##// 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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statprof.py
810 lines | 25.5 KiB | text/x-python | PythonLexer
#!/usr/bin/env python
## statprof.py
## Copyright (C) 2012 Bryan O'Sullivan <bos@serpentine.com>
## Copyright (C) 2011 Alex Fraser <alex at phatcore dot com>
## Copyright (C) 2004,2005 Andy Wingo <wingo at pobox dot com>
## Copyright (C) 2001 Rob Browning <rlb at defaultvalue dot org>
## This library is free software; you can redistribute it and/or
## modify it under the terms of the GNU Lesser General Public
## License as published by the Free Software Foundation; either
## version 2.1 of the License, or (at your option) any later version.
##
## This library is distributed in the hope that it will be useful,
## but WITHOUT ANY WARRANTY; without even the implied warranty of
## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
## Lesser General Public License for more details.
##
## You should have received a copy of the GNU Lesser General Public
## License along with this program; if not, contact:
##
## Free Software Foundation Voice: +1-617-542-5942
## 59 Temple Place - Suite 330 Fax: +1-617-542-2652
## Boston, MA 02111-1307, USA gnu@gnu.org
"""
statprof is intended to be a fairly simple statistical profiler for
python. It was ported directly from a statistical profiler for guile,
also named statprof, available from guile-lib [0].
[0] http://wingolog.org/software/guile-lib/statprof/
To start profiling, call statprof.start():
>>> start()
Then run whatever it is that you want to profile, for example:
>>> import test.pystone; test.pystone.pystones()
Then stop the profiling and print out the results:
>>> stop()
>>> display()
% cumulative self
time seconds seconds name
26.72 1.40 0.37 pystone.py:79:Proc0
13.79 0.56 0.19 pystone.py:133:Proc1
13.79 0.19 0.19 pystone.py:208:Proc8
10.34 0.16 0.14 pystone.py:229:Func2
6.90 0.10 0.10 pystone.py:45:__init__
4.31 0.16 0.06 pystone.py:53:copy
...
All of the numerical data is statistically approximate. In the
following column descriptions, and in all of statprof, "time" refers
to execution time (both user and system), not wall clock time.
% time
The percent of the time spent inside the procedure itself (not
counting children).
cumulative seconds
The total number of seconds spent in the procedure, including
children.
self seconds
The total number of seconds spent in the procedure itself (not
counting children).
name
The name of the procedure.
By default statprof keeps the data collected from previous runs. If you
want to clear the collected data, call reset():
>>> reset()
reset() can also be used to change the sampling frequency from the
default of 1000 Hz. For example, to tell statprof to sample 50 times a
second:
>>> reset(50)
This means that statprof will sample the call stack after every 1/50 of
a second of user + system time spent running on behalf of the python
process. When your process is idle (for example, blocking in a read(),
as is the case at the listener), the clock does not advance. For this
reason statprof is not currently not suitable for profiling io-bound
operations.
The profiler uses the hash of the code object itself to identify the
procedures, so it won't confuse different procedures with the same name.
They will show up as two different rows in the output.
Right now the profiler is quite simplistic. I cannot provide
call-graphs or other higher level information. What you see in the
table is pretty much all there is. Patches are welcome :-)
Threading
---------
Because signals only get delivered to the main thread in Python,
statprof only profiles the main thread. However because the time
reporting function uses per-process timers, the results can be
significantly off if other threads' work patterns are not similar to the
main thread's work patterns.
"""
# no-check-code
from __future__ import absolute_import, division, print_function
import collections
import contextlib
import getopt
import inspect
import json
import os
import signal
import sys
import tempfile
import threading
import time
from . import (
encoding,
pycompat,
)
defaultdict = collections.defaultdict
contextmanager = contextlib.contextmanager
__all__ = ['start', 'stop', 'reset', 'display', 'profile']
skips = set(["util.py:check", "extensions.py:closure",
"color.py:colorcmd", "dispatch.py:checkargs",
"dispatch.py:<lambda>", "dispatch.py:_runcatch",
"dispatch.py:_dispatch", "dispatch.py:_runcommand",
"pager.py:pagecmd", "dispatch.py:run",
"dispatch.py:dispatch", "dispatch.py:runcommand",
"hg.py:<module>", "evolve.py:warnobserrors",
])
###########################################################################
## Utils
def clock():
times = os.times()
return times[0] + times[1]
###########################################################################
## Collection data structures
class ProfileState(object):
def __init__(self, frequency=None):
self.reset(frequency)
def reset(self, frequency=None):
# total so far
self.accumulated_time = 0.0
# start_time when timer is active
self.last_start_time = None
# a float
if frequency:
self.sample_interval = 1.0 / frequency
elif not hasattr(self, 'sample_interval'):
# default to 1000 Hz
self.sample_interval = 1.0 / 1000.0
else:
# leave the frequency as it was
pass
self.remaining_prof_time = None
# for user start/stop nesting
self.profile_level = 0
self.samples = []
def accumulate_time(self, stop_time):
self.accumulated_time += stop_time - self.last_start_time
def seconds_per_sample(self):
return self.accumulated_time / len(self.samples)
state = ProfileState()
class CodeSite(object):
cache = {}
__slots__ = (u'path', u'lineno', u'function', u'source')
def __init__(self, path, lineno, function):
self.path = path
self.lineno = lineno
self.function = function
self.source = None
def __eq__(self, other):
try:
return (self.lineno == other.lineno and
self.path == other.path)
except:
return False
def __hash__(self):
return hash((self.lineno, self.path))
@classmethod
def get(cls, path, lineno, function):
k = (path, lineno)
try:
return cls.cache[k]
except KeyError:
v = cls(path, lineno, function)
cls.cache[k] = v
return v
def getsource(self, length):
if self.source is None:
lineno = self.lineno - 1
fp = None
try:
fp = open(self.path)
for i, line in enumerate(fp):
if i == lineno:
self.source = line.strip()
break
except:
pass
finally:
if fp:
fp.close()
if self.source is None:
self.source = ''
source = self.source
if len(source) > length:
source = source[:(length - 3)] + "..."
return source
def filename(self):
return os.path.basename(self.path)
class Sample(object):
__slots__ = (u'stack', u'time')
def __init__(self, stack, time):
self.stack = stack
self.time = time
@classmethod
def from_frame(cls, frame, time):
stack = []
while frame:
stack.append(CodeSite.get(frame.f_code.co_filename, frame.f_lineno,
frame.f_code.co_name))
frame = frame.f_back
return Sample(stack, time)
###########################################################################
## SIGPROF handler
def profile_signal_handler(signum, frame):
if state.profile_level > 0:
now = clock()
state.accumulate_time(now)
state.samples.append(Sample.from_frame(frame, state.accumulated_time))
signal.setitimer(signal.ITIMER_PROF,
state.sample_interval, 0.0)
state.last_start_time = now
stopthread = threading.Event()
def samplerthread(tid):
while not stopthread.is_set():
now = clock()
state.accumulate_time(now)
frame = sys._current_frames()[tid]
state.samples.append(Sample.from_frame(frame, state.accumulated_time))
state.last_start_time = now
time.sleep(state.sample_interval)
stopthread.clear()
###########################################################################
## Profiling API
def is_active():
return state.profile_level > 0
lastmechanism = None
def start(mechanism='thread'):
'''Install the profiling signal handler, and start profiling.'''
state.profile_level += 1
if state.profile_level == 1:
state.last_start_time = clock()
rpt = state.remaining_prof_time
state.remaining_prof_time = None
global lastmechanism
lastmechanism = mechanism
if mechanism == 'signal':
signal.signal(signal.SIGPROF, profile_signal_handler)
signal.setitimer(signal.ITIMER_PROF,
rpt or state.sample_interval, 0.0)
elif mechanism == 'thread':
frame = inspect.currentframe()
tid = [k for k, f in sys._current_frames().items() if f == frame][0]
state.thread = threading.Thread(target=samplerthread,
args=(tid,), name="samplerthread")
state.thread.start()
def stop():
'''Stop profiling, and uninstall the profiling signal handler.'''
state.profile_level -= 1
if state.profile_level == 0:
if lastmechanism == 'signal':
rpt = signal.setitimer(signal.ITIMER_PROF, 0.0, 0.0)
signal.signal(signal.SIGPROF, signal.SIG_IGN)
state.remaining_prof_time = rpt[0]
elif lastmechanism == 'thread':
stopthread.set()
state.thread.join()
state.accumulate_time(clock())
state.last_start_time = None
statprofpath = encoding.environ.get('STATPROF_DEST')
if statprofpath:
save_data(statprofpath)
return state
def save_data(path):
with open(path, 'w+') as file:
file.write(str(state.accumulated_time) + '\n')
for sample in state.samples:
time = str(sample.time)
stack = sample.stack
sites = ['\1'.join([s.path, str(s.lineno), s.function])
for s in stack]
file.write(time + '\0' + '\0'.join(sites) + '\n')
def load_data(path):
lines = open(path, 'r').read().splitlines()
state.accumulated_time = float(lines[0])
state.samples = []
for line in lines[1:]:
parts = line.split('\0')
time = float(parts[0])
rawsites = parts[1:]
sites = []
for rawsite in rawsites:
siteparts = rawsite.split('\1')
sites.append(CodeSite.get(siteparts[0], int(siteparts[1]),
siteparts[2]))
state.samples.append(Sample(sites, time))
def reset(frequency=None):
'''Clear out the state of the profiler. Do not call while the
profiler is running.
The optional frequency argument specifies the number of samples to
collect per second.'''
assert state.profile_level == 0, "Can't reset() while statprof is running"
CodeSite.cache.clear()
state.reset(frequency)
@contextmanager
def profile():
start()
try:
yield
finally:
stop()
display()
###########################################################################
## Reporting API
class SiteStats(object):
def __init__(self, site):
self.site = site
self.selfcount = 0
self.totalcount = 0
def addself(self):
self.selfcount += 1
def addtotal(self):
self.totalcount += 1
def selfpercent(self):
return self.selfcount / len(state.samples) * 100
def totalpercent(self):
return self.totalcount / len(state.samples) * 100
def selfseconds(self):
return self.selfcount * state.seconds_per_sample()
def totalseconds(self):
return self.totalcount * state.seconds_per_sample()
@classmethod
def buildstats(cls, samples):
stats = {}
for sample in samples:
for i, site in enumerate(sample.stack):
sitestat = stats.get(site)
if not sitestat:
sitestat = SiteStats(site)
stats[site] = sitestat
sitestat.addtotal()
if i == 0:
sitestat.addself()
return [s for s in stats.itervalues()]
class DisplayFormats:
ByLine = 0
ByMethod = 1
AboutMethod = 2
Hotpath = 3
FlameGraph = 4
Json = 5
def display(fp=None, format=3, data=None, **kwargs):
'''Print statistics, either to stdout or the given file object.'''
data = data or state
if fp is None:
import sys
fp = sys.stdout
if len(data.samples) == 0:
print('No samples recorded.', file=fp)
return
if format == DisplayFormats.ByLine:
display_by_line(data, fp)
elif format == DisplayFormats.ByMethod:
display_by_method(data, fp)
elif format == DisplayFormats.AboutMethod:
display_about_method(data, fp, **kwargs)
elif format == DisplayFormats.Hotpath:
display_hotpath(data, fp, **kwargs)
elif format == DisplayFormats.FlameGraph:
write_to_flame(data, fp, **kwargs)
elif format == DisplayFormats.Json:
write_to_json(data, fp)
else:
raise Exception("Invalid display format")
if format != DisplayFormats.Json:
print('---', file=fp)
print('Sample count: %d' % len(data.samples), file=fp)
print('Total time: %f seconds' % data.accumulated_time, file=fp)
def display_by_line(data, fp):
'''Print the profiler data with each sample line represented
as one row in a table. Sorted by self-time per line.'''
stats = SiteStats.buildstats(data.samples)
stats.sort(reverse=True, key=lambda x: x.selfseconds())
print('%5.5s %10.10s %7.7s %-8.8s' %
('% ', 'cumulative', 'self', ''), file=fp)
print('%5.5s %9.9s %8.8s %-8.8s' %
("time", "seconds", "seconds", "name"), file=fp)
for stat in stats:
site = stat.site
sitelabel = '%s:%d:%s' % (site.filename(), site.lineno, site.function)
print('%6.2f %9.2f %9.2f %s' % (stat.selfpercent(),
stat.totalseconds(),
stat.selfseconds(),
sitelabel),
file=fp)
def display_by_method(data, fp):
'''Print the profiler data with each sample function represented
as one row in a table. Important lines within that function are
output as nested rows. Sorted by self-time per line.'''
print('%5.5s %10.10s %7.7s %-8.8s' %
('% ', 'cumulative', 'self', ''), file=fp)
print('%5.5s %9.9s %8.8s %-8.8s' %
("time", "seconds", "seconds", "name"), file=fp)
stats = SiteStats.buildstats(data.samples)
grouped = defaultdict(list)
for stat in stats:
grouped[stat.site.filename() + ":" + stat.site.function].append(stat)
# compute sums for each function
functiondata = []
for fname, sitestats in grouped.iteritems():
total_cum_sec = 0
total_self_sec = 0
total_percent = 0
for stat in sitestats:
total_cum_sec += stat.totalseconds()
total_self_sec += stat.selfseconds()
total_percent += stat.selfpercent()
functiondata.append((fname,
total_cum_sec,
total_self_sec,
total_percent,
sitestats))
# sort by total self sec
functiondata.sort(reverse=True, key=lambda x: x[2])
for function in functiondata:
if function[3] < 0.05:
continue
print('%6.2f %9.2f %9.2f %s' % (function[3], # total percent
function[1], # total cum sec
function[2], # total self sec
function[0]), # file:function
file=fp)
function[4].sort(reverse=True, key=lambda i: i.selfseconds())
for stat in function[4]:
# only show line numbers for significant locations (>1% time spent)
if stat.selfpercent() > 1:
source = stat.site.getsource(25)
stattuple = (stat.selfpercent(), stat.selfseconds(),
stat.site.lineno, source)
print('%33.0f%% %6.2f line %s: %s' % (stattuple), file=fp)
def display_about_method(data, fp, function=None, **kwargs):
if function is None:
raise Exception("Invalid function")
filename = None
if ':' in function:
filename, function = function.split(':')
relevant_samples = 0
parents = {}
children = {}
for sample in data.samples:
for i, site in enumerate(sample.stack):
if site.function == function and (not filename
or site.filename() == filename):
relevant_samples += 1
if i != len(sample.stack) - 1:
parent = sample.stack[i + 1]
if parent in parents:
parents[parent] = parents[parent] + 1
else:
parents[parent] = 1
if site in children:
children[site] = children[site] + 1
else:
children[site] = 1
parents = [(parent, count) for parent, count in parents.iteritems()]
parents.sort(reverse=True, key=lambda x: x[1])
for parent, count in parents:
print('%6.2f%% %s:%s line %s: %s' %
(count / relevant_samples * 100, parent.filename(),
parent.function, parent.lineno, parent.getsource(50)), file=fp)
stats = SiteStats.buildstats(data.samples)
stats = [s for s in stats
if s.site.function == function and
(not filename or s.site.filename() == filename)]
total_cum_sec = 0
total_self_sec = 0
total_self_percent = 0
total_cum_percent = 0
for stat in stats:
total_cum_sec += stat.totalseconds()
total_self_sec += stat.selfseconds()
total_self_percent += stat.selfpercent()
total_cum_percent += stat.totalpercent()
print(
'\n %s:%s Total: %0.2fs (%0.2f%%) Self: %0.2fs (%0.2f%%)\n' %
(
filename or '___',
function,
total_cum_sec,
total_cum_percent,
total_self_sec,
total_self_percent
), file=fp)
children = [(child, count) for child, count in children.iteritems()]
children.sort(reverse=True, key=lambda x: x[1])
for child, count in children:
print(' %6.2f%% line %s: %s' %
(count / relevant_samples * 100, child.lineno,
child.getsource(50)), file=fp)
def display_hotpath(data, fp, limit=0.05, **kwargs):
class HotNode(object):
def __init__(self, site):
self.site = site
self.count = 0
self.children = {}
def add(self, stack, time):
self.count += time
site = stack[0]
child = self.children.get(site)
if not child:
child = HotNode(site)
self.children[site] = child
if len(stack) > 1:
i = 1
# Skip boiler plate parts of the stack
while i < len(stack) and '%s:%s' % (stack[i].filename(), stack[i].function) in skips:
i += 1
if i < len(stack):
child.add(stack[i:], time)
root = HotNode(None)
lasttime = data.samples[0].time
for sample in data.samples:
root.add(sample.stack[::-1], sample.time - lasttime)
lasttime = sample.time
def _write(node, depth, multiple_siblings):
site = node.site
visiblechildren = [c for c in node.children.itervalues()
if c.count >= (limit * root.count)]
if site:
indent = depth * 2 - 1
filename = ''
function = ''
if len(node.children) > 0:
childsite = list(node.children.itervalues())[0].site
filename = (childsite.filename() + ':').ljust(15)
function = childsite.function
# lots of string formatting
listpattern = ''.ljust(indent) +\
('\\' if multiple_siblings else '|') +\
' %4.1f%% %s %s'
liststring = listpattern % (node.count / root.count * 100,
filename, function)
codepattern = '%' + str(55 - len(liststring)) + 's %s: %s'
codestring = codepattern % ('line', site.lineno, site.getsource(30))
finalstring = liststring + codestring
childrensamples = sum([c.count for c in node.children.itervalues()])
# Make frames that performed more than 10% of the operation red
if node.count - childrensamples > (0.1 * root.count):
finalstring = '\033[91m' + finalstring + '\033[0m'
# Make frames that didn't actually perform work dark grey
elif node.count - childrensamples == 0:
finalstring = '\033[90m' + finalstring + '\033[0m'
print(finalstring, file=fp)
newdepth = depth
if len(visiblechildren) > 1 or multiple_siblings:
newdepth += 1
visiblechildren.sort(reverse=True, key=lambda x: x.count)
for child in visiblechildren:
_write(child, newdepth, len(visiblechildren) > 1)
if root.count > 0:
_write(root, 0, False)
def write_to_flame(data, fp, scriptpath=None, outputfile=None, **kwargs):
if scriptpath is None:
scriptpath = encoding.environ['HOME'] + '/flamegraph.pl'
if not os.path.exists(scriptpath):
print("error: missing %s" % scriptpath, file=fp)
print("get it here: https://github.com/brendangregg/FlameGraph",
file=fp)
return
fd, path = tempfile.mkstemp()
file = open(path, "w+")
lines = {}
for sample in data.samples:
sites = [s.function for s in sample.stack]
sites.reverse()
line = ';'.join(sites)
if line in lines:
lines[line] = lines[line] + 1
else:
lines[line] = 1
for line, count in lines.iteritems():
file.write("%s %s\n" % (line, count))
file.close()
if outputfile is None:
outputfile = '~/flamegraph.svg'
os.system("perl ~/flamegraph.pl %s > %s" % (path, outputfile))
print("Written to %s" % outputfile, file=fp)
def write_to_json(data, fp):
samples = []
for sample in data.samples:
stack = []
for frame in sample.stack:
stack.append((frame.path, frame.lineno, frame.function))
samples.append((sample.time, stack))
print(json.dumps(samples), file=fp)
def printusage():
print("""
The statprof command line allows you to inspect the last profile's results in
the following forms:
usage:
hotpath [-l --limit percent]
Shows a graph of calls with the percent of time each takes.
Red calls take over 10%% of the total time themselves.
lines
Shows the actual sampled lines.
functions
Shows the samples grouped by function.
function [filename:]functionname
Shows the callers and callees of a particular function.
flame [-s --script-path] [-o --output-file path]
Writes out a flamegraph to output-file (defaults to ~/flamegraph.svg)
Requires that ~/flamegraph.pl exist.
(Specify alternate script path with --script-path.)""")
def main(argv=None):
if argv is None:
argv = sys.argv
if len(argv) == 1:
printusage()
return 0
displayargs = {}
optstart = 2
displayargs['function'] = None
if argv[1] == 'hotpath':
displayargs['format'] = DisplayFormats.Hotpath
elif argv[1] == 'lines':
displayargs['format'] = DisplayFormats.ByLine
elif argv[1] == 'functions':
displayargs['format'] = DisplayFormats.ByMethod
elif argv[1] == 'function':
displayargs['format'] = DisplayFormats.AboutMethod
displayargs['function'] = argv[2]
optstart = 3
elif argv[1] == 'flame':
displayargs['format'] = DisplayFormats.FlameGraph
else:
printusage()
return 0
# process options
try:
opts, args = pycompat.getoptb(sys.argv[optstart:], "hl:f:o:p:",
["help", "limit=", "file=", "output-file=", "script-path="])
except getopt.error as msg:
print(msg)
printusage()
return 2
displayargs['limit'] = 0.05
path = None
for o, value in opts:
if o in ("-l", "--limit"):
displayargs['limit'] = float(value)
elif o in ("-f", "--file"):
path = value
elif o in ("-o", "--output-file"):
displayargs['outputfile'] = value
elif o in ("-p", "--script-path"):
displayargs['scriptpath'] = value
elif o in ("-h", "help"):
printusage()
return 0
else:
assert False, "unhandled option %s" % o
load_data(path=path)
display(**displayargs)
return 0
if __name__ == "__main__":
sys.exit(main())