Scaling up the performance of more powerful Datalog systems on multicore machines

Scaling up the performance of more powerful Datalog systems on multicore machines Extending RDBMS technology to achieve performance and scalability for queries that are much more powerful than those of SQL-2 has been the goal of deductive database research for more than thirty years. The $$\mathcal {D}e\mathcal {A}\mathcal {L}\mathcal {S}$$ D e A L S system has made major progress toward this goal, by (1) Datalog extensions that support the more powerful recursive queries needed in advanced applications, and (2) superior performance for both traditional recursive queries and those made possible by the new extensions, while (3) delivering competitive performance with commercial RDBMSs on non-recursive queries. In this paper, we focus on the techniques used to support the in-memory evaluation of Datalog programs on multicore machines. In $$\mathcal {D}e\mathcal {A}\mathcal {L}\mathcal {S}$$ D e A L S , a Datalog program is represented as an AND/OR tree, and multiple copies of the same AND/OR tree are used to access the tables in the database concurrently during the parallel evaluation. We describe compilation techniques that (1) recognize when the given program is lock-free, (2) transform a locking program into a lock-free program, and (3) find an efficient parallel plan that correctly evaluates the program while minimizing the use of locks and other overhead required for parallel evaluation. Extensive experiments demonstrate the effectiveness of the proposed techniques. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Scaling up the performance of more powerful Datalog systems on multicore machines

Loading next page...
 
/lp/springer_journal/scaling-up-the-performance-of-more-powerful-datalog-systems-on-GAeWR7qnlR
Publisher
Springer Journals
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-016-0448-z
Publisher site
See Article on Publisher Site

Abstract

Extending RDBMS technology to achieve performance and scalability for queries that are much more powerful than those of SQL-2 has been the goal of deductive database research for more than thirty years. The $$\mathcal {D}e\mathcal {A}\mathcal {L}\mathcal {S}$$ D e A L S system has made major progress toward this goal, by (1) Datalog extensions that support the more powerful recursive queries needed in advanced applications, and (2) superior performance for both traditional recursive queries and those made possible by the new extensions, while (3) delivering competitive performance with commercial RDBMSs on non-recursive queries. In this paper, we focus on the techniques used to support the in-memory evaluation of Datalog programs on multicore machines. In $$\mathcal {D}e\mathcal {A}\mathcal {L}\mathcal {S}$$ D e A L S , a Datalog program is represented as an AND/OR tree, and multiple copies of the same AND/OR tree are used to access the tables in the database concurrently during the parallel evaluation. We describe compilation techniques that (1) recognize when the given program is lock-free, (2) transform a locking program into a lock-free program, and (3) find an efficient parallel plan that correctly evaluates the program while minimizing the use of locks and other overhead required for parallel evaluation. Extensive experiments demonstrate the effectiveness of the proposed techniques.

Journal

The VLDB JournalSpringer Journals

Published: Dec 1, 2016

References

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