Recovery in main memory database systems

  • Vijay Kumar
Physical Aspects 1
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1134)


In this paper we present a recovery mechanism for main memory database, which does not treat volatile RAM as a buffer and uses a limited size non-volatile RAM for efficient logging and archiving. It allows database recovery in parallel to the execution of normal transactions, it combines Undo and Redo into a single operation in many situations and eliminates the need for checkpointing. Under partial recovery our algorithm manages to identify and recovers only dirty data for bringing the entire database into a consistent state.

Key words

MMDBS VRAM NRAM Undo Redo Partial Recovery Write Ahead Logging Active Count Archiving 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    A. C. Amman, M. B. Hanrahan and R. Krishnamurthy, “Design of a Memory Resident DBMS”, Proc. of IEEE Spring Comp. Conf. 1985.Google Scholar
  2. 2.
    Annon Et Al, “A Measure of Transaction Processing Power”, Technical Report 85.2, PN87609, February 1985, Tandem Computers.Google Scholar
  3. 3.
    P. J. Bernstein, V. Hadzilacos and N. Goodman, “Concurrency Control and Recovery in Database Systems”, Addison-Wesley, 1987.Google Scholar
  4. 4.
    Hong-Tai Chou, “Buffer Management of Database Systems”, Ph. D. Thesis, University of Wisconsin, Madison, 1985.Google Scholar
  5. 5.
    G. Copeland, T. Keller, R. Krishnamurthy, and M. Smith, “The Case For Safe RAM”, Proc. 15th VLDB, 1989, Amsterdam.Google Scholar
  6. 6.
    D. J. DeWitt et al., “Implementation Techniques for Main Memory Database Systems”, ACM SIGMOD, 1984.Google Scholar
  7. 7.
    M. H. Eich, “MARS: The Design of a Main Memory Database Machine”, Proc. from the 5th International Workshop on Database Machines, October, 1987.Google Scholar
  8. 8.
    M. H. Eich and Wei-Li Sun,’ Nonvolatile Main Memory: An Overview of Alternatives”, Technical Report 88-CSE-6, Southern Methodist University, Dallas, TX, 1988.Google Scholar
  9. 9.
    L. Gruenwald, “Reload in a Main Memory Database Systems: MARS”, Ph. D. Dissertation, Department of Comp. Sc., and Eng., Southern Methodist Univ., Dec. 1990.Google Scholar
  10. 10.
    R. B. Hagmann, “A Crash Recovery Scheme for a Memory-Resident Database System”, IEEE Transactions on Comp., Vol. c-35, NO. 9, September 1986.Google Scholar
  11. 11.
    H. V. Jagadish, A. Silberschatz, and S. Sudershan, “Recovering from Main-Memory Lapses”, VLDB, 1993.Google Scholar
  12. 12.
    V. Kumar and A. Burger, “Performance Measurement of Some Main Memory Database Recovery Algorithms”, IEEE 7th Int. Conf. on Data Eng., 1991, Kobe, Japan.Google Scholar
  13. 13.
    T. J. Lehman, “Design and Performance Evaluation of a Main Memory Relational Database System”, Ph. D. Thesis, Univ. of Wisconsin-Medison, August 1986.Google Scholar
  14. 14.
    E. Levy and A. Silberschastz, “Incremental recovery in main memory database systems”, IEEE TKD (Special Issue), Vol. 4, No. 6, Dec. 1992.Google Scholar
  15. 15.
    Calton Pu, “On-the-Fly, Incremental Consistent Reading of Entire Database”, Algorithmica, No. 1, Springer-Verlag, New York, 1986.Google Scholar
  16. 16.
    Danial Rosencrantz, “Dynamic Database Dumping”, Proc. SIGMOD Int. Conf. on Management of Data, ACM, 1978.Google Scholar
  17. 17.
    K. Salem and H. Garcia-Molina, “Crash Recovery Mechanisms for Main Storage Database Systems”, Dept. of Comp. Sc, Princeton Univ., CS-TR-034-86.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Vijay Kumar
    • 1
  1. 1.Computer Science Telecommunications 5100 RockhillUniversity of Missouri-Kansas CityKansas CityUSA

Personalised recommendations