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Average Active Sessions - OaktableWorld 2013

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

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Average Active Sessions - OaktableWorld 2013

  1. 1. Average Active Sessions John Beresniewicz, Oracle America
  2. 2. What is this?
  3. 3. Agenda •  DB Time •  Average Active Sessions •  Performance Monitoring •  Comparing Load •  Diving Deep with ASH •  Relationship to Queuing Theory
  4. 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. 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. 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. 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. 8. Average Active Sessions •  NOTE: Time units must synchronize AAS = DB time / elapsed time (during some workload)
  9. 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. 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. 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. 12. Bad Friday?
  13. 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. 14. Enterprise Loadmap
  15. 15. Enterprise Loadmap
  16. 16. Enterprise Loadmap
  17. 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. 18. EM Top Activity
  19. 19. ASH Analytics
  20. 20. Breakdown by SQL_ID
  21. 21. Historical Note: ASH Analytics Mockup
  22. 22. ASH Analytics Loadmap
  23. 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. 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. 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. 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. 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. 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. 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 /

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