arXiv subjects

Jaykumar Tandel

Publications and source records attributed to Jaykumar Tandel.

2 recordsLinked to original sources

arXiv2026

OptBench: An Interactive Workbench for AI/ML-SQL Co-Optimization[Extended Demonstration Proposal]

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 difficult to evaluate fairly because they are typically developed on different platforms and evaluated using different queries. We present OptBench, an interactive workbench for building and benchmarking query optimizers for hybrid SQL+AI/ML queries in a transparent, apples-to-apples manner. OptBench runs all optimizers on a unified backend using DuckDB and exposes an interactive web interface that allows users to (i) construct query optimizers by leveraging and extending abstracted logical plan rewrite actions, (ii) benchmark and compare different optimizer implementations over a suite of diverse queries while recording decision traces and latency, and (iii) visualize logical plans produced by different optimizers side-by-side. The system enables practitioners and researchers to prototype optimizer ideas, inspect plan transformations, and quantitatively compare optimizer designs on multimodal inference queries within a single workbench.

cs.DB↗

arXiv2026

CACTUSDB: Unlock Co-Optimization Opportunities for SQL and AI/ML Inferences

There is a growing demand for supporting inference queries that combine Structured Query Language (SQL) and Artificial Intelligence / Machine Learning (AI/ML) model inferences in database systems, to avoid data denormalization and transfer, facilitate management, and alleviate privacy concerns. Co-optimization techniques for executing inference queries in database systems without accuracy loss fall into four categories: (O1) Relational algebra optimization treating AI/ML models as black-box user-defined functions (UDFs); (O2) Factorized AI/ML inferences; (O3) Tensor-relational transformation; and (O4) General cross-optimization techniques. However, we found none of the existing database systems support all these techniques simultaneously, resulting in suboptimal performance. In this work, we identify two key challenges to address the above problem: (1) the difficulty of unifying all co-optimization techniques that involve disparate data and computation abstractions in one system; and (2) the lack of an optimizer that can effectively explore the exponential search space. To address these challenges, we present CactusDB, a novel system built atop Velox - a high-performance, UDF-centric database engine, open-sourced by Meta. CactusDB features a three-level Intermediate Representations (IR) that supports relational operators, expression operators, and ML functions to enable flexible optimization of arbitrary sub-computations. Additionally, we propose a novel Monte-Carlo Tree Search (MCTS)-based optimizer with query embedding, co-designed with our unique three-level IR, enabling shared and reusable optimization knowledge across different queries. Evaluation of 12 representative inference workloads and 2,000 randomly generated inference queries on well-known datasets, such as MovieLens and TPCx-AI, shows that CactusDB achieves up to 441 times speedup compared to alternative systems.

cs.DB↗