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Database query cost models adapt to new hardware with fewer measurements

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to adapt learned cost models for database query runtimes to new hardware with minimal new measurements. By analyzing query templates and their runtime variations across different environments, they found that a simple speed factor is insufficient. A correction method that accounts for serial and parallel plans, along with template-specific offsets, significantly reduces error, bringing performance close to models trained from scratch on the new hardware. This approach shows promise for efficient model adaptation, though performance can vary on complex query sets. AI

IMPACT Improves efficiency of ML model adaptation for database systems, potentially reducing hardware-specific tuning costs.

RANK_REASON Academic paper detailing a novel method for adapting machine learning models. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Database query cost models adapt to new hardware with fewer measurements

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Academic paper detailing a novel method for adapting machine learning models. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Krish Patel ·

    When Is One Query per Template Enough? Adapting Learned Cost Models to New Hardware

    arXiv:2611.05892v1 Announce Type: cross Abstract: A learned cost model that predicts query runtimes is trained on measurements from one machine. When the database moves to other hardware, its plans stay the same but their runtimes change, each by its own factor. We ask how to ada…

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