arXiv:2606.07923v1 Announce Type: cross Abstract: With the advent of Large Language Models (LLMs), many database systems introduced semantic operators that enabled analytical queries over unstructured data (e.g. text, images, videos). Semantic operators typically incur high inference costs and latencies making semantic (AI) SQL queries challenging to apply on large scale datasets. At the same time, their semantic nature leads database engines to treat them as black boxes, making AISQL queries difficult to optimize. In this paper, we introduce Larch, a framework for optimizing the execution of
The proliferation of LLMs and their integration into database systems necessitates new approaches to query optimization that can handle the unique challenges of semantic predicates and high inference costs.
Efficiently querying unstructured data using semantic operators is critical for unlocking the full analytical potential of AI, impacting data-driven decision-making and business intelligence across industries.
Database systems will evolve to incorporate learned optimization techniques for semantic queries, moving beyond traditional black-box treatment of AI operations to better performance and resource utilization.
- · cloud providers
- · data-driven enterprises
- · AI/ML researchers
- · database vendors
- · legacy database systems
- · companies with high inference costs
- · unoptimized AI SQL applications
Database queries involving AI will become significantly faster and more resource-efficient.
This efficiency will enable more complex and widespread adoption of semantic search and analytical capabilities over vast unstructured datasets.
The enhanced ability to query and analyze unstructured data will accelerate innovation in scientific research, product development, and predictive analytics across various sectors.
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