# 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.
import os
import pickle
import selectors
import signal
import sys
import threading
import time
from .i18n import _
from . import (
encoding,
error,
pycompat,
scmutil,
)
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(encoding.environ[b'NUMBER_OF_PROCESSORS'])
if n > 0:
return n
except (KeyError, ValueError):
pass
return 1
def _numworkers(ui):
s = ui.config(b'worker', b'numcpus')
if s:
try:
n = int(s)
if n >= 1:
return n
except ValueError:
raise error.Abort(_(b'number of cpus must be an integer'))
return min(max(countcpus(), 4), 32)
def ismainthread():
return threading.current_thread() == threading.main_thread()
class _blockingreader:
"""Wrap unbuffered stream such that pickle.load() works with it.
pickle.load() expects that calls to read() and readinto() read as many
bytes as requested. On EOF, it is fine to read fewer bytes. In this case,
pickle.load() raises an EOFError.
"""
def __init__(self, wrapped):
self._wrapped = wrapped
def readline(self):
return self._wrapped.readline()
def readinto(self, buf):
pos = 0
size = len(buf)
with memoryview(buf) as view:
while pos < size:
with view[pos:] as subview:
ret = self._wrapped.readinto(subview)
if not ret:
break
pos += ret
return pos
# issue multiple reads until size is fulfilled (or EOF is encountered)
def read(self, size=-1):
if size < 0:
return self._wrapped.readall()
buf = bytearray(size)
n_read = self.readinto(buf)
del buf[n_read:]
return bytes(buf)
if pycompat.isposix or pycompat.iswindows:
_STARTUP_COST = 0.01
# The Windows worker is thread based. If tasks are CPU bound, threads
# in the presence of the GIL result in excessive context switching and
# this overhead can slow down execution.
_DISALLOW_THREAD_UNSAFE = pycompat.iswindows
else:
_STARTUP_COST = 1e30
_DISALLOW_THREAD_UNSAFE = False
def worthwhile(ui, costperop, nops, threadsafe=True):
"""try to determine whether the benefit of multiple processes can
outweigh the cost of starting them"""
if not threadsafe and _DISALLOW_THREAD_UNSAFE:
return False
linear = costperop * nops
workers = _numworkers(ui)
benefit = linear - (_STARTUP_COST * workers + linear / workers)
return benefit >= 0.15
def worker(
ui, costperarg, func, staticargs, args, hasretval=False, threadsafe=True
):
"""run a function, possibly in parallel in multiple worker
processes.
returns a progress iterator
costperarg - cost of a single task
func - function to run. It is expected to return a progress iterator.
staticargs - arguments to pass to every invocation of the function
args - arguments to split into chunks, to pass to individual
workers
hasretval - when True, func and the current function return an progress
iterator then a dict (encoded as an iterator that yield many (False, ..)
then a (True, dict)). The dicts are joined in some arbitrary order, so
overlapping keys are a bad idea.
threadsafe - whether work items are thread safe and can be executed using
a thread-based worker. Should be disabled for CPU heavy tasks that don't
release the GIL.
"""
enabled = ui.configbool(b'worker', b'enabled')
if enabled and _platformworker is _posixworker and not ismainthread():
# The POSIX worker has to install a handler for SIGCHLD.
# Python up to 3.9 only allows this in the main thread.
enabled = False
if enabled and worthwhile(ui, costperarg, len(args), threadsafe=threadsafe):
return _platformworker(ui, func, staticargs, args, hasretval)
return func(*staticargs + (args,))
def _posixworker(ui, func, staticargs, args, hasretval):
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 ProcessLookupError:
pass
def waitforworkers(blocking=True):
for pid in pids.copy():
p = st = 0
try:
p, st = os.waitpid(pid, (0 if blocking else os.WNOHANG))
except ChildProcessError:
# child would already be reaped, but pids yet been
# updated (maybe interrupted just after waitpid)
pids.discard(pid)
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()
pipes = []
retval = {}
for pargs in partition(args, min(workers, len(args))):
# Every worker gets its own pipe to send results on, so we don't have to
# implement atomic writes larger than PIPE_BUF. Each forked process has
# its own pipe's descriptors in the local variables, and the parent
# process has the full list of pipe descriptors (and it doesn't really
# care what order they're in).
rfd, wfd = os.pipe()
pipes.append((rfd, wfd))
# 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():
for r, w in pipes[:-1]:
os.close(r)
os.close(w)
os.close(rfd)
with os.fdopen(wfd, 'wb') as wf:
for result in func(*(staticargs + (pargs,))):
pickle.dump(result, wf)
wf.flush()
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)
selector = selectors.DefaultSelector()
for rfd, wfd in pipes:
os.close(wfd)
# The stream has to be unbuffered. Otherwise, if all data is read from
# the raw file into the buffer, the selector thinks that the FD is not
# ready to read while pickle.load() could read from the buffer. This
# would delay the processing of readable items.
selector.register(os.fdopen(rfd, 'rb', 0), selectors.EVENT_READ)
def cleanup():
signal.signal(signal.SIGINT, oldhandler)
waitforworkers()
signal.signal(signal.SIGCHLD, oldchldhandler)
selector.close()
return problem[0]
try:
openpipes = len(pipes)
while openpipes > 0:
for key, events in selector.select():
try:
# The pytype error likely goes away on a modern version of
# pytype having a modern typeshed snapshot.
# pytype: disable=wrong-arg-types
res = pickle.load(_blockingreader(key.fileobj))
# pytype: enable=wrong-arg-types
if hasretval and res[0]:
retval.update(res[1])
else:
yield res
except EOFError:
selector.unregister(key.fileobj)
# pytype: disable=attribute-error
key.fileobj.close()
# pytype: enable=attribute-error
openpipes -= 1
except: # re-raises
killworkers()
cleanup()
raise
status = cleanup()
if status:
if status < 0:
os.kill(os.getpid(), -status)
raise error.WorkerError(status)
if hasretval:
yield True, retval
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, hasretval):
class Worker(threading.Thread):
def __init__(
self, taskqueue, resultqueue, func, staticargs, *args, **kwargs
):
threading.Thread.__init__(self, *args, **kwargs)
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 pycompat.queue.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(
_(
b"failed to kill worker threads while "
b"handling an exception\n"
)
)
return
workers = _numworkers(ui)
resultqueue = pycompat.queue.Queue()
taskqueue = pycompat.queue.Queue()
retval = {}
# 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():
res = resultqueue.get()
if hasretval and res[0]:
retval.update(res[1])
else:
yield res
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():
res = resultqueue.get()
if hasretval and res[0]:
retval.update(res[1])
else:
yield res
if hasretval:
yield True, retval
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.
olivia 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]