---
title: "Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.07696v1"
permalink: "https://feed.astralor.com/items/itm_3203b0893ebc14b725f23dae"
published: "2026-07-08T17:55:00.000Z"
score: 78
channel: "ai"
tags: ["Apache Arrow","Jailbreak","PostgreSQL","TPC-H","Large Language Models","Database Bypass"]
---

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

## 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.

## At a glance

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.

## Key points

- 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

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.

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Verify against the original before citing; curated by Astral Feed: https://feed.astralor.com/items/itm_3203b0893ebc14b725f23dae