A buffer that will partially consume an iterator in the background.
Very useful for reading files and merging the data using the excellent http://code.activestate.com/recipes/491285/
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Copy to clipboard1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 | #!/usr/bin/python
# vim: ts=4 sw=4 expandtab fileencoding=UTF-8 :
import Queue
from threading import Thread
# Object used by _background_consumer to signal the source is exhausted
# to the main thread.
_sentinel = object()
class _background_consumer(Thread):
"""Will fill the queue with content of the source in a separate thread.
>>> import Queue
>>> q = Queue.Queue()
>>> c = _background_consumer(q, range(3))
>>> c.start()
>>> q.get(True, 1)
0
>>> q.get(True, 1)
1
>>> q.get(True, 1)
2
>>> q.get(True, 1) is _sentinel
True
"""
def __init__(self, queue, source):
Thread.__init__(self)
self._queue = queue
self._source = source
def run(self):
for item in self._source:
self._queue.put(item)
# Signal the consumer we are done.
self._queue.put(_sentinel)
class ibuffer(object):
"""Buffers content of an iterator polling the contents of the given
iterator in a separate thread.
When the consumer is faster than many producers, this kind of
concurrency and buffering makes sense.
The size parameter is the number of elements to buffer.
The source must be threadsafe.
Next is a slow task:
>>> from itertools import chain
>>> import time
>>> def slow_source():
... for i in range(10):
... time.sleep(0.1)
... yield i
...
>>>
>>> t0 = time.time()
>>> max(chain(*( slow_source() for _ in range(10) )))
9
>>> int(time.time() - t0)
10
Now with the ibuffer:
>>> t0 = time.time()
>>> max(chain(*( ibuffer(5, slow_source()) for _ in range(10) )))
9
>>> int(time.time() - t0)
4
60% FASTER!!!!!11
"""
def __init__(self, size, source):
self._queue = Queue.Queue(size)
self._poller = _background_consumer(self._queue, source)
self._poller.daemon = True
self._poller.start()
def __iter__(self):
return self
def next(self):
item = self._queue.get(True)
if item is _sentinel:
raise StopIteration()
return item
if __name__ == "__main__":
import doctest
doctest.testmod()
|
Created this module to speed up IO but it is insanely slow for that.
Without ibuffer:
python -m timeit -s 'from ibuffer import ibuffer; from glob import iglob; from itertools import chain' "for i in chain.from_iterable([ open(fn) for fn in iglob('/var/log/everything.*') ]): pass" 10 loops, best of 3: 34.9 msec per loop
With ibuffer and queues of 10 elements:
python -m timeit -s 'from ibuffer import ibuffer; from glob import iglob; from itertools import chain' "for i in chain.from_iterable([ ibuffer(10, open(fn)) for fn in iglob('/var/log/everything.*') ]): pass" 10 loops, best of 3: 4.44 sec per loop
With ibuffer and queues of 1000 elements:
[19:30:00] pts/5$ python -m timeit -s 'from ibuffer import ibuffer; from glob import iglob; from itertools import chain' "for i in chain.from_iterable([ ibuffer(1000, open(fn)) for fn in iglob('/var/log/everything.*') ]): pass" 10 loops, best of 3: 2.73 sec per loop
Those timings are with warm caches. I expected this case to be somewhat slower but this is insane!
Suggestions to make it faster?
You may avoid the timeout if you put a sentinel value in the queue when the source is exhausted, instead of setting done=True. That is:
Thanks! That's an excellent way to do it!
I've packaged this idea in https://pypi.python.org/pypi/concurrent-iterator/ .