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Jiabo Yao

dblp:339/0659 · DBLP profile ↗
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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

Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 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 · 67% Memory systems · 33%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems › non-volatile memory › persistent memory
byte-addressable persistent memory
0.712023
Revisiting Log-Structured Merging for KV Stores in Hybrid Memory Systems · ASPLOS (2) 2023
Storage systems
key-value storage
0.712023
Revisiting Log-Structured Merging for KV Stores in Hybrid Memory Systems · ASPLOS (2) 2023
Storage systems › key-value storage
LSM-tree
0.712023
Revisiting Log-Structured Merging for KV Stores in Hybrid Memory Systems · ASPLOS (2) 2023
Memory systems
non-volatile memory
0.712023
Revisiting Log-Structured Merging for KV Stores in Hybrid Memory Systems · ASPLOS (2) 2023
Storage systems
storage reliability
0.712023
Revisiting Log-Structured Merging for KV Stores in Hybrid Memory Systems · ASPLOS (2) 2023
Storage systems › flash and SSD › flash memory management › garbage collection
write amplification
0.712023
Revisiting Log-Structured Merging for KV Stores in Hybrid Memory Systems · ASPLOS (2) 2023

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

zero-copy compaction · 0.7skip list · 0.7bloom filter · 0.7
YearPublicationVenuePosition
2023 Revisiting Log-Structured Merging for KV Stores in Hybrid Memory Systems
abstract
We present MioDB, a novel LSM-tree based key-value (KV) store system designed to fully exploit the advantages of byte-addressable non-volatile memories (NVMs). Our experimental studies reveal that the performance bottleneck of LSM-tree based KV stores using NVMs mainly stems from (1) costly data serialization/deserialization across memory and storage, and (2) unbalanced speed between memory-to-disk data flushing and on-disk data compaction. They may cause unpredictable performance degradation due to write stalls and write amplification. To address these problems, we advocate byte-addressable and persistent skip lists to replace the on-disk data structure of LSM-tree, and design four novel techniques to make the best use of fast NVMs. First, we propose one-piece flushing to minimize the cost of data serialization from DRAM to NVM. Second, we exploit an elastic NVM buffer with multiple levels and zero-copy compaction to eliminate write stalls and reduce write amplification. Third, we propose parallel compaction to orchestrate data flushing and compactions across all levels of LSM-trees. Finally, MioDB increases the depth of LSM-tree and exploits bloom filters to improve the read performance. Our extensive experimental studies demonstrate that MioDB achieves 17.1× and 21.7× lower 99.9th percentile latency, 8.3× and 2.5× higher random write throughput, and up to 5× and 4.9× lower write amplification compared with the state-of-the-art NoveLSM and MatrixKV, respectively.
Zhuohui Duan, Jiabo Yao, Haikun Liu, Xiaofei Liao, Hai Jin 0001, Yu Zhang 0027
ASPLOS (2)2