A question that comes up from time to time on the OTN forums is the one about how much undo do you generate when you insert or update a row. It’s not a question that has an immediate answer – it depends on what you’re actually doing, the amount of change you introduce, and the number of indexes affected. However, there are a few guidelines that will point you in the right direction – and the key is to keep an eye on the used_urec and used_ublk colummns from v$transactions.
Consider the example of inserting data: when you insert a row, you typically generate one undo record (used_urec) for the row, but when you insert many rows using an array insert Oracle has an optimisation mechanism on undo record creation that allows it to create one used_urec to cover all the changes you have made simultaneously to an individual block - so used_urec could be much smaller than the number of rows processed.
However, if you have indexes in place and are doing normal index maintenance on import, then each table row would require each index to be updated, so you would go back to one used_urec per table row plus one used_urec per index maintained per table row.
So, when you look at the “big picture” there’s no obvious correlation between rows inserted and undo generated — until you look at the fine detail of exactly what you’re doing, and whether any optimisations apply. (The details of the optimisation strategies available vary with the chosen insert mechanisms and with version of Oracle)
Just for reference, here’s a link to a little note I wrote some months ago about monitoring undo – it generated a surprising number of comments.
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 22.214.171.124 using an 8KB block size
drop sequence t1_seq; create sequence t1_seq; drop table t1; create table t1 as select rownum id, rpad('x',10) small_vc from all_objects where rownum <= 11 ; execute dbms_stats.gather_table_stats(user,'t1') select * from t1; pause 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$statname before and after the update and print the changes.)
The code simply creates a sequence and a table with 11 rows and no indexes, then updates one specific row in the table. However, where the “pause” appears, I start up 10 separate sessions to do the following:
column seqval new_value m_seq select t1_seq.nextval seqval from dual; update t1 set small_vc = upper(small_vc) where id = &m_seq; pause exit
So when I hit return on the pause for the first session, there are 10 separate active transactions on the single block in my table, one for each row except row 11. (And now you know what the sequence was for.)
Here’s a subset of the statistics from v$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.
Following on from yesterday’s post on consistent reads, I thought I’d make the point that the way you work can make an enormous difference to the amount of work you do. Here’s a silly little demo (in 10.2.0.3):
drop table t1 purge; create table t1 (id number, n1 number); insert into t1 values (1,0); insert into t1 values (2,0); commit; execute dbms_stats.gather_table_stats(user,'t1') execute snap_my_stats.start_snap begin for i in 1..1000 loop update t1 set n1 = i where id = 1; end loop; end; / execute snap_my_stats.end_snap set doc off doc Output - 10.2.0.3 (some rows deleted) Name Value ---- ----- opened cursors cumulative 11 user calls 6 recursive calls 1,068 recursive cpu usage 7 session logical reads 4,041 CPU used when call started 7 CPU used by this session 7 DB time 6 db block gets 1,030 db block gets from cache 1,030 consistent gets 3,011 consistent gets from cache 3,011 consistent gets - examination 4 db block changes 2,015 change write time 4 free buffer requested 1,014 switch current to new buffer 1,000 calls to kcmgas 1,014 calls to get snapshot scn: kcmgss 3,009 redo entries 960 redo size 295,160 undo change vector size 111,584 no work - consistent read gets 1,004 table scans (short tables) 1,001 table scan rows gotten 2,002 table scan blocks gotten 1,001 buffer is pinned count 1,000 execute count 1,009 #
I’ve created two rows in a table, then updated one of them 1,000 times – using a table scan to do the update. I haven’t yet committed my transaction. At this point I’m going to use a second session to run the same update loop on the second row in the table:
begin for i in 1..1000 loop update t1 set n1 = i where id = 2; end loop; end; / Name Value ---- ----- opened cursors cumulative 8 user calls 6 recursive calls 1,009 recursive cpu usage 170 session logical reads 965,999 CPU used when call started 172 CPU used by this session 172 DB time 173 db block gets 1,030 db block gets from cache 1,030 consistent gets 964,969 consistent gets from cache 964,969 consistent gets - examination 961,965 db block changes 3,016 consistent changes 1,001,000 free buffer requested 1,015 CR blocks created 1,001 calls to kcmgas 1,015 calls to get snapshot scn: kcmgss 3,008 redo entries 1,936 redo size 358,652 undo change vector size 111,608 data blocks consistent reads - undo records applied 1,001,000 cleanouts and rollbacks - consistent read gets 1,001 immediate (CR) block cleanout applications 1,001 active txn count during cleanout 2,000 cleanout - number of ktugct calls 1,001 IMU CR rollbacks 41,041 table scans (short tables) 1,001 table scan rows gotten 2,002 table scan blocks gotten 1,001 execute count 1,006
Many of the statistics are (virtually) identical (e.g. “execute count”, “db block gets”, “free buffer requested”); some show an increase by 1,000 (often from zero) – largely because we have to worry 1,000 times about cleaning out the current block and creating a read-consistent version so that we can check if it can be updated.
But the most noticeable changes are in the “CPU time” and “consistent gets” because of the 1,000 times we have to apply 1,000 undo records to the block as we create the read-consistent version of the block. The CPU time has gone from 7 (hundredths of a second) to 172 because of (roughly) 1,000,000 “consistent gets – examination”. As I mentioned yesterday, this matches closely to “data blocks consistent reads – undo records applied” so we know why they are happening. Watch out in your batch jobs – if you have a lot of concurrent update activity going on a significant fraction of the workload may be the constant re-creation of CR clones.
However, there is another interesting detail to watch out for – what happens if I change the update execution path from a tablescan to an indexed access path:
create table t1 (id number, n1 number); insert into t1 values (1,0); insert into t1 values (2,0); commit; execute dbms_stats.gather_table_stats(user,'t1') create index t1_i1 on t1(id); -- Make indexed access path available.
Then with an index hint in my update code, I get the following effects (having done the same update loop on row 1 in the first session, of course):
begin for i in 1..1000 loop update /*+ index(t1) */ t1 set n1 = i where id = 2; -- indexed access path hinted end loop; end; / Name Value ---- ----- opened cursors cumulative 7 user calls 6 recursive calls 1,006 recursive cpu usage 11 session logical reads 2,036 CPU used when call started 11 CPU used by this session 11 DB time 11 db block gets 1,030 db block gets from cache 1,030 consistent gets 1,006 consistent gets from cache 1,006 consistent gets - examination 6 db block changes 2,015 free buffer requested 14 shared hash latch upgrades - no wait 1,000 calls to kcmgas 14 calls to get snapshot scn: kcmgss 1,004 redo entries 960 redo size 295,144 undo change vector size 111,608 index crx upgrade (positioned) 1,000 index scans kdiixs1 1,000 execute count 1,005
The difference is astonishing – where did all the ‘create CR copy’ activity go ?
I’ve pointed out before now that choosing a different execution plan for an update can have a big impact on performance – this is just another example demonstrating the point.
Here’s a quick demo to make a point about consistent reads (prompted by a question on the Oracle-L mailing list):
SQL> drop table t1; Table dropped. SQL> create table t1 (n1 number); Table created. SQL> insert into t1 values(0); 1 row created. SQL> begin 2 for i in 1..1000 loop 3 update t1 set n1 = i; 4 end loop; 5 end; 6 / PL/SQL procedure successfully completed.
Note that I haven’t issued a commit in this session, and all I’ve got is a single row in the table (and because it’s my usual demo setup of locally managed tablespaces with uniform extents of 1MB using freelist management I know that that one row is in the first available block of the table).
How much work is a second session going to do to scan that table ?
SQL> alter system flush buffer_cache; SQL> execute snap_my_stats.start_snap SQL> select * from t1; SQL> set serveroutput on size 1000000 format wrapped SQL> execute snap_my_stats.end_snap --------------------------------- Session stats - 18-Apr 11:33:01 Interval:- 2 seconds --------------------------------- Name Value ---- ----- session logical reads 967 consistent gets 967 consistent gets from cache 967 consistent gets - examination 964 consistent changes 1,001 CR blocks created 1 data blocks consistent reads - undo records applied 1,001 IMU CR rollbacks 41
The snap_my_stats package is similar in concept to Tom Kyte’s “runstats” or Tanel Poder’s “snapper” program to capture changes in values in the dynamic performance views over short time periods. In this case I’ve deleted all but a few of the larger changes, and a couple of small changes.
The figure that stands out (probably) is the “session logical reads” – we’ve done 967 logical I/Os to scan a tables of just one block. The reason for this is that we’ve created a read-consistent copy of that one block (“CR blocks created” = 1), and it has taken a lot of work to create that copy. We’ve had to apply 1,001 undo records (“data blocks consistent reads – undo records applied” = 1001).
Most of those undo records come from individual accesses (which are of the cheaper “consistent gets – examination” type that only need a single get on the “cache buffers chains” latch) to undo blocks, following the “UBA (undo block address)” pointer in the relevant ITL entry of the table block, but since this is a 10g database the last few undo records come out of the “In-memory Undo” of the other session. Basically the cloning operation is something like this:
It is an interesting point that as the first session created undo records it would pin and fill undo blocks – so would only do a few current gets (one for each block) on the undo blocks it was using. As another process reverses out the changes in a CR clone it has to get and release each undo block every time it wants a single undo record … applying undo records introduces far more latch and buffer activity that the original generation of the undo.
It’s worth knowing that there are three statistics relating to applying undo records:
transaction tables consistent reads - undo records applied Estimating "old" commit SCNs during delayed block cleanout data blocks consistent reads - undo records applied Creating CR clones rollback changes - undo records applied The result of a real "rollback;"
The second step in the list of actions is: “Notice uncommitted transaction”. It’s probably worth pointing out that another part of the ITL entry holds the transaction id (“xid”) which implicitly identifies the undo segment and transaction table slot in that segment that has been used to hold the transaction state. The current contents of that slot allow Oracle to determine whether or not (and when, if necessary) the transaction was committed.
About this time last year I wrote a short answer on OTN about Private Redo Threads and In-Memory Undo. Thanks to a follow-up question a year later I’ve been prompted to supply a link to my original answer because it was actually pretty good: OTN Thread “In Memory Undo”.
Oracle uses for its read consistency model a true multi-versioning approach which allows readers to not block writers and vice-versa, writers to not block readers. Obviously this great feature allowing highly concurrent processing doesn't come for free, since somewhere the information to build multiple versions of the same data needs to be stored.
Oracle uses the so called undo information not only to rollback on-going transactions but also to re-construct old versions of blocks if required. Very simplified when reading data Oracle knows the point in time (which corresponds to an internal counter called SCN, System Change Number) that data needs to be consistent with. In the default READ COMMITTED isolation mode this point in time is defined when a statement starts to execute. You could also say at the moment a statement starts to run its result is pre-ordained. When Oracle processes a block it checks if the block is "old" enough and if it discovers that the block content is too new (has been changed by other sessions but the current access is not supposed to see this updated content according to the point-in-time assigned to the statement execution) it will start to create a copy of the block and use the information available from the corresponding undo segment to re-construct an older version of the block. Note that this process can be iterative: If after re-constructing the older version of the block it's still not sufficiently old more undo information will be used to go further back in time.