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Scaling-up reasoning and advanced analytics on BigData

Published online by Cambridge University Press:  05 September 2018

TYSON CONDIE ,
ARIYAM DAS ,
MATTEO INTERLANDI ,
ALEXANDER SHKAPSKY ,
MOHAN YANG  and
CARLO ZANIOLO
TYSON CONDIE
Affiliation:
University of California, Los Angeles, CA, USA (e-mails: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected])
ARIYAM DAS
Affiliation:
University of California, Los Angeles, CA, USA (e-mails: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected])
MATTEO INTERLANDI
Affiliation:
University of California, Los Angeles, CA, USA (e-mails: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected])
ALEXANDER SHKAPSKY
Affiliation:
University of California, Los Angeles, CA, USA (e-mails: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected])
MOHAN YANG
Affiliation:
University of California, Los Angeles, CA, USA (e-mails: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected])
CARLO ZANIOLO
Affiliation:
University of California, Los Angeles, CA, USA (e-mails: [email protected], [email protected], [email protected], [email protected], [email protected], [email protected])
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Abstract

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BigDatalog is an extension of Datalog that achieves performance and scalability on both Apache Spark and multicore systems to the point that its graph analytics outperform those written in GraphX. Looking back, we see how this realizes the ambitious goal pursued by deductive database researchers beginning 40 years ago: this is the goal of combining the rigor and power of logic in expressing queries and reasoning with the performance and scalability by which relational databases managed BigData. This goal led to Datalog which is based on Horn Clauses like Prolog but employs implementation techniques, such as semi-naïve fixpoint and magic sets, that extend the bottom-up computation model of relational systems, and thus obtain the performance and scalability that relational systems had achieved, as far back as the 80s, using data-parallelization on shared-nothing architectures. But this goal proved difficult to achieve because of major issues at (i) the language level and (ii) at the system level. The paper describes how (i) was addressed by simple rules under which the fixpoint semantics extends to programs using count, sum and extrema in recursion, and (ii) was tamed by parallel compilation techniques that achieve scalability on multicore systems and Apache Spark. This paper is under consideration for acceptance in Theory and Practice of Logic Programming.

Keywords

Deductive databasesDatalogBigDataparalleldistributed computing
Type
Survey Article
Information
Theory and Practice of Logic Programming , Volume 18 , Special Issue 5-6: Special Issue on Parallel and Distributed Logic Programming , September 2018 , pp. 806 - 845
Copyright
Copyright © Cambridge University Press 2018 

Footnotes

*This work was supported in part by NSF under Grants IIS-1218471, IIS-1302698 and CNS-1351047, and in part by NIH BigData to Knowledge (BD2K) under Grant U54EB020404.

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