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Semantic Caching for OLAP via LLM-Based Query Canonicalization (Extended Version)

Laurent Bindschaedler

TL;DR

A safety-first middleware cache for dashboard-style OLAP over star schemas is introduced that canonicalizes both SQL and NL into a unified key space -- the OLAP Intent Signature -- capturing measures, grouping levels, filters, and time windows.

Abstract

Analytical workloads exhibit substantial semantic repetition, yet most production caches key entries by SQL surface form (text or AST), fragmenting reuse across BI tools, notebooks, and NL interfaces. We introduce a safety-first middleware cache for dashboard-style OLAP over star schemas that canonicalizes both SQL and NL into a unified key space -- the OLAP Intent Signature -- capturing measures, grouping levels, filters, and time windows. Reuse requires exact intent matches under strict schema validation and confidence-gated NL acceptance; two correctness-preserving derivations (roll-up, filter-down) extend coverage without approximate matching. Across TPC-DS, SSB, and NYC TLC (1,395 queries), we achieve 82% hit rate versus 28% (text) and 56% (AST) with zero false hits; derivations double hit rate on hierarchical queries.

Semantic Caching for OLAP via LLM-Based Query Canonicalization (Extended Version)

TL;DR

A safety-first middleware cache for dashboard-style OLAP over star schemas is introduced that canonicalizes both SQL and NL into a unified key space -- the OLAP Intent Signature -- capturing measures, grouping levels, filters, and time windows.

Abstract

Analytical workloads exhibit substantial semantic repetition, yet most production caches key entries by SQL surface form (text or AST), fragmenting reuse across BI tools, notebooks, and NL interfaces. We introduce a safety-first middleware cache for dashboard-style OLAP over star schemas that canonicalizes both SQL and NL into a unified key space -- the OLAP Intent Signature -- capturing measures, grouping levels, filters, and time windows. Reuse requires exact intent matches under strict schema validation and confidence-gated NL acceptance; two correctness-preserving derivations (roll-up, filter-down) extend coverage without approximate matching. Across TPC-DS, SSB, and NYC TLC (1,395 queries), we achieve 82% hit rate versus 28% (text) and 56% (AST) with zero false hits; derivations double hit rate on hierarchical queries.
Paper Structure (52 sections, 2 figures, 5 tables)

This paper contains 52 sections, 2 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Background and Motivation
  3. Sources of Cache Fragmentation
  4. Motivating Insight
  5. System Design
  6. Scope and Assumptions
  7. Scope Coverage.
  8. Terminology.
  9. Architecture
  10. OLAP Intent Signature
  11. Query Model and Join Handling.
  12. Canonicalization
  13. SQL $\rightarrow$ Signature.
  14. NL $\rightarrow$ Signature.
  15. Validation and Cache Lookup
  16. ...and 37 more sections

Figures (2)

  • Figure 1: Architecture: queries are canonicalized, validated, and hashed as cache keys. Hits return cached results; misses execute on backend. Validation failures bypass the cache (dashed path). Derivation checks (roll-up, filter-down) occur within cache lookup (not shown).
  • Figure 2: Example: an NL question and equivalent SQL query produce identical OLAP Intent Signatures.