Re: Memory usage per top 10x usage per heapy
MrsEntity wrote: Based on heapy, a db based solution would be serious overkill. I've embraced overkill and my life is better for it. Don't confuse overkill with cost. Overkill is your friend. The facts of the case: You need to save some derived strings for each of 2M input lines. Even half the input runs over the 2GB RAM in your (virtual) machine. You're using Ubuntu 12.04 in Virtualbox on Win7/64, Python 2.7/64. That screams sqlite3. It's overkill, in a good way. It's already there for the importing. Other approaches? You could try to keep everything in RAM, but use less. Tim Chase pointed out the memory-efficiency of named tuples. You could save some more by switching to Win7/32, Python 2.7/32; VirtualBox makes trying such alternatives quick and easy. Or you could add memory. Compared to good old 32-bit, 64-bit operation consumes significantly more memory and supports vastly more memory. There's a bit of a mis-match in a 64-bit system with just 2GB of RAM. I know, sounds weird, just two billion bytes of RAM. I'll rephrase: just ten dollars worth of RAM. Less if you buy it where I do. I don't know why the memory profiling tools are misleading you. I can think of plausible explanations, but they'd just be guesses. There's nothing all that surprising in running out of RAM, given what you've explained. A couple K per line is easy to burn. -Bryan -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 09/24/12 23:41, Dennis Lee Bieber wrote: On Mon, 24 Sep 2012 14:59:47 -0700 (PDT), MrsEntity junksh...@gmail.com declaimed the following in gmane.comp.python.general: Hi all, I'm working on some code that parses a 500kb, 2M line file line by line and saves, per line, some derived strings Pardon? A 2million line file will contain, at the minimum 2million line-end characters. That four times 500kB just in the line-ends, ignoring any data. As corrected later in the thread, MrsEntity writes I have, in fact, this very afternoon, invented a means of writing a carriage return character using only 2 bits of information. I am prepared to sell licenses to this revolutionary technology for the low price of $29.95 plus tax. Sorry, that should've been a 500Mb, 2M line file. If only other unnamed persons on the list were so gracious rather than turning the flame-dial to 11. I hope that when people come to the list, *this* is what they see, laugh, and want to participate. Although, MrsEntity could be zombie David A. Huffman, whose encoding scheme actually *can* store 2M lines in 500kb :-) -tkc -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 09/25/2012 12:21 AM, Junkshops wrote: Just curious; which is it, two million lines, or half a million bytes? snip Sorry, that should've been a 500Mb, 2M line file. which machine is 2gb, the Windows machine, or the VM? VM. Winders is 4gb. ...but I would point out that just because you free up the memory from the Python doesn't mean it gets released back to the system. The C runtime manages its own heap, and is pretty persistent about hanging onto memory once obtained. It's not normally a problem, since most small blocks are reused. But it can get fragmented. And i have no idea how well Virtual Box maps the Linux memory map into the Windows one. Right, I understand that - but what's confusing me is that, given the memory use is (I assume) monotonically increasing, the code should never use more than what's reported by heapy once all the data is loaded into memory, given that memory released by the code to the Python runtime is reused. To the best of my ability to tell I'm not storing anything I shouldn't, so the only thing I can think of is that all the object creation and destruction, for some reason, it preventing reuse of memory. I'm at a bit of a loss regarding what to try next. I'm not familiar with heapy, but perhaps it's missing something there. I'm a bit surprised you aren't beyond the 2gb limit, just with the structures you describe for the file. You do realize that each object has quite a few bytes of overhead, so it's not surprising to use several times the size of a file, to store the file in an organized way. I also wonder if heapy has been written to take into account the larger size of pointers in a 64bit build. Perhaps one way to save space would be to use a long to store those md5 values. You'd have to measure it, but I suspect it'd help (at the cost of lots of extra hexlify-type calls). Another thing is to make sure that the md5 object used in your two maps is the same object, and not just one with the same value. -- DaveA -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 25/09/2012 11:51, Tim Chase wrote: [snip] If only other unnamed persons on the list were so gracious rather than turning the flame-dial to 11. Oh heck what have I said this time? -tkc -- Cheers. Mark Lawrence. -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 25 September 2012 00:58, Junkshops junksh...@gmail.com wrote: Hi Tim, thanks for the response. - check how you're reading the data: are you iterating over the lines a row at a time, or are you using .read()/.readlines() to pull in the whole file and then operate on that? I'm using enumerate() on an iterable input (which in this case is the filehandle). - check how you're storing them: are you holding onto more than you think you are? I've used ipython to look through my data structures (without going into ungainly detail, 2 dicts with X numbers of key/value pairs, where X = number of lines in the file), and everything seems to be working correctly. Like I say, heapy output looks reasonable - I don't see anything surprising there. In one dict I'm storing a id string (the first token in each line of the file) with values as (again, without going into massive detail) the md5 of the contents of the line. The second dict has the md5 as the key and an object with __slots__ set that stores the line number of the file and the type of object that line represents. Can you give an example of how these data structures look after reading only the first 5 lines? Oscar -- http://mail.python.org/mailman/listinfo/python-list
Re: gracious responses (was: Memory usage per top 10x usage per heapy)
On 09/25/12 06:10, Mark Lawrence wrote: On 25/09/2012 11:51, Tim Chase wrote: If only other unnamed persons on the list were so gracious rather than turning the flame-dial to 11. Oh heck what have I said this time? You'd *like* to take credit? ;-) Nah, not you or any of the regulars here. The comment was regarding the flame-fest that's been running in some parallel threads over the last ~12hr or so. Mostly instigated by one person with a particularly quick trigger, vitriolic tongue, and a disregard for pythonic code. -tkc -- http://mail.python.org/mailman/listinfo/python-list
Re: gracious responses (was: Memory usage per top 10x usage per heapy)
On Sep 25, 9:39 pm, Tim Chase python.l...@tim.thechases.com wrote: Mostly instigated by one person with a particularly quick trigger, vitriolic tongue, and a disregard for pythonic code. I'm sorry. I'll get me coat. -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
I'm a bit surprised you aren't beyond the 2gb limit, just with the structures you describe for the file. You do realize that each object has quite a few bytes of overhead, so it's not surprising to use several times the size of a file, to store the file in an organized way. I did some back of the envelope calcs which more or less agreed with heapy. The code stores 1 string, which is, on average, about 50 chars or so, and one MD5 hex string per line of code. There's about 40 bytes or so of overhead per string per sys.getsizeof(). I'm also storing an int (24b) and a 10 char string in an object with __slots__ set. Each object, per heapy (this is one area where I might be underestimating things) takes 64 bytes plus instance variable storage, so per line: 50 + 32 + 10 + 3 * 40 + 24 + 64 = 300 bytes per line * 2M lines = ~600MB plus some memory for the dicts, which is about what heapy is reporting (note I'm currently not actually running all 2M lines, I'm just running subsets for my tests). Is there something I'm missing? Here's the heapy output after loading ~300k lines: Partition of a set of 1199849 objects. Total size = 89965376 bytes. Index Count % Size% Cumulative % Kind 0 59 50 3839992043 3839992043 str 1 5 0 2516722428 6356714471 dict 2 28 25 1919987221 8276701692 0xa13330 3 299836 25 7196064 8 89963080100 int 4 4 0 11520 89964232100 collections.defaultdict Note that 3 of the dicts are empty. I assume that 0xa13330 is the address of the object. I'd actually expect to see 900k strings, but the 10 char string is always the same in this case so perhaps the runtime is using the same object...? At this point, top reports python as using 1.1g of virt and 1.0g of res. I also wonder if heapy has been written to take into account the larger size of pointers in a 64bit build. That I don't know, but that would only explain, at most, a 2x increase in memory over the heapy report, wouldn't it? Not the ~10x I'm seeing. Another thing is to make sure that the md5 object used in your two maps is the same object, and not just one with the same value. That's certainly the way the code is written, and heapy seems to confirm that the strings aren't duplicated in memory. Thanks for sticking with me on this, MrsE On 9/25/2012 4:06 AM, Dave Angel wrote: On 09/25/2012 12:21 AM, Junkshops wrote: Just curious; which is it, two million lines, or half a million bytes? snip Sorry, that should've been a 500Mb, 2M line file. which machine is 2gb, the Windows machine, or the VM? VM. Winders is 4gb. ...but I would point out that just because you free up the memory from the Python doesn't mean it gets released back to the system. The C runtime manages its own heap, and is pretty persistent about hanging onto memory once obtained. It's not normally a problem, since most small blocks are reused. But it can get fragmented. And i have no idea how well Virtual Box maps the Linux memory map into the Windows one. Right, I understand that - but what's confusing me is that, given the memory use is (I assume) monotonically increasing, the code should never use more than what's reported by heapy once all the data is loaded into memory, given that memory released by the code to the Python runtime is reused. To the best of my ability to tell I'm not storing anything I shouldn't, so the only thing I can think of is that all the object creation and destruction, for some reason, it preventing reuse of memory. I'm at a bit of a loss regarding what to try next. I'm not familiar with heapy, but perhaps it's missing something there. I'm a bit surprised you aren't beyond the 2gb limit, just with the structures you describe for the file. You do realize that each object has quite a few bytes of overhead, so it's not surprising to use several times the size of a file, to store the file in an organized way. I also wonder if heapy has been written to take into account the larger size of pointers in a 64bit build. Perhaps one way to save space would be to use a long to store those md5 values. You'd have to measure it, but I suspect it'd help (at the cost of lots of extra hexlify-type calls). Another thing is to make sure that the md5 object used in your two maps is the same object, and not just one with the same value. -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
Can you give an example of how these data structures look after
reading only the first 5 lines?
Sure, here you go:
In [38]: mpef._ustore._store
Out[38]: defaultdict(type 'dict', {'Measurement':
{'8991c2dc67a49b909918477ee4efd767':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0fe90,
'7b38b429230f00fe4731e60419e92346':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0fad0,
'b53531471b261c44d52f651add647544':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0f4d0,
'44ea6d949f7c8c8ac3bb4c0bf4943f82':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0f910,
'0de96f928dc471b297f8a305e71ae3e1':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0f550}})
In [39]:
mpef._ustore._store['Measurement']['b53531471b261c44d52f651add647544'].typeStr
Out[39]: 'Measurement'
In [40]:
mpef._ustore._store['Measurement']['b53531471b261c44d52f651add647544'].lineNumber
Out[40]: 5
In [41]: mpef._ustore._idstore
Out[41]: defaultdict(class
'micropheno.exchangeformat.KBaseID.IDStore', {'Measurement':
micropheno.exchangeformat.KBaseID.IDStore object at 0x2f0f950})
In [43]: mpef._ustore._idstore['Measurement']._SIDstore
Out[43]: defaultdict(function lambda at 0x2ece7d0, {'emailRemoved':
defaultdict(function lambda at 0x2c4caa0, {'microPhenoShew2011':
defaultdict(type 'dict', {0: {'MLR_124572462':
'8991c2dc67a49b909918477ee4efd767', 'MLR_124572161':
'7b38b429230f00fe4731e60419e92346', 'SMMLR_12551352':
'b53531471b261c44d52f651add647544', 'SMMLR_12551051':
'0de96f928dc471b297f8a305e71ae3e1', 'SMMLR_12550750':
'44ea6d949f7c8c8ac3bb4c0bf4943f82'}})})})
-MrsE
On 9/25/2012 4:33 AM, Oscar Benjamin wrote:
On 25 September 2012 00:58, Junkshops junksh...@gmail.com
mailto:junksh...@gmail.com wrote:
Hi Tim, thanks for the response.
- check how you're reading the data: are you iterating over
the lines a row at a time, or are you using
.read()/.readlines() to pull in the whole file and then
operate on that?
I'm using enumerate() on an iterable input (which in this case is
the filehandle).
- check how you're storing them: are you holding onto more
than you think you are?
I've used ipython to look through my data structures (without
going into ungainly detail, 2 dicts with X numbers of key/value
pairs, where X = number of lines in the file), and everything
seems to be working correctly. Like I say, heapy output looks
reasonable - I don't see anything surprising there. In one dict
I'm storing a id string (the first token in each line of the file)
with values as (again, without going into massive detail) the md5
of the contents of the line. The second dict has the md5 as the
key and an object with __slots__ set that stores the line number
of the file and the type of object that line represents.
Can you give an example of how these data structures look after
reading only the first 5 lines?
Oscar
--
http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 25 September 2012 19:08, Junkshops junksh...@gmail.com wrote:
Can you give an example of how these data structures look after reading
only the first 5 lines?
Sure, here you go:
In [38]: mpef._ustore._store
Out[38]: defaultdict(type 'dict', {'Measurement':
{'8991c2dc67a49b909918477ee4efd767':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0fe90,
'7b38b429230f00fe4731e60419e92346':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0fad0,
'b53531471b261c44d52f651add647544':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0f4d0,
'44ea6d949f7c8c8ac3bb4c0bf4943f82':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0f910,
'0de96f928dc471b297f8a305e71ae3e1':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0f550}})
Have these exceptions been raised from somewhere before being stored? I
wonder if you're inadvertently keeping execution frames alive. There are
some problems in CPython with this that are related to storing exceptions.
In [39]:
mpef._ustore._store['Measurement']['b53531471b261c44d52f651add647544'].typeStr
Out[39]: 'Measurement'
Seeing how long these hex strings are, I'm confident that you would save a
significant amount of memory by converting them to int.
In [40]:
mpef._ustore._store['Measurement']['b53531471b261c44d52f651add647544'].lineNumber
Out[40]: 5
In [41]: mpef._ustore._idstore
Out[41]: defaultdict(class 'micropheno.exchangeformat.KBaseID.IDStore',
{'Measurement': micropheno.exchangeformat.KBaseID.IDStore object at
0x2f0f950})
In [43]: mpef._ustore._idstore['Measurement']._SIDstore
Out[43]: defaultdict(function lambda at 0x2ece7d0, {'emailRemoved':
defaultdict(function lambda at 0x2c4caa0, {'microPhenoShew2011':
defaultdict(type 'dict', {0: {'MLR_124572462':
'8991c2dc67a49b909918477ee4efd767', 'MLR_124572161':
'7b38b429230f00fe4731e60419e92346', 'SMMLR_12551352':
'b53531471b261c44d52f651add647544', 'SMMLR_12551051':
'0de96f928dc471b297f8a305e71ae3e1', 'SMMLR_12550750':
'44ea6d949f7c8c8ac3bb4c0bf4943f82'}})})})
Also I think lambda functions might be able to keep the frame alive. Are
they by any chance being created in a function that is called in a loop?
def f():
... x = 4
... return lambda : x
...
g = f()
g() # Accesses local variable from kept-alive frame
4
x
Traceback (most recent call last):
File stdin, line 1, in module
NameError: name 'x' is not defined
Oscar
--
http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 09/25/2012 01:39 PM, Junkshops wrote: Procedural point: I know you're trying to conform to the standard that this mailing list uses, but you're off a little, and it's distracting. It's also probably more work for you, and certainly for us. You need an attribution in front of the quoted portions. This next section is by me, but you don't say so. That's because you copy/pasted it from elsewhere in the reply, and didn't copy the ... Dave Angel wrote part. Much easier is to take the reply, and remove the parts you're not going to respond to, putting your own comments in between the parts that are left (as you're doing). And generally, there's no need for anything after your last remark, so you just delete up to your signature, if any. I'm a bit surprised you aren't beyond the 2gb limit, just with the structures you describe for the file. You do realize that each object has quite a few bytes of overhead, so it's not surprising to use several times the size of a file, to store the file in an organized way. I did some back of the envelope calcs which more or less agreed with heapy. The code stores 1 string, which is, on average, about 50 chars or so, and one MD5 hex string per line of code. There's about 40 bytes or so of overhead per string per sys.getsizeof(). I'm also storing an int (24b) and a 10 char string in an object with __slots__ set. Each object, per heapy (this is one area where I might be underestimating things) takes 64 bytes plus instance variable storage, so per line: 50 + 32 + 10 + 3 * 40 + 24 + 64 = 300 bytes per line * 2M lines = ~600MB plus some memory for the dicts, which is about what heapy is reporting (note I'm currently not actually running all 2M lines, I'm just running subsets for my tests). Is there something I'm missing? Here's the heapy output after loading ~300k lines: Partition of a set of 1199849 objects. Total size = 89965376 bytes. Index Count % Size % Cumulative % Kind 0 59 50 38399920 43 38399920 43 str 1 5 0 25167224 28 63567144 71 dict 2 28 25 19199872 21 82767016 92 0xa13330 3 299836 25 7196064 8 89963080 100 int 4 4 0 1152 0 89964232 100 collections.defaultdict Note that 3 of the dicts are empty. I assumet 0xa13330 is the address of the object. I'd actually expect to see 900k strings, but the 10 char string is always the same in this case so perhaps the runtime is using the same object...? CPython currently interns short strings that conform to variable name rules. You can't count on that behavior (and i probably don't have it quite right anyway), but it's probably what you're seeing. At this point, top reports python as using 1.1g of virt and 1.0g of res. I also wonder if heapy has been written to take into account the larger size of pointers in a 64bit build. That I don't know, but that would only explain, at most, a 2x increase in memory over the heapy report, wouldn't it? Not the ~10x I'm seeing. Another thing is to make sure that the md5 object used in your two maps is the same object, and not just one with the same value. That's certainly the way the code is written, and heapy seems to confirm that the strings aren't duplicated in memory. Thanks for sticking with me on this, You're certainly welcome. I suspect that heapy has some limitation in its reporting, and that's what the discrepancy. Oscar points out that you have a bunch of exception objects, which certainly looks suspicious. If you're somehow storing one of these per line, and heapy isn't reporting them, that could be a large discrepancy. He also points out that you have a couple of lambda functions stored in one of your dictionary. A lambda function can be an expensive proposition if you are building millions of them. So can nested functions with non-local variable references, in case you have any of those. Oscar also reminds you of what I suggested for the md5 fields. Stored as ints instead of hex strings could save a good bit. Just remember to use the same one for both dicts, as you've been doing with the strings. Other than that, I'm stumped. -- DaveA -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 9/25/2012 11:17 AM, Oscar Benjamin wrote:
On 25 September 2012 19:08, Junkshops junksh...@gmail.com
mailto:junksh...@gmail.com wrote:
In [38]: mpef._ustore._store
Out[38]: defaultdict(type 'dict', {'Measurement':
{'8991c2dc67a49b909918477ee4efd767':
micropheno.exchangeformat.Exceptions.FileContext object at
0x2f0fe90, '7b38b429230f00fe4731e60419e92346':
micropheno.exchangeformat.Exceptions.FileContext object at
0x2f0fad0, 'b53531471b261c44d52f651add647544':
micropheno.exchangeformat.Exceptions.FileContext object at
0x2f0f4d0, '44ea6d949f7c8c8ac3bb4c0bf4943f82':
micropheno.exchangeformat.Exceptions.FileContext object at
0x2f0f910, '0de96f928dc471b297f8a305e71ae3e1':
micropheno.exchangeformat.Exceptions.FileContext object at
0x2f0f550}})
Have these exceptions been raised from somewhere before being stored?
I wonder if you're inadvertently keeping execution frames alive. There
are some problems in CPython with this that are related to storing
exceptions.
FileContext objects aren't exceptions. They store information about
where the stored object originally came from, so if there's an MD5 or ID
clash with a later line in the file the code can report both the current
line and the older clashing line to the user. I have an Exception
subclass that takes a FileContext as an argument. There are no
exceptions thrown in the file I processed to get the heapy results
earlier in the thread.
In [43]: mpef._ustore._idstore['Measurement']._SIDstore
Out[43]: defaultdict(function lambda at 0x2ece7d0,
{'emailRemoved': defaultdict(function lambda at 0x2c4caa0,
{'microPhenoShew2011': defaultdict(type 'dict', {0:
{'MLR_124572462': '8991c2dc67a49b909918477ee4efd767',
'MLR_124572161': '7b38b429230f00fe4731e60419e92346',
'SMMLR_12551352': 'b53531471b261c44d52f651add647544',
'SMMLR_12551051': '0de96f928dc471b297f8a305e71ae3e1',
'SMMLR_12550750': '44ea6d949f7c8c8ac3bb4c0bf4943f82'}})})})
Also I think lambda functions might be able to keep the frame alive.
Are they by any chance being created in a function that is called in a
loop?
Here's the context for the lambdas:
def __init__(self):
self._SIDstore = defaultdict(lambda: defaultdict(lambda:
defaultdict(dict)))
So the lambda is only being called when a new key is added to the top 3
levels of the datastructure, which in the test case I've been
discussing, only happens once each.
Although the suggestion to change the hex strings to ints is a good one
and I'll do it, what I'm really trying to understand is why there's such
a large difference between the memory use per top (and the fact that the
code appears to thrash swap) and per heapy and my calculations of how
much memory the code should be using.
Cheers, MrsEntity
--
http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 9/25/2012 11:50 AM, Dave Angel wrote: I suspect that heapy has some limitation in its reporting, and that's what the discrepancy. That would be my first suspicion as well - except that heapy's results agree so well with what I expect, and I can't think of any reason I'd be using 10x more memory. If heapy is wrong, then I need to try and figure out what's using up all that memory some other way... but I don't know what that way might be. ... can be an expensive proposition if you are building millions of them. So can nested functions with non-local variable references, in case you have any of those. Not as far as I know. Cheers, MrsEntity -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 25 September 2012 21:26, Junkshops junksh...@gmail.com wrote:
On 9/25/2012 11:17 AM, Oscar Benjamin wrote:
On 25 September 2012 19:08, Junkshops junksh...@gmail.com wrote:
In [38]: mpef._ustore._store
Out[38]: defaultdict(type 'dict', {'Measurement':
{'8991c2dc67a49b909918477ee4efd767':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0fe90,
'7b38b429230f00fe4731e60419e92346':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0fad0,
'b53531471b261c44d52f651add647544':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0f4d0,
'44ea6d949f7c8c8ac3bb4c0bf4943f82':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0f910,
'0de96f928dc471b297f8a305e71ae3e1':
micropheno.exchangeformat.Exceptions.FileContext object at 0x2f0f550}})
Have these exceptions been raised from somewhere before being stored? I
wonder if you're inadvertently keeping execution frames alive. There are
some problems in CPython with this that are related to storing exceptions.
FileContext objects aren't exceptions. They store information about where
the stored object originally came from, so if there's an MD5 or ID clash
with a later line in the file the code can report both the current line and
the older clashing line to the user. I have an Exception subclass that
takes a FileContext as an argument. There are no exceptions thrown in the
file I processed to get the heapy results earlier in the thread.
I don't know whether it would be better or worse but it might be worth
seeing what happens if you replace the FileContext objects with tuples.
In [43]: mpef._ustore._idstore['Measurement']._SIDstore
Out[43]: defaultdict(function lambda at 0x2ece7d0, {'emailRemoved':
defaultdict(function lambda at 0x2c4caa0, {'microPhenoShew2011':
defaultdict(type 'dict', {0: {'MLR_124572462':
'8991c2dc67a49b909918477ee4efd767', 'MLR_124572161':
'7b38b429230f00fe4731e60419e92346', 'SMMLR_12551352':
'b53531471b261c44d52f651add647544', 'SMMLR_12551051':
'0de96f928dc471b297f8a305e71ae3e1', 'SMMLR_12550750':
'44ea6d949f7c8c8ac3bb4c0bf4943f82'}})})})
Also I think lambda functions might be able to keep the frame alive. Are
they by any chance being created in a function that is called in a loop?
Here's the context for the lambdas:
def __init__(self):
self._SIDstore = defaultdict(lambda: defaultdict(lambda:
defaultdict(dict)))
So the lambda is only being called when a new key is added to the top 3
levels of the datastructure, which in the test case I've been discussing,
only happens once each.
I can't see anything wrong with that but then I'm not sure if the lambda
function always keeps its frame alive. If there's only that one line in the
__init__ function then I'd expect it to be fine.
Although the suggestion to change the hex strings to ints is a good one
and I'll do it, what I'm really trying to understand is why there's such a
large difference between the memory use per top (and the fact that the code
appears to thrash swap) and per heapy and my calculations of how much
memory the code should be using.
Perhaps you could see what objgraph comes up with:
http://pypi.python.org/pypi/objgraph
So far as I know objgraph doesn't tell you how big objects are but it does
give a nice graphical representation of which objects are alive and which
other objects they are referenced by. You might find that some other object
is kept alive that you didn't expect.
Oscar
--
http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 9/25/2012 2:17 PM, Oscar Benjamin wrote: I don't know whether it would be better or worse but it might be worth seeing what happens if you replace the FileContext objects with tuples. I originally used a string, and it was slightly better since you don't have the object overhead, but I wanted to code to an interface for the context information so started a Context abstract class that FileContext inherits from (both have __slots__ set). Using an object without __slots__ set was a disaster. However, the difference between a string and an object with __slots__ isn't severe. I can't see anything wrong with that but then I'm not sure if the lambda function always keeps its frame alive. If there's only that one line in the __init__ function then I'd expect it to be fine. That's it, I'm afraid. Perhaps you could see what objgraph comes up with: http://pypi.python.org/pypi/objgraph So far as I know objgraph doesn't tell you how big objects are but it does give a nice graphical representation of which objects are alive and which other objects they are referenced by. You might find that some other object is kept alive that you didn't expect. I'll give it a shot and see what happens. Cheers, MrsEntity -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 09/25/12 16:17, Oscar Benjamin wrote: I don't know whether it would be better or worse but it might be worth seeing what happens if you replace the FileContext objects with tuples. If tuples provide a savings but you find them opaque, you might also consider named-tuples for clarity. -tkc -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On Tue, Sep 25, 2012 at 12:17 PM, Oscar Benjamin oscar.j.benja...@gmail.com wrote: Also I think lambda functions might be able to keep the frame alive. Are they by any chance being created in a function that is called in a loop? I'm pretty sure they don't. Closures don't keep a reference to the calling frame, only to the appropriate cellvars. Also note that whether a function is a closure has nothing to do with whether it was defined by a lambda or a def statement. In fact, there's no difference between functions created by one vs. the other, except that one has an interesting __name__ and the other does not. :-) -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 25 September 2012 23:09, Ian Kelly ian.g.ke...@gmail.com wrote: On Tue, Sep 25, 2012 at 12:17 PM, Oscar Benjamin oscar.j.benja...@gmail.com wrote: Also I think lambda functions might be able to keep the frame alive. Are they by any chance being created in a function that is called in a loop? I'm pretty sure they don't. Closures don't keep a reference to the calling frame, only to the appropriate cellvars. OK, that's good to know. Also note that whether a function is a closure has nothing to do with whether it was defined by a lambda or a def statement. In fact, there's no difference between functions created by one vs. the other, except that one has an interesting __name__ and the other does not. :-) That's true but in my experience most lambda functions are defined inside another function, whereas most ordinary functions are not. Also when creating a closure with an ordinary function it's very clear what you are doing (which is why I don't use lambda functions for this) so I think it's a little easier to accidentally create a closure with a lambda function. Oscar -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 25 September 2012 23:10, Tim Chase python.l...@tim.thechases.com wrote:
On 09/25/12 16:17, Oscar Benjamin wrote:
I don't know whether it would be better or worse but it might be
worth seeing what happens if you replace the FileContext objects
with tuples.
If tuples provide a savings but you find them opaque, you might also
consider named-tuples for clarity.
Do they have the same memory usage?
Since tuples don't have a per-instance __dict__, I'd expect them to be a
lot lighter. I'm not sure if I'm interpreting the results below properly
but they seem to suggest that a namedtuple can have a memory consumption
several times larger than an ordinary tuple.
import sys
import collections
A = collections.namedtuple('A', ['x', 'y'])
sys.getsizeof(a)
72
sys.getsizeof(A(1, 2))
72
sys.getsizeof((1, 2))
72
sys.getsizeof(A(1, 2).__dict__)
280
A(1, 2).__dict__
OrderedDict([('x', 1), ('y', 2)])
sys.getsizeof((1, 2).__dict__)
Traceback (most recent call last):
File stdin, line 1, in module
AttributeError: 'tuple' object has no attribute '__dict__'
A(1, 2).__dict__ is A(3, 4).__dict__
False
Oscar
--
http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 09/25/12 17:55, Oscar Benjamin wrote:
On 25 September 2012 23:10, Tim Chase python.l...@tim.thechases.com wrote:
If tuples provide a savings but you find them opaque, you might also
consider named-tuples for clarity.
Do they have the same memory usage?
Since tuples don't have a per-instance __dict__, I'd expect them to be a
lot lighter. I'm not sure if I'm interpreting the results below properly
but they seem to suggest that a namedtuple can have a memory consumption
several times larger than an ordinary tuple.
I think the how much memory is $METHOD using topic of the thread
is the root of the problem. From my testing of your question:
import collections, sys
A = collections.namedtuple('A', ['x', 'y'])
nt = A(1,3)
t = (1,3)
sys.getsizeof(nt)
72
sys.getsizeof(t)
72
nt_s = set(dir(nt))
t_s = set(dir(t))
t_s ^ nt_s
set(['__module__', '_make', '_asdict', '_replace', '_fields',
'__slots__', 'y', 'x'])
t_s - nt_s
set([])
So a named-tuple has 6+n (where n is the number of fields) extra
attributes, but it seems that namedtuples tuples seem to occupy
the same amount of space (72).
Additionally, pulling up a second console and issuing
ps v | grep [p]ython
shows the memory usage of the process as I perform these, and after
them, and they both show the same usage (actual test was
1) pull up a fresh python
2) import sys, collections; A = collections.namedtuple('A',['x','y'])
3) check memory usage in other window
4a) x = (1,2)
4b) x = A(1,2)
5) check memory usage again in other window
6) quit python
performing 4a on one run, and 4b on the second run.
Both showed identical memory usage as well (Debian Linux (Stable),
stock Python 2.6.6) at the system level.
I don't know if that little testing is actually worth anything, but
at least it's another data-point as we muddle towards helping
MrsEntity/junkshops.
-tkc
--
http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 26 September 2012 00:35, Tim Chase python.l...@tim.thechases.com wrote:
On 09/25/12 17:55, Oscar Benjamin wrote:
On 25 September 2012 23:10, Tim Chase python.l...@tim.thechases.com
wrote:
If tuples provide a savings but you find them opaque, you might also
consider named-tuples for clarity.
Do they have the same memory usage?
Since tuples don't have a per-instance __dict__, I'd expect them to be a
lot lighter. I'm not sure if I'm interpreting the results below properly
but they seem to suggest that a namedtuple can have a memory consumption
several times larger than an ordinary tuple.
I think the how much memory is $METHOD using topic of the thread
is the root of the problem. From my testing of your question:
import collections, sys
A = collections.namedtuple('A', ['x', 'y'])
nt = A(1,3)
t = (1,3)
sys.getsizeof(nt)
72
sys.getsizeof(t)
72
nt_s = set(dir(nt))
t_s = set(dir(t))
t_s ^ nt_s
set(['__module__', '_make', '_asdict', '_replace', '_fields',
'__slots__', 'y', 'x'])
t_s - nt_s
set([])
On my system these is an additional __dict__ attribute and it is bigger
than the original tuple:
$ python
Python 2.7.3 (default, Apr 20 2012, 22:39:59)
[GCC 4.6.3] on linux2
Type help, copyright, credits or license for more information.
import collections, sys
A = collections.namedtuple('A', ['x', 'y'])
nt = A(1,3)
t = (1,3)
set(dir(nt)) - set(dir(t))
set(['__module__', '_replace', '_make', 'y', '__slots__', '_asdict',
'__dict__', 'x', '_fields'])
sys.getsizeof(nt.__dict__)
280
sys.getsizeof(t.__dict__)
Traceback (most recent call last):
File stdin, line 1, in module
AttributeError: 'tuple' object has no attribute '__dict__'
So a named-tuple has 6+n (where n is the number of fields) extra
attributes, but it seems that namedtuples tuples seem to occupy
the same amount of space (72).
Additionally, pulling up a second console and issuing
ps v | grep [p]ython
shows the memory usage of the process as I perform these, and after
them, and they both show the same usage (actual test was
1) pull up a fresh python
2) import sys, collections; A = collections.namedtuple('A',['x','y'])
3) check memory usage in other window
4a) x = (1,2)
4b) x = A(1,2)
5) check memory usage again in other window
6) quit python
performing 4a on one run, and 4b on the second run.
Both showed identical memory usage as well (Debian Linux (Stable),
stock Python 2.6.6) at the system level.
Python uses memory pools for small memory allocations. I don't think it's
possible to tell from the outside how much memory is being used at such a
fine level.
Oscar
--
http://mail.python.org/mailman/listinfo/python-list
Memory usage per top 10x usage per heapy
Hi all, I'm working on some code that parses a 500kb, 2M line file line by line and saves, per line, some derived strings into various data structures. I thus expect that memory use should monotonically increase. Currently, the program is taking up so much memory - even on 1/2 sized files - that on 2GB machine I'm thrashing swap. What's strange is that heapy (http://guppy-pe.sourceforge.net/) is showing that the code uses about 10x less memory than reported by top, and the heapy data seems consistent with what I was expecting based on the objects the code stores. I tried using memory_profiler (http://pypi.python.org/pypi/memory_profiler) but it didn't really provide any illuminating information. The code does create and discard a number of objects per line of the file, but they should not be stored anywhere, and heapy seems to confirm that. So, my questions are: 1) For those of you kind enough to help me figure out what's going on, what additional data would you like? I didn't want swamp everyone with the code and heapy/memory_profiler output but I can do so if it's valuable. 2) How can I diagnose (and hopefully fix) what's causing the massive memory usage when it appears, from heapy, that the code is performing reasonably? Specs: Ubuntu 12.04 in Virtualbox on Win7/64, Python 2.7/64 Thanks very much. -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 09/24/12 16:59, MrsEntity wrote: I'm working on some code that parses a 500kb, 2M line file line by line and saves, per line, some derived strings into various data structures. I thus expect that memory use should monotonically increase. Currently, the program is taking up so much memory - even on 1/2 sized files - that on 2GB machine I'm thrashing swap. It might help to know what comprises the into various data structures. I do a lot of ETL work on far larger files, with similar machine specs, and rarely touch swap. 2) How can I diagnose (and hopefully fix) what's causing the massive memory usage when it appears, from heapy, that the code is performing reasonably? I seem to recall that Python holds on to memory that the VM releases, but that it *should* reuse it later. So you'd get the symptom of the memory-usage always increasing, never decreasing. Things that occur to me: - check how you're reading the data: are you iterating over the lines a row at a time, or are you using .read()/.readlines() to pull in the whole file and then operate on that? - check how you're storing them: are you holding onto more than you think you are? Would it hurt to switch from a dict to store your data (I'm assuming here) to using the anydbm module to temporarily persist the large quantity of data out to disk in order to keep memory usage lower? Without actual code, it's hard to do a more detailed analysis. -tkc -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
Hi Tim, thanks for the response. - check how you're reading the data: are you iterating over the lines a row at a time, or are you using .read()/.readlines() to pull in the whole file and then operate on that? I'm using enumerate() on an iterable input (which in this case is the filehandle). - check how you're storing them: are you holding onto more than you think you are? I've used ipython to look through my data structures (without going into ungainly detail, 2 dicts with X numbers of key/value pairs, where X = number of lines in the file), and everything seems to be working correctly. Like I say, heapy output looks reasonable - I don't see anything surprising there. In one dict I'm storing a id string (the first token in each line of the file) with values as (again, without going into massive detail) the md5 of the contents of the line. The second dict has the md5 as the key and an object with __slots__ set that stores the line number of the file and the type of object that line represents. Would it hurt to switch from a dict to store your data (I'm assuming here) to using the anydbm module to temporarily persist the large quantity of data out to disk in order to keep memory usage lower? That's the thing though - according to heapy, the memory usage *is* low and is more or less what I expect. What I don't understand is why top is reporting such vastly different memory usage. If a memory profiler is saying everything's ok, it makes it very difficult to figure out what's causing the problem. Based on heapy, a db based solution would be serious overkill. -MrsE On 9/24/2012 4:22 PM, Tim Chase wrote: On 09/24/12 16:59, MrsEntity wrote: I'm working on some code that parses a 500kb, 2M line file line by line and saves, per line, some derived strings into various data structures. I thus expect that memory use should monotonically increase. Currently, the program is taking up so much memory - even on 1/2 sized files - that on 2GB machine I'm thrashing swap. It might help to know what comprises the into various data structures. I do a lot of ETL work on far larger files, with similar machine specs, and rarely touch swap. 2) How can I diagnose (and hopefully fix) what's causing the massive memory usage when it appears, from heapy, that the code is performing reasonably? I seem to recall that Python holds on to memory that the VM releases, but that it *should* reuse it later. So you'd get the symptom of the memory-usage always increasing, never decreasing. Things that occur to me: - check how you're reading the data: are you iterating over the lines a row at a time, or are you using .read()/.readlines() to pull in the whole file and then operate on that? - check how you're storing them: are you holding onto more than you think you are? Would it hurt to switch from a dict to store your data (I'm assuming here) to using the anydbm module to temporarily persist the large quantity of data out to disk in order to keep memory usage lower? Without actual code, it's hard to do a more detailed analysis. -tkc -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
On 09/24/2012 05:59 PM, MrsEntity wrote: Hi all, I'm working on some code that parses a 500kb, 2M line file Just curious; which is it, two million lines, or half a million bytes? line by line and saves, per line, some derived strings into various data structures. I thus expect that memory use should monotonically increase. Currently, the program is taking up so much memory - even on 1/2 sized files - that on 2GB machine which machine is 2gb, the Windows machine, or the VM? You could get thrashing at either level. I'm thrashing swap. What's strange is that heapy (http://guppy-pe.sourceforge.net/) is showing that the code uses about 10x less memory than reported by top, and the heapy data seems consistent with what I was expecting based on the objects the code stores. I tried using memory_profiler (http://pypi.python.org/pypi/memory_profiler) but it didn't really provide any illuminating information. The code does create and discard a number of objects per line of the file, but they should not be stored anywhere, and heapy seems to confirm that. So, my questions are: 1) For those of you kind enough to help me figure out what's going on, what additional data would you like? I didn't want swamp everyone with the code and heapy/memory_profiler output but I can do so if it's valuable. 2) How can I diagnose (and hopefully fix) what's causing the massive memory usage when it appears, from heapy, that the code is performing reasonably? Specs: Ubuntu 12.04 in Virtualbox on Win7/64, Python 2.7/64 Thanks very much. Tim raised most of my concerns, but I would point out that just because you free up the memory from the Python doesn't mean it gets released back to the system. The C runtime manages its own heap, and is pretty persistent about hanging onto memory once obtained. It's not normally a problem, since most small blocks are reused. But it can get fragmented. And i have no idea how well Virtual Box maps the Linux memory map into the Windows one. -- DaveA -- http://mail.python.org/mailman/listinfo/python-list
Re: Memory usage per top 10x usage per heapy
Just curious; which is it, two million lines, or half a million bytes? I have, in fact, this very afternoon, invented a means of writing a carriage return character using only 2 bits of information. I am prepared to sell licenses to this revolutionary technology for the low price of $29.95 plus tax. Sorry, that should've been a 500Mb, 2M line file. which machine is 2gb, the Windows machine, or the VM? VM. Winders is 4gb. ...but I would point out that just because you free up the memory from the Python doesn't mean it gets released back to the system. The C runtime manages its own heap, and is pretty persistent about hanging onto memory once obtained. It's not normally a problem, since most small blocks are reused. But it can get fragmented. And i have no idea how well Virtual Box maps the Linux memory map into the Windows one. Right, I understand that - but what's confusing me is that, given the memory use is (I assume) monotonically increasing, the code should never use more than what's reported by heapy once all the data is loaded into memory, given that memory released by the code to the Python runtime is reused. To the best of my ability to tell I'm not storing anything I shouldn't, so the only thing I can think of is that all the object creation and destruction, for some reason, it preventing reuse of memory. I'm at a bit of a loss regarding what to try next. Cheers, MrsE On 9/24/2012 6:14 PM, Dave Angel wrote: On 09/24/2012 05:59 PM, MrsEntity wrote: Hi all, I'm working on some code that parses a 500kb, 2M line file Just curious; which is it, two million lines, or half a million bytes? line by line and saves, per line, some derived strings into various data structures. I thus expect that memory use should monotonically increase. Currently, the program is taking up so much memory - even on 1/2 sized files - that on 2GB machine which machine is 2gb, the Windows machine, or the VM? You could get thrashing at either level. I'm thrashing swap. What's strange is that heapy (http://guppy-pe.sourceforge.net/) is showing that the code uses about 10x less memory than reported by top, and the heapy data seems consistent with what I was expecting based on the objects the code stores. I tried using memory_profiler (http://pypi.python.org/pypi/memory_profiler) but it didn't really provide any illuminating information. The code does create and discard a number of objects per line of the file, but they should not be stored anywhere, and heapy seems to confirm that. So, my questions are: 1) For those of you kind enough to help me figure out what's going on, what additional data would you like? I didn't want swamp everyone with the code and heapy/memory_profiler output but I can do so if it's valuable. 2) How can I diagnose (and hopefully fix) what's causing the massive memory usage when it appears, from heapy, that the code is performing reasonably? Specs: Ubuntu 12.04 in Virtualbox on Win7/64, Python 2.7/64 Thanks very much. Tim raised most of my concerns, but I would point out that just because you free up the memory from the Python doesn't mean it gets released back to the system. The C runtime manages its own heap, and is pretty persistent about hanging onto memory once obtained. It's not normally a problem, since most small blocks are reused. But it can get fragmented. And i have no idea how well Virtual Box maps the Linux memory map into the Windows one. -- http://mail.python.org/mailman/listinfo/python-list
