Machine learning (ML) techniques for optimizing data management problems have
been extensively studied and widely deployed in recent five years. However
traditional ML methods have limitations on generalizability (adapting to
different scenarios) and inference ability (understanding the context).
Fortunately, large language models (LLMs) have shown high generalizability and
human-competitive abilities in understanding context, which are promising for
data management tasks (e.g., database diagnosis, database tuning). However,
existing LLMs have several limitations: hallucination, high cost, and low
accuracy for complicated tasks. To address these challenges, we design LLMDB,
an LLM-enhanced data management paradigm which has generalizability and high
inference ability while avoiding hallucination, reducing LLM cost, and
achieving high accuracy. LLMDB embeds domain-specific knowledge to avoid
hallucination by LLM fine-tuning and prompt engineering. LLMDB reduces the high
cost of LLMs by vector databases which provide semantic search and caching
abilities. LLMDB improves the task accuracy by LLM agent which provides
multiple-round inference and pipeline executions. We showcase three real-world
scenarios that LLMDB can well support, including query rewrite, database
diagnosis and data analytics. We also summarize the open research challenges of
LLMDB.

LLMDB 是一种通过应用机器学习和大型语言模型优化数据管理问题的范例，具有高泛化能力和推理能力，可避免幻觉问题，并通过引入特定领域的知识、矢量数据库和 LLM 代理来提高准确性，其中三个真实场景包括查询重写、数据库诊断和数据分析。

LLM 增强数据管理

LLM-Enhanced Data Management

Database administrators (DBAs) play an important role in managing,
maintaining and optimizing database systems. However, it is hard and tedious
for DBAs to manage a large number of databases and give timely response
(waiting for hours is intolerable in many online cases). In addition, existing
empirical methods only support limited diagnosis scenarios, which are also
labor-intensive to update the diagnosis rules for database version updates.
Recently large language models (LLMs) have shown great potential in various
fields. Thus, we propose D-Bot, an LLM-based database diagnosis system that can
automatically acquire knowledge from diagnosis documents, and generate
reasonable and well-founded diagnosis report (i.e., identifying the root causes
and solutions) within acceptable time (e.g., under 10 minutes compared to hours
by a DBA). The techniques in D-Bot include (i) offline knowledge extraction
from documents, (ii) automatic prompt generation (e.g., knowledge matching,
tool retrieval), (iii) root cause analysis using tree search algorithm, and
(iv) collaborative mechanism for complex anomalies with multiple root causes.
We verify D-Bot on real benchmarks (including 539 anomalies of six typical
applications), and the results show that D-Bot can effectively analyze the root
causes of unseen anomalies and significantly outperforms traditional methods
and vanilla models like GPT-4.

D-Bot 是基于大型语言模型的数据库诊断系统，能够从诊断文档中自动获取知识，并在可接受的时间范围内生成合理和有根据的诊断报告，包括确定根本原因和解决方案。通过在真实基准测试中验证，D-Bot 可以有效地分析未见异常的根本原因，明显优于传统方法和 GPT-4 等基准模型。