这篇论文提出了 Jailbreak，用 LLM 直接读数据库存储文件，不用 JDBC，TPC-H 测试下最高快 27 倍。搞大数据分析的人可以看看。
Jailbreak 是一种新方法，通过直接读取 PostgreSQL 和 MySQL 存储文件实现数据库绕过，避免 JDBC/ODBC 驱动的性能瓶颈。它利用 LLM 辅助代码合成，从数据库文件格式的源代码和文档中再生出表读取组件。在 TPC-H 基准测试中，Jailbreak 生成的读取器将数据输出为 Apache Arrow 格式，可直接被 DuckDB、Apache Spark、cuDF 等引擎消费，端到端分析吞吐量提升最高达 27 倍。论文验证了该方法在 PostgreSQL 和 MySQL 之外对其他文件格式已知的数据库系统具有通用性。
Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass
Analytical workloads operating on data stored in external database systems face a fundamental bottleneck: data access is guarded entirely by the database driver, like JDBC or ODBC, forcing all reads through query execution and other driver layers that are not designed for bulk columnar analytics. We present Jailbreak, an approach that bypasses the database engine entirely by reading storage files directly and materializing data as in-memory columnar buffers. Jailbreak's key insight is that database file formats, while complex, are fully specified by their source code and documentation, artifacts that Large Language Models (LLMs) can ingest to regenerate operator-specific table reading components without human-engineered parsing logic. Jailbreak leverages LLM-assisted code synthesis for database storage decoding, turning a traditionally opaque format into a directly queryable artifact. We evaluate Jailbreak on PostgreSQL and MySQL storage files, targeting analytical snapshot scenarios common in read replicas and offline processing pipelines. The generated reader produces Apache Arrow buffers consumable directly by most of the widely known query engines, including DuckDB, Apache Spark, and GPU-accelerated frameworks such as cuDF and Spark RAPIDS. We validate correctness against JDBC/ODBC-based baselines using the TPC-H benchmark across all query results, and demonstrate significant performance improvements in end-to-end analytical throughput, achieving up to 27x speedups. Our results showcase that LLM-assisted storage reader synthesis is a viable and generalizable methodology for breaking data lock-in across database systems, with applications beyond PostgreSQL and MySQL for any system whose file format is available to the LLM from documentation or source code.