FlowLog: Re-thinking Datalog for Fast and Extensible Static Analysis

2026-07-26

AI Summary

Existing Datalog engines struggle to simultaneously achieve efficiency, scalability, and extensible semantics in static analysis, while also lacking robust support for rule debugging and incremental updates. This work proposes a novel approach that compiles Soufflé-style Datalog programs into executable Differential Dataflow programs, yielding a high-performance, memory-efficient static analysis framework capable of millisecond-scale incremental recomputation. The framework natively supports non-standard semantics—such as k-core analysis—and integrates in-browser performance profiling and rule-tuning capabilities. Evaluated on 24 real-world static analysis benchmarks, the system outperforms state-of-the-art engines in both runtime performance and scalability.

Abstract

Datalog is widely used to build static analyzers, yet existing engines often force a tradeoff between efficiency and extensibility. In practice, static analyses are not run once and forgotten: users edit facts, tune rules, diagnose bottlenecks, and often need semantics beyond standard Datalog, leaving these tasks to ad hoc tooling or invasive engine rewrites. We demonstrate FlowLog, a Datalog compiler that turns Soufflé-style programs into Differential Dataflow executables for efficient and extensible static analysis. Across 24 benchmarks derived from real-world workloads, FlowLog consistently outperforms state-of-the-art engines in runtime while remaining memory-efficient and scaling better. The demonstration walks attendees through a DOOP points-to analysis. Attendees run it, switching the same program from one-shot to incremental evaluation that retracts a fact and updates results in milliseconds; tune it, inspecting per-operator costs in a browser-based profiler and repairing a bad join order; and extend it with a k-core example that uses semantics beyond Datalog.

Problem

• Datalog
• static analysis
• extensibility
• efficiency
• incremental evaluation

Innovation

• Datalog
• Incremental Evaluation
• Static Analysis
• Differential Dataflow
• Extensibility