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annotate rust/hg-core/src/dagops.rs @ 45907:06b64fabf91c

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copies: cache the ancestor checking call when tracing copy A good share of the time spent in this function is spent doing ancestors checking. To avoid spending time in duplicated call, we cache the result of calls. In the slower case, this provide a quite significant performance boost. Below are the result for a set of selected pairs (many of them pathological): (And further down is another table that summarize the current state of filelog based vs changeset base copy tracing) The benchmark have been configured to be killed after 6 minutes of runtime, which mean that any detect slower than 2 minutes will be marked as "killed". This drop some useful information about how much slower these case are? but also prevent 99% of the benchmark time to be spent on case that can be labelled "very slow" anyway. Repo Case Source-Rev Dest-Rev Old-Time New-Time Difference Factor ------------------------------------------------------------------------------------------------------------------------------------ mercurial x_revs_x_added_0_copies ad6b123de1c7 39cfcef4f463 : 0.000044 s, 0.000044 s, +0.000000 s, ? 1.0000 mercurial x_revs_x_added_x_copies 2b1c78674230 0c1d10351869 : 0.000138 s, 0.000138 s, +0.000000 s, ? 1.0000 mercurial x000_revs_x000_added_x_copies 81f8ff2a9bf2 dd3267698d84 : 0.005067 s, 0.005052 s, -0.000015 s, ? 0.9970 pypy x_revs_x_added_0_copies aed021ee8ae8 099ed31b181b : 0.000218 s, 0.000219 s, +0.000001 s, ? 1.0046 pypy x_revs_x000_added_0_copies 4aa4e1f8e19a 359343b9ac0e : 0.000053 s, 0.000055 s, +0.000002 s, ? 1.0377 pypy x_revs_x_added_x_copies ac52eb7bbbb0 72e022663155 : 0.000125 s, 0.000128 s, +0.000003 s, ? 1.0240 pypy x_revs_x00_added_x_copies c3b14617fbd7 ace7255d9a26 : 0.001098 s, 0.001089 s, -0.000009 s, ? 0.9918 pypy x_revs_x000_added_x000_copies df6f7a526b60 a83dc6a2d56f : 0.017546 s, 0.017407 s, -0.000139 s, ? 0.9921 pypy x000_revs_xx00_added_0_copies 89a76aede314 2f22446ff07e : 0.096723 s, 0.094175 s, -0.002548 s, ? 0.9737 pypy x000_revs_x000_added_x_copies 8a3b5bfd266e 2c68e87c3efe : 0.271796 s, 0.238009 s, -0.033787 s, ? 0.8757 pypy x000_revs_x000_added_x000_copies 89a76aede314 7b3dda341c84 : 0.128602 s, 0.125876 s, -0.002726 s, ? 0.9788 pypy x0000_revs_x_added_0_copies d1defd0dc478 c9cb1334cc78 : 7.086742 s, 3.581556 s, -3.505186 s, ? 0.5054 pypy x0000_revs_xx000_added_0_copies bf2c629d0071 4ffed77c095c : 0.016634 s, 0.016721 s, +0.000087 s, ? 1.0052 pypy x0000_revs_xx000_added_x000_copies 08ea3258278e d9fa043f30c0 : 0.254225 s, 0.242367 s, -0.011858 s, ? 0.9534 netbeans x_revs_x_added_0_copies fb0955ffcbcd a01e9239f9e7 : 0.000166 s, 0.000165 s, -0.000001 s, ? 0.9940 netbeans x_revs_x000_added_0_copies 6f360122949f 20eb231cc7d0 : 0.000118 s, 0.000114 s, -0.000004 s, ? 0.9661 netbeans x_revs_x_added_x_copies 1ada3faf6fb6 5a39d12eecf4 : 0.000296 s, 0.000296 s, +0.000000 s, ? 1.0000 netbeans x_revs_x00_added_x_copies 35be93ba1e2c 9eec5e90c05f : 0.001137 s, 0.001124 s, -0.000013 s, ? 0.9886 netbeans x000_revs_xx00_added_0_copies eac3045b4fdd 51d4ae7f1290 : 0.014133 s, 0.013060 s, -0.001073 s, ? 0.9241 netbeans x000_revs_x000_added_x_copies e2063d266acd 6081d72689dc : 0.016988 s, 0.017112 s, +0.000124 s, ? 1.0073 netbeans x000_revs_x000_added_x000_copies ff453e9fee32 411350406ec2 : 0.676361 s, 0.660350 s, -0.016011 s, ? 0.9763 netbeans x0000_revs_xx000_added_x000_copies 588c2d1ced70 1aad62e59ddd : 12.515149 s, 10.032499 s, -2.482650 s, ? 0.8016 mozilla-central x_revs_x_added_0_copies 3697f962bb7b 7015fcdd43a2 : 0.000186 s, 0.000189 s, +0.000003 s, ? 1.0161 mozilla-central x_revs_x000_added_0_copies dd390860c6c9 40d0c5bed75d : 0.000459 s, 0.000462 s, +0.000003 s, ? 1.0065 mozilla-central x_revs_x_added_x_copies 8d198483ae3b 14207ffc2b2f : 0.000273 s, 0.000270 s, -0.000003 s, ? 0.9890 mozilla-central x_revs_x00_added_x_copies 98cbc58cc6bc 446a150332c3 : 0.001503 s, 0.001474 s, -0.000029 s, ? 0.9807 mozilla-central x_revs_x000_added_x000_copies 3c684b4b8f68 0a5e72d1b479 : 0.004862 s, 0.004806 s, -0.000056 s, ? 0.9885 mozilla-central x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 0.088291 s, 0.085150 s, -0.003141 s, ? 0.9644 mozilla-central x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 0.007113 s, 0.007064 s, -0.000049 s, ? 0.9931 mozilla-central x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 0.004687 s, 0.004741 s, +0.000054 s, ? 1.0115 mozilla-central x000_revs_x000_added_x000_copies 7c97034feb78 4407bd0c6330 : 0.198710 s, 0.190133 s, -0.008577 s, ? 0.9568 mozilla-central x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 0.036068 s, 0.035651 s, -0.000417 s, ? 0.9884 mozilla-central x0000_revs_xx000_added_x000_copies f78c615a656c 96a38b690156 : 0.465362 s, 0.440694 s, -0.024668 s, ? 0.9470 mozilla-central x00000_revs_x0000_added_x0000_copies 6832ae71433c 4c222a1d9a00 : 24.519684 s, 18.454163 s, -6.065521 s, ? 0.7526 mozilla-central x00000_revs_x00000_added_x000_copies 76caed42cf7c 1daa622bbe42 : 42.711897 s, 31.562719 s, -11.149178 s, ? 0.7390 mozilla-try x_revs_x_added_0_copies aaf6dde0deb8 9790f499805a : 0.001201 s, 0.001189 s, -0.000012 s, ? 0.9900 mozilla-try x_revs_x000_added_0_copies d8d0222927b4 5bb8ce8c7450 : 0.001216 s, 0.001204 s, -0.000012 s, ? 0.9901 mozilla-try x_revs_x_added_x_copies 092fcca11bdb 936255a0384a : 0.000595 s, 0.000586 s, -0.000009 s, ? 0.9849 mozilla-try x_revs_x00_added_x_copies b53d2fadbdb5 017afae788ec : 0.001856 s, 0.001845 s, -0.000011 s, ? 0.9941 mozilla-try x_revs_x000_added_x000_copies 20408ad61ce5 6f0ee96e21ad : 0.064936 s, 0.063822 s, -0.001114 s, ? 0.9828 mozilla-try x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 0.090601 s, 0.088038 s, -0.002563 s, ? 0.9717 mozilla-try x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 0.007510 s, 0.007389 s, -0.000121 s, ? 0.9839 mozilla-try x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 0.004911 s, 0.004868 s, -0.000043 s, ? 0.9912 mozilla-try x000_revs_x000_added_x000_copies 1346fd0130e4 4c65cbdabc1f : 0.233231 s, 0.222450 s, -0.010781 s, ? 0.9538 mozilla-try x0000_revs_x_added_0_copies 63519bfd42ee a36a2a865d92 : 0.419989 s, 0.370675 s, -0.049314 s, ? 0.8826 mozilla-try x0000_revs_x_added_x_copies 9fe69ff0762d bcabf2a78927 : 0.401521 s, 0.358020 s, -0.043501 s, ? 0.8917 mozilla-try x0000_revs_xx000_added_x_copies 156f6e2674f2 4d0f2c178e66 : 0.179555 s, 0.145235 s, -0.034320 s, ? 0.8089 mozilla-try x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 0.038004 s, 0.037606 s, -0.000398 s, ? 0.9895 mozilla-try x0000_revs_xx000_added_x000_copies 89294cd501d9 7ccb2fc7ccb5 : 52.838482 s, 7.382439 s, -45.456043 s, ? 0.1397 mozilla-try x0000_revs_x0000_added_x0000_copies e928c65095ed e951f4ad123a : 8.705874 s, 7.273506 s, -1.432368 s, ? 0.8355 mozilla-try x00000_revs_x00000_added_0_copies dc8a3ca7010e d16fde900c9c : 1.126708 s, 1.074593 s, -0.052115 s, ? 0.9537 mozilla-try x00000_revs_x0000_added_x0000_copies 8d3fafa80d4b eb884023b810 : 83.854020 s, 27.746195 s, -56.107825 s, ? 0.3309 Below is a table comparing the runtime of the current "filelog centric" algorithm, with the "changeset centric" one, we just modified. The changeset centric algorithm is a significant win in many scenario, but they are still various cases where it is quite slower. When many revision has to be considered the cost of retrieving the copy information, creating new dictionaries, merging dictionaries and checking if revision are ancestors of each other can slow things down. The rest of this series, will introduce a rust version of the copy tracing code to deal with most of theses issues. Repo Case Source-Rev Dest-Rev filelog sidedata Difference Factor --------------------------------------------------------------------------------------------------------------------------------------- mercurial x_revs_x_added_0_copies ad6b123de1c7 39cfcef4f463 : 0.000914 s, 0.000044 s, - 0.000870 s, ? 0.048140 mercurial x_revs_x_added_x_copies 2b1c78674230 0c1d10351869 : 0.001812 s, 0.000138 s, - 0.001674 s, ? 0.076159 mercurial x000_revs_x000_added_x_copies 81f8ff2a9bf2 dd3267698d84 : 0.017954 s, 0.005052 s, - 0.012902 s, ? 0.281386 pypy x_revs_x_added_0_copies aed021ee8ae8 099ed31b181b : 0.001509 s, 0.000219 s, - 0.001290 s, ? 0.145129 pypy x_revs_x000_added_0_copies 4aa4e1f8e19a 359343b9ac0e : 0.206881 s, 0.000055 s, - 0.206826 s, ? 0.000266 pypy x_revs_x_added_x_copies ac52eb7bbbb0 72e022663155 : 0.016951 s, 0.000128 s, - 0.016823 s, ? 0.007551 pypy x_revs_x00_added_x_copies c3b14617fbd7 ace7255d9a26 : 0.019096 s, 0.001089 s, - 0.018007 s, ? 0.057028 pypy x_revs_x000_added_x000_copies df6f7a526b60 a83dc6a2d56f : 0.762506 s, 0.017407 s, - 0.745099 s, ? 0.022829 pypy x000_revs_xx00_added_0_copies 89a76aede314 2f22446ff07e : 1.179211 s, 0.094175 s, - 1.085036 s, ? 0.079863 pypy x000_revs_x000_added_x_copies 8a3b5bfd266e 2c68e87c3efe : 1.249058 s, 0.238009 s, - 1.011049 s, ? 0.190551 pypy x000_revs_x000_added_x000_copies 89a76aede314 7b3dda341c84 : 1.614107 s, 0.125876 s, - 1.488231 s, ? 0.077985 pypy x0000_revs_x_added_0_copies d1defd0dc478 c9cb1334cc78 : 0.001064 s, 3.581556 s, + 3.580492 s, ? 3366.124060 pypy x0000_revs_xx000_added_0_copies bf2c629d0071 4ffed77c095c : 1.061275 s, 0.016721 s, - 1.044554 s, ? 0.015756 pypy x0000_revs_xx000_added_x000_copies 08ea3258278e d9fa043f30c0 : 1.341119 s, 0.242367 s, - 1.098752 s, ? 0.180720 netbeans x_revs_x_added_0_copies fb0955ffcbcd a01e9239f9e7 : 0.027803 s, 0.000165 s, - 0.027638 s, ? 0.005935 netbeans x_revs_x000_added_0_copies 6f360122949f 20eb231cc7d0 : 0.130014 s, 0.000114 s, - 0.129900 s, ? 0.000877 netbeans x_revs_x_added_x_copies 1ada3faf6fb6 5a39d12eecf4 : 0.024990 s, 0.000296 s, - 0.024694 s, ? 0.011845 netbeans x_revs_x00_added_x_copies 35be93ba1e2c 9eec5e90c05f : 0.052201 s, 0.001124 s, - 0.051077 s, ? 0.021532 netbeans x000_revs_xx00_added_0_copies eac3045b4fdd 51d4ae7f1290 : 0.037642 s, 0.013060 s, - 0.024582 s, ? 0.346953 netbeans x000_revs_x000_added_x_copies e2063d266acd 6081d72689dc : 0.197086 s, 0.017112 s, - 0.179974 s, ? 0.086825 netbeans x000_revs_x000_added_x000_copies ff453e9fee32 411350406ec2 : 0.935148 s, 0.660350 s, - 0.274798 s, ? 0.706145 netbeans x0000_revs_xx000_added_x000_copies 588c2d1ced70 1aad62e59ddd : 3.920674 s, 10.032499 s, + 6.111825 s, ? 2.558871 mozilla-central x_revs_x_added_0_copies 3697f962bb7b 7015fcdd43a2 : 0.024232 s, 0.000189 s, - 0.024043 s, ? 0.007800 mozilla-central x_revs_x000_added_0_copies dd390860c6c9 40d0c5bed75d : 0.141483 s, 0.000462 s, - 0.141021 s, ? 0.003265 mozilla-central x_revs_x_added_x_copies 8d198483ae3b 14207ffc2b2f : 0.025775 s, 0.000270 s, - 0.025505 s, ? 0.010475 mozilla-central x_revs_x00_added_x_copies 98cbc58cc6bc 446a150332c3 : 0.084922 s, 0.001474 s, - 0.083448 s, ? 0.017357 mozilla-central x_revs_x000_added_x000_copies 3c684b4b8f68 0a5e72d1b479 : 0.194784 s, 0.004806 s, - 0.189978 s, ? 0.024673 mozilla-central x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 2.161103 s, 0.085150 s, - 2.075953 s, ? 0.039401 mozilla-central x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 0.089347 s, 0.007064 s, - 0.082283 s, ? 0.079063 mozilla-central x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 0.732171 s, 0.004741 s, - 0.727430 s, ? 0.006475 mozilla-central x000_revs_x000_added_x000_copies 7c97034feb78 4407bd0c6330 : 1.157287 s, 0.190133 s, - 0.967154 s, ? 0.164292 mozilla-central x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 6.726568 s, 0.035651 s, - 6.690917 s, ? 0.005300 mozilla-central x0000_revs_xx000_added_x000_copies f78c615a656c 96a38b690156 : 3.266229 s, 0.440694 s, - 2.825535 s, ? 0.134924 mozilla-central x00000_revs_x0000_added_x0000_copies 6832ae71433c 4c222a1d9a00 : 15.860534 s, 18.454163 s, + 2.593629 s, ? 1.163527 mozilla-central x00000_revs_x00000_added_x000_copies 76caed42cf7c 1daa622bbe42 : 20.450475 s, 31.562719 s, +11.112244 s, ? 1.543373 mozilla-try x_revs_x_added_0_copies aaf6dde0deb8 9790f499805a : 0.080442 s, 0.001189 s, - 0.079253 s, ? 0.014781 mozilla-try x_revs_x000_added_0_copies d8d0222927b4 5bb8ce8c7450 : 0.497672 s, 0.001204 s, - 0.496468 s, ? 0.002419 mozilla-try x_revs_x_added_x_copies 092fcca11bdb 936255a0384a : 0.021183 s, 0.000586 s, - 0.020597 s, ? 0.027664 mozilla-try x_revs_x00_added_x_copies b53d2fadbdb5 017afae788ec : 0.230991 s, 0.001845 s, - 0.229146 s, ? 0.007987 mozilla-try x_revs_x000_added_x000_copies 20408ad61ce5 6f0ee96e21ad : 1.118461 s, 0.063822 s, - 1.054639 s, ? 0.057062 mozilla-try x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 2.206083 s, 0.088038 s, - 2.118045 s, ? 0.039907 mozilla-try x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 0.089404 s, 0.007389 s, - 0.082015 s, ? 0.082647 mozilla-try x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 0.733043 s, 0.004868 s, - 0.728175 s, ? 0.006641 mozilla-try x000_revs_x000_added_x000_copies 1346fd0130e4 4c65cbdabc1f : 1.163367 s, 0.222450 s, - 0.940917 s, ? 0.191212 mozilla-try x0000_revs_x_added_0_copies 63519bfd42ee a36a2a865d92 : 0.085456 s, 0.370675 s, + 0.285219 s, ? 4.337612 mozilla-try x0000_revs_x_added_x_copies 9fe69ff0762d bcabf2a78927 : 0.083601 s, 0.358020 s, + 0.274419 s, ? 4.282485 mozilla-try x0000_revs_xx000_added_x_copies 156f6e2674f2 4d0f2c178e66 : 7.366614 s, 0.145235 s, - 7.221379 s, ? 0.019715 mozilla-try x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 6.664464 s, 0.037606 s, - 6.626858 s, ? 0.005643 mozilla-try x0000_revs_xx000_added_x000_copies 89294cd501d9 7ccb2fc7ccb5 : 7.467836 s, 7.382439 s, - 0.085397 s, ? 0.988565 mozilla-try x0000_revs_x0000_added_x0000_copies e928c65095ed e951f4ad123a : 9.801294 s, 7.273506 s, - 2.527788 s, ? 0.742097 mozilla-try x00000_revs_x_added_0_copies 6a320851d377 1ebb79acd503 : 0.091886 s, killed mozilla-try x00000_revs_x00000_added_0_copies dc8a3ca7010e d16fde900c9c : 26.491140 s, 1.074593 s, -25.416547 s, ? 0.040564 mozilla-try x00000_revs_x_added_x_copies 5173c4b6f97c 95d83ee7242d : 0.092863 s, killed mozilla-try x00000_revs_x000_added_x_copies 9126823d0e9c ca82787bb23c : 0.226823 s, killed mozilla-try x00000_revs_x0000_added_x0000_copies 8d3fafa80d4b eb884023b810 : 18.914630 s, 27.746195 s, + 8.831565 s, ? 1.466917 mozilla-try x00000_revs_x00000_added_x0000_copies 1b661134e2ca 1ae03d022d6d : 21.198903 s, killed mozilla-try x00000_revs_x00000_added_x000_copies 9b2a99adc05e 8e29777b48e6 : 24.952268 s, killed Differential Revision: https://phab.mercurial-scm.org/D9296
author Pierre-Yves David <pierre-yves.david@octobus.net>
date Mon, 02 Nov 2020 11:03:56 +0100
parents 26114bd6ec60
children e98fd81bb151
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1 // dagops.rs
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2 //
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3 // Copyright 2019 Georges Racinet <georges.racinet@octobus.net>
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4 //
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5 // This software may be used and distributed according to the terms of the
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6 // GNU General Public License version 2 or any later version.
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7
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8 //! Miscellaneous DAG operations
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9 //!
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10 //! # Terminology
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11 //! - By *relative heads* of a collection of revision numbers (`Revision`), we
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12 //! mean those revisions that have no children among the collection.
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13 //! - Similarly *relative roots* of a collection of `Revision`, we mean those
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14 //! whose parents, if any, don't belong to the collection.
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15 use super::{Graph, GraphError, Revision, NULL_REVISION};
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16 use crate::ancestors::AncestorsIterator;
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17 use std::collections::{BTreeSet, HashSet};
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18
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19 fn remove_parents<S: std::hash::BuildHasher>(
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20 graph: &impl Graph,
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21 rev: Revision,
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22 set: &mut HashSet<Revision, S>,
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23 ) -> Result<(), GraphError> {
47881d2a9d99 rust: dagop.headrevs() Rust counterparts
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24 for parent in graph.parents(rev)?.iter() {
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25 if *parent != NULL_REVISION {
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26 set.remove(parent);
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27 }
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28 }
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29 Ok(())
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30 }
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31
47881d2a9d99 rust: dagop.headrevs() Rust counterparts
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32 /// Relative heads out of some revisions, passed as an iterator.
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33 ///
47881d2a9d99 rust: dagop.headrevs() Rust counterparts
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34 /// These heads are defined as those revisions that have no children
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35 /// among those emitted by the iterator.
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36 ///
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37 /// # Performance notes
47881d2a9d99 rust: dagop.headrevs() Rust counterparts
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38 /// Internally, this clones the iterator, and builds a `HashSet` out of it.
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39 ///
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40 /// This function takes an `Iterator` instead of `impl IntoIterator` to
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41 /// guarantee that cloning the iterator doesn't result in cloning the full
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42 /// construct it comes from.
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43 pub fn heads<'a>(
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44 graph: &impl Graph,
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45 iter_revs: impl Clone + Iterator<Item = &'a Revision>,
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46 ) -> Result<HashSet<Revision>, GraphError> {
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47 let mut heads: HashSet<Revision> = iter_revs.clone().cloned().collect();
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48 heads.remove(&NULL_REVISION);
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49 for rev in iter_revs {
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50 if *rev != NULL_REVISION {
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51 remove_parents(graph, *rev, &mut heads)?;
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52 }
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53 }
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54 Ok(heads)
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55 }
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56
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57 /// Retain in `revs` only its relative heads.
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58 ///
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59 /// This is an in-place operation, so that control of the incoming
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60 /// set is left to the caller.
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61 /// - a direct Python binding would probably need to build its own `HashSet`
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62 /// from an incoming iterable, even if its sole purpose is to extract the
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63 /// heads.
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64 /// - a Rust caller can decide whether cloning beforehand is appropriate
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65 ///
47881d2a9d99 rust: dagop.headrevs() Rust counterparts
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66 /// # Performance notes
47881d2a9d99 rust: dagop.headrevs() Rust counterparts
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67 /// Internally, this function will store a full copy of `revs` in a `Vec`.
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68 pub fn retain_heads<S: std::hash::BuildHasher>(
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69 graph: &impl Graph,
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70 revs: &mut HashSet<Revision, S>,
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71 ) -> Result<(), GraphError> {
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72 revs.remove(&NULL_REVISION);
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73 // we need to construct an iterable copy of revs to avoid itering while
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74 // mutating
47881d2a9d99 rust: dagop.headrevs() Rust counterparts
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75 let as_vec: Vec<Revision> = revs.iter().cloned().collect();
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76 for rev in as_vec {
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77 if rev != NULL_REVISION {
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78 remove_parents(graph, rev, revs)?;
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79 }
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80 }
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81 Ok(())
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82 }
47881d2a9d99 rust: dagop.headrevs() Rust counterparts
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83
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84 /// Roots of `revs`, passed as a `HashSet`
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85 ///
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86 /// They are returned in arbitrary order
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87 pub fn roots<G: Graph, S: std::hash::BuildHasher>(
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88 graph: &G,
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89 revs: &HashSet<Revision, S>,
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90 ) -> Result<Vec<Revision>, GraphError> {
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91 let mut roots: Vec<Revision> = Vec::new();
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92 for rev in revs {
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93 if graph
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94 .parents(*rev)?
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95 .iter()
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96 .filter(|p| **p != NULL_REVISION)
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97 .all(|p| !revs.contains(p))
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98 {
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99 roots.push(*rev);
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100 }
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101 }
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102 Ok(roots)
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103 }
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104
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105 /// Compute the topological range between two collections of revisions
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106 ///
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107 /// This is equivalent to the revset `<roots>::<heads>`.
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108 ///
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109 /// Currently, the given `Graph` has to implement `Clone`, which means
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110 /// actually cloning just a reference-counted Python pointer if
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111 /// it's passed over through `rust-cpython`. This is due to the internal
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112 /// use of `AncestorsIterator`
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113 ///
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114 /// # Algorithmic details
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115 ///
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116 /// This is a two-pass swipe inspired from what `reachableroots2` from
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117 /// `mercurial.cext.parsers` does to obtain the same results.
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118 ///
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119 /// - first, we climb up the DAG from `heads` in topological order, keeping
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120 /// them in the vector `heads_ancestors` vector, and adding any element of
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121 /// `roots` we find among them to the resulting range.
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122 /// - Then, we iterate on that recorded vector so that a revision is always
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123 /// emitted after its parents and add all revisions whose parents are already
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124 /// in the range to the results.
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125 ///
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126 /// # Performance notes
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127 ///
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128 /// The main difference with the C implementation is that
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129 /// the latter uses a flat array with bit flags, instead of complex structures
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130 /// like `HashSet`, making it faster in most scenarios. In theory, it's
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131 /// possible that the present implementation could be more memory efficient
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132 /// for very large repositories with many branches.
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133 pub fn range(
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134 graph: &(impl Graph + Clone),
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135 roots: impl IntoIterator<Item = Revision>,
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136 heads: impl IntoIterator<Item = Revision>,
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137 ) -> Result<BTreeSet<Revision>, GraphError> {
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138 let mut range = BTreeSet::new();
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139 let roots: HashSet<Revision> = roots.into_iter().collect();
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140 let min_root: Revision = match roots.iter().cloned().min() {
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141 None => {
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142 return Ok(range);
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143 }
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144 Some(r) => r,
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145 };
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146
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147 // Internally, AncestorsIterator currently maintains a `HashSet`
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148 // of all seen revision, which is also what we record, albeit in an ordered
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149 // way. There's room for improvement on this duplication.
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150 let ait = AncestorsIterator::new(graph.clone(), heads, min_root, true)?;
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151 let mut heads_ancestors: Vec<Revision> = Vec::new();
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152 for revres in ait {
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153 let rev = revres?;
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154 if roots.contains(&rev) {
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155 range.insert(rev);
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156 }
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157 heads_ancestors.push(rev);
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158 }
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159
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160 for rev in heads_ancestors.into_iter().rev() {
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161 for parent in graph.parents(rev)?.iter() {
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162 if *parent != NULL_REVISION && range.contains(parent) {
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163 range.insert(rev);
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164 }
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165 }
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166 }
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167 Ok(range)
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168 }
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169
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170 #[cfg(test)]
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171 mod tests {
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172
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173 use super::*;
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174 use crate::testing::SampleGraph;
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175
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176 /// Apply `retain_heads()` to the given slice and return as a sorted `Vec`
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177 fn retain_heads_sorted(
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178 graph: &impl Graph,
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179 revs: &[Revision],
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180 ) -> Result<Vec<Revision>, GraphError> {
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181 let mut revs: HashSet<Revision> = revs.iter().cloned().collect();
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182 retain_heads(graph, &mut revs)?;
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183 let mut as_vec: Vec<Revision> = revs.iter().cloned().collect();
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184 as_vec.sort();
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185 Ok(as_vec)
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186 }
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187
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188 #[test]
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189 fn test_retain_heads() -> Result<(), GraphError> {
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190 assert_eq!(retain_heads_sorted(&SampleGraph, &[4, 5, 6])?, vec![5, 6]);
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191 assert_eq!(
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192 retain_heads_sorted(&SampleGraph, &[4, 1, 6, 12, 0])?,
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193 vec![1, 6, 12]
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194 );
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195 assert_eq!(
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196 retain_heads_sorted(&SampleGraph, &[1, 2, 3, 4, 5, 6, 7, 8, 9])?,
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197 vec![3, 5, 8, 9]
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198 );
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199 Ok(())
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200 }
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201
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202 /// Apply `heads()` to the given slice and return as a sorted `Vec`
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203 fn heads_sorted(
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204 graph: &impl Graph,
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205 revs: &[Revision],
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206 ) -> Result<Vec<Revision>, GraphError> {
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207 let heads = heads(graph, revs.iter())?;
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208 let mut as_vec: Vec<Revision> = heads.iter().cloned().collect();
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209 as_vec.sort();
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210 Ok(as_vec)
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211 }
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212
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213 #[test]
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214 fn test_heads() -> Result<(), GraphError> {
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215 assert_eq!(heads_sorted(&SampleGraph, &[4, 5, 6])?, vec![5, 6]);
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216 assert_eq!(
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217 heads_sorted(&SampleGraph, &[4, 1, 6, 12, 0])?,
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218 vec![1, 6, 12]
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219 );
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220 assert_eq!(
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221 heads_sorted(&SampleGraph, &[1, 2, 3, 4, 5, 6, 7, 8, 9])?,
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222 vec![3, 5, 8, 9]
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223 );
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224 Ok(())
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225 }
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226
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227 /// Apply `roots()` and sort the result for easier comparison
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228 fn roots_sorted(
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229 graph: &impl Graph,
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230 revs: &[Revision],
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231 ) -> Result<Vec<Revision>, GraphError> {
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232 let set: HashSet<_> = revs.iter().cloned().collect();
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233 let mut as_vec = roots(graph, &set)?;
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234 as_vec.sort();
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235 Ok(as_vec)
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236 }
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237
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238 #[test]
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239 fn test_roots() -> Result<(), GraphError> {
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240 assert_eq!(roots_sorted(&SampleGraph, &[4, 5, 6])?, vec![4]);
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241 assert_eq!(
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242 roots_sorted(&SampleGraph, &[4, 1, 6, 12, 0])?,
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243 vec![0, 4, 12]
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244 );
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245 assert_eq!(
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246 roots_sorted(&SampleGraph, &[1, 2, 3, 4, 5, 6, 7, 8, 9])?,
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247 vec![1, 8]
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248 );
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249 Ok(())
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250 }
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251
42214
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252 /// Apply `range()` and convert the result into a Vec for easier comparison
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253 fn range_vec(
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254 graph: impl Graph + Clone,
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255 roots: &[Revision],
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256 heads: &[Revision],
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257 ) -> Result<Vec<Revision>, GraphError> {
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258 range(&graph, roots.iter().cloned(), heads.iter().cloned())
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259 .map(|bs| bs.into_iter().collect())
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260 }
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261
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262 #[test]
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263 fn test_range() -> Result<(), GraphError> {
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264 assert_eq!(range_vec(SampleGraph, &[0], &[4])?, vec![0, 1, 2, 4]);
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265 assert_eq!(range_vec(SampleGraph, &[0], &[8])?, vec![]);
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266 assert_eq!(
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267 range_vec(SampleGraph, &[5, 6], &[10, 11, 13])?,
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268 vec![5, 10]
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269 );
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270 assert_eq!(
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271 range_vec(SampleGraph, &[5, 6], &[10, 12])?,
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272 vec![5, 6, 9, 10, 12]
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273 );
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274 Ok(())
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275 }
41242
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276 }