Advertisement

Deferred Maintenance of Indexes and of Materialized Views

  • Harumi Kuno
  • Goetz Graefe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7108)

Abstract

Maintenance of secondary indexes and materialized views can cause the latency and bandwidth of concurrent information capture to degrade by orders of magnitude. In order to preserve performance during temporary bursts of update activity, e.g., during load operations, many systems therefore support deferred maintenance, at least for materialized views. However, deferring maintenance means that index or view contents may become out-of-date. In such cases, a seemingly benign choice among alternative query execution plans affects whether query results represent the latest database contents. We propose here a system that distinguishes between the maintenance of of logical contents and physical structure. This distinction lets us compensate for deferred logical maintenance operations while minimizing the impact of deferred physical maintenance operations, and results in support for concurrent high update rates and immediate, index-based query processing with correct transaction semantics.

Keywords

Query Result Query Execution Partial Index Primary Index Load Operation 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barroso, L.A., Hölzle, U.: The case for energy-proportional computing. IEEE Computer 40(12), 33–37 (2007)CrossRefGoogle Scholar
  2. 2.
    Bender, M.A., Farach-Colton, M., Mosteiro, M.: Insertion sort is O(n log n). In: Fun with Algorithms, pp. 16–23 (2004)Google Scholar
  3. 3.
    Gandhi, A., Gupta, V., Harchol-Balter, M., Kozuch, M.A.: Optimality analysis of energy-performance trade-off for server farm management. Perform. Eval. 67(11), 1155–1171 (2010)CrossRefGoogle Scholar
  4. 4.
    Graefe, G.: Sorting and indexing with partitioned B-trees. In: CIDR (2003)Google Scholar
  5. 5.
    Graefe, G.: Implementing sorting in database systems. ACM Comput. Surv. 38 (September 2006)Google Scholar
  6. 6.
    Graefe, G.: A survey of b-tree locking techniques. ACM Trans. Database Syst. 35, 16:1–16:26 (2010)Google Scholar
  7. 7.
    Graefe, G.: Modern B-tree techniques. Foundations and Trends in Databases 3(4), 203–402 (2011)CrossRefGoogle Scholar
  8. 8.
    Graefe, G., Kuno, H.: Self-selecting, self-tuning, incrementally optimized indexes. In: Proceedings of the 13th International Conference on Extending Database Technology, EDBT 2010, pp. 371–381. ACM, New York (2010)Google Scholar
  9. 9.
    Graefe, G., Kuno, H.: Fast Loads and Queries. In: Hameurlain, A., Küng, J., Wagner, R., Bach Pedersen, T., Tjoa, A.M. (eds.) Transactions on Large-Scale Data. LNCS, vol. 6380, pp. 31–72. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Hamilton, J.: Spot instances, big clusters, & the cloud at work. Amazon Cloud Blog Post (September 2011)Google Scholar
  11. 11.
    Mohan, C., Narang, I.: Algorithms for creating indexes for very large tables without quiescing updates. In: Stonebraker, M. (ed.) Proceedings of the 1992 ACM SIGMOD International Conference on Management of Data, June 2-5, pp. 361–370. ACM Press, San Diego (1992)CrossRefGoogle Scholar
  12. 12.
    O’Neil, P.E.: The escrow transactional method. ACM Trans. Database Syst. 11, 405–430 (1986)CrossRefGoogle Scholar
  13. 13.
    Severance, D.G., Lohman, G.M.: Differential files: their application to the maintenance of large databases. ACM Trans. Database Syst. 1, 256–267 (1976)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Harumi Kuno
    • 1
  • Goetz Graefe
    • 1
  1. 1.Hewlett-Packard LaboratoriesPalo AltoUSA

Personalised recommendations