Hi all,
I have some object on a pre-existing model. Now we want to add a
persistance layer and so SQLAlchemy/SQLite will be our choice.
When I add an object to session a UnmappedInstanceError is raised:
Class 'try_sqlalchemy.example2.applib_model.DescriptorBean'
is mapped, but this instance lacks
Another update!
Maybe the bad thing is to override self.__dict__. Now I set values
without overriding all and
seems to work:
class _Struct(dict):
def __init__(self,**kw):
dict.__init__(self, kw)
for k,v in kw.iteritems():
self.__dict__[k] = v
On Feb 22, 1:25 pm,
When we want to test if a Python program has a leak, we do that via seeing
how many uncollected objects are present. This is done via gc:
import gc
print total number of objects:, len(gc.get_objects())
That's the only real way to measure if the memory used by Python objects is
growing
A few things:
1. the Python dict class cannot be mapped. Classes can only extend from
object or other classes that in turn extend from object.
2. SQLAlchemy instrumentation relies upon Python descriptors (see
http://docs.python.org/howto/descriptor.html) to intercept changes in state on
an
Hi,
thanks for your reply. I haven't yet tested this with a profiler to see
exactly what exactly is happening, but the bottom line is that the
overall memory use grows with each iteration (or transaction processed),
to the point of grinding the server to a halt, and top shows only the
Yes, definitely growing at a rate of 700-800 per iteration.
.oO V Oo.
On 02/22/2012 07:23 PM, Michael Bayer wrote:
When we want to test if a Python program has a leak, we do that via seeing
how many uncollected objects are present. This is done via gc:
import gc
print total number of
I am trying to generate tables/classes dynamically. The code below is
my latest attempt, but I cannot get it to work.
-
class TableName(object):
@declared_attr
def __tablename__(cls): return cls.__name__
class Inherit(object):
On Wed, Feb 22, 2012 at 4:29 PM, Michael Bayer mike...@zzzcomputing.com wrote:
thanks for your reply. I haven't yet tested this with a profiler to see
exactly what exactly is happening, but the bottom line is that the overall
memory use grows with each iteration (or transaction processed), to
On Feb 22, 2012, at 2:46 PM, Claudio Freire wrote:
On Wed, Feb 22, 2012 at 4:29 PM, Michael Bayer mike...@zzzcomputing.com
wrote:
thanks for your reply. I haven't yet tested this with a profiler to see
exactly what exactly is happening, but the bottom line is that the overall
memory use
On Feb 22, 2012, at 3:28 PM, Claudio Freire wrote:
Like I said, it's not a leak situation as much of a fragmentation
situation, where long-lived objects in high memory positions can
prevent the process' heap from shrinking.
[0]
On Wed, Feb 22, 2012 at 5:40 PM, Michael Bayer mike...@zzzcomputing.com wrote:
Saw that a bit, but looking at the tips at the bottom, concrete
implementation changes are not coming to mind. An eternal structure is
ubiquitous in any programming language. sys.modules is a big list of all the
On Wed, Feb 22, 2012 at 5:51 PM, Claudio Freire klaussfre...@gmail.com wrote:
Such caches, for instance, are better made limited in lifespan (say,
giving them a finite lifetime, making them expire, actively cleaning
them from time to time). Structures that are truly required to be
eternal are
On Feb 22, 2012, at 3:51 PM, Claudio Freire wrote:
On Wed, Feb 22, 2012 at 5:40 PM, Michael Bayer mike...@zzzcomputing.com
wrote:
Saw that a bit, but looking at the tips at the bottom, concrete
implementation changes are not coming to mind. An eternal structure is
ubiquitous in any
On Wed, Feb 22, 2012 at 6:21 PM, Michael Bayer mike...@zzzcomputing.com wrote:
IMHO the whole point of using a high level, interpreted language like Python
is that we don't have to be bogged down thinking like C programmers. How
come I've never had a memory fragmentation issue before ?
Okay, thanks to this article:
http://neverfear.org/blog/view/155/Investigating_memory_leaks_in_Python
I made similar plot of object counts in time, showing top 50 types. The
resulting PDF is here (you might wish to download it first, Google
messes it up for me):
On Feb 22, 2012, at 6:36 PM, Vlad K. wrote:
Okay, thanks to this article:
http://neverfear.org/blog/view/155/Investigating_memory_leaks_in_Python
I made similar plot of object counts in time, showing top 50 types. The
resulting PDF is here (you might wish to download it first,
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