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Sriram Melkote

dblp:185/4284 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Indexing and storage engines
secondary index
0.212016
Nitro: A Fast, Scalable In-Memory Storage Engine for NoSQL Global Secondary Index · Proc. VLDB Endow. 2016
Storage systems › storage architecture
in-memory storage
0.212016
Nitro: A Fast, Scalable In-Memory Storage Engine for NoSQL Global Secondary Index · Proc. VLDB Endow. 2016
Storage systems
key-value storage
0.212016
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
0.112016
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
YearPublicationVenuePosition
2016 Nitro: A Fast, Scalable In-Memory Storage Engine for NoSQL Global Secondary Index
abstract
We 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