NDLI: Dictionary-based order-preserving string compression for main memory column stores

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Dictionary-based order-preserving string compression for main memory column stores

Content Provider	ACM Digital Library
Author	Hildenbrand, Stefan Binnig, Carsten Färber, Franz
Abstract	Column-oriented database systems [19, 23] perform better than traditional row-oriented database systems on analytical workloads such as those found in decision support and business intelligence applications. Moreover, recent work [1, 24] has shown that lightweight compression schemes significantly improve the query processing performance of these systems. One such a lightweight compression scheme is to use a dictionary in order to replace long (variable-length) values of a certain domain with shorter (fixedlength) integer codes. In order to further improve expensive query operations such as sorting and searching, column-stores often use order-preserving compression schemes. In contrast to the existing work, in this paper we argue that orderpreserving dictionary compression does not only pay off for attributes with a small fixed domain size but also for long string attributes with a large domain size which might change over time. Consequently, we introduce new data structures that efficiently support an order-preserving dictionary compression for (variablelength) string attributes with a large domain size that is likely to change over time. The main idea is that we model a dictionary as a table that specifies a mapping from string-values to arbitrary integer codes (and vice versa) and we introduce a novel indexing approach that provides efficient access paths to such a dictionary while compressing the index data. Our experiments show that our data structures are as fast as (or in some cases even faster than) other state-of-the-art data structures for dictionaries while being less memory intensive.
Starting Page	283
Ending Page	296
Page Count	14
File Format	PDF
ISBN	9781605585512
DOI	10.1145/1559845.1559877
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-06-29
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Column store String compression
Content Type	Text
Resource Type	Article

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