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# worker.py - master-slave parallelism support
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#
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# Copyright 2013 Facebook, Inc.
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#
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# This software may be used and distributed according to the terms of the
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# GNU General Public License version 2 or any later version.
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from __future__ import absolute_import
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import errno
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import os
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import signal
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import sys
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import threading
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import time
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try:
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import selectors
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selectors.BaseSelector
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except ImportError:
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from .thirdparty import selectors2 as selectors
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from .i18n import _
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from . import (
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encoding,
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error,
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pycompat,
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scmutil,
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util,
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)
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def countcpus():
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'''try to count the number of CPUs on the system'''
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# posix
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try:
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n = int(os.sysconf(r'SC_NPROCESSORS_ONLN'))
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if n > 0:
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return n
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except (AttributeError, ValueError):
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pass
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# windows
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try:
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n = int(encoding.environ['NUMBER_OF_PROCESSORS'])
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if n > 0:
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return n
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except (KeyError, ValueError):
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pass
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return 1
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def _numworkers(ui):
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s = ui.config('worker', 'numcpus')
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if s:
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try:
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n = int(s)
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if n >= 1:
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return n
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except ValueError:
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raise error.Abort(_('number of cpus must be an integer'))
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return min(max(countcpus(), 4), 32)
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if pycompat.isposix or pycompat.iswindows:
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_STARTUP_COST = 0.01
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# The Windows worker is thread based. If tasks are CPU bound, threads
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# in the presence of the GIL result in excessive context switching and
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# this overhead can slow down execution.
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_DISALLOW_THREAD_UNSAFE = pycompat.iswindows
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else:
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_STARTUP_COST = 1e30
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_DISALLOW_THREAD_UNSAFE = False
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def worthwhile(ui, costperop, nops, threadsafe=True):
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'''try to determine whether the benefit of multiple processes can
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outweigh the cost of starting them'''
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if not threadsafe and _DISALLOW_THREAD_UNSAFE:
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return False
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linear = costperop * nops
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workers = _numworkers(ui)
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benefit = linear - (_STARTUP_COST * workers + linear / workers)
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return benefit >= 0.15
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def worker(ui, costperarg, func, staticargs, args, hasretval=False,
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threadsafe=True):
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'''run a function, possibly in parallel in multiple worker
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processes.
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returns a progress iterator
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costperarg - cost of a single task
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func - function to run. It is expected to return a progress iterator.
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staticargs - arguments to pass to every invocation of the function
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args - arguments to split into chunks, to pass to individual
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workers
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hasretval - when True, func and the current function return an progress
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iterator then a dict (encoded as an iterator that yield many (False, ..)
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then a (True, dict)). The dicts are joined in some arbitrary order, so
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overlapping keys are a bad idea.
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threadsafe - whether work items are thread safe and can be executed using
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a thread-based worker. Should be disabled for CPU heavy tasks that don't
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release the GIL.
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'''
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enabled = ui.configbool('worker', 'enabled')
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if enabled and worthwhile(ui, costperarg, len(args), threadsafe=threadsafe):
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return _platformworker(ui, func, staticargs, args, hasretval)
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return func(*staticargs + (args,))
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def _posixworker(ui, func, staticargs, args, hasretval):
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workers = _numworkers(ui)
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oldhandler = signal.getsignal(signal.SIGINT)
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signal.signal(signal.SIGINT, signal.SIG_IGN)
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pids, problem = set(), [0]
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def killworkers():
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# unregister SIGCHLD handler as all children will be killed. This
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# function shouldn't be interrupted by another SIGCHLD; otherwise pids
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# could be updated while iterating, which would cause inconsistency.
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signal.signal(signal.SIGCHLD, oldchldhandler)
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# if one worker bails, there's no good reason to wait for the rest
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for p in pids:
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try:
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os.kill(p, signal.SIGTERM)
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except OSError as err:
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if err.errno != errno.ESRCH:
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raise
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def waitforworkers(blocking=True):
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for pid in pids.copy():
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p = st = 0
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while True:
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try:
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p, st = os.waitpid(pid, (0 if blocking else os.WNOHANG))
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break
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except OSError as e:
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if e.errno == errno.EINTR:
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continue
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elif e.errno == errno.ECHILD:
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# child would already be reaped, but pids yet been
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# updated (maybe interrupted just after waitpid)
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pids.discard(pid)
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break
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else:
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raise
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if not p:
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# skip subsequent steps, because child process should
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# be still running in this case
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continue
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pids.discard(p)
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st = _exitstatus(st)
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if st and not problem[0]:
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problem[0] = st
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def sigchldhandler(signum, frame):
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waitforworkers(blocking=False)
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if problem[0]:
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killworkers()
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oldchldhandler = signal.signal(signal.SIGCHLD, sigchldhandler)
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ui.flush()
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parentpid = os.getpid()
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pipes = []
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retval = {}
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for pargs in partition(args, workers):
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# Every worker gets its own pipe to send results on, so we don't have to
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# implement atomic writes larger than PIPE_BUF. Each forked process has
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# its own pipe's descriptors in the local variables, and the parent
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# process has the full list of pipe descriptors (and it doesn't really
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# care what order they're in).
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rfd, wfd = os.pipe()
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pipes.append((rfd, wfd))
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# make sure we use os._exit in all worker code paths. otherwise the
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# worker may do some clean-ups which could cause surprises like
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# deadlock. see sshpeer.cleanup for example.
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# override error handling *before* fork. this is necessary because
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# exception (signal) may arrive after fork, before "pid =" assignment
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# completes, and other exception handler (dispatch.py) can lead to
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# unexpected code path without os._exit.
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ret = -1
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try:
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pid = os.fork()
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if pid == 0:
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signal.signal(signal.SIGINT, oldhandler)
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signal.signal(signal.SIGCHLD, oldchldhandler)
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def workerfunc():
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for r, w in pipes[:-1]:
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os.close(r)
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os.close(w)
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os.close(rfd)
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for result in func(*(staticargs + (pargs,))):
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os.write(wfd, util.pickle.dumps(result))
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return 0
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ret = scmutil.callcatch(ui, workerfunc)
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except: # parent re-raises, child never returns
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if os.getpid() == parentpid:
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raise
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exctype = sys.exc_info()[0]
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force = not issubclass(exctype, KeyboardInterrupt)
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ui.traceback(force=force)
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finally:
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if os.getpid() != parentpid:
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try:
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ui.flush()
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except: # never returns, no re-raises
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pass
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finally:
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os._exit(ret & 255)
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pids.add(pid)
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selector = selectors.DefaultSelector()
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for rfd, wfd in pipes:
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os.close(wfd)
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selector.register(os.fdopen(rfd, r'rb', 0), selectors.EVENT_READ)
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def cleanup():
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signal.signal(signal.SIGINT, oldhandler)
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waitforworkers()
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signal.signal(signal.SIGCHLD, oldchldhandler)
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selector.close()
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return problem[0]
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try:
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openpipes = len(pipes)
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while openpipes > 0:
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for key, events in selector.select():
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try:
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res = util.pickle.load(key.fileobj)
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if hasretval and res[0]:
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retval.update(res[1])
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else:
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yield res
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except EOFError:
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selector.unregister(key.fileobj)
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key.fileobj.close()
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openpipes -= 1
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except IOError as e:
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if e.errno == errno.EINTR:
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continue
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raise
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except: # re-raises
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killworkers()
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cleanup()
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raise
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status = cleanup()
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if status:
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if status < 0:
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os.kill(os.getpid(), -status)
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sys.exit(status)
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if hasretval:
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yield True, retval
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def _posixexitstatus(code):
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'''convert a posix exit status into the same form returned by
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os.spawnv
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returns None if the process was stopped instead of exiting'''
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if os.WIFEXITED(code):
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return os.WEXITSTATUS(code)
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elif os.WIFSIGNALED(code):
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return -os.WTERMSIG(code)
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def _windowsworker(ui, func, staticargs, args, hasretval):
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class Worker(threading.Thread):
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def __init__(self, taskqueue, resultqueue, func, staticargs, *args,
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**kwargs):
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threading.Thread.__init__(self, *args, **kwargs)
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self._taskqueue = taskqueue
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self._resultqueue = resultqueue
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self._func = func
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self._staticargs = staticargs
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self._interrupted = False
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self.daemon = True
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self.exception = None
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def interrupt(self):
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self._interrupted = True
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def run(self):
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try:
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while not self._taskqueue.empty():
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try:
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args = self._taskqueue.get_nowait()
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for res in self._func(*self._staticargs + (args,)):
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self._resultqueue.put(res)
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# threading doesn't provide a native way to
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# interrupt execution. handle it manually at every
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# iteration.
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if self._interrupted:
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return
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except pycompat.queue.Empty:
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break
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except Exception as e:
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# store the exception such that the main thread can resurface
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# it as if the func was running without workers.
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self.exception = e
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raise
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threads = []
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def trykillworkers():
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# Allow up to 1 second to clean worker threads nicely
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cleanupend = time.time() + 1
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for t in threads:
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t.interrupt()
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for t in threads:
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remainingtime = cleanupend - time.time()
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t.join(remainingtime)
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if t.is_alive():
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# pass over the workers joining failure. it is more
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# important to surface the inital exception than the
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# fact that one of workers may be processing a large
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# task and does not get to handle the interruption.
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ui.warn(_("failed to kill worker threads while "
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"handling an exception\n"))
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return
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workers = _numworkers(ui)
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resultqueue = pycompat.queue.Queue()
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taskqueue = pycompat.queue.Queue()
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retval = {}
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# partition work to more pieces than workers to minimize the chance
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# of uneven distribution of large tasks between the workers
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for pargs in partition(args, workers * 20):
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taskqueue.put(pargs)
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for _i in range(workers):
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t = Worker(taskqueue, resultqueue, func, staticargs)
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threads.append(t)
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t.start()
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try:
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while len(threads) > 0:
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while not resultqueue.empty():
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res = resultqueue.get()
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if hasretval and res[0]:
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retval.update(res[1])
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else:
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yield res
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threads[0].join(0.05)
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finishedthreads = [_t for _t in threads if not _t.is_alive()]
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for t in finishedthreads:
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if t.exception is not None:
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raise t.exception
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threads.remove(t)
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except (Exception, KeyboardInterrupt): # re-raises
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trykillworkers()
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raise
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while not resultqueue.empty():
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res = resultqueue.get()
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if hasretval and res[0]:
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retval.update(res[1])
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else:
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yield res
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if hasretval:
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yield True, retval
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if pycompat.iswindows:
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_platformworker = _windowsworker
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else:
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_platformworker = _posixworker
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_exitstatus = _posixexitstatus
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def partition(lst, nslices):
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'''partition a list into N slices of roughly equal size
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The current strategy takes every Nth element from the input. If
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we ever write workers that need to preserve grouping in input
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we should consider allowing callers to specify a partition strategy.
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mpm is not a fan of this partitioning strategy when files are involved.
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In his words:
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Single-threaded Mercurial makes a point of creating and visiting
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files in a fixed order (alphabetical). When creating files in order,
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a typical filesystem is likely to allocate them on nearby regions on
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disk. Thus, when revisiting in the same order, locality is maximized
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and various forms of OS and disk-level caching and read-ahead get a
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chance to work.
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This effect can be quite significant on spinning disks. I discovered it
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circa Mercurial v0.4 when revlogs were named by hashes of filenames.
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Tarring a repo and copying it to another disk effectively randomized
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the revlog ordering on disk by sorting the revlogs by hash and suddenly
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performance of my kernel checkout benchmark dropped by ~10x because the
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"working set" of sectors visited no longer fit in the drive's cache and
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the workload switched from streaming to random I/O.
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What we should really be doing is have workers read filenames from a
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ordered queue. This preserves locality and also keeps any worker from
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getting more than one file out of balance.
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'''
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for i in range(nslices):
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yield lst[i::nslices]
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