By Hasso Plattner
Recent achievements in and software program improvement, equivalent to multi-core CPUs and DRAM capacities of a number of terabytes in keeping with server, enabled the creation of a progressive know-how: in-memory info administration. This expertise helps the versatile and intensely speedy research of big quantities of firm info. Professor Hasso Plattner and his study team on the Hasso Plattner Institute in Potsdam, Germany, were investigating and educating the corresponding ideas 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 beginners. The ebook is designed for college kids of desktop technological know-how, software program engineering, and IT similar topics. even if, it addresses company specialists, selection makers, software program builders, know-how specialists, and IT analysts alike. Plattner and his staff specialize in exploring the interior mechanics of a column-oriented dictionary-encoded in-memory database. lined subject matters comprise - among others - actual facts garage and entry, easy database operators, compression mechanisms, and parallel subscribe to algorithms. past that, implications for destiny firm functions and their improvement are mentioned. Readers are result in comprehend the unconventional variations and merits of the recent know-how over conventional row-oriented disk-based databases.
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Extra info for A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases
Computers with 40 Gb Infiniband are already on the market and switch manufacturers are talking about 100 Gb switches which even have logic allowing smart switching. This is another location where an optimization can take place—on a low level and very effective for applications. It can be leveraged to improve joins, where calculations often go across multiple nodes. 9 Remote Direct Memory Access 27 Fig. 9 Remote Direct Memory Access Shared memory is another interesting way to directly access memory between multiple nodes.
A calculation for the complete first name and gender columns in our world-population example will exemplify the effects. H. 1007/978-3-642-36524-9_6, Ó Springer-Verlag Berlin Heidelberg 2013 37 38 6 Dictionary Encoding Fig. 1 Compression Example Given is the world population table with 8 billion rows, 200 Byte per row: Attribute # of Distinct Values Size First name Last name Gender Country City Birthday 5 million 8 million 2 200 1 million 40 000 Sum 49 Byte 50 Byte 1 Byte 49 Byte 49 Byte 2 Byte 200 Byte The complete amount of data is: 8 billion rows Á 200 Byte per row ¼ 1:6 TB Each column is split into a dictionary and an attribute vector.
These characteristics facilitate the efficient use of compression techniques, resulting in lower memory consumption and better query performance as will be seen in later chapters. 7 Self Test Questions 1. OLTP OLAP Separation Reasons Why was OLAP separated from OLTP? (a) (b) (c) (d) Due to performance problems For archiving reasons; OLAP is more suitable for tape-archiving Out of security concerns Because some customers only wanted either OLTP or OLAP and did not want to pay for both. D. French, ‘‘One size fits all’’ database architectures do not work for DSS.