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Title: Big data availability: Selective partial checkpointing for in-memory database queries
Authors: Playfair, Daniel
Trehan, Amitabh
McLarnon, Barry
Nikolopoulos, Dimitrios S.
Keywords: Availability
Checkpointing
Database systems
Query processing
Issue Date: 2017
Publisher: © IEEE
Citation: PLAYFAIR, D. ... et al, 2017. Big data availability: Selective partial checkpointing for in-memory database queries. Proceedings of the 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, 5th-8th December 2016, pp. 2785-2794.
Abstract: Fault tolerance is an important challenge for supporting critical big data analytic operations. Most existing solutions only provide fault tolerant data replication, requiring failed queries to be restarted. This approach is insufficient for long-running time-sensitive analytic queries, due to lost query progress. Several solutions provide intra-query fault tolerance. However, these focus on distributed or row-oriented databases and are not suitable for use with the column-oriented in-memory databases increasingly used for highperformance workloads. We propose a new approach for intra-query checkpointing that produces an optimal checkpoint solution for a fixed checkpointing budget to minimise overhead on in-memory column-oriented database clusters. We describe a modified architecture for fault tolerant query execution using this approach. We present a general model for the problem, in which an adversary is free to terminate the execution of the query, eliminating all unsaved work. We present an algorithm that represents a first step towards producing checkpoint plans by optimally placing a single checkpoint. Our analysis shows this approach allows reduced checkpoint overheads while providing resilience for long-running queries.
Description: This paper is closed access.
Version: Published
DOI: 10.1109/BigData.2016.7840926
URI: https://dspace.lboro.ac.uk/2134/27921
Publisher Link: https://doi.org/10.1109/BigData.2016.7840926
ISBN: 9781467390057
9781467390064
Appears in Collections:Closed Access (Computer Science)

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