| Thread pool with same API as (multi)processing.Pool (Python recipe) by david decotigny 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(). Python, 738 lines Download Copy to clipboard 1 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 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 # 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. |
Thread pool with same API as (multi)processing.Pool « Python recipes « ActiveState Code
Thread pool with same API as (multi)processing.Pool « Python recipes « ActiveState Code