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76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
"""缓存装饰器工具"""
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import functools
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import json
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from typing import Any, Callable, Optional
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from src.core.logger import logger
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from src.clients.redis_client import get_redis_client_sync
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def cache_result(key_prefix: str, ttl: int = 60, user_specific: bool = True) -> Callable:
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"""
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缓存函数结果的装饰器
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Args:
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key_prefix: 缓存键前缀
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ttl: 缓存过期时间（秒）
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user_specific: 是否针对用户缓存（从 context.user.id 获取）
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"""
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def decorator(func: Callable) -> Callable:
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@functools.wraps(func)
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async def wrapper(*args, **kwargs) -> Any:
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redis_client = get_redis_client_sync()
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# 如果 Redis 不可用，直接执行原函数
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if redis_client is None:
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return await func(*args, **kwargs)
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# 构建缓存键
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try:
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# 从 args 中获取 context（通常是第一个参数）
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context = args[0] if args else None
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if user_specific and context and hasattr(context, "user") and context.user:
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cache_key = f"{key_prefix}:user:{context.user.id}"
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else:
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cache_key = f"{key_prefix}:global"
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# 如果有额外的参数（如 days），添加到键中
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if hasattr(args[0], "__dict__"):
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# 如果是 dataclass 或对象，获取其属性
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for attr_name in ["days", "limit"]:
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if hasattr(args[0], attr_name):
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attr_value = getattr(args[0], attr_name)
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cache_key += f":{attr_name}:{attr_value}"
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# 尝试从缓存获取
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cached = await redis_client.get(cache_key)
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if cached:
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logger.debug(f"缓存命中: {cache_key}")
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return json.loads(cached)
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# 执行原函数
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result = await func(*args, **kwargs)
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# 保存到缓存
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try:
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await redis_client.setex(
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cache_key, ttl, json.dumps(result, ensure_ascii=False, default=str)
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)
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logger.debug(f"缓存已保存: {cache_key}, TTL: {ttl}s")
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except Exception as e:
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logger.warning(f"保存缓存失败: {e}")
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return result
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except Exception as e:
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logger.warning(f"缓存处理出错: {e}, 直接执行原函数")
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return await func(*args, **kwargs)
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return wrapper
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return decorator
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