I am coming from C++ where I worked on heap memory and there I had to delete the memory of heap which I created on heap using 'new' keyword and I am always in confusion what to do in python for heap memory to stop memory leakage please recommend me any text for detail of python memory allocation and deletion.Thanks
You do not have to do anything: Python first of all uses reference counting. This means that for every object it holds a counter that is incremented when you reference that object through a new variable, and decrements the counter in case you let the variable point to something else. In case the counter hits zero, then the object will be deleted (or scheduled for deletion).
This is not enough however, since two objects can reference each other and thus even if no other variable refer to the objects, these objects keep each other alive. For that, Python has an (optional) garbage collector that does cycle detection. In case such cycles are found, the objects are deleted. You can schedule such collection by calling gc.collect().
In short: Python takes care of memory management itself. Of course it is your task to make sure objects can be released. For instance it is wise not to refer to a large object longer than necessary. You can do this for instance by using the del keyword:
foo = ... # some large object
# ...
# use foo for some tasks
del foo
# ...
# do some other tasks
by using del we have removed the foo variable, and thus we also decremented the counter refering to the object to which foo was refering. As a result, the object foo was refering too can be scheduled for removal (earlier). Of course compilers/interpreters can do liveness analysis, and perhaps find out themselves that you do not use foo anymore, but better be safe than sorry.
So in short: Python manages memory itself by using reference counting and a garbage collector, the thing you have to worry about is that not that much objects are still "alive" if these are no longer necessary.
Python is a high level language. And here you need not worry about memory de-allocation. It is the responsibility of the python runtime to manage memory allocations and de-allocations.
Related
Setup:
I am running a python code where:
I open a file.
For every line in file, I create an object
Do some operations with the object
Note that once I am done with the operations part, I no longer need the object. Every new line is independent.
Relevant Code as per request:
I have commented all the parts of my code, leaving below the following code:
import gc
for l in range(num_lines):
inp = f.readline()[:-1]
collector = [int(i) for i in inp]
M = BooleanFunction(collector)
deg = M.algebraic_degree()
del M
gc.collect()
The problem:
The object once created, is consuming some amount of memory. After performing the operations, I am not able to free it. So while looping over the file, my memory keeps getting accumulated with new objects, and by around 793 lines into the file, my 16 GB of RAM is completely depleted.
What I have tried:
Using the garbage collector:
import gc
del Object
gc.collect()
However, the garbage collector will not free up the RAM (or) python is not giving up the memory to the system. Creating child-processes is an idea, but not what I am up for.
Questions:
Is there any way I can free up all the memory currently occupied by the program to the OS? That means removing all variables (loop vars, global vars, etc). Something similar to what happens when you press CTRL+C to terminate the program, it returns all the memory to the OS.
A way to specifically de-allocate an object (If I am not doing it right).
Previous questions do not answer what if gc.collect() fails to do so and how do I completely give up the memory allocated.
Objects in Python can be garbage-colleted once their reference count drops to zero.
Looking at your code, every variable gets re-assigned in every iteration. So their reference count should be zero.
If that doesn't happen then I can see three main possibilities;
You are unwittingly keeping a reference to that object.
Garbage collection is disabled (gc.disable()) or frozen (gc.freeze() in Python 3.7).
The objects are made by a Python extension written in C that manages its own memory.
Note that (1) or (2) doesn't have to happen in your code. It can also happen in modules that you use.
In your case (2) should not be an issue since you force garbage collection.
For an example of (1), consider what would happen if BooleanFunction was memoized. Then a reference to each object (that you wouldn't see and can't delete) would be kept.
The only way to give all memory back to the OS is to terminate the program.
Edit 1:
Try running your program with the garbage collection debug flags enabled (gc.DEBUG_LEAK). Run gc.get_count() at the end of every loop. And maybe gc.garbage() as well.
For a better understanding of where the memory allocation happens and what exactly happens, you could run your script under the Python debugger. Step through the program line by line while monitoring the resident set size of the Python process with ps in another terminal.
If I run a function in Python 3 (func()) is it possible that objects that are created inside func() but cannot be accessed after it has finished would cause it to increase its memory usage?
For instance, will running
def func():
# Objects being created, that are not able to be used after function call has ended.
while True:
func()
ever cause the program run out of memory, no matter what is in func()?
If the program is continually using memory, what are some possible things that could be going on in func() to cause it to continue using memory after it has been called?
Edit:
I'm only asking about creating objects that can no longer be accessed after the function has ended, so they should be deleted.
Yes, it is possible for a Python function to still use memory after being
called.
Python uses garbage collection (GC) for memory management. Most GCs (I suppose
there could be some exceptions) make no guarantee if or when they will free
the memory of unreferenced objects. Say you have a function
consume_lots_of_memory() and call it as:
while True:
consume_lots_of_memory()
There is no guarantee that all of the memory allocated in the first call
to consume_lots_of_memory() will be released before it is called a
second time. Ideally the GC would run after the call finished, but it
might run half way through the fifth call. So depending on when the GC
runs, you could end up consuming more memory than you would expect and
possibly even run out of memory.
Your function could be modifying global state, and using large amounts of
memory that never gets released. Say you have a module level cache, and a
function cache_lots_of_objects() called as:
module_cache = {}
while True:
cache_lots_of_objects()
Every call to cache_lots_of_objects() only ever adds to the cache, and
the cache just keeps consuming more memory. Even if the GC promptly
releases the non-cached objects created in cache_lots_of_objects(), your
cache could eventually consume all of your memory.
You could be encountering an actual memory leak from Python itself (unlikely
but possible), or from a third-party library improperly using the C API, using
a leaky C library, or incorrectly interfacing with a C library.
One final note about memory usage. Just because Python has freed allocated
objects, it does not necessarily mean that the memory will be released from the process
and returned to the operating system. The reason has to do with how memory is
allocated to a process in chunks (pages). See abarnert's answer
to Releasing memory in Python
for a better explanation than I can offer.
Okay, I got this concept of a class that would allow other classes to import classes on as basis versus if you use it you must import it. How would I go about implementing it? Or, does the Python interpreter already do this in a way? Does it destroy classes not in use from memory, and how so?
I know C++/C are very memory orientated with pointers and all that, but is Python? And I'm not saying I have problem with it; I, more or less, want to make a modification to it for my program's design. I want to write a large program that use hundreds of classes and modules. But I'm afraid if I do this I'll bog the application down, since I have no understanding of how Python handles memory management.
I know it is a vague question, but if somebody would link or point me in the right direction it would be greatly appreciated.
Python -- like C#, Java, Perl, Ruby, Lua and many other languages -- uses garbage collection rather than manual memory management. You just freely create objects and the language's memory manager periodically (or when you specifically direct it to) looks for any objects that are no longer referenced by your program.
So if you want to hold on to an object, just hold a reference to it. If you want the object to be freed (eventually) remove any references to it.
def foo(names):
for name in names:
print name
foo(["Eric", "Ernie", "Bert"])
foo(["Guthtrie", "Eddie", "Al"])
Each of these calls to foo creates a Python list object initialized with three values. For the duration of the foo call they are referenced by the variable names, but as soon as that function exits no variable is holding a reference to them and they are fair game for the garbage collector to delete.
x =10
print (type(x))
memory manager (MM):
x points to 10
y = x
if(id(x) == id(y)):
print('x and y refer to the same object')
(MM):
y points to same 10 object
x=x+1
if(id(x) != id(y)):
print('x and y refer to different objects')
(MM):
x points to another object is 11, previously pointed object was destroyed
z=10
if(id(y) == id(z)):
print('y and z refer to same object')
else:
print('y and z refer different objects')
Python memory management is been divided into two parts.
Stack memory
Heap memory
Methods and variables are created in Stack memory.
Objects and instance variables values are created in Heap memory.
In stack memory - a stack frame is created whenever methods and
variables are created.
These stacks frames are destroyed automaticaly whenever
functions/methods returns.
Python has mechanism of Garbage collector, as soon as variables and
functions returns, Garbage collector clear the dead objects.
Read through following articles about Python Memory Management :
Python : Memory Management (updated to version 3)
Exerpt: (examples can be found in the article):
Memory management in Python involves a private heap containing all
Python objects and data structures. The management of this private
heap is ensured internally by the Python memory manager. The Python
memory manager has different components which deal with various
dynamic storage management aspects, like sharing, segmentation,
preallocation or caching.
At the lowest level, a raw memory allocator ensures that there is
enough room in the private heap for storing all Python-related data by
interacting with the memory manager of the operating system. On top of
the raw memory allocator, several object-specific allocators operate
on the same heap and implement distinct memory management policies
adapted to the peculiarities of every object type. For example,
integer objects are managed differently within the heap than strings,
tuples or dictionaries because integers imply different storage
requirements and speed/space tradeoffs. The Python memory manager thus
delegates some of the work to the object-specific allocators, but
ensures that the latter operate within the bounds of the private heap.
It is important to understand that the management of the Python heap
is performed by the interpreter itself and that the user has no
control over it, even if she regularly manipulates object pointers to
memory blocks inside that heap. The allocation of heap space for
Python objects and other internal buffers is performed on demand by
the Python memory manager through the Python/C API functions listed in
this document.
My 5 cents:
most importantly, python frees memory for referenced objects only (not for classes because they are just containers or custom data types). Again, in python everything is an object, so int, float, string, [], {} and () all are objects. That mean if your program don't reference them anymore they are victims for garbage collection.
Though python uses'Reference count' and 'GC' to free memory (for the objects that are not in used), this free memory is not returned back to the operating system (in windows its different case though). This mean free memory chunk just return back to python interpreter not to the operating system. So utlimately your python process is going to hold the same memory. However, python will use this memory to allocate to some other objects.
Very good explanation for this given at: http://deeplearning.net/software/theano/tutorial/python-memory-management.html
Yes its the same behaviour in python3 as well
Working in Python. I have a function that reads from a queue and creates a dictionary based on some of the XML tags in the record read from the queue, and returns this dictionary. I call this function in a loop forever. The dictionary gets reassigned each time. Does the memory previously used by the dictionary get freed at each reassignment, or does it get orphaned and eventually cause memory problems?
def readq():
qtags = {}
# Omitted code to read the queue record, get XML string, DOMify it
qtags['result'] = "Success"
qtags['call_offer_time'] = get_node_value_by_name(audio_dom, 'call_offer_time')
# More omitted code to extract the rest of the tags
return qtags
while signals.sigterm_caught == False:
tags = readq()
if tags['result'] == "Empty":
time.sleep(SLEEP_TIME)
continue
# Do stuff with the tags
So when I reassign tags each time in that loop, will the memory used by the previous assignment get freed before being allocated by the new assignment?
The memory of an object will be freed if it can be proven (from the knowledge the language implementation has at runtime) that it cannot possibly be accessed any more and the garbage collector sees it fit to make a collection. That's the absolute minimum, and you shouldn't assume any more. And you usually shouldn't have to worry about anything more.
More practically speaking, it may be freed at some point in time between the last reference (where "reference" isn't limited to names in scope, but can be anything that makes the object reachable) being removed and memory running out. It doesn't have to be freed by the Python implementation running your code, it may as well leave the memory cleaning to the OS and forget about any finalizers and such. Note that there can be a noticeable delay between the last reference dying and memory usage actually dropping. But as mentioned before, most implementations go out of their way to avoid excessive memory usage if there is garbage to collect.
Even more practically, you'll propably be running this on CPython (the reference implementation), which always used and most propably will always use reference counting (augmented with a real GC to handle cyclic references), so unless there's a cyclic reference (relatively rare and your code doesn't look like it has them, but can occur e.g. in graph-like structures) it will be freed as soon as the last reference to it is deleted/overwritten. Of course, other implementations aren't that predictable - PyPy alone has half a dozen different garbage collectors, all but one falling under the above paragraph.
No, it will be freed AFTER the new object has been created.
In order for the reference count to go down on the old object, tags has to be pointed to the new object. This happens after readq returns, so at the very least both objects will exist from the beginning of qtags = {} to after tags = readq().
As #delnan stated, soon after tags has been pointed to the new object, the old one will be freed by the garbage collector as there is no longer a reference to it.
Usually Python can keep up with anything you throw at it. The Garbage collector used in Python uses reference counting, so your memory usage should be about constant, you won't see any spikes in memory. Right when you remove a reference (assign the variable to something else), the garbage collector throws the memory back into the "heap" if you will. So don't worry about memory. I have run simulators doing tests for hours rewriting variables, but the memory usage stays about the same. It will be freed when you assign it a new dictionary.
I'm trying to understand the internals of the CPython garbage collector, specifically when the destructor is called. So far, the behavior is intuitive, but the following case trips me up:
Disable the GC.
Create an object, then remove a reference to it.
The object is destroyed and the _____del_____ method is called.
I thought this would only happen if the garbage collector was enabled. Can someone explain why this happens? Is there a way to defer calling the destructor?
import gc
import unittest
_destroyed = False
class MyClass(object):
def __del__(self):
global _destroyed
_destroyed = True
class GarbageCollectionTest(unittest.TestCase):
def testExplicitGarbageCollection(self):
gc.disable()
ref = MyClass()
ref = None
# The next test fails.
# The object is automatically destroyed even with the collector turned off.
self.assertFalse(_destroyed)
gc.collect()
self.assertTrue(_destroyed)
if __name__=='__main__':
unittest.main()
Disclaimer: this code is not meant for production -- I've already noted that this is very implementation-specific and does not work on Jython.
Python has both reference counting garbage collection and cyclic garbage collection, and it's the latter that the gc module controls. Reference counting can't be disabled, and hence still happens when the cyclic garbage collector is switched off.
Since there are no references left to your object after ref = None, its __del__ method is called as a result of its reference count going to zero.
There's a clue in the documentation: "Since the collector supplements the reference counting already used in Python..." (my emphasis).
You can stop the first assertion from firing by making the object refer to itself, so that its reference count doesn't go to zero, for instance by giving it this constructor:
def __init__(self):
self.myself = self
But if you do that, the second assertion will fire. That's because garbage cycles with __del__ methods don't get collected - see the documentation for gc.garbage.
The docs here (original link was to a documentation section which up to Python 3.5 was here, and was later relocated) explain how what's called "the optional garbage collector" is actually a collector of cyclic garbage (the kind that reference counting wouldn't catch) (see also here). Reference counting is explained here, with a nod to its interplay with the cyclic gc:
While Python uses the traditional
reference counting implementation, it
also offers a cycle detector that
works to detect reference cycles. This
allows applications to not worry about
creating direct or indirect circular
references; these are the weakness of
garbage collection implemented using
only reference counting. Reference
cycles consist of objects which
contain (possibly indirect) references
to themselves, so that each object in
the cycle has a reference count which
is non-zero. Typical reference
counting implementations are not able
to reclaim the memory belonging to any
objects in a reference cycle, or
referenced from the objects in the
cycle, even though there are no
further references to the cycle
itself.
Depending on your definition of garbage collector, CPython has two garbage collectors, the reference counting one, and the other one.
The reference counter is always working, and cannot be turned off, as it's quite a fast and lightweight one that does not sigificantly affect the run time of the system.
The other one (some varient of mark and sweep, I think), gets run every so often, and can be disabled. This is because it requires the interpreter to be paused while it is running, and this can happen at the wrong moment, and consume quite a lot of CPU time.
This ability to disable it is there for those time when you expect to be doing something that's time critical, and the lack of this GC won't cause you any problems.