##// END OF EJS Templates
bdiff: adjust criteria for getting optimal longest match in the A side middle...
bdiff: adjust criteria for getting optimal longest match in the A side middle We prefer matches closer to the middle to balance recursion, as introduced in f1ca249696ed. For ranges with uneven length, matches starting exactly in the middle should have preference. That will be optimal for matches of length 1. We will thus accept equality in the half check. For ranges with even length, half was ceil'ed when calculated but we got the preference for low matches from the 'less than half' check. To get the same result as before when we also accept equality, floor it. Without that, test-annotate.t would show some different (still correct but less optimal) results. This will change the heuristics. Tests shows a slightly different output - and sometimes slightly smaller bundles. The bundle size for 4.0 (hg bundle --base null -r 4.0 x.hg) happens to go from 22804885 to 22803824 bytes - an 0.005% reduction.

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profiling.py
176 lines | 5.2 KiB | text/x-python | PythonLexer
# profiling.py - profiling functions
#
# Copyright 2016 Gregory Szorc <gregory.szorc@gmail.com>
#
# 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, print_function
import contextlib
import os
import time
from .i18n import _
from . import (
error,
util,
)
@contextlib.contextmanager
def lsprofile(ui, fp):
format = ui.config('profiling', 'format', default='text')
field = ui.config('profiling', 'sort', default='inlinetime')
limit = ui.configint('profiling', 'limit', default=30)
climit = ui.configint('profiling', 'nested', default=0)
if format not in ['text', 'kcachegrind']:
ui.warn(_("unrecognized profiling format '%s'"
" - Ignored\n") % format)
format = 'text'
try:
from . import lsprof
except ImportError:
raise error.Abort(_(
'lsprof not available - install from '
'http://codespeak.net/svn/user/arigo/hack/misc/lsprof/'))
p = lsprof.Profiler()
p.enable(subcalls=True)
try:
yield
finally:
p.disable()
if format == 'kcachegrind':
from . import lsprofcalltree
calltree = lsprofcalltree.KCacheGrind(p)
calltree.output(fp)
else:
# format == 'text'
stats = lsprof.Stats(p.getstats())
stats.sort(field)
stats.pprint(limit=limit, file=fp, climit=climit)
@contextlib.contextmanager
def flameprofile(ui, fp):
try:
from flamegraph import flamegraph
except ImportError:
raise error.Abort(_(
'flamegraph not available - install from '
'https://github.com/evanhempel/python-flamegraph'))
# developer config: profiling.freq
freq = ui.configint('profiling', 'freq', default=1000)
filter_ = None
collapse_recursion = True
thread = flamegraph.ProfileThread(fp, 1.0 / freq,
filter_, collapse_recursion)
start_time = time.clock()
try:
thread.start()
yield
finally:
thread.stop()
thread.join()
print('Collected %d stack frames (%d unique) in %2.2f seconds.' % (
time.clock() - start_time, thread.num_frames(),
thread.num_frames(unique=True)))
@contextlib.contextmanager
def statprofile(ui, fp):
from . import statprof
freq = ui.configint('profiling', 'freq', default=1000)
if freq > 0:
# Cannot reset when profiler is already active. So silently no-op.
if statprof.state.profile_level == 0:
statprof.reset(freq)
else:
ui.warn(_("invalid sampling frequency '%s' - ignoring\n") % freq)
statprof.start(mechanism='thread')
try:
yield
finally:
data = statprof.stop()
profformat = ui.config('profiling', 'statformat', 'hotpath')
formats = {
'byline': statprof.DisplayFormats.ByLine,
'bymethod': statprof.DisplayFormats.ByMethod,
'hotpath': statprof.DisplayFormats.Hotpath,
'json': statprof.DisplayFormats.Json,
}
if profformat in formats:
displayformat = formats[profformat]
else:
ui.warn(_('unknown profiler output format: %s\n') % profformat)
displayformat = statprof.DisplayFormats.Hotpath
statprof.display(fp, data=data, format=displayformat)
@contextlib.contextmanager
def profile(ui):
"""Start profiling.
Profiling is active when the context manager is active. When the context
manager exits, profiling results will be written to the configured output.
"""
profiler = os.getenv('HGPROF')
if profiler is None:
profiler = ui.config('profiling', 'type', default='stat')
if profiler not in ('ls', 'stat', 'flame'):
ui.warn(_("unrecognized profiler '%s' - ignored\n") % profiler)
profiler = 'stat'
output = ui.config('profiling', 'output')
if output == 'blackbox':
fp = util.stringio()
elif output:
path = ui.expandpath(output)
fp = open(path, 'wb')
else:
fp = ui.ferr
try:
if profiler == 'ls':
proffn = lsprofile
elif profiler == 'flame':
proffn = flameprofile
else:
proffn = statprofile
with proffn(ui, fp):
yield
finally:
if output:
if output == 'blackbox':
val = 'Profile:\n%s' % fp.getvalue()
# ui.log treats the input as a format string,
# so we need to escape any % signs.
val = val.replace('%', '%%')
ui.log('profile', val)
fp.close()
@contextlib.contextmanager
def maybeprofile(ui):
"""Profile if enabled, else do nothing.
This context manager can be used to optionally profile if profiling
is enabled. Otherwise, it does nothing.
The purpose of this context manager is to make calling code simpler:
just use a single code path for calling into code you may want to profile
and this function determines whether to start profiling.
"""
if ui.configbool('profiling', 'enabled'):
with profile(ui):
yield
else:
yield