Analytical workloads operating on data stored in external database systems face a fundamental bottleneck: data access is entirely guarded by the database driver, such as 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.
The key insight of Jailbreak is that database file formats, while complex, are fully specified by their source code and documentation, which 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, transforming 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 well-known query engines, including DuckDB, Apache Spark, and GPU-accelerated frameworks like cuDF and Spark RAPIDS.
We validate correctness against JDBC/ODBC-based baselines using the TPC-H benchmark across all query results, demonstrating 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, applicable beyond PostgreSQL and MySQL to any system whose file format is accessible to the LLM from documentation or source code.
Blogger's Review: The methodology of Jailbreak represents a revolutionary breakthrough in database access by leveraging LLMs, achieving significant performance enhancements. This not only offers a new perspective for data analysis but also points towards future developments in database technology, making it worthy of attention.