By Hasso Plattner
Recent achievements in and software program improvement, similar to multi-core CPUs and DRAM capacities of a number of terabytes consistent with server, enabled the advent of a innovative know-how: in-memory information administration. This expertise helps the versatile and very quick research of huge quantities of company info. Professor Hasso Plattner and his examine workforce on the Hasso Plattner Institute in Potsdam, Germany, were investigating and instructing the corresponding strategies and their adoption within the software program for years.
This publication 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 novices. The publication is designed for college kids of machine technology, software program engineering, and IT similar matters. in spite of the fact that, it addresses enterprise specialists, choice makers, software program builders, expertise specialists, and IT analysts alike. Plattner and his workforce specialize in exploring the internal mechanics of a column-oriented dictionary-encoded in-memory database. coated subject matters comprise - among others - actual facts garage and entry, uncomplicated database operators, compression mechanisms, and parallel sign up for algorithms. past that, implications for destiny company functions and their improvement are mentioned. Readers are result in comprehend the unconventional changes and benefits of the recent expertise over conventional row-oriented disk-based databases.
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Additional resources for A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases
G. Twitter deals with one billion new tweets in five days. g. to detect messages about a new product, competitor activities, or to prevent service abuses. g. sales campaigns or seasonal weather details, market trends for certain products or product classes can be derived. g. for marketing campaigns or even to control the manufacturing rate. Another example for extracting business relevant information from the Web is monitoring search terms. The search engine Google analyzes regional and global search trends.
Finally, several operations such as COUNT or NOT NULL can even be performed without retrieving the real values at all. 4 Self Test Questions 1. Lossless Compression For a column with few distinct values, how can dictionary encoding significantly reduce the required amount of memory without any loss of information? (a) By mapping values to integers using the smallest number of bits possible to represent the given number of distinct values (b) By converting everything into full text values. This allows for better compression techniques, because all values share the same data format.
So one core of a CPU can digest 2 MB per millisecond. This number scales with the amount of cores and if there are ten cores, they can scan 20 GB per second. If there are ten nodes with ten cores each, then that is already 200 GB in per second. Considering a large multi-node system like that, having ten nodes and 40 CPUs per node where the data is distributed across the nodes, it is hard to write an algorithm which needs more than 1 s. This includes large amounts of data. The previously mentioned 200 GB are highly compressed data.