Mercurial > hg
view mercurial/profiling.py @ 41755:a4358f7345b4
context: introduce p[12]copies() methods and debugp[12]copies commands
As mentioned earlier, I'm working on support for storing copy metadata
in the changeset instead of in the filelog.
In order to transition a repo from storing metadata in filelogs to
storing it in the changeset, I'm going to provide a config option for
reading the metadata from the changeset, but falling back to getting
it from the filelog if it's not in the changeset. In this compatiblity
mode, the changeset-optmized algorithms will be used. We will then
need to convert the filelog copy metadata to look like that provided
by changeset copy metadata. This patch introduces methods that do just
that.
By having these methods here, we can start writing changeset-optimized
algorithms that should work already before we add any support for
storing the metadata in the changesets.
This commit also includes new debugp[12]copies commands and exercises
them in test-copies.t.
Differential Revision: https://phab.mercurial-scm.org/D5990
author | Martin von Zweigbergk <martinvonz@google.com> |
---|---|
date | Fri, 18 Jan 2019 13:13:30 -0800 |
parents | 89703e6151e7 |
children | 0ae593e791fb |
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# 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(pycompat.sysstr(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', pycompat.iswindows and 'cpu' or 'real') 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()