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Large Language Model-Enhanced Relational Operators: Taxonomy, Benchmark, and Analysis

Yunxiang Su, Tianjing Zeng, Zhongjun Ding, Yin Lin, Rong Zhu, Zhewei Wei, Bolin Ding, Jingren Zhou

TL;DR

A unified LRO taxonomy is established to align existing LROs, and categorize them into: Select, Match, Impute, Cluster and Order, along with their operands and implementation variants, and compares the end-to-end performance of existing multi-LRO systems against an LRO suite instantiated with these best practices.

Abstract

With the development of large language models (LLMs), numerous studies integrate LLMs through operator-like components to enhance relational data processing tasks, e.g., filters with semantic predicates, knowledge-augmented table imputation, reasoning-driven entity matching and more challenging semantic query processing. These components invoke LLMs while preserving a relational input/output interface, which we refer to as LLM-Enhanced Relational Operators (LROs). From an operator perspective, unfortunately, these existing LROs suffer from fragmented definition, various implementation strategies and inadequate evaluation benchmarks. To this end, in this paper, we first establish a unified LRO taxonomy to align existing LROs, and categorize them into: Select, Match, Impute, Cluster and Order, along with their operands and implementation variants. Second, we design LROBench, a comprehensive benchmark featuring 290 single-LRO queries and 60 multi-LRO queries, spanning 27 databases across more than 10 domains. LROBench covers all operating logics and operand granularities in its single-LRO workload, and provides challenging multi-LRO queries stratified by query complexity. Based on these, we evaluate individual LROs under various implementations, deriving practical insights into LRO design choices and summarizing our empirical best practices. We further compare the end-to-end performance of existing multi-LRO systems against an LRO suite instantiated with these best practices, in order to investigate how to design an effective LRO set for multi-LRO systems targeting complex semantic queries. Last, to facilitate future work, we outline promising future directions and open-source all benchmark data and evaluation code, available at https://github.com/LROBench/LROBench/.

Large Language Model-Enhanced Relational Operators: Taxonomy, Benchmark, and Analysis

TL;DR

A unified LRO taxonomy is established to align existing LROs, and categorize them into: Select, Match, Impute, Cluster and Order, along with their operands and implementation variants, and compares the end-to-end performance of existing multi-LRO systems against an LRO suite instantiated with these best practices.

Abstract

With the development of large language models (LLMs), numerous studies integrate LLMs through operator-like components to enhance relational data processing tasks, e.g., filters with semantic predicates, knowledge-augmented table imputation, reasoning-driven entity matching and more challenging semantic query processing. These components invoke LLMs while preserving a relational input/output interface, which we refer to as LLM-Enhanced Relational Operators (LROs). From an operator perspective, unfortunately, these existing LROs suffer from fragmented definition, various implementation strategies and inadequate evaluation benchmarks. To this end, in this paper, we first establish a unified LRO taxonomy to align existing LROs, and categorize them into: Select, Match, Impute, Cluster and Order, along with their operands and implementation variants. Second, we design LROBench, a comprehensive benchmark featuring 290 single-LRO queries and 60 multi-LRO queries, spanning 27 databases across more than 10 domains. LROBench covers all operating logics and operand granularities in its single-LRO workload, and provides challenging multi-LRO queries stratified by query complexity. Based on these, we evaluate individual LROs under various implementations, deriving practical insights into LRO design choices and summarizing our empirical best practices. We further compare the end-to-end performance of existing multi-LRO systems against an LRO suite instantiated with these best practices, in order to investigate how to design an effective LRO set for multi-LRO systems targeting complex semantic queries. Last, to facilitate future work, we outline promising future directions and open-source all benchmark data and evaluation code, available at https://github.com/LROBench/LROBench/.
Paper Structure (35 sections, 6 equations, 7 figures, 16 tables)

This paper contains 35 sections, 6 equations, 7 figures, 16 tables.

Table of Contents

  1. Introduction
  2. Background
  3. LLM-Enhanced Relational Operator
  4. Relevant Works on LROs
  5. Motivation and Roadmap
  6. Operator Taxonomy
  7. Benchmark Design
  8. Design Principles
  9. Existing Benchmarks
  10. Our New Benchmark
  11. Data Collection
  12. Query Construction
  13. Single-LRO Queries.
  14. Multi-LRO Queries.
  15. Evaluation of Individual LRO
  16. ...and 20 more sections

Figures (7)

  • Figure 1: Examples of standalone-LRO components and multi-LRO systems.
  • Figure 2: An example to illustrate LLM-enhanced operators.
  • Figure 3: Single-LRO query counts by operator and operands.
  • Figure 4: Scoring dimensions of multi-LRO queries.
  • Figure 5: Overall score distribution of multi-LRO queries.
  • ...and 2 more figures

Theorems & Definitions (9)

  • Definition 1: LLM-Enhanced Relational Operator, LRO
  • Example 1
  • Definition 2: Select
  • Definition 3: Match
  • Definition 4: Impute
  • Definition 5: Cluster
  • Definition 6: Order
  • Example 2: Single-LRO Query
  • Example 3: Multi-LRO Query