build(agent): new-agents#a6e6ec iteration
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node_modules/
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.npmrc
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.env
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.env.*
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__tests__/
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coverage/
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dist/
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build/
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.cache/
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*.log
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.DS_Store
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tmp/
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.tmp/
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__pycache__/
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*.pyc
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.venv/
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venv/
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*.egg-info/
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.pytest_cache/
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READY_TO_PUBLISH
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# AGENTS.md
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Architecture and rules for this repository (CatOpt-Query - MVP)
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- Tech stack: Python 3.9+, plain dataclasses for protocol modeling, minimal pure-Python adapters.
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- Testing: pytest-based unit tests.
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- Packaging: pyproject.toml with setuptools build backend. README.md hooks into packaging.
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- Development rules:
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- Do not add backward-incompatible changes unless explicitly required.
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- Tests must pass before publishing (test.sh will enforce this).
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- Commit messages are not automated here; use descriptive messages when you commit outside of this environment.
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- File layout:
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- catopt_query/: library code (protocol models, core, adapters).
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- tests/: unit tests.
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- README.md, AGENTS.md: project docs and governance.
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- MVP goals and acceptance:
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- Protocol modelling correctness (LocalProblem, SharedVariables, etc.).
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- Canonical representation and adapter mapping exist and are testable.
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- Packaging builds and tests pass.
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24
README.md
24
README.md
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# catopt-query-category-theoretic-composit
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CatOpt-Query: MVP scaffolding for a category-theoretic distributed query planning framework.
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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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Overview
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- A minimal, production-oriented Python MVP that models local shard plans (Objects), inter-shard signals (Morphisms), and vendor adapters (Functors).
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- Includes a canonical representation, two starter adapters (PostgreSQL and MongoDB), and a simple coordinator to fuse local plans.
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- Designed as a stepping stone toward the 8–12 week MVP plan described in the project proposal.
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What you’ll find here
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- Core data models for LocalProblem, SharedVariables, DualVariables, PlanDelta, DataContract, and AuditLog.
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- A CanonicalPlan representation and a naive joint-planning coordinator.
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- Adapters for PostgreSQL and MongoDB that map local plans to the canonical representation.
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- Tests validating protocol serialization, adapter mappings, and basic joint planning behavior.
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- A lightweight protocol registry (Graph-of-Contracts concept) skeleton and a small DSL skeleton (data classes-only).
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Getting started
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- Install: python3 -m build && pip install dist/catopt_query-0.1.0-py3-none-any.whl
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- Run tests: pytest -q
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Notes
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- This is an MVP scaffold. It focuses on correctness, testability, and incremental extensibility for the larger CatOpt-Query project.
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- No external DB calls are required for the MVP tests; adapters simulate plan mapping.
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License: MIT
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from .protocol import LocalProblem, SharedVariables, DualVariables, PlanDelta, DataContract, AuditLog
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from .core import CanonicalPlan, aggregate_joint_plan, map_local_to_canonical
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from .adapters.postgres_adapter import map_postgres_to_canonical
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from .adapters.mongo_adapter import map_mongo_to_canonical
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__all__ = [
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"LocalProblem",
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"SharedVariables",
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"DualVariables",
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"PlanDelta",
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"DataContract",
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"AuditLog",
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"CanonicalPlan",
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"aggregate_joint_plan",
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"map_local_to_canonical",
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"map_postgres_to_canonical",
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"map_mongo_to_canonical",
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]
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from .postgres_adapter import map_postgres_to_canonical
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from .mongo_adapter import map_mongo_to_canonical
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__all__ = ["map_postgres_to_canonical", "map_mongo_to_canonical"]
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from __future__ import annotations
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from typing import Dict, Any
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from ..core import CanonicalPlan
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from ..protocol import LocalProblem
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from ..core import map_local_to_canonical
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def map_mongo_to_canonical(local: LocalProblem) -> CanonicalPlan:
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# Lightweight adapter: MongoDB-like LocalProblem to CanonicalPlan
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return map_local_to_canonical(local)
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from __future__ import annotations
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from typing import Dict, Any
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from ..core import CanonicalPlan
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from ..protocol import LocalProblem
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from ..core import map_local_to_canonical
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def map_postgres_to_canonical(local: LocalProblem) -> CanonicalPlan:
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# Very lightweight adapter: map a PostgreSQL-style LocalProblem into a CanonicalPlan
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# In a full implementation, this would translate Postgres-specific plan elements.
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return map_local_to_canonical(local)
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from __future__ import annotations
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from dataclasses import dataclass, field, asdict
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from typing import List, Dict, Any
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import uuid
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import time
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from .protocol import LocalProblem, PlanDelta, SharedVariables, DualVariables
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@dataclass
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class CanonicalPlan:
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plan_id: str
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operations: List[Dict[str, Any]] = field(default_factory=list)
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total_cost: float = 0.0
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details: Dict[str, Any] = field(default_factory=dict)
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def to_dict(self) -> Dict[str, Any]:
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return asdict(self)
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def map_local_to_canonical(local: LocalProblem) -> CanonicalPlan:
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# Minimal translation: convert local plan into a single operation with its cost summary
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op = {
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"shard": local.shard_id,
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"predicates": local.predicates,
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"attributes": local.projected_attributes,
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"costs": local.costs,
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"constraints": local.constraints,
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}
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total = sum(local.costs.values()) if local.costs else 0.0
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return CanonicalPlan(plan_id=str(uuid.uuid4()), operations=[op], total_cost=total, details={"shard_alias": local.shard_id})
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def aggregate_joint_plan(local_plans: List[CanonicalPlan]) -> CanonicalPlan:
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# Naive aggregation: concatenates operations and sums costs
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all_ops: List[Dict[str, Any]] = []
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total = 0.0
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for p in local_plans:
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all_ops.extend(p.operations)
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total += p.total_cost
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return CanonicalPlan(plan_id=str(uuid.uuid4()), operations=all_ops, total_cost=total, details={"source_count": len(local_plans)})
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def aggregate_joint_plan_from_locals(locals: List[LocalProblem]) -> CanonicalPlan:
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cans = [map_local_to_canonical(l) for l in locals]
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return aggregate_joint_plan(cans)
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from __future__ import annotations
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from dataclasses import dataclass, field, asdict
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from typing import Dict, List, Any
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@dataclass
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class LocalProblem:
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shard_id: str
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projected_attributes: List[str]
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predicates: List[str]
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costs: Dict[str, float]
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constraints: Dict[str, Any] = field(default_factory=dict)
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def to_dict(self) -> Dict[str, Any]:
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return asdict(self)
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@dataclass
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class SharedVariables:
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version: int
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payload: Dict[str, Any] = field(default_factory=dict)
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@dataclass
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class DualVariables:
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version: int
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payload: Dict[str, Any] = field(default_factory=dict)
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@dataclass
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class PlanDelta:
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delta_id: str
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timestamp: float
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changes: Dict[str, Any] = field(default_factory=dict)
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@dataclass
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class DataContract:
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schemas: Dict[str, Any] = field(default_factory=dict)
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encryption_rules: Dict[str, Any] = field(default_factory=dict)
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@dataclass
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class AuditLog:
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entries: List[str] = field(default_factory=list)
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[build-system]
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requires = ["setuptools>=61.0", "wheel"]
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build-backend = "setuptools.build_meta"
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[project]
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name = "catopt_query"
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version = "0.1.0"
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description = "MVP: Category-Theoretic Compositional Optimizer for Distributed Query Planning"
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readme = "README.md"
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requires-python = ">=3.9"
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license = { text = "MIT" }
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authors = [ { name = "OpenCode" } ]
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dependencies = []
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[tool.setuptools.packages.find]
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where = ["."]
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#!/usr/bin/env bash
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set -euo pipefail
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# Ensure the repository root is on PYTHONPATH so tests can import the local package
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# even when the package isn't installed yet in the environment.
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export PYTHONPATH=$(pwd)${PYTHONPATH:+":$PYTHONPATH"}
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echo "Running tests..."
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pytest -q
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echo "Running packaging build..."
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python3 -m build
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echo "All tests and build succeeded."
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from catopt_query.adapters.postgres_adapter import map_postgres_to_canonical
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from catopt_query.adapters.mongo_adapter import map_mongo_to_canonical
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from catopt_query.protocol import LocalProblem
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from catopt_query.core import CanonicalPlan
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def test_postgres_adapter_maps_to_canonical():
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lp = LocalProblem(
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shard_id="pg-1",
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projected_attributes=["a"],
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predicates=["a > 1"],
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costs={"cpu": 1.0},
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)
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can = map_postgres_to_canonical(lp)
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assert isinstance(can, CanonicalPlan)
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assert can.total_cost == 1.0
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def test_mongo_adapter_maps_to_canonical():
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lp = LocalProblem(
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shard_id="mongo-1",
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projected_attributes=["b"],
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predicates=["b != NULL"],
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costs={"io": 0.2},
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)
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can = map_mongo_to_canonical(lp)
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assert isinstance(can, CanonicalPlan)
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assert can.total_cost == 0.2
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import math
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from catopt_query.protocol import LocalProblem, SharedVariables, DualVariables, PlanDelta, DataContract, AuditLog
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from catopt_query.core import map_local_to_canonical, aggregate_joint_plan
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from catopt_query.core import CanonicalPlan
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def test_local_problem_serialization():
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lp = LocalProblem(
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shard_id="shard-1",
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projected_attributes=["a", "b"],
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predicates=["a > 0", "b < 100"],
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costs={"cpu": 1.2, "io": 0.5},
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constraints={"timezone": "UTC"},
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)
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d = lp.to_dict()
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assert d["shard_id"] == "shard-1"
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assert d["predicates"] == ["a > 0", "b < 100"]
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def test_local_to_canonical_mapping():
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lp = LocalProblem(
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shard_id="s1",
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projected_attributes=["x"],
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predicates=["x IS NOT NULL"],
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costs={"cpu": 2.0},
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)
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can = map_local_to_canonical(lp)
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assert isinstance(can, CanonicalPlan)
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assert can.total_cost == 2.0
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assert len(can.operations) == 1
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