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1 ATISHH_ H=?N N0L HӅH[A\ÐATIUSH_HGH9CtE1H9CtG[D]A\f. Ho H=M /L HA uID$E1H9CuH=M I\$ /L HE1[]A\ADff. ATIUHH/HH Hx*HHt"I9|$tAH/u*HV*HHuHm tW-HH]A\ 1Il$ +L HH ]A\fH]A\ H)f D AWAVAUATUSH8T$ L$HD$( Ht^HIL|$ 1Lt$Ll$( HLLLL.t)HCH;D$t4H9D$ uHt$|$HHS tH81[]A\A]A^A_ D HK HH $*|$ H $HHхuHCff. ATUHSH_HsH;wt[H ] A\+ D Lg H=B -H HAԅuHs [ ]A\f D ATUHSH.IH9Et [LH ] A\.+f D H= H] H-H HӅt[ ]A\ff. fATUHSH_HGH9C0 H9C H9Ch+ H9C8Y H9C@ H9CP H9C H9CX H9CH H9CptQH9C(tHsXH[ ] A\a*H=J Le ,H HAԅt[ ]A\ @ H=J Le P,H HAԅuHE Lg H=0J ,H HAԅuHEH9CH=
J Le +H HAԅlHEH9Ch @ H=I Le +H HAԅ4HEH9C8 @ H=I Le +H HAԅHExH=I Le P+H HAԅHERH=eI Le +H HAԅHE,H==I Le *H HAԅlHEH= Le *H HAԅ._NyHeapDefs_ rel_new: Invalid relation kind: %d, must be > 0 and < %d. hv_cli_rcs_memoized_kind: nodeset object (immutable) expected. Invalid cmp argument to NyNyObjectClassifier_Compare Compare argument must be a string. Compare argument must be one of < <= == != > >= Invalid value of cmp argument. This classifier supports only equality selection. Unable to determine sizeof(PyGC_Head) from _testinternalcapi.SIZEOF_PYGC_HEAD, assuming %zd Tuple of triples with [0] a CLASSIFIER expected. Tuple of triples with [2] a STRING expected. cli_and: classifiers argument must contain classifier objects. O!:update_referrers_completely update: right argument must be sequence of 2-tuples Item deletion is not implemented for nodegraphs. ng_ass_sub: can not change number of edges (wants to always be fast);
consider using .add_edge() etc. instead. ng_ass_sub: value to assign must be a tuple guppy.sets.setsc.NyNodeSet_Exports Error at initialization of module heapyc __dir__($self, /)
--
Specialized __dir__ implementation for rootstate. The hiding tag: if it is the the same object as the hiding tag
of a HeapView object, the nodegraph will be hidden from that view. NG.is_mapping : boolean kind, read only
True if NG is a 'mapping'. Then, only one edge is allowed for each
source; indexing returns the actual target object instead of a tuple
of targets. NG.is_sorted : boolean kind, read only
True if NG is sorted. It will become unsorted after any update. It
will need to be sorted to make it possible to find edges
(implementation uses binary search). Any indexing operation will
automatically sort it if it was not already sorted. The flag is
currently used from Python to see if the nodegraph has been used at
least once after update, so that it will not be cleared too early. HV._hiding_tag_
The hiding tag defining what objects are hidden from the view defined
by HV. Objects that contain a _hiding_tag_ object which is identical
to HV._hiding_tag_, will be hidden from view, in the following cases:
o The object is of a type that has been registered for hiding
via _hiding_tag, or is of a subtype of such a type.
o The object is of instance type. Such an object will be checked
for a _hiding_tag_ item in its __dict__.
HV.is_hiding_calling_interpreter : boolean kind
If True, the data of the interpreter using the HV will be hidden from
the heap view as seen from RootState.
This is used when multiple Python interpreters are used. One
interpreter will be monitoring the operation of the other
interpreter(s). It would set is_hiding_calling_interpreter to True in
the HV it is using. Its own data will then be hidden from view, making
memory leak detection more practical. HV.root
An object that is used as the starting point when traversing the
heap. It is normally set to the special RootState object, which has
special functionality for finding the objects in the internals of the
Python interpreter structures. It can be set to any other object,
especially for test purposes.
See also: RootState HV.static_types : NodeSet, read only
The 'static types' that have been found.
The static types are the type objects that are not heap allocated, but
are defined directly in C code. HeapView searches for these among all
reachable objects (at a suitable time or as needed). classifier returning object type classifier returning index of matching kind classifier returning object size classifier returning object producer classifier returning singleton set containing object itself classifier returning the object itself classifier based on a combination of other subclassifiers guppy.heapy.heapyc.RootStateType guppy.heapy.heapyc.ObjectClassifier RootState edges[%d].src edges[%d].tgt modules i%d_%s modules_by_index sysdict importlib codec_search_path codec_search_cache codec_error_registry builtins_copy import_func before_forkers after_forkers_parent after_forkers_child audit_hooks c_profileobj i%d_t%lu_%s i%d_t%lu_f%d c_traceobj curexc_type curexc_value curexc_traceback async_exc async_gen_firstiter async_gen_finalizer context Output from malloc_stats
Other statistics
__dict__ i%d_%n t%lu_%n f%d%n no such thread state number i%d_%U gc get_objects O!:register_hiding_type OO:numedges _hiding_tag_ OO:relate ob_type O!O!O!|O!i:shpathstep O!O!:reachable pop from empty list delete_extra_type ._NyHeapDefs_ OO!:hv_new iO:rel_new O:partition O:Horizon.__new__ < OOO:select O!OOO:user_defined O!O!O!:cli_rcs O!:cli_prod hexversion _testinternalcapi SIZEOF_PYGC_HEAD O!O!O!:cli_inrel O!:cli_indisize O!O!:cli_findex Tuple of TUPLES expected. Tuple of TRIPLES expected. O!O!OO:cli_dictof O!O!:cli_and O!:update_dictowners O!O!:update_referrers OO: Ambiguos mapping |OO:NodeGraph.__new__ ObjectClassifier RootStateType malloc_usable_size malloc_stats _PyObject_DebugMallocStats _Py_RefTotal module initialization failed X relator iterable is_mapping cond_cli cond_kind classify memoized_kind start avoid G S AvoidEdges find_one root heapdefs heapyc xmemstats __dir__ is_sorted add_edge add_edges_n1 as_flat_list clear domain_covers domain_restricted get_domain get_range invert inverted relimg update updated nodegraph-iterator news self epartition <= == != > >= limitframe is_hiding_calling_interpreter static_types cli_id cli_idset cli_none cli_type cli_user_defined indisize_sum heap reachable_x register_hidden_exact_type register__hiding_tag__type update_referrers_completely classifier returning None user defined classifier hv_cli_rcs classifier returning ... hv_cli_prod guppy.heapy.heapyc.NodeTuple guppy.heapy.heapyc.Relation guppy.heapy.heapyc.NodeGraph guppy.heapy.heapyc.Horizon guppy.heapy.heapyc.HeapView 999999 >====>ň horizon_get_org_dealloc horizon_patched_dealloc horizon_remove HV.update_referrers_completely(X:nodegraph)
Update referrer graph X 'completely'.
[Experimental algorithm that updates X with the referrers to all
objects in the heap (of visible nodes as defined in HV). It is not
normally used.] HV.update_referrers(X:NodeGraph, Y:NodeSet)
Update referrer graph X for Y.
The visible heap defined by HV will be traversed from the root of HV
so that the edges of every path from the root to nodes in Y will be
represented, inverted, in X. HV.update_dictowners(owners:NodeGraph)
Update owners with ownership edges.
The dict owners graph will be updated with an edge from each dict
object in the heap, to either its owner or to None. HV.limitframe : frame | None
The traversal limiting frame.
If limitframe is set to a frame object, the frames that are more
recently entered than limitframe will be hidden when traversing the
heap from the root RootState. It will start traversing from limitframe
rather than from the most recent frame as it would otherwise do. HV.shpathstep(G:NodeGraph, U:NodeSet, S:NodeSet
[,AvoidEdges:NodeGraph [,find_one:bool]]) -> NodeSet
This method implements one step of a shortest path algorithm.
The arguments are:
G Updated by the method, with the edges from nodes in the
source set to the new nodes visited.
U The source set for this step.
S The set of already visited nodes.
AvoidEdges Edges to avoid.
find_one If True, at most one edge will be found from each node
in the source set. Normally, all edges will be found.
Return value: The new nodes visited. This may be used for the
U argument the next time the method is called.
See also: shpgraph_algorithm in Path.py. HV.relimg(S:iterable) -> NodeSet
Return the 'relational image of HV wrt S'. That is, the set of nodes
that are directly referred to from the nodes in S via the visible heap
reachability relation as defined by HV. HV.relate(src, tgt) -> relation structure
Return a description of the relation between src and tgt. This is used
for descriptions of edges in paths.
[The result is in a special format that I choose to not define here
since it is for special low-level use and subject to change.] HV.register_hidden_exact_type(type)
Register a type of objects that should be hidden from the heap view
defined by HV. Objects of the exact type registered -- not including
subtypes -- will be hidden.
See also: register__hiding_tag__type. HV.register__hiding_tag__type(type)
Register a type of objects that may be hidden from the heap view
defined by HV. The type must have a slot named _hiding_tag_. An object
that is an instance of the type, or of a subtype, is hidden when its
_hiding_tag_ is HV._hiding_tag_. HV.reachable_x(X:NodeSet, Y:NodeSet) -> NodeSet
Return the set of objects reached via a path in the visible heap as
defined by HV, from some object in X, avoiding any object in Y except
at the end of the path. HV.reachable(X:NodeSet, Y:NodeSet) -> NodeSet
Return the set of objects reached via a path in the visible heap as
defined by HV, from some object in X, avoiding any object in Y. HV.numedges(src, tgt) -> int
Return the number of edges from src to tgt. HV.indisize_sum(S:iterable) -> int
Return the sum of the 'individual size' of the objects in S.
See also HeapView.__doc. HV.heap() -> NodeSet
Return a set containing all 'visible objects' in the heap view
defined by HV. See also HeapView.__doc__. HV.cli_type() -> ObjectClassifier
Return a classifier that classifies by type.
The classification of each object is the type, as given by its
C-level member 'ob_type'. (This is the same as the type returned
by the Python-level builtin 'type'.) HV.cli_none() -> ObjectClassifier
Return a classifier that classifies all objects the same.
The classification of each object is None.
HV.cli_inrel(referrers, memo) -> ObjectClassifier
Return a classifier that classifes by "incoming relations".
The classification of an object is the set of incoming relations.
referrers A NodeGraph object used to
map each object to its referrers.
memo A dict object used to
memoize the classification sets.
HV.cli_findex(tuple, memo) -> ObjectClassifier
HV.cli_rcs(referrers, classifier, memo) -> ObjectClassifier
Return a classifier that classifies by "Referrer Classification Set".
The classification of an object is the classifications of its
referrers, collected in an immutable NodeSet object. Arguments:
referrers A NodeGraph object used to
map each object to its referrers.
classifier A ObjectClassifier object used to
classify each referrer.
memo A dict object used to
memoize the classification sets.
HV.cli_prod(memo) -> ObjectClassifier
Return a classifier that classifes by "producer".
The classification of an object is the file name and line number
in which the object is produced (allocated).
memo A dict object used to
memoize the classification sets.
HV.cli_id() -> ObjectClassifier
Return a classifier that classifies by set of identity.
The classification of an object is a singleton immnodeset containing the object itself. HV.cli_id() -> ObjectClassifier
Return a classifier that classifies by identity.
The classification of an object is the object itself. HV.cli_dictof(owners, ownerclassifier, notdictkind, notownedkind) -> ObjectClassifier
Return a classifier, that classifies by "Dict Owner".
The classification of an object is the notdictkind,
unless the object is a dict object. If the dict is 'owned' by some owner,
the classification will be
the class (as by the ownerclass argument ) of its owner.
If it is not owned, the returned kind will be notowned argument.
Arguments:
owners A NodeGraph object used to map each dict object to
its owner, or to None if it has no owner. The
graph will be automatically updated, from heap
information defined by HV, whenever an attempt
is made to classify a dict that maps to nothing.
ownerclassifier
notdictkind
notownedkind
Tuple with comparison based on addresses on the elements.
HV.cli_and(classifiers, memo) -> ObjectClassifier
Return a classifier that combines the classifications of other classifiers.
The classification returned from the returned classifier is a tuple containing
the classifications from the classifiers in the classifiers argument.
HV.delete_extra_type(weakref)
Delete extra type information. For internal use as a weak-ref callback. HeapView(root, heapdefs:tuple)
Create a new HeapView object with arguments:
root The initial value of the root member.
heapdefs Definitions of specially treated extension types.
A HeapView object provides methods to get memory related information
about the system heap and about individual objects.
It implements much of the low-level functionality for the Heapy
system. It is intended to provide what can not be done at all or would
be much slower if programmed directly in Python. It is not intended to
be used directly by a user, but to be wrapped in higher level objects.
Some terms that are referred to in the method descriptions:
Visible objects.
The HeapView object attempts to restrict its view of the heap to only
the 'visible objects'. This is to make it possible to analyse the heap
via a Python library that inevitably itself is continually allocating
and deallocating objects. These should be hidden from the heap view
presented. This is primarily done via a special tag attribute, see
'_hiding_tag_' and 'register__hiding_tag__type'. Frames can be hidden
with another mechanism, see 'limitframe'. For hiding all objects of a
special type, 'register_hidden_exact_type' may be used. It is also
possible to use a separate interpreter and hide its root objects, see
'is_hiding_calling_interpreter'.
Classifiers.
The methods named cli_* are factory methods that create objects of
type ObjectClassifier. The principal difference between classifiers is
how a single object is classified. The single-object classification
function is available in classifier objects; it is the classify
method. There are also methods that operate on collections of objects,
namely partition and select. These eliminate the per-object
Python-level function call overhead that would occur if the classify
method were to be called from Python for each object in a collection.
See also the ObjectClassifier type.
Individual size.
The individual size of an object is its individually allocated memory size.
It includes:
o The basic object size, as can be found out in a standard way.
o The extra memory for variable size objects.
o For GC collected objects, the size of the GC information.
o An alignment to the next highest multiple of a pointer size.
o The size of any other memory allocated that belongs to the object.
Some types of objects have extra memory allocated that can not be
accounted for in the standard way. This memory should nevertheless be
included in the individual size. To determine the size of these
objects, special functions are needed. These are defined for standard
builtin types, such as lists and dicts. Other types should be defined
via the heapdefs argument to the HeapView constructor.
The individual size does not include:
o Subobjects that are accounted for separately.
o Overhead for the memory allocation system. This varies depending
on the kind of memory allocator, the requested size, etc.
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Print extra memory statistics. What is printed depends on the system
configuration. The type of an object with special functionality that gives access to
internals of the Python interpreter and thread structures. It is used
as a top level root when traversing the heap to to make sure to find
some special objects that may otherwise be hidden.
There are no references from the RootState object to the special
objects. But the heap traversal and related functions defined for
RootStateType look into the Python interpreter and thread structures.
The visibility is controlled by options set in the HeapView object
which is passed to the traversal function. This makes it possible to
hide an interpreter and/or some frames referring to system objects
that should not be traversed. (See the attributes
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The objects found in interpreter and thread structures are related to
the RootState object via attributes with special names. These names
have a special form which will be described below. The name starts
with either an interpreter designator or a thread designator. It is
then followed by the name of a member in the corresponding interpreter
or thread structure. These names are the same as the names of the
members in the C structures defining them. Some of the names may be
dependent on the Python interpreter version used.
The attribute names are used for two purposes:
o To be the name used in the result of the 'relate' operation between
the RootState object and some object that is referred to via an
internal Python interpreter or thread structure.
o To be used as attribute names when selecting objects
from the RootState object. This may be used to get at such
an object knowing only its attribute name.
An attribute name is of one of the following three forms.
i_
i_t_
i_t_f
The interpreter number identifies a particular interpreter structure.
Often there is only one interpreter used, in which case the number is
0. It is possible to use more than one interpreter. The interpreters
are then numbered from 0 and up in the order they were started. [This
applies as long as no interpreter is terminated while there is still a
newer interpreter running. Then the newer interpreters will be
renumbered. If this is found to be a problem, a solution may be
devised for a newer release.]
The interpreter attribute is a member with PyObject pointer type
in the PyInterpreterState structure and can be, but not limited to,
one of the following:
modules
sysdict
builtins
codec_search_path
codec_search_cache
codec_error_registry
The thread numbers are taken from the thread identity number assigned
by Python. [ In older versions without thread identity numbers the hex
address will be used.]
The thread attribute is a member with PyObject pointer type
in the PyThreadState structure and can be, but not limited to,
one of the following:
c_profileobj
c_traceobj
curexc_type
curexc_value
curexc_traceback
exc_type
exc_value
exc_traceback
dict
async_exc
The frame list is treated specially. The frame list is continually
changed and the object that the frame member points to is not valid
for long enough to be useful. Therefore frames are referred to by a
special designator using the format shown above with a frame
number. The frame number is the number of the frame starting from 0
but counting in the reversed order of the frame list. Thus the first
started frame is 0, and in general the most recent frame has a number
that is the number of frames it has before it in call order.
NodeGraph([iterable [,is_mapping]])
Construct a new NodeGraph object. The arguments are:
iterable An iterable object that will be used to
initialize the new nodegraph. It should yield a
sequence of edges of the form (source, target).
is_mapping A boolean which, if True, will cause the nodegraph
to be treated like a 'mapping'. It will then, for the
purpose of indexing, be expected to contain a single
target for each source node.
A NodeGraph object contains pairs of nodes (edges) and can be indexed
on the first node of the pair (the source of an edge) to find all
second nodes of such pairs (the targets of those edges).
NodeGraph objects are used internally in the heapy system, for example
to record dict ownership and shortest-path graphs.
They may be used generally for mapping and dict-like purposes, but
differ in the following:
o The mapping is based on object identity - no equality or hashing is
assumed, so any object can be used as a key. Only the address is used.
To distinguish this usage from that of ordinary dicts and sets, such
objects are called 'nodes'.
o There may be any number of targets associated with each source.
o Performance characteristics differ from dicts, in somewhat subtle ways.
NG.updated(X:iterable) -> NodeGraph
Return a copy of NG updated with the edges from X,
specified as pairs of the form (source, target). NG.update(X:iterable)
Update NG with the edges from X,
specified as pairs of the form (source, target). NG.relimg(X:iterable) -> NodeSet
Return the relational image of NG wrt X. That is, the set of nodes
that are the target of some edge that have its source in X. NG.inverted() -> NodeGraph
Return a copy of NG with the edges inverted. NG.invert()
Invert the edges of NG. NG.get_range() -> NodeSet
Return the set of nodes that are the target of some edge in NG. NG.get_domain() -> NodeSet
Return the set of nodes that are the source of some edge in NG. NG.domain_restricted(X:iterable) -> NodeGraph
Return a new NodeGraph, containing those edges in NG that have source in X. NG.domain_covers(X:iterable) -> bool
Return True if each node in X is the source of some edge in NG, False otherwise. NG.copy() -> NodeGraph
Return a copy of NG. NG.clear()
Remove all items from NG. NG.as_flat_list() -> list
Return the edges of NG in the form [src0, tgt0, src1, tgt1 ...]. NG.add_edges_n1(srcs:iterable, tgt)
Add to NG, for each src in srcs, an edge from src to tgt. NG.add_edge(source, target)
Add to NG, an edge from source to target. H.news(X:iterable) -> NodeSet
Return the set of objects in X that is not in the set of objects of H.
If H was created from the contents of the heap at a particular time,
H.news(X) will return the set of objects in X that were allocated
after H was created.
Horizon(X:iterable)
Create a new Horizon object from X.
The objects in X will be used to initialize a set of objects within
the Horizon object. There are no official references to these objects,
but as some of these objects become deallocated, they will be removed
from the set of objects within the Horizon object. The objects within
the set of objects within the Horizon object can be compared to
another set of objects via the news() method. This can be used to see
what objects have been allocated but not deallocated since the Horizon
object was created.
C.select(X:iterable, kind:object, cmp:string) -> list
Select objects of a particular kind.
Each object in X is classified by C to get its kind and this is
compared with the kind argument to determine if the object will
be returned in the return list. The cmp argumt tells how the
kind of the object OK is compared to the kind argument AK
and can be one of < <= == != > >= .
The comparison '<=' used depends on the classifier. Not all
classifier may have such a comparison available. The common
cases where it is available are:
For the type classifier:
A <= B means A is a subtype of B.
For the size classifier:
A <= B means that the size A is less or equal than B.
For the referenced-by classifier:
A <= B means that A is a subset of B.
C.partition(X:iterable) -> dict
Return a partition of a set of objects.
Each object in X is classified by C to get its kind. The partition
returned is a mapping from each different kind to a list containing
the objects of that kind. C.classify(object) -> object
Return the kind of an object.
The object is classified by C, to get its kind which is then returned. This is the type of objects created by the hv_cli_* factory methods of
HeapView objects. See HeapView.__doc__ and the factory methods for the
different kinds of classifiers that are supported.
HV.cli_indisize(memo) -> ObjectClassifier
Return a classifier that classifies by "individual size".
The classification of each object is an int, containing the
object's individual memory size. The argument is:
memo A dict used to memoize the classification objects. This module contains low level functionality for the heapy system.
It is intended to be wrapped in higher level library classes.
Summary of module content.
Classes
HeapView Gives a parameterized view of the heap.
Horizon Limits the view back to some moment in time.
NodeGraph Graph of nodes (address-treated objects).
ObjectClassifier Classifies objects on various criteria.
RootStateType Root of heap traversal using Python internals.
Functions
interpreter Start a new interpreter.
set_async_exc Raise an exception in another thread.
xmemstats Print system-dependent memory statistics.
Object
RootState The single instance of RootStateType.
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ng_inverted ng_updated cli_epartition moduledef module_methods hp_xmemstats_doc rootstate_methods ng_as_mapping ng_members ng_getset ng_doc ng_methods ng_add_edge_doc ng_add_edges_n1_doc ng_as_flat_list_doc ng_clear_doc ng_copy_doc ng_domain_covers_doc ng_domain_restricted_doc ng_get_domain_doc ng_get_range_doc ng_invert_doc ng_inverted_doc ng_relimg_doc ng_update_doc ng_updated_doc horizon_methods news_doc cli_members cli_methods cli_classify_doc cli_partition_doc cli_select_doc hv_getset hv_limitframe_doc hv_members hv_methods hv_cli_and_doc hv_cli_dictof_doc hv_cli_findex_doc hv_cli_id_doc hv_cli_idset_doc hv_cli_indisize_doc hv_cli_inrel_doc hv_cli_none_doc hv_cli_prod_doc hv_cli_rcs_doc hv_cli_type_doc hv_cli_user_defined_doc hv_delete_extra_type_doc hv_indisize_sum_doc hv_heap_doc hv_numedges_doc hv_reachable_doc hv_reachable_x_doc hv_register_hidden_exact_type_doc hv_register__hiding_tag__type_doc hv_relate_doc hv_relimg_doc hv_shpathstep_doc hv_update_dictowners_doc 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