High-Performance SQL Engine Based on Semantic Analysis (MMIT Proceedings, 2026)
The rising complexity of modern DBMS architectures and the increasing volume of processed data transform classical query execution methods into a bottleneck for information systems. This paper examines the design and software implementation of a high-performance relational SQL engine utilizing semantic analysis and rule-based optimization (RBO) techniques. A strict pipelined architecture of the system is described, incorporating an AST parser, a binder, and a query tree optimizer based on relevant algorithms. The study justifies the rejection of the classical Volcano iterator execution model in favor of a batch-vectorized columnar approach implemented via NumPy library tools. The integration of telemetry and visualization modules using Graphviz enables the developed engine to serve as a transparent platform for further scientific research in the field of systems software. Developing a custom SQL engine provides an insider's view into the mechanics of transforming high-level queries into fast, low-level operations.
Keywords: SQL engine; semantic analysis; query optimization; DBMS architecture; query parsing; Abstract Syntax Tree (AST)