I have a large hierarchical data set in Python. After I am done with it, I need to get rid of it -- so I just do a del on the root node of the hierarchy.
Would it be OK to manually do a gc.collect() -- is it a good practice to remove large data quickly or should I not do it and let Python do it's business?
What are (if any) the correct patterns to use gc manually?
The CPython garbage collector is still largely based on reference counting, so if your data structure is truly hierarchical (does not contain circular references), a del on the last reference to it should clear it from memory and there's no need to use the gc module.
That being said, I'd recommend not even using del. It's far more elegant to set up your functions in such a way that the last reference to a data structure simply disappears when the last function to use it returns:
def load():
return some_huge_data_structure
def process(ds):
do_whatever_to(ds)
process(load()) # after this, the huge DS will be gone
When CPython garbage collects something it doesn't always actually return that memory back to the operating system.
Python uses a complicated system of memory "arenas" and "pools" (see http://www.evanjones.ca/memoryallocator/ for example). Objects live in those pools and arenas, and memory is only returned to the OS when a whole memory arena has been garbage collected.
That means that in a worst case you could have 1000 objects that occupy 250MB of memory, just because each object lives in its own arena, which might be 256k large. Now Python allocates memory in a pretty clever way, so this worst case (almost) never happens.
If you constantly allocate and de-allocate tons of very differently-sized objects, then you might into these memory fragmentation problems. In that case Python doesn't return much memory to the OS, and sadly you can't do much about it.
Related
Pretty simple question:
I have some code to show some graphs, and it prepares data for the graphs, and I don't want to waste memory (limited)... is there a way to have a "local scope" so when we get to the end, everything inside is freed?
I come from C++ where you can define code inside { ... } so at the end everything is freed, and you don't have to care about anything
Anything like that in python?
The only thing I can think of is:
def tmp():
... code ...
tmp()
but is very ugly, and for sure I don't want to list all the del x at the end
If anything holds a reference to your object, it cannot be freed. By default, anything at the global scope is going to be held in the global namespace (globals()), and as far as the interpreter knows, the very next line of source code could reference it (or, another module could import it from this current module), so globals cannot be implicitly freed, ever.
This forces your hand to either explicitly delete references to objects with del, or to put them within the local scope of a function. This may seem ugly, but if you follow the philosophy that a function should do one thing and one thing well (thanks Unix!), you will already segment your code into functions already. On the one-off exceptions where you allocate a lot of memory early on in your function, and no longer need it midway through, you can del the reference to it.
I know this isn't the answer you want to hear, but its the reality of Python. You could accomplish something similar by nesting function defs or classs inside, but this is kinda hacky (or in the class case, which wouldn't require calling/instantiating, extremely hacky).
I will also mention, there is a gc built in module for interacting with the garbage collector. Here, you can trigger an immediate garbage collection (otherwise python will eventually get around to collecting the things you del refs to), as well as inspect how many references a given object has.
If you're curious where the allocations are happening, you can also use the built in tracemalloc module to trace said allocations.
Mechanism that handles freeing memory in Python is called "Garbage Collector" and it means there's no reason to use del in overwhelming majority of Python code.
When programming in Python, you are "not supposed" to care about such low level things as allocating and freeing memory for your variables.
That being said, putting your code into functions (although preferrably called something clearer than tmp()) is most definitely a good idea as it will make your code much more readable and "Pythonic"
Coming from C++ and already stumbled to one of the main diferences (drawbacks) of python and this is memory management.Python Garbage Collector will delete all the objects that will fall out of scope.Freeing up memory of objects althought doesnt guarantee that this memory will return actually to the system but instead a rather big portion will be kept reserved by the python programm even if not used.If you face a memory problem and you want to free your memory back to the system the only safe method is to run the memory intensive function into a seperate process.Every process in python have its own interpreter and any memory consumed by this process will return to the system when the process exits.
In Python, when you write x=10, it reserves a memory location and essentially stores 10, right? Then, if you write x=20 will 20 replace the value of 10 (like C/C++ does) or will it write 20 to a new memory location and consider the old 10 as garbage?
Thanks in advance ;)
You do not have to manually free memory that you use.
Perhaps this is useful also.
garbage collection
The process of freeing memory when it is not used anymore. Python performs garbage collection via reference counting and a cyclic garbage collector that is able to detect and break reference cycles.
Sample on allocation (ints are immutable)
something=10
print(id(something)) # memory address
something=12
print(id(something))
140159603405344
140159603405408
You don't know. The Python Language Specification does not talk about things like "memory location" or "address".
It simply specifies the semantics of the code. Implementors are free to implement those semantics however they may wish.
For GraalPython, for example, I would guess that the compiler would completely optimize away the variable.
I am doing some experiments with the Python garbage collector, I would like to check if a memory address is used or not. In the following example, I have de-referenced the string (surely) at ls[2]. If I run the garbage collector, I can still see surely at the original address. I would like to be sure that the address is now writable. Is there a way to check it in Python?
from ctypes import string_at
from sys import getsizeof
import gc
ls = ['This','will be','surely','deleted']
idsurely= id(ls[2])
sizesurely = getsizeof(ls[2])
ls[2] = 'probably'
print(ls)
print(string_at(idsurely,sizesurely))
gc.collect()
# I check there is nothing in the garbage
print(gc.garbage)
print(string_at(idsurely,sizesurely))
I am interested in this mainly from a theoretical point of view so I am not saying that is something that has practical usage. My goal is to show how memory works for a tutorial. I want to show that the data is still there and that just that the bytes at the address can be now written. So the output of the script is up to now as expected. I just want to prove the last passage.
Not possible.
There is no central registry of used or unused memory addresses in Python. There isn't even a central registry of all objects (the cyclic GC doesn't know about all of them), and even if you had a registry of all objects, that wouldn't be enough to determine what memory locations are in use. Additionally, you can't just read arbitrary memory addresses, or write to arbitrary deallocated addresses. That'll quickly lead to segfaults or worse.
Finally, I would strongly advise against using this kind of thing in a tutorial even if you did find something to make it work. When you put something in a tutorial, a large fraction of people reading the tutorial are going to think it's something they're supposed to learn. Programming newbies should not be mislead into thinking that examining possibly-deallocated memory locations is something they should be doing.
Your experiments are way off base. id (solely as a CPython implementation detail) does get the memory address of the object in question, but we're talking about the Python object itself, not the data it contains. sys.getsizeof returns a number that roughly corresponds to how much memory the object occupies, but there is no guarantee that memory is contiguous.
By sheer coincidence, this almost works on str (though it will perform a buffer overread if the string in question has cached copies of its UTF-8 or wchar_t form, so you're risking crashing your program), but even then your test is flawed; CPython interns string literals that look like legal variable names, so if the string in question appears as a literal anywhere else in your program (including as the name of some class or function in some module you imported), it won't actually go away when you replace it. Similar implicit caches can occur if the literal string appears in any function, anywhere (it ends up being not only interned, but stored in the constants for that function).
Update: On testing, in an actual script, the reference count for 'surely' when you hold onto a copy of it is 3, which drops to 2 when you replace it with 'probably'. Turns out constants are being cached even at global scope. The only reason the interactive interpreter doesn't exhibit this behavior is that it effectively evals each line separately, so the constant cache is discarded when the eval completes.
And even if all that's not a problem, most (almost all) memory managers (CPython's specialized small object heap and the general heap it's built on) don't actually zero out memory when its released, so if you do look at the same address shortly after it really was released, it'll probably have pretty similar data in it.
Lastly, your gc.collect() call won't change anything except by coincidence (of whatever happens during gc possibly allocating memory by side-effect). str is not a garbage collected type, as it cannot contain references to other Python objects, so it's impossible for it to be a link in a reference cycle, and the CPython garbage collector is solely concerned with collecting cyclic garbage; CPython is reference counted, so anything that's not part of a reference cycle is cleaned up automatically and immediately when the last reference disappears.
The short answer this all leads up to is: There is no way to determine, within CPython, non-heuristically, if a particular memory address has been released to the free store and made available for reuse. CPython's memory management scheme is pure implementation detail, and exposing APIs at that level of detail would create compatibility concerns when people depended on them.
The closest you're going to get is using something like the tracemalloc module to perform basic snapshotting and compute differences in the snapshot. That's not going to give you a window into whether a specific address is still in use though AFAICT; at best it can tell you where an address that's definitely in use was allocated.
The other approach (specific to CPython) you can use is to just check the reference counts before replacing the object; sys.getrefcount for a given name/attribute reports 2, then deling (or rebinding) that name/attribute will release it (assuming no threads that might create additional references between the test and the del/rebind). You expect 2, not 1, because calling sys.getrefcount creates a temporary reference to the object in question. If it reports a number greater than 2, deling/rebinding could still lead to the object being deleted eventually when the cyclic garbage collectors runs, if the object was part of a reference cycle, but for a reference count of 2 (or 1 for something otherwise unnamed, e.g. sys.getrefcount(''.join(('f', '9')) or the like), the behavior will be deterministic.
From the documentation about gc:
... the collector supplements the reference counting already used in Python...
And from gc.is_tracked():
Returns True if the object is currently tracked by the garbage collector, False otherwise. As a general rule, instances of atomic types aren’t tracked and instances of non-atomic types (containers, user-defined objects…) are.
Strings are not tracked by the garbage collector:
In [1]: import gc
In [2]: test = 'surely'
Out[2]: 'surely'
In [3]: gc.is_tracked(test)
Out[3]: False
Looking at the documentation, there doesn't seem to be a method for accessing the reference counting from within the language.
Note that at least for me, using string_at doesn't work from the interactive interpreter. It does work in a script.
If a variable is assigned any new content, will the memory allocated for the "old content" be "properly" free'd? For example, in the following script, will the memory for variable "a" as an array of zeros be free'd after "a" is assigned some new stuff
import numpy
a = numpy.zeros(1000)
a = a+1
I would imaging Python is smart enough to do everything cleanly, using the so-called 'garbage collection', which I never really be able to read through. Any confirmation? I'd appreciate it.
Eventually, the old memory will be freed, though you cannot predict when this will happen. It is dependent on the Python implementation and many other factors.
That said, for the example you gave and the CPython implementation, the old array should be garbage collected during the assignment.
(Note that NumPy arrays are a particularly complex example for discussing garbage-collector behaviour.)
You can find the answer by playing with gc module (and probably finetuning). It provides the ability to disable the collector, tune the collection frequency, and set debugging options. It also provides access to unreachable objects that the collector found but cannot free.
See http://docs.python.org/library/gc.html
My question is why does python use both reference counting and mark-and-sweep for gc? Why not only mark-and-sweep?
My initial guess is that using reference counting can easily remove non-cyclic referenced objects, this may somewhat speed up mark-and-sweep and gain memory immediately. Don't know if my guess is right?
Any thoughts?
Thanks a lot.
Python (the language) doesn't say which form of garbage collection it uses. The main implementation (often known as CPython) acts as you describe. Other versions such as Jython or IronPython use a purely garbage collected system.
Yes, there is a benefit of earlier collection with reference counting, but the main reason CPython uses it is historical. Originally there was no garbage collection for cyclic objects so cycles led to memory leaks. The C APIs and data structures are based heavily around the principle of reference counting. When real garbage collection was added it wasn't an option to break the existing binary APIs and all the libraries that depended on them so the reference counting had to remain.
Reference counting deallocates objects sooner than garbage collection.
But as reference counting can't handle reference cycles between unreachable objects, Python uses a garbage collector (really just a cycle collector) to collect those cycles when they exist.
My initial guess is that using reference counting can easily remove non-cyclic referenced objects, this may somewhat speed up mark-and-sweep and gain memory immediately. Don't know if my guess is right?
Yes. As soon as the refcount goes to zero and object can be removed. This won't happen in a cyclic referenced object. AFAIK, mark and sweep is a costly operation and the simplest way to implement it requires you to "stop the world" while objects are marked. When all of the objects are traversed, andy object not marked (as reachable) is released.