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Benchmarking DBMS’s for Communication Cost Analysis

Benchmarking DBMS’s for Communication Cost Analysis. A Work Term Report Presentation Tony Young M.Math Candidate May 27 th , 2005. Introduction. What is a federated system? Travelocity Remote searches of airline databases Performs bookings, adds payment details, etc. Google Scholar

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Benchmarking DBMS’s for Communication Cost Analysis

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  1. Benchmarking DBMS’s for Communication Cost Analysis A Work Term Report Presentation Tony Young M.Math Candidate May 27th, 2005

  2. Introduction • What is a federated system? • Travelocity • Remote searches of airline databases • Performs bookings, adds payment details, etc. • Google Scholar • Remote searches of ACM, IEEE, etc. databases • Presents consolidated view of papers matching common search criteria

  3. Outline • Introduction • Organization • Optimization • Global Cost Modeling • Experiments • Experimental Procedure • Results • Conclusion • Future Work

  4. Organization • Multidatabase Language Approach • Pass-through Querying • Global Schema Approach

  5. Organization • Global schema approach • Burden of integration is on global DBA • Logical global schema • Functional compensation • Possibly high maintenance

  6. Organization • Global Schema Approach Physical Org. Logical Org.

  7. Optimization • Optimization challenges for the FDBS • Remote site autonomy • Remote parameters • Translation • Heterogeneous capabilities • Additional costs • From the perspective of the remote source, the FDBS is just another application requesting data!

  8. Optimization • Omni module in iAnywhere ASA • Supports GS approach and pass-through querying • Performance of global queries is not as good as local queries

  9. Global Cost Modeling • Many factors must be taken into account • Optimization Cost (OPT) • Communication Cost (COMM) • Execution Cost (EXEC) • Sub-query/Method Call Costs (SM) • Reformatting Costs (RF) • Working Cost Model

  10. Global Cost Modeling • Interest for this project is communication cost • LS = Link Speed • S = Source/DBMS • DS = Data Size • DT = Data Type • PF = Prefetch Status • PS = Packet Size • R = Processor Speed

  11. Experiments • Goal • Determine if communication cost can be modeled using simple network applications • Determine what factors affect communication cost • Two sets of experiments • Pure network benchmarking • DBMS benchmarking • Varied each factor mentioned previously, one at a time

  12. Experimental Procedure • Hot cache • 30 trials • Experimental error below 5% • Parameters varied during both sets of experiments • Semantics of prefetching for network benchmarking

  13. Experimental Procedure • Applications • DBCreate • NetBench • DBBench • ResultParse

  14. Experimental Procedure • Recall the working cost model • Used two types of queries • SELECT * ROW • SELECT MAX(COLUMN) MAX • Ensure no indexes were created • Determining communication cost

  15. Experimental Procedure • Recording query execution time

  16. Experimental Procedure • Many ways to calculate • Similar overhead in both types of queries • Assumptions • Hot cache • Transfer of max() value negligible • Loop evaluation is negligible

  17. Results • Results Table • DBMS (S)

  18. Results • Link Speed (LS)

  19. Results • Link Speed (LS)

  20. Results • Data Size (DS)

  21. Results • Data Type (DT)

  22. Results • Prefetch Status (PF)

  23. Results • Packet Size (PS)

  24. Results • Server CPU Speed (CPU)

  25. Results • Other notes • Dominant Factors • Consistency • Efficiency of Link Usage

  26. Conclusion • Many factors need to be included in cost models • Dominant Factors • Affecting Factors • Communication cost is not a pure networking problem

  27. Conclusion • Each DBMS is different in added overhead • Systems are consistent in overhead • Efficiency of link use could improve • Ease of control of the factors • Easily controllable • Not easily controllable • Much work still to be done!

  28. Future Work • Collection of additional data • Generation and testing of a communication cost model • Gathering and analysis of other global cost model parameters

  29. Acknowledgements • iAnywhere for their support • Glenn and Ivan • Support and countless questions • Mike, Anil, Ani, Dan, Matthew • Help and guidance • Mark, Scott and Dave • Hardware loans • Karim, Graham and Ian • Software help • Frank • Arranging the work term and help with the report and talk

  30. Want More? • Check out the work term report at http://www.tonyyoung.ca/wtr.pdf

  31. Optimization • Semijoin algorithm • Site selection • Remote reduction • Global reduction • Assembly • Minimizes communication costs • Exploits heterogeneous capabilities

  32. Optimization • Replicate algorithm • Site selection • Data transfer • Query execution • Assembly • Minimizes query response time • Exploits varying hardware configurations

  33. Optimization • Difference between semijoin and replicate • Assumptions made • Execution location

  34. Optimization • Garlic • Fire access STAR’s • Fire join STAR’s • Fire FinishRoot STAR • Hybrid of semijoin and replicate algorithms • Large amount of overhead

  35. Motivation • Proliferation of heterogeneous DBMS’s • Data sharing within organizations • Differing rates of technology adoption • Mergers and acquisitions • Geographic separation of teams

  36. Want More? • Check out the work term report at http://www.tonyyoung.ca/wtr.pdf

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