Garbage Collection Mechanism and Memory Management in Python

Problem Description
As a high-level language, Python's memory management is primarily achieved through an automatic garbage collection (GC) mechanism. Interviews often examine understanding of core mechanisms such as reference counting, generational collection, mark-and-sweep, as well as how to avoid memory leaks. This section will provide a detailed analysis of Python's garbage collection principles and their application scenarios.

1. Memory Management Basics: Reference Counting
Principle: Each object in Python maintains a reference counter, recording how many variables or data structures currently point to it.

• When an object is referenced (e.g., a = obj), the counter increments by 1.
• When a reference becomes invalid (e.g., a variable goes out of scope or is reassigned), the counter decrements by 1.
• When the counter reaches 0, the object is immediately destroyed, and memory is released.

Example:

x = [1, 2, 3]  # The list object's reference count = 1  
y = x          # Reference count +1, becomes 2  
del x          # Reference count -1, becomes 1  
y = None       # Reference count -1, becomes 0, object is collected  

Advantages and Disadvantages:

• Advantages: High real-time performance; most objects can be collected promptly.
• Disadvantages: Cannot resolve circular reference issues (e.g., two objects referencing each other).

2. Circular Reference Problem and Mark-and-Sweep
Scenario:

class Node:  
    def __init__(self):  
        self.parent = None  
        self.child = None  

a = Node()  
b = Node()  
a.child = b  # a references b  
b.parent = a  # b references a, forming a circular reference  
del a, b      # Reference count remains 1, cannot be collected  

Mark-and-Sweep Mechanism:

1. Marking Phase: Starting from root objects (e.g., global variables, variables in the call stack), traverse all reachable objects and mark them as "alive."
2. Sweeping Phase: Collect all unmarked objects (i.e., unreachable objects).

• This process pauses the entire program (Stop-The-World) and occurs at a low frequency.

3. Generational Collection Optimizes Efficiency
Principle: Objects are divided into three generations (generation 0, 1, 2) based on their lifetime, with new objects placed in generation 0.

• Young Generation (Generation 0): Frequently checked; surviving objects are promoted to the next generation.
• Old Generation (Generations 1 and 2): Checked less frequently because objects that have survived for a long time are more likely to continue surviving.

Threshold Trigger:

• When the number of allocated objects minus the number of freed objects exceeds a threshold, collection for the corresponding generation is triggered.
• Thresholds can be viewed with gc.get_threshold() and adjusted with gc.set_threshold().

4. Practical Application and Debugging Techniques
Avoiding Memory Leaks:

• Timely break circular references (e.g., using weakref weak references).
• Avoid global variables holding large data for extended periods.

Debugging Tools:

import gc  
gc.set_debug(gc.DEBUG_LEAK)  # Print collection information  
gc.collect()                 # Manually trigger full generational collection  

Summary:
Python's garbage collection is a hybrid mechanism primarily based on reference counting, supplemented by mark-and-sweep and generational collection. Understanding its principles helps in writing efficient and safe code, especially when dealing with complex data structures where vigilance against circular references is required.