期刊全稱(chēng) | Building a Columnar Database on RAMCloud | 期刊簡(jiǎn)稱(chēng) | Database Design for | 影響因子2023 | Christian Tinnefeld | 視頻video | http://file.papertrans.cn/192/191809/191809.mp4 | 發(fā)行地址 | Comprehensively analyzes existing and new solutions for disk-based parallel database management systems.Demonstrates the feasibility of a parallel main memory DBMS based on shared-storage.Provides a s | 學(xué)科分類(lèi) | In-Memory Data Management Research | 圖書(shū)封面 |  | 影響因子 | 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. | Pindex | Book 2016 |
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