# 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

from .i18n import _

from . import (

    error,

    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 error.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 = set(), [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 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:

                        break # ignore ECHILD

                    else:

                        raise

            if p:

                pids.remove(p)

                st = _exitstatus(st)

            if st and not problem[0]:

                problem[0] = st

                killworkers()

    def sigchldhandler(signum, frame):

        waitforworkers(blocking=False)

    oldchldhandler = signal.signal(signal.SIGCHLD, sigchldhandler)

    for pargs in partition(args, workers):

        pid = os.fork()

        if pid == 0:

            signal.signal(signal.SIGINT, oldhandler)

            signal.signal(signal.SIGCHLD, oldchldhandler)

            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.add(pid)

    os.close(wfd)

    fp = os.fdopen(rfd, '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)

if os.name != 'nt':

    _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]