PACTree

Kim, Wook-Hee; Krishnan, R.; Fu, Xinwei; Kashyap, Sanidhya; Min, Changwoo

doi:10.1145/3477132.3483589

Cited by 38 publications

(12 citation statements)

References 27 publications

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“…Persistent Key Index. We choose PACTree [50] for our Key Index for its scalability and performance. PACTree is a persistent index structure that uses asynchronous updates to maximize concurrency.…”

Section: Methodsmentioning

confidence: 99%

“…Prism uses a central Persistent Key Index to avoid the downsides of the shared-nothing approach which manages a per-shard index. With recent advances in index designs [50,64,66], a key index is no longer a scalability bottleneck. In this work, we employ PACTree [50], a state-of-the-art persistent range index.…”

Section: Persistent Key Index On Nvmmentioning

confidence: 99%

“…With recent advances in index designs [50,64,66], a key index is no longer a scalability bottleneck. In this work, we employ PACTree [50], a state-of-the-art persistent range index. Note that our design is not dependent on a specific key index design, so Prism can replace it with any other range index.…”

Section: Persistent Key Index On Nvmmentioning

confidence: 99%

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Prism: Optimizing Key-Value Store for Modern Heterogeneous Storage Devices

Song

Kim

Monga

et al. 2023

Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems,

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As data generation has been on an upward trend, storing vast volumes of data cost-effectively as well as efficiently accessing them is paramount. At the same time, today's storage landscape continues to diversify, from high-bandwidth storage devices such as NVMe SSDs to low-latency non-volatile memory (e.g., Intel Optane DCPMM). These heterogeneous storage devices have the potential to deliver high performance in terms of bandwidth and latency with cost efficiency, while achieving the performance and cost targets together still remains a challenging problem.We provide our solution, Prism, a novel key-value store that utilizes modern heterogeneous storage devices. Prism uses heterogeneous storage devices synergistically to harness the advantages of each storage device while suppressing their downsides. We devise new techniques to balance the latency-bandwidth tradeoff when reading from SSD. For ensuring multicore scalability and crash consistency of data across heterogeneous storage media, Prism proposes cross-storage concurrency control and cross-storage crash consistency protocols. Our evaluation shows that Prism outperforms state-of-the-art key-value stores by up to 13.1× with significantly lower tail latency.

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Section: Methodsmentioning

confidence: 99%

Section: Persistent Key Index On Nvmmentioning

confidence: 99%

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Prism: Optimizing Key-Value Store for Modern Heterogeneous Storage Devices

Song

Kim

Monga

et al. 2023

Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems,

Self Cite

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show abstract

“…SkipList [12] is another index structure that can tolerate transient inconsistencies because of the insert and delete operation of internal nodes. Recently proposed hybrid range indexes [9,10] improve the performance and scalability by tolerating transient inconsistencies between the search layer and the data layer. We exploit the concept of tolerating transient inconsistencies between the search layer and the data layer.…”

Section: Search Layer and Data Layer In Range Index Structurementioning

confidence: 99%

“…These inconsistent transient states are called Transient Inconsistency. Moreover, other recent studies [9,10] show that separating the search layer (the internal nodes) and the data layer (the data nodes) in a concurrent range index structure can improve scalability and performance. This structure also can take advantage of disaggregated memory, which is an emerging architecture in a distributed memory system.…”

Section: Introductionmentioning

confidence: 99%

Converting Concurrent Range Index Structure to Range Index Structure for Disaggregated Memory

Koo,

Hwang,

Park

et al. 2023

Applied Sciences

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In this work, we propose the Spread approach, which tailors a concurrent range index structure to a range index structure for disaggregated memory connected via RDMA (Remote Direct Memory Access). The Spread approach leverages the concept of tolerating transient inconsistencies in a concurrent range index structure to reduce the amount of expensive RDMA operations. Based on the Spread approach, we converted Blink-tree, a concurrent range index structure, to a range index structure for disaggregated memory called RF-tree. In our experimental study, RF-tree shows comparable performance to Sherman, a state-of-the-art and carefully crafted range index structure for disaggregated memory.

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PFtree: Optimizing Persistent Adaptive Radix Tree for PM Systems on eADR Platform

Zhang

Sun

et al. 2023

Lecture Notes in Computer Science

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PACTree

Cited by 38 publications

References 27 publications

Prism: Optimizing Key-Value Store for Modern Heterogeneous Storage Devices

Prism: Optimizing Key-Value Store for Modern Heterogeneous Storage Devices

Converting Concurrent Range Index Structure to Range Index Structure for Disaggregated Memory

PFtree: Optimizing Persistent Adaptive Radix Tree for PM Systems on eADR Platform

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