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A comparison of the use of virtual versus physical snapshots for supporting update-intensive workloads

Published: 21 May 2012 Publication History

Abstract

Deployments of networked sensors fuel online applications that feed on real-time sensor data. This scenario calls for techniques that support the management of workloads that contain queries as well as very frequent updates. This paper compares two well-chosen approaches to exploiting the parallelism offered by modern processors for supporting such workloads. A general approach to avoiding contention among parallel hardware threads and thus exploiting the parallelism available in processors is to maintain two copies, or snapshots, of the data: one for the relatively long-duration queries and one for the frequent and very localized updates. The snapshot that receives the updates is frequently made available to queries, so that queries see up-to-date data. The snapshots may be physical or virtual. Physical snapshots are created using the C library memcpy function. Virtual snapshots are created by the fork system function that creates a new process that initially has the same data snapshot as the process it was forked from. When the new process carries out updates, this triggers the actual memory copying in a copy-on-write manner at memory page granularity. This paper characterizes the circumstances under which each technique is preferable. The use of physical snapshots is surprisingly efficient.

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Cited By

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  • (2023)MoonKV: Optimizing Update-intensive Workloads for NVM-based Key-value Stores2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00057(478-487)Online publication date: 1-Dec-2023
  • (2022)Polynesia: Enabling High-Performance and Energy-Efficient Hybrid Transactional/Analytical Databases with Hardware/Software Co-Design2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00270(2997-3011)Online publication date: May-2022
  • (2016)Space odysseyProceedings of the Third International Workshop on Exploratory Search in Databases and the Web10.1145/2948674.2948677(12-18)Online publication date: 26-Jun-2016
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cover image ACM Conferences
DaMoN '12: Proceedings of the Eighth International Workshop on Data Management on New Hardware
May 2012
72 pages
ISBN:9781450314459
DOI:10.1145/2236584
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 21 May 2012

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Overall Acceptance Rate 94 of 127 submissions, 74%

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View all
  • (2023)MoonKV: Optimizing Update-intensive Workloads for NVM-based Key-value Stores2023 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM58522.2023.00057(478-487)Online publication date: 1-Dec-2023
  • (2022)Polynesia: Enabling High-Performance and Energy-Efficient Hybrid Transactional/Analytical Databases with Hardware/Software Co-Design2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00270(2997-3011)Online publication date: May-2022
  • (2016)Space odysseyProceedings of the Third International Workshop on Exploratory Search in Databases and the Web10.1145/2948674.2948677(12-18)Online publication date: 26-Jun-2016
  • (2014)Processing of extreme moving-object update and query workloads in main memoryThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-014-0353-223:5(817-841)Online publication date: 1-Oct-2014
  • (2013)DaisyACM SIGMOD Record10.1145/2430456.243046741:4(39-44)Online publication date: 17-Jan-2013

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