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hgweb: cache fctx.parents() in annotate command (issue5414)...
hgweb: cache fctx.parents() in annotate command (issue5414) 9c37df347485 introduced a call to fctx.parents() for each line in annotate output. This function call isn't cheap, as it requires linkrev adjustment. Since multiple lines in annotate output tend to belong to the same file revision, a cache of fctx.parents() lookups for each input should be effective in the common case. So we implement one. Since the cache has to precompute parents so an aborted generator doesn't leave an incomplete cache, we could just return a list. However, we preserve the generator for backwards compatibility. The effect of this change when requesting /annotate/96ca0ecdcfa/ browser/locales/en-US/chrome/browser/downloads/downloads.dtd on the mozilla-aurora repo is significant: p1(9c37df347485) 5.5s 9c37df347485: 66.3s this patch: 10.8s We're still slower than before. But only by ~2x instead of ~12x. On the tip revisions of layout/base/nsCSSFrameConstructor.cpp file in the mozilla-unified repo, time went from 12.5s to 14.5s and back to 12.5s. I'm not sure why the mozilla-aurora repo is so slow. Looking at the code of basefilectx.parents(), there is room for further improvements. Notably, we still perform redundant calls to filelog.renamed() and basefilectx._parentfilectx(). And basefilectx.annotate() also makes similar calls, so there is potential for object reuse. However, introducing caches here are not appropriate for the stable branch.

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profiling.py
164 lines | 4.8 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 sys
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):
try:
import statprof
except ImportError:
raise error.Abort(_(
'statprof not available - install using "easy_install 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()
try:
yield
finally:
statprof.stop()
statprof.display(fp)
@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='ls')
if profiler not in ('ls', 'stat', 'flame'):
ui.warn(_("unrecognized profiler '%s' - ignored\n") % profiler)
profiler = 'ls'
output = ui.config('profiling', 'output')
if output == 'blackbox':
fp = util.stringio()
elif output:
path = ui.expandpath(output)
fp = open(path, 'wb')
else:
fp = sys.stderr
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