26. Design of CPython’s Garbage Collector

Author:Pablo Galindo Salgado

26.1. Abstract

The main garbage collection algorithm used by CPython is reference counting. The basic idea is that CPython counts how many different places there are that have a reference to an object. Such a place could be another object, or a global (or static) C variable, or a local variable in some C function. When an object’s reference count becomes zero, the object is deallocated. If it contains references to other objects, their reference counts are decremented. Those other objects may be deallocated in turn, if this decrement makes their reference count become zero, and so on. The reference count field can be examined using the sys.getrefcount function (notice that the value returned by this function is always 1 more as the function also has a reference to the object when called):

>>> x = object()
>>> sys.getrefcount(x)
2
>>> y = x
>>> sys.getrefcount(x)
3
>>> del y
>>> sys.getrefcount(x)
2

The main problem with the reference counting scheme is that it does not handle reference cycles. For instance, consider this code:

>>> container = []
>>> container.append(container)
>>> sys.getrefcount(container)
3
>>> del container

In this example, container holds a reference to itself, so even when we remove our reference to it (the variable “container”) the reference count never falls to 0 because it still has its own internal reference. Therefore it would never be cleaned just by simple reference counting. For this reason some additional machinery is needed to clean these reference cycles between objects once they become unreachable. This is the cyclic garbage collector, usually called just Garbage Collector (GC), even though reference counting is also a form of garbage collection.

26.2. Memory layout and object structure

Normally the C structure supporting a regular Python object looks as follows:

object -----> +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+ \
              |                    ob_refcnt                  | |
              +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+ | PyObject_HEAD
              |                    *ob_type                   | |
              +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+ /
              |                      ...                      |

In order to support the garbage collector, the memory layout of objects is altered to accommodate extra information before the normal layout:

              +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+ \
              |                    *_gc_next                  | |
              +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+ | PyGC_Head
              |                    *_gc_prev                  | |
object -----> +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+ /
              |                    ob_refcnt                  | \
              +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+ | PyObject_HEAD
              |                    *ob_type                   | |
              +--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+ /
              |                      ...                      |

In this way the object can be treated as a normal python object and when the extra information associated to the GC is needed the previous fields can be accessed by a simple type cast from the original object: ((PyGC_Head *)(the_object)-1).

As is explained later in the Optimization: reusing fields to save memory section, these two extra fields are normally used to keep doubly linked lists of all the objects tracked by the garbage collector (these lists are the GC generations, more on that in the Optimization: generations section), but they are also reused to fullfill other purposes when the full doubly linked list structure is not needed as a memory optimization.

Doubly linked lists are used because they efficiently support most frequently required operations. In general, the collection of all objects tracked by GC are partitioned into disjoint sets, each in its own doubly linked list. Between collections, objects are partitioned into “generations”, reflecting how often they’ve survived collection attempts. During collections, the generation(s) being collected are further partitioned into, e.g., sets of reachable and unreachable objects. Doubly linked lists support moving an object from one partition to another, adding a new object, removing an object entirely (objects tracked by GC are most often reclaimed by the refcounting system when GC isn’t running at all!), and merging partitions, all with a small constant number of pointer updates. With care, they also support iterating over a partition while objects are being added to - and removed from - it, which is frequently required while GC is running.

Specific APIs are offered to allocate, deallocate, initialize, track, and untrack objects with GC support. These APIs can be found in the Garbage Collector C API documentation.

Apart from this object structure, the type object for objects supporting garbage collection must include the Py_TPFLAGS_HAVE_GC in its tp_flags slot and provide an implementation of the tp_traverse handler. Unless it can be proven that the objects cannot form reference cycles with only objects of its type or unless the type is immutable, a tp_clear implementation must also be provided.

26.3. Identifiying reference cycles

The algorithm that CPython uses to detect those reference cycles is implemented in the gc module. The garbage collector only focuses on cleaning container objects (i.e. objects that can contain a reference to one or more objects). These can be arrays, dictionaries, lists, custom class instances, classes in extension modules, etc. One could think that cycles are uncommon but the truth is that many internal references needed by the interpreter create cycles everywhere. Some notable examples:

  • Exceptions contain traceback objects that contain a list of frames that contain the exception itself.
  • Module-level functions reference the module’s dict (which is needed to resolve globals), which in turn contains entries for the module-level functions.
  • Instances have references to their class which itself references its module, and the module contains references to everything that is inside (and maybe other modules) and this can lead back to the original instance.
  • When representing data structures like graphs, it is very typical for them to have internal links to themselves.

To correctly dispose of these objects once they become unreachable, they need to be identified first. Inside the function that identifies cycles, two double-linked lists are maintained: one list contains all objects to be scanned, and the other will contain all objects “tentatively” unreachable.

To understand how the algorithm works, Let’s take the case of a circular linked list which has one link referenced by a variable A, and one self-referencing object which is completely unreachable:

>>> import gc

>>> class Link:
...    def __init__(self, next_link=None):
...        self.next_link = next_link

>>> link_3 = Link()
>>> link_2 = Link(link_3)
>>> link_1 = Link(link_2)
>>> link_3.next_link = link_1
>>> A = link_1
>>> del link_1, link_2, link_3

>>> link_4 = Link()
>>> link_4.next_link = link_4

>>> gc.collect()
2

When the GC starts, it has all the container objects it wants to scan on the first linked list. The objective is to move all the unreachable objects. Since most objects turn out to be reachable, it is much more efficient to move the unreachable as this involves fewer pointer updates.

Every object that supports garbage collection will have an extra reference count field initialized to the reference count (gc_ref in the figures) of that object when the algorithm starts. This is because the algorithm needs to modify the reference count to do the computations and in this way the interpreter will not modify the real reference count field.

../_images/python-cyclic-gc-1-new-page.png

The GC then iterates over all containers in the first list and decrements by one the gc_ref field of any other object that container is referencing. Doing this makes use of the tp_traverse slot in the container class (implemented using the C API or inherited by a superclass) to know what objects are referenced by each container. After all the objects have been scanned, only the objects that have references from outside the “objects to scan” list will have gc_refs > 0.

../_images/python-cyclic-gc-2-new-page.png

Notice that having gc_refs == 0 does not imply that the object is unreachable. This is because another object that is reachable from the outside (gc_refs > 0) can still have references to it. For instance, the link_2 object in our example ended having gc_refs == 0 but is referenced still by the link_1 object that is reachable from the outside. To obtain the set of objects that are really unreachable, the garbage collector re-scans the container objects using the tp_traverse slot; this time with a different traverse function that marks objects with gc_refs == 0 as “tentatively unreachable” and then moves them to the tentatively unreachable list. The following image depicts the state of the lists in a moment when the GC processed the link_3 and link_4 objects but has not processed link_1 and link_2 yet.

../_images/python-cyclic-gc-3-new-page.png

Then the GC scans the next link_1 object. Because it has gc_refs == 1, the gc does not do anything special because it knows it has to be reachable (and is already in what will become the reachable list):

../_images/python-cyclic-gc-4-new-page.png

When the GC encounters an object which is reachable (gc_refs > 0), it traverses its references using the tp_traverse slot to find all the objects that are reachable from it, moving them to the end of the list of reachable objects (where they started originally) and setting its gc_refs field to 1. This is what happens to link_2 and link_3 below as they are reachable from link_1. From the state in the previous image and after examining the objects referred to by link_1 the GC knows that link_3 is reachable after all, so it is moved back to the original list and its gc_refs field is set to one so if the GC visits it again, it does know that is reachable. To avoid visiting a object twice, the GC marks all objects that are already visited once (by unsetting the PREV_MASK_COLLECTING flag) so if an object that has already been processed is referred by some other object, the GC does not process it twice.

../_images/python-cyclic-gc-5-new-page.png

Notice that once an object that was marked as “tentatively unreachable” and later is moved back to the reachable list, it will be visited again by the garbage collector as now all the references that that objects has need to be processed as well. This process is really a breadth first search over the object graph. Once all the objects are scanned, the GC knows that all container objects in the tentatively unreachable list are really unreachable and can thus be garbage collected.

Pragmatically, it’s important to note that no recursion is required by any of this, and neither does it in any other way require additional memory proportional to the number of objects, number of pointers, or the lengths of pointer chains. Apart from O(1) storage for internal C needs, the objects themselves contain all the storage the GC algorithms require.

26.3.1. Why moving unreachable objects is better

It sounds logical to move the unreachable objects under the premise that most objects are usually reachable, until you think about it: the reason it pays isn’t actually obvious.

Suppose we create objects A, B, C in that order. They appear in the young generation in the same order. If B points to A, and C to B, and C is reachable from outside, then the adjusted refcounts after the first step of the algorithm runs will be 0, 0, and 1 respectively because the only reachable object from the outside is C.

When the next step of the algorithm finds A, A is moved to the unreachable list. The same for B when it’s first encountered. Then C is traversed, B is moved back to the reachable list. B is eventually traversed, and then A is moved back to the reachable list.

So instead of not moving at all, the reachable objects B and A are each moved twice. Why is this a win? A straightforward algorithm to move the reachable objects instead would move A, B, and C once each. The key is that this dance leaves the objects in order C, B, A - it’s reversed from the original order. On all subsequent scans, none of them will move. Since most objects aren’t in cycles, this can save an unbounded number of moves across an unbounded number of later collections. The only time the cost can be higher is the first time the chain is scanned.

26.4. Destroying unreachable objects

Once the GC knows the list of unreachable objects, a very delicate process starts with the objective of completely destroying these objects. Roughly, the process follows these steps in order:

  1. Handle and clean weak references (if any). If an object that is in the unreachable set is going to be destroyed and has weak references with callbacks, these callbacks need to be honored. This process is very delicate as any error can cause objects that will be in an inconsistent state to be resurrected or reached by some python functions invoked from the callbacks. In addition, weak references that also are part of the unreachable set (the object and its weak reference are in cycles that are unreachable) need to be cleaned immediately, without executing the callback. Otherwise it will be triggered later, when the tp_clear slot is called, causing havoc. Ignoring the weak reference’s callback is fine because both the object and the weakref are going away, so it’s legitimate to say the weak reference is going away first.
  2. If an object has legacy finalizers (tp_del slot) move them to the gc.garbage list.
  3. Call the finalizers (tp_finalize slot) and mark the objects as already finalized to avoid calling them twice if they resurrect of if other finalizers have removed the object first.
  4. Deal with resurrected objects. If some objects have been resurrected, the GC finds the new subset of objects that are still unreachable by running the cycle detection algorithm again and continues with them.
  5. Call the tp_clear slot of every object so all internal links are broken and the reference counts fall to 0, triggering the destruction of all unreachable objects.

26.5. Optimization: generations

In order to limit the time each garbage collection takes, the GC uses a popular optimization: generations. The main idea behind this concept is the assumption that most objects have a very short lifespan and can thus be collected shortly after their creation. This has proven to be very close to the reality of many Python programs as many temporarily objects are created and destroyed very fast. The older an object is the less likely it is to become unreachable.

To take advantage of this fact, all container objects are segregated across three spaces/generations. Every new object starts in the first generation (generation 0). The previous algorithm is executed only over the objects of a particular generation and if an object survives a collection of its generation it will be moved to the next one (generation 1), where it will be surveyed for collection less often. If the same object survives another GC round in this new generation (generation 1) it will be moved to the last generation (generation 2) where it will be surveyed the least often.

Generations are collected when the number of objects that they contain reach some predefined threshold, which is unique for each generation and is lower the older the generations are. These thresholds can be examined using the gc.get_threshold function:

>>> import gc
>>> gc.get_threshold()
(700, 10, 10)

The content of these generations can be examined using the gc.get_objects(generation=NUM) function and collections can be triggered specifically in a generation by calling gc.collect(generation=NUM).

>>> import gc
>>> class MyObj:
...     pass
...

# Move everything to the last generation so its easier to inspect
# the younger generations.

>>> gc.collect()
0

# Create a reference cycle

>>> x = MyObj()
>>> x.self = x

# Initially the object is in the younguest generation.

>>> gc.get_objects(generation=0)
[..., <__main__.MyObj object at 0x7fbcc12a3400>, ...]

# After a collection of the younguest generation the object
# moves to the next generation.

>>> gc.collect(generation=0)
0
>>> gc.get_objects(generation=0)
[]
>>> gc.get_objects(generation=1)
[..., <__main__.MyObj object at 0x7fbcc12a3400>, ...]

26.5.1. Collecting the oldest generation

In addition to the various configurable thresholds, the GC only triggers a full collection of the oldest generation if the ratio long_lived_pending / long_lived_total is above a given value (hardwired to 25%). The reason is that, while “non-full” collections (i.e., collections of the young and middle generations) will always examine roughly the same number of objects (determined by the aforementioned thresholds) the cost of a full collection is proportional to the total number of long-lived objects, which is virtually unbounded. Indeed, it has been remarked that doing a full collection every <constant number> of object creations entails a dramatic performance degradation in workloads which consist of creating and storing lots of long-lived objects (e.g. building a large list of GC-tracked objects would show quadratic performance, instead of linear as expected). Using the above ratio, instead, yields amortized linear performance in the total number of objects (the effect of which can be summarized thusly: “each full garbage collection is more and more costly as the number of objects grows, but we do fewer and fewer of them”).

26.6. Optimization: reusing fields to save memory

In order to save memory, the two linked list pointers in every object with GC support are reused for several purposes. This is a common optimization known as “fat pointers” or “tagged pointers”: pointers that carry additional data, “folded” into the pointer, meaning stored inline in the data representing the address, taking advantage of certain properties of memory addressing. This is possible as most architectures align certain types of data to the size of the data, often a word or multiple thereof. This discrepancy leaves a few of the least significant bits of the pointer unused, which can be used for tags or to keep other information – most often as a bit field (each bit a separate tag) – as long as code that uses the pointer masks out these bits before accessing memory. E.g., on a 32-bit architecture (for both addresses and word size), a word is 32 bits = 4 bytes, so word-aligned addresses are always a multiple of 4, hence end in 00, leaving the last 2 bits available; while on a 64-bit architecture, a word is 64 bits word = 8 bytes, so word-aligned addresses end in 000, leaving the last 3 bits available.

The CPython GC makes use of two fat pointers that corresponds to the extra fields of PyGC_Head discussed in the Memory layout and object structure section:

Warning

Because the presence of extra information, “tagged” or “fat” pointers cannot be dereferenced directly and the extra information must be stripped off before obtaining the real memory address. Special care needs to be taken with functions that directly manipulate the linked lists, as these functions normally assume the pointers inside the lists are in a consistent state.

  • The _gc_prev` field is normally used as the “previous” pointer to maintain the doubly linked list but its lowest two bits are used to keep the flags PREV_MASK_COLLECTING and _PyGC_PREV_MASK_FINALIZED. Between collections, the only flag that can be present is _PyGC_PREV_MASK_FINALIZED that indicates if an object has been already finalized. During collections _gc_prev is temporarily used for storing a copy of the reference count (gc_refs), in addition to two flags, and the GC linked list becomes a singly linked list until _gc_prev is restored.
  • The _gc_next field is used as the “next” pointer to maintain the doubly linked list but during collection its lowest bit is used to keep the NEXT_MASK_UNREACHABLE flag that indicates if an object is tentatively unreachable during the cycle detection algorithm. This is a drawback to using only doubly linked lists to implement partitions: while most needed operations are constant-time, there is no efficient way to determine which partition an object is currently in. Instead, when that’s needed, ad hoc tricks (like the NEXT_MASK_UNREACHABLE flag) are employed.

26.7. Optimization: delay tracking containers

Certain types of containers cannot participate in a reference cycle, and so do not need to be tracked by the garbage collector. Untracking these objects reduces the cost of garbage collections. However, determining which objects may be untracked is not free, and the costs must be weighed against the benefits for garbage collection. There are two possible strategies for when to untrack a container:

  1. When the container is created.
  2. When the container is examined by the garbage collector.

As a general rule, instances of atomic types aren’t tracked and instances of non-atomic types (containers, user-defined objects…) are. However, some type-specific optimizations can be present in order to suppress the garbage collector footprint of simple instances. Some examples of native types that benefit from delayed tracking:

  • Tuples containing only immutable objects (integers, strings etc, and recursively, tuples of immutable objects) do not need to be tracked. The interpreter creates a large number of tuples, many of which will not survive until garbage collection. It is therefore not worthwhile to untrack eligible tuples at creation time. Instead, all tuples except the empty tuple are tracked when created. During garbage collection it is determined whether any surviving tuples can be untracked. A tuple can be untracked if all of its contents are already not tracked. Tuples are examined for untracking in all garbage collection cycles. It may take more than one cycle to untrack a tuple.
  • Dictionaries containing only immutable objects also do not need to be tracked. Dictionaries are untracked when created. If a tracked item is inserted into a dictionary (either as a key or value), the dictionary becomes tracked. During a full garbage collection (all generations), the collector will untrack any dictionaries whose contents are not tracked.

The garbage collector module provides the python function is_tracked(obj), which returns the current tracking status of the object. Subsequent garbage collections may change the tracking status of the object.

>>> gc.is_tracked(0)
False
>>> gc.is_tracked("a")
False
>>> gc.is_tracked([])
True
>>> gc.is_tracked({})
False
>>> gc.is_tracked({"a": 1})
False
>>> gc.is_tracked({"a": []})
True