Using the AWR History Tables to compare performance

Using the DBA_HIST AWR history tables, we can compare the top wait events between different days or different time periods.

Let us assume that we find that batch jobs which are executed at night particularly between 1 and 2 AM are experiencing performance issues.

In this case we are comparing performance of a particular database on the 20th and 21st of July for the time periods 01:00 to 02:00.

SQL>
1 select snap_id,to_char(BEGIN_INTERVAL_TIME,’DD-MON-YY HH24:MI:SS’) “Runtime”
2 from dba_hist_snapshot
3* where trunc(BEGIN_INTERVAL_TIME)=’21-JUL-2009′ order by snap_id;

SNAP_ID Runtime
———- ——————
10781 21-JUL-09 00:00:14
10782 21-JUL-09 01:01:01
10783 21-JUL-09 02:00:27
10784 21-JUL-09 03:00:42
……..

Since the snapshots are collected every hour, for the same time period on the previous day we substract 24 – so the snap_ids for the 20th of July are 10758 and 10759.

select * from
(select event, waits “Waits”, time “Wait Time (s)”, pct*100 “Percent of Total”, waitclass “Wait Class”
from (select e.event_name event, e.total_waits – nvl(b.total_waits,0) waits,
(e.time_waited_micro – nvl(b.time_waited_micro,0))/1000000 time
, (e.time_waited_micro – nvl(b.time_waited_micro,0))/(select sum(e1.time_waited_micro – nvl(b1.time_waited_micro,0))
from dba_hist_system_event b1 , dba_hist_system_event e1
where b1.snap_id(+) = b.snap_id and e1.snap_id = e.snap_id and b1.dbid(+) = b.dbid
and e1.dbid = e.dbid and b1.instance_number(+) = b.instance_number
and e1.instance_number = e.instance_number
and b1.event_id(+) = e1.event_id
and e1.total_waits > nvl(b1.total_waits,0)
and e1.wait_class <> ‘Idle’
) pct
, e.wait_class waitclass
from
dba_hist_system_event b ,
dba_hist_system_event e
where b.snap_id(+) = &pBgnSnap
and e.snap_id = &pEndSnap
and b.event_id(+) = e.event_id
and e.total_waits > nvl(b.total_waits,0)
and e.wait_class <> ‘Idle’
order by waits desc
)
where rownum < 11)
;

Note: we are filtering the rows to display only the Top 10 Wait Events

SnapID’s 10758 and 10759 (20th July)

EVENT                                         Waits Wait Time (s) Percent of Total Wait Class
---------------------------------------- ---------- ------------- ---------------- --------------------
control file sequential read                4803994     79.589894        2.4698686 System I/O
PX qref latch                               1451898      3.587572       .111331112 Other
db file sequential read                       57463    1914.86419       59.4229078 User I/O
SQL*Net message to client                     52040       .077309       .002399087 Network
db file scattered read                         9169    223.497921        6.9356858 User I/O
log file parallel write                        6692    371.933451       11.5420025 System I/O
SQL*Net break/reset to client                  6610       3.75458       .116513778 Application
log file sync                                  3175    211.309884       6.55746127 Commit
direct path read                               2077      1.349465       .041877191 User I/O
control file parallel write                    1857    112.049356        3.4771649 System I/O

10 rows selected.

SnapId’s 10782 and 10783 (21st July)

EVENT                                         Waits Wait Time (s) Percent of Total Wait Class
---------------------------------------- ---------- ------------- ---------------- --------------------
control file sequential read                6006299    101.375645       .512584025 System I/O
SQL*Net message to client                   1045461       1.16951       .005913374 Network
db file sequential read                      312287    7147.10849       36.1378085 User I/O
log file sync                                 86597    8044.46297       40.6750873 Commit
log file parallel write                       62614    3257.61227       16.4714119 System I/O
SQL*Net more data from client                 25708      6.923053        .03500492 Network
SQL*Net break/reset to client                 24824    361.693308       1.82882399 Application
db file scattered read                        18289    178.619015       .903148421 User I/O
SQL*Net more data to client                   14721       .531947       .002689675 Network
db file parallel write                         3128    310.442873       1.56968725 System I/O

10 rows selected.

Looking at this output for the same time period on two days, we find that on the 21st of July the top wait events seem to be all I/O related and if we see the wait event “log file sync” on the second day is significantly higher than the first day. The wait event “db file sequential read” is also significantly higher on the second day as well as compared to the first day.

We can use this information to quickly triage the problem and make the following checks: