Introduces Python's internal memory management mechanism and how to avoid memory leaks and deal with memory fragmentation.

Author: Zen and the Art of Computer Programming

1 Introduction

In Python, memory management is a fairly complex topic. Understanding its working principles, principles and mechanisms is very important to optimize Python applications. This article will introduce Python memory management from three aspects:

1) Python object memory layout and allocation method 2) Garbage Collection 3) The concept, advantages and disadvantages of Python memory pool

Readers need to have basic Python programming knowledge and understand Python's basic concepts such as data structures, reference counting, and garbage collection. At the same time, you also need to understand the relevant principles and operation methods of Python memory management.

2.Python object memory layout and allocation method

2.1 Object types

First, let's define some terms to better understand Python memory management.

• All data in Python is an object, including integers, floating point numbers, strings, lists, tuples, dictionaries, etc.
• Each object consists of a memory block, which is used to store the object's value and its information, such as the object's type, address value, reference count, etc.

2.2 Reference Counting

In Python, memory management is done through reference counting. Whenever a new object or variable is created, the system automatically creates a reference count pointing to it. When the reference count reaches 0, Python will reclaim the memory space occupied by this object.

Reference counting has the following functions:

1. Track memory usage and destroy an object when it is no longer referenced elsewhere.
2. To avoid memory leaks, when two variables refer to the same object, as long as one variable is destroyed, the reference count of the other variable will be decremented.
3. Simplified parallel GC implementation, if the reference count is not zero, it means that this object is still in usey