By Hasso Plattner
Recent achievements in and software program improvement, comparable to multi-core CPUs and DRAM capacities of a number of terabytes in keeping with server, enabled the advent of a progressive expertise: in-memory facts administration. This know-how helps the versatile and intensely quickly research of huge quantities of firm information. Professor Hasso Plattner and his examine staff on the Hasso Plattner Institute in Potsdam, Germany, were investigating and educating the corresponding innovations and their adoption within the software program for years.
This ebook is predicated 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 beginners. The publication is designed for college students of desktop technology, software program engineering, and IT similar topics. besides the fact that, it addresses company specialists, determination 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. coated subject matters comprise - among others - actual info garage and entry, uncomplicated database operators, compression mechanisms, and parallel subscribe to algorithms. past that, implications for destiny firm functions and their improvement are mentioned. Readers are bring about comprehend the novel ameliorations and benefits of the hot 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
Instead, changed or deleted tuples can be inserted with according time stamps or invalidation flags. The additional benefit of this insert-only approach is that the complete transactional data history and a tuple’s life cycle are saved in the database automatically. More details about the insert-only approach will be provided in Chap. 26. The further fact that workloads are not that different after all leads to the vision of reuniting the two systems and to combine OLTP and OLAP data in one system.
In the literature, it is claimed that OLTP workloads are write-intensive, whereas OLAP-workloads are read-only and that the two workloads rely on ‘‘Opposing Laws of Database Physics’’ [Fre95]. H. 1007/978-3-642-36524-9_3, Ó Springer-Verlag Berlin Heidelberg 2013 15 16 3 Enterprise Application Characteristics Yet, research in current enterprise systems showed that this statement is not true [KGZP10, KKG+11]. The main difference between systems that handle these query types is that OLTP systems handle more queries with a single select or queries that are highly selective returning only a few tuples, whereas OLAP systems calculate aggregations for only a few columns of a table, but for a large number of tuples.
Even the insertion of only one value somewhere except the end of the dictionary causes a re-sorting, since the position of already present values behind the inserted value has to be moved one position up. While sorting the dictionary is not that costly, updating the corresponding attribute vector is. In our example, about 8 billion values have to be checked or updated if a new first name is added to the dictionary. 3 Operations on Encoded Values The first and most important effect of dictionary encoding is that all operations concerning the table data are now done via attribute vectors, which solely consist of integers.