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copies-rust: tokenize all paths into integer...
copies-rust: tokenize all paths into integer Copy information for each changesets tend to affect a small new number of path. However, each of these path might be handled a large number of time. Handling HgPathBuf (aka `Vec<u8>`) is expensive. Handling integer is cheap. With this patch we: - turn any input path into an integer "token" early, - do all the internal logic using such "token", - turn "token" back into path right before returning a result. This gives use a quite significant performance boost in our slower cases. Repo Case Source-Rev Dest-Rev # of revisions old time new time Difference Factor time per rev --------------------------------------------------------------------------------------------------------------------------------------------------------------- pypy x000_revs_x000_added_x_copies 8a3b5bfd266e 2c68e87c3efe : 6780 revs, 0.092828 s, 0.081225 s, -0.011603 s, × 0.8750, 11 µs/rev pypy x0000_revs_x_added_0_copies d1defd0dc478 c9cb1334cc78 : 43645 revs, 0.711975 s, 0.586011 s, -0.125964 s, × 0.8231, 13 µs/rev pypy x0000_revs_xx000_added_x000_copies 08ea3258278e d9fa043f30c0 : 11316 revs, 0.124505 s, 0.114173 s, -0.010332 s, × 0.9170, 10 µs/rev netbeans x000_revs_x000_added_x000_copies ff453e9fee32 411350406ec2 : 5750 revs, 0.072072 s, 0.061004 s, -0.011068 s, × 0.8464, 10 µs/rev netbeans x0000_revs_xx000_added_x000_copies 588c2d1ced70 1aad62e59ddd : 66949 revs, 0.682732 s, 0.535874 s, -0.146858 s, × 0.7849, 8 µs/rev mozilla-central x00000_revs_x0000_added_x0000_copies 6832ae71433c 4c222a1d9a00 : 153721 revs, 1.935918 s, 1.781383 s, -0.154535 s, × 0.9202, 11 µs/rev mozilla-central x00000_revs_x00000_added_x000_copies 76caed42cf7c 1daa622bbe42 : 204976 revs, 2.827320 s, 2.603867 s, -0.223453 s, × 0.9210, 12 µs/rev mozilla-try x0000_revs_xx000_added_x000_copies 89294cd501d9 7ccb2fc7ccb5 : 97052 revs, 3.243010 s, 1.529120 s, -1.713890 s, × 0.4715, 15 µs/rev mozilla-try x00000_revs_x_added_0_copies 6a320851d377 1ebb79acd503 : 363753 revs, 5.693818 s, 4.842699 s, -0.851119 s, × 0.8505, 13 µs/rev mozilla-try x00000_revs_x_added_x_copies 5173c4b6f97c 95d83ee7242d : 362229 revs, 5.677655 s, 4.761732 s, -0.915923 s, × 0.8387, 13 µs/rev mozilla-try x00000_revs_x000_added_x_copies 9126823d0e9c ca82787bb23c : 359344 revs, 5.563370 s, 4.733912 s, -0.829458 s, × 0.8509, 13 µs/rev mozilla-try x00000_revs_x0000_added_x0000_copies 8d3fafa80d4b eb884023b810 : 192665 revs, 2.864099 s, 2.593410 s, -0.270689 s, × 0.9055, 13 µs/rev mozilla-try x00000_revs_x00000_added_x0000_copies 1b661134e2ca 1ae03d022d6d : 228985 revs, 113.297287 s, 41.041198 s, -72.256089 s, × 0.3622, 179 µs/rev mozilla-try x00000_revs_x00000_added_x000_copies 9b2a99adc05e 8e29777b48e6 : 382065 revs, 59.498652 s, 27.915689 s, -31.582963 s, × 0.4692, 73 µs/rev Full timing comparison between this revision and the previous one: 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, 0.000042 s, 0.000042 s, +0.000000 s, × 1.0000, 42 µs/rev mercurial x_revs_x_added_x_copies 2b1c78674230 0c1d10351869 : 6 revs, 0.000104 s, 0.000110 s, +0.000006 s, × 1.0577, 18 µs/rev mercurial x000_revs_x000_added_x_copies 81f8ff2a9bf2 dd3267698d84 : 1032 revs, 0.004913 s, 0.004918 s, +0.000005 s, × 1.0010, 4 µs/rev pypy x_revs_x_added_0_copies aed021ee8ae8 099ed31b181b : 9 revs, 0.000191 s, 0.000195 s, +0.000004 s, × 1.0209, 21 µs/rev pypy x_revs_x000_added_0_copies 4aa4e1f8e19a 359343b9ac0e : 1 revs, 0.000050 s, 0.000049 s, -0.000001 s, × 0.9800, 49 µs/rev pypy x_revs_x_added_x_copies ac52eb7bbbb0 72e022663155 : 7 revs, 0.000112 s, 0.000112 s, +0.000000 s, × 1.0000, 16 µs/rev pypy x_revs_x00_added_x_copies c3b14617fbd7 ace7255d9a26 : 1 revs, 0.000288 s, 0.000324 s, +0.000036 s, × 1.1250, 324 µs/rev pypy x_revs_x000_added_x000_copies df6f7a526b60 a83dc6a2d56f : 6 revs, 0.010411 s, 0.010611 s, +0.000200 s, × 1.0192, 1768 µs/rev pypy x000_revs_xx00_added_0_copies 89a76aede314 2f22446ff07e : 4785 revs, 0.052852 s, 0.050835 s, -0.002017 s, × 0.9618, 10 µs/rev pypy x000_revs_x000_added_x_copies 8a3b5bfd266e 2c68e87c3efe : 6780 revs, 0.092828 s, 0.081225 s, -0.011603 s, × 0.8750, 11 µs/rev pypy x000_revs_x000_added_x000_copies 89a76aede314 7b3dda341c84 : 5441 revs, 0.063269 s, 0.061291 s, -0.001978 s, × 0.9687, 11 µs/rev pypy x0000_revs_x_added_0_copies d1defd0dc478 c9cb1334cc78 : 43645 revs, 0.711975 s, 0.586011 s, -0.125964 s, × 0.8231, 13 µs/rev pypy x0000_revs_xx000_added_0_copies bf2c629d0071 4ffed77c095c : 2 revs, 0.012771 s, 0.012824 s, +0.000053 s, × 1.0042, 6412 µs/rev pypy x0000_revs_xx000_added_x000_copies 08ea3258278e d9fa043f30c0 : 11316 revs, 0.124505 s, 0.114173 s, -0.010332 s, × 0.9170, 10 µs/rev netbeans x_revs_x_added_0_copies fb0955ffcbcd a01e9239f9e7 : 2 revs, 0.000082 s, 0.000085 s, +0.000003 s, × 1.0366, 42 µs/rev netbeans x_revs_x000_added_0_copies 6f360122949f 20eb231cc7d0 : 2 revs, 0.000111 s, 0.000108 s, -0.000003 s, × 0.9730, 54 µs/rev netbeans x_revs_x_added_x_copies 1ada3faf6fb6 5a39d12eecf4 : 3 revs, 0.000171 s, 0.000175 s, +0.000004 s, × 1.0234, 58 µs/rev netbeans x_revs_x00_added_x_copies 35be93ba1e2c 9eec5e90c05f : 9 revs, 0.000708 s, 0.000719 s, +0.000011 s, × 1.0155, 79 µs/rev netbeans x000_revs_xx00_added_0_copies eac3045b4fdd 51d4ae7f1290 : 1421 revs, 0.010608 s, 0.010175 s, -0.000433 s, × 0.9592, 7 µs/rev netbeans x000_revs_x000_added_x_copies e2063d266acd 6081d72689dc : 1533 revs, 0.015635 s, 0.015569 s, -0.000066 s, × 0.9958, 10 µs/rev netbeans x000_revs_x000_added_x000_copies ff453e9fee32 411350406ec2 : 5750 revs, 0.072072 s, 0.061004 s, -0.011068 s, × 0.8464, 10 µs/rev netbeans x0000_revs_xx000_added_x000_copies 588c2d1ced70 1aad62e59ddd : 66949 revs, 0.682732 s, 0.535874 s, -0.146858 s, × 0.7849, 8 µs/rev mozilla-central x_revs_x_added_0_copies 3697f962bb7b 7015fcdd43a2 : 2 revs, 0.000090 s, 0.000090 s, +0.000000 s, × 1.0000, 45 µs/rev mozilla-central x_revs_x000_added_0_copies dd390860c6c9 40d0c5bed75d : 8 revs, 0.000210 s, 0.000281 s, +0.000071 s, × 1.3381, 35 µs/rev mozilla-central x_revs_x_added_x_copies 8d198483ae3b 14207ffc2b2f : 9 revs, 0.000182 s, 0.000187 s, +0.000005 s, × 1.0275, 20 µs/rev mozilla-central x_revs_x00_added_x_copies 98cbc58cc6bc 446a150332c3 : 7 revs, 0.000594 s, 0.000660 s, +0.000066 s, × 1.1111, 94 µs/rev mozilla-central x_revs_x000_added_x000_copies 3c684b4b8f68 0a5e72d1b479 : 3 revs, 0.003102 s, 0.003385 s, +0.000283 s, × 1.0912, 1128 µs/rev mozilla-central x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 6 revs, 0.060234 s, 0.069812 s, +0.009578 s, × 1.1590, 11635 µs/rev mozilla-central x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 1593 revs, 0.006300 s, 0.006503 s, +0.000203 s, × 1.0322, 4 µs/rev mozilla-central x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 41 revs, 0.004817 s, 0.004988 s, +0.000171 s, × 1.0355, 121 µs/rev mozilla-central x000_revs_x000_added_x000_copies 7c97034feb78 4407bd0c6330 : 7839 revs, 0.065451 s, 0.063963 s, -0.001488 s, × 0.9773, 8 µs/rev mozilla-central x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 615 revs, 0.026282 s, 0.026225 s, -0.000057 s, × 0.9978, 42 µs/rev mozilla-central x0000_revs_xx000_added_x000_copies f78c615a656c 96a38b690156 : 30263 revs, 0.206873 s, 0.201377 s, -0.005496 s, × 0.9734, 6 µs/rev mozilla-central x00000_revs_x0000_added_x0000_copies 6832ae71433c 4c222a1d9a00 : 153721 revs, 1.935918 s, 1.781383 s, -0.154535 s, × 0.9202, 11 µs/rev mozilla-central x00000_revs_x00000_added_x000_copies 76caed42cf7c 1daa622bbe42 : 204976 revs, 2.827320 s, 2.603867 s, -0.223453 s, × 0.9210, 12 µs/rev mozilla-try x_revs_x_added_0_copies aaf6dde0deb8 9790f499805a : 2 revs, 0.000842 s, 0.000845 s, +0.000003 s, × 1.0036, 422 µs/rev mozilla-try x_revs_x000_added_0_copies d8d0222927b4 5bb8ce8c7450 : 2 revs, 0.000870 s, 0.000862 s, -0.000008 s, × 0.9908, 431 µs/rev mozilla-try x_revs_x_added_x_copies 092fcca11bdb 936255a0384a : 4 revs, 0.000165 s, 0.000161 s, -0.000004 s, × 0.9758, 40 µs/rev mozilla-try x_revs_x00_added_x_copies b53d2fadbdb5 017afae788ec : 2 revs, 0.001145 s, 0.001163 s, +0.000018 s, × 1.0157, 581 µs/rev mozilla-try x_revs_x000_added_x000_copies 20408ad61ce5 6f0ee96e21ad : 1 revs, 0.026500 s, 0.032414 s, +0.005914 s, × 1.2232, 32414 µs/rev mozilla-try x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 6 revs, 0.059407 s, 0.070149 s, +0.010742 s, × 1.1808, 11691 µs/rev mozilla-try x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 1593 revs, 0.006325 s, 0.006526 s, +0.000201 s, × 1.0318, 4 µs/rev mozilla-try x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 41 revs, 0.005171 s, 0.005187 s, +0.000016 s, × 1.0031, 126 µs/rev mozilla-try x000_revs_x000_added_x000_copies 1346fd0130e4 4c65cbdabc1f : 6657 revs, 0.066837 s, 0.065047 s, -0.001790 s, × 0.9732, 9 µs/rev mozilla-try x0000_revs_x_added_0_copies 63519bfd42ee a36a2a865d92 : 40314 revs, 0.314252 s, 0.301129 s, -0.013123 s, × 0.9582, 7 µs/rev mozilla-try x0000_revs_x_added_x_copies 9fe69ff0762d bcabf2a78927 : 38690 revs, 0.304160 s, 0.280683 s, -0.023477 s, × 0.9228, 7 µs/rev mozilla-try x0000_revs_xx000_added_x_copies 156f6e2674f2 4d0f2c178e66 : 8598 revs, 0.089223 s, 0.084897 s, -0.004326 s, × 0.9515, 9 µs/rev mozilla-try x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 615 revs, 0.026711 s, 0.026620 s, -0.000091 s, × 0.9966, 43 µs/rev mozilla-try x0000_revs_xx000_added_x000_copies 89294cd501d9 7ccb2fc7ccb5 : 97052 revs, 3.243010 s, 1.529120 s, -1.713890 s, × 0.4715, 15 µs/rev mozilla-try x0000_revs_x0000_added_x0000_copies e928c65095ed e951f4ad123a : 52031 revs, 0.756500 s, 0.738709 s, -0.017791 s, × 0.9765, 14 µs/rev mozilla-try x00000_revs_x_added_0_copies 6a320851d377 1ebb79acd503 : 363753 revs, 5.693818 s, 4.842699 s, -0.851119 s, × 0.8505, 13 µs/rev mozilla-try x00000_revs_x00000_added_0_copies dc8a3ca7010e d16fde900c9c : 34414 revs, 0.590904 s, 0.596946 s, +0.006042 s, × 1.0102, 17 µs/rev mozilla-try x00000_revs_x_added_x_copies 5173c4b6f97c 95d83ee7242d : 362229 revs, 5.677655 s, 4.761732 s, -0.915923 s, × 0.8387, 13 µs/rev mozilla-try x00000_revs_x000_added_x_copies 9126823d0e9c ca82787bb23c : 359344 revs, 5.563370 s, 4.733912 s, -0.829458 s, × 0.8509, 13 µs/rev mozilla-try x00000_revs_x0000_added_x0000_copies 8d3fafa80d4b eb884023b810 : 192665 revs, 2.864099 s, 2.593410 s, -0.270689 s, × 0.9055, 13 µs/rev mozilla-try x00000_revs_x00000_added_x0000_copies 1b661134e2ca 1ae03d022d6d : 228985 revs, 113.297287 s, 41.041198 s, -72.256089 s, × 0.3622, 179 µs/rev mozilla-try x00000_revs_x00000_added_x000_copies 9b2a99adc05e 8e29777b48e6 : 382065 revs, 59.498652 s, 27.915689 s, -31.582963 s, × 0.4692, 73 µs/rev Full timing comparison between this revision and the filelog copy tracing. Repo Case Source-Rev Dest-Rev # of revisions filelog sidedata Difference Factor time per rev --------------------------------------------------------------------------------------------------------------------------------------------------------------- mercurial x_revs_x_added_0_copies ad6b123de1c7 39cfcef4f463 : 1 revs, 0.000903 s, 0.000042 s, -0.000861 s, × 0.0465, 41 µs/rev mercurial x_revs_x_added_x_copies 2b1c78674230 0c1d10351869 : 6 revs, 0.001861 s, 0.000110 s, -0.001751 s, × 0.0591, 18 µs/rev mercurial x000_revs_x000_added_x_copies 81f8ff2a9bf2 dd3267698d84 : 1032 revs, 0.018577 s, 0.004918 s, -0.013659 s, × 0.2647, 4 µs/rev pypy x_revs_x_added_0_copies aed021ee8ae8 099ed31b181b : 9 revs, 0.001519 s, 0.000195 s, -0.001324 s, × 0.1283, 21 µs/rev pypy x_revs_x000_added_0_copies 4aa4e1f8e19a 359343b9ac0e : 1 revs, 0.213855 s, 0.000049 s, -0.350d73 s, × 0.0002, 48 µs/rev pypy x_revs_x_added_x_copies ac52eb7bbbb0 72e022663155 : 7 revs, 0.017022 s, 0.000112 s, -0.016910 s, × 0.0065, 15 µs/rev pypy x_revs_x00_added_x_copies c3b14617fbd7 ace7255d9a26 : 1 revs, 0.019398 s, 0.000324 s, -0.019074 s, × 0.0167, 323 µs/rev pypy x_revs_x000_added_x000_copies df6f7a526b60 a83dc6a2d56f : 6 revs, 0.769467 s, 0.010611 s, -0.758856 s, × 0.0137, 1768 µs/rev pypy x000_revs_xx00_added_0_copies 89a76aede314 2f22446ff07e : 4785 revs, 1.221952 s, 0.050835 s, -1.171117 s, × 0.0416, 10 µs/rev pypy x000_revs_x000_added_x_copies 8a3b5bfd266e 2c68e87c3efe : 6780 revs, 1.304007 s, 0.081225 s, -1.222782 s, × 0.0622, 11 µs/rev pypy x000_revs_x000_added_x000_copies 89a76aede314 7b3dda341c84 : 5441 revs, 1.686610 s, 0.061291 s, -1.625319 s, × 0.0363, 11 µs/rev pypy x0000_revs_x_added_0_copies d1defd0dc478 c9cb1334cc78 : 43645 revs, 0.001107 s, 0.586011 s, +0.584904 s, × 529.36, 13 µs/rev pypy x0000_revs_xx000_added_0_copies bf2c629d0071 4ffed77c095c : 2 revs, 1.100760 s, 0.012824 s, -1.087936 s, × 0.0116, 6408 µs/rev pypy x0000_revs_xx000_added_x000_copies 08ea3258278e d9fa043f30c0 : 11316 revs, 1.350547 s, 0.114173 s, -1.236374 s, × 0.0845, 10 µs/rev netbeans x_revs_x_added_0_copies fb0955ffcbcd a01e9239f9e7 : 2 revs, 0.027864 s, 0.000085 s, -0.027779 s, × 0.0030, 42 µs/rev netbeans x_revs_x000_added_0_copies 6f360122949f 20eb231cc7d0 : 2 revs, 0.132479 s, 0.000108 s, -0.132371 s, × 0.0008, 53 µs/rev netbeans x_revs_x_added_x_copies 1ada3faf6fb6 5a39d12eecf4 : 3 revs, 0.025405 s, 0.000175 s, -0.025230 s, × 0.0068, 58 µs/rev netbeans x_revs_x00_added_x_copies 35be93ba1e2c 9eec5e90c05f : 9 revs, 0.053244 s, 0.000719 s, -0.052525 s, × 0.0135, 79 µs/rev netbeans x000_revs_xx00_added_0_copies eac3045b4fdd 51d4ae7f1290 : 1421 revs, 0.038017 s, 0.010175 s, -0.027842 s, × 0.2676, 7 µs/rev netbeans x000_revs_x000_added_x_copies e2063d266acd 6081d72689dc : 1533 revs, 0.198308 s, 0.015569 s, -0.182739 s, × 0.0785, 10 µs/rev netbeans x000_revs_x000_added_x000_copies ff453e9fee32 411350406ec2 : 5750 revs, 0.949749 s, 0.061004 s, -0.888745 s, × 0.0642, 10 µs/rev netbeans x0000_revs_xx000_added_x000_copies 588c2d1ced70 1aad62e59ddd : 66949 revs, 3.932262 s, 0.535874 s, -3.396388 s, × 0.1362, 8 µs/rev mozilla-central x_revs_x_added_0_copies 3697f962bb7b 7015fcdd43a2 : 2 revs, 0.024490 s, 0.000090 s, -0.024400 s, × 0.0036, 44 µs/rev mozilla-central x_revs_x000_added_0_copies dd390860c6c9 40d0c5bed75d : 8 revs, 0.143885 s, 0.000281 s, -0.143604 s, × 0.0019, 35 µs/rev mozilla-central x_revs_x_added_x_copies 8d198483ae3b 14207ffc2b2f : 9 revs, 0.025471 s, 0.000187 s, -0.025284 s, × 0.0073, 20 µs/rev mozilla-central x_revs_x00_added_x_copies 98cbc58cc6bc 446a150332c3 : 7 revs, 0.086013 s, 0.000660 s, -0.085353 s, × 0.0076, 94 µs/rev mozilla-central x_revs_x000_added_x000_copies 3c684b4b8f68 0a5e72d1b479 : 3 revs, 0.200726 s, 0.003385 s, -0.197341 s, × 0.0168, 1127 µs/rev mozilla-central x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 6 revs, 2.224171 s, 0.069812 s, -2.154359 s, × 0.0313, 11633 µs/rev mozilla-central x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 1593 revs, 0.090780 s, 0.006503 s, -0.084277 s, × 0.0716, 4 µs/rev mozilla-central x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 41 revs, 0.764805 s, 0.004988 s, -0.759817 s, × 0.0065, 121 µs/rev mozilla-central x000_revs_x000_added_x000_copies 7c97034feb78 4407bd0c6330 : 7839 revs, 1.161405 s, 0.063963 s, -1.097442 s, × 0.0550, 8 µs/rev mozilla-central x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 615 revs, 6.816186 s, 0.026225 s, -6.789961 s, × 0.0038, 42 µs/rev mozilla-central x0000_revs_xx000_added_x000_copies f78c615a656c 96a38b690156 : 30263 revs, 3.374819 s, 0.201377 s, -3.173442 s, × 0.0596, 6 µs/rev mozilla-central x00000_revs_x0000_added_x0000_copies 6832ae71433c 4c222a1d9a00 : 153721 revs, 16.285469 s, 1.781383 s, -14.504086 s, × 0.1093, 11 µs/rev mozilla-central x00000_revs_x00000_added_x000_copies 76caed42cf7c 1daa622bbe42 : 204976 revs, 21.207733 s, 2.603867 s, -18.603866 s, × 0.1227, 12 µs/rev mozilla-try x_revs_x_added_0_copies aaf6dde0deb8 9790f499805a : 2 revs, 0.080843 s, 0.000845 s, -0.079998 s, × 0.0104, 422 µs/rev mozilla-try x_revs_x000_added_0_copies d8d0222927b4 5bb8ce8c7450 : 2 revs, 0.511068 s, 0.000862 s, -0.510206 s, × 0.0016, 430 µs/rev mozilla-try x_revs_x_added_x_copies 092fcca11bdb 936255a0384a : 4 revs, 0.021573 s, 0.000161 s, -0.021412 s, × 0.0074, 40 µs/rev mozilla-try x_revs_x00_added_x_copies b53d2fadbdb5 017afae788ec : 2 revs, 0.227726 s, 0.001163 s, -0.226563 s, × 0.0051, 581 µs/rev mozilla-try x_revs_x000_added_x000_copies 20408ad61ce5 6f0ee96e21ad : 1 revs, 1.120448 s, 0.032414 s, -1.088034 s, × 0.0289, 32381 µs/rev mozilla-try x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 6 revs, 2.241713 s, 0.070149 s, -2.171564 s, × 0.0312, 11689 µs/rev mozilla-try x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 1593 revs, 0.090633 s, 0.006526 s, -0.084107 s, × 0.0720, 4 µs/rev mozilla-try x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 41 revs, 0.770403 s, 0.005187 s, -0.765216 s, × 0.0067, 126 µs/rev mozilla-try x000_revs_x000_added_x000_copies 1346fd0130e4 4c65cbdabc1f : 6657 revs, 1.184557 s, 0.065047 s, -1.119510 s, × 0.0549, 9 µs/rev mozilla-try x0000_revs_x_added_0_copies 63519bfd42ee a36a2a865d92 : 40314 revs, 0.085790 s, 0.301129 s, +0.215339 s, × 3.5100, 7 µs/rev mozilla-try x0000_revs_x_added_x_copies 9fe69ff0762d bcabf2a78927 : 38690 revs, 0.080616 s, 0.280683 s, +0.200067 s, × 3.4817, 7 µs/rev mozilla-try x0000_revs_xx000_added_x_copies 156f6e2674f2 4d0f2c178e66 : 8598 revs, 7.712554 s, 0.084897 s, -7.627657 s, × 0.0110, 9 µs/rev mozilla-try x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 615 revs, 6.937294 s, 0.026620 s, -6.910674 s, × 0.0038, 43 µs/rev mozilla-try x0000_revs_xx000_added_x000_copies 89294cd501d9 7ccb2fc7ccb5 : 97052 revs, 7.712313 s, 1.529120 s, -6.183193 s, × 0.1982, 15 µs/rev mozilla-try x0000_revs_x0000_added_x0000_copies e928c65095ed e951f4ad123a : 52031 revs, 9.966910 s, 0.738709 s, -9.228201 s, × 0.0741, 14 µs/rev mozilla-try x00000_revs_x_added_0_copies 6a320851d377 1ebb79acd503 : 363753 revs, 0.090397 s, 4.842699 s, +4.752302 s, × 53.571, 13 µs/rev mozilla-try x00000_revs_x00000_added_0_copies dc8a3ca7010e d16fde900c9c : 34414 revs, 27.817167 s, 0.596946 s, -27.220221 s, × 0.0214, 17 µs/rev mozilla-try x00000_revs_x_added_x_copies 5173c4b6f97c 95d83ee7242d : 362229 revs, 0.091305 s, 4.761732 s, +4.670427 s, × 52.151, 13 µs/rev mozilla-try x00000_revs_x000_added_x_copies 9126823d0e9c ca82787bb23c : 359344 revs, 0.231183 s, 4.733912 s, +4.502729 s, × 20.476, 13 µs/rev mozilla-try x00000_revs_x0000_added_x0000_copies 8d3fafa80d4b eb884023b810 : 192665 revs, 19.830617 s, 2.593410 s, -17.237207 s, × 0.1307, 13 µs/rev mozilla-try x00000_revs_x00000_added_x0000_copies 1b661134e2ca 1ae03d022d6d : 228985 revs, 21.743873 s, 41.041198 s, +19.297325 s, × 1.8874, 179 µs/rev mozilla-try x00000_revs_x00000_added_x000_copies 9b2a99adc05e 8e29777b48e6 : 382065 revs, 25.935037 s, 27.915689 s, +1.980652 s, × 1.0763, 73 µs/rev Differential Revision: https://phab.mercurial-scm.org/D9493

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dagops.rs
276 lines | 8.9 KiB | application/rls-services+xml | RustLexer
// dagops.rs
//
// Copyright 2019 Georges Racinet <georges.racinet@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.
//! Miscellaneous DAG operations
//!
//! # Terminology
//! - By *relative heads* of a collection of revision numbers (`Revision`), we
//! mean those revisions that have no children among the collection.
//! - Similarly *relative roots* of a collection of `Revision`, we mean those
//! whose parents, if any, don't belong to the collection.
use super::{Graph, GraphError, Revision, NULL_REVISION};
use crate::ancestors::AncestorsIterator;
use std::collections::{BTreeSet, HashSet};
fn remove_parents<S: std::hash::BuildHasher>(
graph: &impl Graph,
rev: Revision,
set: &mut HashSet<Revision, S>,
) -> Result<(), GraphError> {
for parent in graph.parents(rev)?.iter() {
if *parent != NULL_REVISION {
set.remove(parent);
}
}
Ok(())
}
/// Relative heads out of some revisions, passed as an iterator.
///
/// These heads are defined as those revisions that have no children
/// among those emitted by the iterator.
///
/// # Performance notes
/// Internally, this clones the iterator, and builds a `HashSet` out of it.
///
/// This function takes an `Iterator` instead of `impl IntoIterator` to
/// guarantee that cloning the iterator doesn't result in cloning the full
/// construct it comes from.
pub fn heads<'a>(
graph: &impl Graph,
iter_revs: impl Clone + Iterator<Item = &'a Revision>,
) -> Result<HashSet<Revision>, GraphError> {
let mut heads: HashSet<Revision> = iter_revs.clone().cloned().collect();
heads.remove(&NULL_REVISION);
for rev in iter_revs {
if *rev != NULL_REVISION {
remove_parents(graph, *rev, &mut heads)?;
}
}
Ok(heads)
}
/// Retain in `revs` only its relative heads.
///
/// This is an in-place operation, so that control of the incoming
/// set is left to the caller.
/// - a direct Python binding would probably need to build its own `HashSet`
/// from an incoming iterable, even if its sole purpose is to extract the
/// heads.
/// - a Rust caller can decide whether cloning beforehand is appropriate
///
/// # Performance notes
/// Internally, this function will store a full copy of `revs` in a `Vec`.
pub fn retain_heads<S: std::hash::BuildHasher>(
graph: &impl Graph,
revs: &mut HashSet<Revision, S>,
) -> Result<(), GraphError> {
revs.remove(&NULL_REVISION);
// we need to construct an iterable copy of revs to avoid itering while
// mutating
let as_vec: Vec<Revision> = revs.iter().cloned().collect();
for rev in as_vec {
if rev != NULL_REVISION {
remove_parents(graph, rev, revs)?;
}
}
Ok(())
}
/// Roots of `revs`, passed as a `HashSet`
///
/// They are returned in arbitrary order
pub fn roots<G: Graph, S: std::hash::BuildHasher>(
graph: &G,
revs: &HashSet<Revision, S>,
) -> Result<Vec<Revision>, GraphError> {
let mut roots: Vec<Revision> = Vec::new();
for rev in revs {
if graph
.parents(*rev)?
.iter()
.filter(|p| **p != NULL_REVISION)
.all(|p| !revs.contains(p))
{
roots.push(*rev);
}
}
Ok(roots)
}
/// Compute the topological range between two collections of revisions
///
/// This is equivalent to the revset `<roots>::<heads>`.
///
/// Currently, the given `Graph` has to implement `Clone`, which means
/// actually cloning just a reference-counted Python pointer if
/// it's passed over through `rust-cpython`. This is due to the internal
/// use of `AncestorsIterator`
///
/// # Algorithmic details
///
/// This is a two-pass swipe inspired from what `reachableroots2` from
/// `mercurial.cext.parsers` does to obtain the same results.
///
/// - first, we climb up the DAG from `heads` in topological order, keeping
/// them in the vector `heads_ancestors` vector, and adding any element of
/// `roots` we find among them to the resulting range.
/// - Then, we iterate on that recorded vector so that a revision is always
/// emitted after its parents and add all revisions whose parents are already
/// in the range to the results.
///
/// # Performance notes
///
/// The main difference with the C implementation is that
/// the latter uses a flat array with bit flags, instead of complex structures
/// like `HashSet`, making it faster in most scenarios. In theory, it's
/// possible that the present implementation could be more memory efficient
/// for very large repositories with many branches.
pub fn range(
graph: &(impl Graph + Clone),
roots: impl IntoIterator<Item = Revision>,
heads: impl IntoIterator<Item = Revision>,
) -> Result<BTreeSet<Revision>, GraphError> {
let mut range = BTreeSet::new();
let roots: HashSet<Revision> = roots.into_iter().collect();
let min_root: Revision = match roots.iter().cloned().min() {
None => {
return Ok(range);
}
Some(r) => r,
};
// Internally, AncestorsIterator currently maintains a `HashSet`
// of all seen revision, which is also what we record, albeit in an ordered
// way. There's room for improvement on this duplication.
let ait = AncestorsIterator::new(graph.clone(), heads, min_root, true)?;
let mut heads_ancestors: Vec<Revision> = Vec::new();
for revres in ait {
let rev = revres?;
if roots.contains(&rev) {
range.insert(rev);
}
heads_ancestors.push(rev);
}
for rev in heads_ancestors.into_iter().rev() {
for parent in graph.parents(rev)?.iter() {
if *parent != NULL_REVISION && range.contains(parent) {
range.insert(rev);
}
}
}
Ok(range)
}
#[cfg(test)]
mod tests {
use super::*;
use crate::testing::SampleGraph;
/// Apply `retain_heads()` to the given slice and return as a sorted `Vec`
fn retain_heads_sorted(
graph: &impl Graph,
revs: &[Revision],
) -> Result<Vec<Revision>, GraphError> {
let mut revs: HashSet<Revision> = revs.iter().cloned().collect();
retain_heads(graph, &mut revs)?;
let mut as_vec: Vec<Revision> = revs.iter().cloned().collect();
as_vec.sort();
Ok(as_vec)
}
#[test]
fn test_retain_heads() -> Result<(), GraphError> {
assert_eq!(retain_heads_sorted(&SampleGraph, &[4, 5, 6])?, vec![5, 6]);
assert_eq!(
retain_heads_sorted(&SampleGraph, &[4, 1, 6, 12, 0])?,
vec![1, 6, 12]
);
assert_eq!(
retain_heads_sorted(&SampleGraph, &[1, 2, 3, 4, 5, 6, 7, 8, 9])?,
vec![3, 5, 8, 9]
);
Ok(())
}
/// Apply `heads()` to the given slice and return as a sorted `Vec`
fn heads_sorted(
graph: &impl Graph,
revs: &[Revision],
) -> Result<Vec<Revision>, GraphError> {
let heads = heads(graph, revs.iter())?;
let mut as_vec: Vec<Revision> = heads.iter().cloned().collect();
as_vec.sort();
Ok(as_vec)
}
#[test]
fn test_heads() -> Result<(), GraphError> {
assert_eq!(heads_sorted(&SampleGraph, &[4, 5, 6])?, vec![5, 6]);
assert_eq!(
heads_sorted(&SampleGraph, &[4, 1, 6, 12, 0])?,
vec![1, 6, 12]
);
assert_eq!(
heads_sorted(&SampleGraph, &[1, 2, 3, 4, 5, 6, 7, 8, 9])?,
vec![3, 5, 8, 9]
);
Ok(())
}
/// Apply `roots()` and sort the result for easier comparison
fn roots_sorted(
graph: &impl Graph,
revs: &[Revision],
) -> Result<Vec<Revision>, GraphError> {
let set: HashSet<_> = revs.iter().cloned().collect();
let mut as_vec = roots(graph, &set)?;
as_vec.sort();
Ok(as_vec)
}
#[test]
fn test_roots() -> Result<(), GraphError> {
assert_eq!(roots_sorted(&SampleGraph, &[4, 5, 6])?, vec![4]);
assert_eq!(
roots_sorted(&SampleGraph, &[4, 1, 6, 12, 0])?,
vec![0, 4, 12]
);
assert_eq!(
roots_sorted(&SampleGraph, &[1, 2, 3, 4, 5, 6, 7, 8, 9])?,
vec![1, 8]
);
Ok(())
}
/// Apply `range()` and convert the result into a Vec for easier comparison
fn range_vec(
graph: impl Graph + Clone,
roots: &[Revision],
heads: &[Revision],
) -> Result<Vec<Revision>, GraphError> {
range(&graph, roots.iter().cloned(), heads.iter().cloned())
.map(|bs| bs.into_iter().collect())
}
#[test]
fn test_range() -> Result<(), GraphError> {
assert_eq!(range_vec(SampleGraph, &[0], &[4])?, vec![0, 1, 2, 4]);
assert_eq!(range_vec(SampleGraph, &[0], &[8])?, vec![]);
assert_eq!(
range_vec(SampleGraph, &[5, 6], &[10, 11, 13])?,
vec![5, 10]
);
assert_eq!(
range_vec(SampleGraph, &[5, 6], &[10, 12])?,
vec![5, 6, 9, 10, 12]
);
Ok(())
}
}