Mercurial > hg
view mercurial/worker.py @ 37711:65a23cc8e75b
cborutil: implement support for streaming encoding, bytestring decoding
The vendored cbor2 package is... a bit disappointing.
On the encoding side, it insists that you pass it something with
a write() to send data to. That means if you want to emit data to
a generator, you have to construct an e.g. io.BytesIO(), write()
to it, then get the data back out. There can be non-trivial overhead
involved.
The encoder also doesn't support indefinite types - bytestrings, arrays,
and maps that don't have a known length. Again, this is really
unfortunate because it requires you to buffer the entire source and
destination in memory to encode large things.
On the decoding side, it supports reading indefinite length types.
But it buffers them completely before returning. More sadness.
This commit implements "streaming" encoders for various CBOR types.
Encoding emits a generator of hunks. So you can efficiently stream
encoded data elsewhere.
It also implements support for emitting indefinite length bytestrings,
arrays, and maps.
On the decoding side, we only implement support for decoding an
indefinite length bytestring from a file object. It will emit a
generator of raw chunks from the source.
I didn't want to reinvent so many wheels. But profiling the wire
protocol revealed that the overhead of constructing io.BytesIO()
instances to temporarily hold results has a non-trivial overhead.
We're talking >15% of execution time for operations like
"transfer the fulltexts of all files in a revision." So I can
justify this effort.
Fortunately, CBOR is a relatively straightforward format. And we have
a reference implementation in the repo we can test against.
Differential Revision: https://phab.mercurial-scm.org/D3303
author | Gregory Szorc <gregory.szorc@gmail.com> |
---|---|
date | Sat, 14 Apr 2018 16:36:15 -0700 |
parents | 5bc7ff103081 |
children | 8fb9985382be |
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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 __future__ import absolute_import import errno import os import signal import sys import threading import time from .i18n import _ from . import ( encoding, error, pycompat, scmutil, util, ) def countcpus(): '''try to count the number of CPUs on the system''' # posix try: n = int(os.sysconf(r'SC_NPROCESSORS_ONLN')) if n > 0: return n except (AttributeError, ValueError): pass # windows try: n = int(encoding.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 error.Abort(_('number of cpus must be an integer')) return min(max(countcpus(), 4), 32) if pycompat.isposix or pycompat.iswindows: _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 ''' enabled = ui.configbool('worker', 'enabled') if enabled and 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 = set(), [0] def killworkers(): # unregister SIGCHLD handler as all children will be killed. This # function shouldn't be interrupted by another SIGCHLD; otherwise pids # could be updated while iterating, which would cause inconsistency. signal.signal(signal.SIGCHLD, oldchldhandler) # 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 as err: if err.errno != errno.ESRCH: raise def waitforworkers(blocking=True): for pid in pids.copy(): p = st = 0 while True: try: p, st = os.waitpid(pid, (0 if blocking else os.WNOHANG)) break except OSError as e: if e.errno == errno.EINTR: continue elif e.errno == errno.ECHILD: # child would already be reaped, but pids yet been # updated (maybe interrupted just after waitpid) pids.discard(pid) break else: raise if not p: # skip subsequent steps, because child process should # be still running in this case continue pids.discard(p) st = _exitstatus(st) if st and not problem[0]: problem[0] = st def sigchldhandler(signum, frame): waitforworkers(blocking=False) if problem[0]: killworkers() oldchldhandler = signal.signal(signal.SIGCHLD, sigchldhandler) ui.flush() parentpid = os.getpid() for pargs in partition(args, workers): # make sure we use os._exit in all worker code paths. otherwise the # worker may do some clean-ups which could cause surprises like # deadlock. see sshpeer.cleanup for example. # override error handling *before* fork. this is necessary because # exception (signal) may arrive after fork, before "pid =" assignment # completes, and other exception handler (dispatch.py) can lead to # unexpected code path without os._exit. ret = -1 try: pid = os.fork() if pid == 0: signal.signal(signal.SIGINT, oldhandler) signal.signal(signal.SIGCHLD, oldchldhandler) def workerfunc(): os.close(rfd) for i, item in func(*(staticargs + (pargs,))): os.write(wfd, '%d %s\n' % (i, item)) return 0 ret = scmutil.callcatch(ui, workerfunc) except: # parent re-raises, child never returns if os.getpid() == parentpid: raise exctype = sys.exc_info()[0] force = not issubclass(exctype, KeyboardInterrupt) ui.traceback(force=force) finally: if os.getpid() != parentpid: try: ui.flush() except: # never returns, no re-raises pass finally: os._exit(ret & 255) pids.add(pid) os.close(wfd) fp = os.fdopen(rfd, r'rb', 0) def cleanup(): signal.signal(signal.SIGINT, oldhandler) waitforworkers() signal.signal(signal.SIGCHLD, oldchldhandler) status = problem[0] if status: if status < 0: os.kill(os.getpid(), -status) sys.exit(status) try: for line in util.iterfile(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) def _windowsworker(ui, func, staticargs, args): class Worker(threading.Thread): def __init__(self, taskqueue, resultqueue, func, staticargs, group=None, target=None, name=None, verbose=None): threading.Thread.__init__(self, group=group, target=target, name=name, verbose=verbose) self._taskqueue = taskqueue self._resultqueue = resultqueue self._func = func self._staticargs = staticargs self._interrupted = False self.daemon = True self.exception = None def interrupt(self): self._interrupted = True def run(self): try: while not self._taskqueue.empty(): try: args = self._taskqueue.get_nowait() for res in self._func(*self._staticargs + (args,)): self._resultqueue.put(res) # threading doesn't provide a native way to # interrupt execution. handle it manually at every # iteration. if self._interrupted: return except util.empty: break except Exception as e: # store the exception such that the main thread can resurface # it as if the func was running without workers. self.exception = e raise threads = [] def trykillworkers(): # Allow up to 1 second to clean worker threads nicely cleanupend = time.time() + 1 for t in threads: t.interrupt() for t in threads: remainingtime = cleanupend - time.time() t.join(remainingtime) if t.is_alive(): # pass over the workers joining failure. it is more # important to surface the inital exception than the # fact that one of workers may be processing a large # task and does not get to handle the interruption. ui.warn(_("failed to kill worker threads while " "handling an exception\n")) return workers = _numworkers(ui) resultqueue = util.queue() taskqueue = util.queue() # partition work to more pieces than workers to minimize the chance # of uneven distribution of large tasks between the workers for pargs in partition(args, workers * 20): taskqueue.put(pargs) for _i in range(workers): t = Worker(taskqueue, resultqueue, func, staticargs) threads.append(t) t.start() try: while len(threads) > 0: while not resultqueue.empty(): yield resultqueue.get() threads[0].join(0.05) finishedthreads = [_t for _t in threads if not _t.is_alive()] for t in finishedthreads: if t.exception is not None: raise t.exception threads.remove(t) except (Exception, KeyboardInterrupt): # re-raises trykillworkers() raise while not resultqueue.empty(): yield resultqueue.get() if pycompat.iswindows: _platformworker = _windowsworker else: _platformworker = _posixworker _exitstatus = _posixexitstatus def partition(lst, nslices): '''partition a list into N slices of roughly equal size The current strategy takes every Nth element from the input. If we ever write workers that need to preserve grouping in input we should consider allowing callers to specify a partition strategy. mpm is not a fan of this partitioning strategy when files are involved. In his words: Single-threaded Mercurial makes a point of creating and visiting files in a fixed order (alphabetical). When creating files in order, a typical filesystem is likely to allocate them on nearby regions on disk. Thus, when revisiting in the same order, locality is maximized and various forms of OS and disk-level caching and read-ahead get a chance to work. This effect can be quite significant on spinning disks. I discovered it circa Mercurial v0.4 when revlogs were named by hashes of filenames. Tarring a repo and copying it to another disk effectively randomized the revlog ordering on disk by sorting the revlogs by hash and suddenly performance of my kernel checkout benchmark dropped by ~10x because the "working set" of sectors visited no longer fit in the drive's cache and the workload switched from streaming to random I/O. What we should really be doing is have workers read filenames from a ordered queue. This preserves locality and also keeps any worker from getting more than one file out of balance. ''' for i in range(nslices): yield lst[i::nslices]