Skippy: Enabling Long-Lived Snapshots of the Long-Lived Past

  • Authors:

  • Ross Shaull;Liuba Shrira;Hao Xu

  • Affiliations:

  • Department of Computer Science, Brandeis University, Waltham, Massachusetts, USA. rshaull@cs.brandeis.edu;Department of Computer Science, Brandeis University, Waltham, Massachusetts, USA. liuba@cs.brandeis.edu;Department of Computer Science, Brandeis University, Waltham, Massachusetts, USA. hxu@cs.brandeis.edu

  • Venue:
  • ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
  • Year:
  • 2008

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Abstract

Decreasing disk costs have made it practical to retain long-lived snapshots, enabling new applications that analyze past states and infer about future states. Current approaches offer no satisfactory way to organize long-lived snapshots because they disrupt the database in either short or long run. Split snapshots are a recent approach that overcomes some of the limitations. An unsolved problem has been how to support efficient application code access to arbitrarily long-lived snapshots. We describe Skippy, a new approach that solves this problem. Performance evaluation of Skippy, based on theoretical analysis and experimental measurements, indicates that the new approach is effective and efficient.