##// END OF EJS Templates
repair: migrate revlogs during upgrade...
repair: migrate revlogs during upgrade Our next step for in-place upgrade is to migrate store data. Revlogs are the biggest source of data within the store and a store is useless without them, so we implement their migration first. Our strategy for migrating revlogs is to walk the store and call `revlog.clone()` on each revlog. There are some minor complications. Because revlogs have different storage options (e.g. changelog has generaldelta and delta chains disabled), we need to obtain the correct class of revlog so inserted data is encoded properly for its type. Various attempts at implementing progress indicators that didn't lead to frustration from false "it's almost done" indicators were made. I initially used a single progress bar based on number of revlogs. However, this quickly churned through all filelogs, got to 99% then effectively froze at 99.99% when it got to the manifest. So I converted the progress bar to total revision count. This was a little bit better. But the manifest was still significantly slower than filelogs and it took forever to process the last few percent. I then tried both revision/chunk bytes and raw bytes as the denominator. This had the opposite effect: because so much data is in manifests, it would churn through filelogs without showing much progress. When it got to manifests, it would fill in 90+% of the progress bar. I finally gave up having a unified progress bar and instead implemented 3 progress bars: 1 for filelog revisions, 1 for manifest revisions, and 1 for changelog revisions. I added extra messages indicating the total number of revisions of each so users know there are more progress bars coming. I also added extra messages before and after each stage to give extra details about what is happening. Strictly speaking, this isn't necessary. But the numbers are impressive. For example, when converting a non-generaldelta mozilla-central repository, the messages you see are: migrating 2475593 total revisions (1833043 in filelogs, 321156 in manifests, 321394 in changelog) migrating 1.67 GB in store; 2508 GB tracked data migrating 267868 filelogs containing 1833043 revisions (1.09 GB in store; 57.3 GB tracked data) finished migrating 1833043 filelog revisions across 267868 filelogs; change in size: -415776 bytes migrating 1 manifests containing 321156 revisions (518 MB in store; 2451 GB tracked data) That "2508 GB" figure really blew me away. I had no clue that the raw tracked data in mozilla-central was that large. Granted, 2451 GB is in the manifest and "only" 57.3 GB is in filelogs. But still. It's worth noting that gratuitous loading of source revlogs in order to display numbers and progress bars does serve a purpose: it ensures we can open all source revlogs. We don't want to spend several minutes copying revlogs only to encounter a permissions error or similar later. As part of this commit, we also add swapping of the store directory to the upgrade function. After revlogs are converted, we move the old store into the backup directory then move the temporary repo's store into the old store's location. On well-behaved systems, this should be 2 atomic operations and the window of inconsistency show be very narrow. There are still a few improvements to be made to store copying and upgrading. But this commit gets the bulk of the work out of the way.

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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 time
from .i18n import _
from . import (
error,
pycompat,
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 = pycompat.osgetenv('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