Analysis of Python's underlying technology: how to implement garbage collection mechanism

Analysis of Python’s underlying technology: How to implement the garbage collection mechanism requires specific code examples

Introduction:
Python as a high-level programming language is extremely convenient in development and flexible, but its underlying implementation is quite complex. This article will focus on exploring Python's garbage collection mechanism, including the principles, algorithms, and specific implementation code examples of garbage collection. I hope that through this article’s analysis of Python’s garbage collection mechanism, readers can have a deeper understanding of Python’s underlying technology.

1. Principle of garbage collection
First of all, we need to clarify what garbage collection is. Garbage collection is an automated memory management mechanism that is responsible for automatically releasing memory space that is no longer used to prevent programs from crashing or performance degradation due to memory leaks.

Python's garbage collection mechanism mainly uses two methods: "reference counting" and "mark-clear".

1. Reference counting
   Reference counting is a simple and efficient garbage collection method. It maintains a reference counter for each object. When the object is referenced, the counter is incremented by 1. When the object is no longer referenced, the counter is decremented by 1. When the counter reaches 0, it means that the object is no longer used and can be recycled.

However, there is a problem with the reference counting method, which is circular reference. When there are cyclic references between two or more objects, their reference counts will not become 0, resulting in the inability to be recycled. To solve this problem, Python introduced the "mark-sweep" algorithm.

2. Mark-clear
   Mark-clear is a more complex garbage collection algorithm. It traverses all objects, marks all surviving objects, and then clears unmarked objects. This process can be composed of two phases: marking phase and cleaning phase.

Marking phase: Starting from the root object, recursively traverse all reachable objects and mark them as active objects.

Cleaning phase: Traverse the entire heap, find unmarked objects, and release the memory space they occupy.

2. Garbage collection algorithm
Python's garbage collection algorithm includes two main algorithms: mark-clear algorithm and generational collection algorithm.

1. Mark-and-clear algorithm
   The mark-and-clear algorithm is the most basic and slowest garbage collection algorithm. It traverses the entire object tree and marks all reachable objects as live objects. Then, during the cleanup phase, all untagged objects will be released.

The following is a code example of the mark-sweep algorithm:

class GarbageCollector:
    def __init__(self):
        self.marked = set()

    def mark(self, obj):
        if obj in self.marked:
            return
        self.marked.add(obj)
        if isinstance(obj, Container):
            for o in obj.references():
                self.mark(o)

    def sweep(self):
        unreachable = set()
        for o in objects:
            if o not in self.marked:
                unreachable.add(o)
        for o in unreachable:
            del o

    def collect(self):
        self.mark(root_object)
        self.sweep()

2. Generational collection algorithm
   The generational collection algorithm is another commonly used garbage collection algorithm in Python . It divides objects into different generations, each generation has a different cycle. Typically, newly created objects are assigned to generation 0, while objects in generations 1 and 2 are gradually upgraded over time.

The generational recycling algorithm believes that newly created objects are usually recycled quickly, while objects that survive longer are more likely to survive longer. Therefore, it will recycle newly created objects more frequently and recycle longer-lived objects relatively less often.

The following is a code example of the generational recycling algorithm:

import gc

# 设置回收阈值，分别对应不同代的对象
gc.set_threshold(700, 10, 10)

# 创建一个对象
class MyClass:
    pass

# 分配到第0代
my_object = MyClass()

# 手动触发垃圾回收
gc.collect()

3. Summary
Python’s garbage collection mechanism is an important part of Python’s underlying technology. This article analyzes the principles of garbage collection, the two garbage collection methods of reference counting and mark-sweep, as well as the two garbage collection algorithms of mark-sweep and generational collection. For Python developers, understanding Python's garbage collection mechanism can help write more efficient and high-performance code.

Through the introduction of this article, I believe that readers have a deeper understanding of how to implement the garbage collection mechanism through Python's underlying technical analysis. I hope this article can inspire readers and help them in their daily development work. If you have any questions or comments, please feel free to discuss them with us.

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