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MAP Query Architecture — Algebra-First Approach (DRAFT)

Core Principle

Decouple declarative graph query languages from graph execution by introducing a MAP-native Graph Algebra as the stable internal intermediate representation (IR).

1. Holons Core: Graph Algebra Execution Layer

  • MAP Holons Core implements a set of graph algebra operations (IR).
  • Operations are composable, imperative, and deterministic.

  • Exposed via API / TypeScript SDK for:

    • Programmatic graph navigation
    • Internal system use
    • Tooling and UX layers

Examples of algebraic ops (illustrative):

  • matchNode, matchEdge
  • traverse
  • filter
  • project
  • join
  • group, aggregate
  • sort, limit

This layer is:

  • Language-agnostic
  • Optimizable
  • The true execution substrate of MAP

2. Declarative Query Engine (Front-End)

  • OpenCypher is the initial declarative query language.
  • Chosen as a stepping stone toward ISO GQL.

  • The Query Engine:

    • Parses OpenCypher
    • Transforms it into MAP Graph Algebra
    • Does not execute queries directly

This preserves:

  • Standards alignment
  • Long-term GQL compatibility
  • Freedom to evolve execution semantics independently

3. Design Space for Query Optimization

  • Graph Algebra acts as an IR suitable for optimization:

    • Reordering operations
    • Predicate pushdown
    • Cost-based planning
    • Lazy or distributed execution (future)

Optimization occurs between parsing and execution, not in the language layer.

4. Algebra Command Log

  • Executed algebra operations are recorded as a command log.

  • The log represents:

    • User-guided graph navigation
    • Programmatic exploration paths
    • System-driven query execution

Properties:

  • Serializable
  • Replayable
  • Deterministic

5. Algebra → Declarative Translation (Replay & Sharing)

  • Algebra command logs can be translated back into OpenCypher:

    • Save user navigation as declarative queries
    • Enable later replay
    • Support sharing and reproducibility
    • Over time, the same logs can target ISO GQL.

This creates a reversible loop:

  • Declarative → Algebra → Execution
  • Imperative navigation → Algebra → Declarative

6. Strategic Outcomes

  • Clean separation of concerns:

    • Language ≠ Execution

    • Future-proofing:

    • OpenCypher today

    • ISO GQL tomorrow

    • Rich UX possibilities:

    • Explainable navigation

    • AI-assisted query generation
    • Shareable graph workflows

Summary

MAP treats Graph Algebra as the truth.

Declarative query languages are:

  • Compilers into algebra
  • Not execution engines themselves

This architecture enables:

  • Standards compliance
  • Optimization
  • Replayability
  • Long-term evolvability of the platform