There are two basic applications for weak references which have
been noted by Python programmers: object caches and reduction of
pain from circular references.
Caches (weak dictionaries)
There is a need to allow objects to be maintained that represent
external state, mapping a single instance to the external
reality, where allowing multiple instances to be mapped to the
same external resource would create unnecessary difficulty
maintaining synchronization among instances. In these cases,
a common idiom is to support a cache of instances; a factory
function is used to return either a new or existing instance.
The difficulty in this approach is that one of two things must
be tolerated: either the cache grows without bound, or there
needs to be explicit management of the cache elsewhere in the
application. The later can be very tedious and leads to more
code than is really necessary to solve the problem at hand,
and the former can be unacceptable for long-running processes
or even relatively short processes with substantial memory
- External objects that need to be represented by a single
instance, no matter how many internal users there are. This
can be useful for representing files that need to be written
back to disk in whole rather than locked & modified for
- Objects that are expensive to create, but may be needed by
multiple internal consumers. Similar to the first case, but
not necessarily bound to external resources, and possibly
not an issue for shared state. Weak references are only
useful in this case if there is some flavor of "soft"
references or if there is a high likelihood that users of
individual objects will overlap in lifespan.
- DOMs require a huge amount of circular (to parent & document
nodes) references, but these could be eliminated using a weak
dictionary mapping from each node to its parent. This
might be especially useful in the context of something like
xml.dom.pulldom, allowing the .unlink() operation to become
This proposal is divided into the following sections:
- Proposed Solution
- Implementation Strategy
- Possible Applications
- Previous Weak Reference Work in Python
- Weak References in Java
The full text of one early proposal is included as an appendix
since it does not appear to be available on the net.
Aspects of the Solution Space
There are two distinct aspects to the weak references problem:
- Invalidation of weak references
- Presentation of weak references to Python code
Past approaches to weak reference invalidation have often hinged
on storing a strong reference and being able to examine all the
instances of weak reference objects, and invalidating them when
the reference count of their referent goes to one (indicating that
the reference stored by the weak reference is the last remaining
reference). This has the advantage that the memory management
machinery in Python need not change, and that any type can be
The disadvantage of this approach to invalidation is that it
assumes that the management of the weak references is called
sufficiently frequently that weakly-referenced objects are noticed
within a reasonably short time frame; since this means a scan over
some data structure to invalidate references, an operation which
is O(N) on the number of weakly referenced objects, this is not
effectively amortized for any single object which is weakly
referenced. This also assumes that the application is calling
into code which handles weakly-referenced objects with some
frequency, which makes weak-references less attractive for library
An alternate approach to invalidation is that the de-allocation
code to be aware of the possibility of weak references and make a
specific call into the weak-reference management code to all
invalidation whenever an object is deallocated. This requires a
change in the tp_dealloc handler for weakly-referencable objects;
an additional call is needed at the "top" of the handler for
objects which support weak-referencing, and an efficient way to
map from an object to a chain of weak references for that object
is needed as well.
Two ways that weak references are presented to the Python layer
have been as explicit reference objects upon which some operation
is required in order to retrieve a usable reference to the
underlying object, and proxy objects which masquerade as the
original objects as much as possible.
Reference objects are easy to work with when some additional layer
of object managemenet is being added in Python; references can be
checked for liveness explicitly, without having to invoke
operations on the referents and catching some special exception
raised when an invalid weak reference is used.
However, a number of users favor the proxy approach simply because
the weak reference looks so much like the original object.
Weak references should be able to point to any Python object that
may have substantial memory size (directly or indirectly), or hold
references to external resources (database connections, open
A new module, weakref, will contain new functions used to create
weak references. weakref.ref() will create a "weak reference
object" and optionally attach a callback which will be called when
the object is about to be finalized. weakref.mapping() will
create a "weak dictionary". A third function, weakref.proxy(),
will create a proxy object that behaves somewhat like the original
A weak reference object will allow access to the referenced object
if it hasn't been collected and to determine if the object still
exists in memory. Retrieving the referent is done by calling the
reference object. If the referent is no longer alive, this will
return None instead.
A weak dictionary maps arbitrary keys to values, but does not own
a reference to the values. When the values are finalized, the
(key, value) pairs for which it is a value are removed from all
the mappings containing such pairs. Like dictionaries, weak
dictionaries are not hashable.
Proxy objects are weak references that attempt to behave like the
object they proxy, as much as they can. Regardless of the
underlying type, proxies are not hashable since their ability to
act as a weak reference relies on a fundamental mutability that
will cause failures when used as dictionary keys -- even if the
proper hash value is computed before the referent dies, the
resulting proxy cannot be used as a dictionary key since it cannot
be compared once the referent has expired, and comparability is
necessary for dictionary keys. Operations on proxy objects after
the referent dies cause weakref.ReferenceError to be raised in
most cases. "is" comparisons, type(), and id() will continue to
work, but always refer to the proxy and not the referent.
The callbacks registered with weak references must accept a single
parameter, which will be the weak reference or proxy object
itself. The object cannot be accessed or resurrected in the
The implementation of weak references will include a list of
reference containers that must be cleared for each weakly-
referencable object. If the reference is from a weak dictionary,
the dictionary entry is cleared first. Then, any associated
callback is called with the object passed as a parameter. Once
all callbacks have been called, the object is finalized and
Many built-in types will participate in the weak-reference
management, and any extension type can elect to do so. The type
structure will contain an additional field which provides an
offset into the instance structure which contains a list of weak
reference structures. If the value of the field is <= 0, the
object does not participate. In this case, weakref.ref(),
<weakdict>.__setitem__() and .setdefault(), and item assignment will
raise TypeError. If the value of the field is > 0, a new weak
reference can be generated and added to the list.
This approach is taken to allow arbitrary extension types to
participate, without taking a memory hit for numbers or other
Standard types which support weak references include instances,
functions, and bound & unbound methods. With the addition of
class types ("new-style classes") in Python 2.2, types grew
support for weak references. Instances of class types are weakly
referencable if they have a base type which is weakly referencable,
the class not specify __slots__, or a slot is named __weakref__.
Generators also support weak references.
Tkinter -- could avoid circular references by using weak
references from widgets to their parents. Objects won't be
discarded any sooner in the typical case, but there won't be so
much dependence on the programmer calling .destroy() before
releasing a reference. This would mostly benefit long-running
Previous Weak Reference Work in Python
Dianne Hackborn has proposed something called "virtual references".
'vref' objects are very similar to java.lang.ref.WeakReference
objects, except there is no equivalent to the invalidation
queues. Implementing a "weak dictionary" would be just as
difficult as using only weak references (without the invalidation
queue) in Java. Information on this has disappeared from the Web,
but is included below as an Appendix.
Marc-André Lemburg's mx.Proxy package:
The weakdict module by Dieter Maurer is implemented in C and
Python. It appears that the Web pages have not been updated since
Python 1.5.2a, so I'm not yet sure if the implementation is
compatible with Python 2.0.
PyWeakReference by Alex Shindich:
Eric Tiedemann has a weak dictionary implementation:
Weak References in Java
Java provides three forms of weak references, and one interesting
helper class. The three forms are called "weak", "soft", and
"phantom" references. The relevant classes are defined in the
For each of the reference types, there is an option to add the
reference to a queue when it is invalidated by the memory
allocator. The primary purpose of this facility seems to be that
it allows larger structures to be composed to incorporate
weak-reference semantics without having to impose substantial
additional locking requirements. For instance, it would not be
difficult to use this facility to create a "weak" hash table which
removes keys and referents when a reference is no longer used
elsewhere. Using weak references for the objects without some
sort of notification queue for invalidations leads to much more
tedious implementation of the various operations required on hash
tables. This can be a performance bottleneck if deallocations of
the stored objects are infrequent.
Java's "weak" references are most like Dianne Hackborn's old vref
proposal: a reference object refers to a single Python object,
but does not own a reference to that object. When that object is
deallocated, the reference object is invalidated. Users of the
reference object can easily determine that the reference has been
invalidated, or a NullObjectDereferenceError can be raised when
an attempt is made to use the referred-to object.
The "soft" references are similar, but are not invalidated as soon
as all other references to the referred-to object have been
released. The "soft" reference does own a reference, but allows
the memory allocator to free the referent if the memory is needed
elsewhere. It is not clear whether this means soft references are
released before the malloc() implementation calls sbrk() or its
equivalent, or if soft references are only cleared when malloc()
"Phantom" references are a little different; unlike weak and soft
references, the referent is not cleared when the reference is
added to its queue. When all phantom references for an object
are dequeued, the object is cleared. This can be used to keep an
object alive until some additional cleanup is performed which
needs to happen before the objects .finalize() method is called.
Unlike the other two reference types, "phantom" references must be
associated with an invalidation queue.
Appendix -- Dianne Hackborn's vref proposal (1995)
[This has been indented and paragraphs reflowed, but there have be
no content changes. --Fred]
Proposal: Virtual References
In an attempt to partly address the recurring discussion
concerning reference counting vs. garbage collection, I would like
to propose an extension to Python which should help in the
creation of "well structured" cyclic graphs. In particular, it
should allow at least trees with parent back-pointers and
doubly-linked lists to be created without worry about cycles.
The basic mechanism I'd like to propose is that of a "virtual
reference," or a "vref" from here on out. A vref is essentially a
handle on an object that does not increment the object's reference
count. This means that holding a vref on an object will not keep
the object from being destroyed. This would allow the Python
programmer, for example, to create the aforementioned tree
structure tree structure, which is automatically destroyed when it
is no longer in use -- by making all of the parent back-references
into vrefs, they no longer create reference cycles which keep the
tree from being destroyed.
In order to implement this mechanism, the Python core must ensure
that no -real- pointers are ever left referencing objects that no
longer exist. The implementation I would like to propose involves
two basic additions to the current Python system:
1. A new "vref" type, through which the Python programmer creates
and manipulates virtual references. Internally, it is
basically a C-level Python object with a pointer to the Python
object it is a reference to. Unlike all other Python code,
however, it does not change the reference count of this object.
In addition, it includes two pointers to implement a
doubly-linked list, which is used below.
2. The addition of a new field to the basic Python object
[PyObject_Head in object.h], which is either NULL, or points to
the head of a list of all vref objects that reference it. When
a vref object attaches itself to another object, it adds itself
to this linked list. Then, if an object with any vrefs on it
is deallocated, it may walk this list and ensure that all of
the vrefs on it point to some safe value, e.g. Nothing.
This implementation should hopefully have a minimal impact on the
current Python core -- when no vrefs exist, it should only add one
pointer to all objects, and a check for a NULL pointer every time
an object is deallocated.
Back at the Python language level, I have considered two possible
semantics for the vref object --
==> Pointer semantics:
In this model, a vref behaves essentially like a Python-level
pointer; the Python program must explicitly dereference the vref
to manipulate the actual object it references.
An example vref module using this model could include the
function "new"; When used as 'MyVref = vref.new(MyObject)', it
returns a new vref object such that MyVref.object ==
MyObject. MyVref.object would then change to Nothing if
MyObject is ever deallocated.
For a concrete example, we may introduce some new C-style syntax:
& -- unary operator, creates a vref on an object, same as vref.new().
* -- unary operator, dereference a vref, same as VrefObject.object.
We can then define:
1. type(&MyObject) == vref.VrefType
2. *(&MyObject) == MyObject
3. (*(&MyObject)).attr == MyObject.attr
4. &&MyObject == Nothing
5. *MyObject -> exception
Rule #4 is subtle, but comes about because we have made a vref
to (a vref with no real references). Thus the outer vref is
cleared to Nothing when the inner one inevitably disappears.
==> Proxy semantics:
In this model, the Python programmer manipulates vref objects
just as if she were manipulating the object it is a reference
of. This is accomplished by implementing the vref so that all
operations on it are redirected to its referenced object. With
this model, the dereference operator (*) no longer makes sense;
instead, we have only the reference operator (&), and define:
1. type(&MyObject) == type(MyObject)
2. &MyObject == MyObject
3. (&MyObject).attr == MyObject.attr
4. &&MyObject == MyObject
Again, rule #4 is important -- here, the outer vref is in fact a
reference to the original object, and -not- the inner vref.
This is because all operations applied to a vref actually apply
to its object, so that creating a vref of a vref actually
results in creating a vref of the latter's object.
The first, pointer semantics, has the advantage that it would be
very easy to implement; the vref type is extremely simple,
requiring at minimum a single attribute, object, and a function to
create a reference.
However, I really like the proxy semantics. Not only does it put
less of a burden on the Python programmer, but it allows you to do
nice things like use a vref anywhere you would use the actual
object. Unfortunately, it would probably an extreme pain, if not
practically impossible, to implement in the current Python
implementation. I do have some thoughts, though, on how to do
this, if it seems interesting; one possibility is to introduce new
type-checking functions which handle the vref. This would
hopefully older C modules which don't expect vrefs to simply
return a type error, until they can be fixed.
Finally, there are some other additional capabilities that this
system could provide. One that seems particularly interesting to
me involves allowing the Python programmer to add "destructor"
function to a vref -- this Python function would be called
immediately prior to the referenced object being deallocated,
allowing a Python program to invisibly attach itself to another
object and watch for it to disappear. This seems neat, though I
haven't actually come up with any practical uses for it, yet... :)
This document has been placed in the public domain.