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Sanket Hase

dblp:163/3985 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none

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

Databases, data management, data science and information retrieval · 2

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.

Databases, data mining, and information retrieval
2 papers
Database system architecture and tuning · 56% Distributed and cloud data management · 17% Query processing and optimization · 17%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Database system architecture and tuning
main-memory database
0.422015
Distributed Architecture of Oracle Database In-memory · Proc. VLDB Endow. 2015
Oracle Database In-Memory: A dual format in-memory database · ICDE 2015
Database system architecture and tuning › main-memory database
distributed in-memory database
0.212015
Distributed Architecture of Oracle Database In-memory · Proc. VLDB Endow. 2015
Distributed and cloud data management
distributed query processing
0.212015
Distributed Architecture of Oracle Database In-memory · Proc. VLDB Endow. 2015
Query processing and optimization
parallel query processing
0.212015
Distributed Architecture of Oracle Database In-memory · Proc. VLDB Endow. 2015
Indexing and storage engines
columnar storage
0.112015
Oracle Database In-Memory: A dual format in-memory database · ICDE 2015
Database system architecture and tuning
hybrid transactional and analytical processing
0.112015
Distributed Architecture of Oracle Database In-memory · Proc. VLDB Endow. 2015
Transaction processing and concurrency control › consistency
transactional consistency
0.112015
Oracle Database In-Memory: A dual format in-memory database · ICDE 2015

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

row-column dual format · 0.2columnar storage · 0.2NUMA-aware execution · 0.2
YearPublicationVenuePosition
2015 Oracle Database In-Memory: A dual format in-memory database
abstract
The Oracle Database In-Memory Option allows Oracle to function as the industry-first dual-format in-memory database. Row formats are ideal for OLTP workloads which typically use indexes to limit their data access to a small set of rows, while column formats are better suited for Analytic operations which typically examine a small number of columns from a large number of rows. Since no single data format is ideal for all types of workloads, our approach was to allow data to be simultaneously maintained in both formats with strict transactional consistency between them.
Tirthankar Lahiri, Shasank Chavan, Maria Colgan, Dinesh Das, Amit Ganesh, Mike Gleeson, Sanket Hase, Allison Holloway, Jesse Kamp, Teck-Hua Lee, Juan Loaiza, Neil MacNaughton, Vineet Marwah, Niloy Mukherjee, Atrayee Mullick, Sujatha Muthulingam, Vivekanandhan Raja, Marty Roth, Ekrem Soylemez, Mohamed Zaït
ICDE7
2015 Distributed Architecture of Oracle Database In-memory
abstract
Over the last few years, the information technology industry has witnessed revolutions in multiple dimensions. Increasing ubiquitous sources of data have posed two connected challenges to data management solutions -- processing unprecedented volumes of data, and providing ad-hoc real-time analysis in mainstream production data stores without compromising regular transactional workload performance. In parallel, computer hardware systems are scaling out elastically, scaling up in the number of processors and cores, and increasing main memory capacity extensively. The data processing challenges combined with the rapid advancement of hardware systems has necessitated the evolution of a new breed of main-memory databases optimized for mixed OLTAP environments and designed to scale. The Oracle RDBMS In-memory Option (DBIM) is an industry-first distributed dual format architecture that allows a database object to be stored in columnar format in main memory highly optimized to break performance barriers in analytic query workloads, simultaneously maintaining transactional consistency with the corresponding OLTP optimized row-major format persisted in storage and accessed through database buffer cache. In this paper, we present the distributed, highly-available, and fault-tolerant architecture of the Oracle DBIM that enables the RDBMS to transparently scale out in a database cluster, both in terms of memory capacity and query processing throughput. We believe that the architecture is unique among all mainstream in-memory databases. It allows complete application-transparent, extremely scalable and automated distribution of Oracle RDBMS objects in-memory across a cluster, as well as across multiple NUMA nodes within a single server. It seamlessly provides distribution awareness to the Oracle SQL execution framework through affinitized fault-tolerant parallel execution within and across servers without explicit optimizer plan changes or query rewrites.
Niloy Mukherjee, Shasank Chavan, Maria Colgan, Dinesh Das, Mike Gleeson, Sanket Hase, Allison Holloway, Hui Jin 0001, Jesse Kamp, Kartik Kulkarni, Tirthankar Lahiri, Juan Loaiza, Neil MacNaughton, Vineet Marwah, Atrayee Mullick, Andy Witkowski, Mohamed Zaït
Proc. VLDB Endow.6