Training-Free Query Optimization via LLM-Based Plan Similarity

• URL: http://arxiv.org/abs/2506.05853v2
• Date: Mon, 07 Jul 2025 09:14:21 GMT
• Title: Training-Free Query Optimization via LLM-Based Plan Similarity
• Authors: Nikita Vasilenko, Alexander Demin, Vladimir Boorlakov,
• Abstract summary: We introduce LLM-PM (LLM-based Plan Mapping), a framework that embeds the default execution plan of a query.<n> evaluated on the JOB-CEB benchmark, LLM-PM achieves an average speed-up of 21% latency reduction.
• Score: 45.9982965995401
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model (LLM) embeddings offer a promising new avenue for database query optimization. In this paper, we explore how pre-trained execution plan embeddings can guide SQL query execution without the need for additional model training. We introduce LLM-PM (LLM-based Plan Mapping), a framework that embeds the default execution plan of a query, finds its k nearest neighbors among previously executed plans, and recommends database hintsets based on neighborhood voting. A lightweight consistency check validates the selected hint, while a fallback mechanism searches the full hint space when needed. Evaluated on the JOB-CEB benchmark using OpenGauss, LLM-PM achieves an average speed-up of 21% query latency reduction. This work highlights the potential of LLM-powered embeddings to deliver practical improvements in query performance and opens new directions for training-free, embedding-based optimizer guidance systems.

Related papers

• Can Prompt Difficulty be Online Predicted for Accelerating RL Finetuning of Reasoning Models? [62.579951798437115]
  This work investigates iterative approximate evaluation for arbitrary prompts.<n>It introduces Model Predictive Prompt Selection (MoPPS), a Bayesian risk-predictive framework.<n>MoPPS reliably predicts prompt difficulty and accelerates training with significantly reduced rollouts.
  arXiv  Detail & Related papers  (2025-07-07T03:20:52Z)
• LLM4Hint: Leveraging Large Language Models for Hint Recommendation in Offline Query Optimization [7.00597706249493]
  This paper explores how Large Language Model (LLM) can be incorporated to enhance the generalization of learned phrases.<n>We propose textbfLLM4Hint that leverages moderate-sized backbone LLMs to recommend query optimization hints.
  arXiv  Detail & Related papers  (2025-07-04T08:32:17Z)
• Query-Efficient Planning with Language Models [8.136901056728945]
  Planning in complex environments requires an agent to efficiently query a world model to find a sequence of actions from start to goal.<n>Recent work has shown that Large Language Models (LLMs) can potentially help with planning by searching over promising states and adapting to feedback from the world.<n>We show that while both approaches improve upon comparable baselines, using an LLM as a generative planner results in significantly fewer interactions.
  arXiv  Detail & Related papers  (2024-12-09T02:51:21Z)
• The Unreasonable Effectiveness of LLMs for Query Optimization [4.50924404547119]
  We show that embeddings of query text contain useful semantic information for query optimization. We show that a simple binary deciding between alternative query plans, trained on a small number of embedded query vectors, can outperform existing systems.
  arXiv  Detail & Related papers  (2024-11-05T07:10:00Z)
• DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing [10.712756715779822]
  Large Language Models (LLMs) have shown promise in data processing.<n>These frameworks focus on reducing cost when executing user-specified operations.<n>This is problematic for complex tasks and data.<n>We present DocETL, a system that optimize complex document processing pipelines.
  arXiv  Detail & Related papers  (2024-10-16T03:22:35Z)
• Exploring and Benchmarking the Planning Capabilities of Large Language Models [57.23454975238014]
  This work lays the foundations for improving planning capabilities of large language models (LLMs) We construct a comprehensive benchmark suite encompassing both classical planning benchmarks and natural language scenarios. We investigate the use of many-shot in-context learning to enhance LLM planning, exploring the relationship between increased context length and improved planning performance.
  arXiv  Detail & Related papers  (2024-06-18T22:57:06Z)
• TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios [51.66718740300016]
  TableLLM is a robust large language model (LLM) with 8 billion parameters.<n>TableLLM is purpose-built for proficiently handling data manipulation tasks.<n>We have released the model checkpoint, source code, benchmarks, and a web application for user interaction.
  arXiv  Detail & Related papers  (2024-03-28T11:21:12Z)
• No more optimization rules: LLM-enabled policy-based multi-modal query optimizer [9.370719876854228]
  Large language model (LLM) has marked a pivotal moment in the field of machine learning and deep learning. In this paper, we investigate the query optimization ability of LLM and use LLM to design LaPuda, a novel LLM and Policy based multi-modal query.
  arXiv  Detail & Related papers  (2024-03-20T13:44:30Z)
• JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning [58.71541261221863]
  Join order selection (JOS) is the problem of ordering join operations to minimize total query execution cost. We present JoinGym, a query optimization environment for bushy reinforcement learning (RL) Under the hood, JoinGym simulates a query plan's cost by looking up intermediate result cardinalities from a pre-computed dataset.
  arXiv  Detail & Related papers  (2023-07-21T17:00:06Z)
• Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting [65.00288634420812]
  Pairwise Ranking Prompting (PRP) is a technique to significantly reduce the burden on Large Language Models (LLMs) Our results are the first in the literature to achieve state-of-the-art ranking performance on standard benchmarks using moderate-sized open-sourced LLMs.
  arXiv  Detail & Related papers  (2023-06-30T11:32:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.