When Large Language Models Meet Database Deadlocks: A New Approach to Intelligent Victim Selection

This article introduces an open-source project by fatihsoysalcom on GitHub (llm-database-deadlock-victim-selection-simulation, MIT license, released on June 14, 2026). Its core idea is to use the intelligent decision-making capabilities of Large Language Models (LLMs) to solve the victim selection problem in database deadlock scenarios. By simulating multi-dimensional transaction attribute evaluation, the project demonstrates the application potential of AI in the field of traditional database optimization. Related article: Veritabanı Kilitlenme Mağduru Seçiminde Yapay Zeka Devrimi: LLM'ler Sahneye Çıkıyor

A database deadlock refers to a system impasse where two or more transactions wait for each other to release resources. Traditional victim selection strategies are based on simple heuristic rules, such as choosing the transaction that holds the fewest locks, has the shortest execution time, or the lowest priority. However, these methods are overly simplified and cannot fully consider the complexity and diversity of transactions in real-world scenarios.

This project proposes using the intelligent reasoning capabilities of LLMs to assist or replace traditional algorithms. The core is to enable LLMs to comprehensively evaluate multi-dimensional attributes of transactions, including priority, held resources, execution progress, criticality, etc., to make more detailed and context-aware choices.

The project uses a scoring heuristic approach: a comprehensive score is calculated for each candidate transaction, and the one with the highest score is selected as the victim. Unlike traditional methods, LLMs output a step-by-step reasoning process to ensure interpretability (helping DBAs understand the reasons behind decisions). The selection goal is to minimize system impact: reduce rework, ensure critical business operations, and quickly release resources.

The project is implemented in Python with concise code. Users can save it as main.py and run python main.py to view simulation results. It is suitable as a teaching example, prototype verification (exploring the potential of LLMs in database optimization), and benchmark testing (comparing effects with traditional algorithms).

This project reveals the trend of LLMs penetrating from natural language processing to system optimization. In autonomous database operation and maintenance, LLM-assisted deadlock handling is an important direction. A feasible solution is a hybrid intelligent architecture: traditional algorithms handle regular scenarios, complex cases are handed over to LLMs; use LLM reasoning results to optimize traditional heuristic rules.

The project has limitations: 1. It uses simulated data; transaction attributes in real production environments are more complex and dynamic. 2. Whether LLM reasoning latency is acceptable in high-concurrency scenarios needs to be verified. 3. Frequent calls to LLM APIs incur costs, so the benefits and overhead need to be balanced.

This project demonstrates the cross-innovation between traditional database technology and modern LLMs. The potential of LLMs is not limited to chatbots; they also have great potential in underlying fields such as system optimization and decision support. In the future, as model efficiency improves and costs decrease, more AI-enhanced database components may emerge.