1. CoPhy: A Scalable, Portable, and Interactive Index Advisor for Large Workloads Debabrata Dash, Anastasia Ailamaki, Neoklis Polyzotis 1

2. High Cost of DB Tuning Enterprises spend a lot on DBMS 2 Sources: MS Azure, Forrester Research; 2010oracle.com/us/products/database/039433.pdf Need to reduce administration and tuning cost

3. A New Approach to Index Tuning Existing ApproachesCoPhy PortabilityNoYes ScalabilitySampling/pruningYes GeneralityNoYes Quality FeedbackNot with constraintsYes InteractivityNoYes CoPhy: Convert to a compact Binary Integer Program (BIP). Solve using mature solvers. − BIP: CoPhy: Convert to a compact Binary Integer Program (BIP). Solve using mature solvers. − BIP: Index Tuning: Select indexes that maximize performance

4. Outline Introduction BIP formulation –Existing formulation –Discovering structure –Exploiting the structure –Benefits Experimental Results Conclusion 4

5. Candidates Index Tuning Problem Index Tuning T1T1 T 2 I 1 I 2 I 3 I 4 T 1 Join T 2 Optimal Indexes Constraints ?? Workload

6. Existing Approaches 6 Index Advisor What-If DBMS Optimizer Index Advisor What-If DBMS Optimizer Fast What-If Optimizer [INUM07,C-PQO08] Greedy approaches Bottom-up [CN97, VZ+00] Top-down [BC05] BIP-based approach [CS96, PA07]

7. Existing BIP 7 x 1 x 2 x 3 x 4 {0,1} Min. Cost Select one atomic conf. Index presence {0,1} Program size = O(# T 1 Indexes x # T 2 Indexes) T1T1 T 2 I 1 I 2 I 3 I 4 t 1 t 2 are corresponding costs are atomic configurations

8. Index Advisor What-If DBMS Optimizer Fast What-If Optimizer CoPhy vs. Existing Approaches 8 What-If DBMS Optimizer Fast What-If Optimizer Index Advisor CoPhy [INUM07,C-PQO08]

9. Fast What-If: INUM 9 A template plan can be reused for many index combinations Place Holder Template Plan I1 I3 I1 I4 I1I4 I1I3 What-If Optimizer T 1 Join T 2 Plan Instantiated Plan Place Holder Instantiated Plan

10. Cost Structure 10 Linear Composability of Query Costs Linear Composability is exhibited by both INUM, C-PQO Atomic Configuration Cost of template plan under A Cost of optimal plan under A

11. Exploiting Linear Composability 11 Exposing the cost model leads to linearly growing BIPs T1T1 T 2 I 1 I 2 I 3 I 4 x 1 x 2 x 3 x 4 t 1 t 2 Program size = O(# T 1 Indexes + # T 2 Indexes) BIP Solver explores the index combinations with the knowledge of the objective Program size = O(# T 1 Indexes + # T 2 Indexes) BIP Solver explores the index combinations with the knowledge of the objective

12. More Complex BIPs Complex queries Update costs Complex Constraints [Bruno08]: –Storage constraint –Index constraints –Column constraints –Generators –Soft constraints 12 BIP formulation does not restrict the expressive power of the DBA We extend the BIP to handle:

13. CoPhy’s Architecture 13 What-If DBMS Optimizer INUM BIP Solver BIP Generator Candidate Generator Candidate Generator Workload Constraints Selected Indexes Selected Indexes Bounds CoPhy Theorem: CoPhy computes an optimal index configuration

14. Unique Features Enabled by the BIP Portability: No change to the optimizer –Requires only the what-if APIs Scalability: By solving large BIPs in seconds –No need to select workload, candidate indexes Generality: The formulation can be reused Quality feedback: All modern BIP solvers provide this –Can stop at near-optimal values Interactive tuning: By solving BIPs incrementally –Interactively add/drop candidate indexes –Enables efficient multi-objective optimization 14

15. Outline Introduction BIP formulation –Existing formulation –Discovering structure –Exploiting the structure –Benefits Experimental Results Conclusion 15

16. Experimental Setup Two commercial DBMS -- System A, System B 1 GB TPC-H database 1 GB index size constraint Algorithms: –Tool A, Tool B – the commercial designers –ILP – The state of the art BIP [PA07] –CoPhy A, CoPhy B – Our approach on the systems Queries generated using 15 TPC-H templates Metric: 16

17. Replacing heuristic algorithms improves savings Using larger set of candidates also helps Replacing heuristic algorithms improves savings Using larger set of candidates also helps Speedup Comparison 17 # of queries System A # of queries System B Better # Candidates: CoPhy ~2000, Tool A ~200, Tool B ~50

18. Tool Execution Time Comparison 18 Scalable index tuning eliminates the workload selection problem Better # of queries System B # of queries System A # of queries System A

19. Conclusion Index tuning using a novel compact BIP –Generic, scalable, efficient, and high quality –Quality feedback –Incremental index selection –Multi-objective optimization Future Work: –Incorporating other workload types –Applying the approach to other tuning problems 19

20. Backup Sildes 20

21. BIP for Multiple Plans 21 15 x 23 x 24 x 25 x 26 Matching logic One plan per query Minimize cost 25 T1T1 T2T2 I 1 I 2 I 3 I 4

22. More Complex BIPs Storage constraint 22 Build indexes when used Size under a fixed constant

23. CoPhy vs. FLP 23 Offloading the search process to the solver improves both the problem construction and solving times Better