Raqlet: Cross-Paradigm Compilation for Recursive Queries

2025-08-05

AI Summary

Relational databases, graph databases, and deductive systems exhibit paradigmatic fragmentation in supporting recursive queries, hindering interoperability and standardization. Method: This paper proposes a source-to-source compilation framework based on a multi-layer intermediate representation (PGIR, DLIR, SQIR), employing formal semantic modeling and syntax-directed translation to enable bidirectional, semantics-preserving translation among Cypher, SQL/PGQ, and Datalog for recursive queries. Contribution/Results: The framework establishes a cross-paradigm semantic bridge that supports static analysis, optimization transformations, and execution engine adaptation. It provides a formally verifiable reference implementation for recursive queries, significantly improving query portability and execution consistency across heterogeneous systems. By unifying recursive query semantics across database paradigms, the work advances standardization efforts and fosters interoperability in graph and relational data processing.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Knowledge Representation and Reasoning: Description Logics
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

We introduce Raqlet, a source-to-source compilation framework that addresses the fragmentation of recursive querying engines spanning relational (recursive SQL), graph (Cypher, GQL), and deductive (Datalog) systems. Recent standards such as SQL:2023's SQL/PGQ and the GQL standard provide a common foundation for querying graph data within relational and graph databases; however, real-world support remains inconsistent across systems. Raqlet bridges this gap by translating recursive queries across paradigms through leveraging intermediate representations (IRs) grounded in well-defined semantics; it translates Cypher or SQL/PGQ to PGIR (inspired by Cypher), then into DLIR (inspired by Datalog), and finally to SQIR (inspired by recursive SQL). Raqlet provides a shared semantic basis that can serve as a golden reference implementation for language standards, while supporting static analysis and transformations (e.g., magic-set transformation) for performance tuning. Our vision is to make Raqlet a robust platform that enables rapid cross-paradigm prototyping, portable recursive queries, and formal reasoning about recursion even when targeting diverse query execution engines.

Problem

• Bridges fragmentation in recursive query engines across relational, graph, and deductive systems
• Translates recursive queries between paradigms using intermediate representations
• Provides a shared semantic basis for language standards and performance tuning

Innovation

• Source-to-source compilation for recursive queries
• Intermediate representations for cross-paradigm translation
• Shared semantic basis for language standards