Demo Data from SIGIR 2026 / reviewers in the wild / expert
Sriram Melkote
dblp:185/4284
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2016
—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 · 87% Parallel and multicore computing · 13% | |
| Databases, data mining, and information retrieval
1 paper |
Indexing and storage engines · 100% |
Topics — the 4 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Indexing and storage engines
secondary index |
0.2 | 1 | 2016 | Nitro: A Fast, Scalable In-Memory Storage Engine for NoSQL Global Secondary Index · Proc. VLDB Endow. 2016 |
Storage systems › storage architecture
in-memory storage |
0.2 | 1 | 2016 | Nitro: A Fast, Scalable In-Memory Storage Engine for NoSQL Global Secondary Index · Proc. VLDB Endow. 2016 |
Storage systems
key-value storage |
0.2 | 1 | 2016 | Nitro: A Fast, Scalable In-Memory Storage Engine for NoSQL Global Secondary Index · Proc. VLDB Endow. 2016 |
Parallel and multicore computing › concurrent data structures
lock-free data structures |
0.1 | 1 | 2016 | Nitro: A Fast, Scalable In-Memory Storage Engine for NoSQL Global Secondary Index · Proc. VLDB Endow. 2016 |
Methods — techniques the papers use, named apart from their topics
latch-free data structures · 0.5database snapshots · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Nitro: A Fast, Scalable In-Memory Storage Engine for NoSQL Global Secondary IndexabstractWe present Nitro, a high-performance in-memory key-value storage engine used in Couchbase 4.5 Global Secondary Indexes. The Nitro storage engine is well suited for the recent hardware trends like large amounts of memory and many CPU cores. The storage engine leverages latch-free data structures and tries to achieve linear scalability for the index read-write operations. The Nitro storage engine offers concurrent readers and writers, lightweight database snapshots, stable scan, backup and recovery operations. We integrated Nitro into the Couchbase Global Secondary Indexes (GSI) and observed significant improvement in performance compared to our disk oriented storage engine configured with the same amount of memory for buffer cache. On a 32 core machine, we observed an end-to-end GSI server insertion throughput of 1,650,000 entries/sec and index update throughput of 822,000 entries/sec. A single instance of Nitro data structure running on a 40 core machine achieved a peak insertion throughput of 4 million index entries/sec and entry lookup throughput of 10 million lookups/sec. Sarath Lakshman, Sriram Melkote, John Liang, Ravi Mayuram |
Proc. VLDB Endow. | 2 |