worker: avoid potential partial write of pickled data

Previously, the code wrote the pickled data using os.write(). However,
os.write() can write less bytes than passed to it. To trigger the problem, the
pickled data had to be larger than 2147479552 bytes on my system.

Instead, open a file object and pass it to pickle.dump(). This also has the
advantage that it doesn’t buffer the whole pickled data in memory.

Note that the opened file must be buffered because pickle doesn’t support
unbuffered streams because unbuffered streams’ write() method might write less
bytes than passed to it (like os.write()) but pickle.dump() relies on that all
bytes are written (see https://github.com/python/cpython/issues/93050).

The side effect of using a file object and a with statement is that wfd is
explicitly closed now while it seems like before it was implicitly closed by
process exit.

mercurial/worker.py

@@ -1,471 +1,473 b''
 # 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 errno
 import os
 import pickle
 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,
 )
 
 
 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 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 = []
     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,))):
-                        os.write(wfd, pickle.dumps(result))
+                            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)
                     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)
         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]