3、Python持续集成与部署

Python持续集成与部署

1. 持续集成(CI)基础

持续集成是一种软件开发实践，团队成员频繁地将代码集成到共享仓库中，每次集成都通过自动化构建和测试进行验证，从而尽早发现集成错误。

1.1 持续集成的核心原则

mindmap root((持续集成
原则)) 频繁提交每天至少提交一次小批量提交自动化构建一键构建构建服务器自动化测试单元测试集成测试快速反馈构建状态可视化失败通知主干开发避免长期分支特性开关

1.2 持续集成工作流

sequenceDiagram participant D as 开发者 participant R as 代码仓库 participant CI as CI服务器 D->>D: 本地开发 D->>D: 本地测试 D->>R: 提交代码 R->>CI: 触发CI流程 CI->>CI: 代码检出 CI->>CI: 安装依赖 CI->>CI: 静态代码分析 CI->>CI: 运行测试 CI->>CI: 构建应用 CI->>D: 反馈结果

1.3 常见CI工具

GitHub Actions: GitHub集成的CI/CD服务
GitLab CI/CD: GitLab集成的CI/CD服务
Jenkins: 开源自动化服务器
CircleCI: 云端CI/CD服务
Travis CI: 开源项目流行的CI服务

2. GitHub Actions详解

GitHub Actions是GitHub提供的CI/CD服务，它允许您自动化软件开发工作流程。

2.1 基本概念

工作流(Workflow): 由一个或多个作业组成的可配置自动化过程
事件(Event): 触发工作流的特定活动，如push、pull request等
作业(Job): 在同一运行器上执行的一组步骤
步骤(Step): 可以运行命令或操作的单个任务
操作(Action): 可重用的工作流组件
运行器(Runner): 运行工作流的服务器

2.2 工作流配置文件

GitHub Actions工作流使用YAML文件定义，存储在仓库的.github/workflows目录中。

yaml 复制代码

# .github/workflows/python-ci.yml
name: Python CI

on:
  push:
    branches: [ main, develop ]
  pull_request:
    branches: [ main, develop ]

jobs:
  test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: [3.8, 3.9, 3.10]

    steps:
    - uses: actions/checkout@v2
    - name: Set up Python ${{ matrix.python-version }}
      uses: actions/setup-python@v2
      with:
        python-version: ${{ matrix.python-version }}
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install flake8 pytest
        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
    - name: Lint with flake8
      run: |
        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
    - name: Test with pytest
      run: |
        pytest

2.3 高级功能

矩阵构建

矩阵构建允许您在不同环境中测试代码：

yaml 复制代码

strategy:
  matrix:
    python-version: [3.8, 3.9, 3.10]
    os: [ubuntu-latest, windows-latest, macos-latest]

缓存依赖

缓存依赖可以加速工作流：

yaml 复制代码

- name: Cache pip dependencies
  uses: actions/cache@v2
  with:
    path: ~/.cache/pip
    key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements.txt') }}
    restore-keys: |
      ${{ runner.os }}-pip-

工作流之间的数据共享

使用工件(Artifacts)在作业之间共享数据：

yaml 复制代码

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      - name: Build package
        run: python setup.py sdist bdist_wheel
      - name: Upload artifacts
        uses: actions/upload-artifact@v2
        with:
          name: dist
          path: dist/
          
  publish:
    needs: build
    runs-on: ubuntu-latest
    steps:
      - name: Download artifacts
        uses: actions/download-artifact@v2
        with:
          name: dist
          path: dist/
      - name: Publish to PyPI
        uses: pypa/gh-action-pypi-publish@release/v1
        with:
          user: __token__
          password: ${{ secrets.PYPI_API_TOKEN }}

flowchart TD A[构建作业] -->|上传工件| B[工件存储] B -->|下载工件| C[发布作业] style A fill:#d0e0ff,stroke:#333,stroke-width:2px style B fill:#ffe0d0,stroke:#333,stroke-width:2px style C fill:#d0ffe0,stroke:#333,stroke-width:2px

3. 持续部署(CD)基础

持续部署是持续集成的扩展，它自动将通过测试的代码部署到生产环境或类生产环境中。

3.1 持续部署流程

graph LR A[代码提交] --> B[持续集成] B --> C{测试通过?} C -->|是| D[自动部署到测试环境] C -->|否| E[通知开发者] D --> F[自动化测试] F --> G{测试通过?} G -->|是| H[自动部署到生产环境] G -->|否| E style A fill:#d0e0ff,stroke:#333,stroke-width:2px style B fill:#ffe0d0,stroke:#333,stroke-width:2px style C fill:#ffd0e0,stroke:#333,stroke-width:2px style D fill:#d0ffe0,stroke:#333,stroke-width:2px style E fill:#e0d0ff,stroke:#333,stroke-width:2px style F fill:#ffffd0,stroke:#333,stroke-width:2px style G fill:#d0ffff,stroke:#333,stroke-width:2px style H fill:#ffd0ff,stroke:#333,stroke-width:2px

3.2 部署策略

蓝绿部署

蓝绿部署维护两个相同的生产环境，一个活跃(蓝)，一个待命(绿)。新版本部署到待命环境，测试通过后，将流量切换到待命环境。

sequenceDiagram participant U as 用户 participant LB as 负载均衡器 participant B as 蓝环境(当前活跃) participant G as 绿环境(待命) U->>LB: 请求 LB->>B: 转发请求 Note over G: 部署新版本 Note over G: 测试新版本 Note over LB: 切换流量 U->>LB: 请求 LB->>G: 转发请求 Note over B: 现在是待命环境

金丝雀部署

金丝雀部署将新版本逐步推送给一小部分用户，然后逐渐增加比例，直到所有用户都使用新版本。

pie title "金丝雀部署流量分配" "新版本" : 10 "旧版本" : 90

pie title "金丝雀部署流量分配(50%)" "新版本" : 50 "旧版本" : 50

pie title "金丝雀部署流量分配(100%)" "新版本" : 100 "旧版本" : 0

滚动部署

滚动部署逐步更新服务实例，一次更新一小部分实例，直到所有实例都更新完成。

graph TD subgraph "步骤1" A1[实例1: 旧版本] A2[实例2: 旧版本] A3[实例3: 旧版本] A4[实例4: 旧版本] end subgraph "步骤2" B1[实例1: 新版本] B2[实例2: 旧版本] B3[实例3: 旧版本] B4[实例4: 旧版本] end subgraph "步骤3" C1[实例1: 新版本] C2[实例2: 新版本] C3[实例3: 旧版本] C4[实例4: 旧版本] end subgraph "步骤4" D1[实例1: 新版本] D2[实例2: 新版本] D3[实例3: 新版本] D4[实例4: 旧版本] end subgraph "步骤5" E1[实例1: 新版本] E2[实例2: 新版本] E3[实例3: 新版本] E4[实例4: 新版本] end A1 --> B1 A2 --> B2 A3 --> B3 A4 --> B4 B1 --> C1 B2 --> C2 B3 --> C3 B4 --> C4 C1 --> D1 C2 --> D2 C3 --> D3 C4 --> D4 D1 --> E1 D2 --> E2 D3 --> E3 D4 --> E4 style A1 fill:#ffe0d0,stroke:#333,stroke-width:2px style A2 fill:#ffe0d0,stroke:#333,stroke-width:2px style A3 fill:#ffe0d0,stroke:#333,stroke-width:2px style A4 fill:#ffe0d0,stroke:#333,stroke-width:2px style B1 fill:#d0ffe0,stroke:#333,stroke-width:2px style B2 fill:#ffe0d0,stroke:#333,stroke-width:2px style B3 fill:#ffe0d0,stroke:#333,stroke-width:2px style B4 fill:#ffe0d0,stroke:#333,stroke-width:2px style C1 fill:#d0ffe0,stroke:#333,stroke-width:2px style C2 fill:#d0ffe0,stroke:#333,stroke-width:2px style C3 fill:#ffe0d0,stroke:#333,stroke-width:2px style C4 fill:#ffe0d0,stroke:#333,stroke-width:2px style D1 fill:#d0ffe0,stroke:#333,stroke-width:2px style D2 fill:#d0ffe0,stroke:#333,stroke-width:2px style D3 fill:#d0ffe0,stroke:#333,stroke-width:2px style D4 fill:#ffe0d0,stroke:#333,stroke-width:2px style E1 fill:#d0ffe0,stroke:#333,stroke-width:2px style E2 fill:#d0ffe0,stroke:#333,stroke-width:2px style E3 fill:#d0ffe0,stroke:#333,stroke-width:2px style E4 fill:#d0ffe0,stroke:#333,stroke-width:2px

4. Python应用部署方案

4.1 Web应用部署

使用Gunicorn和Nginx部署Flask/Django应用

graph LR A[客户端] --> B[Nginx] B --> C[Gunicorn] C --> D[Flask/Django应用] D --> E[数据库] style A fill:#d0e0ff,stroke:#333,stroke-width:2px style B fill:#ffe0d0,stroke:#333,stroke-width:2px style C fill:#d0ffe0,stroke:#333,stroke-width:2px style D fill:#ffd0e0,stroke:#333,stroke-width:2px style E fill:#e0d0ff,stroke:#333,stroke-width:2px

部署步骤：

安装Gunicorn和应用依赖
配置Gunicorn服务
配置Nginx作为反向代理
设置防火墙
配置SSL证书

Gunicorn服务配置示例(gunicorn.service):

ini 复制代码

[Unit]
Description=Gunicorn instance to serve myapp
After=network.target

[Service]
User=myuser
Group=myuser
WorkingDirectory=/home/myuser/myapp
Environment="PATH=/home/myuser/myapp/venv/bin"
ExecStart=/home/myuser/myapp/venv/bin/gunicorn --workers 3 --bind unix:myapp.sock -m 007 wsgi:app

[Install]
WantedBy=multi-user.target

Nginx配置示例:

nginx 复制代码

server {
    listen 80;
    server_name example.com www.example.com;

    location / {
        include proxy_params;
        proxy_pass http://unix:/home/myuser/myapp/myapp.sock;
    }
}

使用Docker容器化部署

graph LR A[客户端] --> B[Nginx容器] B --> C[应用容器] C --> D[数据库容器] style A fill:#d0e0ff,stroke:#333,stroke-width:2px style B fill:#ffe0d0,stroke:#333,stroke-width:2px style C fill:#d0ffe0,stroke:#333,stroke-width:2px style D fill:#ffd0e0,stroke:#333,stroke-width:2px

Dockerfile示例:

dockerfile 复制代码

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

EXPOSE 5000

CMD ["gunicorn", "--bind", "0.0.0.0:5000", "wsgi:app"]

docker-compose.yml示例:

yaml 复制代码

version: '3'

services:
  web:
    build: .
    restart: always
    ports:
      - "5000:5000"
    depends_on:
      - db
    environment:
      - DATABASE_URL=postgresql://postgres:password@db:5432/myapp
      
  db:
    image: postgres:13
    volumes:
      - postgres_data:/var/lib/postgresql/data/
    environment:
      - POSTGRES_PASSWORD=password
      - POSTGRES_DB=myapp
      
  nginx:
    image: nginx:latest
    ports:
      - "80:80"
    volumes:
      - ./nginx/conf.d:/etc/nginx/conf.d
    depends_on:
      - web

volumes:
  postgres_data:

4.2 Python包发布

发布到PyPI

sequenceDiagram participant D as 开发者 participant B as 构建工具 participant T as TestPyPI participant P as PyPI participant U as 用户 D->>B: 准备包 B->>D: 生成分发包 D->>T: 上传到TestPyPI D->>D: 测试安装 D->>P: 上传到PyPI U->>P: pip install package

准备包的步骤：

创建setup.py或pyproject.toml
创建README.md和LICENSE
创建MANIFEST.in(如果需要)
构建分发包
上传到PyPI

setup.py示例:

python 复制代码

from setuptools import setup, find_packages

setup(
    name="mypackage",
    version="0.1.0",
    packages=find_packages(),
    install_requires=[
        "requests>=2.25.0",
        "numpy>=1.20.0",
    ],
    author="Your Name",
    author_email="your.email@example.com",
    description="A short description of your package",
    long_description=open("README.md").read(),
    long_description_content_type="text/markdown",
    url="https://github.com/yourusername/mypackage",
    classifiers=[
        "Programming Language :: Python :: 3",
        "License :: OSI Approved :: MIT License",
        "Operating System :: OS Independent",
    ],
    python_requires=">=3.6",
)

pyproject.toml示例(使用Poetry):

toml 复制代码

[tool.poetry]
name = "mypackage"
version = "0.1.0"
description = "A short description of your package"
authors = ["Your Name <your.email@example.com>"]
readme = "README.md"
repository = "https://github.com/yourusername/mypackage"
license = "MIT"

[tool.poetry.dependencies]
python = "^3.6"
requests = "^2.25.0"
numpy = "^1.20.0"

[tool.poetry.dev-dependencies]
pytest = "^6.2.5"
black = "^21.9b0"

[build-system]
requires = ["poetry-core>=1.0.0"]
build-backend = "poetry.core.masonry.api"

构建和上传命令:

bash 复制代码

# 使用setuptools
python -m pip install --upgrade pip
pip install setuptools wheel twine
python setup.py sdist bdist_wheel
twine upload --repository-url https://test.pypi.org/legacy/ dist/*
twine upload dist/*

# 使用Poetry
poetry build
poetry publish --repository testpypi
poetry publish

使用GitHub Actions自动发布

yaml 复制代码

# .github/workflows/publish.yml
name: Publish Python Package

on:
  release:
    types: [created]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install setuptools wheel twine
    - name: Build and publish
      env:
        TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
      run: |
        python setup.py sdist bdist_wheel
        twine upload dist/*

4.3 Serverless部署

AWS Lambda部署

graph LR A[客户端] --> B[API Gateway] B --> C[Lambda函数] C --> D[DynamoDB] style A fill:#d0e0ff,stroke:#333,stroke-width:2px style B fill:#ffe0d0,stroke:#333,stroke-width:2px style C fill:#d0ffe0,stroke:#333,stroke-width:2px style D fill:#ffd0e0,stroke:#333,stroke-width:2px

Lambda函数示例:

python 复制代码

import json

def lambda_handler(event, context):
    """
    AWS Lambda函数处理程序
    """
    # 解析请求
    body = json.loads(event.get('body', '{}'))
    name = body.get('name', 'World')
    
    # 处理业务逻辑
    message = f"Hello, {name}!"
    
    # 返回响应
    return {
        'statusCode': 200,
        'headers': {
            'Content-Type': 'application/json'
        },
        'body': json.dumps({
            'message': message
        })
    }

使用Serverless Framework部署:

yaml 复制代码

# serverless.yml
service: my-python-service

provider:
  name: aws
  runtime: python3.9
  region: us-east-1
  
functions:
  hello:
    handler: handler.lambda_handler
    events:
      - http:
          path: hello
          method: post
          cors: true

部署命令:

bash 复制代码

# 安装Serverless Framework
npm install -g serverless

# 部署
serverless deploy

5. 监控与日志

5.1 应用监控

使用Prometheus和Grafana监控Python应用

graph LR A[Python应用] --> B[Prometheus] B --> C[Grafana] style A fill:#d0e0ff,stroke:#333,stroke-width:2px style B fill:#ffe0d0,stroke:#333,stroke-width:2px style C fill:#d0ffe0,stroke:#333,stroke-width:2px

Flask应用集成Prometheus示例:

python 复制代码

from flask import Flask
from prometheus_flask_exporter import PrometheusMetrics

app = Flask(__name__)
metrics = PrometheusMetrics(app)

# 静态信息
metrics.info('app_info', 'Application info', version='1.0.0')

@app.route('/')
def main():
    return 'Hello World!'

@app.route('/user/<name>')
@metrics.counter('user_counter', 'Number of user visits', labels={'name': lambda: request.view_args['name']})
def user(name):
    return f'Hello {name}!'

if __name__ == '__main__':
    app.run(host='0.0.0.0')

使用APM(应用性能监控)工具

graph LR A[Python应用] --> B[APM代理] B --> C[APM服务器] C --> D[可视化界面] style A fill:#d0e0ff,stroke:#333,stroke-width:2px style B fill:#ffe0d0,stroke:#333,stroke-width:2px style C fill:#d0ffe0,stroke:#333,stroke-width:2px style D fill:#ffd0e0,stroke:#333,stroke-width:2px

使用Elastic APM监控Flask应用示例:

python 复制代码

from flask import Flask
from elasticapm.contrib.flask import ElasticAPM

app = Flask(__name__)

app.config['ELASTIC_APM'] = {
    'SERVICE_NAME': 'my-flask-app',
    'SERVER_URL': 'http://apm-server:8200',
    'ENVIRONMENT': 'production',
}

apm = ElasticAPM(app)

@app.route('/')
def main():
    return 'Hello World!'

if __name__ == '__main__':
    app.run(host='0.0.0.0')

5.2 日志管理

使用ELK Stack管理日志

graph LR A[Python应用] --> B[Logstash] B --> C[Elasticsearch] C --> D[Kibana] style A fill:#d0e0ff,stroke:#333,stroke-width:2px style B fill:#ffe0d0,stroke:#333,stroke-width:2px style C fill:#d0ffe0,stroke:#333,stroke-width:2px style D fill:#ffd0e0,stroke:#333,stroke-width:2px

Python日志配置示例:

python 复制代码

import logging
import logstash

# 创建logger
logger = logging.getLogger('python-logger')
logger.setLevel(logging.INFO)

# 创建logstash处理器
logstash_handler = logstash.TCPLogstashHandler(
    'logstash-host', 5000, version=1)

# 添加处理器到logger
logger.addHandler(logstash_handler)

# 使用logger
logger.info('Python应用启动')
try:
    # 一些可能出错的代码
    result = 1 / 0
except Exception as e:
    logger.error('发生错误', exc_info=True)

使用结构化日志

python 复制代码

import structlog

# 配置结构化日志
structlog.configure(
    processors=[
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ],
    context_class=dict,
    logger_factory=structlog.stdlib.LoggerFactory(),
)

# 创建logger
logger = structlog.get_logger()

# 使用logger
logger.info("用户登录", user_id="123", ip_address="192.168.1.1")
logger.error("数据库连接失败", db_host="db.example.com", error_code=500)

6. 基础设施即代码(IaC)

6.1 使用Terraform管理云资源

graph LR A[Terraform配置] --> B[Terraform计划] B --> C[Terraform应用] C --> D[云资源] style A fill:#d0e0ff,stroke:#333,stroke-width:2px style B fill:#ffe0d0,stroke:#333,stroke-width:2px style C fill:#d0ffe0,stroke:#333,stroke-width:2px style D fill:#ffd0e0,stroke:#333,stroke-width:2px

Terraform配置示例(AWS):

hcl 复制代码

provider "aws" {
  region = "us-east-1"
}

resource "aws_instance" "web" {
  ami           = "ami-0c55b159cbfafe1f0"
  instance_type = "t2.micro"
  
  tags = {
    Name = "WebServer"
  }
}

resource "aws_s3_bucket" "app_data" {
  bucket = "my-app-data-bucket"
  acl    = "private"
  
  versioning {
    enabled = true
  }
  
  tags = {
    Name = "AppData"
  }
}

6.2 使用Ansible配置服务器

graph LR A[Ansible Playbook] --> B[Ansible控制节点] B --> C[目标服务器1] B --> D[目标服务器2] B --> E[目标服务器3] style A fill:#d0e0ff,stroke:#333,stroke-width:2px style B fill:#ffe0d0,stroke:#333,stroke-width:2px style C fill:#d0ffe0,stroke:#333,stroke-width:2px style D fill:#d0ffe0,stroke:#333,stroke-width:2px style E fill:#d0ffe0,stroke:#333,stroke-width:2px

Ansible Playbook示例:

yaml 复制代码

---
- name: 配置Web服务器
  hosts: webservers
  become: yes
  
  tasks:
    - name: 安装Python和依赖
      apt:
        name:
          - python3
          - python3-pip
          - nginx
        state: present
        update_cache: yes
        
    - name: 复制应用代码
      copy:
        src: /local/path/to/app
        dest: /var/www/app
        
    - name: 安装Python依赖
      pip:
        requirements: /var/www/app/requirements.txt
        
    - name: 配置Nginx
      template:
        src: nginx.conf.j2
        dest: /etc/nginx/sites-available/default
      notify: 重启Nginx
        
    - name: 启动应用
      systemd:
        name: myapp
        state: started
        enabled: yes
        
  handlers:
    - name: 重启Nginx
      systemd:
        name: nginx
        state: restarted

6.3 使用Docker Compose管理容器

graph TD A[Docker Compose配置] --> B[容器1] A --> C[容器2] A --> D[容器3] B --> E[共享网络] C --> E D --> E style A fill:#d0e0ff,stroke:#333,stroke-width:2px style B fill:#ffe0d0,stroke:#333,stroke-width:2px style C fill:#ffe0d0,stroke:#333,stroke-width:2px style D fill:#ffe0d0,stroke:#333,stroke-width:2px style E fill:#d0ffe0,stroke:#333,stroke-width:2px

Docker Compose配置示例:

yaml 复制代码

version: '3'

services:
  web:
    build: ./web
    ports:
      - "5000:5000"
    depends_on:
      - db
    environment:
      - FLASK_APP=app.py
      - FLASK_ENV=production
      - DATABASE_URL=postgresql://postgres:password@db:5432/myapp
    volumes:
      - ./web:/app
      
  db:
    image: postgres:13
    volumes:
      - postgres_data:/var/lib/postgresql/data/
    environment:
      - POSTGRES_PASSWORD=password
      - POSTGRES_DB=myapp
      
  redis:
    image: redis:6
    ports:
      - "6379:6379"

volumes:
  postgres_data:

7. 练习：设置CI/CD流水线

7.1 为Python项目设置GitHub Actions

创建一个简单的Python项目，并为其设置GitHub Actions CI/CD流水线。

项目结构:

markdown 复制代码

my_project/
├── my_package/
│   ├── __init__.py
│   └── main.py
├── tests/
│   ├── __init__.py
│   └── test_main.py
├── .github/
│   └── workflows/
│       └── ci.yml
├── requirements.txt
├── setup.py
└── README.md

main.py:

python 复制代码

def add(a, b):
    """Add two numbers and return the result."""
    return a + b

def subtract(a, b):
    """Subtract b from a and return the result."""
    return a - b

def multiply(a, b):
    """Multiply two numbers and return the result."""
    return a * b

def divide(a, b):
    """Divide a by b and return the result."""
    if b == 0:
        raise ValueError("Cannot divide by zero")
    return a / b

test_main.py:

python 复制代码

import unittest
from my_package.main import add, subtract, multiply, divide

class TestMathFunctions(unittest.TestCase):
    def test_add(self):
        self.assertEqual(add(1, 2), 3)
        self.assertEqual(add(-1, 1), 0)
        self.assertEqual(add(-1, -1), -2)
        
    def test_subtract(self):
        self.assertEqual(subtract(3, 2), 1)
        self.assertEqual(subtract(2, 3), -1)
        self.assertEqual(subtract(-1, -1), 0)
        
    def test_multiply(self):
        self.assertEqual(multiply(2, 3), 6)
        self.assertEqual(multiply(-2, 3), -6)
        self.assertEqual(multiply(-2, -3), 6)
        
    def test_divide(self):
        self.assertEqual(divide(6, 3), 2)
        self.assertEqual(divide(-6, 3), -2)
        self.assertEqual(divide(-6, -3), 2)
        
        with self.assertRaises(ValueError):
            divide(6, 0)

if __name__ == "__main__":
    unittest.main()

ci.yml:

yaml 复制代码

name: Python CI

on:
  push:
    branches: [ main ]
  pull_request:
    branches: [ main ]

jobs:
  test:
    runs-on: ubuntu-latest
    strategy:
      matrix:
        python-version: [3.8, 3.9, 3.10]

    steps:
    - uses: actions/checkout@v2
    - name: Set up Python ${{ matrix.python-version }}
      uses: actions/setup-python@v2
      with:
        python-version: ${{ matrix.python-version }}
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install flake8 pytest pytest-cov
        if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
        pip install -e .
    - name: Lint with flake8
      run: |
        flake8 my_package tests
    - name: Test with pytest
      run: |
        pytest --cov=my_package tests/
    - name: Upload coverage to Codecov
      uses: codecov/codecov-action@v1

setup.py:

python 复制代码

from setuptools import setup, find_packages

setup(
    name="my_package",
    version="0.1.0",
    packages=find_packages(),
    install_requires=[],
    author="Your Name",
    author_email="your.email@example.com",
    description="A simple math package",
)

7.2 设置自动部署到PyPI

在上面的CI/CD流水线基础上，添加自动部署到PyPI的功能。

创建一个新的工作流文件.github/workflows/publish.yml:

yaml 复制代码

name: Publish Python Package

on:
  release:
    types: [created]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v2
    - name: Set up Python
      uses: actions/setup-python@v2
      with:
        python-version: '3.x'
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install setuptools wheel twine
    - name: Build and publish
      env:
        TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }}
        TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }}
      run: |
        python setup.py sdist bdist_wheel
        twine upload dist/*

8. 总结

本章介绍了Python持续集成与部署的最佳实践，包括：

持续集成(CI)基础，包括核心原则、工作流和常见工具
GitHub Actions详解，包括基本概念、工作流配置和高级功能
持续部署(CD)基础，包括部署流程和不同的部署策略
Python应用部署方案，包括Web应用部署、Python包发布和Serverless部署
监控与日志，包括应用监控和日志管理
基础设施即代码(IaC)，包括使用Terraform、Ansible和Docker Compose
通过实践练习设置CI/CD流水线

通过遵循这些最佳实践，您可以建立高效、可靠的软件交付流程，提高开发效率和产品质量。