By Hasso Plattner
Recent achievements in and software program improvement, comparable to multi-core CPUs and DRAM capacities of a number of terabytes according to server, enabled the creation of a progressive know-how: in-memory info administration. This know-how helps the versatile and very speedy research of big quantities of firm facts. Professor Hasso Plattner and his study team on the Hasso Plattner Institute in Potsdam, Germany, were investigating and educating the corresponding strategies and their adoption within the software program for years.
This ebook relies at the first on-line path at the openHPI e-learning platform, which used to be introduced in autumn 2012 with greater than 13,000 inexperienced persons. The booklet is designed for college kids of laptop technological know-how, software program engineering, and IT similar matters. despite the fact that, it addresses company specialists, choice makers, software program builders, know-how specialists, and IT analysts alike. Plattner and his team specialise in exploring the internal mechanics of a column-oriented dictionary-encoded in-memory database. lined themes contain - among others - actual facts garage and entry, easy database operators, compression mechanisms, and parallel sign up for algorithms. past that, implications for destiny firm functions and their improvement are mentioned. Readers are bring about comprehend the unconventional adjustments and merits of the recent expertise 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
If only the 17 needed attributes are queried instead of the full tuple representation of all 300 attributes, an instant advantage of factor eight to 20 for data to be scanned can be achieved. As disk is not the bottleneck any longer, but access to main memory has to be considered, an important aspect is to work on a minimal set of data. So far, application programmers were fond of ‘‘SELECT *’’ statements. The difference in runtime between selecting specific fields or all fields in row-oriented storage is insignificant and in case changes to an application need more fields, the data was already there (which besides is a weak argument for using SELECT * and retrieving unnecessary data).
Thus, runtimes of in-memory processing can be calculated (although it might be complicated). Observations from using in-memory databases show that response times are smooth—always the same—and not varying like it is the case with disks and their response time variations due to disk seeks. 5 Architecture Overview The architecture shown in Fig. 1 grants an overview of the components of SanssouciDB. SanssouciDB is split in three different logical layers fulfilling specific tasks inside the database system.
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. Data explosion Consider the formula 1 race car tracking example, with each race car having 512 sensors, each sensor records 32 events per second whereby each event is 64 byte in size. How much data is produced by a F1 team, if a team has two cars in the race and the race takes 2 h? For easier calculation, assume 1,000 byte = 1 kB, 1,000 kB = 1 MB, 1,000 MB = 1 GB.
A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases by Hasso Plattner