def find_top_k_with_FastRBTree(filename = TDATA, k = 10):
"""
Profile result:
5 million strings:
memory consuming: 259 MB
time consuming: 39.9689998627
[(753, 'bf'),
(753, 'qj'),
(753, 'zb'),
(753, 'vz'),
(763, 'ma'),
(755, 'lx'),
(779, 'qp'),
(768, 'bg'),
(758, 'eq'),
(767, 'tf')]
"""
result = []
t = FastRBTree()
with open(filename) as f:
for line in f:
key = line.strip()
t[key] = t.setdefault(key, 0) + 1
# heapq
for key, val in t.iter_items():
if len(result) < k:
heapq.heappush(result, (val, key))
else:
heapq.heappushpop(result, (val, key))
return result
