Main Memory Database Systems
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Main Memory Database Systems
This article provides an overview of recent developments in main-memory database systems. With growing memory sizes and memory prices dropping by a factor of 10 every 5 years, data having a "primary home" in memory is now a reality. Main-memory databases eschew many of the traditional architectural pillars of relational database systems that optimized for disk-resident data. The result of these memory-optimized designs are systems that feature several innovative approaches to fundamental issues (e.g., concurrency control, query processing) that achieve orders of magnitude performance improvements over traditional designs. Our survey covers five main issues and architectural choices that need to be made when building a high performance main-memory optimized database: data organization and storage, indexing, concurrency control, durability and recovery techniques, and query processing and compilation. We focus our survey on four commercial and research systems: H-Store/VoltDB, Hekaton, HyPer, and SAP HANA. These systems are diverse in their design choices and form a representative sample of the state of the art in main-memory database systems. We also cover other commercial and academic systems, along with current and future research trends.
Implementation techniques for main memory database systems
Author: University of California, Berkeley. Electronics Research Laboratory. POSTGRES Project
language: en
Publisher:
Release Date: 1984
With the availability of very large, relatively inexpensive main memories, it is becoming possible to keep large databases resident in main memory. This paper considers the changes necessary to permit a relational database system to take advantage of large amounts of main memory. The authors evaluate AVL vs. B+- tree access methods for main memory databases, hash-based query processing strategies vs. sort-merge, and study recovery issues when most or all of the database fits in main memory. As expected, B+- trees are the preferred storage mechanism unless more than 80-90% of the database fits in main memory. A somewhat surprising result is that hash based query processing strategies are advantageous for large memory situations. Keywords: Access; Algorithms. (Author).
Main Memory Management on Relational Database Systems
Author: Pedro Mejia Alvarez
language: en
Publisher: Springer Nature
Release Date: 2022-08-12
This book provides basic knowledge about main memory management in relational databases as it is needed to support large-scale applications processed completely in memory. In business operations, real-time predictability and high speed is a must. Hence every opportunity must be exploited to improve performance, including reducing dependency on the hard disk, adding more memory to make more data resident in the memory, and even deploying an in-memory system where all data can be kept in memory. The book provides one chapter for each of the main related topics, i.e. the memory system, memory management, virtual memory, and databases and their memory systems, and it is complemented by a short survey of six commercial systems: TimesTen, MySQL, VoltDB, Hekaton, HyPer/ScyPer, and SAP HANA.