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).

BigDatalog 是 Datalog 的扩展，旨在实现在 Apache Spark 和多核系统上的性能和可扩展性，其图分析性能优于 GraphX，通过实现技术（例如半朴素极限和魔术集）解决了在递归中使用数量、总和和极值等问题。