摘要：一般情況下，的垃圾收集器會被用于檢測上面這樣的循環(huán)引用，并刪除掉它們。你可以通過強制垃圾收集器運行，并檢查列表里有什么來驗證上述結(jié)論。

-- [since Python 3.4, circular references are handled much better](http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/#comment-2882030670)

Nice post. Note that starting from Python 3.4, circular references are handled much better (docs imply it should be
rare that they are not collected -- but don"t give specifics about how to make that happen). For example the
example you give is no longer a problem in Python 3.5 (probably not in 3.4 either, but can"t test it right now).

用像 Python, Ruby 這樣的解釋型語言編程很方便的一個方面就是，通常情況下，你可以避免處理內(nèi)存管理相關(guān)的事情。然而，有一個眾所周知的情況 Python 一定會有內(nèi)存泄漏，這就是當你在對象創(chuàng)建中聲明了一個循環(huán)引用，而且在類聲明中實現(xiàn)了一個自定義的 __del__ 解構(gòu)方法。例如，考慮如下例子：

One of the more convenient aspects of writing code in interpreted languages such as Python or Ruby is that you normally can avoid dealing with memory management. However, one known case where Python will definitely leak memory is when you declare circular references in your object declarations and implement a custom __del__ destructor method in one these classes. For instance, consider the following example:

class A(object):
    def __init__(self, b_instance):
      self.b = b_instance

class B(object):
    def __init__(self):
        self.a = A(self)
    def __del__(self):
        print "die"

def test():
    b = B()

test()

當函數(shù) test() 被調(diào)用時，它聲明了一個對象 B，在 B 的 __init__ 函數(shù)中，把自己當成變量傳給了 A，A 然后在 __init__ 函數(shù)中聲明了對 B 的引用，這就造成了一個循環(huán)引用。一般情況下，python 的垃圾收集器會被用于檢測上面這樣的循環(huán)引用，并刪除掉它們。然而，因為自定義的 ___del__ 方法，垃圾收集器會把這個循環(huán)引用相關(guān)對象標記為 “uncollectable”。從設(shè)計上說，垃圾收集器并不知道循環(huán)引用對象的銷毀順序，所以也就不會去處理它們。你可以通過強制垃圾收集器運行，并檢查 gc.garbage 列表里有什么來驗證上述結(jié)論。

When the function test() is invoked, it declares an instance of B, which passes itself to A, which then sets a reference to B, resulting in a circular reference. Normally Python"s garbage collector, which is used to detect these types of cyclic references, would remove it. However, because of the custom destructor (the __del__ method), it marks this item as "uncollectable". By design, it doesn"t know the order in which to destroy the objects, so leaves them alone (see Python"s garbage collection documentation for more background). You can verify this aspect by forcing the Python garbage collector to run and inspecting what is set inside the gc.garbage array:

import gc
gc.collect()
print gc.garbage
[<__main__.B object at 0x7f59f57c98d0>]

你可以通過 objgraph 庫可視化這些循環(huán)引用。

You can also see these circular references visually by using the objgraph library, which relies on Python"s gc module to inspect the references to your Python objects. Note that objgraph library also deliberately plots the the custom __del__ methods in a red circle to spotlight a possible issue.

為了避免循環(huán)引用，你通常需要使用 weak reference，向 python 解釋器聲明：如果剩余的引用屬于 weak reference，或者使用了 context manager 或 with 語法，那么內(nèi)存可以被垃圾收集器回收并用于重新聲明對象。

To avoid circular references, you usually need to use weak references, declaring to the interpreter that the memory can be reclaimed for an object if the remaining references are of these types, or to use context managers and the with statement (for an example of this latter approach, see how it was solved for the happybase library).

# -*- encoding: utf-8 -*-
from __future__ import print_function

import gc
import traceback
import types
from tornado import web, ioloop, gen
from tornado.http1connection import HTTP1ServerConnection


def find_circular_references(garbage=None):
    """
    從 garbage 中尋找循環(huán)引用
    """
    def inner(level):
        """
        處理內(nèi)層的數(shù)據(jù)
        """
        for item in level:
            item_id = id(item)
            if item_id not in garbage_ids:
                continue
            if item_id in visited_ids:
                continue
            if item_id in stack_ids:
                candidate = stack[stack.index(item):]
                candidate.append(item)
                found.append(candidate)
                continue

            stack.append(item)
            stack_ids.add(item_id)
            inner(gc.get_referents(item))
            stack.pop()
            stack_ids.remove(item_id)
            visited_ids.add(item_id)

    ######### 開始初始化 ########

    # 獲取傳入的 garbage 或者通過 gc 模塊獲取 garbage 列表
    garbage = garbage or gc.garbage

    # 已經(jīng)找到的循環(huán)引用列表 type: list of list
    found = []

    # 存放 item 的堆
    stack = []

    # 存放 item_id 的 set
    stack_ids = set()

    # 保存 garbage 里每個對象的 id
    garbage_ids = set(map(id, garbage))

    # 保存 visited item 的 id
    visited_ids = set()

    ######## 初始化結(jié)束 ########

    # 進入遞歸函數(shù) inner
    inner(garbage)
    inner = None
    return found


class CollectHandler(web.RequestHandler):
    @gen.coroutine
    def get(self):
        # collect_result = None
        collect_result = gc.collect()
        garbage = gc.garbage
        # for i in garbage[:5]:
        #     print(gc.get_referents(i), "
")
        self.write("Collected: {}
".format(collect_result))
        self.write("Garbage: {}
".format(len(gc.garbage)))
        for circular in find_circular_references():
            print("
==========
 Circular 
==========")
            for item in circular:
                print("    ", repr(item))
            for item in circular:
                if isinstance(item, types.FrameType):
                    print("
Locals:", item.f_locals)
                    print("
Traceback:", repr(item))
                    traceback.print_stack(item)

class DummyHandler(web.RequestHandler):
    @gen.coroutine
    def get(self):
        self.write("ok
")
        self.finish()

application = web.Application([
    (r"/dummy/", DummyHandler),
    (r"/collect/", CollectHandler),
], debug=True)

if __name__ == "__main__":
    gc.disable()
    gc.collect()
    gc.set_debug(gc.DEBUG_STATS | gc.DEBUG_LEAK)
    print("GC disabled")

    print("Start on 8888")
    application.listen(8888)
    ioloop.IOLoop.current().start()