Semantic-Aware Neural Query Optimization: Bridging the Gap Between Distributed Frameworks and Large Language Models at Terabyte Scale

While processing and managing datasets of the same order of terabytes is commonplace in any business environment, distributed SQL engines such as Apache Spark SQL, Trino, etc., have been proliferating. Despite their advanced Cost-Based Optimizers (CBOs) and adaptive execution plans, the underlying statistical heuristics have turned out to be less useful in the presence of large-scale datasets, high-dimensional data, and data skew. On the architectural side, the NP-Hard join order problem, the cost of shuffle, and the fragility of the pipeline execution model in MPP systems are considered. It is observed that learn-based optimizers in documentation and records, such as Neo, Bao, and LITHE, along with customary and pre-existing learned optimization strategies used today in industrial systems, lack semantic reasoning capabilities to optimize queries through rewriting and physically planning hints. To address this, the Semantic-Aware Neural Query Optimization (SANQO) framework is proposed. SANQO integrates Large Language Models (LLMs) as a supervisory agent within the database kernel, utilizing a novel retrieve-then-rewrite paradigm to inject precise execution hints (e.g., broadcast thresholds, skew salting) and perform structural rewrites that elude static evaluation. Through a rigorous comparative evaluation with related work, this article demonstrates that SANQO offers a plausible, strictly superior pathway for optimizing distributed workloads, transforming the database from a passive execution engine into an active, reasoning system.