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function cache_result

Maturity: 48

A decorator factory that creates a caching decorator for function results with a configurable time-to-live (TTL). Currently a placeholder implementation that passes through function calls without actual caching.

File:
/tf/active/vicechatdev/CDocs/controllers/__init__.py
Lines:
215 - 231
Complexity:
simple

Purpose

This decorator is designed to cache function results to improve performance by avoiding redundant computations. It accepts a TTL parameter to control how long cached results remain valid. The current implementation is a skeleton that preserves function metadata using functools.wraps but does not implement actual caching logic. It serves as a framework for future caching implementation, possibly using Redis, memcached, or in-memory dictionaries.

Source Code

def cache_result(ttl=300):
    """
    Decorator to cache function results.
    This is a placeholder for future implementation.
    
    Args:
        ttl: Time to live for cached result (seconds)
    """
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            # Future: Check cache and return cached result if available
            result = func(*args, **kwargs)
            # Future: Store result in cache
            return result
        return wrapper
    return decorator

Parameters

Name Type Default Kind
ttl - 300 positional_or_keyword

Parameter Details

ttl: Time-to-live in seconds for the cached result. Defaults to 300 seconds (5 minutes). This parameter will control how long a cached result is considered valid before the function needs to be re-executed. Currently not enforced as caching logic is not implemented.

Return Value

Returns a decorator function that wraps the target function. The decorator preserves the original function's signature and metadata while adding caching capability (when implemented). The wrapped function returns the same value as the original function would return.

Dependencies

  • functools

Required Imports

from functools import wraps

Usage Example

@cache_result(ttl=600)
def expensive_computation(x, y):
    """Simulate an expensive operation."""
    import time
    time.sleep(2)
    return x * y + x ** y

# First call executes the function
result1 = expensive_computation(3, 4)
print(f"Result: {result1}")  # Takes ~2 seconds

# Second call would use cache (when implemented)
result2 = expensive_computation(3, 4)
print(f"Cached result: {result2}")  # Would be instant with caching

# Using default TTL of 300 seconds
@cache_result()
def fetch_user_data(user_id):
    return {"id": user_id, "name": "John Doe"}

user = fetch_user_data(123)

Best Practices

  • This is currently a placeholder implementation - actual caching logic needs to be implemented before production use
  • When implementing, ensure thread-safety if used in multi-threaded environments
  • Consider using hashable arguments only, or implement custom key generation for complex objects
  • Be cautious with caching functions that have side effects or depend on external state
  • Choose appropriate TTL values based on data volatility - shorter for frequently changing data, longer for stable data
  • Consider memory implications when caching large result sets
  • Future implementation should handle cache invalidation and cleanup of expired entries
  • Document which functions are cached to avoid confusion about stale data

Tags

decorator caching performance memoization ttl optimization placeholder future-implementation function-wrapper

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