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.
Related
I am running some memory-heavy scripts which iterate over documents in a database, and due to memory constraints on the server I manually delete references to the large object at the conclusion of each iteration:
for document in database:
initial_function_calls()
big_object = memory_heavy_operation(document)
save_to_file(big_object)
del big_object
additional_function_calls()
The initial_function_calls() and additional_function_calls() are each slightly memory-heavy. Do I see any benefit by explicitly deleting the reference to the large object for garbage collection? Alternatively, does leaving it and having it point to a new object in the next iteration suffice?
As often in these cases; it depends. :-/
I'm assuming we're talking about CPython here.
Using del or re-assigning a name reduces the reference count for an object. Only if that reference could reaches 0 can it be de-allocated. So if you inadvertently stashed a reference to big_object away somewhere, using del won't help.
When garbage collection is triggered depends on the amount of allocations and de-allocations. See the documentation for gc.set_threshold().
If you're pretty sure that there are no further references, you could use gc.collect() to force a garbage collection run. That might help if your code doesn't do a lot of other allocations.
One thing to keep in mind is that if the big_object is created by a C extension module (like e.g. numpy), it could manage its own memory. In that case the garbage collection won't affect it! Also small integers and small strings are pre-allocated and won't be garbage collected. You can use gc.is_tracked() to check if an object is managed by the garbage collector.
What I would suggest is that you run your program with and without del+gc.collect(), and monitor the amount of RAM used. On UNIX-like systems, look at the resident set size. You could also use sys._debugmallocstats().
Unless you see the resident set size grow and grow, I wouldn't worry about it.
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.
Consider the following code for illustration propose:
import mod
f1s = ["A1", "B1", "C1"]
f2s = ["A2", "B2", "C2"]
for f1, f2 in zip(f1s,f2s):
# Creating an object
acumulator = mod.AcumulatorObject()
# Using object
acumulator.append(f1)
acumulator.append(f2)
# Output of object
acumulator.print()
So, I use an instance of a class at the beginning of the for to perform an operation. For each tuple in the for I need to perform the same action, however I can not use the same object because it would add the effect of the last iteration. Therefore, at the beginning of every iteration I create a new instance.
My question is if by doing this a memory leak is created? What action I have to do for each object created? (Delete it maybe? Or by assign the new object to the same name it is cleared?)
tl,dr; no
The reference implementation of Python uses reference counting for garbage collection. There are other implementations that use different GC strategies and this affects the precise time at which __del__ methods are called, which may or may not be reliable or timely in PyPy, Jython or IronPython. These differences are not important unless when you are dealing with resources like file pointers and other expensive system resources.
In cPython the GC will wipe out objects when the referencing count is zero. For example, when you do acumulator = mod.AcumulatorObject() inside a for loop, a new object replaces the old one at the next iteration - and since there are no other variables referencing the old object it will be garbage collected in the next GC pass. The reference implementation cPython will spoil you with things like releasing resources automatically when they go out of scope but YMMV regarding other implementations.
That is why many people commented memory leaks are not of concern in Python.
You have complete control over cPython's garbage collector using the cg module. The default settings are pretty conservative and in 10 years doing Python for a living I never had to fire a GC cycle manually - but I've seen a situation where delaying it helped performance:
Yes, I had previously played with sys.setcheckinterval. I changed it to 1000 (from its default of 100), but it didn't do any measurable difference. Disabling Garbage Collection has helped - thanks. This has been the biggest speedup so far - saving about 20% (171 minutes for the whole run, down to 135 minutes) - I'm not sure what the error bars are on that, but it must be a statistically significant increase.
Just follow best practices like wrapping system resources using with or (try/finally blocks) and you should have no problems.
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.