mercurial/profiling.py
author Durham Goode <durham@fb.com>
Wed, 03 May 2017 10:43:59 -0700
changeset 32151 4d504e541d3d
parent 30975 22fbca1d11ed
child 32417 f40dc6f7c12f
permissions -rw-r--r--
rebase: use matcher to optimize manifestmerge The old merge code would call manifestmerge and calculate the complete diff between the source to the destination. In many cases, like rebase, the vast majority of differences between the source and destination are irrelevant because they are differences between the destination and the common ancestor only, and therefore don't affect the merge. Since most actions are 'keep', all the effort to compute them is wasted. Instead, let's compute the difference between the source and the common ancestor and only perform the diff of those files against the merge destination. When using treemanifest, this lets us avoid loading almost the entire tree when rebasing from a very old ancestor. This speeds up rebase of an old stack of 27 commits by 20x. In mozilla-central, without treemanifest, when rebasing a commit from default~100000 to default, this speeds up the manifestmerge step from 2.6s to 1.2s. However, the additional diff adds an overhead to all manifestmerge calls, especially for flat manifests. When rebasing a commit from default~1 to default it appears to add 100ms in mozilla-central. While we could put this optimization behind a flag, I think the fact that it makes merge O(number of changes being applied) instead of O(number of changes between X and Y) justifies it.

# 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,
    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 = 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', 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,
            '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 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', 0.999)
            kwargs.update(minthreshold=showmin, maxthreshold=showmax)

        statprof.display(fp, data=data, format=displayformat, **kwargs)

@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 = encoding.environ.get('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