I guess with the actual dataset I'll be able to improve the memory
usage a bit, with BioPython::trie. That would probably be enough
optimization to continue working with some comfort. On this test code
BioPython::trie gives a bit of improvement in terms of memory. Not
much though...
>>> d = dict()
>>> for i in xrange(0, 1000000): d[unicode(i).encode('utf-8')] =
>>> array.array('i', (i, i+1, i+2, i+3, i+4, i+5, i+6))
1000000 keys, ['VmPeak:\t 125656 kB', 'VmSize:\t 125656 kB'],
3.525858 seconds, 283618.896034 keys per second
>>> from Bio import trie
>>> d = trie.trie()
>>> for i in xrange(0, 1000000): d[unicode(i).encode('utf-8')] =
>>> array.array('i', (i, i+1, i+2, i+3, i+4, i+5, i+6))
1000000 keys, ['VmPeak:\t 108932 kB', 'VmSize:\t 108932 kB'],
4.142797 seconds, 241382.814950 keys per second
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