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UREM: A High-performance Unified and Resilient Enhancement Method for Multi- and High-Dimensional Indexes

Ming Sheng, Shuliang Wang, Yong Zhang, Yi Luo, Xianbo Liu, Zeming Li

TL;DR

UREM tackles the problem of improving query performance for both multi- and high-dimensional indexes across static and dynamic workloads by introducing a unified, decoupled, layout-oriented enhancement. It combines offline layout optimization based on a unified historical-query representation with online partial-layout reorganization, driven by data- and query-driven adjustments and a sliding-window pool of candidate layouts. The approach achieves large performance gains across 20 indexes and multiple datasets, with notable speedups in PBF and ANNS tasks and robust stability under query shifts. The work demonstrates strong practical impact by enabling common data platforms to boost existing indexes without tight coupling to specific index structures, and provides theoretical and empirical analyses of complexity and cost tradeoffs. Overall, UREM advances unified resilience in data layout optimization for diverse indexing scenarios, offering scalable, cross-platform applicability and substantial runtime benefits.

Abstract

Numerous multi- or high-dimensional indexes with distinct advantages have been proposed on various platforms to meet application requirements. To achieve higher-performance queries, most indexes employ enhancement methods, including structure-oriented and layout-oriented enhancement methods. Existing structure-oriented methods tailored to specific indexes work well under static workloads but lack generality and degrade under dynamic workloads. The layout-oriented methods exhibit good generality and perform well under dynamic workloads, but exhibit suboptimal performance under static workloads. Therefore, it is an open challenge to develop a unified and resilient enhancement method that can improve query performance for different indexes adaptively under different scenarios. In this paper, we propose UREM, which is the first high-performance Unified and Resilient Enhancement Method designed for both multi- and high-dimensional indexes, capable of adapting to different scenarios. Specifically, UREM (1) can be uniformly applied with different indexes on various platforms; (2) enhances the query performance of indexes by layout optimization under static workloads; (3) enables indexes to stabilize performance when queries shift through partial layout reorganization. We evaluate UREM on 20 widely used indexes. Experimental results demonstrate that UREM improves the query performance of multi- and high-dimensional indexes by up to 5.73x and 9.18x under static workloads, and by an average of 5.72x and 9.47x under dynamic workloads. Moreover, some traditional indexes enhanced by UREM even achieve performance comparable to or even surpassing that of recent advanced indexes.

UREM: A High-performance Unified and Resilient Enhancement Method for Multi- and High-Dimensional Indexes

TL;DR

UREM tackles the problem of improving query performance for both multi- and high-dimensional indexes across static and dynamic workloads by introducing a unified, decoupled, layout-oriented enhancement. It combines offline layout optimization based on a unified historical-query representation with online partial-layout reorganization, driven by data- and query-driven adjustments and a sliding-window pool of candidate layouts. The approach achieves large performance gains across 20 indexes and multiple datasets, with notable speedups in PBF and ANNS tasks and robust stability under query shifts. The work demonstrates strong practical impact by enabling common data platforms to boost existing indexes without tight coupling to specific index structures, and provides theoretical and empirical analyses of complexity and cost tradeoffs. Overall, UREM advances unified resilience in data layout optimization for diverse indexing scenarios, offering scalable, cross-platform applicability and substantial runtime benefits.

Abstract

Numerous multi- or high-dimensional indexes with distinct advantages have been proposed on various platforms to meet application requirements. To achieve higher-performance queries, most indexes employ enhancement methods, including structure-oriented and layout-oriented enhancement methods. Existing structure-oriented methods tailored to specific indexes work well under static workloads but lack generality and degrade under dynamic workloads. The layout-oriented methods exhibit good generality and perform well under dynamic workloads, but exhibit suboptimal performance under static workloads. Therefore, it is an open challenge to develop a unified and resilient enhancement method that can improve query performance for different indexes adaptively under different scenarios. In this paper, we propose UREM, which is the first high-performance Unified and Resilient Enhancement Method designed for both multi- and high-dimensional indexes, capable of adapting to different scenarios. Specifically, UREM (1) can be uniformly applied with different indexes on various platforms; (2) enhances the query performance of indexes by layout optimization under static workloads; (3) enables indexes to stabilize performance when queries shift through partial layout reorganization. We evaluate UREM on 20 widely used indexes. Experimental results demonstrate that UREM improves the query performance of multi- and high-dimensional indexes by up to 5.73x and 9.18x under static workloads, and by an average of 5.72x and 9.47x under dynamic workloads. Moreover, some traditional indexes enhanced by UREM even achieve performance comparable to or even surpassing that of recent advanced indexes.
Paper Structure (41 sections, 2 theorems, 3 equations, 12 figures, 4 tables, 3 algorithms)

This paper contains 41 sections, 2 theorems, 3 equations, 12 figures, 4 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Existing Enhancement Methods
  4. Challenges of Existing Enhancement Methods
  5. Overview
  6. Layout Optimization
  7. Historical Queries Representation
  8. Data- and Query-driven Data Adjustments
  9. Vertical Concatenation
  10. Vertical Concatenation
  11. Feature-based Transformation
  12. Gravity-based Movement
  13. Partitioning Process
  14. Partial Layout Reorganization
  15. Generating Partial Layouts
  16. ...and 26 more sections

Key Result

Theorem 1

The total cost under dynamic workloads is minimized when the size $N_l$ of the partial layout $l_i$ satisfies the condition $\frac{k(1-p)^{k-1}}{N_d} \left(\log N_l - C \right) + \frac{1-(1-p)^k}{N_l} = - \frac{\lambda \cdot B}{A}$, where $p=\frac{N_l}{N_d}$, $A$ is the total number of queries, $B$

Figures (12)

  • Figure 1: UREM overview.
  • Figure 2: Layout optimization.
  • Figure 3: The CDF curves of KEY values and clustering evaluation results before and after data adjustments.
  • Figure 4: Partial layout reorganization.
  • Figure 5: Partial layout generation and data distribution estimation.
  • ...and 7 more figures

Theorems & Definitions (2)

  • Theorem 1
  • Theorem 2