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Operating on Hierarchical Enterprise Data in an In-Memory Column Store

Authors:
Christian Tinnefeld
Bjoern Wagner
Hasso Plattner

Keywords: hierarchical data; enterprise data management; in- memory column store; SanssouciDB

Abstract:
Enterprise data management is currently sepa- rated into online transactional processing (OLTP) and on- line analytical processing (OLAP). This separation brings disadvantages such as the need for costly data replication, maintaining redundant systems or the inability to run data reports on the latest transactional data. Academia and industry are working on a reunification by storing all data in a single columnar in-memory database which is designed to sustain high transactional workloads while simultaneously handle complex analytical tasks as well. Since enterprise data often includes hierarchical data, this paper focuses on modeling, storing, and operating on hierarchical data in an in-memory columnar database. The paper contributes by describing the implementation of the most frequently used hierarchical data operations on such a database while maintaining the ability to execute performant analytical queries on such data as well. A set of benchmarks demonstrates that hierarchical data operations can be executed up to three times faster on an in- memory column store than on an in-memory row store. The paper closes with a discussion which enterprise applications can benefit from this contributions.

Pages: 58 to 63

Copyright: Copyright (c) IARIA, 2012

Publication date: February 29, 2012

Published in: conference

ISSN: 2308-4332

ISBN: 978-1-61208-185-4

Location: Saint Gilles, Reunion

Dates: from February 29, 2012 to March 5, 2012