Building a Columnar Database on RAMCloud - Database Design for the Low-Latency Enabled Data Center (Hardcover, 1st ed. 2016)

This book examines the field of parallel database management systems and illustrates the great variety of solutions based on a shared-storage or a shared-nothing architecture. Constantly dropping memory prices and the desire to operate with low-latency responses on large sets of data paved the way for main memory-based parallel database management systems. However, this area is currently dominated by the shared-nothing approach in order to preserve the in-memory performance advantage by processing data locally on each server. The main argument this book makes is that such an unilateral development will cease due to the combination of the following three trends: a) Today's network technology features remote direct memory access (RDMA) and narrows the performance gap between accessing main memory on a server and of a remote server to and even below a single order of magnitude. b) Modern storage systems scale gracefully, are elastic and provide high-availability. c) A modern storage system such as Stanford's RAM Cloud even keeps all data resident in the main memory. Exploiting these characteristics in the context of a main memory-based parallel database management system is desirable. The book demonstrates that the advent of RDMA-enabled network technology makes the creation of a parallel main memory DBMS based on a shared-storage approach feasible.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	In-Memory Data Management Research
Release date:	July 2015
First published:	2016
Authors:	Christian Tinnefeld
Dimensions:	235 x 155 x 10mm (L x W x T)
Format:	Hardcover - Cloth over boards
Pages:	130
Edition:	1st ed. 2016
ISBN-13:	978-3-319-20710-0
Categories:	Books > Computing & IT > Computer hardware & operating systems > Storage media & peripherals Books > Business & Economics > Business & management > Business mathematics & systems > General Books > Computing & IT > Applications of computing > Databases > Data mining
LSN:	3-319-20710-5
Barcode:	9783319207100

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