Proactive performance monitoring with adaptive thresholds
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Presentation given at UKOUG 2008 conference on the Adaptive Thresholds technology in Oracle database 10.2+ and Enterprise Manager 11. Adaptive Thresholds allows users to do consistent and effective performance monitoring across systems and architectures by using statistical characterization of metric streams to automatically set and adapt monitoring thresholds independent of application workload.
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Proactive performance monitoring with adaptive thresholds
- 2. <Insert Picture Here> Proactive Performance Monitoring with Adaptive Thresholds John Beresniewicz Consulting Member of Technical Staff Oracle USA
- 3. The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.
- 4. <Insert Picture Here> Agenda • Performance Monitoring • Understanding Metrics • Baselines and Adaptive Thresholds • Enterprise Manager Use Cases
- 5. <Insert Picture Here> Performance Monitoring
- 6. A brief history • Availability monitoring • Simple Boolean (up/down) using ping • Notification frameworks constructed • Performance monitoring • Fixed thresholds over system-level counters (V$SYSSTAT) • Use existing frameworks • Vendor metric madness • More metrics must be better • Users complaints are still the primary alerting mechanism
- 7. Performance alerting is difficult • Performance is subjective and variable • Better or worse, not best or worst • Applications vary in performance characteristics • Workloads vary predictably within system • Many metrics, few good signals • DB Time metrics far superior to counter-based ones • Metrics lack semantic framework • Do alerts point at symptoms, causes, both? • Setting thresholds manually is labor intensive • The M x N problem (M targets and N metrics)
- 8. <Insert Picture Here> Understanding Metrics
- 9. Classifying metrics • Identify a set of basic metrics • PERFORMANCE: Time-based metrics • KING KONG: Average Active Sessions • Response time per Txn, Response time per call • WORKLOAD TYPE • What kind of work is system doing? • Typically the “per txn” metrics • WORKLOAD VOLUME • How much demand is being placed on system? • Typically the “per sec” metrics • Triage performance effects by correlating with causes
- 10. Demand varies predictably Autocorrelation of calls per second for email system
- 11. Executions per second over a week • Weekdays show clear hour-of-day pattern • Weekends different • What threshold to set?
- 12. Average active sessions Scotty, I think we have a problem
- 13. Outliers or events? In stable system, metrics should be statistically stable and rare observations may signal events Are these significant?
- 14. <Insert Picture Here> Baselines and Adaptive Thresholds
- 15. Operational requirements • Set alert thresholds automatically • Determine thresholds relative to baseline behavior • Adjust thresholds for expected workload changes • Adapt thresholds to system evolution
- 16. AWR Baselines • Captured AWR snapshots representing expected performance under common workload • Capture can be pre-configured using templates • SYSTEM_MOVING_WINDOW • Trailing N days of data • Compare performance against recent history • N is settable in days, 3 weeks or 5 weeks are nice settings • Out-of-box baseline in RDBMS 11g
- 17. Time-grouping • Captures workload periodicity by grouping data into common diurnal time buckets • Daily periodicity • All hours, Day-Night, Hour-of-Day • Weekly periodicity • All days, Weekday-Weekend, Day-of-Week • Time-grouping combines daily and weekly periodicities
- 18. Metric statistics • Basic metrics only • Computed over SYSTEM_MOVING_WINDOW • Standard stats: MIN, MAX, AVG, STDDEV • Percentiles: • Measured: 25, 50 (median), 75, 90, 95, 99 • Estimated: 99.9, 99.99 • Computed over time-groups • Automatically determined in 11g • Computed weekly • Saturday 12 midnight Scheduler job
- 20. Adaptive alert thresholds • Percent of maximum thresholds • User input multiplier over time group maximum • Good for detecting load peaks • Significance level thresholds • Signal on unusual metric values • HIGH (95 pctile) • VERY HIGH (99 pctile) • SEVERE (99.9 pctile) • EXTREME (99.99 pctile) • Computed and set automatically • Thresholds can reset every hour (MMON task)
- 21. <Insert Picture Here> Enterprise Manager User Interface
- 22. Early 10g visualization: seismograph
- 23. Enterprise Manager entry points • DB home page: Related Links • 10g: Metric Baselines • Need to enable metric persistence • Static and moving window baselines • Time grouping selected by user • 11g: Baseline Metric Thresholds • Out-of-box metric persistence and statistics computation • Improved use case based interface • Automatic time grouping selection • Statistics computed over SYSTEM_MOVING_WINDOW
- 24. RDBMS 11g use case goals • Quickly configure Adaptive Thresholds • Adjust thresholds in context • Identify signals for known problem • Advanced metric analysis
- 25. Baseline Metric Thresholds page
- 26. Quickly configure Adaptive Thresholds
- 28. Quick configure: Data Warehouse
- 29. Adjust thresholds in context
- 30. Adjust thresholds in context
- 31. Identify signals for known problem
- 32. Identify signals for known problem