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Yunshan Tu

dblp:266/6219 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2020
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 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 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Storage systems › i/o architecture › i/o subsystem
i/o stack
0.412020
Latte: A Native Table Engine On Nvme Storage · ICDE 2020
Storage systems
storage engine
0.412020
Latte: A Native Table Engine On Nvme Storage · ICDE 2020
Storage systems › logging
undo logging
0.412020
Latte: A Native Table Engine On Nvme Storage · ICDE 2020
Storage systems › flash and SSD › flash memory management › garbage collection
write amplification
0.412020
Latte: A Native Table Engine On Nvme Storage · ICDE 2020

Methods — techniques the papers use, named apart from their topics

parallel scheduling · 0.4
YearPublicationVenuePosition
2020 Latte: A Native Table Engine On Nvme Storage
abstract
Most database systems rely on complex multi-layer and compatibility-oriented storage stacks, which results in sub-optimal database management system (DBMS) performance and significant write amplification. A heavy storage stack can be tolerated in the slow disk era because its storage overhead is completely overlapped by hardware delay. However, with advances in storage technologies, emerging NVMe devices have reached the same level of latency as software, which in turn has caused the storage stack to become a new bottleneck. On the other hand, NVMe devices not only improve I/O efficiency but also introduce distinctive hardware features that require software modifications to take advantage of themTo fully exploit the hardware potential of NVMe devices, we propose a lightweight native storage stack called Lightstack to minimize the software overhead. The core of Lightstack is an efficient table storage engine, LATTE, which abstracts the essential data service of the database's 2D table. LATTE is designed from the ground up to use NVMe devices efficiently. It directly accesses NVMe devices to reduce single I/O latency and utilizes a parallel scheduling strategy to leverage multiple deep I/O queues and CPU cores. Besides, undo logging on heterogeneous storage is proposed to mitigate the write amplification further. We also implement a working prototype and evaluate it with standard benchmarks on the Intel Optane DC P4800X NVMe SSD and the DC P3608 Series NVMe SSD. Experimental results show that LATTE has up to 3.6-6.5× the throughput of MySQL's InnoDB and MyRocks engines, with latency as low as 28% in the same hardware environment.
Jiajia Chu, Yunshan Tu, Chuliang Weng
ICDE2