Demo Data from EDBT 2026 / reviewers in the wild / expert
Hariharan Gopalakrishnan
dblp:157/2516
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Storage systems · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems
flash and SSD |
0.3 | 1 | 2017 | WiscKey: Separating Keys from Values in SSD-Conscious Storage · ACM Trans. Storage 2017 |
Storage systems › storage performance
i/o amplification |
0.3 | 1 | 2017 | WiscKey: Separating Keys from Values in SSD-Conscious Storage · ACM Trans. Storage 2017 |
Storage systems
key-value storage |
0.3 | 1 | 2017 | WiscKey: Separating Keys from Values in SSD-Conscious Storage · ACM Trans. Storage 2017 |
Storage systems › key-value storage
LSM-tree |
0.3 | 1 | 2017 | WiscKey: Separating Keys from Values in SSD-Conscious Storage · ACM Trans. Storage 2017 |
Storage systems › key-value storage
persistent key-value store |
0.1 | 1 | 2017 | WiscKey: Separating Keys from Values in SSD-Conscious Storage · ACM Trans. Storage 2017 |
Methods — techniques the papers use, named apart from their topics
key-value separation · 0.3LSM-tree · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | WiscKey: Separating Keys from Values in SSD-Conscious StorageabstractWe present WiscKey, a persistent LSM-tree-based key-value store with a performance-oriented data layout that separates keys from values to minimize I/O amplification. The design of WiscKey is highly SSD optimized, leveraging both the sequential and random performance characteristics of the device. We demonstrate the advantages of WiscKey with both microbenchmarks and YCSB workloads. Microbenchmark results show that WiscKey is 2.5 × to 111 × faster than LevelDB for loading a database (with significantly better tail latencies) and 1.6 × to 14 × faster for random lookups. WiscKey is faster than both LevelDB and RocksDB in all six YCSB workloads. Lanyue Lu, Thanumalayan Sankaranarayana Pillai, Hariharan Gopalakrishnan, Andrea C. Arpaci-Dusseau, Remzi H. Arpaci-Dusseau |
ACM Trans. Storage | 3 |