Mercurial > hg
view contrib/perf-utils/perf-revlog-write-plot.py @ 42423:0ae593e791fb
profiling: show actual time spent in hotpath display
To get, for instance:
...
\ 6.6% 4.08s lock.py: __exit__ line 1566: ...
| 6.5% 4.01s exchange.py: close line 1191: ...
| 6.5% 4.01s transaction.py: _active line 1443: ...
| 6.5% 4.01s transaction.py: close line 47: ...
| 6.2% 3.84s scmutil.py: wrapped line 529: ...
| 6.2% 3.81s localrepo.py: wrapper line 2114: ...
| 6.2% 3.81s localrepo.py: updatecaches line 177: ...
...
instead of:
...
\ 6.6% lock.py: __exit__ line 1566: ...
| 6.5% exchange.py: close line 1191: ...
| 6.5% transaction.py: _active line 1443: ...
| 6.5% transaction.py: close line 47: ...
| 6.2% scmutil.py: wrapped line 529: ...
| 6.2% localrepo.py: wrapper line 2114: ...
| 6.2% localrepo.py: updatecaches line 177: ...
...
I find that if it's not displayed, I frequently end up estimating the
numbers by hand.
Differential Revision: https://phab.mercurial-scm.org/D6477
author | Valentin Gatien-Baron <vgatien-baron@janestreet.com> |
---|---|
date | Wed, 05 Jun 2019 12:51:21 -0400 |
parents | c3e5ce3a9483 |
children | 2372284d9457 |
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#!/usr/bin/env python # # Copyright 2018 Paul Morelle <Paul.Morelle@octobus.net> # # This software may be used and distributed according to the terms of the # GNU General Public License version 2 or any later version. # # This script use the output of `hg perfrevlogwrite -T json --details` to draw # various plot related to write performance in a revlog # # usage: perf-revlog-write-plot.py details.json from __future__ import absolute_import, print_function import json import re import numpy as np import scipy.signal from matplotlib import ( pyplot as plt, ticker as mticker, ) def plot(data, title=None): items = {} re_title = re.compile(r'^revisions #\d+ of \d+, rev (\d+)$') for item in data: m = re_title.match(item['title']) if m is None: continue rev = int(m.group(1)) items[rev] = item min_rev = min(items.keys()) max_rev = max(items.keys()) ary = np.empty((2, max_rev - min_rev + 1)) for rev, item in items.items(): ary[0][rev - min_rev] = rev ary[1][rev - min_rev] = item['wall'] fig = plt.figure() comb_plt = fig.add_subplot(211) other_plt = fig.add_subplot(212) comb_plt.plot(ary[0], np.cumsum(ary[1]), color='red', linewidth=1, label='comb') plots = [] p = other_plt.plot(ary[0], ary[1], color='red', linewidth=1, label='wall') plots.append(p) colors = { 10: ('green', 'xkcd:grass green'), 100: ('blue', 'xkcd:bright blue'), 1000: ('purple', 'xkcd:dark pink'), } for n, color in colors.items(): avg_n = np.convolve(ary[1], np.full(n, 1. / n), 'valid') p = other_plt.plot(ary[0][n - 1:], avg_n, color=color[0], linewidth=1, label='avg time last %d' % n) plots.append(p) med_n = scipy.signal.medfilt(ary[1], n + 1) p = other_plt.plot(ary[0], med_n, color=color[1], linewidth=1, label='median time last %d' % n) plots.append(p) formatter = mticker.ScalarFormatter() formatter.set_scientific(False) formatter.set_useOffset(False) comb_plt.grid() comb_plt.xaxis.set_major_formatter(formatter) comb_plt.legend() other_plt.grid() other_plt.xaxis.set_major_formatter(formatter) leg = other_plt.legend() leg2plot = {} for legline, plot in zip(leg.get_lines(), plots): legline.set_picker(5) leg2plot[legline] = plot def onpick(event): legline = event.artist plot = leg2plot[legline] visible = not plot[0].get_visible() for l in plot: l.set_visible(visible) if visible: legline.set_alpha(1.0) else: legline.set_alpha(0.2) fig.canvas.draw() if title is not None: fig.canvas.set_window_title(title) fig.canvas.mpl_connect('pick_event', onpick) plt.show() if __name__ == '__main__': import sys if len(sys.argv) > 1: print('reading from %r' % sys.argv[1]) with open(sys.argv[1], 'r') as fp: plot(json.load(fp), title=sys.argv[1]) else: print('reading from stdin') plot(json.load(sys.stdin))