AI 中文总结
针对现有数据库调优方法未考虑组件相互依赖性及不足以处理多样工作负载等问题，提出IDSTune框架，通过大语言模型驱动多智能体协作联合优化多个配置组件，经实验验证其性能提升显著且调优速度快，适应性强。
AI 中文摘要
数据库调优对于现代数据库管理系统（DBMS）实现高性能至关重要。现有方法通常仅优化单个组件，如旋钮、索引或物化视图，而未考虑它们的相互依赖性。这是因为这些组件需要不同的调优策略且难以集成在统一框架中。直接扩展方法或简单组合单独方法往往无法捕捉跨组件协作和共享调优信号，且现有方法不足以处理多样工作负载、不断演变的数据和动态查询模式。为解决这些限制，我们提出IDSTune，一个通过大语言模型驱动的多智能体协作联合优化多个配置组件的集成调优框架。IDSTune分两个阶段运行：工作负载压缩，提取并选择与任务相关的特征；配置推荐，专业智能体在集中协调器监督下协作生成并优化旋钮、索引和物化视图的配置。通过纳入反馈和外部知识检索，IDSTune实现高效且全局一致的调优。大量实验表明，IDSTune性能提升高达38%，调优速度快57%，在多样场景中具有很强的适应性。
英文摘要
Database tuning is critical for achieving high performance in modern database management systems (DBMSs). Existing methods typically optimize a single component---knobs, indexes, or materialized views---without accounting for their interdependencies. This limitation arises because these components require different tuning strategies and are difficult to integrate within a unified framework. As a result, directly extending a method to multiple components or simply combining separate methods often fails to capture cross-component collaboration and shared tuning signals. Moreover, existing methods are insufficient for handling diverse workloads, evolving data, and dynamic query patterns. To address these limitations, we propose IDSTune, an integrated tuning framework that jointly optimizes multiple configuration components through LLM-driven multi-agent collaboration. IDSTune operates in two phases: (i) workload compression, which extracts and selects task-relevant features, and (ii) configuration recommendation, where specialized agents collaboratively generate and refine configurations for knobs, indexes, and materialized views under the supervision of a centralized coordinator. By incorporating feedback and external knowledge retrieval, IDSTune achieves efficient and globally consistent tuning. Extensive experiments show that IDSTune achieves up to 38% performance improvement and 57% faster tuning, with strong adaptability across diverse scenarios.
