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Dynamic Data Layout Optimization with Worst-case Guarantees

Kexin Rong, Paul Liu, Sarah Ashok Sonje, Moses Charikar

TL;DR

This paper introduces OReO, an online reorganization optimizer for dynamic data layouts that balances query processing costs with reorganization costs in unknown workloads. By modeling the problem as a dynamic variant of Metrical Task Systems (D-UMTS) and decoupling layout generation from reorganization decisions, OReO achieves provable worst-case guarantees with a competitive ratio that is logarithmic in the maximum dynamic state space. Empirical results on TPC-H, TPC-DS, and VMware Telemetry workloads show up to 32% end-to-end improvements over static layouts, with end-to-end costs closely approaching the online oracle bounds. The work demonstrates the practical viability of workload-aware, online data layout optimization and outlines avenues for future extensions, including multi-table scenarios and non-uniform cost metrics.

Abstract

Many data analytics systems store and process large datasets in partitions containing millions of rows. By mapping rows to partitions in an optimized way, it is possible to improve query performance by skipping over large numbers of irrelevant partitions during query processing. This mapping is referred to as a data layout. Recent works have shown that customizing the data layout to the anticipated query workload greatly improves query performance, but the performance benefits may disappear if the workload changes. Reorganizing data layouts to accommodate workload drift can resolve this issue, but reorganization costs could exceed query savings if not done carefully. In this paper, we present an algorithmic framework OReO that makes online reorganization decisions to balance the benefits of improved query performance with the costs of reorganization. Our framework extends results from Metrical Task Systems to provide a tight bound on the worst-case performance guarantee for online reorganization, without prior knowledge of the query workload. Through evaluation on real-world datasets and query workloads, our experiments demonstrate that online reorganization with OReO can lead to an up to 32% improvement in combined query and reorganization time compared to using a single, optimized data layout for the entire workload.

Dynamic Data Layout Optimization with Worst-case Guarantees

TL;DR

This paper introduces OReO, an online reorganization optimizer for dynamic data layouts that balances query processing costs with reorganization costs in unknown workloads. By modeling the problem as a dynamic variant of Metrical Task Systems (D-UMTS) and decoupling layout generation from reorganization decisions, OReO achieves provable worst-case guarantees with a competitive ratio that is logarithmic in the maximum dynamic state space. Empirical results on TPC-H, TPC-DS, and VMware Telemetry workloads show up to 32% end-to-end improvements over static layouts, with end-to-end costs closely approaching the online oracle bounds. The work demonstrates the practical viability of workload-aware, online data layout optimization and outlines avenues for future extensions, including multi-table scenarios and non-uniform cost metrics.

Abstract

Many data analytics systems store and process large datasets in partitions containing millions of rows. By mapping rows to partitions in an optimized way, it is possible to improve query performance by skipping over large numbers of irrelevant partitions during query processing. This mapping is referred to as a data layout. Recent works have shown that customizing the data layout to the anticipated query workload greatly improves query performance, but the performance benefits may disappear if the workload changes. Reorganizing data layouts to accommodate workload drift can resolve this issue, but reorganization costs could exceed query savings if not done carefully. In this paper, we present an algorithmic framework OReO that makes online reorganization decisions to balance the benefits of improved query performance with the costs of reorganization. Our framework extends results from Metrical Task Systems to provide a tight bound on the worst-case performance guarantee for online reorganization, without prior knowledge of the query workload. Through evaluation on real-world datasets and query workloads, our experiments demonstrate that online reorganization with OReO can lead to an up to 32% improvement in combined query and reorganization time compared to using a single, optimized data layout for the entire workload.
Paper Structure (35 sections, 2 theorems, 1 equation, 6 figures, 2 tables, 5 algorithms)

This paper contains 35 sections, 2 theorems, 1 equation, 6 figures, 2 tables, 5 algorithms.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Motivation: Dynamic Layout Optimization in Practice
  4. Background: Metrical Task Systems
  5. Overview
  6. Problem Formulation
  7. Framework Overview
  8. Assumptions and Limitations
  9. Dynamic Reorganization via MTS
  10. Overview of the Algorithm of Borodin, Linial, and Saks
  11. Supporting Dynamic State Spaces
  12. Improving Reorganization with a Predictor
  13. On-the-fly Layout Generation
  14. Generating Diverse Data Layouts
  15. Expanding the Dynamic State Space
  16. ...and 20 more sections

Key Result

Theorem 4.1

alg:update-states solves D-UMTS with competitive ratio $2H(|S_{max}|) \leq 2(1+\log |\mathcal{S}_{max}|)$ where $H(n)$ is the $n$-th harmonic number and $\mathcal{S}_{max}$ is the largest set of states created by the update queries over the course of the stream.

Figures (6)

  • Figure 1: Overview of OReO, an algorithmic framework for making online data reorganization decisions to minimize the total costs of query processing and data reorganization over an unknown query sequence.
  • Figure 2: Example of a Qd-tree and its partition-level metadata.
  • Figure 3: Comparison of total query and reorganization time in Spark enabled by OReO with baselines. The top half of each bar with the hatches represents reorganization time, while the bottom half represents query time. Overall, dynamic reorganization improves upon a static, optimized layout by up to 32% in total compute time.
  • Figure 4: OReO's query costs is 74% and 44% larger than those of the optimal offline algorithm that observes the entire workload in advance and can switch states. Vertical gray lines indicate when the workload switches to a different query template.
  • Figure 5: Impact of reorganization cost ($\alpha$) on the overall performance. As reorganization becomes more expensive, the total gains from dynamic reorganization decrease. The decrease is not monotonic due to changes in reorganization strategies.
  • ...and 1 more figures

Theorems & Definitions (5)

  • Theorem 4.1
  • proof
  • Remark 4.1
  • Theorem 4.2
  • proof