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Querying Everything Everywhere All at Once: Supervaluationism for the Agentic Lakehouse

Jacopo Tagliabue

Abstract

Agentic analytics is turning the lakehouse into a multi-version system: swarms of (human or AI) producers materialize competing pipelines in data branches, while (human or AI) consumers need answers without knowing the underlying data life-cycle. We demonstrate a new system that answers questions across branches rather than at a single snapshot. Our prototype focuses on a novel query path that evaluates queries under supervaluationary semantics. In the absence of comparable multi-branch querying capabilities in mainstream OLAP systems, we open source the demo code as a concrete baseline for the OLAP community.

Querying Everything Everywhere All at Once: Supervaluationism for the Agentic Lakehouse

Abstract

Agentic analytics is turning the lakehouse into a multi-version system: swarms of (human or AI) producers materialize competing pipelines in data branches, while (human or AI) consumers need answers without knowing the underlying data life-cycle. We demonstrate a new system that answers questions across branches rather than at a single snapshot. Our prototype focuses on a novel query path that evaluates queries under supervaluationary semantics. In the absence of comparable multi-branch querying capabilities in mainstream OLAP systems, we open source the demo code as a concrete baseline for the OLAP community.
Paper Structure (19 sections, 9 equations, 3 figures, 2 tables)

This paper contains 19 sections, 9 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Motivation
  3. System Overview
  4. Architecture
  5. Supervaluationism
  6. Logic
  7. Engineering
  8. Limitations and future work
  9. Demonstration scenarios
  10. Experiments
  11. Related work
  12. AI Disclaimer
  13. Supervaluationism: formal remarks
  14. Language.
  15. Branch models.
  16. ...and 4 more sections

Figures (3)

  • Figure 1: Architecture of the demo system. Agentic pipelines write predictions into isolated data branches of the lakehouse. The UI translates English questions into SQL, then executes them using a modified OLAP engine that evaluates answers across branches under supervaluationary (glut-tolerant) semantics.
  • Figure 2: Speedup of the native engine over the ad hoc strategy as a function of branch count. The JOIN query with window functions (Q3) benefits from shared-table reuse; the boolean query (Q4) achieves near-constant time via short-circuit evaluation.
  • Figure 3: Median latency (ms) vs. number of branches. Left: Q1, both engines scale linearly but the native engine's unified plan carries coordination overhead (hash-repartitioning for GROUP BY). Center: Q3, the ad hoc engine scales at ${\sim}160$ ms/branch (recomputing window functions over the shared table), while the native engine stays nearly flat thanks to CTE reuse. Right: Q4, the native engine's short-circuit optimization achieves near-constant ${\sim}11$ ms latency.