Hi,

I need a memory efficient int-int dict in Python that would support the following operations in O(log n) time:

d[k] = v  # replace if present
v = d[k]  # None or a negative number if not present

I need to hold ~250M pairs, so it really has to be tight.

Do you happen to know a suitable implementation (Python 2.7)?

EDIT Removed impossible requirement and other nonsense. Thanks, Craig and Kylotan!

To rephrase. Here's a trivial int-int dictionary with 1M pairs:

>>> import random, sys
>>> from guppy import hpy
>>> h = hpy()
>>> h.setrelheap()
>>> d = {}
>>> for _ in xrange(1000000):
...     d[random.randint(0, sys.maxint)] = random.randint(0, sys.maxint)
... 
>>> h.heap()
Partition of a set of 1999530 objects. Total size = 49161112 bytes.
 Index  Count   %     Size   % Cumulative  % Kind (class / dict of class)
     0      1   0 25165960  51  25165960  51 dict (no owner)
     1 1999521 100 23994252  49  49160212 100 int

On average, a pair of integers uses 49 bytes.

Here's an array of 2M integers:

>>> import array, random, sys
>>> from guppy import hpy
>>> h = hpy()
>>> h.setrelheap()
>>> a = array.array('i')
>>> for _ in xrange(2000000):
...     a.append(random.randint(0, sys.maxint))
... 
>>> h.heap()
Partition of a set of 14 objects. Total size = 8001108 bytes.
 Index  Count   %     Size   % Cumulative  % Kind (class / dict of class)
     0      1   7  8000028 100   8000028 100 array.array

On average, a pair of integers uses 8 bytes.

I accept that 8 bytes/pair in a dictionary is rather hard to achieve in general. Rephrased question: is there a memory-efficient implementation of int-int dictionary that uses considerably less than 49 bytes/pair?