Scaling-Up Reasoning and Advanced Analytics on BigData

TL;DR

This paper introduces BigDatalog, an extension of Datalog that achieves high performance and scalability on big data platforms like Spark and multicore systems, enabling advanced graph analytics and reasoning.

Contribution

It presents new language and system techniques that extend Datalog with aggregate functions and scalable parallel execution, bridging logic programming with big data analytics.

Findings

BigDatalog outperforms GraphX in graph analytics tasks.

New rules enable recursion with count, sum, and extrema.

Parallel compilation techniques ensure scalability on multicore and Spark.

Abstract

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 forty 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 Big Data. This goal led to Datalog which is based on Horn Clauses like Prolog but employs implementation techniques, such as Semi-naive 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 (TPLP).