1. 1 Self-tuning DB Technology & Info Services: from Wishful Thinking to Viable Engineering Gerhard Weikum weikum@cs.uni-sb.de Acknowledgements to collaborators: Surajit Chaudhuri, Christoff Hasse, Arnd Christian König, Achim Kraiss, Axel Mönkeberg, Peter Muth, Guido Nerjes, Elizabeth ONeil, Patrick ONeil, Peter Scheuermann, Markus Sinnwell, Peter Zabback Teamwork is essential. It allows you to blame someone else.

2. 2 Outline Auto-Tuning: What and Why? Where Do We Go From Here? - Dreams and Directions - The Feedback-Control Approach Example 1: Load Control Where Do We Stand Today? - Myths and Facts - The COMFORT Experience Example 2: Workflow System Configuration

3. 3 Auto-Tuning: What and Why? DBA manual (10 years ago): tuning experts are expensive system cost dominated and growth limited by human care & feed automate sys admin and tuning! Härder 1981: mission impossible

4. 4 Intriguing and Treacherous Approaches Instant tuning: rules of thumb KIWI principle: kill it with iron DBA joystick method: feedback control loop Columbus / Sisyphus approach: trial and error + ok for page size, striping unit, min cache size – insufficient for max cache size, MPL limit, etc. + ok if applied with care – waste of money otherwise + ok with simulation tools – risky with production system An engineer is someone who can do for a dime what any fool can do for a dollar. + ok when it converges under stationary workload – susceptible to instability

5. 5 Outline Auto-tuning: What and Why? Where Do We Go From Here? - Dreams and Directions - The Feedback-Control Approach Example 1: Load Control Where Do We Stand Today? - Myths and Facts - The COMFORT Experience Example 2: Workflow System Configuration

6. 6 Feedback Control Loop for Automatic Tuning Observe Predict React Need a quantitative model !

7. 7 Performance Predictability is Key Our ability to analyze and predict the performance of the enormously complex software systems... are painfully inadequate ( Report of the US Presidents Technology Advisory Committee 1998 ) ability to predict workload knobs performance !!! !!! ??? is prerequisite for finding the right knob settings workload knobs performance goal !!! ??? !!!

8. 8 Level, Scope, and Time Horizon of Tuning Issues (workflow) system configuration time scope level index selection query opt. & db stats mgt. caching load control data placement

9. 9 Level, Scope, and Time Horizon of Tuning Issues (workflow) system configuration time scope level index selection query opt. & db stats mgt. caching load control data placement

10. uncontrolled memory or lock contention can lead to performance catastrophe Load Control for Locking (MPL Tuning)

11. Locking for Concurrent Transactions

12. Locking and Lock Contention t lock conflict lock wait lock thrashing

13. How Difficult Can This Be? typical Sisyphus problem arriving transactions DBS active trans, trans. queue 1020304050 0.2 0.4 0.6 0.8 1.0 MPL response time [s]

14. Adaptive Load Control transaction admission transaction cancellation transaction execution aborted trans. committed trans. arriving trans. conflict ratio conflict ratio = critical conflict ratio 1.3 restarted trans. backed up by math (Tay, Thomasian)

15. 15 WFMS Architecture for E-Services... WF server type 1 App server type 1 Clients Comm server WF server type 2 App server type n...

16. 16 Workflow System Configuration Tool Workflow Repository Operational Workflow System Config. Admin ModelingCalibration Evaluation Recommendation Monitoring Mapping Hypothetical config Max. Throughput Avg. waiting time Expected downtime

17. 17 Workflow System Configuration Tool Workflow Repository Operational Workflow System Config. Admin ModelingCalibration Evaluation Recommendation Monitoring Mapping Min-cost re-config. Goals: min(throughput) max(waiting time) max(downtime) + constraints Long-term feedback control aims at global, user- perceived metrics and uses more advanced math for prediction

18. 18 Outline The Problem – 10 Years Ago and Now Where Do We Go From Here? - Dreams and Directions - The Feedback-Control Approach Example 1: Load Control Where Do We Stand Today? - Myths and Facts - The COMFORT Experience Example 2: Workflow System Configuration

19. 19 Where Do We Stand Today? - Good News Advances in Engineering: Eliminate second-order knobs Robust rules of thumb for some knobs KIWI method where applicable Scientific Progress: + Feedback control approach + Storage systems have become self-managing + Index selection wizards hard to beat + Materialized view wizards + Synopses selection and space allocation for DB statistics well understood

20. Index Selection: Live or Let DieOrders ONo ODate... Parts PNo PName... LineItems ONo LNo Qty Price ODate ShipDate... Customers CNo CName City State Discount... Data Workload Select... From... Where City =... And State =... Select... From... Where ShipDate – ODate >... Select... From... Where L.Ono = O.ONo And Price <... Update... Set ShipDate =... Insert LineItems... 100 x / min 20 x / min 15 x / min 300 x / min NP-hard combinatorial problem with complex cost formulas Create Index... ONo Price ShipDate City State

21. 21 Where Do We Stand Today? - Good News Advances in Engineering: Eliminate second-order knobs Robust rules of thumb for some knobs KIWI method where applicable Scientific Progress: + Feedback control approach + Storage systems have become self-managing + Index selection wizards hard to beat + Materialized view wizards + Synopses selection and space allocation for DB statistics well understood

22. 22 Where Do We Stand Today? - Bad News, wrong Complex problems have simple, easy-to-understand answers – Automatic system tuning based on few principles: – Interactions across components and interference among different workload classes can make entire system unpredictable

23. 23 Outline The Problem – 10 Years Ago and Now Where Do We Go From Here? - Dreams and Directions - The Feedback-Control Approach Example 1: Load Control Where Do We Stand Today? - Myths and Facts - The COMFORT Experience Example 2: Workflow System Configuration

24. 24 Autonomic Computing: Path to Nirvana ? My interpretation: need component design for predictability: self-inspection, self-analysis, self-tuning Vision: all computer systems must be self-managed, self-organizing, and self-healing Motivation: ambient intelligence (sensors in every room, your body etc.) reducing complexity and improving manageability of very large systems aka. observation, prediction, reaction Role model: biological, self-regulating systems (really ???)

25. 25 Summary & Concluding Remarks Major advances towards automatic tuning during last decade: Major challenges remain: path towards autonomic systems requires rethinking & simplifying component architectures with design-for-predictability paradigm workload-aware feedback control approach fruitful math models and online stats are vital assets low-hanging fruit engineering successful important contributions from research community (AutoRAID, AutoAdmin, LEO, Härder/Rahm book, etc.) Problem is long-standing but very difficult and requires good research stamina Success is a lousy teacher. (Bill Gates)