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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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Plain-Language Brief

The paper introduces Jailbreak, an approach that allows bypassing database engines by reading storage files directly and materializing data as in-memory columnar buffers. This method utilizes Large Language Models (LLMs) to regenerate operator-specific table reading components without human-engineered parsing logic. The generated reader produces Apache Arrow buffers consumable by various query engines, demonstrating significant performance improvements over traditional methods.

What the Document Says

  • The paper presents Jailbreak, a method that bypasses database engines for analytical workloads.
  • Jailbreak reads storage files directly and materializes data as in-memory columnar buffers.
  • Large Language Models (LLMs) are used to regenerate operator-specific table reading components without human-engineered parsing logic.
  • The approach generates a reader that produces Apache Arrow buffers consumable by various query engines.
  • Evaluation is conducted on PostgreSQL and MySQL storage files, focusing on analytical snapshot scenarios common in read replicas and offline processing pipelines.
  • The generated reader demonstrates significant performance improvements, achieving up to 27x speedups compared to traditional methods.
  • Correctness is validated against JDBC/ODBC-based baselines using the TPC-H benchmark across all query results.
  • Applications of this method extend beyond PostgreSQL and MySQL for any system whose file format is available to the LLM.

Key Concepts Mentioned

  • Jailbreak
  • Large Language Models (LLMs)
  • storage files
  • Apache Arrow buffers
  • PostgreSQL
  • MySQL
  • TPC-H benchmark

Claims Made by the Document

  • Jailbreak bypasses database engines, improving analytical workloads.
  • The approach significantly improves performance compared to traditional methods.
  • LLM-assisted storage reader synthesis is a viable and generalizable methodology for breaking data lock-in.

Evidence Used

  • The paper presents an evaluation demonstrating significant performance improvements in end-to-end analytical throughput.
  • Correctness validation against JDBC/ODBC-based baselines using the TPC-H benchmark is mentioned.

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.