
REDO RECORD - Thread:1 RBA: 0x0005c6.00000057.00f8 LEN: 0x0100 VLD: 0x01
CHANGE #1 TYP:0 CLS:30 AFN:2 DBA:0x00807017 OBJ:4294967295 SCN:0x0000.04f86221 SEQ:  1 OP:5.1
	ktubu redo: slt: 30 rci: 100 opc: 11.1 objn: 98261 objd: 98261 tsn: 5
CHANGE #2 TYP:0 CLS: 1 AFN:5 DBA:0x01800130 OBJ:98261 SCN:0x0000.04f86221 SEQ:  1 OP:11.5
	KDO Op code: URP row dependencies Disabled


Simple subquery:
----------------
REDO RECORD - Thread:1 RBA: 0x0005c7.00000044.0090 LEN: 0x0160 VLD: 0x01
CHANGE #1 TYP:0 CLS:26 AFN:2 DBA:0x00801610 OBJ:4294967295 SCN:0x0000.04f86288 SEQ:  1 OP:5.1
	ktubu redo: slt: 36 rci: 42 opc: 11.1 objn: 98261 objd: 98261 tsn: 5
CHANGE #2 TYP:2 CLS: 1 AFN:5 DBA:0x0180012c OBJ:98261 SCN:0x0000.04f86224 SEQ:  1 OP:11.19
	Array Update of 1 rows: 
	KDO Op code:  21 row dependencies Disabled

When we update using the aggregate subquery Oracle generates two redo records per row: the first locks the row (op code 11.4), the second modifies it (op code 11.5). When we do the update through a simple subquery Oracle generates a single redo record per row, which is an “array update” (op code 11.19).

This anomaly may look familiar – but this time it’s nothing to do with the audit_trail, it just seems to be the way things work. At present I can’t think of any good reason for the difference, but maybe there’s something subtle I’m overlooking.