Difference between @cached_property and @lru_cache decorator in python

Difference between @cached_property and @lru_cache decorator in python

Both @cached_property and @lru_cache are decorators in Python that help optimize performance by caching function or property results. However, they serve different purposes and are used in slightly different contexts.

  • @cached_property:

The @cached_property decorator is part of the cached_property module in the backports.cached_property library (introduced in Python 3.8). It's used to cache the result of a property method in a class. This can be useful when calculating a property's value is computationally expensive, but the value doesn't change frequently.

from cached_property import cached_property class MyClass: def __init__(self, data): self._data = data @cached_property def expensive_property(self): # Expensive computation return self._data * 2 obj = MyClass(5) print(obj.expensive_property) # Calculated and cached print(obj.expensive_property) # Returned from cache, not recalculated

In this example, the expensive_property is only computed once and then cached for future accesses within the same instance.

  • @lru_cache:

The @lru_cache decorator is part of the functools module and is used for caching the results of function calls. It's primarily used to memoize functions with a potentially large number of distinct inputs to avoid recalculating results.

from functools import lru_cache @lru_cache(maxsize=None) # maxsize=None means cache all function calls def fibonacci(n): if n <= 1: return n return fibonacci(n - 1) + fibonacci(n - 2) print(fibonacci(10)) # Calculated and cached print(fibonacci(10)) # Returned from cache, not recalculated

In this example, the lru_cache decorator stores the results of the fibonacci function calls in a cache. This avoids redundant calculations and improves performance.

In summary, @cached_property is used to cache the result of a property method within a class instance, while @lru_cache is used to cache the results of function calls. They are used in different contexts and for different optimization purposes.

Examples

  1. "What is the difference between @cached_property and @lru_cache decorator in Python?" Description: Explanation of the distinctions between the @cached_property decorator, used for caching object properties, and the @lru_cache decorator, used for function result caching.

    # Example: Using @cached_property decorator in Python from functools import cached_property class MyClass: @cached_property def my_property(self): # Calculate or fetch property value return 42

  2. "How to use @cached_property decorator in Python?" Description: Illustration of how to apply the @cached_property decorator in Python to cache the result of an object's property.

    # Example: Using @cached_property decorator in Python from functools import cached_property class MyClass: @cached_property def my_property(self): # Calculate or fetch property value return 42

  3. "Python @cached_property vs @lru_cache: usage and differences" Description: Detailed comparison of the usage and differences between the @cached_property and @lru_cache decorators in Python for caching object properties and function results, respectively.

    # Example: Using @lru_cache decorator in Python from functools import lru_cache @lru_cache(maxsize=None) def fibonacci(n): if n < 2: return n return fibonacci(n-1) + fibonacci(n-2)

  4. "When to use @cached_property and when to use @lru_cache in Python?" Description: Guidance on choosing between the @cached_property and @lru_cache decorators in Python based on specific use cases and caching requirements.

    # Example: Choosing between @cached_property and @lru_cache in Python from functools import cached_property, lru_cache class MyClass: @cached_property def my_property(self): # Calculate or fetch property value return 42 @lru_cache(maxsize=None) def fibonacci(n): if n < 2: return n return fibonacci(n-1) + fibonacci(n-2)

  5. "Python cached_property vs lru_cache performance comparison" Description: Comparison of the performance characteristics between using the @cached_property and @lru_cache decorators in Python for caching object properties and function results, respectively.

    # Example: Comparing performance of @cached_property and @lru_cache in Python from functools import cached_property, lru_cache class MyClass: @cached_property def my_property(self): # Calculate or fetch property value return 42 @lru_cache(maxsize=None) def fibonacci(n): if n < 2: return n return fibonacci(n-1) + fibonacci(n-2)

  6. "Understanding the internals of @cached_property and @lru_cache in Python" Description: Explanation of how the @cached_property and @lru_cache decorators work internally in Python, including their caching mechanisms and behavior.

    # Example: Internals of @cached_property and @lru_cache in Python from functools import cached_property, lru_cache class MyClass: @cached_property def my_property(self): # Calculate or fetch property value return 42 @lru_cache(maxsize=None) def fibonacci(n): if n < 2: return n return fibonacci(n-1) + fibonacci(n-2)

  7. "Python cached_property example" Description: Example demonstrating the usage of the @cached_property decorator in Python to cache the result of an object's property.

    # Example: Using @cached_property decorator in Python from functools import cached_property class MyClass: @cached_property def my_property(self): # Calculate or fetch property value return 42

  8. "How to cache function results with @lru_cache in Python?" Description: Instructions on how to cache the results of a function using the @lru_cache decorator in Python for optimized performance.

    # Example: Using @lru_cache decorator in Python from functools import lru_cache @lru_cache(maxsize=None) def fibonacci(n): if n < 2: return n return fibonacci(n-1) + fibonacci(n-2)

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