By Hasso Plattner
Recent achievements in and software program improvement, comparable to multi-core CPUs and DRAM capacities of a number of terabytes in step with server, enabled the advent of a innovative expertise: in-memory info administration. This expertise helps the versatile and very quick research of huge quantities of firm information. Professor Hasso Plattner and his learn crew on the Hasso Plattner Institute in Potsdam, Germany, were investigating and educating the corresponding techniques and their adoption within the software program for years.
This publication relies on an internet path that used to be first introduced in autumn 2012 with greater than 13,000 enrolled scholars and marked the winning place to begin of the openHPI e-learning platform. The path is especially designed for college students of computing device technology, software program engineering, and IT comparable topics, yet addresses company specialists, software program builders, know-how specialists, and IT analysts alike. Plattner and his crew specialize in exploring the interior mechanics of a column-oriented dictionary-encoded in-memory database. lined issues contain - among others - actual information garage and entry, simple 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 novel variations and benefits of the hot know-how over conventional row-oriented, disk-based databases.
In this thoroughly revised 2nd variation, we comprise the suggestions of hundreds of thousands after all contributors on openHPI and take into consideration most recent developments in demanding- and software program. greater figures, motives, and examples extra ease the certainty of the suggestions provided. We introduce complicated facts administration thoughts akin to obvious mixture caches and supply new showcases that reveal the potential for in-memory databases for 2 varied industries: retail and lifestyles sciences.
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Additional resources for A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases
4 shows a rough comparison between a Uniform Memory Access (UMA) and a Non-Uniform Memory Access (NUMA) system architecture. A UMA system is characterized by a deterministic access time for an arbitrary memory address independent of which processor makes the request as every memory chip is accessed through a central memory bus as shown in Fig. 4a. For NUMA systems on the other hand the access time depends on the memory location relative to the processor. Local (adjacent) memory can be accessed faster than nonlocal (adjacent to another processor) memory or shared memory (shared amongst processors) as shown in Fig.
Each of those systems offers a high level of parallel computing for a price of about $50,000. Despite the introduction of massive parallelism, the disk totally dominated all thinking and performance optimizations not long ago. It was extremely slow, but necessary to store data. Compared to the speed development of CPUs, the development of disk performance could not keep up. This resulted in a complete distortion of the whole model of working with databases and large amounts of data. Today, the increasing size of main memory available in servers combined with its ever decreasing price initiate a shift from disk-based to main-memory based database management systems.
In the early 2000s multi-core architectures were introduced, starting a trend towards growing parallelism. Today, a typical enterprise computing board comprises eight CPUs and 10 to 16 cores per CPU, adding up to 80 to 128 cores per server. An address space of 64 bit ensures that the increasing amount of main memory can be addressed, with current servers supporting up to 6 TB. The maximum data throughput between a CPU and DRAM typically approximates 85 GB/s and it is possible to transfer more than 6 GB/s between servers via a typical InfiniBand FDR 4x link.
A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases by Hasso Plattner