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🗣 Wd提问: 站群缓存预热

🤖 Ai回答:
站群缓存预热是指在网站集群系统中，提前将热门内容或重要页面加载到缓存中，以提高访问速度和用户体验的技术方案。以下是详细的实现方案：

1、缓存预热策略

主动预热
python
预热脚本示例
import requests
import threading
from concurrent.futures import ThreadPoolExecutor

class CachePreheater:
def __init__(self, sites, cache_servers):
self.sites = sites
self.cache_servers = cache_servers

def preheat_urls(self, urls):
批量预热URL
with ThreadPoolExecutor(max_workers=10) as executor:
for url in urls:
executor.submit(self.preheat_single_url, url)

def preheat_single_url(self, url):
单个URL预热
try:
response = requests.get(url, headers={'X-Preheat': 'true'})
if response.status_code == 200:
print(f预热成功: {url})
except Exception as e:
print(f预热失败: {url}, 错误: {e})

被动预热
nginx
Nginx配置智能缓存
upstream backend {
server 192.168.1.10:8080;
server 192.168.1.11:8080;
}

server {
listen 80;

location / {
先检查缓存
proxy_cache my_cache;
proxy_cache_valid 200 1h;

如果缓存未命中，则回源并自动缓存
proxy_pass http://backend;
proxy_cache_use_stale updating;
}
}

2、站群管理架构

多站点统一管理
yaml
站点配置文件
sites:
name: 主站
domain: www.65.hk
priority: 1
preheat_patterns:
/api/home/*
/product/detail/*

name: 子站A
domain: sitea.65.hk
priority: 2
preheat_patterns:
/category/*
/article/*

分布式缓存预热
python
import redis
import json
from datetime import datetime, timedelta

class DistributedPreheater:
def __init__(self):
self.redis_client = redis.Redis(host='localhost', port=6379, db=0)
self.lock_timeout = 300 锁超时时间5分钟

def acquire_lock(self, key):
获取分布式锁
lock_key = flock:{key}
return self.redis_client.set(
lock_key,
str(datetime.now().timestamp()),
nx=True,
ex=self.lock_timeout
)

def release_lock(self, key):
释放分布式锁
lock_key = flock:{key}
self.redis_client.delete(lock_key)

def schedule_preheat(self, site_id, urls):
调度预热任务
task_key = fpreheat_task:{site_id}
task_data = {
'urls': urls,
'created_at': datetime.now().isoformat(),
'status': 'pending'
}
self.redis_client.lpush(task_key, json.dumps(task_data))

3、智能预热算法

基于访问统计的预热
python
import sqlite3
from collections import defaultdict

class SmartPreheater:
def __init__(self, db_path):
self.db_path = db_path
self.init_db()

def init_db(self):
初始化数据库
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()
cursor.execute('''
CREATE TABLE IF NOT EXISTS access_log (
id INTEGER PRIMARY KEY,
url TEXT,
site_id TEXT,
access_time TIMESTAMP,
user_agent TEXT
)
''')
conn.commit()
conn.close()

def get_hot_urls(self, site_id, limit=100):
获取热门URL
conn = sqlite3.connect(self.db_path)
cursor = conn.cursor()

query = '''
SELECT url, COUNT(*) as access_count
FROM access_log
WHERE site_id = ?
AND access_time datetime('now', '-24 hours')
GROUP BY url
ORDER BY access_count DESC
LIMIT ?
'''

cursor.execute(query, (site_id, limit))
results = cursor.fetchall()
conn.close()

return [row[0] for row in results]

def calculate_preheat_priority(self, urls_stats):
计算预热优先级
priorities = {}
total_access = sum(urls_stats.values())

for url, count in urls_stats.items():
基础优先级：访问频率
base_priority = count / total_access

时间衰减因子
time_factor = self.calculate_time_decay(url)

内容类型权重
content_weight = self.get_content_weight(url)

priorities[url] = base_priority * time_factor * content_weight

return sorted(priorities.items(), key=lambda x: x[1], reverse=True)

4、实时监控与优化

性能监控
python
import time
import psutil
from prometheus_client import Counter, Histogram, Gauge

class PreheatMonitor:
def __init__(self):
self.preheat_requests = Counter(
'preheat_requests_total',
'Total number of preheat requests'
)
self.preheat_duration = Histogram(
'preheat_duration_seconds',
'Duration of preheat operations'
)
self.cache_hit_rate = Gauge(
'cache_hit_rate',
'Cache hit rate percentage'
)

def monitor_preheat(self, func):
装饰器：监控预热操作
def wrapper(*args, kwargs):
start_time = time.time()
self.preheat_requests.inc()

try:
result = func(*args, kwargs)
duration = time.time() start_time
self.preheat_duration.observe(duration)
return result
except Exception as e:
print(f预热监控异常: {e})
raise
return wrapper

自适应预热
python
class AdaptivePreheater:
def __init__(self):
self.performance_history = []
self.current_strategy = balanced

def adjust_strategy(self, metrics):
根据性能指标调整预热策略
avg_response_time = metrics.get('avg_response_time', 0)
cache_hit_rate = metrics.get('cache_hit_rate', 0)
system_load = psutil.cpu_percent()

根据不同指标调整策略
if avg_response_time 2.0:
self.current_strategy = conservative 保守策略
elif cache_hit_rate < 0.7:
self.current_strategy = aggressive 激进策略
elif system_load 80:
self.current_strategy = light 轻量策略
else:
self.current_strategy = balanced 平衡策略

def execute_preheat(self, urls):
执行自适应预热
strategy_config = {
'conservative': {'batch_size': 10, 'concurrency': 2},
'aggressive': {'batch_size': 100, 'concurrency': 20},
'light': {'batch_size': 5, 'concurrency': 1},
'balanced': {'batch_size': 50, 'concurrency': 10}
}

config = strategy_config[self.current_strategy]
return self.batch_preheat(urls, **config)

5、定时任务与自动化

Cron任务配置
bash
crontab 配置
每小时预热热门内容
0 * * * * /usr/bin/python3 /opt/preheat/hourly_preheat.py

每天凌晨预热新内容
0 2 * * * /usr/bin/python3 /opt/preheat/daily_preheat.py

每5分钟更新缓存状态
*/5 * * * * /usr/bin/python3 /opt/preheat/cache_status.py

自动化脚本
python
!/usr/bin/env python3
hourly_preheat.py

import os
import sys
import logging
from datetime import datetime

添加项目路径
sys.path.append('/opt/preheat')

from preheater import SmartPreheater, CachePreheater
from monitor import PreheatMonitor

def main():
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s %(levelname)s %(message)s'
)

logger = logging.getLogger(__name__)

try:
logger.info(开始执行定时预热任务)

初始化组件
smart_preheater = SmartPreheater('/var/db/access.db')
cache_preheater = CachePreheater(
sites=['site1', 'site2'],
cache_servers=['redis1', 'redis2']
)

获取需要预热的URL
hot_urls =

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