A Course in In-Memory Data Management: The Inner Mechanics by Hasso Plattner

By Hasso Plattner

Recent achievements in and software program improvement, corresponding to multi-core CPUs and DRAM capacities of a number of terabytes in line with server, enabled the creation of a innovative expertise: in-memory information administration. This know-how helps the versatile and intensely quick research of big quantities of company information. Professor Hasso Plattner and his learn staff on the Hasso Plattner Institute in Potsdam, Germany, were investigating and instructing the corresponding thoughts and their adoption within the software program for years.

This e-book is predicated on an internet path that used to be first introduced in autumn 2012 with greater than 13,000 enrolled scholars and marked the profitable start line of the openHPI e-learning platform. The direction is especially designed for college kids of machine technological know-how, software program engineering, and IT similar topics, yet addresses company specialists, software program builders, know-how specialists, and IT analysts alike. Plattner and his staff concentrate on exploring the internal mechanics of a column-oriented dictionary-encoded in-memory database. lined issues contain - among others - actual facts garage and entry, uncomplicated database operators, compression mechanisms, and parallel subscribe to algorithms. past that, implications for destiny firm purposes and their improvement are mentioned. step-by-step, readers will comprehend the unconventional changes and benefits of the hot know-how over conventional row-oriented, disk-based databases.

In this thoroughly revised 2nd version, we contain the suggestions of millions in fact contributors on openHPI and take into consideration most up-to-date developments in tough- and software program. enhanced figures, reasons, and examples extra ease the certainty of the thoughts offered. We introduce complex info administration strategies comparable to obvious mixture caches and supply new showcases that exhibit the potential for in-memory databases for 2 diversified industries: retail and lifestyles sciences.

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There are two major requirements for a modern database management system: • Data from various sources need to be combined in a single database management system, and • This data needs to be analyzed in real-time to support interactive decisionmaking. The following sections outline typical use cases for modern enterprises and derive associated requirements for an entirely new enterprise data management system. 1 Processing of Event Data Event data influences enterprises more and more today. It is characterized by the following aspects: • Each event dataset itself is small (some bytes or kilobytes) compared to the size of traditional enterprise data, such as all data contained in a single sales order, and H.

Those stalls are mainly caused by in-page data placements that do not utilize the CPU caches properly. In many cases, the actual computation accounts for less than 40 % of the execution time. Besides, Harizopoulos et al. found that the buffer management of disk-based databases alone contributes 31 % to the overall instruction count [HAMS08] Consequently, the reason for the performance advantages of in-memory over disk-based databases derives from optimized data structures and algorithms avoiding memory and resource stalls together with the removal of additional indirections.

1 Memory Cells In early computer systems, the clock rate of the CPU was the same as the frequency of the memory bus and register access was only slightly faster than memory access. CPU frequencies however heavily increased over the last years following Moore’s H. 1007/978-3-642-55270-0__4, © Springer-Verlag Berlin Heidelberg 2014 23 24 4 Changes in Hardware Law1 [Moo65] while frequencies of memory buses and latencies of memory chips did not grow with this same speed. As a result memory accesses became increasingly expensive as more CPU cycles were wasted waiting for memory access.

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