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changelog: add class to represent parsed changelog revisions...
changelog: add class to represent parsed changelog revisions Currently, changelog entries are parsed into their respective components at read time. Many operations are only interested in a subset of fields of a changelog entry. The parsing and storing of all the fields adds avoidable overhead. This patch introduces the "changelogrevision" class. It takes changelog raw text and exposes the parsed results as attributes. The code for parsing changelog entries has been moved into its construction function. changelog.read() has been modified to use the new class internally while maintaining its existing API. Future patches will make revision parsing lazy. We implement the construction function of the new class with __new__ instead of __init__ so we can use a named tuple to represent the empty revision. This saves overhead and complexity of coercing later versions of this class to represent an empty instance. While we are here, we add a method on changelog to obtain an instance of the new type. The overhead of constructing the new class regresses performance of revsets accessing this data: author(mpm) 0.896565 0.929984 desc(bug) 0.887169 0.935642 105% date(2015) 0.878797 0.908094 extra(rebase_source) 0.865446 0.922624 106% author(mpm) or author(greg) 1.801832 1.902112 105% author(mpm) or desc(bug) 1.812438 1.860977 date(2015) or branch(default) 0.968276 1.005824 author(mpm) or desc(bug) or date(2015) or extra(rebase_source) 3.656193 3.743381 Once lazy parsing is implemented, these revsets will all be faster than before. There is no performance change on revsets that do not access this data. There /could/ be a performance regression on operations that perform several changelog reads. However, I can't think of anything outside of revsets and `hg log` (basically the same as a revset) that would be impacted.

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worker.py
184 lines | 5.7 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
from .i18n import _
from . import error
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 = [], [0]
for pargs in partition(args, workers):
pid = os.fork()
if pid == 0:
signal.signal(signal.SIGINT, oldhandler)
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.append(pid)
pids.reverse()
os.close(wfd)
fp = os.fdopen(rfd, 'rb', 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():
for _pid in pids:
st = _exitstatus(os.wait()[1])
if st and not problem[0]:
problem[0] = st
killworkers()
t = threading.Thread(target=waitforworkers)
t.start()
def cleanup():
signal.signal(signal.SIGINT, oldhandler)
t.join()
status = problem[0]
if status:
if status < 0:
os.kill(os.getpid(), -status)
sys.exit(status)
try:
for line in 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]