Main memory database recovery strategies

Magalhães, Arlino; Brayner, Ângelo; Monteiro, José Maria

doi:10.5753/sbbd_estendido.2022.21861

Anais Estendidos Do XXXVII Simpósio Brasileiro De Banco De Dados (SBBD Estendido 2022) 2022

DOI: 10.5753/sbbd_estendido.2022.21861

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Main memory database recovery strategies

Arlino Magalhães

Ângelo Brayner

José Maria Monteiro

Abstract: Main Memory Database (MMDB) technology has been an efficient alternative for high-performance, real-time, mission-critical applications. However, MMDBs are more vulnerable to crashes due to memory volatility. Although the recovery component looks similar on disk- and memory-oriented systems, these systems differ dramatically in the way they implement their architectural components. This tutorial aims to provide a complete review of MMDB recovery techniques. To achieve this goal, the tutorial reviews key databa… Show more

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Index checkpoints for instant recovery in in-memory database systems

Lee

Xie

et al. 2022

Proc. VLDB Endow.

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We observe that the time bottleneck during the recovery phase of an IMDB (In-Memory DataBase system) shifts from log replaying to index rebuilding after the state-of-art techniques for instant recovery have been applied. In this paper, we investigate index checkpoints to eliminate this bottleneck. However, improper designs may lead to inconsistent index checkpoints or incur severe performance degradation. For the correctness challenge, we combine two techniques, i.e. , deferred deletion of index entries, and on-demand clean-up of dangling index entries after recovery, to achieve data correctness. For the efficiency challenge, we propose three wait-free index checkpoint algorithms, i.e., ChainIndex, MirrorIndex, IACoW , for supporting efficient normal processing and fast recovery. We implement our proposed solutions in HiEngine, an IMDB being developed as part of Huawei's next-generation cloud-native database product. We evaluate the impact of index checkpoint persistence on recovery and transaction performance using two workloads ( i.e. , TPC-C and Microbench). We analyze the pros and cons of each algorithm. Our experimental results show that HiEngine can be recovered instantly ( i.e. , in ~10 s) with only slight ( i.e. , 5% - 11%) performance degradation. Therefore, we strongly recommend integrating index checkpointing into IMDBs if recovery time is a crucial product metric.

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Index checkpoints for instant recovery in in-memory database systems

Lee

Xie

et al. 2022

Proc. VLDB Endow.

View full text Add to dashboard Cite

show abstract

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Main memory database recovery strategies

Cited by 1 publication

References 5 publications

Index checkpoints for instant recovery in in-memory database systems

Index checkpoints for instant recovery in in-memory database systems

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