Fast checkpoint recovery algorithms for frequently consistent applications

  • Authors:

  • Tuan Cao;Marcos Vaz Salles;Benjamin Sowell;Yao Yue;Alan Demers;Johannes Gehrke;Walker White

  • Affiliations:

  • Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA;Cornell University, Ithaca, NY, USA

  • Venue:
  • Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
  • Year:
  • 2011

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Abstract

Advances in hardware have enabled many long-running applications to execute entirely in main memory. As a result, these applications have increasingly turned to database techniques to ensure durability in the event of a crash. However, many of these applications, such as massively multiplayer online games and main-memory OLTP systems, must sustain extremely high update rates - often hundreds of thousands of updates per second. Providing durability for these applications without introducing excessive overhead or latency spikes remains a challenge for application developers. In this paper, we take advantage of frequent points of consistency in many of these applications to develop novel checkpoint recovery algorithms that trade additional space in main memory for significantly lower overhead and latency. Compared to previous work, our new algorithms do not require any locking or bulk copies of the application state. Our experimental evaluation shows that one of our new algorithms attains nearly constant latency and reduces overhead by more than an order of magnitude for low to medium update rates. Additionally, in a heavily loaded main-memory transaction processing system, it still reduces overhead by more than a factor of two.