FlowLog: Efficient and Extensible Datalog via Incrementality

TL;DR

FlowLog is a new Datalog engine that combines logical optimization and database primitives to efficiently handle recursive computations, outperforming existing systems in scalability and flexibility.

Contribution

FlowLog introduces an explicit relational IR per-rule, enabling Datalog-aware optimizations and seamless integration with database primitives for recursive workloads.

Findings

Outperforms state-of-the-art Datalog engines and databases

Achieves superior scalability in recursive workloads

Supports both batch and incremental Datalog processing

Abstract

Datalog-based languages are regaining popularity as a powerful abstraction for expressing recursive computations in domains such as program analysis and graph processing. However, existing systems often face a trade-off between efficiency and extensibility. Engines like Souffle achieve high efficiency through domain-specific designs, but lack general-purpose flexibility. Others, like RecStep, offer modularity by layering Datalog on traditional databases, but struggle to integrate Datalog-specific optimizations. This paper bridges this gap by presenting FlowLog, a new Datalog engine that uses an explicit relational IR per-rule to cleanly separate recursive control (e.g., semi-naive execution) from each rule's logical plan. This boundary lets us retain fine-grained, Datalog-aware optimizations at the logical layer, but also reuse off-the-shelf database primitives at execution. At the logical level (i.e. IR), we apply proven SQL optimizations, such as logic fusion and subplan reuse. To address high volatility in recursive workloads, we adopt a robustness-first approach that pairs a structural optimizer (avoiding worst-case joins) with sideways information passing (early filtering). Built atop Differential Dataflow--a mature framework for streaming analytics--FlowLog supports both batch and incremental Datalog and adds novel recursion-aware optimizations called Boolean (or algebraic) specialization. Our evaluation shows that FlowLog outperforms state-of-the-art Datalog engines and modern databases across a broad range of recursive workloads, achieving superior scalability while preserving a simple and extensible architecture.