Wednesday 3 July 2024

How to Use Python Built-In Decoration to Improve Performance Significantly?

 Python decorators can significantly improve performance by optimizing certain aspects of code execution, such as caching, memoization, and just-in-time (JIT) compilation. Here are some built-in and widely used decorators that can enhance performance:

1. @lru_cache from functools
The @lru_cache decorator is used for memoization, which can drastically improve performance by caching the results of expensive function calls and reusing them when the same inputs occur again.

from functools import lru_cache

@lru_cache(maxsize=128)
def expensive_function(x, y):
    # Simulate a time-consuming computation
    return x * y

# Example usage
result = expensive_function(10, 20)

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2. @cached_property from functools
The @cached_property decorator is used to cache the result of a property method. This is useful when you have a property that is expensive to compute and its value does not change over the lifetime of the instance.

from functools import cached_property
class DataProcessor:
    def __init__(self, data):
        self.data = data
    
    @cached_property
    def processed_data(self):
        # Simulate an expensive computation
        return [d * 2 for d in self.data]
# Example usage
processor = DataProcessor([1, 2, 3])
result = processor.processed_data
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3. @jit from numba
The @jit decorator from the numba library can be used to perform Just-In-Time (JIT) compilation, which can significantly speed up numerical computations by converting Python code to optimized machine code at runtime.

from numba import jit

@jit(nopython=True)
def fast_function(x, y):
    result = 0
    for i in range(x):
        result += i * y
    return result

# Example usage
result = fast_function(100000, 2)

#clcoding.com
4. @profile from line_profiler
The @profile decorator is used to measure the time spent in individual functions, which helps in identifying performance bottlenecks.

# First, install the line_profiler package
# pip install line_profiler

@profile
def slow_function():
    result = 0
    for i in range(100000):
        result += i
    return result

# Example usage
result = slow_function()

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5. @staticmethod and @classmethod
Using @staticmethod and @classmethod decorators can improve performance by reducing the overhead associated with instance methods when the method does not need access to instance-specific data.

class MyClass:
    @staticmethod
    def static_method(x, y):
        return x + y

    @classmethod
    def class_method(cls, x, y):
        return x * y

# Example usage
result_static = MyClass.static_method(10, 20)
result_class = MyClass.class_method(10, 20)

#clcoding.com
6. @singledispatch from functools
The @singledispatch decorator allows you to create generic functions that can have different implementations based on the type of the first argument. This can lead to performance improvements by avoiding complex conditional logic.

from functools import singledispatch

@singledispatch
def process_data(data):
    raise NotImplementedError("Unsupported type")

@process_data.register
def _(data: int):
    return data * 2

@process_data.register
def _(data: str):
    return data.upper()

# Example usage
result_int = process_data(10)
result_str = process_data("hello")

#clcoding.com


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