Exploiting early sorting and early partitioning for decision support query processing

Exploiting early sorting and early partitioning for decision support query processing Decision support queries typically involve several joins, a grouping with aggregation, and/or sorting of the result tuples. We propose two new classes of query evaluation algorithms that can be used to speed up the execution of such queries. The algorithms are based on (1) early sorting and (2) early partitioning– or a combination of both. The idea is to push the sorting and/or the partitioning to the leaves, i.e., the base relations, of the query evaluation plans (QEPs) and thereby avoid sorting or partitioning large intermediate results generated by the joins. Both early sorting and early partitioning are used in combination with hash-based algorithms for evaluating the join(s) and the grouping. To enable early sorting, the sort order generated at an early stage of the QEP is retained through an arbitrary number of so-called order-preserving hash joins. To make early partitioning applicable to a large class of decision support queries, we generalize the so-called hash teams proposed by Graefe et al. [GBC98]. Hash teams allow to perform several hash-based operations (join and grouping) on the same attribute in one pass without repartitioning intermediate results. Our generalization consists of indirectly partitioning the input data. Indirect partitioning means partitioning the input data on an attribute that is not directly needed for the next hash-based operation, and it involves the construction of bitmaps to approximate the partitioning for the attribute that is needed in the next hash-based operation. Our performance experiments show that such QEPs based on early sorting, early partitioning, or both in combination perform significantly better than conventional strategies for many common classes of decision support queries. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Exploiting early sorting and early partitioning for decision support query processing

Loading next page...
 
/lp/springer_journal/exploiting-early-sorting-and-early-partitioning-for-decision-support-GjMmZeX0Ad
Publisher
Springer-Verlag
Copyright
Copyright © 2000 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s007780000030
Publisher site
See Article on Publisher Site

Abstract

Decision support queries typically involve several joins, a grouping with aggregation, and/or sorting of the result tuples. We propose two new classes of query evaluation algorithms that can be used to speed up the execution of such queries. The algorithms are based on (1) early sorting and (2) early partitioning– or a combination of both. The idea is to push the sorting and/or the partitioning to the leaves, i.e., the base relations, of the query evaluation plans (QEPs) and thereby avoid sorting or partitioning large intermediate results generated by the joins. Both early sorting and early partitioning are used in combination with hash-based algorithms for evaluating the join(s) and the grouping. To enable early sorting, the sort order generated at an early stage of the QEP is retained through an arbitrary number of so-called order-preserving hash joins. To make early partitioning applicable to a large class of decision support queries, we generalize the so-called hash teams proposed by Graefe et al. [GBC98]. Hash teams allow to perform several hash-based operations (join and grouping) on the same attribute in one pass without repartitioning intermediate results. Our generalization consists of indirectly partitioning the input data. Indirect partitioning means partitioning the input data on an attribute that is not directly needed for the next hash-based operation, and it involves the construction of bitmaps to approximate the partitioning for the attribute that is needed in the next hash-based operation. Our performance experiments show that such QEPs based on early sorting, early partitioning, or both in combination perform significantly better than conventional strategies for many common classes of decision support queries.

Journal

The VLDB JournalSpringer Journals

Published: Dec 1, 2000

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off