What is the principle of memory management in Python?

What is the principle of memory management in Python?

Python is a high-level, dynamically typed programming language with automatic garbage collection. The principle of Python memory management is based on the reference counting mechanism and garbage collection mechanism.

The reference counting mechanism is the basis of Python memory management. Each object has a reference counter that records the number of times the object is referenced. When an object is created, its reference counter is initialized to 1. When an object is referenced, its reference counter is incremented by 1. Conversely, when an object's reference becomes invalid, its reference counter is decremented by one. When the reference counter of an object becomes 0, it means that the object is not referenced, and Python will automatically recycle it and release the memory.

The garbage collection mechanism is a supplement to Python memory management. Although reference counting can effectively reclaim most objects that are no longer used, it can lead to circular reference problems. A circular reference occurs when two objects refer to each other and there are no external references between the two objects. In this case, the object's reference count will never reach 0, causing a memory leak. To solve this problem, Python introduced a garbage collection mechanism.

In Python's garbage collection mechanism, the garbage collection algorithm will be executed regularly. By checking the reference relationships of objects, objects that are no longer referenced will be found and recycled. Python's garbage collection algorithms mainly include mark sweep and generational collection.

The mark-and-sweep algorithm is a simple and effective garbage collection algorithm. It works by starting from the root object, marking all accessible objects, and then clearing all unmarked objects. This allows you to recycle all objects that are no longer referenced.

The generational collection algorithm is a more efficient garbage collection algorithm. It is based on the observation that "young objects are likely to be recycled quickly, while old objects are likely to persist for a long time". Generational recycling divides memory into different generations, and each generation has different recycling strategies. Newly created objects are allocated in the young generation, and objects that are still active after multiple collections are promoted to the old generation. This can reduce the frequency of garbage collection and improve efficiency.

The following is a code example that shows the memory management principle in Python:

class Person:
    def __init__(self, name):
        self.name = name

p1 = Person("Tom")  # 创建一个Person对象，引用计数器为1
p2 = p1  # p2引用p1，引用计数器增加为2
p3 = p1  # p3引用p1，引用计数器增加为3

del p1  # p1的引用失效，引用计数器减少为2
del p2  # p2的引用失效，引用计数器减少为1
del p3  # p3的引用失效，引用计数器减少为0，对象回收

# 在这个例子中，当p3的引用失效时，对象就被自动回收，释放内存

In summary, Python's memory management principle is based on the reference counting mechanism and garbage collection mechanism. The reference counting mechanism records the number of times an object is referenced through a reference counter. When the reference counter reaches 0, the object is recycled. The garbage collection mechanism solves the problem of circular references. Through a periodic recycling algorithm, objects that are no longer referenced are found and recycled. This ensures Python's memory usage efficiency and stability.

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