PLOP is a cost-based optimizer that finds optimal placements for semantic LLM operators in hybrid query plans via dynamic programming, delivering up to 1.5x speedup and 4.29x cost reduction on 44 benchmark queries while preserving accuracy.
Cortex AISQL: A Production SQL Engine for Unstructured Data
8 Pith papers cite this work. Polarity classification is still indexing.
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abstract
Snowflake's Cortex AISQL is a production SQL engine that integrates native semantic operations directly into SQL. This integration allows users to write declarative queries that combine relational operations with semantic reasoning, enabling them to query both structured and unstructured data effortlessly. However, making semantic operations efficient at production scale poses fundamental challenges. Semantic operations are more expensive than traditional SQL operations, possess distinct latency and throughput characteristics, and their cost and selectivity are unknown during query compilation. Furthermore, existing query engines are not designed to optimize semantic operations. The AISQL query execution engine addresses these challenges through three novel techniques informed by production deployment data from Snowflake customers. First, AI-aware query optimization treats AI inference cost as a first-class optimization objective, reasoning about large language model (LLM) cost directly during query planning to achieve 2-8$\times$ speedups. Second, adaptive model cascades reduce inference costs by routing most rows through a fast proxy model while escalating uncertain cases to a powerful oracle model, achieving 2-6$\times$ speedups while maintaining 90-95% of oracle model quality. Third, semantic join query rewriting lowers the quadratic time complexity of join operations to linear through reformulation as multi-label classification tasks, achieving 15-70$\times$ speedups with often improved prediction quality. AISQL is deployed in production at Snowflake, where it powers diverse customer workloads across analytics, search, and content understanding.
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SemJoin dynamically routes semantic joins via an LLM advisor to Cluster Join or Classifier strategies, achieving 20-33 higher F1 than ABJ and lower token cost than FDJ on three datasets.
Larch uses a GNN-MDP formulation and a selectivity predictor plus dynamic programming to reorder semantic filter evaluation, cutting token usage 3x-19x versus prior systems on real and synthetic workloads.
Semantic Histograms treat semantic image filters as implicit range queries in embedding space and use two specificity estimators whose ensemble reduces end-to-end query optimization and execution overhead by up to 86%.
MLSkip demonstrates that lightweight metadata enables data skipping for ReLU-based ML filters, with 27.4% average pruning using min-max and 38.31% using 2D convex hulls on TPC benchmarks, for a 1.07x end-to-end speedup.
AvalancheBench introduces a benchmark for data agents based on recovering a known latent world from observations, reporting that the best coding agent recovers only 26% on an e-commerce case.
AADvark extends agent-aided CAD design to dynamic 3D assemblies with movable parts by integrating constraint solvers and visual feedback to create a verification signal for the agent.
Introduces the LLM ORDER BY semantic operator with algorithmic improvements, a semantic-aware external merge sort, and a budget-aware optimizer that selects near-optimal access paths for LLM-based ordering.
citing papers explorer
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PLOP: Cost-Based Placement of Semantic Operators in Hybrid Query Plans
PLOP is a cost-based optimizer that finds optimal placements for semantic LLM operators in hybrid query plans via dynamic programming, delivering up to 1.5x speedup and 4.29x cost reduction on 44 benchmark queries while preserving accuracy.
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SemJoin: Semantic Join Optimization
SemJoin dynamically routes semantic joins via an LLM advisor to Cluster Join or Classifier strategies, achieving 20-33 higher F1 than ABJ and lower token cost than FDJ on three datasets.
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Larch: Learned Query Optimization for Semantic Predicates
Larch uses a GNN-MDP formulation and a selectivity predictor plus dynamic programming to reorder semantic filter evaluation, cutting token usage 3x-19x versus prior systems on real and synthetic workloads.
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Selectivity Estimation for Semantic Filters on Image Data
Semantic Histograms treat semantic image filters as implicit range queries in embedding space and use two specificity estimators whose ensemble reduces end-to-end query optimization and execution overhead by up to 86%.
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MLSkip: Data Skipping for ML Filters via Lightweight Metadata
MLSkip demonstrates that lightweight metadata enables data skipping for ReLU-based ML filters, with 27.4% average pruning using min-max and 38.31% using 2D convex hulls on TPC benchmarks, for a 1.07x end-to-end speedup.
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AvalancheBench: Evaluating Enterprise Data Agents Through Latent World Recovery
AvalancheBench introduces a benchmark for data agents based on recovering a known latent world from observations, reporting that the best coding agent recovers only 26% on an e-commerce case.
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Agent-Aided Design for Dynamic CAD Models
AADvark extends agent-aided CAD design to dynamic 3D assemblies with movable parts by integrating constraint solvers and visual feedback to create a verification signal for the agent.
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Access Paths for Efficient Ordering with Large Language Models
Introduces the LLM ORDER BY semantic operator with algorithmic improvements, a semantic-aware external merge sort, and a budget-aware optimizer that selects near-optimal access paths for LLM-based ordering.