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Query suspend and resume

Published: 11 June 2007 Publication History

Abstract

Suppose a long-running analytical query is executing on a database server and has been allocated a large amount of physical memory. A high-priority task comes in and we need to run it immediately with all available resources. We have several choices. We could swap out the old query to disk, but writing out a large execution state may take too much time. Another option is to terminate the old query and restart it after the new task completes, but we would waste all the work already performed by the old query. Yet another alternative is to periodically checkpoint the query during execution, but traditional synchronous checkpointing carries high overhead. In this paper, we advocate a database-centric approach to implementing query suspension and resumption, with negligible execution overhead, bounded suspension cost, and efficient resumption. The basic idea is to let each physical query operator perform lightweight checkpointing according to its own semantics, and coordinate asynchronous checkpoints among operators through a novel contracting mechanism. At the time of suspension, we find an optimized suspend plan for the query, which may involve a combination of dumping current state to disk and going back to previous checkpoints. The plan seeks to minimize the suspend/resume overhead while observing the constraint on suspension time. Our approach requires only small changes to the iterator interface, which we have implemented in the PREDATOR database system. Experiments with our implementation demonstrate significant advantages of our approach over traditional alternatives.

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Cited By

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  • (2024)Riveter: Adaptive Query Suspension and Resumption Framework for Cloud Native Databases2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00304(3975-3988)Online publication date: 13-May-2024
  • (2023)Tempura: a general cost-based optimizer framework for incremental data processing (Journal Version)The VLDB Journal10.1007/s00778-023-00785-132:6(1315-1342)Online publication date: 20-Mar-2023
  • (2022)Dynamic Fault Tolerance for Multi-Node Query ProcessingIEICE Transactions on Information and Systems10.1587/transinf.2021DAP0004E105.D:5(909-919)Online publication date: 1-May-2022
  • Show More Cited By

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Published In

cover image ACM Conferences
SIGMOD '07: Proceedings of the 2007 ACM SIGMOD international conference on Management of data
June 2007
1210 pages
ISBN:9781595936868
DOI:10.1145/1247480
  • General Chairs:
  • Lizhu Zhou,
  • Tok Wang Ling,
  • Program Chair:
  • Beng Chin Ooi
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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Publication History

Published: 11 June 2007

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Author Tags

  1. optimization
  2. processing
  3. query
  4. resume
  5. suspend

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

View all
  • (2024)Riveter: Adaptive Query Suspension and Resumption Framework for Cloud Native Databases2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00304(3975-3988)Online publication date: 13-May-2024
  • (2023)Tempura: a general cost-based optimizer framework for incremental data processing (Journal Version)The VLDB Journal10.1007/s00778-023-00785-132:6(1315-1342)Online publication date: 20-Mar-2023
  • (2022)Dynamic Fault Tolerance for Multi-Node Query ProcessingIEICE Transactions on Information and Systems10.1587/transinf.2021DAP0004E105.D:5(909-919)Online publication date: 1-May-2022
  • (2020)TempuraProceedings of the VLDB Endowment10.14778/3421424.342142714:1(14-27)Online publication date: 1-Sep-2020
  • (2020)AmberProceedings of the VLDB Endowment10.14778/3377369.337738113:5(740-753)Online publication date: 19-Feb-2020
  • (2020)PhoeniQ: Failure-Tolerant Query Processing in Multi-node EnvironmentsDatabase and Expert Systems Applications10.1007/978-3-030-59003-1_5(71-85)Online publication date: 14-Sep-2020
  • (2019)Intermittent query processingProceedings of the VLDB Endowment10.14778/3342263.334227812:11(1427-1441)Online publication date: 1-Jul-2019
  • (2018)ChiProceedings of the VLDB Endowment10.14778/3231751.323176511:10(1303-1316)Online publication date: 1-Jun-2018
  • (2018)Workload Management in Database Management Systems: A TaxonomyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2017.276704430:7(1386-1402)Online publication date: 1-Jul-2018
  • (2018)Autonomic workload performance tuning in large-scale data repositoriesKnowledge and Information Systems10.1007/s10115-018-1272-0Online publication date: 4-Sep-2018
  • Show More Cited By

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