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localrepo: iteratively derive local repository type...
localrepo: iteratively derive local repository type This commit implements the dynamic local repository type derivation that was explained in the recent commit bfeab472e3c0 "localrepo: create new function for instantiating a local repo object." Instead of a static localrepository class/type which must be customized after construction, we now dynamically construct a type by building up base classes/types to represent specific repository interfaces. Conceptually, the end state is similar to what was happening when various extensions would monkeypatch the __class__ of newly-constructed repo instances. However, the approach is inverted. Instead of making the instance then customizing it, we do the customization up front by influencing the behavior of the type then we instantiate that custom type. This approach gives us much more flexibility. For example, we can use completely separate classes for implementing different aspects of the repository. For example, we could have one class representing revlog-based file storage and another representing non-revlog based file storage. When then choose which implementation to use based on the presence of repo requirements. A concern with this approach is that it creates a lot more types and complexity and that complexity adds overhead. Yes, it is true that this approach will result in more types being created. Yes, this is more complicated than traditional "instantiate a static type." However, I believe the alternatives to supporting alternate storage backends are just as complicated. (Before I arrived at this solution, I had patches storing factory functions on local repo instances for e.g. constructing a file storage instance. We ended up having a handful of these. And this was logically identical to assigning custom methods. Since we were logically changing the type of the instance, I figured it would be better to just use specialized types instead of introducing levels of abstraction at run-time.) On the performance front, I don't believe that having N base classes has any significant performance overhead compared to just a single base class. Intuition says that Python will need to iterate the base classes to find an attribute. However, CPython caches method lookups: as long as the __class__ or MRO isn't changing, method attribute lookup should be constant time after first access. And non-method attributes are stored in __dict__, of which there is only 1 per object, so the number of base classes for __dict__ is irrelevant. Anyway, this commit splits up the monolithic completelocalrepository interface into sub-interfaces: 1 for file storage and 1 representing everything else. We've taught ``makelocalrepository()`` to call a series of factory functions which will produce types implementing specific interfaces. It then calls type() to create a new type from the built-up list of base types. This commit should be considered a start and not the end state. I suspect we'll hit a number of problems as we start to implement alternate storage backends: * Passing custom arguments to __init__ and setting custom attributes on __dict__. * Customizing the set of interfaces that are needed. e.g. the "readonly" intent could translate to not requesting an interface providing methods related to writing. * More ergonomic way for extensions to insert themselves so their callbacks aren't unconditionally called. * Wanting to modify vfs instances, other arguments passed to __init__. That being said, this code is usable in its current state and I'm convinced future commits will demonstrate the value in this approach. Differential Revision: https://phab.mercurial-scm.org/D4642

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worker.py
369 lines | 13.1 KiB | text/x-python | PythonLexer
# 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()
status = problem[0]
if status:
if status < 0:
os.kill(os.getpid(), -status)
sys.exit(status)
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
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 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]