Parallel Data Processing
In the following, we discuss how to achieve parallelism in in-memory and traditional database management systems. Pipelined parallelism and data parallelism are two approaches to speed up query processing.
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A Course in In-Memory Data Management The Inner Mechanics of In-Memory Databases
A Course in In-Memory Data Management
Hasso Plattner
A Course in In-Memory Data Management The Inner Mechanics of In-Memory Databases
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Hasso Plattner Hasso Plattner Institute Potsdam, Brandenburg Germany
ISBN 978-3-642-36523-2 DOI 10.1007/978-3-642-36524-9
ISBN 978-3-642-36524-9
(eBook)
Springer Heidelberg New York Dordrecht London Library of Congress Control Number: 2013932332 Ó Springer-Verlag Berlin Heidelberg 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer. Permissions for use may be obtained through RightsLink at the Copyright Clearance Center. Violations are liable to prosecution under the respective Copyright Law. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. While the advice and information in this book are believed to be true and accurate at the date of publication, neither the authors nor the editors nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein. Printed on acid-free paper Springer is part of Springer Science?Business Media (www.springer.com)
Preface
Why We Wrote This Book Our research group at the HPI has conducted research in the area of in-memory data management for enterprise applications since 2006. The ideas and concepts of a dictionary-encoded column-oriented in-memory database gained much traction due to the success of SAP HANA as the cutting-edge industry product and from followers trying to catch up. As this topic reached a broader audience, we felt the need for proper education in this area. This is of utmost importance as students and developers have to understand the underlying concepts and technology in order to make use of it. At our institute, we have been teaching in-memory data management in a Master’s course since 2009. When I
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