Consistent Gets

There’s an interesting question on the OTN database forum at present – why does an update of 300,000 rows take a billion buffer visits. (There are 25 indexes on the table – so you might point a finger at then initially, but only one of the indexes is going to be changed by the update so that should only account for around an extra 10 gets per row in a clean environment.)

The answer to the question hadn’t been reached by the time I wrote this note – and this note isn’t intended as a suggested cause of the issue, it’s just an example of the type of thing that could cause an apparent excess of buffer visits. Here’s a little bit of code I’ve just tested on using an 8KB block size

drop sequence t1_seq;
create sequence t1_seq;

drop table t1;

create table t1 as
	rownum id, rpad('x',10) small_vc
	rownum <= 11

execute dbms_stats.gather_table_stats(user,'t1')

select * from t1;


execute snap_my_stats.start_snap
update t1 set small_vc = upper('small_vc') where id = 11;
execute snap_my_stats.end_snap

(The calls to the “snap_my_stats” package simply record the current contents of v$mystat joined to v$statname before and after the update and print the changes.)

The code simply creates a sequence and a table with 11 rows and no indexes, then updates one specific row in the table. However, where the “pause” appears, I start up 10 separate sessions to do the following:

column seqval new_value m_seq

select t1_seq.nextval seqval from dual;

update t1 set small_vc = upper(small_vc) where id = &m_seq;



So when I hit return on the pause for the first session, there are 10 separate active transactions on the single block in my table, one for each row except row 11. (And now you know what the sequence was for.)

Here’s a subset of the statistics from v$mystat after my update statement – remember, all I’ve done is update one row in one block using a tablescan:

Name                                                                     Value
----                                                                     -----
session logical reads                                                       45
db block gets                                                                3
db block gets from cache                                                     3
consistent gets                                                             42
consistent gets from cache                                                  42
consistent gets from cache (fastpath)                                        3
consistent gets - examination                                               39
db block changes                                                             7
consistent changes                                                          13
calls to kcmgrs                                                             26
calls to kcmgas                                                              1
calls to get snapshot scn: kcmgss                                            7
redo entries                                                                 3
redo size                                                                  764
undo change vector size                                                    236
data blocks consistent reads - undo records applied                         13
active txn count during cleanout                                            20
table scan blocks gotten                                                     1

Note the last statistics – just one block accessed by tablescan – compared to the session logical reads at 45 buffer visits.
That 45 buffer visits comes from 3 current (db) block gets and 42 consistent gets.
Of the 42 consistent gets 39 are examinations, which – in the absence of indexes and hash clusters are visits to undo blocks
The 39 undo visits are to find 13 undo records to apply, and 26 visits (to undo segment headers) to find 13 transaction SCNs.

What you’re seeing is one session doing (relatively speaking) a lot of work to hide the effects of other sessions which have not yet committed their transactions. (This was only a quick test, so I haven’t examined why the larger figures appear in multiples of 13 rather than multiples of 10 – the number of other transactions – and since this note is just trying to demonstrate a concept I won’t be looking into it any further.)

If you have a number of “non-interfering” transactions – i.e. transactions that don’t actually lock each other out – on a single table then you could find that they spend more time hiding each other’s work than they do doing their own work.


The numbers change significantly if I commit the 10 transactions (but wait until they’ve all executed, so they are all active at the same time) before I do the update to the 11th row.

The numbers changed even more surprisingly when I forgot to collect stats on the table in my initial example of the test.