planet/rules.md

rules.md

必须强制执行的约束。违反时立即终止并报错。

---

Code Style - Imports

Python

# Group order: stdlib → third-party → local
import json
from datetime import datetime
from typing import List, Optional

import redis
from fastapi import APIRouter, Depends
from sqlalchemy.orm import Session

from app.core.config import settings
from app.models.user import User
from app.schemas.user import UserCreate

TypeScript

// Group order: React → Third-party → Local
import React, { useState, useEffect } from 'react';
import { useQuery } from '@tanstack/react-query';
import axios from 'axios';

import { useAuthStore } from '@/stores/auth';
import { api } from '@/services/api';

Rules:

• Sort alphabetically within groups
• Use absolute imports for external packages, relative for local modules
• NEVER use wildcard imports (from module import *)

---

Code Style - Formatting

• Python: 4-space indentation, Black formatter, max line 100
• TypeScript: 2-space indentation, Prettier, max line 100
• Run formatter before committing
• No trailing whitespace
• Empty line at end of file

---

Code Style - Type Hints

# Use strict typing - NO Any
from typing import List, Dict, Optional, Union
from datetime import datetime

def get_gpu_clusters(
    country: Optional[str] = None,
    min_gpu_count: int = 0,
) -> List[Dict[str, Union[str, int, float]]]:
    ...

Rules:

• Use type hints throughout
• NEVER use Any - use unknown or specific unions
• Define interfaces/types for all data structures
• Generic types preferred over type casting

---

Code Style - Naming Conventions

Pattern	Usage	Example
camelCase	Variables, functions, methods	gpuCluster, getData()
PascalCase	Classes, components, types	GPUCluster, DataSourceConfig
SCREAMING_SNAKE_CASE	Constants, env vars	API_KEY, DATABASE_URL
kebab-case	File names, CSS	data-source-config.css

Rules:

• Descriptive names - avoid abbreviations except well-known ones (id, ok, err)
• Max function length: 50 lines
• Max file length: 500 lines

---

Code Style - Error Handling

# Use custom exceptions
class DataSourceError(Exception):
    """Raised when data source fetch fails"""
    pass

# Proper error handling with logging
try:
    data = await fetch_data(source)
except requests.RequestException as e:
    logger.error(f"Failed to fetch from {source}: {e}")
    raise DataSourceError(f"Source {source} unavailable") from e

Rules:

• NEVER swallow errors silently
• Use custom exceptions for domain errors -区分可恢复错误和不可恢复错误
• Log errors with appropriate level (warn/error)
• Include context in all error messages
• Propagate errors to caller unless explicitly handled

---

Security - NON-NEGOTIABLE

• NEVER commit .env, secrets, keys, or credentials
• Use environment variables for all credentials
• Validate and sanitize all user inputs
• Use parameterized queries for database operations (SQL injection prevention)
• JWT tokens with short expiration (15 min)
• Redis for token blacklist (logout support)
• Hash passwords with bcrypt/argon2 - NEVER store plain text

---

Git Workflow

# Create feature branch
git checkout -b feature/data-collector-huggingface

# Commit message format
git commit -m "feat: add Hugging Face data collector"
git commit -m "fix: resolve WebSocket heartbeat timeout"
git commit -m "docs: update API documentation"

# Before opening PR
git fetch origin && git rebase origin/main
./.venv/bin/python -m pytest -s backend/tests && bun run build

Rules:

• Feature branches from main
• Clear commit messages - "Add user authentication", not "fix"
• NEVER force push to main
• Run tests and lint before committing

---

Dependencies

Rules:

• Verify package legitimacy before adding
• Prefer well-maintained, widely-used libraries
• Pin dependency versions in pyproject.toml, uv.lock, and package.json
• Review security advisories with pip-audit and bun audit
• Frontend package management and script execution use Bun only
• Frontend commands must use bun install / bun run ...
• NEVER use npm / pnpm / yarn for the frontend project
• NEVER add unknown packages

---

Data Collector Pattern - MANDATORY

class BaseCollector:
    async def fetch(self) -> List[Dict]:
        """Fetch data from source"""
        ...

    def transform(self, raw_data: Dict) -> NormalizedData:
        """Transform to internal format"""
        ...

    async def run(self):
        """Full pipeline: fetch -> transform -> save"""
        raw = await self.fetch()
        data = self.transform(raw)
        await self.save(data)

Rules:

• Each data source has its own collector class
• Collectors MUST inherit from BaseCollector
• Implement fetch() and transform() methods
• Support incremental and full sync modes

---

WebSocket Communication - MANDATORY

# Data frame format
{
    "timestamp": "2024-01-15T10:30:00Z",
    "type": "update",  # or "full"
    "payload": {
        "gpu_clusters": [...],
        "submarine_cables": [...],
        "ixp_nodes": [...]
    }
}

# Heartbeat every 30 seconds

Rules:

• UE5 communicates via WebSocket (not REST)
• Send data frames at configured intervals (default: 5 minutes)
• Include camera position in control frames
• Support auto-cruise and manual interaction modes

---

General Guidelines

• Keep functions small and focused (single responsibility)
• Write self-documenting code; comment why, not what
• One concern per file/module
• Dependency injection for testability
• Feature flags for incomplete features
• Use config files for environment-specific settings

---

Query Performance - MANDATORY

• NEVER load whole tables into Python just to do filtering, pagination, counting, dedupe, or summary aggregation
• Filters, sorting, pagination, count, distinct, and grouped statistics MUST be pushed down to the database whenever the ORM/query builder can express them
• Summary/dashboard endpoints should prefer dedicated aggregate queries or aggregate endpoints, not multiple full-table scans
• For hot paths, avoid selecting large JSON/text payload columns unless the response really needs them
• If an endpoint returns a list, default to database-side pagination instead of scalars().all() followed by Python slicing
• When you suspect a query is slow, first check for:
  • full-table ORM loads
  • Python-side post-filtering
  • repeated summary queries that can be merged
  • repeated per-request recomputation that should be cached or aggregated once