GH-117759: Update GC docs for incremental collection (GH-126695)

This commit is contained in:
Mark Shannon 2024-11-14 09:50:00 +00:00 committed by GitHub
parent ff0ef0a54b
commit 3966d8d626
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 86 additions and 50 deletions

View File

@ -108,7 +108,7 @@ As is explained later in the
[Optimization: reusing fields to save memory](#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](#Optimization-generations) section), but
that in the [Optimization: incremental collection](#Optimization-incremental-collection) section), but
they are also reused to fulfill other purposes when the full doubly linked list
structure is not needed as a memory optimization.
@ -351,38 +351,90 @@ follows these steps in order:
the reference counts fall to 0, triggering the destruction of all unreachable
objects.
Optimization: generations
=========================
Optimization: incremental collection
====================================
In order to limit the time each garbage collection takes, the GC
implementation for the default build 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 soon after their
creation. This has proven to be very close to the reality of many Python
In order to bound the length of each garbage collection pause, the GC implementation
for the default build uses incremental collection with two generations.
Generational garbage collection takes advantage of what is known as the weak
generational hypothesis: Most objects die young.
This has proven to be very close to the reality of many Python
programs as many temporary objects are created and destroyed very quickly.
To take advantage of this fact, all container objects are segregated into
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.
two generations: young and old. Every new object starts in the young generation.
Each garbage collection scans the entire young generation and part of the old generation.
The GC implementation for the free-threaded build does not use multiple
generations. Every collection operates on the entire heap.
The time taken to scan the young generation can be controlled by controlling its
size, but the size of the old generation cannot be controlled.
In order to keep pause times down, scanning of the old generation of the heap
occurs in increments.
To keep track of what has been scanned, the old generation contains two lists:
* Those objects that have not yet been scanned, referred to as the `pending` list.
* Those objects that have been scanned, referred to as the `visited` list.
To detect and collect all unreachable objects in the heap, the garbage collector
must scan the whole heap. This whole heap scan is called a full scavenge.
Increments
----------
Each full scavenge is performed in a series of increments.
For each full scavenge, the combined increments will cover the whole heap.
Each increment is made up of:
* The young generation
* The old generation's least recently scanned objects
* All objects reachable from those objects that have not yet been scanned this full scavenge
The surviving objects (those that are not collected) are moved to the back of the
`visited` list in the old generation.
When a full scavenge starts, no objects in the heap are considered to have been scanned,
so all objects in the old generation must be in the `pending` space.
When all objects in the heap have been scanned a cycle ends, and all objects are moved
to the `pending` list again. To avoid having to traverse the entire list, which list is
`pending` and which is `visited` is determined by a field in the `GCState` struct.
The `visited` and `pending` lists can be swapped by toggling this bit.
Correctness
-----------
The [algorithm for identifying cycles](#Identifying-reference-cycles) will find all
unreachable cycles in a list of objects, but will not find any cycles that are
even partly outside of that list.
Therefore, to be guaranteed that a full scavenge will find all unreachable cycles,
each cycle must be fully contained within a single increment.
To make sure that no partial cycles are included in the increment we perform a
[transitive closure](https://en.wikipedia.org/wiki/Transitive_closure)
over reachable, unscanned objects from the initial increment.
Since the transitive closure of objects reachable from an object must be a (non-strict)
superset of any unreachable cycle including that object, we are guaranteed that a
transitive closure cannot contain any partial cycles.
We can exclude scanned objects, as they must have been reachable when scanned.
If a scanned object becomes part of an unreachable cycle after being scanned, it will
not be collected this at this time, but it will be collected in the next full scavenge.
> [!NOTE]
> The GC implementation for the free-threaded build does not use incremental collection.
> Every collection operates on the entire heap.
In order to decide when to run, the collector keeps track of the number of object
allocations and deallocations since the last collection. When the number of
allocations minus the number of deallocations exceeds `threshold_0`,
collection starts. Initially only generation 0 is examined. If generation 0 has
been examined more than `threshold_1` times since generation 1 has been
examined, then generation 1 is examined as well. With generation 2,
things are a bit more complicated; see
[Collecting the oldest generation](#Collecting-the-oldest-generation) for
more information. These thresholds can be examined using the
allocations minus the number of deallocations exceeds `threshold0`,
collection starts. `threshold1` determines the fraction of the old
collection that is included in the increment.
The fraction is inversely proportional to `threshold1`,
as historically a larger `threshold1` meant that old generation
collections were performed less frequently.
`threshold2` is ignored.
These thresholds can be examined using the
[`gc.get_threshold()`](https://docs.python.org/3/library/gc.html#gc.get_threshold)
function:
@ -402,8 +454,8 @@ specifically in a generation by calling `gc.collect(generation=NUM)`.
... pass
...
# Move everything to the last generation so it's easier to inspect
# the younger generations.
# Move everything to the old generation so it's easier to inspect
# the young generation.
>>> gc.collect()
0
@ -413,40 +465,24 @@ specifically in a generation by calling `gc.collect(generation=NUM)`.
>>> x = MyObj()
>>> x.self = x
# Initially the object is in the youngest generation.
# Initially the object is in the young generation.
>>> gc.get_objects(generation=0)
[..., <__main__.MyObj object at 0x7fbcc12a3400>, ...]
# After a collection of the youngest generation the object
# moves to the next generation.
# moves to the old generation.
>>> gc.collect(generation=0)
0
>>> gc.get_objects(generation=0)
[]
>>> gc.get_objects(generation=1)
[]
>>> gc.get_objects(generation=2)
[..., <__main__.MyObj object at 0x7fbcc12a3400>, ...]
```
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 (that is, 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 (for example, 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").
Optimization: reusing fields to save memory
===========================================