Scaling concurrent log-structured data stores

Golan-Gueta, Guy; Bortnikov, Edward; Hillel, Eshcar; Keidar, Idit

doi:10.1145/2741948.2741973

Cited by 67 publications

(30 citation statements)

References 27 publications

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“…The difference is that FloDB [15] only uses two layers, while Accordion [19] uses multiple layers to provide better concurrency and memory utilization. For multi-core machines, cLSM [34] presents a set of new concurrency control algorithms to improve concurrency.…”

Section: Discussionmentioning

confidence: 99%

LSM-based storage techniques: a survey

2019

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Recently, the Log-Structured Merge-tree (LSMtree) has been widely adopted for use in the storage layer of modern NoSQL systems. Because of this, there have been a large number of research efforts, from both the database community and the operating systems community, that try to improve various aspects of LSM-trees. In this paper, we provide a survey of recent research efforts on LSM-trees so that readers can learn the state-of-the-art in LSM-based storage techniques. We provide a general taxonomy to classify the literature of LSM-trees, survey the efforts in detail, and discuss their strengths and trade-offs. We further survey several representative LSM-based open-source NoSQL systems and discuss some potential future research directions resulting from the survey.

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Section: Discussionmentioning

confidence: 99%

LSM-based storage techniques: a survey

2019

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“…bLSM [28] presented a spring-and-gear merge scheduler to reduce periodic write stalls. cLSM [18] is optimized for multi-core machines using non-blocking concurrency control mechanisms. Monkey [16] optimized the memory allocation of Bloom filters for LSM-trees.…”

Section: Related Workmentioning

confidence: 99%

Efficient data ingestion and query processing for LSM-based storage systems

Luo

Carey

2019

Proc. VLDB Endow.

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In recent years, the Log Structured Merge (LSM) tree has been widely adopted by NoSQL and NewSQL systems for its superior write performance. Despite its popularity, however, most existing work has focused on LSM-based keyvalue stores with only a single LSM-tree; auxiliary structures, which are critical for supporting ad-hoc queries, have received much less attention. In this paper, we focus on efficient data ingestion and query processing for generalpurpose LSM-based storage systems. We first propose and evaluate a series of optimizations for efficient batched point lookups, significantly improving the range of applicability of LSM-based secondary indexes. We then present several new and efficient maintenance strategies for LSM-based storage systems. Finally, we have implemented and experimentally evaluated the proposed techniques in the context of the Apache AsterixDB system, and we present the results here. PVLDB Reference Format:Chen Luo, Michael J. Carey. Efficient Data Ingestion and Query Processing for LSM-Based Storage Systems (Extended Version). PVLDB, 12(5): xxxx-yyyy, 2019.

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“…To address this challenge, many key-value stores adopt the log-structured merge (LSM) architecture [35,36]. Examples include LevelDB [5], RocksDB [12], cLSM [26], bLSM [39], HyperLevelDB [6] and HBase [11]. LSM data stores are suitable for applications that require low latency accesses, such as message queues that undergo a high number of updates, and for maintaining session states in userfacing applications [12].…”

Section: Introductionmentioning

confidence: 99%