QCATS: Query Context-Aware Transformer Slicing for Efficient Predictive Query Processing

Abstract:In-database predictive query processing increasingly applies Transformer-based models within relational pipelines. However, existing in-database inference typically exposes only tuple-level model inputs to the inference runtime, leaving relational predicates and metadata statistics invisible to neural execution planning. In this paper, we propose QCATS, a query context-aware transformer slicing framework that enables efficient sparse inference inside database systems. QCATS executes at query granularity: instead of routing individual tokens or tuples during inference, it uses query predicates and metadata statistics to pre-select context-aligned FFN slices before model execution. The framework comprises offline expert construction and lightweight query-level routing that dynamically selects experts during execution. QCATS further introduces system optimizations, including asynchronous CPU-GPU pipelines and routing-aware batching. Experiments on four predictive-query workloads with BERT-base and Qwen-0.6B show that QCATS achieves up to 4.42x latency reduction while preserving prediction accuracy comparable to dense baselines.

Databases (cs.DB); Artificial Intelligence (cs.AI)

arXiv:2610.09894 [cs.DB]

(or arXiv:2610.09894v1 [cs.DB] for this version)

https://doi.org/10.48550/arXiv.2610.09894 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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