第二十五天 - Web安全防护 - WAF原理与实现 - 练习：请求过滤中间件

一、Celery核心机制解析

1.1 分布式架构四要素

# celery_config.py
BROKER_URL = 'redis://:password@localhost:6379/0'  # 消息中间件
RESULT_BACKEND = 'redis://:password@localhost:6379/1'  # 结果存储
TASK_SERIALIZER = 'json'
ACCEPT_CONTENT = ['json']
TIMEZONE = 'Asia/Shanghai'

核心组件对比：

组件	作用	常用实现
Broker	任务消息传递	RabbitMQ/Redis
Worker	任务执行节点	Celery Worker
Backend	结果存储	Redis/PostgreSQL
Monitor	任务监控	Flower/Prometheus

1.2 第一个分布式任务

# tasks.py
from celery import Celery

app = Celery('demo', broker='redis://localhost:6379/0')

@app.task
def send_email(to, content):
    # 模拟耗时操作
    import time
    time.sleep(3)
    return f"Email to {to} sent: {content[:20]}..."

快速验证：

# 启动Worker
celery -A tasks worker --loglevel=info

# 在Python Shell中调用
from tasks import send_email
result = send_email.delay('[email protected]', 'Your order #1234 has shipped!')
print(result.get(timeout=10))  # 获取执行结果

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二、Celery高级应用技巧

2.1 复杂工作流设计

# 订单处理流水线
@app.task
def validate_order(order_id):
    return {'order_id': order_id, 'status': 'valid'}

@app.task
def process_payment(order_info):
    return {**order_info, 'paid': True}

@app.task
def ship_order(payment_result):
    return {**payment_result, 'tracking_no': 'EXPRESS123'}

# 链式调用
from celery import chain
order_chain = chain(
    validate_order.s(1001),
    process_payment.s(),
    ship_order.s()
).apply_async()

2.2 任务监控与报警

# 异常处理装饰器
@app.task(bind=True, max_retries=3)
def risky_operation(self):
    try:
        # 可能失败的操作
        1 / 0
    except Exception as exc:
        self.retry(exc=exc, countdown=2 ** self.request.retries)

# 实时报警集成
from celery.signals import task_failure

@task_failure.connect
def alert_on_failure(sender=None, task_id=None, **kwargs):
    import requests
    requests.post('https://报警接口地址', json={
        'task': sender.name,
        'error': str(kwargs.get('exception'))
    })

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三、构建分布式监控系统

3.1 系统架构设计

                       +----------------+
                       |   Flask API    |
                       +-------+--------+
                               | 触发监控任务
                               v
+-------------+       +--------+--------+
|   Redis     <-------+   Celery Beat   |
+------+------+       +--------+--------+
       ^                       |
       | 存储任务              | 分发任务
       v                       v
+------+------+       +--------+--------+
|   Worker1   |       |   Worker2       |
| (HTTP监测)  |       | (磁盘检查)      |
+-------------+       +-----------------+

3.2 核心监控任务实现

# monitor_tasks.py
@app.task
def check_http_endpoint(url):
    import requests
    start = time.time()
    try:
        resp = requests.get(url, timeout=10)
        return {
            'url': url,
            'status': 'UP' if resp.ok else 'DOWN',
            'response_time': time.time() - start
        }
    except Exception as e:
        return {'url': url, 'error': str(e)}

@app.task
def check_disk_usage(host):
    import paramiko
    client = paramiko.SSHClient()
    client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
    client.connect(host, username='monitor', key_filename='~/.ssh/monitor_key')
    
    stdin, stdout, stderr = client.exec_command('df -h /')
    output = stdout.read().decode()
    client.close()
    
    return parse_disk_output(output)  # 解析函数需自定义

# 定时任务配置
from celery.schedules import crontab

app.conf.beat_schedule = {
    'check-homepage-every-5m': {
        'task': 'monitor_tasks.check_http_endpoint',
        'schedule': crontab(minute='*/5'),
        'args': ('https://www.yourdomain.com',)
    },
    'daily-disk-check': {
        'task': 'monitor_tasks.check_disk_usage',
        'schedule': crontab(hour=3, minute=0),
        'args': ('server01',)
    }
}

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四、实战：可视化监控面板

4.1 使用Flower实时监控

# 启动监控面板
celery -A monitor_tasks flower --port=5555

访问http://localhost:5555可以看到：

• 实时任务执行状态
• Worker节点负载情况
• 任务历史统计图表

4.2 Prometheus集成方案

# prometheus_exporter.py
from prometheus_client import start_http_server, Counter

TASKS_STARTED = Counter('celery_tasks_started', 'Total tasks started')
TASKS_FAILED = Counter('celery_tasks_failed', 'Total tasks failed')

@task_prerun.connect
def count_task_start(sender=None, **kwargs):
    TASKS_STARTED.inc()

@task_failure.connect
def count_task_failure(sender=None, **kwargs):
    TASKS_FAILED.inc()

# 启动指标服务
start_http_server(8000)

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五、生产环境最佳实践

5.1 部署架构优化

# 使用Supervisor管理进程
[program:celery_worker]
command=celery -A proj worker --loglevel=info --concurrency=4
directory=/opt/yourproject
autostart=true
autorestart=true

[program:celery_beat]
command=celery -A proj beat
directory=/opt/yourproject
autostart=true

5.2 安全加固措施

# 启用任务结果加密
app.conf.result_backend_transport_options = {
    'visibility_timeout': 3600,
    'signed_data': True  # 启用签名
}

# 路由保护
app.conf.task_routes = {
    'critical_tasks.*': {'queue': 'secure'},
    '*.default': {'queue': 'regular'}
}

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六、知识体系进阶

6.1 扩展学习路径

1. 消息队列深度：RabbitMQ vs Kafka
2. 容器化部署：Docker + Kubernetes
3. 分布式追踪：OpenTelemetry
4. 自动扩缩容：Celery Autoscale

6.2 推荐工具链

工具类型	推荐方案
消息队列	RabbitMQ
监控系统	Prometheus + Grafana
任务可视化	Flower
部署管理	Supervisor/Docker

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