Mercurial > hg
view mercurial/statprof.py @ 45972:8b99c473aae2
copies-rust: move is_ancestor caching within the rust code
Now that the OrdMap merging is fast, smaller things start to matters.
We move the caching of `is_ancestor` call within the Rust code. This avoid
round-trip to Python and help us to shave more time on our slower case:
Repo Cases Source-Rev Dest-Rev Old-Time New-Time Difference Factor
------------------------------------------------------------------------------------------------------------------------------------
pypy x0000_revs_x_added_0_copies d1defd0dc478 c9cb1334cc78 : 2.780174 s, 2.137894 s, -0.642280 s, × 0.7690
mozilla-try x0000_revs_xx000_added_x000_copies 89294cd501d9 7ccb2fc7ccb5 : 9.843481 s, 8.100385 s, -1.743096 s, × 0.8229
Note: I would happily have used native code for ancestors computation, however
I failed (did not tried hard) to created a rust version that goes as fast as
the current C version.
Below are full tables for:
- this change compared to the previous change
- this change compared to filelog performance
Repo Cases Source-Rev Dest-Rev Old-Time New-Time Difference Factor
------------------------------------------------------------------------------------------------------------------------------------
mercurial x_revs_x_added_0_copies ad6b123de1c7 39cfcef4f463 : 0.000049 s, 0.000047 s, -0.000002 s, × 0.9592
mercurial x_revs_x_added_x_copies 2b1c78674230 0c1d10351869 : 0.000182 s, 0.000181 s, -0.000001 s, × 0.9945
mercurial x000_revs_x000_added_x_copies 81f8ff2a9bf2 dd3267698d84 : 0.005872 s, 0.005852 s, -0.000020 s, × 0.9966
pypy x_revs_x_added_0_copies aed021ee8ae8 099ed31b181b : 0.000229 s, 0.000229 s, +0.000000 s, × 1.0000
pypy x_revs_x000_added_0_copies 4aa4e1f8e19a 359343b9ac0e : 0.000058 s, 0.000058 s, +0.000000 s, × 1.0000
pypy x_revs_x_added_x_copies ac52eb7bbbb0 72e022663155 : 0.000148 s, 0.000146 s, -0.000002 s, × 0.9865
pypy x_revs_x00_added_x_copies c3b14617fbd7 ace7255d9a26 : 0.001205 s, 0.001206 s, +0.000001 s, × 1.0008
pypy x_revs_x000_added_x000_copies df6f7a526b60 a83dc6a2d56f : 0.025662 s, 0.025275 s, -0.000387 s, × 0.9849
pypy x000_revs_xx00_added_0_copies 89a76aede314 2f22446ff07e : 0.080113 s, 0.080303 s, +0.000190 s, × 1.0024
pypy x000_revs_x000_added_x_copies 8a3b5bfd266e 2c68e87c3efe : 0.153030 s, 0.152641 s, -0.000389 s, × 0.9975
pypy x000_revs_x000_added_x000_copies 89a76aede314 7b3dda341c84 : 0.098774 s, 0.099107 s, +0.000333 s, × 1.0034
pypy x0000_revs_x_added_0_copies d1defd0dc478 c9cb1334cc78 : 2.780174 s, 2.137894 s, -0.642280 s, × 0.7690
pypy x0000_revs_xx000_added_0_copies bf2c629d0071 4ffed77c095c : 0.022218 s, 0.022202 s, -0.000016 s, × 0.9993
pypy x0000_revs_xx000_added_x000_copies 08ea3258278e d9fa043f30c0 : 0.252125 s, 0.228946 s, -0.023179 s, × 0.9081
netbeans x_revs_x_added_0_copies fb0955ffcbcd a01e9239f9e7 : 0.000186 s, 0.000186 s, +0.000000 s, × 1.0000
netbeans x_revs_x000_added_0_copies 6f360122949f 20eb231cc7d0 : 0.000133 s, 0.000133 s, +0.000000 s, × 1.0000
netbeans x_revs_x_added_x_copies 1ada3faf6fb6 5a39d12eecf4 : 0.000320 s, 0.000320 s, +0.000000 s, × 1.0000
netbeans x_revs_x00_added_x_copies 35be93ba1e2c 9eec5e90c05f : 0.001336 s, 0.001339 s, +0.000003 s, × 1.0022
netbeans x000_revs_xx00_added_0_copies eac3045b4fdd 51d4ae7f1290 : 0.015573 s, 0.015694 s, +0.000121 s, × 1.0078
netbeans x000_revs_x000_added_x_copies e2063d266acd 6081d72689dc : 0.018667 s, 0.018457 s, -0.000210 s, × 0.9888
netbeans x000_revs_x000_added_x000_copies ff453e9fee32 411350406ec2 : 0.112534 s, 0.111691 s, -0.000843 s, × 0.9925
netbeans x0000_revs_xx000_added_x000_copies 588c2d1ced70 1aad62e59ddd : 1.231869 s, 1.166017 s, -0.065852 s, × 0.9465
mozilla-central x_revs_x_added_0_copies 3697f962bb7b 7015fcdd43a2 : 0.000197 s, 0.000197 s, +0.000000 s, × 1.0000
mozilla-central x_revs_x000_added_0_copies dd390860c6c9 40d0c5bed75d : 0.000637 s, 0.000626 s, -0.000011 s, × 0.9827
mozilla-central x_revs_x_added_x_copies 8d198483ae3b 14207ffc2b2f : 0.000303 s, 0.000303 s, +0.000000 s, × 1.0000
mozilla-central x_revs_x00_added_x_copies 98cbc58cc6bc 446a150332c3 : 0.001663 s, 0.001679 s, +0.000016 s, × 1.0096
mozilla-central x_revs_x000_added_x000_copies 3c684b4b8f68 0a5e72d1b479 : 0.007008 s, 0.006947 s, -0.000061 s, × 0.9913
mozilla-central x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 0.127385 s, 0.133070 s, +0.005685 s, × 1.0446
mozilla-central x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 0.008740 s, 0.008705 s, -0.000035 s, × 0.9960
mozilla-central x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 0.005783 s, 0.005913 s, +0.000130 s, × 1.0225
mozilla-central x000_revs_x000_added_x000_copies 7c97034feb78 4407bd0c6330 : 0.102184 s, 0.101373 s, -0.000811 s, × 0.9921
mozilla-central x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 0.046220 s, 0.046526 s, +0.000306 s, × 1.0066
mozilla-central x0000_revs_xx000_added_x000_copies f78c615a656c 96a38b690156 : 0.315271 s, 0.313954 s, -0.001317 s, × 0.9958
mozilla-central x00000_revs_x0000_added_x0000_copies 6832ae71433c 4c222a1d9a00 : 3.478747 s, 3.367395 s, -0.111352 s, × 0.9680
mozilla-central x00000_revs_x00000_added_x000_copies 76caed42cf7c 1daa622bbe42 : 4.766435 s, 4.691820 s, -0.074615 s, × 0.9843
mozilla-try x_revs_x_added_0_copies aaf6dde0deb8 9790f499805a : 0.001214 s, 0.001199 s, -0.000015 s, × 0.9876
mozilla-try x_revs_x000_added_0_copies d8d0222927b4 5bb8ce8c7450 : 0.001221 s, 0.001216 s, -0.000005 s, × 0.9959
mozilla-try x_revs_x_added_x_copies 092fcca11bdb 936255a0384a : 0.000613 s, 0.000613 s, +0.000000 s, × 1.0000
mozilla-try x_revs_x00_added_x_copies b53d2fadbdb5 017afae788ec : 0.001904 s, 0.001906 s, +0.000002 s, × 1.0011
mozilla-try x_revs_x000_added_x000_copies 20408ad61ce5 6f0ee96e21ad : 0.093000 s, 0.092766 s, -0.000234 s, × 0.9975
mozilla-try x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 0.132194 s, 0.136074 s, +0.003880 s, × 1.0294
mozilla-try x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 0.009069 s, 0.009067 s, -0.000002 s, × 0.9998
mozilla-try x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 0.006169 s, 0.006243 s, +0.000074 s, × 1.0120
mozilla-try x000_revs_x000_added_x000_copies 1346fd0130e4 4c65cbdabc1f : 0.115540 s, 0.114463 s, -0.001077 s, × 0.9907
mozilla-try x0000_revs_x_added_0_copies 63519bfd42ee a36a2a865d92 : 0.435381 s, 0.433683 s, -0.001698 s, × 0.9961
mozilla-try x0000_revs_x_added_x_copies 9fe69ff0762d bcabf2a78927 : 0.415461 s, 0.411278 s, -0.004183 s, × 0.9899
mozilla-try x0000_revs_xx000_added_x_copies 156f6e2674f2 4d0f2c178e66 : 0.155946 s, 0.155133 s, -0.000813 s, × 0.9948
mozilla-try x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 0.048521 s, 0.048933 s, +0.000412 s, × 1.0085
mozilla-try x0000_revs_xx000_added_x000_copies 89294cd501d9 7ccb2fc7ccb5 : 9.843481 s, 8.100385 s, -1.743096 s, × 0.8229
mozilla-try x0000_revs_x0000_added_x0000_copies e928c65095ed e951f4ad123a : 1.465128 s, 1.446720 s, -0.018408 s, × 0.9874
mozilla-try x00000_revs_x00000_added_0_copies dc8a3ca7010e d16fde900c9c : 1.374283 s, 1.369537 s, -0.004746 s, × 0.9965
mozilla-try x00000_revs_x0000_added_x0000_copies 8d3fafa80d4b eb884023b810 : 5.255158 s, 5.186079 s, -0.069079 s, × 0.9869
Repo Case Source-Rev Dest-Rev filelog sidedata Difference Factor
--------------------------------------------------------------------------------------------------------------------------------------
mercurial x_revs_x_added_0_copies ad6b123de1c7 39cfcef4f463 : 0.000892 s, 0.000047 s, -0.000845 s, × 0.052691
mercurial x_revs_x_added_x_copies 2b1c78674230 0c1d10351869 : 0.001823 s, 0.000181 s, -0.001642 s, × 0.099287
mercurial x000_revs_x000_added_x_copies 81f8ff2a9bf2 dd3267698d84 : 0.018063 s, 0.005852 s, -0.012211 s, × 0.323977
pypy x_revs_x_added_0_copies aed021ee8ae8 099ed31b181b : 0.001505 s, 0.000229 s, -0.001276 s, × 0.152159
pypy x_revs_x000_added_0_copies 4aa4e1f8e19a 359343b9ac0e : 0.205895 s, 0.000058 s, -0.205837 s, × 0.000282
pypy x_revs_x_added_x_copies ac52eb7bbbb0 72e022663155 : 0.017021 s, 0.000146 s, -0.016875 s, × 0.008578
pypy x_revs_x00_added_x_copies c3b14617fbd7 ace7255d9a26 : 0.019422 s, 0.001206 s, -0.018216 s, × 0.062095
pypy x_revs_x000_added_x000_copies df6f7a526b60 a83dc6a2d56f : 0.767740 s, 0.025275 s, -0.742465 s, × 0.032921
pypy x000_revs_xx00_added_0_copies 89a76aede314 2f22446ff07e : 1.188515 s, 0.080303 s, -1.108212 s, × 0.067566
pypy x000_revs_x000_added_x_copies 8a3b5bfd266e 2c68e87c3efe : 1.251968 s, 0.152641 s, -1.099327 s, × 0.121921
pypy x000_revs_x000_added_x000_copies 89a76aede314 7b3dda341c84 : 1.616799 s, 0.099107 s, -1.517692 s, × 0.061298
pypy x0000_revs_x_added_0_copies d1defd0dc478 c9cb1334cc78 : 0.001057 s, 2.137894 s, +2.136837 s, × 2022.605487
pypy x0000_revs_xx000_added_0_copies bf2c629d0071 4ffed77c095c : 1.069485 s, 0.022202 s, -1.047283 s, × 0.020760
pypy x0000_revs_xx000_added_x000_copies 08ea3258278e d9fa043f30c0 : 1.350162 s, 0.228946 s, -1.121216 s, × 0.169569
netbeans x_revs_x_added_0_copies fb0955ffcbcd a01e9239f9e7 : 0.028008 s, 0.000186 s, -0.027822 s, × 0.006641
netbeans x_revs_x000_added_0_copies 6f360122949f 20eb231cc7d0 : 0.132281 s, 0.000133 s, -0.132148 s, × 0.001005
netbeans x_revs_x_added_x_copies 1ada3faf6fb6 5a39d12eecf4 : 0.025311 s, 0.000320 s, -0.024991 s, × 0.012643
netbeans x_revs_x00_added_x_copies 35be93ba1e2c 9eec5e90c05f : 0.052957 s, 0.001339 s, -0.051618 s, × 0.025285
netbeans x000_revs_xx00_added_0_copies eac3045b4fdd 51d4ae7f1290 : 0.038011 s, 0.015694 s, -0.022317 s, × 0.412880
netbeans x000_revs_x000_added_x_copies e2063d266acd 6081d72689dc : 0.198639 s, 0.018457 s, -0.180182 s, × 0.092917
netbeans x000_revs_x000_added_x000_copies ff453e9fee32 411350406ec2 : 0.955713 s, 0.111691 s, -0.844022 s, × 0.116867
netbeans x0000_revs_xx000_added_x000_copies 588c2d1ced70 1aad62e59ddd : 3.838886 s, 1.166017 s, -2.672869 s, × 0.303738
mozilla-central x_revs_x_added_0_copies 3697f962bb7b 7015fcdd43a2 : 0.024548 s, 0.000197 s, -0.024351 s, × 0.008025
mozilla-central x_revs_x000_added_0_copies dd390860c6c9 40d0c5bed75d : 0.143394 s, 0.000626 s, -0.142768 s, × 0.004366
mozilla-central x_revs_x_added_x_copies 8d198483ae3b 14207ffc2b2f : 0.026046 s, 0.000303 s, -0.025743 s, × 0.011633
mozilla-central x_revs_x00_added_x_copies 98cbc58cc6bc 446a150332c3 : 0.085440 s, 0.001679 s, -0.083761 s, × 0.019651
mozilla-central x_revs_x000_added_x000_copies 3c684b4b8f68 0a5e72d1b479 : 0.195656 s, 0.006947 s, -0.188709 s, × 0.035506
mozilla-central x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 2.190874 s, 0.133070 s, -2.057804 s, × 0.060738
mozilla-central x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 0.090208 s, 0.008705 s, -0.081503 s, × 0.096499
mozilla-central x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 0.747367 s, 0.005913 s, -0.741454 s, × 0.007912
mozilla-central x000_revs_x000_added_x000_copies 7c97034feb78 4407bd0c6330 : 1.152863 s, 0.101373 s, -1.051490 s, × 0.087932
mozilla-central x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 6.598336 s, 0.046526 s, -6.551810 s, × 0.007051
mozilla-central x0000_revs_xx000_added_x000_copies f78c615a656c 96a38b690156 : 3.255015 s, 0.313954 s, -2.941061 s, × 0.096452
mozilla-central x00000_revs_x0000_added_x0000_copies 6832ae71433c 4c222a1d9a00 : 15.668041 s, 3.367395 s, -12.300646 s, × 0.214921
mozilla-central x00000_revs_x00000_added_x000_copies 76caed42cf7c 1daa622bbe42 : 20.439638 s, 4.691820 s, -15.747818 s, × 0.229545
mozilla-try x_revs_x_added_0_copies aaf6dde0deb8 9790f499805a : 0.080923 s, 0.001199 s, -0.079724 s, × 0.014817
mozilla-try x_revs_x000_added_0_copies d8d0222927b4 5bb8ce8c7450 : 0.498456 s, 0.001216 s, -0.497240 s, × 0.002440
mozilla-try x_revs_x_added_x_copies 092fcca11bdb 936255a0384a : 0.020798 s, 0.000613 s, -0.020185 s, × 0.029474
mozilla-try x_revs_x00_added_x_copies b53d2fadbdb5 017afae788ec : 0.226930 s, 0.001906 s, -0.225024 s, × 0.008399
mozilla-try x_revs_x000_added_x000_copies 20408ad61ce5 6f0ee96e21ad : 1.113005 s, 0.092766 s, -1.020239 s, × 0.083347
mozilla-try x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 2.230671 s, 0.136074 s, -2.094597 s, × 0.061001
mozilla-try x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 0.089672 s, 0.009067 s, -0.080605 s, × 0.101113
mozilla-try x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 0.740221 s, 0.006243 s, -0.733978 s, × 0.008434
mozilla-try x000_revs_x000_added_x000_copies 1346fd0130e4 4c65cbdabc1f : 1.185881 s, 0.114463 s, -1.071418 s, × 0.096521
mozilla-try x0000_revs_x_added_0_copies 63519bfd42ee a36a2a865d92 : 0.086072 s, 0.433683 s, +0.347611 s, × 5.038607
mozilla-try x0000_revs_x_added_x_copies 9fe69ff0762d bcabf2a78927 : 0.081321 s, 0.411278 s, +0.329957 s, × 5.057464
mozilla-try x0000_revs_xx000_added_x_copies 156f6e2674f2 4d0f2c178e66 : 7.528370 s, 0.155133 s, -7.373237 s, × 0.020606
mozilla-try x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 6.757368 s, 0.048933 s, -6.708435 s, × 0.007241
mozilla-try x0000_revs_xx000_added_x000_copies 89294cd501d9 7ccb2fc7ccb5 : 7.643752 s, 8.100385 s, +0.456633 s, × 1.059739
mozilla-try x0000_revs_x0000_added_x0000_copies e928c65095ed e951f4ad123a : 9.704242 s, 1.446720 s, -8.257522 s, × 0.149081
mozilla-try x00000_revs_x_added_0_copies 6a320851d377 1ebb79acd503 : 0.092845 s, killed
mozilla-try x00000_revs_x00000_added_0_copies dc8a3ca7010e d16fde900c9c : 26.626870 s, 1.369537 s, -25.257333 s, × 0.051434
mozilla-try x00000_revs_x_added_x_copies 5173c4b6f97c 95d83ee7242d : 0.092953 s, killed
mozilla-try x00000_revs_x000_added_x_copies 9126823d0e9c ca82787bb23c : 0.227131 s, killed
mozilla-try x00000_revs_x0000_added_x0000_copies 8d3fafa80d4b eb884023b810 : 18.884666 s, 5.186079 s, -13.698587 s, × 0.274619
mozilla-try x00000_revs_x00000_added_x0000_copies 1b661134e2ca 1ae03d022d6d : 21.451622 s, killed
mozilla-try x00000_revs_x00000_added_x000_copies 9b2a99adc05e 8e29777b48e6 : 25.152558 s, killed
Differential Revision: https://phab.mercurial-scm.org/D9303
author | Pierre-Yves David <pierre-yves.david@octobus.net> |
---|---|
date | Fri, 02 Oct 2020 15:41:31 +0200 |
parents | 89a2afe31e82 |
children | 068307b638f4 |
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## 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) 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 = b'' function = b'' if len(node.children) > 0: childsite = list(pycompat.itervalues(node.children))[0].site filename = (childsite.filename() + b':').ljust(15) function = childsite.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, ) codepattern = b'%' + (b'%d' % (55 - len(liststring))) + b's %d: %s' codestring = codepattern % ( b'line', 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())