Authors:
Till Kolditz
;
Dirk Habich
;
Patrick Damme
;
Wolfgang Lehner
;
Dmitrii Kuvaiskii
;
Oleksii Oleksenko
and
Christof Fetzer
Affiliation:
Technische Universität Dresden, Germany
Keyword(s):
In-memory Database Systems, Data Integrity, Lightweight Data Compression, AN Encoding.
Related
Ontology
Subjects/Areas/Topics:
Data Engineering
;
Data Integrity
;
Data Management and Quality
;
Data Structures and Data Management Algorithms
;
Databases and Data Security
;
Information and Systems Security
Abstract:
Nowadays, database systems pursuit a main memory-centric architecture, where the entire business-related
data is stored and processed in a compressed form in main memory. In this case, the performance gain is
massive because database operations can benefit from its higher bandwidth and lower latency. However,
current main memory-centric database systems utilize general-purpose error detection and correction solutions
to address the emerging problem of increasing dynamic error rate of main memory. The costs of these generalpurpose
methods dramatically increases with increasing error rates. To reduce these costs, we have to exploit
context knowledge of database systems for resiliency. Therefore, we introduce our vision of resiliency-aware
data compression in this paper, where we want to exploit the benefits of both fields in an integrated approach
with low performance and memory overhead. In detail, we present and evaluate a first approach using AN
encoding and two different compressio
n schemes to show the potentials and challenges of our vision.
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