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

By Hasso Plattner

ISBN-10: 364236523X

ISBN-13: 9783642365232

ISBN-10: 3642365248

ISBN-13: 9783642365249

Recent achievements in and software program improvement, corresponding to multi-core CPUs and DRAM capacities of a number of terabytes consistent with server, enabled the advent of a innovative expertise: in-memory info administration. This expertise helps the versatile and very quickly research of big quantities of firm facts. Professor Hasso Plattner and his examine team on the Hasso Plattner Institute in Potsdam, Germany, were investigating and instructing the corresponding recommendations and their adoption within the software program for years.

This ebook is predicated at the first on-line direction at the openHPI e-learning platform, which was once introduced in autumn 2012 with greater than 13,000 newbies. The e-book is designed for college students of desktop technology, software program engineering, and IT similar topics. even if, it addresses enterprise specialists, choice makers, software program builders, expertise specialists, and IT analysts alike. Plattner and his crew specialise in exploring the internal mechanics of a column-oriented dictionary-encoded in-memory database. coated themes contain - among others - actual information garage and entry, uncomplicated database operators, compression mechanisms, and parallel sign up for algorithms. past that, implications for destiny firm functions and their improvement are mentioned. Readers are result in comprehend the novel variations and benefits of the hot expertise over conventional row-oriented disk-based databases.

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Extra resources for A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases

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1 Prefix Encoding In real-world databases, we often find the case that a column contains one predominant value and the remaining values have low redundancy. In this case, we would store the same value very often in an uncompressed format. Prefix encoding is the simplest way to handle this case more efficiently. To apply prefix encoding, the data sets need to be sorted by the column with the predominant value and the attribute vector has to start with the predominant value. H. 1007/978-3-642-36524-9_7, Ó Springer-Verlag Berlin Heidelberg 2013 43 44 7 Compression To compress the column, the predominant value should not be stored explicitly every time it occurs.

They achieve much higher compression rates, but encoding and decoding is prohibitively expensive for their usage in our context. An in-depth discussion of many compression techniques can be found in [AMF06]. 1 Prefix Encoding In real-world databases, we often find the case that a column contains one predominant value and the remaining values have low redundancy. In this case, we would store the same value very often in an uncompressed format. Prefix encoding is the simplest way to handle this case more efficiently.

Hennessy, D. Patterson, Computer Architecture: A Quantitative Approach (Morgan Kaufmann, San Francisco, 2003) [Int08] Intel Inc. , Intel 64 and IA-32 architectures optimization reference manual (2011) [Moo65] G. Moore, Cramming more components onto integrated circuits. Electronics 38, 114 ff. A. R. T. J. Hosseini, A novel zero-aware fourtransistor SRAM cell for high density and low power cache application, in International Conference on Advanced Computer Theory and Engineering, ICACTE ’08, pp. 571–575 (2008) [SKP12] D.

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