KVell

Lepers, Baptiste; Balmau, Oana; Gupta, Komal; Zwaenepoel, Willy

doi:10.1145/3341301.3359628

Cited by 84 publications

(2 citation statements)

References 20 publications

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“…Several efficient snapshot algorithms have been proposed to construct a snapshot with low overhead, including Copyon-Write (CoW) [104,106] and Zigzag [37], and multiple database vendors and research prototypes adopt memory snapshot approach for hybrid transactional and analytical processing, e.g., Hyper-O 1 [85], SwingDB [116], AnKer [149], Kvell+ [100], and update-aware NDP [167].…”

Section: In-memory Snapshot Algorithmmentioning

confidence: 99%

A survey on hybrid transactional and analytical processing

Song,

Zhou,

Cui

et al. 2024

The VLDB Journal

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To provide applications with the ability to analyze fresh data and eliminate the time-consuming ETL workflow, hybrid transactional and analytical (HTAP) systems have been developed to serve online transaction processing and online analytical processing workloads in a single system. In recent years, HTAP systems have attracted considerable interest from both academia and industry. Several new architectures and technologies have been proposed. This paper provides a comprehensive overview of these HTAP systems. We review recently published papers and technical reports in this field and broadly classify existing HTAP systems into two categories based on their data formats: monolithic and hybrid HTAP. We further classify hybrid HTAP into four sub-categories based on their storage architecture: row-oriented, column-oriented, separated, and hybrid. Based on such a taxonomy, we outline each stream’s design challenges and performance issues (e.g., the contradictory format demand for monolithic HTAP). We then discuss potential solutions and their trade-offs by reviewing noteworthy research findings. Finally, we summarize emerging HTAP applications, benchmarks, future trends, and open problems.

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Section: In-memory Snapshot Algorithmmentioning

confidence: 99%

A survey on hybrid transactional and analytical processing

Song,

Zhou,

Cui

et al. 2024

The VLDB Journal

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“…KVell [24] describes the design of a fast, persistent KV store leveraging NVMe SSDs. The simple design of an unstructured SSD-resident log paired with an in-memory index is similar to the design of our backup node.…”

Section: Related Workmentioning

confidence: 99%

High availability in cheap distributed key value storage

Kim

Wong

Ganger

et al. 2020

Proceedings of the 11th ACM Symposium on Cloud Computing

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Memory-based storage currently offers the highest-performance distributed storage, keeping the primary copy of all data in DRAM. Recent advances in non-volatile main memory (NVMM) technologies promise latency similar to DRAM at reduced cost and energy, but will make providing high availability more challenging. Previous approaches to failure recovery involve maintaining multiple identical replicas or relying on fast offline restoration of data from backup replicas stored on SSD. Unfortunately, NVMM's combination of lower write throughput and increased storage density means that offline restoration can no longer provide sufficiently fast recovery, and maintaining multiple identical replicas is generally cost prohibitive. CANDStore is a strongly consistent, distributed, replicated key-value store that uses a new fast crash recovery protocol. As a result, CANDStore can use NVMM and NVMe SSD technology to provide low-latency distributed storage that is cheaper and higher-availability than existing main memory-based distributed storage. Our evaluation shows that CANDStore's recovery protocol enables the system to restore performance and meet SLOs after the failure of a primary node 4.5-10.5x faster than offline recovery.

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Towards Optimizing Deduplication on Persistent Memory

Wang

et al. 2021

Lecture Notes in Computer Science

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KVell

Cited by 84 publications

References 20 publications

A survey on hybrid transactional and analytical processing

A survey on hybrid transactional and analytical processing

High availability in cheap distributed key value storage

Towards Optimizing Deduplication on Persistent Memory

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