arXiv:2605.31097v1 Announce Type: cross Abstract: Mainstream relational databases ship a uniform feature set across deployments, although individual workloads exercise only a fraction of the available subsystems. We investigate whether a database can instead be generated on demand with a feature set matched to the target workload. We present SpecDB, a system that uses large language models (LLMs) to synthesize customized relational databases. We survey 9 production systems and decompose them into 10 functional modules, each further divided into implementation variants. To capture cross-module
The proliferation of powerful LLMs and the increasing specialization of computational workloads are converging, making customized database generation more feasible and necessary.
This development can significantly optimize data infrastructure by tailoring databases to specific workloads, reducing overhead, and improving performance for AI-driven applications and complex systems.
Traditional 'one-size-fits-all' relational database paradigms could evolve towards dynamically generated, workload-specific databases, fundamentally changing how data storage and retrieval are managed.
- · Cloud providers
- · Organizations with complex data workloads
- · AI software developers
- · Database optimization tools
- · Generic relational database vendors
- · Database administrators managing inefficient systems
- · Companies with undifferentiated database offerings
Workloads become more efficient with databases precisely tailored to their needs.
The cost and complexity of database management could decrease, democratizing access to highly optimized data infrastructure.
This could enable entirely new classes of applications and services that were previously infeasible due to database inefficiencies.
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Read at arXiv cs.AI