Breaking Database Lock-in: Agentic Regeneration of High Performance Storage Readers for Database Bypass
arXiv:2607.07696v1 Announce Type: cross Abstract: 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 specifi
The increasing scale and complexity of analytical workloads, particularly with AI, are pushing the limits of traditional database drivers, necessitating more efficient data access methods.
This development allows direct access to database storage formats, bypassing traditional bottlenecks and unlocking significantly higher performance for analytical and AI-driven applications.
Data analytics and AI applications can now achieve greater efficiency by directly interfacing with raw storage, reducing reliance on conventional database access layers and potentially lowering infrastructure costs.
- · AI/ML developers
- · Data warehousing platforms
- · High-performance computing
- · Cloud providers
- · Traditional database driver manufacturers
- · Legacy database vendors
- · Overly complex SQL layers
Analytical workloads achieve a significant performance uplift, speeding up data processing and model training.
New architectural patterns emerge for data lakes and data warehouses, favoring direct storage access over traditional database interfaces.
Increased competition among data infrastructure providers, with an emphasis on efficient storage format handling and high-throughput data pipelines.
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Read at arXiv cs.AI