TATune: A RocksDB Knob Tuning System Based on Transformer by Yun-Zhang Hu, Hui Wang

Subjects: “…RocksDB…”
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Selective Power-Loss-Protection Method for Write Buffer in ZNS SSDs by Junseok Yang, Seokjun Lee, Sungyong Ahn

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RangeKV: An Efficient Key-Value Store Based on Hybrid DRAM-NVM-SSD Storage Structure by Ling Zhan, Kai Lu, Zhilong Cheng, Jiguang Wan

“…We implement RangeKV based on RocksDB and conduct a comparative test and performance evaluation with RocksDB and NoveLSM. …”
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ZenFS+: Nurturing Performance and Isolation to ZenFS by Myounghoon Oh, Seehwan Yoo, Jongmoo Choi, Jeongsu Park, Chang-Eun Choi

“…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. …”
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Hybrid Transactional/Analytical Processing Amplifies IO in LSM-Trees by Jongbin Kim, Jaechan Ahn, Kitaek Lee, Minsoo Ryu, Hyungsoo Jung

“…We integrated our techniques into RocksDB and demonstrated that the modified RocksDB exhibits reduced IO amplification under HTAP workloads with negligible resource consumption.…”
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Requirements and Trade-Offs of Compression Techniques in Key–Value Stores: A Survey by Charles Jaranilla, Jongmoo Choi

“…Then, we quantitatively evaluate the compression ratio and performance using RocksDB under diverse compression techniques, block sizes, value sizes, and workloads. …”
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Rethinking Update-in-Place Key-Value Stores for Modern Storage by Markakis, Markos

“…Several widely-used key-value stores, like RocksDB, are designed around log-structured merge trees (LSMs). …”
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Efficient Key-Value Data Placement for ZNS SSD by Gijun Oh, Junseok Yang, Sungyong Ahn

“…The proposed method was implemented by modifying ZenFS of RocksDB and, according to the result of the performance evaluation, the space efficiency could be improved by up to 75%.…”
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Gen-Z memory pool system implementation and performance measurement by Won-ok Kwon, Song-Woo Sok, Chan-ho Park, Myeong-Hoon Oh, Seokbin Hong

“…Besides, it showed low latency in RocksDB's fillseq dbbench using the ext4 direct access filesystem.…”
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InK: In-Kernel Key-Value Storage with Persistent Memory by Minjong Ha, Sang-Hoon Kim

“…We implemented InK based on the Linux kernel and evaluated its performance with Yahoo Cloud Service Benchmark (YCSB) and RocksDB. Evaluation results confirms that InK has advantages over LSM-tree-based key-value store systems in terms of throughput and tail latency.…”
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HoaKV: High-Performance KV Store Based on the Hot-Awareness in Mixed Workloads by Jingyu Liu, Xiaoqin Fan, Youxi Wu, Yong Zheng, Lu Liu

“…In the mixed read and write workloads experments show that HoaKV performs significantly better than several state-of-the-art KV store technologies such as LevelDB, RocksDB, PebblesDB, and WiscKey.…”
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Shinjuku: Preemptive scheduling for µsecond-scale tail latency by Kaffes, K, Chong, T, Humphries, JT, Belay, Adam M, Mazières, D, Kozyrakis, C

“…We demonstrate that Shinjuku provides significant tail latency and throughput improvements over IX and ZygOS for a wide range of workload scenarios. For the case of a RocksDB server processing both point and range queries, Shinjuku achieves up to 6.6× higher throughput and 88% lower tail latency.…”
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Real and synthetic data sets for benchmarking key-value stores focusing on various data types and sizes by Hyuk-Yoon Kwon

“…Last, we show that the crawled and generated data sets are actually utilized for the well-known key-value stores such as Level DB of Google, RocksDB of Facebook, and Berkeley DB of Oracle. Actually, the presented real and synthetic data sets have been used for comparing the performance of them. …”
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ParaTM: Transparent Embedding of Hardware Transactional Memory for Traditional Applications by Kangmin Lee, Heeseung Jo

“…ParaTM achieved 1.75<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula>, 4.76<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula>, and 1.53<inline-formula> <tex-math notation="LaTeX">$\times$ </tex-math></inline-formula> better performance compared to the traditional lock mechanism for LevelDB, RocksDB, and Memcached, respectively.…”
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Constructing a Lightweight Key-Value Store Based on the Windows Native Features by Hyuk-Yoon Kwon

“…Through extensive experiments using synthetic and real data sets, we show that the performance of WR-Store is comparable to or even better than the state-of-the-art systems (i.e., RocksDB, BerkeleyDB, and LevelDB). Especially, we show the scalability of WR-Store. …”
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