Thread pool with same API as (multi)processing.Pool « Python recipes « ActiveState Code

ActiveState Code (http://code.activestate.com/recipes/576519/)

1

There are probably <write your guess here>s of recipes presenting how to implement a pool of threads. Now that multiprocessing is becoming mainstream, this recipe takes multiprocessing.Pool as a model and re-implements it entirely with threads. Even the comments should look familiar... This recipe also adds 2 new methods: imap_async() and imap_unordered_async().
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# Author: David Decotigny, Oct 1 2008
# @brief Pool of threads similar to multiprocessing.Pool
# See http://docs.python.org/dev/library/multiprocessing.html
# Differences: added imap_async and imap_unordered_async, and terminate()
# has to be called explicitly (it's not registered by atexit).
#
# The general idea is that we submit works to a workqueue, either as
# single Jobs (one function to call), or JobSequences (batch of
# Jobs). Each Job is associated with an ApplyResult object which has 2
# states: waiting for the Job to complete, or Ready. Instead of
# waiting for the jobs to finish, we wait for their ApplyResult object
# to become ready: an event mechanism is used for that.
# When we apply a function to several arguments in "parallel", we need
# a way to wait for all/part of the Jobs to be processed: that's what
# "collectors" are for; they group and wait for a set of ApplyResult
# objects. Once a collector is ready to be used, we can use a
# CollectorIterator to iterate over the result values it's collecting.
#
# The methods of a Pool object use all these concepts and expose
# them to their caller in a very simple way.

import sys, threading, Queue, traceback

## Item pushed on the work queue to tell the worker threads to terminate
SENTINEL = "QUIT"
def is_sentinel(obj):
"""Predicate to determine whether an item from the queue is the
signal to stop"""
return type(obj) is str and obj == SENTINEL

class TimeoutError(Exception):
"""Raised when a result is not available within the given timeout"""
pass

class PoolWorker(threading.Thread):
"""Thread that consumes WorkUnits from a queue to process them"""
def __init__(self, workq, *args, **kwds):
"""\param workq: Queue object to consume the work units from"""
threading.Thread.__init__(self, *args, **kwds)
self._workq = workq

def run(self):
"""Process the work unit, or wait for sentinel to exit"""
while 1:
workunit = self._workq.get()
if is_sentinel(workunit):
# Got sentinel
break

# Run the job / sequence
workunit.process()

class Pool(object):
"""
The Pool class represents a pool of worker threads. It has methods
which allows tasks to be offloaded to the worker processes in a
few different ways
"""

def __init__(self, nworkers, name="Pool"):
"""
\param nworkers (integer) number of worker threads to start
\param name (string) prefix for the worker threads' name
"""
self._workq = Queue.Queue()
self._closed = False
self._workers = []
for idx in xrange(nworkers):
thr = PoolWorker(self._workq, name="Worker-%s-%d" % (name, idx))
try:
thr.start()
except:
# If one thread has a problem, undo everything
self.terminate()
raise
else:
self._workers.append(thr)

def apply(self, func, args=(), kwds=dict()):
"""Equivalent of the apply() builtin function. It blocks till
the result is ready."""
return self.apply_async(func, args, kwds).get()

def map(self, func, iterable, chunksize=None):
"""A parallel equivalent of the map() builtin function. It
blocks till the result is ready.

This method chops the iterable into a number of chunks which
it submits to the process pool as separate tasks. The
(approximate) size of these chunks can be specified by setting
chunksize to a positive integer."""
return self.map_async(func, iterable, chunksize).get()

def imap(self, func, iterable, chunksize=1):
"""
An equivalent of itertools.imap().

The chunksize argument is the same as the one used by the
map() method. For very long iterables using a large value for
chunksize can make make the job complete much faster than
using the default value of 1.

Also if chunksize is 1 then the next() method of the iterator
returned by the imap() method has an optional timeout
parameter: next(timeout) will raise processing.TimeoutError if
the result cannot be returned within timeout seconds.
"""
collector = OrderedResultCollector(as_iterator=True)
self._create_sequences(func, iterable, chunksize, collector)
return iter(collector)

def imap_unordered(self, func, iterable, chunksize=1):
"""The same as imap() except that the ordering of the results
from the returned iterator should be considered
arbitrary. (Only when there is only one worker process is the
order guaranteed to be "correct".)"""
collector = UnorderedResultCollector()
self._create_sequences(func, iterable, chunksize, collector)
return iter(collector)

def apply_async(self, func, args=(), kwds=dict(), callback=None):
"""A variant of the apply() method which returns an
ApplyResult object.

If callback is specified then it should be a callable which
accepts a single argument. When the result becomes ready,
callback is applied to it (unless the call failed). callback
should complete immediately since otherwise the thread which
handles the results will get blocked."""
assert not self._closed # No lock here. We assume it's atomic...
apply_result = ApplyResult(callback=callback)
job = Job(func, args, kwds, apply_result)
self._workq.put(job)
return apply_result

def map_async(self, func, iterable, chunksize=None, callback=None):
"""A variant of the map() method which returns a ApplyResult
object.

If callback is specified then it should be a callable which
accepts a single argument. When the result becomes ready
callback is applied to it (unless the call failed). callback
should complete immediately since otherwise the thread which
handles the results will get blocked."""
apply_result = ApplyResult(callback=callback)
collector = OrderedResultCollector(apply_result, as_iterator=False)
self._create_sequences(func, iterable, chunksize, collector)
return apply_result

def imap_async(self, func, iterable, chunksize=None, callback=None):
"""A variant of the imap() method which returns an ApplyResult
object that provides an iterator (next method(timeout)
available).

If callback is specified then it should be a callable which
accepts a single argument. When the resulting iterator becomes
ready, callback is applied to it (unless the call
failed). callback should complete immediately since otherwise
the thread which handles the results will get blocked."""
apply_result = ApplyResult(callback=callback)
collector = OrderedResultCollector(apply_result, as_iterator=True)
self._create_sequences(func, iterable, chunksize, collector)
return apply_result

def imap_unordered_async(self, func, iterable, chunksize=None,
callback=None):
"""A variant of the imap_unordered() method which returns an
ApplyResult object that provides an iterator (next
method(timeout) available).

If callback is specified then it should be a callable which
accepts a single argument. When the resulting iterator becomes
ready, callback is applied to it (unless the call
failed). callback should complete immediately since otherwise
the thread which handles the results will get blocked."""
apply_result = ApplyResult(callback=callback)
collector = UnorderedResultCollector(apply_result)
self._create_sequences(func, iterable, chunksize, collector)
return apply_result

def close(self):
"""Prevents any more tasks from being submitted to the
pool. Once all the tasks have been completed the worker
processes will exit."""
# No lock here. We assume it's sufficiently atomic...
self._closed = True

def terminate(self):
"""Stops the worker processes immediately without completing
outstanding work. When the pool object is garbage collected
terminate() will be called immediately."""
self.close()

# Clearing the job queue
try:
while 1:
self._workq.get_nowait()
except Queue.Empty:
pass

# Send one sentinel for each worker thread: each thread will die
# eventually, leaving the next sentinel for the next thread
for thr in self._workers:
self._workq.put(SENTINEL)

def join(self):
"""Wait for the worker processes to exit. One must call
close() or terminate() before using join()."""
for thr in self._workers:
thr.join()

def _create_sequences(self, func, iterable, chunksize, collector = None):
"""
Create the WorkUnit objects to process and pushes them on the
work queue. Each work unit is meant to process a slice of
iterable of size chunksize. If collector is specified, then
the ApplyResult objects associated with the jobs will notify
collector when their result becomes ready.

\return the list of WorkUnit objects (basically: JobSequences)
pushed onto the work queue
"""
assert not self._closed # No lock here. We assume it's atomic...
sequences = []
results = []
it_ = iter(iterable)
exit_loop = False
while not exit_loop:
seq = []
for i in xrange(chunksize or 1):
try:
arg = it_.next()
except StopIteration:
exit_loop = True
break
apply_result = ApplyResult(collector)
job = Job(func, (arg,), {}, apply_result)
seq.append(job)
results.append(apply_result)
sequences.append(JobSequence(seq))

for seq in sequences:
self._workq.put(seq)

return sequences

class WorkUnit(object):
"""ABC for a unit of work submitted to the worker threads. It's
basically just an object equipped with a process() method"""
def process(self):
"""Do the work. Shouldn't raise any exception"""
raise NotImplementedError("Children must override Process")

class Job(WorkUnit):
"""A work unit that corresponds to the execution of a single function"""
def __init__(self, func, args, kwds, apply_result):
"""
\param func/args/kwds used to call the function
\param apply_result ApplyResult object that holds the result
of the function call
"""
WorkUnit.__init__(self)
self._func = func
self._args = args
self._kwds = kwds
self._result = apply_result

def process(self):
"""
Call the function with the args/kwds and tell the ApplyResult
that its result is ready. Correctly handles the exceptions
happening during the execution of the function
"""
try:
result = self._func(*self._args, **self._kwds)
except:
self._result._set_exception()
else:
self._result._set_value(result)

class JobSequence(WorkUnit):
"""A work unit that corresponds to the processing of a continuous
sequence of Job objects"""
def __init__(self, jobs):
WorkUnit.__init__(self)
self._jobs = jobs

def process(self):
"""
Call process() on all the Job objects that have been specified
"""
for job in self._jobs:
job.process()

class ApplyResult(object):
"""An object associated with a Job object that holds its result:
it's available during the whole life the Job and after, even when
the Job didn't process yet. It's possible to use this object to
wait for the result/exception of the job to be available.

The result objects returns by the Pool::*_async() methods are of
this type"""
def __init__(self, collector = None, callback = None):
"""
\param collector when not None, the notify_ready() method of
the collector will be called when the result from the Job is
ready
\param callback when not None, function to call when the
result becomes available (this is the paramater passed to the
Pool::*_async() methods.
"""
self._success = False
self._event = threading.Event()
self._data = None
self._collector = None
self._callback = callback

if collector is not None:
collector.register_result(self)
self._collector = collector

def get(self, timeout = None):
"""
Returns the result when it arrives. If timeout is not None and
the result does not arrive within timeout seconds then
TimeoutError is raised. If the remote call raised an exception
then that exception will be reraised by get().
"""
if not self.wait(timeout):
raise TimeoutError("Result not available within %fs" % timeout)
if self._success:
return self._data
raise self._data[0], self._data[1], self._data[2]

def wait(self, timeout = None):
"""Waits until the result is available or until timeout
seconds pass."""
self._event.wait(timeout)
return self._event.isSet()

def ready(self):
"""Returns whether the call has completed."""
return self._event.isSet()

def successful(self):
"""Returns whether the call completed without raising an
exception. Will raise AssertionError if the result is not
ready."""
assert self.ready()
return self._success

def _set_value(self, value):
"""Called by a Job object to tell the result is ready, and
provides the value of this result. The object will become
ready and successful. The collector's notify_ready() method
will be called, and the callback method too"""
assert not self.ready()
self._data = value
self._success = True
self._event.set()
if self._collector is not None:
self._collector.notify_ready(self)
if self._callback is not None:
try:
self._callback(value)
except:
traceback.print_exc()

def _set_exception(self):
"""Called by a Job object to tell that an exception occured
during the processing of the function. The object will become
ready but not successful. The collector's notify_ready()
method will be called, but NOT the callback method"""
# traceback.print_exc()
assert not self.ready()
self._data = sys.exc_info()
self._success = False
self._event.set()
if self._collector is not None:
self._collector.notify_ready(self)

class AbstractResultCollector(object):
"""ABC to define the interface of a ResultCollector object. It is
basically an object which knows whuich results it's waiting for,
and which is able to get notify when they get available. It is
also able to provide an iterator over the results when they are
available"""

def __init__(self, to_notify):
"""
\param to_notify ApplyResult object to notify when all the
results we're waiting for become available. Can be None.
"""
self._to_notify = to_notify

def register_result(self, apply_result):
"""Used to identify which results we're waiting for. Will
always be called BEFORE the Jobs get submitted to the work
queue, and BEFORE the __iter__ and _get_result() methods can
be called
\param apply_result ApplyResult object to add in our collection
"""
raise NotImplementedError("Children classes must implement it")

def notify_ready(self, apply_result):
"""Called by the ApplyResult object (already registered via
register_result()) that it is now ready (ie. the Job's result
is available or an exception has been raised).
\param apply_result ApplyResult object telling us that the job
has been processed
"""
raise NotImplementedError("Children classes must implement it")

def _get_result(self, idx, timeout = None):
"""Called by the CollectorIterator object to retrieve the
result's values one after another (order defined by the
implementation)
\param idx The index of the result we want, wrt collector's order
\param timeout integer telling how long to wait (in seconds)
for the result at index idx to be available, or None (wait
forever)
"""
raise NotImplementedError("Children classes must implement it")

def __iter__(self):
"""Return a new CollectorIterator object for this collector"""
return CollectorIterator(self)

class CollectorIterator(object):
"""An iterator that allows to iterate over the result values
available in the given collector object. Equipped with an extended
next() method accepting a timeout argument. Created by the
AbstractResultCollector::__iter__() method"""
def __init__(self, collector):
"""\param AbstractResultCollector instance"""
self._collector = collector
self._idx = 0

def __iter__(self):
return self

def next(self, timeout = None):
"""Return the next result value in the sequence. Raise
StopIteration at the end. Can raise the exception raised by
the Job"""
try:
apply_result = self._collector._get_result(self._idx, timeout)
except IndexError:
# Reset for next time
self._idx = 0
raise StopIteration
except:
self._idx = 0
raise
self._idx += 1
assert apply_result.ready()
return apply_result.get(0)

class UnorderedResultCollector(AbstractResultCollector):
"""An AbstractResultCollector implementation that collects the
values of the ApplyResult objects in the order they become ready. The
CollectorIterator object returned by __iter__() will iterate over
them in the order they become ready"""

def __init__(self, to_notify = None):
"""
\param to_notify ApplyResult object to notify when all the
results we're waiting for become available. Can be None.
"""
AbstractResultCollector.__init__(self, to_notify)
self._cond = threading.Condition()
self._collection = []
self._expected = 0

def register_result(self, apply_result):
"""Used to identify which results we're waiting for. Will
always be called BEFORE the Jobs get submitted to the work
queue, and BEFORE the __iter__ and _get_result() methods can
be called
\param apply_result ApplyResult object to add in our collection
"""
self._expected += 1

def _get_result(self, idx, timeout = None):
"""Called by the CollectorIterator object to retrieve the
result's values one after another, in the order the results have
become available.
\param idx The index of the result we want, wrt collector's order
\param timeout integer telling how long to wait (in seconds)
for the result at index idx to be available, or None (wait
forever)
"""
self._cond.acquire()
try:
if idx >= self._expected:
raise IndexError
elif idx < len(self._collection):
return self._collection[idx]
elif idx != len(self._collection):
# Violation of the sequence protocol
raise IndexError()
else:
self._cond.wait(timeout=timeout)
try:
return self._collection[idx]
except IndexError:
# Still not added !
raise TimeoutError("Timeout while waiting for results")
finally:
self._cond.release()

def notify_ready(self, apply_result):
"""Called by the ApplyResult object (already registered via
register_result()) that it is now ready (ie. the Job's result
is available or an exception has been raised).
\param apply_result ApplyResult object telling us that the job
has been processed
"""
first_item = False
self._cond.acquire()
try:
self._collection.append(apply_result)
first_item = (len(self._collection) == 1)

self._cond.notifyAll()
finally:
self._cond.release()

if first_item and self._to_notify is not None:
self._to_notify._set_value(iter(self))

class OrderedResultCollector(AbstractResultCollector):
"""An AbstractResultCollector implementation that collects the
values of the ApplyResult objects in the order they have been
submitted. The CollectorIterator object returned by __iter__()
will iterate over them in the order they have been submitted"""

def __init__(self, to_notify = None, as_iterator = True):
"""
\param to_notify ApplyResult object to notify when all the
results we're waiting for become available. Can be None.
\param as_iterator boolean telling whether the result value
set on to_notify should be an iterator (available as soon as 1
result arrived) or a list (available only after the last
result arrived)
"""
AbstractResultCollector.__init__(self, to_notify)
self._results = []
self._lock = threading.Lock()
self._remaining = 0
self._as_iterator = as_iterator

def register_result(self, apply_result):
"""Used to identify which results we're waiting for. Will
always be called BEFORE the Jobs get submitted to the work
queue, and BEFORE the __iter__ and _get_result() methods can
be called
\param apply_result ApplyResult object to add in our collection
"""
self._results.append(apply_result)
self._remaining += 1

def _get_result(self, idx, timeout = None):
"""Called by the CollectorIterator object to retrieve the
result's values one after another (order defined by the
implementation)
\param idx The index of the result we want, wrt collector's order
\param timeout integer telling how long to wait (in seconds)
for the result at index idx to be available, or None (wait
forever)
"""
res = self._results[idx]
res.wait(timeout)
return res

def notify_ready(self, apply_result):
"""Called by the ApplyResult object (already registered via
register_result()) that it is now ready (ie. the Job's result
is available or an exception has been raised).
\param apply_result ApplyResult object telling us that the job
has been processed
"""
got_first = False
got_last = False
self._lock.acquire()
try:
assert self._remaining > 0
got_first = (len(self._results) == self._remaining)
self._remaining -= 1
got_last = (self._remaining == 0)
finally:
self._lock.release()

if self._to_notify is not None:
if self._as_iterator and got_first:
self._to_notify._set_value(iter(self))
elif not self._as_iterator and got_last:
try:
lst = [r.get(0) for r in self._results]
except:
self._to_notify._set_exception()
else:
self._to_notify._set_value(lst)

def _test():
"""Some tests"""
import thread, time

def f(x):
return x*x

def work(seconds):
print "[%d] Start to work for %fs..." % (thread.get_ident(), seconds)
time.sleep(seconds)
print "[%d] Work done (%fs)." % (thread.get_ident(), seconds)
return "%d slept %fs" % (thread.get_ident(), seconds)

### Test copy/pasted from multiprocessing
pool = Pool(9) # start 4 worker threads

result = pool.apply_async(f, (10,)) # evaluate "f(10)" asynchronously
print result.get(timeout=1) # prints "100" unless slow computer

print pool.map(f, range(10)) # prints "[0, 1, 4,..., 81]"

it = pool.imap(f, range(10))
print it.next() # prints "0"
print it.next() # prints "1"
print it.next(timeout=1) # prints "4" unless slow computer

# Test apply_sync exceptions
result = pool.apply_async(time.sleep, (3,))
try:
print result.get(timeout=1) # raises `TimeoutError`
except TimeoutError:
print "Good. Got expected timeout exception."
else:
assert False, "Expected exception !"
print result.get()

def cb(s):
print "Result ready: %s" % s

# Test imap()
for res in pool.imap(work, xrange(10, 3, -1), chunksize=4):
print "Item:", res

# Test imap_unordered()
for res in pool.imap_unordered(work, xrange(10, 3, -1)):
print "Item:", res

# Test map_async()
result = pool.map_async(work, xrange(10), callback=cb)
try:
print result.get(timeout=1) # raises `TimeoutError`
except TimeoutError:
print "Good. Got expected timeout exception."
else:
assert False, "Expected exception !"
print result.get()

# Test imap_async()
result = pool.imap_async(work, xrange(3, 10), callback=cb)
try:
print result.get(timeout=1) # raises `TimeoutError`
except TimeoutError:
print "Good. Got expected timeout exception."
else:
assert False, "Expected exception !"
for i in result.get():
print "Item:", i
print "### Loop again:"
for i in result.get():
print "Item2:", i

# Test imap_unordered_async()
result = pool.imap_unordered_async(work, xrange(10, 3, -1), callback=cb)
try:
print result.get(timeout=1) # raises `TimeoutError`
except TimeoutError:
print "Good. Got expected timeout exception."
else:
assert False, "Expected exception !"
for i in result.get():
print "Item1:", i
for i in result.get():
print "Item2:", i
r = result.get()
for i in r:
print "Item3:", i
for i in r:
print "Item4:", i
for i in r:
print "Item5:", i

#
# The case for the exceptions
#

# Exceptions in imap_unordered_async()
result = pool.imap_unordered_async(work, xrange(2, -10, -1), callback=cb)
time.sleep(3)
try:
for i in result.get():
print "Got item:", i
except IOError:
print "Good. Got expected exception:"
traceback.print_exc()

# Exceptions in imap_async()
result = pool.imap_async(work, xrange(2, -10, -1), callback=cb)
time.sleep(3)
try:
for i in result.get():
print "Got item:", i
except IOError:
print "Good. Got expected exception:"
traceback.print_exc()

# Stop the test: need to stop the pool !!!
pool.terminate()
print "End of tests"

if __name__ == "__main__":
_test()

Be careful to call Pool::terminate() explicitly because the worker threads are not "daemon" threads; otherwise your program will hang forever instead of terminating. This is the main difference in usage wrt multiprocessing::Pool (which registers most of its objects for deletion by atexit).

When we say that an ApplyResult becomes "ready" above, it means that its associated Job either completed without exception (in which case the ApplyResult is also "successful"), or with an exception. In the first case, calling get() on the ApplyResult object will return the value of the result. In the second, it will raise the exception and the backtrace goes up to the root cause in the worker thread.

This recipe has been tested with python 2.5 and 2.6b3.

For more information about the general API, refer to http://docs.python.org/dev/library/multiprocessing.html#module-multiprocessing.pool for example.