OptBench: An Interactive Workbench for AI/ML-SQL Co-Optimization[Extended Demonstration Proposal]
Jaykumar Tandel, Douglas Oscarson, Jia Zou

TL;DR
OptBench is an interactive platform that enables building, benchmarking, and visualizing hybrid SQL+AI/ML query optimizers on a unified system, facilitating fair comparisons and rapid prototyping.
Contribution
It introduces a unified workbench for developing and evaluating SQL+AI/ML query optimizers with transparent benchmarking and visualization capabilities.
Findings
Supports diverse hybrid query workloads
Enables fair comparison of optimizer strategies
Facilitates rapid prototyping and analysis
Abstract
Database workloads are increasingly nesting artificial intelligence (AI) and machine learning (ML) pipelines and AI/ML model inferences with data processing, yielding hybrid SQL+AI/ML queries that mix relational operators with expensive, opaque AI/ML operators, often expressed as UDFs. These workloads are challenging to optimize because ML operators behave like black boxes, data-dependent effects such as sparsity, selectivity, and cardinalities can dominate runtime, domain experts often rely on practical heuristics that are difficult to develop with monolithic optimizers, and AI/ML operators introduce numerous co-optimization opportunities such as factorization, pushdown, ML-to-SQL conversion, and linear-algebra-to-relational-algebra rewrites, significantly enlarging the search space of equivalent execution plans. At the same time, research prototypes for SQL+ML optimization are…
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Taxonomy
TopicsAdvanced Database Systems and Queries · Scientific Computing and Data Management · Cloud Computing and Resource Management
