nKV in Action: Accelerating KV-Stores on Native Computational Storage with Near-Data Processing
Summary: nKV uses native computational storage and near-data processing to accelerate a KV-store with reduced data movement. In-situ GET/SCAN and Betweenness Centrality yield 1.4x-2.7x gains on COSMOS+ hardware. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Tobias Vinçon (Reutlingen University)
- 2. Lukas Weber (Technical University of Darmstadt)
- 3. Arthur Bernhardt (Reutlingen University)
- 4. Christian Riegger (Reutlingen University)
- 5. Sergey Hardock (Reutlingen University)
- 6. Christian Knoedler (Reutlingen University)
- 7. Florian Stock (Reutlingen University)
- 8. Leonardo Solis-Vasquez (Reutlingen University)
- 9. Sajjad Tamimi (Reutlingen University)
- 10. Andreas Koch (Technical University of Darmstadt)
- 11. Ilia Petrov (Reutlingen University)
BibTeX Citation
@article{vincon_vldb20,
title = {{nKV in Action: Accelerating KV-Stores on Native Computational Storage with Near-Data Processing}},
author = {Vinçon, Tobias and Weber, Lukas and Bernhardt, Arthur and Riegger, Christian and Hardock, Sergey and Knoedler, Christian and Stock, Florian and Solis-Vasquez, Leonardo and Tamimi, Sajjad and Koch, Andreas and Petrov, Ilia},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {2981--2984},
doi = {10.14778/3415478.3415524},
url = {https://doi.org/10.14778/3415478.3415524},
year = {2020}
}
Incoming Citations (Sorted by Pagerank)
Showing 3 of 3 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,577 | Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine | 2022 | VLDB | 7.2930211e-05 |
| 5,917 | Near-Data Processing in Database Systems on Native Computational Storage under HTAP Workloads | 2022 | VLDB | 6.043185e-05 |
| 7,263 | Catalyst: Optimizing Cache Management for Large In-memory Key-value Systems | 2023 | VLDB | 5.6611756e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,130 | Query Processing on Smart SSDs: Opportunities and Challenges | 2013 | SIGMOD | 0.00012052236 |
| 1,704 | YourSQL: A High-Performance Database System Leveraging In-Storage Computing | 2016 | VLDB | 9.9631993e-05 |
| 4,862 | Caribou: Intelligent Distributed Storage | 2017 | VLDB | 6.4741258e-05 |
| 6,702 | JAFAR: Near-Data Processing for Databases | 2015 | SIGMOD | 5.7980528e-05 |
| 7,569 | Less Watts, More Performance: An Intelligent Storage Engine for Data Appliances | 2013 | SIGMOD | 5.5950861e-05 |
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Semantically Similar Papers
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|---|---|---|---|---|
| 1 | 8,919 | MirrorKV: An Efficient Key-Value Store on Hybrid Cloud Storage with Balanced Performance of Compaction and Querying | 2023 | SIGMOD |
| 2 | 9,507 | BonsaiKV: Towards Fast, Scalable, and Persistent Key-Value Stores with Tiered, Heterogeneous Memory System | 2024 | VLDB |
| 3 | 7,821 | Concurrent Log-Structured Memory for Many-Core Key-Value Stores | 2018 | VLDB |
| 4 | 4,993 | Key-Value Storage Engines | 2020 | SIGMOD |
| 5 | 4,058 | Fast Scans on Key-Value Stores | 2017 | VLDB |
| 6 | 11,002 | From FASTER to F2: Evolving Concurrent Key-Value Store Designs for Large Skewed Workloads | 2025 | VLDB |
| 7 | 9,506 | FluidKV: Seamlessly Bridging the Gap between Indexing Performance and Memory-Footprint on Ultra-Fast Storage | 2024 | VLDB |
| 8 | 614 | Faster: A Concurrent Key-Value Store with In-Place Updates | 2018 | SIGMOD |
| 9 | 2,498 | Mega-KV: A Case for GPUs to Maximize the Throughput of In-Memory Key-Value Stores | 2015 | VLDB |
| 10 | 5,917 | Near-Data Processing in Database Systems on Native Computational Storage under HTAP Workloads | 2022 | VLDB |