LLMs as Database Administrators: A Survey of AI-Driven Schema Design and Index Recommendation

Large language models are reshaping database administration by enabling automation across tasks — schema design, index recommendation, configuration tuning, and diagnosis — that classical cost-model-driven tools handle only narrowly. This survey covers ten representative systems published between 2023 and 2026, organized under a four-axis taxonomy of task scope, LLM integration paradigm, deployment model, and autonomy level, with deep-dive comparisons on the two most mature tasks: index recommendation and schema design. For index recommendation, LLM-based advisors such as LLMIA, LLMIdxAdvis, and MAAdvisor match or exceed production baselines like Microsoft's DTA, though a persistent gap between recommendation quality and validation cost remains unresolved. For schema design, the literature is earlier-stage and lacks shared benchmarks. Across all systems, three findings recur: database feedback loops separate effective advisors from naive prompting baselines, hallucination takes domain-specific forms requiring targeted mitigation, and the tension between frontier-model capability and on-premise deployment constraints is unresolved. Five open challenges — schema scale, cost-model coupling, workload drift, trust and explainability, and standardized benchmarking — define the road ahead for LLM-driven database administration.

LLMs, database administration, index recommendation, schema design, in-context learning, retrieval-augmented generation, multi-agent systems, database tuning, DBA automation, large language models,