Graph queries in a next-generation Datalog system

Abstract

Recent theoretical advances have enabled the use of special monotonic aggregates in recursion. These special aggregates make possible the concise expression and efficient implementation of a rich new set of advanced applications. Among these applications, graph queries are particularly important because of their pervasiveness in data intensive application areas. In this demonstration, we present our Deductive Application Language (DeAL) System, the first of a new generation of Deductive Database Systems that support applications that could not be expressed using regular stratification, or could be expressed using XY-stratification (also supported in DeAL) but suffer from inefficient execution. Using example queries, we will (i) show how complex graph queries can be concisely expressed using DeAL and (ii) illustrate the formal semantics and efficient implementation of these powerful new monotonic constructs.

A declarative extension of horn clauses, and its significance for datalog and its applications

“…Furthermore, Datalog FS programs can be efficiently implemented extending Datalog bottom-up implementation technology through generalized seminaive fixpoint and magic-set method, and the newly introduced max-optimization method. A first Datalog FS prototype, called DeAL (Deductive Application Language), is undergoing testing at UCLA (Shkapsky et al 2013).…”

Main memory evaluation of recursive queries on multicore machines

“…A clear conclusion emerging from these experiments is that, for multicore machines, the simple SSC algorithms perform better than other algorithms in terms of speed and significantly better in terms of memory utilization. We thus introduced an algorithm, called SSC12, which combines the strengths of SSC1 and SSC2, and thus provides the obvious target algorithm for the compiler of our Datalog system DeAL [28] on multicore machines. However, our experiments also confirmed that performance of SSC12 (and other algorithms) on multicore machines will always be limited by the memory bandwidth bottleneck.…”

“…From this study we seek to derive simple criteria for deciding which system, out of the many available on the cloud, can be most cost-effective for the application at hand. The ability of our Datalog compiler [28] to retarget recursive queries for different platforms is based on its ability to transform linear recursive rules into nonlinear ones, which was described through simple examples in Section II. Many important algorithms that use TC-like rules in conjunction with monotonic aggregates [19] are amenable to such platform-driven porting and optimization.…”

Fixpoint semantics and optimization of recursive Datalog programs with aggregates

“…A growing body of research on scalable data analytics has brought a renaissance of interest in Datalog because of its ability to specify, declaratively, advanced data-intensive applications that execute efficiently over different systems and architectures, including massively parallel ones (Seo et al 2013;Shkapsky et al 2013;Yang and Zaniolo 2014;Aref et al 2015;Wang et al 2015;Yang et al 2015;Shkapsky et al 2016;Yang et al 2017). A common thread in this new generation of Datalog systems is the use of aggregates in recursion, since aggregates enable the concise expression and efficient support of much more powerful algorithms than those expressible by programs that are stratified w.r.t.…”