sql

本文提出一种将线性Datalog程序编译成递归SQL查询的方法，通过中间语言Midlog转换，利用现有数据库引擎执行程序分析，显著提高性能。

SQL/PGQ standards enable hybrid relational and graph query processing, yet experiments reveal substantial performance disparities—even for semantically equivalent SQL and PGQ queries—due to syntax-driven, model-isolated optimization in existing systems, lacking cross-model synergy. This paper proposes the first holistic unified optimization framework that transcends syntactic boundaries between SQL and PGQ, instead leveraging query semantics and data characteristics to automatically select optimal execution strategies. Key technical innovations include: (i) cross-model query normalization via equivalence-aware analysis; (ii) cost-aware operator fusion; and (iii) adaptive execution plan generation. Experimental evaluation demonstrates that the framework significantly narrows performance gaps among functionally identical queries, achieving an average 3.2× speedup. It thus provides critical enabling technology for efficient, standardized SQL/PGQ deployment.

This paper addresses the limitations of traditional database systems—including outdated abstractions (SQL, relational algebra, ER modeling, ORM), restricted expressive power, and poor integration with modern programming languages—by proposing the Relational Mapping Type Model (RMTM), a unified data model that subsumes both the relational and ER models. Methodologically, it introduces a mapping-primitive query language grounded in RMTM, formally satisfying seven principled design criteria; this language operates directly over tuples, relations, and hierarchical database structures, enabling paradigm-level unification and elimination of SQL/RA/ERM/ORM distinctions. The approach integrates type-theoretic modeling, declarative semantic definitions, and a compatibility layer to ensure native programming-language integration and multi-level abstraction coherence. Experimental evaluation demonstrates that replacing only the data model—without modifying underlying algorithms—yields up to 3× performance improvement; moreover, the query language exhibits significantly greater expressiveness than SQL and natively aligns with contemporary software stacks.

Relational databases’ flat data model exhibits impedance mismatch with the nested data structures required by modern applications. This paper introduces the graph-relational database model, which unifies the formal rigor of the relational model with the expressive power of graph-structured nesting to support composable, type-safe complex queries. Our approach comprises three core contributions: (1) EdgeQL—a statically typed, SQL-like query language designed for expressive, safe navigation and transformation of nested, graph-shaped data; (2) the Gel compiler system, which jointly compiles EdgeQL schemas and queries—incorporating both static and dynamic semantics—into highly optimized, native PostgreSQL SQL; and (3) end-to-end type safety without runtime overhead, achieving execution performance comparable to hand-written SQL. Experimental evaluation demonstrates that our system substantially outperforms conventional ORMs, delivering superior balance among query expressivity, developer productivity, and runtime efficiency.

This paper addresses the challenge of data integration across heterogeneous databases. It proposes an algebraic approach grounded in category theory and functional programming. The method models database schemas and instances as many-sorted equational theories and their initial algebras, respectively, and employs adjoint functors to enable rigorous cross-schema data migration. Innovatively, it unifies category theory, many-sorted equational logic, and functional programming paradigms; introduces a pushout-based schema mapping construction; and defines an algebraic query language—with for/where/return syntax—endowed with formal semantics. The authors implement AQL, an open-source tool supporting formally specified schema mappings, automated data migration, and verifiable query compilation. This framework constitutes the first theoretically rigorous integration of these three foundational paradigms, simultaneously ensuring mathematical precision and enhancing the automation and reliability of data integration.

本文提出了一种基于标记有向树的新型数据库模型，通过定义一系列树操作和查询语言来解决数据访问和分析问题。

该研究通过在双范畴数据库模式中引入右伴随以实现通用量化，解决关系除法查询问题，并提供模态算子解释、一阶谓词逻辑及查询优化规则。

Existing approaches to window function optimization suffer from stringent applicability conditions and limited generalizability, lacking a unified reasoning framework. This work proposes the first systematic inference framework for window function optimization, which derives algebraically equivalent transformations that can be safely applied by leveraging frame analysis, partition analysis, and a novel co-evaluation strategy. This enables predicate pushdown even in the presence of dependencies on window results. Implemented in an open-source query engine, the framework consistently preserves or improves performance, achieving speedups of up to 40.7× on common queries, with gains becoming more pronounced as data scale increases.

This study addresses the challenge of enforcing data constraints in SQL Server applications by proposing an event-driven approach based on Visual Basic for Applications (VBA). The core innovation lies in the introduction of a novel "constraint-driven design" pseudocode algorithm, which transforms the rigorous enforcement of database constraints into a standardized development workflow. Notably, this algorithm exhibits cross-platform generality, enabling seamless compatibility with NoSQL databases and heterogeneous environments while significantly reducing system adaptation costs. Experimental evaluations demonstrate that the proposed method maintains optimal data quality while validating its versatility and efficiency across diverse platforms. Ultimately, this work provides a cost-effective solution for multi-platform data constraint management, bridging the gap between strict relational integrity requirements and flexible, modern deployment architectures.

This study addresses the challenge of unifying attribute graph models and SQL querying within relational databases. The authors propose reinterpreting SQL foreign key semantics as reference keys, enabling natural modeling of labeled property graphs directly on standard relational table structures. They further extend SQL to support efficient graph data insertion and complex pattern matching. This approach achieves deep integration of relational and graph models within a single system without requiring an additional storage engine. Experimental results demonstrate that the proposed method effectively enables graph structure construction and advanced graph querying capabilities, significantly enhancing relational databases’ support for graph-oriented operations.