An Efficient Interpreter for Datalog by De-specializing Relations

Datalog is becoming increasingly popular as a standard tool for a variety of use cases. Modern Datalog engines can achieve high performance by specializing data structures for relational operations. For example, the Datalog engine Souffle achieves high performance with a synthesizer that specializes data structures for relations. However, the synthesizer cannot always be deployed, and a fast interpreter is required. This work introduces the design and implementation of the Souffle Tree Interpreter (STI). Key for the performance of the STI is the support for fast operations on relations. We obtain fast operations by de-specializing data structures so that they can work in a virtual execution environment. Our new interpreter achieves a competitive performance slowdown between 1.32 and 5.67x when compared to synthesized code. If compile time overheads of the synthesizer are also considered, the interpreter can be 6.46x faster on average for the first run.