##// END OF EJS Templates
transaction: make entries a private attribute (API)...
transaction: make entries a private attribute (API) This attribute is tracking changes to append-only files. It is an implementation detail and should not be exposed as part of the public interface. But code in repair was accessing it, so it seemingly does belong as part of the public API. But that code in repair is making assumptions about how storage works and is grossly wrong when alternate storage backends are in play. We'll need some kind of "strip" API at the storage layer that knows how to handle things in a storage-agnostic manner. I don't think accessing a private attribute on the transaction is any worse than what this code is already doing. So I'm fine with violating the abstraction for transactions. And with this change, all per-instance attributes on transaction have been made private except for "changes" and "hookargs." Both are used by multiple consumers and look like they need to be part of the public interface. .. api:: Various attributes of ``transaction.transaction`` are now ``_`` prefixed to indicate they shouldn't be used by external consumers. Differential Revision: https://phab.mercurial-scm.org/D4634

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profiling.py
251 lines | 7.9 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
from .i18n import _
from . import (
encoding,
error,
extensions,
pycompat,
util,
)
def _loadprofiler(ui, profiler):
"""load profiler extension. return profile method, or None on failure"""
extname = profiler
extensions.loadall(ui, whitelist=[extname])
try:
mod = extensions.find(extname)
except KeyError:
return None
else:
return getattr(mod, 'profile', None)
@contextlib.contextmanager
def lsprofile(ui, fp):
format = ui.config('profiling', 'format')
field = ui.config('profiling', 'sort')
limit = ui.configint('profiling', 'limit')
climit = ui.configint('profiling', 'nested')
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')
filter_ = None
collapse_recursion = True
thread = flamegraph.ProfileThread(fp, 1.0 / freq,
filter_, collapse_recursion)
start_time = util.timer()
try:
thread.start()
yield
finally:
thread.stop()
thread.join()
print('Collected %d stack frames (%d unique) in %2.2f seconds.' % (
util.timer() - start_time, thread.num_frames(),
thread.num_frames(unique=True)))
@contextlib.contextmanager
def statprofile(ui, fp):
from . import statprof
freq = ui.configint('profiling', 'freq')
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)
track = ui.config('profiling', 'time-track')
statprof.start(mechanism='thread', track=track)
try:
yield
finally:
data = statprof.stop()
profformat = ui.config('profiling', 'statformat')
formats = {
'byline': statprof.DisplayFormats.ByLine,
'bymethod': statprof.DisplayFormats.ByMethod,
'hotpath': statprof.DisplayFormats.Hotpath,
'json': statprof.DisplayFormats.Json,
'chrome': statprof.DisplayFormats.Chrome,
}
if profformat in formats:
displayformat = formats[profformat]
else:
ui.warn(_('unknown profiler output format: %s\n') % profformat)
displayformat = statprof.DisplayFormats.Hotpath
kwargs = {}
def fraction(s):
if isinstance(s, (float, int)):
return float(s)
if s.endswith('%'):
v = float(s[:-1]) / 100
else:
v = float(s)
if 0 <= v <= 1:
return v
raise ValueError(s)
if profformat == 'chrome':
showmin = ui.configwith(fraction, 'profiling', 'showmin', 0.005)
showmax = ui.configwith(fraction, 'profiling', 'showmax')
kwargs.update(minthreshold=showmin, maxthreshold=showmax)
elif profformat == 'hotpath':
# inconsistent config: profiling.showmin
limit = ui.configwith(fraction, 'profiling', 'showmin', 0.05)
kwargs[r'limit'] = limit
statprof.display(fp, data=data, format=displayformat, **kwargs)
class profile(object):
"""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.
"""
def __init__(self, ui, enabled=True):
self._ui = ui
self._output = None
self._fp = None
self._fpdoclose = True
self._profiler = None
self._enabled = enabled
self._entered = False
self._started = False
def __enter__(self):
self._entered = True
if self._enabled:
self.start()
return self
def start(self):
"""Start profiling.
The profiling will stop at the context exit.
If the profiler was already started, this has no effect."""
if not self._entered:
raise error.ProgrammingError()
if self._started:
return
self._started = True
profiler = encoding.environ.get('HGPROF')
proffn = None
if profiler is None:
profiler = self._ui.config('profiling', 'type')
if profiler not in ('ls', 'stat', 'flame'):
# try load profiler from extension with the same name
proffn = _loadprofiler(self._ui, profiler)
if proffn is None:
self._ui.warn(_("unrecognized profiler '%s' - ignored\n")
% profiler)
profiler = 'stat'
self._output = self._ui.config('profiling', 'output')
try:
if self._output == 'blackbox':
self._fp = util.stringio()
elif self._output:
path = self._ui.expandpath(self._output)
self._fp = open(path, 'wb')
elif pycompat.iswindows:
# parse escape sequence by win32print()
class uifp(object):
def __init__(self, ui):
self._ui = ui
def write(self, data):
self._ui.write_err(data)
def flush(self):
self._ui.flush()
self._fpdoclose = False
self._fp = uifp(self._ui)
else:
self._fpdoclose = False
self._fp = self._ui.ferr
if proffn is not None:
pass
elif profiler == 'ls':
proffn = lsprofile
elif profiler == 'flame':
proffn = flameprofile
else:
proffn = statprofile
self._profiler = proffn(self._ui, self._fp)
self._profiler.__enter__()
except: # re-raises
self._closefp()
raise
def __exit__(self, exception_type, exception_value, traceback):
propagate = None
if self._profiler is not None:
propagate = self._profiler.__exit__(exception_type, exception_value,
traceback)
if self._output == 'blackbox':
val = 'Profile:\n%s' % self._fp.getvalue()
# ui.log treats the input as a format string,
# so we need to escape any % signs.
val = val.replace('%', '%%')
self._ui.log('profile', val)
self._closefp()
return propagate
def _closefp(self):
if self._fpdoclose and self._fp is not None:
self._fp.close()