Is there a way to access the memory heap in Python? I'm interested in being able to access all of the objects allocated in memory of the running instance.
You can't get direct access, but the gc module should do most of what you want. A simple gc.get_objects() call will return all the objects tracked by the collector. This isn't everything since the CPython garbage collector is only concerned with potential reference cycles (so built-in types that can't refer to other objects, e.g. int, float, str, etc.) won't appear in the resulting list, but they'll all be referenced by something in that list (if they weren't, their reference count would be zero and they'd have been disposed of).
Aside from that, you might get some more targeted use out of the inspect module, especially stack frame inspection, using the traceback module for "easy formatting" or manually digging into the semi-documented frame objects themselves, either of which would allow you to narrow the scope down to a particular active scope on the stack frame.
For the closest to the heap solution, you could use the tracemalloc module to trace and record allocations as they happen, or the pdb debugger to do live introspection from the outside (possibly adding breakpoint() calls to your code to make it stop automatically when you reach that point to let you look around).
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I'm working on a C extension and am at the point where I want to track down memory leaks. From reading Python's documentation it's hard to understand when to increment / decrement reference count of Python objects. Also, after couple days spending trying to embed Python interpreter (in order to compile the extension as a standalone program), I had to give up this endeavor. So, tools like Valgrind are helpless here.
So far, by trial and error I learned that, for example, Py_DECREF(Py_None) is a bad thing... but is this true of any constant? I don't know.
My major confusions so far can be listed like this:
Do I have to decrement refcount on anything created by PyWhatever_New() if it doesn't outlive the procedure that created it?
Does every Py_INCREF need to be matched by Py_DECREF, or should there be one more of one / the other?
If a call to Python procedure resulted in a PyObject*, do I need to increment it to ensure that I can still use it (forever), or decrement it to ensure that eventually it will be garbage-collected, or neither?
Are Python objects created through C API on the stack allocated on stack or on heap? (It is possible that Py_INCREF reallocates them on heap for example).
Do I need to do anything special to Python objects created in C code before passing them to Python code? What if Python code outlives C code that created Python objects?
Finally, I understand that Python has both reference counting and garbage collector: if that's the case, how critical is it if I mess up the reference count (i.e. not decrement enough), will GC eventually figure out what to do with those objects?
Most of this is covered in Reference Count Details, and the rest is covered in the docs on the specific questions you're asking about. But, to get it all in one place:
Py_DECREF(Py_None) is a bad thing... but is this true of any constant?
The more general rule is that calling Py_DECREF on anything you didn't get a new/stolen reference to, and didn't call Py_INCREF on, is a bad thing. Since you never call Py_INCREF on anything accessible as a constant, this means you never call Py_DECREF on them.
Do I have to decrement refcount on anything created by PyWhatever_New()
Yes. Anything that returns a "new reference" has to be decremented. By convention, anything that ends in _New should return a new reference, but it should be documented anyway (e.g., see PyList_New).
Does every Py_INCREF need to be matched by Py_DECREF, or should there be one more of one / the other?
The number in your own code may not necessarily balance. The total number has to balance, but there are increments and decrements happening inside Python itself. For example, anything that returns a "new reference" has already done an inc, while anything that "steals" a reference will do the dec on it.
Are Python objects created through C API on the stack allocated on stack or on heap? (It is possible that Py_INCREF reallocates them on heap for example).
There's no way to create objects through C API on the stack. The C API only has functions that return pointers to objects.
Most of these objects are allocated on the heap. Some are actually in static memory.
But your code should not care anyway. You never allocate or delete them; they get allocated in the PySpam_New and similar functions, and deallocate themselves when you Py_DECREF them to 0, so it doesn't matter to you where they are.
(The except is constants that you can access via their global names, like Py_None. Those, you obviously know are in static storage.)
Do I need to do anything special to Python objects created in C code before passing them to Python code?
No.
What if Python code outlives C code that created Python objects?
I'm not sure what you mean by "outlives" here. Your extension module is not going to get unloaded while any objects depend on its code. (In fact, until at least 3.8, your module probably never going to get unloaded until shutdown.)
If you just mean the function that _New'd up an object returning, that's not an issue. You have to go very far out of your way to allocate any Python objects on the stack. And there's no way to pass things like a C array of objects, or a C string, into Python code without converting them to a Python tuple of objects, or a Python bytes or str. There are a few cases where, e.g., you could stash a pointer to something on the stack in a PyCapsule and pass that—but that's the same as in any C program, and… just don't do it.
Finally, I understand that Python has both reference counting and garbage collector
The garbage collector is just a cycle breaker. If you have objects that are keeping each other alive with a reference cycle, you can rely on the GC. But if you've leaked references to an object, the GC will never clean it up.
I know that python uses reference counting for garbage collection.
Every object that is allocated on the heap has counter that counts the number of object that refer to it, when the counter hits zero, the object is delete.
but how python handle with circle pointer?
if one of then delete the second stay with 1 counter but need to be delete.
The way this is handled is dependent on the python implementation. The reference implementation, the one you're probably using, is sometimes called CPython, because it is written in C.
CPython uses reference counting to clean up object which are obviously no longer used. However, every once in a while, it pauses execution of the program, and begins will the objects directly referenced by variables alive in the program. Then, it follows all references as long as it can, marking which objects have been visited. Once it has followed all references, it finds all the objects which aren't reachable from the main program, and deletes them. This is called tracing garbage collection, of which mark and sweep is a particular implementation.
If you want, and you're sure your program has no circular references, you can turn this feature off to improve performance. If you have circular references, however, you'll accidentally cause memory leaks, so it's usually not worth doing unless you're really worried about performance.
In my project, I periodically use pickling to represent the internal state of the process for persistence. As a part of normal operation, references to objects are added to and removed from multiple other objects.
For example Person might have an attribute called address_list (a list) that contains the Address objects representing all the properties they are trying to sell. Another object, RealEstateAgent, might have an attribute called addresses_for_sale (also a list) which contains the same type of Address objects, but only those ones that are listed at their agency.
If a seller takes their property off the market, or it is sold, the Address is removed from both lists.
Both Persons and RealEstateAgents are members of a central object (Masterlist) list for pickling. My problem is that as I add and remove properties and pickle the Masterlist object repeatedly over time, the size of the pickle file grows, even when I have removed (del actually) more properties than I have added. I realize that, in pickling Masterlist, there is a circular reference. There are many circular references in my application.
I examined the pickle file using pickletools.dis(), and while it's hard to human-read, I see references to Addresses that have been removed. I am sure they are removed, because, even after unpickling, they do not exist in their respective lists.
While the application functions correctly before and after pickling/unpickling, the growing filesize is an issue as the process is meant to be long running, and reinitializing it is not an option.
My example is notional, and it might be a stretch to ask, but I'm wondering if anyone has experience with either garbage collection issues using pickles, when they contain circular references or anything else that might point me in the right direction to debugging this. Maybe some tools that would be helpful.
Many thanks
You might want to try objgraph… it can seriously aid you in tracking down memory leaks and circular references and pointer relationships between objects.
http://mg.pov.lt/objgraph/
I use it when debugging pickles (in my own pickling package called dill).
Also, certain pickled objects will (down the pickle chain) pickle globals, and is often a cause of circular references within pickled objects.
I also have a suite of pickle debugging tools in dill. See dill.detect at https://github.com/uqfoundation, where there are several methods that can be used to diagnose objects you are tying to pickle. For instance, if you set dill.detect.trace(True), it will print out all the internal calls to pickle objects while your object is being dumped.
I'm trying to debug a memory leak (see question Memory leak in Python Twisted: where is it?).
When the garbage collector is running, does it have access to all Python objects created by the Python interpreter? If we suppose Python C libraries are not leaking, should RSS memory usage grow linearly with respect to the GC object count? What about sys.getobjects?
CPython uses two mechanisms to clean up garbage. One is reference counting, which affects all objects but which can't clean up objects that (directly or indirectly) refer to each other. That's where the actual garbage collector comes in: python has the gc module, which searches for cyclic references in objects it knows about. Only objects that can potentially be part of a reference cycle need to worry about participating in the cyclic gc. So, for example, lists do, but strings do not; strings don't reference any other objects. (In fact, the story is a little more complicated, as there's two ways of participating in cyclic gc, but that isn't really relevant here.)
All Python classes (and instances thereof) automatically get tracked by the cyclic gc. Types defined in C aren't, unless they put in a little effort. All the builtin types that could be part of a cycle do. But this does mean the gc module only knows about the types that play along.
Apart from the collection mechanism there's also the fact that Python has its own aggregating memory allocator (obmalloc), which allocates entire memory arenas and uses the memory for most of the smaller objects it creates. Python now does free these arenas when they're completely empty (for a long time it didn't), but actually emptying an arena is fairly rare: because CPython objects aren't movable, you can't just move some stragglers to another arena.
The RSS does not grow linearly with the number of Python objects, because Python objects can vary in size. An int object is usually much smaller than a big list.
I suppose that you mean gc.get_objects when you wrote sys.getobjects. This function gives you a list of all reachable objects. If you suppose a leak, you can iterate this list and try to find objects that should have been freed already. (For instance you might know that all objects of a certain type are to be freed at a certain point.)
A Python class designed to be unable to be involved in cycles is not tracked by the GC.
class V(object):
__slots__ = ()
Instances of V cannot have any attribute. Its size is 16, like the size of object().
sys.getsizeof(V()) and v().sizeof() return the same value: 16.
V isn't useful, but I imagine that other classes derived from base types (e.g. tuple), that only add methods, can be crafted so that reference counting is enough to manage them in memory.
From what I've read about cpython it seems like it does reference counting + something extra to detect/free objects pointing to each other.(Correct me if I'm wrong). Could someone explain the something extra? Also does this guarantee* no cycle leaking? If not is there any research into an algorithm proven to add to reference counting to make it never leak*? Would this be just running a non reference counting tracing gc every so often?
*discounting bugs and problems with modules using foreign function interface
As explained in the documentation for gc.garbage, there is no guarantee that no leaks occur; specifically, cyclic objects with __del__ methods are not collected by default. For such objects, the cyclic links have to be manually broken to enable further GC.
From what I understand by browsing the CPython sourcecode, the interpreter keeps references to all objects under its control. The "extra" garbage collector runs a mark-and-sweep-like algorithm through the heap, remembers for each object if it is reachable from the "outside" and, if not, deletes it. (The GC is generational, but it may be run explicitly from the gc module with a generation argument.)
The only efficient algorithm that I could think of that satisfies your criteria would indeed be a "full" GC algorithm to augment reference counting and this is what seems to be implemented in Python. I'm not an expert in these matters though.