TridentKV: A Read-Optimized LSM-Tree Based KV Store via Adaptive Indexing and Space-Efficient Partitioning

Lu, Kai; Zhao, Nannan; Wan, Jiguang; Fei, Changhong; Zhao, Wei; Deng, Tongliang

doi:10.1109/tpds.2021.3118599

Cited by 19 publications

(7 citation statements)

References 26 publications

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“…Write with a guard, PebblesDB [20] divides the key space into fragments and proposes a fragmented LSM treebased KVS. Bourbon [21] uses learned index for quickly pinpointing the index location in the sstable, TridentKV [22] also uses adaptive index for read-optimization. Remix [23] proposes re-indexing the tree for range query, S3 [24] uses CPU-optimized data structure [25] and semi-ordered skiplist, respectively.…”

Section: Discussionmentioning

confidence: 99%

ZenFS+: Nurturing Performance and Isolation to ZenFS

Yoo

Choi

et al. 2023

IEEE Access

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This paper proposes ZenFS+, a new storage backend of RocksDB for small-zone ZNS SSD. RocksDB has complicated internal operations such as flush and compaction. Flush and compaction run in separate threads, and it is well known that they closely interact around the sstables. Due to the concurrent storage I/O between flush and compaction, ZenFS presents unsatisfactory performance for small-zone ZNS SSD. We believe that ZenFS+ presents how ZNS SSD can support the performance and isolation of modern keyvalue stores. Leveraging the ZNS SSD, ZenFS+ intelligently identifies the independent zones group (IZG), which reveals the device's internal parallelism. With the IZG information, ZenFS+ effectively isolates the flush workloads of RocksDB from compaction. Besides, ZenFS+ spreads the sstables to multiple IZGs so that the storage write can leverage the hardware parallelism. ZenFS+ presents up to 4.8x higher storage throughput for write-intensive microbenchmark and stabilizing 99.9P tail latency by 1/51 from existing ZenFS. Further, ZenFS+ implements proactive garbage collection, and presents sustainable performance over longer lifespan of the device.

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Section: Discussionmentioning

confidence: 99%

ZenFS+: Nurturing Performance and Isolation to ZenFS

Yoo

Choi

et al. 2023

IEEE Access

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“…SPDK for High-Performance Storage Stack: Several studies have adopted SPDK for building high-performance storage systems, including file systems, such as EvFS [15] and FSP [39] and KVS such as SpanDB [14] and TridentKV [16]. However, the focus remained on how to use SPDK, whereas the inherent impact of KVS operations on SPDK performance has not been clearly studied.…”

Section: Related Workmentioning

confidence: 99%

“…Due to these characteristics, the SPDK can fully leverage the true capabilities of NVMe SSDs. Consequently, there are ongoing efforts to integrate the SPDK into the development of high-performance KVS solutions [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

OctoFAS: A Two-Level Fair Scheduler That Increases Fairness in Network-Based Key-Value Storage

Park,

Park

et al. 2024

Electronics

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We identified a fairness problem in a network-based key-value storage system using Intel Storage Performance Development Kit (SPDK) in a multitenant environment. In such an environment, each tenant’s I/O service rate is not fairly guaranteed compared to that of other tenants. To address the fairness problem, we propose OctoFAS, a two-level fair scheduler designed to improve overall throughput and fairness among tenants. The two-level scheduler of OctoFAS consists of (i) inter-core scheduling and (ii) intra-core scheduling. Through inter-core scheduling, OctoFAS addresses the load imbalance problem that is inherent in SPDK on the storage server by dynamically migrating I/O requests from overloaded cores to underloaded cores, thereby increasing overall throughput. Intra-core scheduling prioritizes handling requests from starving tenants over well-fed tenants within core-specific event queues to ensure fair I/O services among multiple tenants. OctoFAS is deployed on a Linux cluster with SPDK. Through extensive evaluations, we found that OctoFAS ensures that the total system throughput remains high and balanced, while enhancing fairness by approximately 10% compared to the baseline, when both scheduling levels operate in a hybrid fashion.

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“…In this paper, we choose LevelDB as an experimental environment since it is well-known, open-source, and popularly used by both industry and academia. Note that our proposal can be applied to other key-value stores that are based on the LSM (Log-Structured Merge)-tree structure [15,26].…”

Section: Leveldbmentioning

confidence: 99%

An Empirical Study of Segmented Linear Regression Search in LevelDB

et al. 2023

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The purpose of this paper is proposing a novel search mechanism, called SLR (Segmented Linear Regression) search, based on the concept of learned index. It is motivated by our observation that a lot of big data, collected and used by previous studies, have a linearity property, meaning that keys and their stored locations show a strong linear correlation. This observation leads us to design SLR search where we apply segmentation into the well-known machine learning algorithm, linear regression, for identifying a location from a given key. We devise two segmentation techniques, equal-size and error-aware, with the consideration of both prediction accuracy and segmentation overhead. We implement our proposal in LevelDB, Google’s key-value store, and verify that it can improve search performance by up to 12.7%. In addition, we find that the equal-size technique provides efficiency in training while the error-aware one is tolerable to noisy data.

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TridentKV: A Read-Optimized LSM-Tree Based KV Store via Adaptive Indexing and Space-Efficient Partitioning

Cited by 19 publications

References 26 publications

ZenFS+: Nurturing Performance and Isolation to ZenFS

ZenFS+: Nurturing Performance and Isolation to ZenFS

OctoFAS: A Two-Level Fair Scheduler That Increases Fairness in Network-Based Key-Value Storage

An Empirical Study of Segmented Linear Regression Search in LevelDB

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