# 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

try:

    import selectors

    selectors.BaseSelector

except ImportError:

    from .thirdparty import selectors2 as selectors

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:

    _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, 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

    staticargs - arguments to pass to every invocation of the function

    args - arguments to split into chunks, to pass to individual

    workers

    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('worker', 'enabled')

    if enabled and worthwhile(ui, costperarg, len(args), threadsafe=threadsafe):

        return _platformworker(ui, func, staticargs, args)

    return func(*staticargs + (args,))

def _posixworker(ui, func, staticargs, args):

    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()

    pipes = []

    for pargs in partition(args, workers):

        # 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)

                    for result in func(*(staticargs + (pargs,))):

                        os.write(wfd, util.pickle.dumps(result))

                    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)

        selector.register(os.fdopen(rfd, r'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:

                    yield util.pickle.load(key.fileobj)

                except EOFError:

                    selector.unregister(key.fileobj)

                    key.fileobj.close()

                    openpipes -= 1

                except IOError as e:

                    if e.errno == errno.EINTR:

                        continue

                    raise

    except: # re-raises

        killworkers()

        cleanup()

        raise

    status = cleanup()

    if status:

        if status < 0:

            os.kill(os.getpid(), -status)

        sys.exit(status)

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, *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(_("failed to kill worker threads while "

                          "handling an exception\n"))

                return

    workers = _numworkers(ui)

    resultqueue = pycompat.queue.Queue()

    taskqueue = pycompat.queue.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]