Problem space: Distributed databases across shards and heterogeneous storage backends (relational, document, time-series) struggle with cross-shard queries, join planning, and pushdown optimization under varying data locality and bandwidth constraint
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README.md

CatOpt-Query: MVP scaffolding for a category-theoretic distributed query planning framework.

Overview

  • A minimal, production-oriented Python MVP that models local shard plans (Objects), inter-shard signals (Morphisms), and vendor adapters (Functors).
  • Includes a canonical representation, two starter adapters (PostgreSQL and MongoDB), and a simple coordinator to fuse local plans.
  • Designed as a stepping stone toward the 812 week MVP plan described in the project proposal.

What youll find here

  • Core data models for LocalProblem, SharedVariables, DualVariables, PlanDelta, DataContract, and AuditLog.
  • A CanonicalPlan representation and a naive joint-planning coordinator.
  • Adapters for PostgreSQL and MongoDB that map local plans to the canonical representation.
  • Tests validating protocol serialization, adapter mappings, and basic joint planning behavior.
  • A lightweight protocol registry (Graph-of-Contracts concept) skeleton and a small DSL skeleton (data classes-only).

Getting started

  • Install: python3 -m build && pip install dist/catopt_query-0.1.0-py3-none-any.whl
  • Run tests: pytest -q

Notes

  • This is an MVP scaffold. It focuses on correctness, testability, and incremental extensibility for the larger CatOpt-Query project.
  • No external DB calls are required for the MVP tests; adapters simulate plan mapping.

License: MIT