1 research outputs found
Provenance for Large-scale Datalog
Logic programming languages such as Datalog have become popular as Domain
Specific Languages (DSLs) for solving large-scale, real-world problems, in
particular, static program analysis and network analysis. The logic
specifications which model analysis problems, process millions of tuples of
data and contain hundreds of highly recursive rules. As a result, they are
notoriously difficult to debug. While the database community has proposed
several data-provenance techniques that address the Declarative Debugging
Challenge for Databases, in the cases of analysis problems, these
state-of-the-art techniques do not scale.
In this paper, we introduce a novel bottom-up Datalog evaluation strategy for
debugging: our provenance evaluation strategy relies on a new provenance
lattice that includes proof annotations, and a new fixed-point semantics for
semi-naive evaluation. A debugging query mechanism allows arbitrary provenance
queries, constructing partial proof trees of tuples with minimal height. We
integrate our technique into Souffle, a Datalog engine that synthesizes C++
code, and achieve high performance by using specialized parallel data
structures. Experiments are conducted with DOOP/DaCapo, producing proof
annotations for tens of millions of output tuples. We show that our method has
a runtime overhead of 1.27x on average while being more flexible than existing
state-of-the-art techniques.Comment: 28 pages, 18 figures, technical repor