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Source note3 min read

Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass

Source

arXiv

arxiv.org/abs/2607.07696v1

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Source

Plain-Language Brief

The researchers present an approach called Jailbreak that allows analytical workloads to bypass the traditional database driver and read storage files directly, using Large Language Models (LLMs) to regenerate table reading components. This method was evaluated on PostgreSQL and MySQL storage files and showed significant performance improvements, up to 27x speedups, compared to JDBC/ODBC-based baselines.

What the Document Says

  • The paper introduces an approach called Jailbreak that bypasses the database engine entirely for analytical workloads.
  • Jailbreak reads storage files directly and materializes data as in-memory columnar buffers.
  • Database file formats are fully specified by their source code and documentation, which can be ingested by LLMs to regenerate operator-specific table reading components.
  • The generated reader produces Apache Arrow buffers consumable by various query engines like DuckDB, Apache Spark, cuDF, and Spark RAPIDS.
  • Evaluation was done on PostgreSQL and MySQL storage files for analytical snapshot scenarios common in read replicas and offline processing pipelines.
  • The generated reader was validated against JDBC/ODBC-based baselines using the TPC-H benchmark across all query results.
  • Significant performance improvements were demonstrated, with up to 27x speedups in end-to-end analytical throughput.
  • LLM-assisted storage reader synthesis was shown as a viable and generalizable methodology for breaking data lock-in across database systems.

Key Concepts Mentioned

  • Jailbreak
  • Large Language Models (LLMs)
  • database driver
  • JDBC
  • ODBC
  • PostgreSQL
  • MySQL
  • Apache Arrow buffers
  • DuckDB
  • Apache Spark
  • cuDF
  • Spark RAPIDS
  • TPC-H benchmark

Claims Made by the Document

  • Jailbreak bypasses the database engine for analytical workloads.
  • The method generates a reader that produces Apache Arrow buffers consumable by various query engines.
  • LLM-assisted storage reader synthesis is a viable and generalizable methodology for breaking data lock-in.

Evidence Used

  • The paper describes the approach and its components.
  • Evaluation results demonstrate performance improvements.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.