By Hasso Plattner
-Presents the interior mechanics of an in-memory database
-Includes 34 studying devices with greater than a hundred self-tests and accomplished explanation
-Explains an enormous new develoment in firm computing
Recent achievements in and software program improvement, equivalent to multi-core CPUs and DRAM capacities of a number of terabytes in line with server, enabled the advent of a innovative expertise: in-memory info administration. This expertise helps the versatile and intensely quickly research of huge quantities of firm facts. Professor Hasso Plattner and his study staff on the Hasso Plattner Institute in Potsdam, Germany, were investigating and instructing the corresponding techniques 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 newcomers. The e-book is designed for college kids of laptop technology, software program engineering, and IT similar matters. even though, it addresses company specialists, determination makers, software program builders, know-how specialists, and IT analysts alike. Plattner and his workforce specialize in exploring the interior mechanics of a column-oriented dictionary-encoded in-memory database. lined issues comprise - among others - actual info garage and entry, simple database operators, compression mechanisms, and parallel sign up for algorithms. past that, implications for destiny company functions and their improvement are mentioned. Readers are bring about comprehend the unconventional changes and merits of the hot know-how over conventional row-oriented disk-based databases.
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Extra resources for A Course in In-Memory Data Management
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. e. valueIDs. In a dictionary-encoded column, the attribute vectors now only store valueIDs, which correspond to the valueIDs in the dictionary.
Using in-memory databases enables ATP checks to be performed on the latest data without using pre-aggregated totals. Thus, manufacturing and Scheduling rescheduling decisions can be taken on real-time data. Furthermore, removing aggregates simplifies the overall system architecture significantly, while adding flexibility. 7 Self Test Questions 1. Compression Factor What is the average compression factor for accounting data in an in-memory column-oriented database? (a) (b) (c) (d) 100x 10x 50x 5x 14 2 New Requirements for Enterprise Computing 2.
8 Scaling Main Memory Systems An example system that consists of multiple nodes can be seen in Fig. 5. One node has eight CPUs with eight cores, so each system has 64 cores, and there are four nodes. Each of them has a terabyte of RAM and SSDs for persistence. Everything which is below DRAM is for logging, archiving, and for emergency reconstruction of data, which means reloading the data after the power supply was turned off. The networks which connect the nodes are continuously increasing in speed.