Demo Data from EDBT 2026 / reviewers in the wild / expert
Lucas Vasconcelos Santana
dblp:350/5885
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2023
0009-0002-6831-0139ORCID · reported
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 · 82% Cloud and datacenter computing · 18% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cloud and datacenter computing
cloud storage |
0.7 | 1 | 2023 | Disaggregating RocksDB: A Production Experience · Proc. ACM Manag. Data 2023 |
Storage systems › distributed storage
disaggregated storage |
0.7 | 1 | 2023 | Disaggregating RocksDB: A Production Experience · Proc. ACM Manag. Data 2023 |
Storage systems › file systems
distributed file system |
0.7 | 1 | 2023 | Disaggregating RocksDB: A Production Experience · Proc. ACM Manag. Data 2023 |
Storage systems
key-value storage |
0.7 | 1 | 2023 | Disaggregating RocksDB: A Production Experience · Proc. ACM Manag. Data 2023 |
Storage systems › key-value storage
LSM-tree |
0.7 | 1 | 2023 | Disaggregating RocksDB: A Production Experience · Proc. ACM Manag. Data 2023 |
Storage systems
crash recovery |
0.2 | 1 | 2023 | Disaggregating RocksDB: A Production Experience · Proc. ACM Manag. Data 2023 |
Storage systems
storage reliability |
0.2 | 1 | 2023 | Disaggregating RocksDB: A Production Experience · Proc. ACM Manag. Data 2023 |
Methods — techniques the papers use, named apart from their topics
append-only distributed file system · 0.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | Disaggregating RocksDB: A Production ExperienceabstractAs in the general industry, there is a trend in Meta's data centers to migrate data from locally attached SSDs to cloud storage. We extended RocksDB [26], a widely used open-source storage engine designed and built for local SSDs, to leverage disaggregated storage. RocksDB's design, such as its data and log files' access patterns, makes an append-only distributed file system a desirable underlying storage. At Meta, we built disaggregated RocksDB using Tectonic File System [35], which so far had mainly been used for our data warehouse and blob storage stacks. We identified that metadata overhead and tail latencies were Tectonic's major performance gaps and addressed them accordingly. We improved the reliability, performance and other requirements with both general and customized optimizations to the core engine in RocksDB. We also took the time to deeply understand the common challenges presented by applications running on RocksDB and implemented enhancements to address them. This architecture enabled RocksDB to adapt to a more distributed architecture for performance enhancements. Siying Dong, Shiva Shankar P., Satadru Pan, Anand Ananthabhotla, Dhanabal Ekambaram, Shobhit Dayal, Nishant Vinaybhai Parikh, Yanqin Jin, Albert Kim, Sushil Patil, Jay Zhuang, Sam Dunster, Akanksha Mahajan 0001, Anirudh Chelluri, Chaitanya Datye, Lucas Vasconcelos Santana, Omkar Gawde |
Proc. ACM Manag. Data | 17 |