view mercurial/statprof.py @ 46582:b0a3ca02d17a

copies-rust: implement PartialEqual manually Now that we know that each (dest, rev) pair has at most a unique CopySource, we can simplify comparison a lot. This "simple" step buy a good share of the previous slowdown back in some case: Repo Case Source-Rev Dest-Rev # of revisions old time new time Difference Factor time per rev --------------------------------------------------------------------------------------------------------------------------------------------------------------- mozilla-try x00000_revs_x00000_added_x000_copies 9b2a99adc05e 8e29777b48e6 : 382065 revs, 43.304637 s, 34.443661 s, -8.860976 s, × 0.7954, 90 µs/rev Full benchmark: Repo Case Source-Rev Dest-Rev # of revisions old time new time Difference Factor time per rev --------------------------------------------------------------------------------------------------------------------------------------------------------------- mercurial x_revs_x_added_0_copies ad6b123de1c7 39cfcef4f463 : 1 revs, 0.000043 s, 0.000043 s, +0.000000 s, × 1.0000, 43 µs/rev mercurial x_revs_x_added_x_copies 2b1c78674230 0c1d10351869 : 6 revs, 0.000114 s, 0.000117 s, +0.000003 s, × 1.0263, 19 µs/rev mercurial x000_revs_x000_added_x_copies 81f8ff2a9bf2 dd3267698d84 : 1032 revs, 0.004937 s, 0.004892 s, -0.000045 s, × 0.9909, 4 µs/rev pypy x_revs_x_added_0_copies aed021ee8ae8 099ed31b181b : 9 revs, 0.000339 s, 0.000196 s, -0.000143 s, × 0.5782, 21 µs/rev pypy x_revs_x000_added_0_copies 4aa4e1f8e19a 359343b9ac0e : 1 revs, 0.000049 s, 0.000050 s, +0.000001 s, × 1.0204, 50 µs/rev pypy x_revs_x_added_x_copies ac52eb7bbbb0 72e022663155 : 7 revs, 0.000202 s, 0.000117 s, -0.000085 s, × 0.5792, 16 µs/rev pypy x_revs_x00_added_x_copies c3b14617fbd7 ace7255d9a26 : 1 revs, 0.000409 s, 0.6f1f4a s, -0.000087 s, × 0.7873, 322 µs/rev pypy x_revs_x000_added_x000_copies df6f7a526b60 a83dc6a2d56f : 6 revs, 0.011984 s, 0.011949 s, -0.000035 s, × 0.9971, 1991 µs/rev pypy x000_revs_xx00_added_0_copies 89a76aede314 2f22446ff07e : 4785 revs, 0.050820 s, 0.050802 s, -0.000018 s, × 0.9996, 10 µs/rev pypy x000_revs_x000_added_x_copies 8a3b5bfd266e 2c68e87c3efe : 6780 revs, 0.087953 s, 0.088090 s, +0.000137 s, × 1.0016, 12 µs/rev pypy x000_revs_x000_added_x000_copies 89a76aede314 7b3dda341c84 : 5441 revs, 0.062902 s, 0.062079 s, -0.000823 s, × 0.9869, 11 µs/rev pypy x0000_revs_x_added_0_copies d1defd0dc478 c9cb1334cc78 : 43645 revs, 0.679234 s, 0.635337 s, -0.043897 s, × 0.9354, 14 µs/rev pypy x0000_revs_xx000_added_0_copies bf2c629d0071 4ffed77c095c : 2 revs, 0.013095 s, 0.013262 s, +0.000167 s, × 1.0128, 6631 µs/rev pypy x0000_revs_xx000_added_x000_copies 08ea3258278e d9fa043f30c0 : 11316 revs, 0.120910 s, 0.120085 s, -0.000825 s, × 0.9932, 10 µs/rev netbeans x_revs_x_added_0_copies fb0955ffcbcd a01e9239f9e7 : 2 revs, 0.000087 s, 0.000085 s, -0.000002 s, × 0.9770, 42 µs/rev netbeans x_revs_x000_added_0_copies 6f360122949f 20eb231cc7d0 : 2 revs, 0.000107 s, 0.000110 s, +0.000003 s, × 1.0280, 55 µs/rev netbeans x_revs_x_added_x_copies 1ada3faf6fb6 5a39d12eecf4 : 3 revs, 0.000186 s, 0.000177 s, -0.000009 s, × 0.9516, 59 µs/rev netbeans x_revs_x00_added_x_copies 35be93ba1e2c 9eec5e90c05f : 9 revs, 0.000754 s, 0.000743 s, -0.000011 s, × 0.9854, 82 µs/rev netbeans x000_revs_xx00_added_0_copies eac3045b4fdd 51d4ae7f1290 : 1421 revs, 0.010443 s, 0.010168 s, -0.000275 s, × 0.9737, 7 µs/rev netbeans x000_revs_x000_added_x_copies e2063d266acd 6081d72689dc : 1533 revs, 0.015697 s, 0.015946 s, +0.000249 s, × 1.0159, 10 µs/rev netbeans x000_revs_x000_added_x000_copies ff453e9fee32 411350406ec2 : 5750 revs, 0.063528 s, 0.062712 s, -0.000816 s, × 0.9872, 10 µs/rev netbeans x0000_revs_xx000_added_x000_copies 588c2d1ced70 1aad62e59ddd : 66949 revs, 0.545515 s, 0.523832 s, -0.021683 s, × 0.9603, 7 µs/rev mozilla-central x_revs_x_added_0_copies 3697f962bb7b 7015fcdd43a2 : 2 revs, 0.000089 s, 0.000090 s, +0.000001 s, × 1.0112, 45 µs/rev mozilla-central x_revs_x000_added_0_copies dd390860c6c9 40d0c5bed75d : 8 revs, 0.000265 s, 0.000264 s, -0.000001 s, × 0.9962, 33 µs/rev mozilla-central x_revs_x_added_x_copies 8d198483ae3b 14207ffc2b2f : 9 revs, 0.000381 s, 0.000187 s, -0.000194 s, × 0.4908, 20 µs/rev mozilla-central x_revs_x00_added_x_copies 98cbc58cc6bc 446a150332c3 : 7 revs, 0.000672 s, 0.000665 s, -0.000007 s, × 0.9896, 95 µs/rev mozilla-central x_revs_x000_added_x000_copies 3c684b4b8f68 0a5e72d1b479 : 3 revs, 0.003497 s, 0.003556 s, +0.000059 s, × 1.0169, 1185 µs/rev mozilla-central x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 6 revs, 0.073204 s, 0.071345 s, -0.001859 s, × 0.9746, 11890 µs/rev mozilla-central x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 1593 revs, 0.006482 s, 0.006551 s, +0.000069 s, × 1.0106, 4 µs/rev mozilla-central x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 41 revs, 0.005066 s, 0.005078 s, +0.000012 s, × 1.0024, 123 µs/rev mozilla-central x000_revs_x000_added_x000_copies 7c97034feb78 4407bd0c6330 : 7839 revs, 0.065707 s, 0.065823 s, +0.000116 s, × 1.0018, 8 µs/rev mozilla-central x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 615 revs, 0.026800 s, 0.027050 s, +0.000250 s, × 1.0093, 43 µs/rev mozilla-central x0000_revs_xx000_added_x000_copies f78c615a656c 96a38b690156 : 30263 revs, 0.203856 s, 0.202443 s, -0.001413 s, × 0.9931, 6 µs/rev mozilla-central x00000_revs_x0000_added_x0000_copies 6832ae71433c 4c222a1d9a00 : 153721 revs, 1.293394 s, 1.261583 s, -0.031811 s, × 0.9754, 8 µs/rev mozilla-central x00000_revs_x00000_added_x000_copies 76caed42cf7c 1daa622bbe42 : 204976 revs, 1.698239 s, 1.643869 s, -0.054370 s, × 0.9680, 8 µs/rev mozilla-try x_revs_x_added_0_copies aaf6dde0deb8 9790f499805a : 2 revs, 0.000875 s, 0.000868 s, -0.000007 s, × 0.9920, 434 µs/rev mozilla-try x_revs_x000_added_0_copies d8d0222927b4 5bb8ce8c7450 : 2 revs, 0.000891 s, 0.000887 s, -0.000004 s, × 0.9955, 443 µs/rev mozilla-try x_revs_x_added_x_copies 092fcca11bdb 936255a0384a : 4 revs, 0.000292 s, 0.000168 s, -0.000124 s, × 0.5753, 42 µs/rev mozilla-try x_revs_x00_added_x_copies b53d2fadbdb5 017afae788ec : 2 revs, 0.003939 s, 0.001160 s, -0.002779 s, × 0.2945, 580 µs/rev mozilla-try x_revs_x000_added_x000_copies 20408ad61ce5 6f0ee96e21ad : 1 revs, 0.033027 s, 0.033016 s, -0.000011 s, × 0.9997, 33016 µs/rev mozilla-try x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 6 revs, 0.073703 s, 0.073312 s, -0.39ae31 s, × 0.9947, 12218 µs/rev mozilla-try x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 1593 revs, 0.006469 s, 0.006485 s, +0.000016 s, × 1.0025, 4 µs/rev mozilla-try x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 41 revs, 0.005278 s, 0.005494 s, +0.000216 s, × 1.0409, 134 µs/rev mozilla-try x000_revs_x000_added_x000_copies 1346fd0130e4 4c65cbdabc1f : 6657 revs, 0.064995 s, 0.064879 s, -0.000116 s, × 0.9982, 9 µs/rev mozilla-try x0000_revs_x_added_0_copies 63519bfd42ee a36a2a865d92 : 40314 revs, 0.301041 s, 0.301469 s, +0.000428 s, × 1.0014, 7 µs/rev mozilla-try x0000_revs_x_added_x_copies 9fe69ff0762d bcabf2a78927 : 38690 revs, 0.285575 s, 0.297113 s, +0.011538 s, × 1.0404, 7 µs/rev mozilla-try x0000_revs_xx000_added_x_copies 156f6e2674f2 4d0f2c178e66 : 8598 revs, 0.085597 s, 0.085890 s, +0.000293 s, × 1.0034, 9 µs/rev mozilla-try x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 615 revs, 0.027118 s, 0.027718 s, +0.000600 s, × 1.0221, 45 µs/rev mozilla-try x0000_revs_xx000_added_x000_copies 89294cd501d9 7ccb2fc7ccb5 : 97052 revs, 2.119204 s, 2.048949 s, -0.070255 s, × 0.9668, 21 µs/rev mozilla-try x0000_revs_x0000_added_x0000_copies e928c65095ed e951f4ad123a : 52031 revs, 0.701479 s, 0.685924 s, -0.015555 s, × 0.9778, 13 µs/rev mozilla-try x00000_revs_x_added_0_copies 6a320851d377 1ebb79acd503 : 363753 revs, 4.482399 s, 4.482891 s, +0.000492 s, × 1.0001, 12 µs/rev mozilla-try x00000_revs_x00000_added_0_copies dc8a3ca7010e d16fde900c9c : 34414 revs, 0.574082 s, 0.577633 s, +0.003551 s, × 1.0062, 16 µs/rev mozilla-try x00000_revs_x_added_x_copies 5173c4b6f97c 95d83ee7242d : 362229 revs, 4.480366 s, 4.397816 s, -0.082550 s, × 0.9816, 12 µs/rev mozilla-try x00000_revs_x000_added_x_copies 9126823d0e9c ca82787bb23c : 359344 revs, 4.369070 s, 4.370538 s, +0.001468 s, × 1.0003, 12 µs/rev mozilla-try x00000_revs_x0000_added_x0000_copies 8d3fafa80d4b eb884023b810 : 192665 revs, 1.592506 s, 1.570439 s, -0.022067 s, × 0.9861, 8 µs/rev mozilla-try x00000_revs_x00000_added_x0000_copies 1b661134e2ca 1ae03d022d6d : 228985 revs, 87.824489 s, 88.388512 s, +0.564023 s, × 1.0064, 386 µs/rev mozilla-try x00000_revs_x00000_added_x000_copies 9b2a99adc05e 8e29777b48e6 : 382065 revs, 43.304637 s, 34.443661 s, -8.860976 s, × 0.7954, 90 µs/rev private : 459513 revs, 33.853687 s, 27.370148 s, -6.483539 s, × 0.8085, 59 µs/rev Differential Revision: https://phab.mercurial-scm.org/D9653
author Pierre-Yves David <pierre-yves.david@octobus.net>
date Wed, 16 Dec 2020 11:11:05 +0100
parents 8b0a3ff5ed12
children c1fe758c1530
line wrap: on
line source

## 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 threading
import time

from .pycompat import open
from . import (
    encoding,
    pycompat,
)

defaultdict = collections.defaultdict
contextmanager = contextlib.contextmanager

__all__ = [b'start', b'stop', b'reset', b'display', b'profile']

skips = {
    "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], times[4])


###########################################################################
## Collection data structures


class ProfileState(object):
    def __init__(self, frequency=None):
        self.reset(frequency)
        self.track = b'cpu'

    def reset(self, frequency=None):
        # total so far
        self.accumulated_time = (0.0, 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 pycompat.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):
        increment = (
            stop_time[0] - self.last_start_time[0],
            stop_time[1] - self.last_start_time[1],
        )
        self.accumulated_time = (
            self.accumulated_time[0] + increment[0],
            self.accumulated_time[1] + increment[1],
        )

    def seconds_per_sample(self):
        return self.accumulated_time[self.timeidx] / len(self.samples)

    @property
    def timeidx(self):
        if self.track == b'real':
            return 1
        return 0


state = ProfileState()


class CodeSite(object):
    cache = {}

    __slots__ = ('path', 'lineno', 'function', 'source')

    def __init__(self, path, lineno, function):
        assert isinstance(path, bytes)
        self.path = path
        self.lineno = lineno
        assert isinstance(function, bytes)
        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
            try:
                with open(self.path, b'rb') as fp:
                    for i, line in enumerate(fp):
                        if i == lineno:
                            self.source = line.strip()
                            break
            except:
                pass
            if self.source is None:
                self.source = b''

        source = self.source
        if len(source) > length:
            source = source[: (length - 3)] + b"..."
        return source

    def filename(self):
        return os.path.basename(self.path)

    def skipname(self):
        return '%s:%s' % (self.filename(), self.function)


class Sample(object):
    __slots__ = ('stack', '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(
                    pycompat.sysbytes(frame.f_code.co_filename),
                    frame.f_lineno,
                    pycompat.sysbytes(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)

        timestamp = state.accumulated_time[state.timeidx]
        state.samples.append(Sample.from_frame(frame, timestamp))

        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]

        timestamp = state.accumulated_time[state.timeidx]
        state.samples.append(Sample.from_frame(frame, timestamp))

        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=b'thread', track=b'cpu'):
    '''Install the profiling signal handler, and start profiling.'''
    state.track = track  # note: nesting different mode won't work
    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 == b'signal':
            signal.signal(signal.SIGPROF, profile_signal_handler)
            signal.setitimer(
                signal.ITIMER_PROF, rpt or state.sample_interval, 0.0
            )
        elif mechanism == b'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 == b'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 == b'thread':
            stopthread.set()
            state.thread.join()

        state.accumulate_time(clock())
        state.last_start_time = None
        statprofpath = encoding.environ.get(b'STATPROF_DEST')
        if statprofpath:
            save_data(statprofpath)

    return state


def save_data(path):
    with open(path, b'w+') as file:
        file.write(b"%f %f\n" % state.accumulated_time)
        for sample in state.samples:
            time = sample.time
            stack = sample.stack
            sites = [
                b'\1'.join([s.path, b'%d' % s.lineno, s.function])
                for s in stack
            ]
            file.write(b"%d\0%s\n" % (time, b'\0'.join(sites)))


def load_data(path):
    lines = open(path, b'rb').read().splitlines()

    state.accumulated_time = [float(value) for value in lines[0].split()]
    state.samples = []
    for line in lines[1:]:
        parts = line.split(b'\0')
        time = float(parts[0])
        rawsites = parts[1:]
        sites = []
        for rawsite in rawsites:
            siteparts = rawsite.split(b'\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, b"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 pycompat.itervalues(stats)]


class DisplayFormats:
    ByLine = 0
    ByMethod = 1
    AboutMethod = 2
    Hotpath = 3
    FlameGraph = 4
    Json = 5
    Chrome = 6


def display(fp=None, format=3, data=None, **kwargs):
    '''Print statistics, either to stdout or the given file object.'''
    if data is None:
        data = state

    if fp is None:
        import sys

        fp = sys.stdout
    if len(data.samples) == 0:
        fp.write(b'No samples recorded.\n')
        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)
    elif format == DisplayFormats.Chrome:
        write_to_chrome(data, fp, **kwargs)
    else:
        raise Exception(b"Invalid display format")

    if format not in (DisplayFormats.Json, DisplayFormats.Chrome):
        fp.write(b'---\n')
        fp.write(b'Sample count: %d\n' % len(data.samples))
        fp.write(b'Total time: %f seconds (%f wall)\n' % data.accumulated_time)


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())

    fp.write(
        b'%5.5s %10.10s   %7.7s  %-8.8s\n'
        % (b'%  ', b'cumulative', b'self', b'')
    )
    fp.write(
        b'%5.5s  %9.9s  %8.8s  %-8.8s\n'
        % (b"time", b"seconds", b"seconds", b"name")
    )

    for stat in stats:
        site = stat.site
        sitelabel = b'%s:%d:%s' % (site.filename(), site.lineno, site.function)
        fp.write(
            b'%6.2f %9.2f %9.2f  %s\n'
            % (
                stat.selfpercent(),
                stat.totalseconds(),
                stat.selfseconds(),
                sitelabel,
            )
        )


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."""
    fp.write(
        b'%5.5s %10.10s   %7.7s  %-8.8s\n'
        % (b'%  ', b'cumulative', b'self', b'')
    )
    fp.write(
        b'%5.5s  %9.9s  %8.8s  %-8.8s\n'
        % (b"time", b"seconds", b"seconds", b"name")
    )

    stats = SiteStats.buildstats(data.samples)

    grouped = defaultdict(list)
    for stat in stats:
        grouped[stat.site.filename() + b":" + stat.site.function].append(stat)

    # compute sums for each function
    functiondata = []
    for fname, sitestats in pycompat.iteritems(grouped):
        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
        fp.write(
            b'%6.2f %9.2f %9.2f  %s\n'
            % (
                function[3],  # total percent
                function[1],  # total cum sec
                function[2],  # total self sec
                function[0],
            )
        )  # file:function

        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)
                if sys.version_info.major >= 3 and not isinstance(
                    source, bytes
                ):
                    source = pycompat.bytestr(source)

                stattuple = (
                    stat.selfpercent(),
                    stat.selfseconds(),
                    stat.site.lineno,
                    source,
                )

                fp.write(b'%33.0f%% %6.2f   line %d: %s\n' % stattuple)


def display_about_method(data, fp, function=None, **kwargs):
    if function is None:
        raise Exception(b"Invalid function")

    filename = None
    if b':' in function:
        filename, function = function.split(b':')

    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 pycompat.iteritems(parents)]
    parents.sort(reverse=True, key=lambda x: x[1])
    for parent, count in parents:
        fp.write(
            b'%6.2f%%   %s:%s   line %s: %s\n'
            % (
                count / relevant_samples * 100,
                pycompat.fsencode(parent.filename()),
                pycompat.sysbytes(parent.function),
                parent.lineno,
                pycompat.sysbytes(parent.getsource(50)),
            )
        )

    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()

    fp.write(
        b'\n    %s:%s    Total: %0.2fs (%0.2f%%)    Self: %0.2fs (%0.2f%%)\n\n'
        % (
            pycompat.sysbytes(filename or b'___'),
            pycompat.sysbytes(function),
            total_cum_sec,
            total_cum_percent,
            total_self_sec,
            total_self_percent,
        )
    )

    children = [(child, count) for child, count in pycompat.iteritems(children)]
    children.sort(reverse=True, key=lambda x: x[1])
    for child, count in children:
        fp.write(
            b'        %6.2f%%   line %s: %s\n'
            % (
                count / relevant_samples * 100,
                child.lineno,
                pycompat.sysbytes(child.getsource(50)),
            )
        )


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 stack[i].skipname() in skips:
                    i += 1
                if i < len(stack):
                    child.add(stack[i:], time)
            else:
                # Normally this is done by the .add() calls
                child.count += 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
    showtime = kwargs.get('showtime', True)

    def _write(node, depth, multiple_siblings):
        site = node.site
        visiblechildren = [
            c
            for c in pycompat.itervalues(node.children)
            if c.count >= (limit * root.count)
        ]
        if site:
            indent = depth * 2 - 1
            filename = (site.filename() + b':').ljust(15)
            function = site.function

            # lots of string formatting
            listpattern = (
                b''.ljust(indent)
                + (b'\\' if multiple_siblings else b'|')
                + b' %4.1f%%'
                + (b' %5.2fs' % node.count if showtime else b'')
                + b'  %s %s'
            )
            liststring = listpattern % (
                node.count / root.count * 100,
                filename,
                function,
            )
            # 4 to account for the word 'line'
            spacing_len = max(4, 55 - len(liststring))
            prefix = b''
            if spacing_len == 4:
                prefix = b', '

            codepattern = b'%s%s %d: %s%s'
            codestring = codepattern % (
                prefix,
                b'line'.rjust(spacing_len),
                site.lineno,
                b''.ljust(max(0, 4 - len(str(site.lineno)))),
                site.getsource(30),
            )

            finalstring = liststring + codestring
            childrensamples = sum(
                [c.count for c in pycompat.itervalues(node.children)]
            )
            # Make frames that performed more than 10% of the operation red
            if node.count - childrensamples > (0.1 * root.count):
                finalstring = b'\033[91m' + finalstring + b'\033[0m'
            # Make frames that didn't actually perform work dark grey
            elif node.count - childrensamples == 0:
                finalstring = b'\033[90m' + finalstring + b'\033[0m'
            fp.write(finalstring + b'\n')

        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[b'HOME'] + b'/flamegraph.pl'
    if not os.path.exists(scriptpath):
        fp.write(b'error: missing %s\n' % scriptpath)
        fp.write(b'get it here: https://github.com/brendangregg/FlameGraph\n')
        return

    lines = {}
    for sample in data.samples:
        sites = [s.function for s in sample.stack]
        sites.reverse()
        line = b';'.join(sites)
        if line in lines:
            lines[line] = lines[line] + 1
        else:
            lines[line] = 1

    fd, path = pycompat.mkstemp()

    with open(path, b"w+") as file:
        for line, count in pycompat.iteritems(lines):
            file.write(b"%s %d\n" % (line, count))

    if outputfile is None:
        outputfile = b'~/flamegraph.svg'

    os.system(b"perl ~/flamegraph.pl %s > %s" % (path, outputfile))
    fp.write(b'Written to %s\n' % outputfile)


_pathcache = {}


def simplifypath(path):
    """Attempt to make the path to a Python module easier to read by
    removing whatever part of the Python search path it was found
    on."""

    if path in _pathcache:
        return _pathcache[path]
    hgpath = encoding.__file__.rsplit(os.sep, 2)[0]
    for p in [hgpath] + sys.path:
        prefix = p + os.sep
        if path.startswith(prefix):
            path = path[len(prefix) :]
            break
    _pathcache[path] = path
    return path


def write_to_json(data, fp):
    samples = []

    for sample in data.samples:
        stack = []

        for frame in sample.stack:
            stack.append(
                (
                    pycompat.sysstr(frame.path),
                    frame.lineno,
                    pycompat.sysstr(frame.function),
                )
            )

        samples.append((sample.time, stack))

    data = json.dumps(samples)
    if not isinstance(data, bytes):
        data = data.encode('utf-8')

    fp.write(data)


def write_to_chrome(data, fp, minthreshold=0.005, maxthreshold=0.999):
    samples = []
    laststack = collections.deque()
    lastseen = collections.deque()

    # The Chrome tracing format allows us to use a compact stack
    # representation to save space. It's fiddly but worth it.
    # We maintain a bijection between stack and ID.
    stack2id = {}
    id2stack = []  # will eventually be rendered

    def stackid(stack):
        if not stack:
            return
        if stack in stack2id:
            return stack2id[stack]
        parent = stackid(stack[1:])
        myid = len(stack2id)
        stack2id[stack] = myid
        id2stack.append(dict(category=stack[0][0], name='%s %s' % stack[0]))
        if parent is not None:
            id2stack[-1].update(parent=parent)
        return myid

    # The sampling profiler can sample multiple times without
    # advancing the clock, potentially causing the Chrome trace viewer
    # to render single-pixel columns that we cannot zoom in on.  We
    # work around this by pretending that zero-duration samples are a
    # millisecond in length.

    clamp = 0.001

    # We provide knobs that by default attempt to filter out stack
    # frames that are too noisy:
    #
    # * A few take almost all execution time. These are usually boring
    #   setup functions, giving a stack that is deep but uninformative.
    #
    # * Numerous samples take almost no time, but introduce lots of
    #   noisy, oft-deep "spines" into a rendered profile.

    blacklist = set()
    totaltime = data.samples[-1].time - data.samples[0].time
    minthreshold = totaltime * minthreshold
    maxthreshold = max(totaltime * maxthreshold, clamp)

    def poplast():
        oldsid = stackid(tuple(laststack))
        oldcat, oldfunc = laststack.popleft()
        oldtime, oldidx = lastseen.popleft()
        duration = sample.time - oldtime
        if minthreshold <= duration <= maxthreshold:
            # ensure no zero-duration events
            sampletime = max(oldtime + clamp, sample.time)
            samples.append(
                dict(
                    ph='E',
                    name=oldfunc,
                    cat=oldcat,
                    sf=oldsid,
                    ts=sampletime * 1e6,
                    pid=0,
                )
            )
        else:
            blacklist.add(oldidx)

    # Much fiddling to synthesize correctly(ish) nested begin/end
    # events given only stack snapshots.

    for sample in data.samples:
        stack = tuple(
            (
                (
                    '%s:%d'
                    % (simplifypath(pycompat.sysstr(frame.path)), frame.lineno),
                    pycompat.sysstr(frame.function),
                )
                for frame in sample.stack
            )
        )
        qstack = collections.deque(stack)
        if laststack == qstack:
            continue
        while laststack and qstack and laststack[-1] == qstack[-1]:
            laststack.pop()
            qstack.pop()
        while laststack:
            poplast()
        for f in reversed(qstack):
            lastseen.appendleft((sample.time, len(samples)))
            laststack.appendleft(f)
            path, name = f
            sid = stackid(tuple(laststack))
            samples.append(
                dict(
                    ph='B',
                    name=name,
                    cat=path,
                    ts=sample.time * 1e6,
                    sf=sid,
                    pid=0,
                )
            )
        laststack = collections.deque(stack)
    while laststack:
        poplast()
    events = [
        sample for idx, sample in enumerate(samples) if idx not in blacklist
    ]
    frames = collections.OrderedDict(
        (str(k), v) for (k, v) in enumerate(id2stack)
    )
    data = json.dumps(dict(traceEvents=events, stackFrames=frames), indent=1)
    if not isinstance(data, bytes):
        data = data.encode('utf-8')
    fp.write(data)
    fp.write(b'\n')


def printusage():
    print(
        r"""
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[b'function'] = None
    if argv[1] == 'hotpath':
        displayargs[b'format'] = DisplayFormats.Hotpath
    elif argv[1] == 'lines':
        displayargs[b'format'] = DisplayFormats.ByLine
    elif argv[1] == 'functions':
        displayargs[b'format'] = DisplayFormats.ByMethod
    elif argv[1] == 'function':
        displayargs[b'format'] = DisplayFormats.AboutMethod
        displayargs[b'function'] = argv[2]
        optstart = 3
    elif argv[1] == 'flame':
        displayargs[b'format'] = DisplayFormats.FlameGraph
    else:
        printusage()
        return 0

    # process options
    try:
        opts, args = pycompat.getoptb(
            sys.argv[optstart:],
            b"hl:f:o:p:",
            [b"help", b"limit=", b"file=", b"output-file=", b"script-path="],
        )
    except getopt.error as msg:
        print(msg)
        printusage()
        return 2

    displayargs[b'limit'] = 0.05
    path = None
    for o, value in opts:
        if o in ("-l", "--limit"):
            displayargs[b'limit'] = float(value)
        elif o in ("-f", "--file"):
            path = value
        elif o in ("-o", "--output-file"):
            displayargs[b'outputfile'] = value
        elif o in ("-p", "--script-path"):
            displayargs[b'scriptpath'] = value
        elif o in ("-h", "help"):
            printusage()
            return 0
        else:
            assert False, b"unhandled option %s" % o

    if not path:
        print('must specify --file to load')
        return 1

    load_data(path=path)

    display(**pycompat.strkwargs(displayargs))

    return 0


if __name__ == "__main__":
    sys.exit(main())