Debugging Large-Scale Datalog: A Scalable Provenance Evaluation Strategy
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 that 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 article, 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-naïve evaluation. A debugging query mechanism allows arbitrary provenance queries, constructing partial proof trees of tuples with minimal height. We integrate our technique into Soufflé, 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 1.31× on average while being more flexible than existing state-of-the-art techniques.
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POPLLink to publication DOI
|egg: Fast and Extensible Equality SaturationDistinguished Paper|
Max Willsey University of Washington, USA, Chandrakana Nandi University of Washington, USA, Yisu Remy Wang University of Washington, Oliver Flatt University of Utah, Zachary Tatlock University of Washington, Seattle, Pavel Panchekha University of UtahLink to publication DOI Pre-print
|Debugging Large-Scale Datalog: A Scalable Provenance Evaluation StrategyTOPLAS|
David Zhao The University of Sydney, Pavle Subotic Microsoft and Mathematical Institute, Serbian Academy of Sciences and Arts (SASA), Bernhard Scholz University of Sydney, AustraliaLink to publication DOI