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# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
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lib/
lib64/
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sdist/
var/
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*.egg-info/
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*.egg
# Virtual environments
venv/
env/
ENV/
.venv
# IDE
.vscode/
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*.swp
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*~
# Testing
.pytest_cache/
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# Byte-compiled / optimized / DLL files
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repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.1.6
hooks:
- id: ruff
args: [--fix]
- id: ruff-format

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# Use Python 3.11 slim image
FROM python:3.11-slim
# Set environment variables
ENV PYTHONUNBUFFERED=1
ENV PYTHONDONTWRITEBYTECODE=1
# Set Aliyun mirrors for apt
RUN sed -i 's/deb.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources && \
sed -i 's/security.debian.org/mirrors.aliyun.com/g' /etc/apt/sources.list.d/debian.sources
# Install system dependencies
RUN apt-get update && apt-get install -y \
curl \
build-essential \
&& rm -rf /var/lib/apt/lists/*
# Install uv using pip with Aliyun mirror
RUN pip install --no-cache-dir -i https://mirrors.aliyun.com/pypi/simple/ uv
# Set work directory
WORKDIR /app
# Copy pyproject.toml
COPY pyproject.toml ./
# Install Python dependencies using uv with Aliyun mirror
RUN uv sync --frozen --no-install-project
# Copy application code
COPY app/ ./app/
COPY main.py ./
# Expose port
EXPOSE 8000
# Run the application
CMD ["uv", "run", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]

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.PHONY: install dev build run worker clean lint format pre-commit-install pre-commit-run
# Default target
help:
@echo "Available commands:"
@echo " install Install dependencies using uv"
@echo " dev Run FastAPI in development mode"
@echo " run Run FastAPI server"
@echo " worker Run Celery worker"
@echo " build Build Docker images"
@echo " up Start all services with Docker Compose"
@echo " down Stop all services"
@echo " clean Remove build artifacts"
@echo " lint Run ruff linting"
@echo " format Format code with ruff"
@echo " pre-commit-install Install pre-commit hooks"
@echo " pre-commit-run Run pre-commit on all files"
# Install dependencies
install:
uv sync
# Development server
dev:
uv run uvicorn main:app --reload --host 0.0.0.0 --port 8000
# Production server
run:
uv run uvicorn main:app --host 0.0.0.0 --port 8000
# Celery worker
worker:
uv run celery -A app.core.celery_app worker --loglevel=info
# Docker commands
build:
docker-compose build
up:
docker-compose up -d
down:
docker-compose down
# Clean artifacts
clean:
find . -type d -name __pycache__ -exec rm -rf {} + 2>/dev/null || true
find . -type f -name "*.pyc" -delete 2>/dev/null || true
find . -type f -name "*.pyo" -delete 2>/dev/null || true
find . -type d -name "*.egg-info" -exec rm -rf {} + 2>/dev/null || true
rm -rf .venv dist build 2>/dev/null || true
# Code quality
lint:
uv run ruff check .
format:
uv run ruff format .
# Pre-commit
pre-commit-install:
uv run pre-commit install
pre-commit-run:
uv run pre-commit run --all-files

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# FastAPI Celery Example
一个基于 FastAPI 和 Celery 的异步任务处理示例项目，展示了如何在现代 Python Web 应用中实现高效的异步任务队列。
## 项目介绍
本项目是一个完整的 FastAPI + Celery 应用示例，演示了如何：
- 使用 FastAPI 构建高性能的 REST API
- 集成 Celery 进行异步任务处理
- 使用 Redis 作为消息代理和结果后端
- 实现统一的日志管理系统
- 配置现代化的代码质量工具链
- 使用容器化技术简化部署
### 为什么选择这个架构？
- **FastAPI**: 现代化的异步 Web 框架，提供出色的性能和自动 API 文档生成
- **Celery**: 强大的分布式任务队列，支持多种消息代理
- **Redis**: 高速的内存数据库，适合作为任务队列的后端
- **Loguru**: 简单易用的日志库，支持结构化日志和文件轮转
- **uv**: 快速的 Python 包管理器，比 pip 更快
- **Ruff**: 快速的 Python 代码检查和格式化工具
- **Docker**: 容器化技术，确保环境一致性和易部署
## Features
- FastAPI web framework
- Celery for asynchronous tasks
- Redis as message broker and result backend
- Loguru for unified logging
- Docker containerization
- Code quality with Ruff and pre-commit hooks
- Dependency management with uv
## Project Structure
```
.
├── app/
│ ├── api/
│ │ ├── __init__.py
│ │ └── routes.py # API endpoints
│ ├── core/
│ │ ├── __init__.py
│ │ ├── celery_app.py # Celery configuration
│ │ ├── config.py # Application settings
│ │ └── logging.py # Loguru setup
│ └── tasks/
│ ├── __init__.py
│ └── tasks.py # Celery tasks
├── main.py # FastAPI application
├── pyproject.toml # Project dependencies and ruff config
├── Dockerfile # Docker image
├── docker-compose.yml # Multi-service setup
├── Makefile # Build and run commands
├── .pre-commit-config.yaml # Pre-commit hooks
└── README.md # This file
```
## Installation
### Prerequisites
- Python 3.11+
- uv (https://github.com/astral-sh/uv)
- Docker and Docker Compose (for containerized deployment)
### Local Development
1. Clone the repository:
```bash
git clone <repository-url>
cd fastapi-celery-example
```
2. Install dependencies:
```bash
make install
```
3. Install pre-commit hooks:
```bash
make pre-commit-install
```
## 架构说明
### 系统架构
```
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ FastAPI App │ │ Redis │ │ Celery Worker │
│ │◄──►│ Message Broker │◄──►│ │
│ • REST API │ │ Result Backend │ │ • Task Processing│
│ • Task Submission│ │ │ │ • Async Execution│
└─────────────────┘ └─────────────────┘ └─────────────────┘
```
### 工作流程
1. **客户端请求**: 用户通过 FastAPI 端点提交任务
2. **任务入队**: FastAPI 将任务发送到 Redis 消息队列
3. **异步处理**: Celery Worker 从队列中获取任务并异步执行
4. **结果存储**: 任务结果存储回 Redis
5. **结果查询**: 客户端可以通过任务 ID 查询执行结果
### 主要组件
- **app/api/routes.py**: FastAPI 路由定义，处理 HTTP 请求
- **app/core/celery_app.py**: Celery 应用配置
- **app/core/logging.py**: 统一日志配置
- **app/tasks/tasks.py**: 具体的任务实现
- **app/core/config.py**: 应用配置管理
## 用法指南
### Local Development
1. Start Redis (required for Celery):
```bash
docker run -d -p 6379:6379 redis:7-alpine
```
2. Run the FastAPI server:
```bash
make dev
```
3. In another terminal, run the Celery worker:
```bash
make worker
```
4. Visit http://localhost:8000 to access the API
### Docker Deployment
1. Build and start all services:
```bash
make up
```
2. The API will be available at http://localhost:8000
3. Stop services:
```bash
make down
```
## API 端点详解
### 基础端点
- `GET /` - 应用根端点，返回欢迎信息
### 任务提交端点
- `POST /add`
- **功能**: 提交两个整数的加法任务
- **请求体**: `{"x": int, "y": int}`
- **响应**: `{"task_id": "uuid", "status": "submitted", "message": "..."}`
- `POST /long-task`
- **功能**: 提交长时间运行的任务（用于演示异步处理）
- **请求体**: `{"duration": int}` (秒数)
- **响应**: `{"task_id": "uuid", "status": "submitted", "message": "..."}`
### 结果查询端点
- `GET /result/{task_id}`
- **功能**: 查询任务执行结果
- **响应状态**:
- `pending`: 任务正在执行
- `success`: 任务成功完成，返回结果
- `failure`: 任务执行失败，返回错误信息
## 详细使用示例
```bash
# Submit an addition task
curl -X POST "http://localhost:8000/add" \
-H "Content-Type: application/json" \
-d '{"x": 5, "y": 3}'
# Response: {"task_id": "uuid", "status": "submitted", "message": "Addition task submitted: 5 + 3"}
# Check result
curl "http://localhost:8000/result/uuid"
# Response: {"task_id": "uuid", "status": "success", "result": 8}
```
### 完整工作流程示例
```bash
# 1. 提交加法任务
curl -X POST "http://localhost:8000/add" \
-H "Content-Type: application/json" \
-d '{"x": 10, "y": 20}'
# 返回: {"task_id": "550e8400-e29b-41d4-a716-446655440000", "status": "submitted", "message": "Addition task submitted: 10 + 20"}
# 2. 立即查询结果（可能还在处理中）
curl "http://localhost:8000/result/550e8400-e29b-41d4-a716-446655440000"
# 返回: {"task_id": "550e8400-e29b-41d4-a716-446655440000", "status": "pending"}
# 3. 稍后再次查询（任务完成）
curl "http://localhost:8000/result/550e8400-e29b-41d4-a716-446655440000"
# 返回: {"task_id": "550e8400-e29b-41d4-a716-446655440000", "status": "success", "result": 30}
# 4. 提交长时间任务
curl -X POST "http://localhost:8000/long-task" \
-H "Content-Type: application/json" \
-d '{"duration": 5}'
# 5. 在另一个终端查看 Celery worker 日志
# Worker 将处理任务并记录执行过程
```
### 错误处理
当任务执行失败时，结果查询会返回：
```json
{
"task_id": "uuid",
"status": "failure",
"error": "具体错误信息"
}
```
### 任务状态说明
- **PENDING**: 任务已提交，正在等待处理
- **PROGRESS**: 任务正在执行中（如果任务支持进度报告）
- **SUCCESS**: 任务成功完成，结果可用
- **FAILURE**: 任务执行失败，包含错误信息
- **RETRY**: 任务失败后正在重试（如果配置了重试机制）
## 监控和调试
### 日志查看
应用会生成两种日志输出：
1. **控制台日志**: 实时显示应用运行状态
2. **文件日志**: 保存到 `app.log` 文件，支持自动轮转
```bash
# 查看实时日志
tail -f app.log
# 查看 FastAPI 日志
make dev
# 查看 Celery worker 日志（新终端）
make worker
```
### 健康检查
```bash
# 检查 API 健康状态
curl http://localhost:8000/
# 检查 Redis 连接
docker exec -it <redis-container> redis-cli ping
```
### 常见问题排查
1. **任务一直处于 PENDING 状态**
- 检查 Celery worker 是否正在运行
- 确认 Redis 服务是否可访问
2. **连接 Redis 失败**
- 确保 Redis 容器正在运行
- 检查网络配置和端口映射
3. **任务执行失败**
- 查看 Celery worker 的错误日志
- 检查任务代码中的异常处理
### 性能监控
- 使用 `/result/{task_id}` 端点监控任务执行时间
- 查看 `app.log` 中的任务处理日志
- 监控 Redis 的内存和连接数
## 日志系统
All logs are written to both console and `app.log` file with rotation.
## 部署和扩展
### 生产环境部署
1. **使用 Docker Compose**:
```bash
make up
```
2. **水平扩展 Worker**:
```yaml
# 在 docker-compose.yml 中增加 worker 实例
worker:
deploy:
replicas: 3 # 根据负载调整
```
3. **负载均衡**:
- 使用 Nginx 或 Traefik 作为反向代理
- 配置多个 FastAPI 实例
### 扩展建议
1. **添加任务优先级**:
```python
@celery_app.task(bind=True, priority=10)
def high_priority_task(self):
# 高优先级任务
pass
```
2. **实现任务进度跟踪**:
```python
from celery import current_task
@celery_app.task(bind=True)
def task_with_progress(self):
for i in range(100):
current_task.update_state(state='PROGRESS', meta={'progress': i})
# 执行任务逻辑
```
3. **添加任务超时和重试**:
```python
@celery_app.task(bind=True, autoretry_for=(Exception,), retry_kwargs={'max_retries': 3})
def robust_task(self):
# 任务逻辑
pass
```
4. **监控集成**:
- 集成 Prometheus/Grafana 进行监控
- 使用 Celery Events 进行任务监控
## 配置管理
Environment variables can be set in a `.env` file:
- `REDIS_URL`: Redis connection URL (default: redis://localhost:6379/0)
- `CELERY_BROKER_URL`: Celery broker URL (default: redis://localhost:6379/0)
- `CELERY_RESULT_BACKEND`: Celery result backend URL (default: redis://localhost:6379/0)
## 开发指南
### 添加新任务
1. 在 `app/tasks/tasks.py` 中定义新任务：
```python
@celery_app.task(bind=True)
def your_new_task(self, param1, param2):
log.info(f"Executing your_new_task with {param1}, {param2}")
# 任务逻辑
return result
```
2. 在 `app/api/routes.py` 中添加对应的 API 端点：
```python
@router.post("/your-task")
async def submit_your_task(param1: str, param2: int):
task = your_new_task.delay(param1, param2)
return {"task_id": task.id, "status": "submitted"}
```
### 代码规范
运行代码质量检查：
```bash
make lint # 运行 ruff 代码检查
make format # 格式化代码
make pre-commit-run # 运行所有 pre-commit 检查
```
代码规范要求：
- 使用 `uv run ruff check .` 检查代码质量
- 使用 `uv run ruff format .` 格式化代码
- 提交前运行 `make pre-commit-run` 确保所有检查通过
- 为所有新增功能添加适当的日志记录
### 测试建议
- 使用 pytest 编写单元测试
- 测试异步任务的提交和结果查询
- 验证错误处理和边界情况
- 使用测试 Redis 实例避免影响生产环境
## 贡献
欢迎提交 Issue 和 Pull Request！在提交前请确保：
1. 代码通过所有 linting 和格式化检查
2. 添加适当的测试用例
3. 更新相关文档
4. 遵循现有的代码风格和架构模式
## License
MIT License

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app/__init__.py Normal file
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from fastapi import APIRouter, BackgroundTasks
from app.tasks.tasks import add_numbers, long_running_task
from app.core.logging import log
router = APIRouter()
@router.get("/")
async def root():
"""Root endpoint"""
log.info("Root endpoint accessed")
return {"message": "FastAPI Celery Example"}
@router.post("/add")
async def add_task(x: int, y: int, background_tasks: BackgroundTasks):
"""Submit an addition task to Celery"""
log.info(f"Submitting addition task: {x} + {y}")
# Submit task to Celery
task = add_numbers.delay(x, y)
return {"task_id": task.id, "status": "submitted", "message": f"Addition task submitted: {x} + {y}"}
@router.get("/result/{task_id}")
async def get_result(task_id: str):
"""Get the result of a Celery task"""
from app.core.celery_app import celery_app
log.info(f"Checking result for task: {task_id}")
task_result = celery_app.AsyncResult(task_id)
if task_result.state == "PENDING":
return {"task_id": task_id, "status": "pending"}
elif task_result.state == "SUCCESS":
return {"task_id": task_id, "status": "success", "result": task_result.result}
else:
return {"task_id": task_id, "status": "failure", "error": str(task_result.info)}
@router.post("/long-task")
async def long_task(duration: int = 10):
"""Submit a long running task"""
log.info(f"Submitting long running task for {duration} seconds")
task = long_running_task.delay(duration)
return {"task_id": task.id, "status": "submitted", "message": f"Long running task submitted for {duration} seconds"}

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from celery import Celery
from app.core.config import settings
celery_app = Celery(
"fastapi_celery_example",
broker=settings.celery_broker_url,
backend=settings.celery_result_backend,
include=["app.tasks.tasks"],
)
# Optional configuration
celery_app.conf.update(
task_serializer="json",
accept_content=["json"],
result_serializer="json",
timezone="UTC",
enable_utc=True,
)

14
app/core/config.py Normal file
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from pydantic_settings import BaseSettings
class Settings(BaseSettings):
app_name: str = "FastAPI Celery Example"
debug: bool = True
redis_url: str = "redis://localhost:6379/0"
celery_broker_url: str = "redis://localhost:6379/0"
celery_result_backend: str = "redis://localhost:6379/0"
model_config = {"env_file": ".env"}
settings = Settings()

33
app/core/logging.py Normal file
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from loguru import logger
import sys
from pathlib import Path
def setup_logging(log_level: str = "INFO", log_file: str = "app.log"):
"""Setup logging configuration with loguru"""
# Remove default handler
logger.remove()
# Add console handler
logger.add(
sys.stdout,
level=log_level,
format="<green>{time:YYYY-MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>",
)
# Add file handler
log_path = Path(log_file)
logger.add(
log_path,
level=log_level,
format="{time:YYYY-MM-DD HH:mm:ss} | {level: <8} | {name}:{function}:{line} - {message}",
rotation="10 MB",
retention="1 week",
)
return logger
# Global logger instance
log = setup_logging()

0
app/tasks/__init__.py Normal file
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22
app/tasks/tasks.py Normal file
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from app.core.celery_app import celery_app
from app.core.logging import log
@celery_app.task(bind=True)
def add_numbers(self, x: int, y: int) -> int:
"""Simple task to add two numbers"""
log.info(f"Adding numbers: {x} + {y}")
result = x + y
log.info(f"Result: {result}")
return result
@celery_app.task(bind=True)
def long_running_task(self, duration: int = 10):
"""Simulate a long running task"""
import time
log.info(f"Starting long running task for {duration} seconds")
time.sleep(duration)
log.info("Long running task completed")
return f"Task completed after {duration} seconds"

34
docker-compose.yml Normal file
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services:
redis:
image: redis:7-alpine
ports:
- "6379:6379"
volumes:
- ./dataredis_data:/data
web:
build: .
ports:
- "8000:8000"
environment:
- REDIS_URL=redis://redis:6379/0
- CELERY_BROKER_URL=redis://redis:6379/0
- CELERY_RESULT_BACKEND=redis://redis:6379/0
depends_on:
- redis
volumes:
- ./app:/app/app
- ./main.py:/app/main.py
worker:
build: .
command: uv run celery -A app.core.celery_app worker --loglevel=info
environment:
- REDIS_URL=redis://redis:6379/0
- CELERY_BROKER_URL=redis://redis:6379/0
- CELERY_RESULT_BACKEND=redis://redis:6379/0
depends_on:
- redis
volumes:
- ./app:/app/app
- ./main.py:/app/main.py

13
main.py Normal file
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from fastapi import FastAPI
from app.api.routes import router
from app.core.config import settings
from app.core.logging import log
app = FastAPI(
title=settings.app_name,
debug=settings.debug,
)
app.include_router(router)
log.info("FastAPI application started")

28
pyproject.toml Normal file
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[project]
name = "fastapi-celery-example"
version = "0.1.0"
description = "A FastAPI example with Celery integration"
authors = [{name = "Your Name", email = "your.email@example.com"}]
dependencies = [
"fastapi>=0.104.1",
"uvicorn[standard]>=0.24.0",
"celery>=5.3.4",
"redis>=5.0.1",
"loguru>=0.7.2",
"pydantic-settings>=2.0.0",
]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["app"]
[tool.ruff.lint]
# 显式禁用 E501（虽然默认已禁）
ignore = ["E501"]
[tool.ruff]
# 或设置更宽松的行长度（影响 ruff format）
line-length = 120

13
tasks.txt Normal file
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@ -0,0 +1,13 @@
1. 使用makefile管理安装和启动服务
2. 封装loguru，涉及到log时 统一用loguru打印并保存日志
3. docker中涉及到apt、pip、uv 更新或安装时 使用阿里云镜像
4. 使用uv替代pip
5. 每个项目都需要安装并使用pre-commit，使用ruff检测并格式化代码，其中pyproject.toml的内容为：
```
[tool.ruff]
# 显式禁用 E501（虽然默认已禁）
ignore = ["E501"]
# 或设置更宽松的行长度（影响 ruff format）
line-length = 120
```

704
uv.lock generated Normal file
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requires-python = ">=3.12"
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