Practical throughput estimation for parallel databases

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Practical throughput estimation for parallel databases

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Methods for estimating the performance of database management systems can aid the design of database systems by identifying potential performance bottle-necks or by predicting the relative performance of different designs. Performance estimation is critical in parallel database systems with distributed memory, where an effective overall performance depends on a good choice among a wide range of ways of placing data. An approach is described for performance estimation for shared-nothing parallel database systems. It estimates system throughput for a given benchmark or set of queries, and can exercise different data placement schemes to determine the data layout that provides the best throughput value.

References

    1. 1)
      • M. Stonebraker . The case for shared nothing. Database Eng. Bull. , 1 , 4 - 9
    2. 2)
      • Kersten, M., Kwakkel, F.: `Design and implementation of a DBMS performance assessment tool', Proc. DEXA' 93 Database and Expert Systems Applications, 1993, , p. 265–276.
    3. 3)
      • S. Zhou , M.H. Williams , H. Taylor . A comparative study of data placement in a parallel DBMS. IEEE Trans.
    4. 4)
      • M. Mannino , P. Chu , T. Sager . Statistical profile estimation in database systems. ACM Comput Surv. , 3 , 191 - 221
    5. 5)
      • Bergsten, B., Couprie, M., Valduriez, P.: `Prototyping DBS3, a shared-memory parallel database system', Proc. 1st int. conf. on Parallel and Distributed Information Systems, December 1991, Florida.
    6. 6)
      • Ghandeharizadeh, S., deWitt, D.: `Hybrid-range partitioning strategy: a new declustering strategy for multiprocessor database machines', Proc. 16th VLDB conf, 1990, Brisbane, Australia, p. 481–492.
    7. 7)
      • S. Dietrich , M. Brown , E. Cortes-Rello , S. Wunderlin . A practitioner's introduction to database performance benchmarks and measurements. Comput. J. , 4 , 322 - 331
    8. 8)
      • F. Andres , M. Couprie , Y. Viemont , A. Makinouchi . (1991) A multienvironment cost evaluator for parallel database systems, Database systems for advanced applications' 91.
    9. 9)
      • G. Copeland , W. Alexander , E. Boughter , T. Keller . Data placement in Bubba. SIG-MOD Rec , 3 , 99 - 108
    10. 10)
      • Wong, K.F., Paci, M.: `Performance evaluation of an OLTP application on the EDS database server using abehavioural simulation model', ECRC Technical Report, 1991.
    11. 11)
      • Sacca, D., Wiederhold, G.: `Database partitioning in a cluster of processors', Proc. 9th VLDB conf., 1983, Florence, Italy, p. 242–247.
    12. 12)
      • D. Dewitt , S. Ghandeharizadeh , D. Schneider , A. Bricker , H. Hsiao , R. Rasmussen . The Gamma database machine project. IEEE Trans. , 1 , 44 - 62
    13. 13)
      • F. Andres , B. Bergsten , P. Borla-Salamet , P. Broughton , C. Chachaty , M. Couprie , B. Finance , G. Gardarin , K. Glynn , B. Hart , S. Kellett , S. Leunig , M. Lopez , M. Ward , P. Valduriez , M. Ziane . (1990) EDS—collaborating for a high performance parallel relational database, Proc. ann. ESPRIT conf. brussels.
    14. 14)
      • J. Gray . (1993) Transaction Processing Performance Council(TPC):TPC benchmark C, The benchmark handbook for database and transaction processing systems (2e).
    15. 15)
      • P. Apers . Data allocation in distributed database systems. ACM Trans. Database Syst. , 3 , 263 - 304
    16. 16)
      • H. Boral , W. Alexander , L. Clay , G. Copeland , S. Danforth , M. Franklin , B. Hart , M. Smith , P. Valuduriez . Prototyping bubba, a highly parallel database system. IEEE Trans. , 1 , 4 - 24
    17. 17)
      • J. Gray . (1993) Transaction Processing Performance Council (TPC):TPC benchmark B, The benchmark handbook for database and transaction processing systems (2e).
    18. 18)
      • K.Y. Whang , R. Krishnamorthy . Query optimization in a memory-resident domain relational calculus database systems. ACM TODS , 1 , 67 - 75
    19. 19)
      • Hua, K., Lee, C., Young, H.: `An efficient load balancing strategy for shared-nothing database systems', Proc.database and Expert Systems Applications 92, 1992, springer-verlag, , p. 469–474.
    20. 20)
      • Hua, K., Lee, C.: `An adaptive data placement scheme for parallel database computer systems', Proc. 16th vldb conf., 1990, Brisbane, Australia, p. 493–506.
    21. 21)
      • C. Turbyfill , C. Orji , D. Bitton , J. Gray . (1993) AS3AP: an ANSI SQL standard scaleable and portable benchmark for relational database systems, The benchmark handbook for database and transaction processing systems (2e).
    22. 22)
      • Iblza-Espiga, M.B., Williams, M.H.: `Data placement strategy for a parallel database system', Proc.Database and Expert Systems Applications 92, 1992, springer-verlag, , p. 48–54.
    23. 23)
      • P. Mishra , M. Eich . Join processing in relational databases. ACM Comput. Suru. , 1 , 63 - 113
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