python-patterns-advanced

name	python-patterns-advanced
description	Advanced Python patterns — concurrency (threading, multiprocessing, async/await), hexagonal architecture with FastAPI, RFC 7807 error handling, memory optimization, pyproject.toml tooling, and anti-patterns. Extends python-patterns.

Python Advanced Patterns

This skill extends python-patterns with concurrency, architecture, memory optimization, and tooling.

When to Activate

• Writing concurrent or async Python code
• Designing hexagonal/clean architecture with FastAPI
• Optimizing memory usage in Python
• Configuring Python tooling (ruff, mypy, pytest)
• Reviewing anti-patterns
• Structuring a FastAPI project with domain, application, and adapter layers
• Wiring dependency injection and output port protocols without a DI framework
• Implementing RFC 7807 Problem Details error handling across all HTTP exception types

Concurrency Patterns

Threading for I/O-Bound Tasks

import concurrent.futures
import threading

def fetch_url(url: str) -> str:
    """Fetch a URL (I/O-bound operation)."""
    import urllib.request
    with urllib.request.urlopen(url) as response:
        return response.read().decode()

def fetch_all_urls(urls: list[str]) -> dict[str, str]:
    """Fetch multiple URLs concurrently using threads."""
    with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:
        future_to_url = {executor.submit(fetch_url, url): url for url in urls}
        results = {}
        for future in concurrent.futures.as_completed(future_to_url):
            url = future_to_url[future]
            try:
                results[url] = future.result()
            except Exception as e:
                results[url] = f"Error: {e}"
    return results

Multiprocessing for CPU-Bound Tasks

def process_data(data: list[int]) -> int:
    """CPU-intensive computation."""
    return sum(x ** 2 for x in data)

def process_all(datasets: list[list[int]]) -> list[int]:
    """Process multiple datasets using multiple processes."""
    with concurrent.futures.ProcessPoolExecutor() as executor:
        results = list(executor.map(process_data, datasets))
    return results

Async/Await for Concurrent I/O

import asyncio

async def fetch_async(url: str) -> str:
    """Fetch a URL asynchronously."""
    import aiohttp
    async with aiohttp.ClientSession() as session:
        async with session.get(url) as response:
            return await response.text()

async def fetch_all(urls: list[str]) -> dict[str, str]:
    """Fetch multiple URLs concurrently."""
    tasks = [fetch_async(url) for url in urls]
    results = await asyncio.gather(*tasks, return_exceptions=True)
    return dict(zip(urls, results))

Package Organization

Hexagonal Project Layout (FastAPI / Clean Python)

For services with real domain logic, use hexagonal structure. Protocol is the natural fit for ports in Python:

src/
  domain/
    model/          # Pure Python dataclasses — zero framework imports
      market.py     # Market, MarketStatus, create_market(), publish_market()
      money.py      # Money, add_money() — value object
    port/
      in_/          # Input port ABCs / Protocols
        use_cases.py
      out/          # Output port Protocols
        repositories.py
    event/          # Domain event dataclasses
  application/
    use_cases/      # Use case implementations
      create_market.py
  adapter/
    in_/
      http/         # FastAPI routers + Pydantic request/response models
    out/
      persistence/  # SQLAlchemy/Prisma repositories implementing output ports
      client/       # External API clients
  config/
    container.py    # DI wiring (manual or with dependency-injector)
tests/
  unit/             # Test use cases with mocked ports — no DB, no HTTP
  integration/      # Test adapters against real infrastructure

Domain Model — Zero Framework Imports

# domain/model/market.py
from __future__ import annotations
from dataclasses import dataclass, replace
from enum import Enum


class MarketStatus(Enum):
    DRAFT = "DRAFT"
    ACTIVE = "ACTIVE"


class InvalidMarketError(Exception):
    pass


class MarketAlreadyPublishedError(Exception):
    pass


@dataclass(frozen=True)
class Market:
    id: str | None
    name: str
    slug: str
    status: MarketStatus = MarketStatus.DRAFT


def create_market(name: str, slug: str) -> Market:
    """Factory — enforces creation invariants."""
    if not name or not name.strip():
        raise InvalidMarketError("name is required")
    return Market(id=None, name=name.strip(), slug=slug)


def publish_market(market: Market) -> Market:
    """Behavior function — returns new immutable Market."""
     market.status != MarketStatus.DRAFT:
         MarketAlreadyPublishedError()
     replace(market, status=MarketStatus.ACTIVE)

Output Port — Protocol (Structural Typing)

# domain/port/out/repositories.py
from typing import Protocol
from domain.model.market import Market, MarketStatus


class MarketRepository(Protocol):
    async def save(self, market: Market) -> Market: ...
    async def find_by_slug(self, slug: str) -> Market | None: ...
    async def find_all(
        self, status: MarketStatus | None = None, limit: int = 20, offset: int = 0
    ) -> list[Market]: ...

Use Case Implementation

# application/use_cases/create_market.py
from domain.model.market import Market, create_market
from domain.port.out.repositories import MarketRepository


class CreateMarketUseCase:
    def __init__(self, repository: MarketRepository) -> None:  # output port injected
        self._repository = repository

    async def execute(self, name: str, slug: str) -> Market:
        market = create_market(name, slug)           # domain logic
        return await self._repository.save(market)  # persistence via port

Inbound Adapter — FastAPI Router

# adapter/in_/http/market_router.py
from fastapi import APIRouter, HTTPException, status
from pydantic import BaseModel, field_validator
from application.use_cases.create_market import CreateMarketUseCase
from domain.model.market import InvalidMarketError

router = APIRouter(prefix="/markets", tags=["markets"])


class CreateMarketRequest(BaseModel):
    name: str
    slug: str

    @field_validator("slug")
    @classmethod
    def validate_slug(cls, v: str) -> str:
        import re
        if not re.match(r"^[a-z0-9-]+$", v):
            raise ValueError("slug must be lowercase alphanumeric with hyphens")
        return v


class MarketResponse(BaseModel):
    name: str
    slug: str
    status: str


def create_market_router(use_case: CreateMarketUseCase) -> APIRouter:
    @router.post("/", status_code=status.HTTP_201_CREATED, response_model=MarketResponse)
    async def () -> MarketResponse:
        
        market =  use_case.execute(body.name, body.slug)
         MarketResponse(name=market.name, slug=market.slug, status=market.status.value)
     router

Error Handling — RFC 7807 / RFC 9457 Problem Details

All HTTP errors must use Content-Type: application/problem+json. Register exception handlers on app startup:

# adapter/in_/http/exception_handlers.py
from fastapi import FastAPI, Request
from fastapi.exceptions import RequestValidationError
from fastapi.responses import JSONResponse
from domain.model.market import InvalidMarketError, MarketAlreadyPublishedError


def problem_response(status: int, type_: str, title: str,
                     detail: str | None = None, instance: str | None = None,
                     **extensions) -> JSONResponse:
    body = {
        "type": type_,
        "title": title,
        "status": status,
        **({"detail": detail} if detail else {}),
        **({"instance": instance} if instance else {}),
        **extensions,
    }
    return JSONResponse(content=body, status_code=status,
                        headers={"Content-Type": "application/problem+json"})


def register_exception_handlers(app: FastAPI) -> None:

    @app.exception_handler(RequestValidationError)
    async def handle_validation(request: Request, exc: RequestValidationError) -> JSONResponse:
        return problem_response(
            ,
            ,
            ,
            detail=,
            instance=(request.url),
            errors=[{: .join((l)  l  e[]), : e[]}
                     e  exc.errors()],
        )


      () -> JSONResponse:
         problem_response(
            ,
            ,
            ,
            detail=(exc),
            instance=(request.url),
        )


      () -> JSONResponse:
         problem_response(
            ,
            ,
            ,
            detail=(exc),
            instance=(request.url),
        )


      () -> JSONResponse:
         problem_response(, , )

See skill: problem-details for the full RFC 7807/9457 specification and field reference.

DI Wiring — FastAPI Dependency Injection or Manual

# config/container.py
from sqlalchemy.ext.asyncio import AsyncSession
from adapter.out.persistence.market_repo import SqlAlchemyMarketRepository
from application.use_cases.create_market import CreateMarketUseCase


def get_create_market_use_case(session: AsyncSession) -> CreateMarketUseCase:
    repo = SqlAlchemyMarketRepository(session)  # outbound adapter
    return CreateMarketUseCase(repo)            # use case with injected port


# In FastAPI app setup:
# register_exception_handlers(app)
# app.include_router(create_market_router(get_create_market_use_case(session)))

Testing Use Cases (Mock the Protocol)

# tests/unit/test_create_market.py
import pytest
from unittest.mock import AsyncMock, MagicMock
from application.use_cases.create_market import CreateMarketUseCase
from domain.model.market import Market, MarketStatus, InvalidMarketError


class MockMarketRepository:
    """Inline mock satisfying MarketRepository Protocol."""
    def __init__(self):
        self.saved: list[Market] = []

    async def save(self, market: Market) -> Market:
        saved = Market(id="market-1", name=market.name, slug=market.slug, status=market.status)
        self.saved.append(saved)
        return saved

    async def find_by_slug(self, slug: str) -> Market | None:
        return None

    async def find_all(self, status=None, limit=20, offset=0) -> list[Market]:
        return []


@pytest.mark.asyncio
async def test_create_market_saves_and_returns():
    repo = MockMarketRepository()
    use_case = CreateMarketUseCase(repo)

    market = await use_case.execute(, )

     market.name == 
     (repo.saved) == 



  ():
    use_case = CreateMarketUseCase(MockMarketRepository())

     pytest.raises(InvalidMarketError):
         use_case.execute(, )

Note: This hexagonal structure is for FastAPI and clean Python backends. For Django, do not use this structure — Django's ORM couples domain and persistence by design. See skill: django-patterns for Django-specific DDD patterns.

Import Conventions

# Good: Import order - stdlib, third-party, local
import os
import sys
from pathlib import Path

import requests
from fastapi import FastAPI

from mypackage.models import User
from mypackage.utils import format_name

# Good: Use isort for automatic import sorting
# pip install isort

__init__.py for Package Exports

# mypackage/__init__.py
"""mypackage - A sample Python package."""

__version__ = "1.0.0"

# Export main classes/functions at package level
from mypackage.models import User, Post
from mypackage.utils import format_name

__all__ = ["User", "Post", "format_name"]

Memory and Performance

Using __slots__ for Memory Efficiency

# Bad: Regular class uses __dict__ (more memory)
class Point:
    def __init__(self, x: float, y: float):
        self.x = x
        self.y = y

# Good: __slots__ reduces memory usage
class Point:
    __slots__ = ['x', 'y']

    def __init__(self, x: float, y: float):
        self.x = x
        self.y = y

Generator for Large Data

# Bad: Returns full list in memory
def read_lines(path: str) -> list[str]:
    with open(path) as f:
        return [line.strip() for line in f]

# Good: Yields lines one at a time
def read_lines(path: str) -> Iterator[str]:
    with open(path) as f:
        for line in f:
            yield line.strip()

Avoid String Concatenation in Loops

# Bad: O(n²) due to string immutability
result = ""
for item in items:
    result += str(item)

# Good: O(n) using join
result = "".join(str(item) for item in items)

# Good: Using StringIO for building
from io import StringIO

buffer = StringIO()
for item in items:
    buffer.write(str(item))
result = buffer.getvalue()

Python Tooling Integration

Essential Commands

# Code formatting (ruff replaces black + isort)
ruff format .
ruff check --fix .

# Linting
ruff check .

# Type checking
mypy .

# Testing
pytest --cov=mypackage --cov-report=html

# Security scanning
bandit -r .

# Dependency management
pip-audit

pyproject.toml Configuration

[project]
name = "mypackage"
version = "1.0.0"
requires-python = ">=3.13"
dependencies = [
    "requests>=2.32.0",
    "pydantic>=2.10.0",
]

[project.optional-dependencies]
dev = [
    "pytest>=8.3.0",
    "pytest-cov>=6.0.0",
    "ruff>=0.11.0",
    "mypy>=1.15.0",
]

[tool.ruff]
line-length = 88

[tool.ruff.lint]
select = ["E", "F", "I", "N", "W"]

[tool.ruff.format]
# ruff format replaces black — no [tool.black] needed

[tool.mypy]
python_version = "3.13"
warn_return_any = true
warn_unused_configs = true
disallow_untyped_defs = true

[tool.pytest.ini_options]
testpaths = ["tests"]
addopts = "--cov=mypackage --cov-report=term-missing"

Quick Reference: Python Idioms

Idiom	Description
EAFP	Easier to Ask Forgiveness than Permission
Context managers	Use with for resource management
List comprehensions	For simple transformations
Generators	For lazy evaluation and large datasets
Type hints	Annotate function signatures
Dataclasses	For data containers with auto-generated methods
__slots__	For memory optimization
f-strings	For string formatting
pathlib.Path	For path operations
enumerate	For index-element pairs in loops

Anti-Patterns to Avoid

# Bad: Mutable default arguments
def append_to(item, items=[]):
    items.append(item)
    return items

# Good: Use None and create new list
def append_to(item, items=None):
    if items is None:
        items = []
    items.append(item)
    return items

# Bad: Checking type with type()
if type(obj) == list:
    process(obj)

# Good: Use isinstance
if isinstance(obj, list):
    process(obj)

# Bad: Comparing to None with ==
if value == None:
    process()

# Good: Use is
if value is None:
    process()

# Bad: from module import *
from os.path import *

# Good: Explicit imports
from os.path import join, exists

# Bad: Bare except
try:
    risky_operation()
except:
    pass

# Good: Specific exception
try:
    risky_operation()
except SpecificError as e:
    logger.error(f"Operation failed: {e}")

Remember: Python code should be readable, explicit, and follow the principle of least surprise. When in doubt, prioritize clarity over cleverness.

NamedTuples and Decorators are covered in python-patterns base skill. Use NamedTuple over plain tuple for named field access; use class-based decorators for stateful wrapping.