Re: Python resident memory retention Evan Jones' improvements

[Matt Ernst]

On Oct 2, 4:47 pm, Andrew MacIntyre andy...@bullseye.apana.org.au
wrote:
 There are two things you need to be aware of in this situation:

 - not all Python's memory is allocated through Python's specialised
    malloc() - int and float objects in particular (in 2.x at least) are
    allocated directly via a privately managed free list, and any
    allocation requiring more than 256 bytes is directed to the platform
    malloc().  Any memory not allocated via Python's malloc() is not
    subject to the memory release facility referred to above.

    Python 2.6 does improve the management of memory consumed by int and
    float objects via the garbage collector.

 - while Python attempts to maximally utilise memory arenas to improve
    the chances of being able to free them, Python's malloc() does not do
    any compaction of memory (ie moving allocations between arenas) within
    existing arenas.  Nor does garbage collection do this.  So fragmented
    allocations can cause the retention of nearly empty arenas.

 I suspect that what you see with the second test script above is caused
 by some sort of fragmentation.

Thank you for the explanation. Since my real application uses many
small objects, it makes sense that memory cannot be reclaimed in the
absence of compaction.

Matt
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Python resident memory retention Evan Jones' improvements

[Matt Ernst]

My apologies in advance if this has been addressed before. Google does
not presently seem to return search results for this group from more
than a couple of months ago.

I have some long-running Python processes that slowly increase in
resident memory size, and whose resident size goes down only when they
are restarted. I spent hours with gc and heapy but was unable to
identify obvious culprits. I eventually tracked the problem down to
buffering data to a queue for later processing. Putting to the queue
increases resident size, but getting from it never decreases resident
size. In fact I see the same behavior when I use plain lists instead
of queue objects.

I thought Evan Jones altered Python to deal with this very problem,
and the change went into the release of 2.5.

Here is Tim Peters announcing the change:
http://mail.python.org/pipermail/python-dev/2006-March/061991.html

He included this simple test program to show the improvement:

x = []
for i in xrange(100):
   x.append([])
raw_input(full )
del x[:]
raw_input(empty )


If you look at resident size in the full stage, the interpreter has
grown to tens of megabytes. If you look at it in the empty stage, it
goes back down to less than 10 megabytes. But if you run this trivial
variation on the same program, memory use goes up and stays up:


x = []
for i in xrange(100):
   x.append([])
raw_input(full )
del x[:]
for i in xrange(100):
   x.append([])
del x[:]
raw_input(empty )


At the empty prompt resident memory size has not decreased. I see
this pattern of behavior in CPython 3.1.1, 2.6.3, 2.5.2, and Jython
2.5.1. I have tested under 32 and 64 bit Intel Linux.

At this point I suspect that I am not going to be able to force my
long-running processes to shrink their resident size, since I can't
force it in much simpler tests. I am curious about why it happens
though. That the second program should retain a larger resident memory
footprint than the first is (to me) quite surprising.
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