Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass
Score
78首次提出利用LLM自动生成数据库存储读取器这一新颖思路，信息增量高。
论文结构清晰，实验设计合理，验证全面，但可扩展性讨论略少。
实际分析场景中可显著提升性能，降低数据库依赖，实用价值突出。
当前LLM应用和数据库性能优化皆为热点，论文发布时间较新，时效性好。
来源arXiv，属于知名预印本平台，且实验基于公开基准和系统，可信度高。
Base 86.5 × T2 Source weight ×0.9 = 78
Curation line 70Selected
Why selected
The paper presents an innovative method with rigorous experiments and significant performance gains, offering valuable insights at the intersection of databases and AI.
AI commentary
「This paper caught my eye: it cleverly uses LLM's code generation ability to directly parse database storage formats, bypassing the performance trap of traditional drivers. For data engineers, it's a powerful tool to boost analytics efficiency; for researchers, it opens a new intersection of AI and systems performance optimization. Highly recommended for teams tackling database bottlenecks in large data pipelines.」
Quick overview
This paper presents Jailbreak, an approach that leverages LLMs to learn database file formats from source code and documentation, automatically generating high-performance storage readers that bypass database drivers (e.g., JDBC/ODBC) and directly read storage files into in-memory columnar buffers. Evaluated on PostgreSQL and MySQL with TPC-H benchmark, it achieves up to 27x end-to-end analytical throughput improvement with full correctness validation against JDBC/ODBC baselines. The results demonstrate the feasibility and generalizability of LLM-assisted code synthesis for breaking database lock-in.
- Jailbreak uses LLMs to automatically generate database storage readers that bypass traditional drivers, enabling high-performance columnar data access directly from storage files.
- Achieves up to 27x speedup in end-to-end analytical throughput compared to JDBC/ODBC baselines on TPC-H benchmark.
- Approach is generalizable to any database system whose file format is available from documentation or source code.
- Generated Apache Arrow buffers can be consumed directly by popular query engines like DuckDB, Apache Spark, cuDF, etc.
Full text
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