A more recent presentation on the all-important topic of Average Active Sessions (AAS) for Oracle performance analysis.

1. Average Active Sessions
John Beresniewicz, Oracle America

2. What is this?

3. Agenda
• DB Time
• Average Active Sessions
• Performance Monitoring
• Comparing Load
• Diving Deep with ASH
• Relationship to Queuing Theory

4. Database Time (DB Time)
• Time spent in the database by foreground sessions
• Includes CPU time, IO wait time and active wait time
• Excludes idle wait time
Database time is total time spent by user processes
either actively working or actively waiting in a
database call.

5. EM Performance Page
• DB Time per Second
• Broken down into CPU + Wait Classes
• Averaged over 1 minute intervals
• Sources: v$waitclassmetric_history; v$sysmetric_history
• Question: What are the chart units and label?

6. Historical Note
on Naming
• We knew what we
were graphing, but
what was it?
• Team met multiple
times to discuss
• This is from when we
finally “got it”

7. What Are the Units?
• Time / time = unit-less (?)
• DB time accumulates in micro, milli, or centi-seconds
• Time-normalized sysmetrics are per second of elapsed
• Centi-seconds (foreground time) per second (elapsed)
• Centi-users per second
• User seconds per elapsed second (normalize time units)
• Active session seconds per second
• Active sessions

8. Average Active Sessions
• NOTE: Time units must synchronize
AAS = DB time / elapsed time
(during some workload)

9. Average Active Sessions
• A time-based measure of “user load” on the database
• Where the “time” is really user time…important
• The derivative (calculus) of DB Time over time
• This is why Top Activity and Perf Page are literal pictures of
DB Time
• The “velocity” of DB Time accumulation in the
database

10. Performance Monitoring
• DB Time increases when user load increases
• Average Active Sessions measures the rate of increase
• DB Time increases when performance degrades
• Average Active Sessions captures the rate of
increase of DB Time over time
• Therefore, AAS is the best single metric to monitor for
overall performance

11. Performance monitoring
• Average Active Sessions captures both load and
performance
• Severe performance degradation will spike the metric
• Server-generated metrics for monitoring:
• Database Time Per Sec (10g)
• Average Active Sessions (11g)
• Adaptive Thresholds technology
• Set thresholds to high percentile (99th) values (unusual spike)
• Moving window baseline and seasonality adjust to expected
load changes

12. Bad Friday?

13. Comparing Database Load
• How to compare load on two different databases?
• How to compare load on same database from two
different time periods?
• Answer: normalize DB Time by time
• That is, use Average Active Sessions!
• Remember, DB Time is “user time”
• It is “fungible”

14. Enterprise Loadmap

15. Enterprise Loadmap

16. Enterprise Loadmap

17. Diving Deep with ASH
• ASH is used to estimate DB Time accumulation over
some time interval, and thus also AAS
• With ASH we can break down total AAS within a
Database across many dimensions of interest
• Come to “ASH Deep-dive: Advanced Performance
Analysis Tips”
• Wednesday 3:30 pm, Moscone South Room 104

18. EM Top Activity

19. ASH Analytics

20. Breakdown by SQL_ID

21. Historical Note: ASH Analytics Mockup

22. ASH Analytics Loadmap

23. Relationship to Queuing Theory
• Consider the Oracle database as a black-box service
center
• User calls come in (SQL) from outside
• Results computed inside and returned to user
• What is the relationship of Average Active Sessions to
black-box queuing models?
• Average Active Sessions measures an important queuing
theoretic concept

24. Little’s Law for Queuing Systems
N = X * R
N = number of active requests in system
X = serviced request throughput
R = average service time per request

25. Little’s Law: Database Black-box server
N = X * R
N = ????
X = User Calls per Second
R = Response (DB time) per Call
Average Active Sessions
This explains why DB time increases with both
performance degradation and load increase.

26. Compute AAS from AWR snapshots
• Step 1: Prepare raw data stream by joining:
• DBA_HIST_SNAPSHOT
• DBA_HIST_SYS_TIME_MODEL
• Statistic “DB Time”
• Step 2: Compute elapsed time and DB Time deltas
per snapshot
• Step 3: Compute Average Active Sessions per
snapshot
• DELTA(DB Time) / DELTA(Elapsed time)

27. Step 1: Prepare Raw Data Stream
WITH snapDBtime
as
(select
SN.snap_id as snap_id
,SN.instance_number as inst_num
,ROUND(SN.startup_time,'MI') as startup_time
,ROUND(SN.begin_interval_time,'MI') as begin_time
,ROUND(SN.end_interval_time,'MI') as end_time
,TM.value / 1000000 as DBtime_secs
from
dba_hist_snapshot SN
,dba_hist_sys_time_model TM
where
SN.dbid = TM.dbid
and SN.instance_number = TM.instance_number
and SN.snap_id = TM.snap_id
and TM.stat_name = 'DB time'
),

28. Step 2: Compute Time Deltas
DeltaDBtime as
(select
inst_num
,snap_id
,startup_time
,end_time
,DBtime_secs
,CASE WHEN begin_time = startup_time
THEN DBtime_secs
ELSE
DBtime_secs - LAG(DBtime_secs,1)
OVER (PARTITION BY inst_num, startup_time
ORDER BY snap_id ASC)
END
as DBtime_secs_delta
,(end_time - begin_time)*24*60*60 as elapsed_secs
from
snapDBtime
order by inst_num, snap_id ASC
)

29. Step 3: Compute Avg Active Sessions
select
inst_num
,snap_id
,ROUND(DBtime_secs,1)
as DBtime_secs
,ROUND(DBtime_secs_delta / elapsed_secs,3)
as AvgActive_sess
from
DeltaDBtime
/