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Programming and Computer Software

, Volume 30, Issue 6, pp 337–346 | Cite as

Survey of Architectures of Parallel Database Systems

  • L. B. Sokolinsky
Article

Abstract

The paper is devoted to the classification, design, and analysis of architectures of parallel database systems. A formalization of the notion “parallel database system” is suggested, which relies on a concept of a virtual machine. Based on this formalization, a new approach to the classification of architectures of parallel database systems is suggested. Requirements to parallel database systems are formulated, which serve as criteria for comparing various architectures. Various classes of architectures of parallel database systems are considered and compared.

Keywords

Operating System Artificial Intelligence Virtual Machine Database System Parallel Database System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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REFERENCES

  1. 1.
    Ozsu, M.T. and Valduriez, P., Principles of Distributed Database System, Englewood Cliffs: Prentice-Hall, 1991.Google Scholar
  2. 2.
    DeWitt, D.J. and Gray, J., Parallel Database Systems: The Future of High-Performance Database Systems, Commun. ACM, 1992, vol. 35, no. 6, pp. 85–98.Google Scholar
  3. 3.
    Voevodin, Vl.V. and Kapitonova, A.P., Metody opisaniya i klassifikatsii arkhitektur vychislitel'nykh system, (Methods of Description and Classification of Architectures of Computing Systems), Moscow: Mosk. Gos. Univ., 1994.Google Scholar
  4. 4.
    Flynn, M.J. and Rudd, K.W., Parallel Architectures, ACM Computing Surv., 1996, vol. 28, no. 1, pp. 67–70.Google Scholar
  5. 5.
    Dasgupta, S.A., Hierarchical Taxonomic System for Computer Architectures, IEEE Comput., 1990, vol. 23, no. 3, pp. 64–74.Google Scholar
  6. 6.
    Korneev, V.V., Parallel'nye vychislitel'nye sistemy(Parallel Computing Systems)}, Moscow: Nolidzh, 1999.Google Scholar
  7. 7.
    Stonebraker, M., The Case for Shared Nothing, Database Eng. Bull., 1986, vol. 9, no. 1, pp. 4–9.Google Scholar
  8. 8.
    Norman, M.G., Zurek, T., and Thanisch, P., Much Ado about Shared-Nothing, ACM SIGMOD Record, 1996, vol. 25, no. 3, pp. 16–21.Google Scholar
  9. 9.
    Carr, J.L. and Hennessy, J.L., WSClock—A Simple and Effective Algorithm for Virtual Memory Management, Proc. of the Eighth Symp. on Operating System Principles (Asilomar Conf. Grounds, Pacific Grove, 1981), New York: ACM, 1981, pp. 87–95.Google Scholar
  10. 10.
    Amza, C. et al., ThreadMarks: Shared Memory Computing on Networks of Workstations, IEEE Comput., 1996, vol. 29, no. 2, pp. 18–28.Google Scholar
  11. 11.
    Patterson, D.A., Gibson, G.A., and Katz, R.H., A Case for Redundant Arrays of Inexpensive Disks (RAID), Proc. 1988 ACM SIGMOD Int. Conf. on Management of Data (Chicago, 1988), ACM, 1988, pp. 109–116.Google Scholar
  12. 12.
    Cheng, J.M. et al., IBM Database 2 Performance: Design, Implementation, and Tuning, IBM Systems J., 1984, vol. 23, no. 2, pp. 189–210.Google Scholar
  13. 13.
    Davison, W., Parallel Index Building in Informix OnLine 6.0, Proc. of the 1992 ACM SIGMOD Int. Conf. on Management of Data (San Diego, 1992), ACM, 1992, p. 103.Google Scholar
  14. 14.
    Stonebraker, M., Katz, R.H., and Patterson, D.A., and Ousterhout, J.K., The Design of XPRS, Fourteenth Int. Conf. on Very Large Data Bases, (Los Angeles, 1988), Morgan Kaufmann, 1988, pp. 318–330.Google Scholar
  15. 15.
    Bergsten, B., Couprie, M., and Lopez, M., DBS3: A Parallel Data Base System for Shared Store (Synopsis), in Issues, Architectures, and Algorithms (Proc. of the 2nd Int. Conf. on Parallel and Distributed Information Systems (PDIS 1993), San Diego, 1993), IEEE Comput. Soc., 1993, pp. 260–262.Google Scholar
  16. 16.
    Graefe, G., Volcano—An Extensible and Parallel Query Evaluation System, IEEE Trans. Knowledge Data Engineering, 1994, vol. 6, no. 1, pp. 120–135.Google Scholar
  17. 17.
    Rahm, E., Parallel Query Processing in Shared Disk Database Systems, ACM SIGMOD Record, 1993, vol. 22, no. 4, pp. 32–37.Google Scholar
  18. 18.
    Strickland, J.P., Uhrowczik, P.P., and Watts, V.L., IMS/VS: An Evolving System, IBM Systems J., 1982, vol. 21, no. 3, pp. 490–510.Google Scholar
  19. 19.
    Linder, B., Oracle Parallel RDBMS on Massively Parallel Systems, in Issues, Architectures, and Algorithms (Proc. of the 2nd Int. Conf. on Parallel and Distributed Information Systems (PDIS 1993), San Diego, 1993), IEEE Comput. Soc., 1993, pp. 67–68.Google Scholar
  20. 20.
    Dubova, N., Supercomputers nCube, Otkrytye sistemy, 1995, no. 2, pp. 42–47.Google Scholar
  21. 21.
    Kronenberg, N.P., Levy, H.M., and Strecker, W.D., VAXclusters: A Closely-Coupled Distributed System, ACM Trans. Comput. Systems, 1986, vol. 4, no. 2, pp. 130–146.Google Scholar
  22. 22.
    Nick, J.M., Moore, B.B., Chung, J.-Y., and Bowen, N.S., S/390 Cluster Technology: Parallel Sysplex, IBM Systems J., 1997, vol. 36, no. 2, pp. 172–201.Google Scholar
  23. 23.
    Lorie, R., et al., Adding Intra-Transaction Parallelism to an Existing DBMS: Early Experience, Data Engineering Bull., 1989, vol. 12, no. 1, pp. 2–8.Google Scholar
  24. 24.
    Boral, H., Alexander, W., Clay, L., Copeland, G., Sanforth, S., Franklin, M., Hart, B., Smith, M., and Valduriez, P., Prototyping Bubba: A Highly Parallel Database System, IEEE Trans. Knowledge Data Eng., 1990, vol. 2, no. 1, pp. 4–24.Google Scholar
  25. 25.
    Skelton, C.J. et al., EDS: A Parallel Computer System for Advanced Information Processing, Lecture Notes in Computer Science (Proc. of the 4th Int. PARLE Conf., Paris, 1992), Springer, 1992, vol. 605, pp. 1–18.Google Scholar
  26. 26.
    DeWitt, D.J. et al., The Gamma Database Machine Project, IEEE Trans. Knowledge Data Eng.1990}, vol. 2, no. 1, pp. 44–62.Google Scholar
  27. 27.
    Von Bultzingsloewen, G. et al., KARDAMON—A Dataflow Database Machine for Real-Time Applications, SIGMOD Record, 1988, vol. 17, no. 1, pp. 44–50.Google Scholar
  28. 28.
    Apers, P.M.G., van den Berg, C.A., Flokstra, J., Grefen, P.W.P.J., Kersten, M.L., and Wilschut, A.N., Prisma/DB: A Parallel Main-Memory Relational DBMS, IEEE Trans. Knowledge Data Eng., 1992, vol. 4, no. 6, pp. 541–554.Google Scholar
  29. 29.
    Englert, S., Glasstone, R., and Hasan, W., Parallelism and Its Price: A Case Study of NonStop SQL/MP, ACM SIGMOD Record, 1995, vol. 24, no. 4, pp. 61–71.Google Scholar
  30. 30.
    Clay, D., Informix Parallel Data Query (PDQ), in Issues, Architectures, and Algorithms (Proc. of the 2nd Int. Conf. on Parallel and Distributed Information Systems (PDIS 1993), San Diego, 1993), IEEE Comput. Soc., 1993, pp. 71–72.Google Scholar
  31. 31.
    Page, J., A Study of a Parallel Database Machine and Its Performance: The NCR/Teradata DBC/1012. Advanced Database Systems, Lecture Notes in Computer Science (Proc. of the 10th British Natl. Conf. on Databases. BNCOD 10, Aberdeen, 1992), Springer, 1992, vol. 618, pp. 115–137.Google Scholar
  32. 32.
    Baru, C.K. et al., DB2 Parallel Edition, IBM System J., 1995, vol. 34, no. 2, pp. 292–322.Google Scholar
  33. 33.
    Bergsten, B., Couprie, M., and Valduriez, P., Overview of Parallel Architectures for Databases, Comput. J., 1993, vol. 36, no. 8, pp. 734–740.Google Scholar
  34. 34.
    Hua, K.A., Lee, C., and Peir, J.-K., Interconnecting Shared-Everything Systems for Efficient Parallel Query Processing, Proc. First Int. Conf. on Parallel and Distributed Information Systems (PDIS 1991) (Miami Beach, 1991), IEEE-CS, 1991, pp. 262–270.Google Scholar
  35. 35.
    Valduriez, P., Parallel Database Systems: The Case for Shared-Something, Proc. of the 9th Int. Conf. on Data Eng. (Vienna, 1993), IEEE Comput. Soc., 1993, pp. 460–465.Google Scholar
  36. 36.
    Ballinger, C. and Fryer, R., Born to Be Parallel: Why Parallel Origins Give Teradata an Enduring Performance Edge, IEEE Data Eng. Bull., 1997, vol. 20, no. 2, pp. 3–12.Google Scholar
  37. 37.
    Pramanik, S. and Tout, W.R., The NUMA with Clusters of Processors for Parallel Join, IEEE Trans. Knowledge Data Eng., 1997, vol. 9, no. 4, pp. 653–666.Google Scholar
  38. 38.
    Copeland, G.P. and Keller, T., A Comparison of High-Availability Media Recovery Techniques, Proc. of the 1989 ACM SIGMOD Int. Conf. on Management of Data (Portland, 1989), ACM, 1989, pp. 98–109.Google Scholar
  39. 39.
    Graefe, G., Query Evaluation Techniques for Large Databases, ACM Computing Surv., 1993, vol. 25, no. 2, pp. 73–169.Google Scholar
  40. 40.
    Sokolinsky, L.B., Organization of Parallel Query Processing in Multiprocessor Database Machines with Hierarchical Architecture, Programmirovanie, 2001, no. 6, pp. 13–29.Google Scholar
  41. 41.
    Sokolinsky, L.B., Design and Evaluation of Database Multiprocessor Architecture with High Data Availability, Proc. of the 12th Int. DEXA Workshop (Munich, 2001), IEEE Comput. Soc., 2001, pp. 115–120.Google Scholar
  42. 42.
    Bouganim, L., Florescu, D., and Valduriez, P., Dynamic Load Balancing in Hierarchical Parallel Database Systems, Proc. 22th Int. Conf. on Very Large Data Bases (VLDB'96) (Mumbai, India, 1996), Morgan Kaufmann, 1996, pp. 436–447.Google Scholar
  43. 43.
    Xu, Y. and Dandamudi, S.P., Performance Evaluation of a Two-Level Hierarchical Parallel Database System, Proc. Int. Conf. Computers and Their Applications, Tempe, Arizona, 1997, pp. 242–247.Google Scholar
  44. 44.
    Shmidt, V., IBM SP2 Systems, Otkrytye Sistemy, 1995, no. 6, pp. 53–60.Google Scholar
  45. 45.
    Shnitman, V., Fault-Tolerant Servers ServerNet, Otkrytye Sistemy, 1996, no. 3, pp. 5–11.Google Scholar
  46. 46.
    Rahm, E., Framework for Workload Allocation in Distributed Transaction Processing Systems, J. Systems Software, 1992, vol. 18, pp. 171–190.Google Scholar
  47. 47.
    Kim, W., Highly Available Systems for Database Applications, ACM Computing Surv., 1984, vol. 16, no. 1, pp. 71–98.Google Scholar
  48. 48.
    Christodoulakis, S., Estimating Record Selectivities, Information Systems, 1983, vol. 8, no. 2, pp. 105–115.Google Scholar
  49. 49.
    Lynch, C.A., Selectivity Estimation and Query Optimization in Large Databases with Highly Skewed Distribution of Column Values, Proc. of the Fourteenth Int. Conf. on Very Large Data Bases, (Los Angeles, 1988), Morgan Kaufmann, 1998, pp. 240–251.Google Scholar
  50. 50.
    Montgomery, A.Y., D'Souza, D.J., and Lee, S.B., The Cost of Relational Algebraic Operations on Skewed Data: Estimates and Experiments, in Information Processing 83 (Proc. of the IFIP 9th World Comput. Congr., Paris, 1983), North-Holland, 1983, pp. 235–241.Google Scholar
  51. 51.
    Zipf, G.K., Human Behavior and the Principle of Least Effort: An Introduction to Human Ecology, Cambridge: Addison-Wesley, 1949.Google Scholar
  52. 52.
    Walton, C.B., Dale, A.G., and Jenevein, R.M., A Taxonomy and Performance Model of Data Skew Effects in Parallel Joins, Proc. of the 17th Int. Conf. on Very Large Data Bases (Barcelona, 1991), Morgan Kaufmann, 1991, pp. 537–548.Google Scholar
  53. 53.
    Lakshmi, M.S. and Yu, P.S., Effectiveness of Parallel Joins, IEEE Trans. Knowledge Data Eng., 1990, vol. 2, no. 4, pp. 410–424.Google Scholar
  54. 54.
    Pfister, G., Sizing Up Parallel Architectures, Database Programming Design OnLine (http://www.dbpd.com), 1998, vol. 11, no. 5.Google Scholar
  55. 55.
    Mohan, C. and Narang, I., Efficient Locking and Caching of Data in the Multisystem Shared Disks Transaction Environment, Lecture Notes in Computer Science (Proc. of the 3rd Int. Conf. on Extending Database Technol., Vienna, 1992), Vienna: Springer, 1992, pp. 453–468.Google Scholar
  56. 56.
    Gray, J. and Reuter, A., Transaction Processing: Concepts and Techniques, Morgan Kaufmann, 1993.Google Scholar
  57. 57.
    Hsiao, H.I. and DeWitt, D.J., A Performance Study of Three High Availability Data Replication Strategies, Distributed Parallel Databases, 1993, vol. 1, no. 1, pp. 53–80.Google Scholar
  58. 58.
    Sokolinsky, L.B., Interprocessor Communication Support in the Omega Parallel Database System, Proc. of the 1st Int. Workshop on Comput. Sci. and Information Technol. (CSIT'99), Moscow, 1999.Google Scholar
  59. 59.
    Sokolinsky, L.B., Operating System Support for a Parallel DBMS with a Hierarchical Shared-Nothing Architecture, Proc. of the Third East Eur. Conf. “Advances in Databases and Information Systems” (ADBIS'99) (Maribor, Slovenia, 1999), Maribor: Institute of Informatics, 1999, pp. 38–45.Google Scholar

Copyright information

© MAIK “Nauka/Interperiodica” 2004

Authors and Affiliations

  • L. B. Sokolinsky
    • 1
  1. 1.South Ural State UniversityChelyabinskRussia Received February 26, 2004

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