Mercurial > public > mercurial-scm > hg-stable
view mercurial/worker.py @ 21577:c62c5ce750ee
revert: group related data in tuple in the dispatch table
The dispatch table used to be:
- action if in target manifest
- action if not in target manifest
- make backup if in target manifest
- make backup if not in target manifest
We turn this into two (action, make backup) tuples.
This helps both readability of the dispatch table and handling of each case.
This also prepares a refactoring where the different actions we performs, whether
"file is in target manifest" or not, are determined before reaching this loop.
author | Pierre-Yves David <pierre-yves.david@fb.com> |
---|---|
date | Tue, 13 May 2014 17:28:19 -0700 |
parents | 1e5b38a919dd |
children | b3e51675f98e |
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# worker.py - master-slave parallelism support # # Copyright 2013 Facebook, Inc. # # This software may be used and distributed according to the terms of the # GNU General Public License version 2 or any later version. from i18n import _ import errno, os, signal, sys, threading import util def countcpus(): '''try to count the number of CPUs on the system''' # posix try: n = int(os.sysconf('SC_NPROCESSORS_ONLN')) if n > 0: return n except (AttributeError, ValueError): pass # windows try: n = int(os.environ['NUMBER_OF_PROCESSORS']) if n > 0: return n except (KeyError, ValueError): pass return 1 def _numworkers(ui): s = ui.config('worker', 'numcpus') if s: try: n = int(s) if n >= 1: return n except ValueError: raise util.Abort(_('number of cpus must be an integer')) return min(max(countcpus(), 4), 32) if os.name == 'posix': _startupcost = 0.01 else: _startupcost = 1e30 def worthwhile(ui, costperop, nops): '''try to determine whether the benefit of multiple processes can outweigh the cost of starting them''' linear = costperop * nops workers = _numworkers(ui) benefit = linear - (_startupcost * workers + linear / workers) return benefit >= 0.15 def worker(ui, costperarg, func, staticargs, args): '''run a function, possibly in parallel in multiple worker processes. returns a progress iterator costperarg - cost of a single task func - function to run staticargs - arguments to pass to every invocation of the function args - arguments to split into chunks, to pass to individual workers ''' if worthwhile(ui, costperarg, len(args)): return _platformworker(ui, func, staticargs, args) return func(*staticargs + (args,)) def _posixworker(ui, func, staticargs, args): rfd, wfd = os.pipe() workers = _numworkers(ui) oldhandler = signal.getsignal(signal.SIGINT) signal.signal(signal.SIGINT, signal.SIG_IGN) pids, problem = [], [0] for pargs in partition(args, workers): pid = os.fork() if pid == 0: signal.signal(signal.SIGINT, oldhandler) try: os.close(rfd) for i, item in func(*(staticargs + (pargs,))): os.write(wfd, '%d %s\n' % (i, item)) os._exit(0) except KeyboardInterrupt: os._exit(255) # other exceptions are allowed to propagate, we rely # on lock.py's pid checks to avoid release callbacks pids.append(pid) pids.reverse() os.close(wfd) fp = os.fdopen(rfd, 'rb', 0) def killworkers(): # if one worker bails, there's no good reason to wait for the rest for p in pids: try: os.kill(p, signal.SIGTERM) except OSError, err: if err.errno != errno.ESRCH: raise def waitforworkers(): for _ in pids: st = _exitstatus(os.wait()[1]) if st and not problem[0]: problem[0] = st killworkers() t = threading.Thread(target=waitforworkers) t.start() def cleanup(): signal.signal(signal.SIGINT, oldhandler) t.join() status = problem[0] if status: if status < 0: os.kill(os.getpid(), -status) sys.exit(status) try: for line in fp: l = line.split(' ', 1) yield int(l[0]), l[1][:-1] except: # re-raises killworkers() cleanup() raise cleanup() def _posixexitstatus(code): '''convert a posix exit status into the same form returned by os.spawnv returns None if the process was stopped instead of exiting''' if os.WIFEXITED(code): return os.WEXITSTATUS(code) elif os.WIFSIGNALED(code): return -os.WTERMSIG(code) if os.name != 'nt': _platformworker = _posixworker _exitstatus = _posixexitstatus def partition(lst, nslices): '''partition a list into N slices of equal size''' n = len(lst) chunk, slop = n / nslices, n % nslices end = 0 for i in xrange(nslices): start = end end = start + chunk if slop: end += 1 slop -= 1 yield lst[start:end]