"""Cache decorators for easy integration with existing functions. This module provides decorators to automatically cache function results with configurable TTL strategies and cache key generation. """ import functools import hashlib import inspect import json import logging from typing import Any, Callable, Optional, TypeVar from backend.caching.factory import get_cache from backend.caching.redis_cache import CacheDataType, TTLStrategy logger = logging.getLogger(__name__) T = TypeVar("T") def cached( namespace: str = "default", data_type: CacheDataType = CacheDataType.ANALYSIS_RESULTS, ttl: Optional[int] = None, key_prefix: Optional[str] = None, include_self: bool = False, ) -> Callable[[Callable[..., T]], Callable[..., T]]: """Decorator to cache function results. Args: namespace: Cache namespace for key generation. data_type: Type of data being cached (for TTL strategy). ttl: Custom TTL override (seconds). key_prefix: Optional prefix for cache key. include_self: Include 'self' in cache key for instance methods. Returns: Decorated function with caching. Example: >>> @cached(namespace="market", data_type=CacheDataType.MARKET_DATA) >>> def get_stock_price(ticker: str) -> float: ... # Expensive API call ... return fetch_price_from_api(ticker) >>> >>> # First call - fetches from API and caches >>> price1 = get_stock_price("AAPL") >>> >>> # Second call - returns cached value >>> price2 = get_stock_price("AAPL") """ def decorator(func: Callable[..., T]) -> Callable[..., T]: @functools.wraps(func) def wrapper(*args, **kwargs) -> T: cache = get_cache() # Generate cache key from function arguments cache_key = _generate_cache_key( namespace, func.__name__, args, kwargs, key_prefix=key_prefix, include_self=include_self, ) # Try to get from cache cached_value = cache.get(cache_key) if cached_value is not None: logger.debug(f"Cache HIT for {cache_key}") return cached_value logger.debug(f"Cache MISS for {cache_key}") # Call function and cache result result = func(*args, **kwargs) # Get TTL for this data type cache_ttl = ttl if ttl is not None else TTLStrategy.get_ttl(data_type) # Cache the result cache.set(cache_key, result, ttl=cache_ttl) return result # Add cache control methods to the wrapper wrapper.cache_key = lambda *args, **kwargs: _generate_cache_key( namespace, func.__name__, args, kwargs, key_prefix=key_prefix, include_self=include_self, ) wrapper.invalidate = lambda *args, **kwargs: get_cache().delete( wrapper.cache_key(*args, **kwargs) ) return wrapper return decorator def cached_async( namespace: str = "default", data_type: CacheDataType = CacheDataType.ANALYSIS_RESULTS, ttl: Optional[int] = None, key_prefix: Optional[str] = None, include_self: bool = False, ) -> Callable[[Callable[..., Any]], Callable[..., Any]]: """Decorator to cache async function results. Args: namespace: Cache namespace for key generation. data_type: Type of data being cached (for TTL strategy). ttl: Custom TTL override (seconds). key_prefix: Optional prefix for cache key. include_self: Include 'self' in cache key for instance methods. Returns: Decorated async function with caching. Example: >>> @cached_async(namespace="portfolio", data_type=CacheDataType.PORTFOLIO_METRICS) >>> async def analyse_portfolio(portfolio_id: str) -> dict: ... # Expensive async analysis ... return await perform_analysis(portfolio_id) >>> >>> # First call - performs analysis and caches >>> result1 = await analyse_portfolio("portfolio-123") >>> >>> # Second call - returns cached value >>> result2 = await analyse_portfolio("portfolio-123") """ def decorator(func: Callable[..., Any]) -> Callable[..., Any]: @functools.wraps(func) async def wrapper(*args, **kwargs) -> Any: cache = get_cache() # Generate cache key from function arguments cache_key = _generate_cache_key( namespace, func.__name__, args, kwargs, key_prefix=key_prefix, include_self=include_self, ) # Try to get from cache cached_value = cache.get(cache_key) if cached_value is not None: logger.debug(f"Cache HIT for {cache_key}") return cached_value logger.debug(f"Cache MISS for {cache_key}") # Call async function and cache result result = await func(*args, **kwargs) # Get TTL for this data type cache_ttl = ttl if ttl is not None else TTLStrategy.get_ttl(data_type) # Cache the result cache.set(cache_key, result, ttl=cache_ttl) return result # Add cache control methods to the wrapper wrapper.cache_key = lambda *args, **kwargs: _generate_cache_key( namespace, func.__name__, args, kwargs, key_prefix=key_prefix, include_self=include_self, ) wrapper.invalidate = lambda *args, **kwargs: get_cache().delete( wrapper.cache_key(*args, **kwargs) ) return wrapper return decorator def cache_invalidate(namespace: str, pattern: str = "*") -> int: """Invalidate cache entries matching a pattern. Args: namespace: Cache namespace. pattern: Pattern to match (default: all in namespace). Returns: Number of entries invalidated. Example: >>> # Invalidate all portfolio cache entries >>> cache_invalidate("portfolio", "portfolio-123:*") >>> >>> # Invalidate all market data >>> cache_invalidate("market") """ cache = get_cache() full_pattern = f"{namespace}:{pattern}" deleted = cache.delete_pattern(full_pattern) logger.info(f"Invalidated {deleted} cache entries matching {full_pattern}") return deleted def _generate_cache_key( namespace: str, func_name: str, args: tuple, kwargs: dict, key_prefix: Optional[str] = None, include_self: bool = False, ) -> str: """Generate deterministic cache key from function arguments. Args: namespace: Cache namespace. func_name: Function name. args: Positional arguments. kwargs: Keyword arguments. key_prefix: Optional prefix. include_self: Include 'self' parameter for instance methods. Returns: Generated cache key. """ # Filter out 'self' from args unless explicitly included filtered_args = args if not include_self and args and hasattr(args[0], "__dict__"): # Likely an instance method - skip first arg (self) filtered_args = args[1:] # Create a deterministic representation of arguments key_data = { "args": [_serialise_arg(arg) for arg in filtered_args], "kwargs": {k: _serialise_arg(v) for k, v in sorted(kwargs.items())}, } # Generate hash of arguments key_json = json.dumps(key_data, sort_keys=True) args_hash = hashlib.md5(key_json.encode()).hexdigest()[:16] # Build cache key parts = [namespace, func_name, args_hash] if key_prefix: parts.insert(0, key_prefix) return ":".join(parts) def _serialise_arg(arg: Any) -> Any: """Serialise argument for cache key generation. Args: arg: Argument to serialise. Returns: Serialisable representation. """ # Handle common types if isinstance(arg, (str, int, float, bool, type(None))): return arg # Handle lists and tuples if isinstance(arg, (list, tuple)): return [_serialise_arg(item) for item in arg] # Handle dictionaries if isinstance(arg, dict): return {k: _serialise_arg(v) for k, v in sorted(arg.items())} # Handle Pydantic models if hasattr(arg, "model_dump"): return arg.model_dump() # Handle objects with __dict__ if hasattr(arg, "__dict__"): return str(arg.__dict__) # Fallback to string representation return str(arg)