Abstract:LLMs are increasingly used for software modernization, code translation, and database migration. However, LLM-based Oracle2PostgreSQL migration stays constrained by excessive token consumption, lengthy-context degradation, dialect-specific semantic differences, and the chance of semantic drift throughout query transformation. Direct inclusion of massive Oracle SQL/PL-SQL artefacts, schema definitions, procedural logic, and migration directions into the model context will increase price and should scale back era high quality. This paper exhibits token optimization as a constrained transformation drawback in LLM-primarily based Oracle2PostgreSQL migration. The study formalizes and evaluates twelve token optimization strategies: baseline representation, context pruning, minification, DSL-based semantic compression, metadata augmentation, context refactoring, schema distillation, adaptive routing, AST-based minification, identifier masking, output constraint enforcement, and hybrid optimization. The strategies are evaluated on samples of 10 and one hundred Oracle SQL queries using Valid Syntax Rate, Exact Match, Semantic Match, CodeBLEU, and Token Efficiency. The outcomes present that mild context pruning preserves semantic quality nearly on the baseline stage, reaching 89.75% Semantic Match on the 100-query pattern in contrast with 89.80% for the unoptimized baseline. Adaptive routing provides the best practical commerce-off, decreasing input tokens by 8.72% and output tokens by 5.49% whereas sustaining 88.40% Semantic Match and rising Token Efficiency by 6.67%. Aggressive schema distillation will increase Token Efficiency by 132.22% but results in a 44.50-share-level decrease in Semantic Match. The findings exhibit that token optimization cannot be handled as easy immediate shortening; it should be evaluated as a multi-objective migration problem balancing cost, syntactic validity, semantic preservation, and structural fidelity.
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