EDBT 2026 Demo / reviewers in the wild / expert
Hongdi Ren
dblp:352/6598
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1 · 1 since 2021
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 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Storage systems › key-value storage
compaction strategy |
0.7 | 1 | 2023 | PM-Blade: A Persistent Memory Augmented LSM-tree Storage for Database · ICDE 2023 |
Storage systems › key-value storage
LSM-tree |
0.7 | 1 | 2023 | PM-Blade: A Persistent Memory Augmented LSM-tree Storage for Database · ICDE 2023 |
Storage systems › flash and SSD
write amplification reduction |
0.7 | 1 | 2023 | PM-Blade: A Persistent Memory Augmented LSM-tree Storage for Database · ICDE 2023 |
Methods — techniques the papers use, named apart from their topics
cost-based compaction · 0.7coroutines · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | PM-Blade: A Persistent Memory Augmented LSM-tree Storage for DatabaseabstractIn this paper, we present PM-Blade, an LSM-tree structured storage augmented with persistent memory (or non-volatile memory). PM-Blade utilizes persistent memory to optimize read performance and reduce write amplification, which are essential to Meituan’s online retail applications. Distinguished from existing designs, PM-Blade leverages persistent memory to drastically increase the capacity of the level-0 layer of LSM-tree. An enlarged level-0 layer allows a large amount of hot or warm data to be retained in persistent memory, enabling high read performance. At the same time, it works as a large write buffer that absorbs write amplification. To make the best of the design, we devised an internal compaction method and used a cost-based compaction strategy to maximize the utility of the level-0 layer. We implemented the compaction method using coroutines to improve its efficiency and resource utilization. We evaluated PM-Blade through extensive experiments, in which PM-Blade outperformed several open-source alternatives on standard benchmarks and a real-world workload of Meituan. Huiqi Hu, Xuan Zhou 0001, Enlong Xie, Hongdi Ren, Le Jin |
ICDE | 5 |