rwijk@ORA11GR1> create table percentages_per_year (ym,percentage)
2 as
3 select date '2005-12-01' + numtoyminterval(level,'month')
4 , case mod(level,3) when 0 then 5 else 10 end
5 from dual
6 connect by level <= 48
7 /
Table created.
rwijk@ORA11GR1> select * from percentages_per_year
2 /
YM PERCENTAGE
------------------- ----------
01-01-2006 00:00:00 10
01-02-2006 00:00:00 10
01-03-2006 00:00:00 5
01-04-2006 00:00:00 10
01-05-2006 00:00:00 10
01-06-2006 00:00:00 5
01-07-2006 00:00:00 10
01-08-2006 00:00:00 10
01-09-2006 00:00:00 5
01-10-2006 00:00:00 10
01-11-2006 00:00:00 10
01-12-2006 00:00:00 5
01-01-2007 00:00:00 10
01-02-2007 00:00:00 10
01-03-2007 00:00:00 5
01-04-2007 00:00:00 10
01-05-2007 00:00:00 10
01-06-2007 00:00:00 5
01-07-2007 00:00:00 10
01-08-2007 00:00:00 10
01-09-2007 00:00:00 5
01-10-2007 00:00:00 10
01-11-2007 00:00:00 10
01-12-2007 00:00:00 5
01-01-2008 00:00:00 10
01-02-2008 00:00:00 10
01-03-2008 00:00:00 5
01-04-2008 00:00:00 10
01-05-2008 00:00:00 10
01-06-2008 00:00:00 5
01-07-2008 00:00:00 10
01-08-2008 00:00:00 10
01-09-2008 00:00:00 5
01-10-2008 00:00:00 10
01-11-2008 00:00:00 10
01-12-2008 00:00:00 5
01-01-2009 00:00:00 10
01-02-2009 00:00:00 10
01-03-2009 00:00:00 5
01-04-2009 00:00:00 10
01-05-2009 00:00:00 10
01-06-2009 00:00:00 5
01-07-2009 00:00:00 10
01-08-2009 00:00:00 10
01-09-2009 00:00:00 5
01-10-2009 00:00:00 10
01-11-2009 00:00:00 10
01-12-2009 00:00:00 5
48 rows selected.
The data is such that all percentages in a year add up to exactly 100. To ensure this is always the case in the future, I implement it as a business rule using an on commit-time fast refreshable materialized view:
rwijk@ORA11GR1> create materialized view log on percentages_per_year
2 with sequence, rowid (ym,percentage) including new values
3 /
Materialized view log created.
rwijk@ORA11GR1> create materialized view mv
2 refresh fast on commit
3 as
4 select to_char(ym,'yyyy') year
5 , sum(percentage) year_percentage
6 , count(percentage) count_percentage
7 , count(*) count_all
8 from percentages_per_year
9 group by to_char(ym,'yyyy')
10 /
Materialized view created.
rwijk@ORA11GR1> alter materialized view mv
2 add constraint year_percentage_100_ck
3 check (year_percentage=100)
4 /
Materialized view altered.
rwijk@ORA11GR1> select * from mv
2 /
YEAR YEAR_PERCENTAGE COUNT_PERCENTAGE COUNT_ALL
---- --------------- ---------------- ----------
2009 100 12 12
2008 100 12 12
2007 100 12 12
2006 100 12 12
4 rows selected.
rwijk@ORA11GR1> exec dbms_stats.gather_table_stats(user,'percentages_per_year')
PL/SQL procedure successfully completed.
Now the idea is to run 4 jobs, each updating 12 records of one year. To see what's going on inside each job, I create a logging table with logging procedures that run autonomous transactions:
rwijk@ORA11GR1> create table t_log
2 ( year number(4)
3 , starttime timestamp(6)
4 , endtime timestamp(6)
5 )
6 /
Table created.
rwijk@ORA11GR1> create procedure log_start (p_year in t_log.year%type)
2 is
3 pragma autonomous_transaction;
4 begin
5 insert into t_log (year,starttime)
6 values (p_year,systimestamp)
7 ;
8 commit;
9 end log_start;
10 /
Procedure created.
rwijk@ORA11GR1> create procedure log_end (p_year in t_log.year%type)
2 is
3 pragma autonomous_transaction;
4 begin
5 update t_log
6 set endtime = systimestamp
7 where year = p_year
8 ;
9 commit;
10 end log_end;
11 /
Procedure created.
The jobs will run a procedure called update_percentages. The procedure sets its module and action column, to be able to identify the trace files that are created. It logs itself before the update and after the commit:
rwijk@ORA11GR1> create procedure update_percentages (p_year in number)
2 is
3 begin
4 dbms_application_info.set_module('p',to_char(p_year))
5 ;
6 dbms_session.session_trace_enable
7 ;
8 log_start(p_year)
9 ;
10 update percentages_per_year
11 set percentage = case when extract(month from ym) <= 8 then 9 else 7 end
12 where extract(year from ym) = p_year
13 ;
14 commit
15 ;
16 log_end(p_year)
17 ;
18 end update_percentages;
19 /
Procedure created.
And now let 4 jobs run:
rwijk@ORA11GR1> declare
2 l_job_id binary_integer;
3 begin
4 for i in 2006 .. 2009
5 loop
6 dbms_job.submit(l_job_id,'rwijk.update_percentages('||to_char(i)||');');
7 end loop;
8 end;
9 /
PL/SQL procedure successfully completed.
rwijk@ORA11GR1> commit
2 /
Commit complete.
rwijk@ORA11GR1> pause
After all jobs have run concurrently and have finished, the logging table looks like this:
rwijk@ORA11GR1> select year
2 , starttime
3 , endtime
4 , endtime - starttime duration
5 from t_log
6 order by endtime
7 /
YEAR STARTTIME ENDTIME
----- ------------------------------- -------------------------------
DURATION
-------------------------------
2007 14-JAN-10 09.59.45.625000 PM 14-JAN-10 09.59.45.843000 PM
+000000000 00:00:00.218000
2006 14-JAN-10 09.59.45.609000 PM 14-JAN-10 09.59.45.875000 PM
+000000000 00:00:00.266000
2008 14-JAN-10 09.59.45.656000 PM 14-JAN-10 09.59.45.906000 PM
+000000000 00:00:00.250000
2009 14-JAN-10 09.59.45.625000 PM 14-JAN-10 09.59.45.937000 PM
+000000000 00:00:00.312000
4 rows selected.
You can see the overlap of the processes. The process for year 2007 has finished first, and doesn't experience any noticeable wait. The other three processes do. A look at the trace file shows one line like this:
WAIT #8: nam='enq: JI - contention' ela= 85497 name|mode=1246298118 view object #=106258 0=0 obj#=-1 tim=7377031494
This line is followed after the COMMIT (XCTEND rlbk=0) in the trace file. The wait event is called "enq: JI - contention", where JI stands for materialized view refresh :-). In the documention it says that JI stands for "Enqueue used during AJV snapshot refresh". So it's still unclear where those letters JI really stand for, although the J may be from the J in AJV. And google suggests that AJV stands for "Aggregate join view". Maybe there are so many two-letter enqueue names already that they just picked one?
In the WAIT line from the trace file, we see three parameters after the "ela= 85497". We can see these parameters in the v$event_name view too:
rwijk@ORA11GR1> select event#
2 , name
3 , parameter1
4 , parameter2
5 , parameter3
6 , wait_class
7 from v$event_name
8 where name = 'enq: JI - contention'
9 /
EVENT# NAME
---------- ----------------------------------------------------------------
PARAMETER1
----------------------------------------------------------------
PARAMETER2
----------------------------------------------------------------
PARAMETER3
----------------------------------------------------------------
WAIT_CLASS
----------------------------------------------------------------
818 enq: JI - contention
name|mode
view object #
0
Other
1 row selected.
The first parameter is "name|mode", which equals a rather strange number 1246298118. This pretty-old-but-still-very-useful article of Anjo Kolk helped me interpreting the number:
rwijk@ORA11GR1> select chr(bitand(1246298118,-16777216)/16777215) ||
2 chr(bitand(1246298118,16711680)/65535) “Lock”
3 , bitand(1246298118, 65535) “Mode”
4 from dual
5 /
Lo Mode
-- ----------
JI 6
1 row selected.
Where lock mode 6 means an exclusive lock.
The second parameter is the object_id as you can find it in the all_objects view. The third parameter isn't useful here.
So a materialized view cannot be fast refreshed more than once in a given period. It's serialized during the commit. Normally when I update record 1 and commit and you update record 2 and commit, we don't have to wait for each other. But when having an on commit-time fast refreshable materialized view on top of the table, we do have to wait when two sessions do some totally unrelated transactions concurrently against the same table. Is this a problem? Not when the table is modified infrequently or only by a single session. But it can be a big problem when applied to a table that modifies a lot concurrently.
So be sure to use on commit-time fast refreshable materialized views for implementing business rules only on tables that are not concurrently accessed or infrequently changed. Or be prepared to be called by the users of your system ...