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. They \emph{run} it, switching the same program from one-shot to incremental evaluation that retracts a fact and updates results in milliseconds; \emph{tune} it, inspecting per-operator costs in a browser-based profiler and repairing a bad join order; and \emph{extend} it with a k-core example that uses semantics beyond Datalog.