Title

Gemda: A Multidimensional Data Partitioning Technique For Multiprocessor Database Systems

Keywords

Data allocation; Data fragmentation; Parallel database system; Query processing; System utilization

Abstract

Several studies have repeatedly demonstrated that both the performance and scalability of a shared-nothing parallel database system depend on the physical layout of data across the processing nodes of the system. Today, data is allocated in these systems using horizontal partitioning strategies. This approach has a number of drawbacks. If a query involves the partitioning attribute, then typically only a small number of the processing nodes can be used to speedup the execution of this query. On the other hand, if the predicate of a selection query includes an attribute other than the partitioning attribute, then the entire data space must be searched. Again, this results in waste of computing resources. In recent years, several multidimensional data declustering techniques have been proposed to address these problems. However, these schemes are too restrictive (e.g., FX, ECC, etc.), or optimized for a certain type of queries (e.g., DM, HCAM, etc.). In this paper, we introduce a new technique which is flexible, and performs well for general queries. We prove its optimality properties, and present experimental results showing that our scheme outperforms DM and HCAM by a significant margin.

Publication Date

5-1-2001

Publication Title

Distributed and Parallel Databases

Volume

9

Issue

3

Number of Pages

211-236

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1023/A:1019265612794

Socpus ID

0035339817 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/0035339817

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