In-Memory Data Management: An Inflection Point for Enterprise Applications
Springer Science & Business Media, 8 мар. 2011 г. - Всего страниц: 236
In the last 50 years the world has been completely transformed through the use of IT. We have now reached a new inflection point. Here we present, for the first time, how in-memory computing is changing the way businesses are run. Today, enterprise data is split into separate databases for performance reasons. Analytical data resides in warehouses, synchronized periodically with transactional systems. This separation makes flexible, real-time reporting on current data impossible. Multi-core CPUs, large main memories, cloud computing and powerful mobile devices are serving as the foundation for the transition of enterprises away from this restrictive model. We describe techniques that allow analytical and transactional processing at the speed of thought and enable new ways of doing business. The book is intended for university students, IT-professionals and IT-managers, but also for senior management who wish to create new business processes by leveraging in-memory computing.
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PART I An Inflection Point for Enterprise Applications
1 Desirability Feasibility Viability The Impact of InMemory
2 Why Are Enterprise Applications So Diverse?
3 SanssouciDB Blueprint for an InMemory Enterprise Database System
PART II SanssouciDB A Single Source of Truth through InMemory
4 The Technical Foundations of SanssouciDB
5 Organizing and Accessing Data in SanssouciDB
7 Finally a Real Business Intelligence System Is at Hand
8 Scaling SanssouciDB in the Cloud
9 The InMemory Revolution Has Begun
About the Authors
PART III How InMemory Changes the Game
6 Application Development
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aggregation algorithms allows amounts of data analytical processing analytical queries analytical systems architecture attributes benchmark blades business object cache line cache misses Cloud Computing column store column-oriented compression concurrently cores cost CPU caches created data structures data warehouse database system DBMS dictionary differential buffer disk Encoding enterprise applications example execution Figure flash memory hardware hash table Hasso Plattner implementation In-Memory Data Management in-memory database in-memory technology input insert join latency layer layout load machine main memory main store memory access memory hierarchy merge process mixed workload multi-core multi-tenant multiple node OLAP OLTP operational and analytical operational systems optimized parallel partition performance processor query processing real-time relational result retrieved January 14th sales order SanssouciDB scan scheduling Section server shared-nothing star schema storage tasks threads transaction tuples updates users value IDs virtual Zeier