view mercurial/tagmerge.py @ 45892:06b64fabf91c

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 687b865b95ad
children 89a2afe31e82
line wrap: on
line source

# tagmerge.py - merge .hgtags files
#
# Copyright 2014 Angel Ezquerra <angel.ezquerra@gmail.com>
#
# This software may be used and distributed according to the terms of the
# GNU General Public License version 2 or any later version.

# This module implements an automatic merge algorithm for mercurial's tag files
#
# The tagmerge algorithm implemented in this module is able to resolve most
# merge conflicts that currently would trigger a .hgtags merge conflict. The
# only case that it does not (and cannot) handle is that in which two tags point
# to different revisions on each merge parent _and_ their corresponding tag
# histories have the same rank (i.e. the same length). In all other cases the
# merge algorithm will choose the revision belonging to the parent with the
# highest ranked tag history. The merged tag history is the combination of both
# tag histories (special care is taken to try to combine common tag histories
# where possible).
#
# In addition to actually merging the tags from two parents, taking into
# account the base, the algorithm also tries to minimize the difference
# between the merged tag file and the first parent's tag file (i.e. it tries to
# make the merged tag order as as similar as possible to the first parent's tag
# file order).
#
# The algorithm works as follows:
# 1. read the tags from p1, p2 and the base
#     - when reading the p1 tags, also get the line numbers associated to each
#       tag node (these will be used to sort the merged tags in a way that
#       minimizes the diff to p1). Ignore the file numbers when reading p2 and
#       the base
# 2. recover the "lost tags" (i.e. those that are found in the base but not on
#    p1 or p2) and add them back to p1 and/or p2
#     - at this point the only tags that are on p1 but not on p2 are those new
#       tags that were introduced in p1. Same thing for the tags that are on p2
#       but not on p2
# 3. take all tags that are only on p1 or only on p2 (but not on the base)
#     - Note that these are the tags that were introduced between base and p1
#       and between base and p2, possibly on separate clones
# 4. for each tag found both on p1 and p2 perform the following merge algorithm:
#     - the tags conflict if their tag "histories" have the same "rank" (i.e.
#       length) AND the last (current) tag is NOT the same
#     - for non conflicting tags:
#         - choose which are the high and the low ranking nodes
#             - the high ranking list of nodes is the one that is longer.
#               In case of draw favor p1
#             - the merged node list is made of 3 parts:
#                 - first the nodes that are common to the beginning of both
#                   the low and the high ranking nodes
#                 - second the non common low ranking nodes
#                 - finally the non common high ranking nodes (with the last
#                   one being the merged tag node)
#             - note that this is equivalent to putting the whole low ranking
#               node list first, followed by the non common high ranking nodes
#     - note that during the merge we keep the "node line numbers", which will
#       be used when writing the merged tags to the tag file
# 5. write the merged tags taking into account to their positions in the first
#    parent (i.e. try to keep the relative ordering of the nodes that come
#    from p1). This minimizes the diff between the merged and the p1 tag files
#    This is done by using the following algorithm
#     - group the nodes for a given tag that must be written next to each other
#         - A: nodes that come from consecutive lines on p1
#         - B: nodes that come from p2 (i.e. whose associated line number is
#              None) and are next to one of the a nodes in A
#         - each group is associated with a line number coming from p1
#     - generate a "tag block" for each of the groups
#         - a tag block is a set of consecutive "node tag" lines belonging to
#           the same tag and which will be written next to each other on the
#           merged tags file
#     - sort the "tag blocks" according to their associated number line
#         - put blocks whose nodes come all from p2 first
#     - write the tag blocks in the sorted order

from __future__ import absolute_import

from .i18n import _
from .node import (
    hex,
    nullid,
)
from . import (
    tags as tagsmod,
    util,
)

hexnullid = hex(nullid)


def readtagsformerge(ui, repo, lines, fn=b'', keeplinenums=False):
    '''read the .hgtags file into a structure that is suitable for merging

    Depending on the keeplinenums flag, clear the line numbers associated
    with each tag. This is done because only the line numbers of the first
    parent are useful for merging.
    '''
    filetags = tagsmod._readtaghist(
        ui, repo, lines, fn=fn, recode=None, calcnodelines=True
    )[1]
    for tagname, taginfo in filetags.items():
        if not keeplinenums:
            for el in taginfo:
                el[1] = None
    return filetags


def grouptagnodesbyline(tagnodes):
    '''
    Group nearby nodes (i.e. those that must be written next to each other)

    The input is a list of [node, position] pairs, corresponding to a given tag
    The position is the line number where the node was found on the first parent
    .hgtags file, or None for those nodes that came from the base or the second
    parent .hgtags files.

    This function groups those [node, position] pairs, returning a list of
    groups of nodes that must be written next to each other because their
    positions are consecutive or have no position preference (because their
    position is None).

    The result is a list of [position, [consecutive node list]]
    '''
    firstlinenum = None
    for hexnode, linenum in tagnodes:
        firstlinenum = linenum
        if firstlinenum is not None:
            break
    if firstlinenum is None:
        return [[None, [el[0] for el in tagnodes]]]
    tagnodes[0][1] = firstlinenum
    groupednodes = [[firstlinenum, []]]
    prevlinenum = firstlinenum
    for hexnode, linenum in tagnodes:
        if linenum is not None and linenum - prevlinenum > 1:
            groupednodes.append([linenum, []])
        groupednodes[-1][1].append(hexnode)
        if linenum is not None:
            prevlinenum = linenum
    return groupednodes


def writemergedtags(fcd, mergedtags):
    '''
    write the merged tags while trying to minimize the diff to the first parent

    This function uses the ordering info stored on the merged tags dict to
    generate an .hgtags file which is correct (in the sense that its contents
    correspond to the result of the tag merge) while also being as close as
    possible to the first parent's .hgtags file.
    '''
    # group the node-tag pairs that must be written next to each other
    for tname, taglist in list(mergedtags.items()):
        mergedtags[tname] = grouptagnodesbyline(taglist)

    # convert the grouped merged tags dict into a format that resembles the
    # final .hgtags file (i.e. a list of blocks of 'node tag' pairs)
    def taglist2string(tlist, tname):
        return b'\n'.join([b'%s %s' % (hexnode, tname) for hexnode in tlist])

    finaltags = []
    for tname, tags in mergedtags.items():
        for block in tags:
            block[1] = taglist2string(block[1], tname)
        finaltags += tags

    # the tag groups are linked to a "position" that can be used to sort them
    # before writing them
    # the position is calculated to ensure that the diff of the merged .hgtags
    # file to the first parent's .hgtags file is as small as possible
    finaltags.sort(key=lambda x: -1 if x[0] is None else x[0])

    # finally we can join the sorted groups to get the final contents of the
    # merged .hgtags file, and then write it to disk
    mergedtagstring = b'\n'.join([tags for rank, tags in finaltags if tags])
    fcd.write(mergedtagstring + b'\n', fcd.flags())


def singletagmerge(p1nodes, p2nodes):
    '''
    merge the nodes corresponding to a single tag

    Note that the inputs are lists of node-linenum pairs (i.e. not just lists
    of nodes)
    '''
    if not p2nodes:
        return p1nodes
    if not p1nodes:
        return p2nodes

    # there is no conflict unless both tags point to different revisions
    # and have a non identical tag history
    p1currentnode = p1nodes[-1][0]
    p2currentnode = p2nodes[-1][0]
    if p1currentnode != p2currentnode and len(p1nodes) == len(p2nodes):
        # cannot merge two tags with same rank pointing to different nodes
        return None

    # which are the highest ranking (hr) / lowest ranking (lr) nodes?
    if len(p1nodes) >= len(p2nodes):
        hrnodes, lrnodes = p1nodes, p2nodes
    else:
        hrnodes, lrnodes = p2nodes, p1nodes

    # the lowest ranking nodes will be written first, followed by the highest
    # ranking nodes
    # to avoid unwanted tag rank explosion we try to see if there are some
    # common nodes that can be written only once
    commonidx = len(lrnodes)
    for n in range(len(lrnodes)):
        if hrnodes[n][0] != lrnodes[n][0]:
            commonidx = n
            break
        lrnodes[n][1] = p1nodes[n][1]

    # the merged node list has 3 parts:
    # - common nodes
    # - non common lowest ranking nodes
    # - non common highest ranking nodes
    # note that the common nodes plus the non common lowest ranking nodes is the
    # whole list of lr nodes
    return lrnodes + hrnodes[commonidx:]


def merge(repo, fcd, fco, fca):
    '''
    Merge the tags of two revisions, taking into account the base tags
    Try to minimize the diff between the merged tags and the first parent tags
    '''
    ui = repo.ui
    # read the p1, p2 and base tags
    # only keep the line numbers for the p1 tags
    p1tags = readtagsformerge(
        ui, repo, fcd.data().splitlines(), fn=b"p1 tags", keeplinenums=True
    )
    p2tags = readtagsformerge(
        ui, repo, fco.data().splitlines(), fn=b"p2 tags", keeplinenums=False
    )
    basetags = readtagsformerge(
        ui, repo, fca.data().splitlines(), fn=b"base tags", keeplinenums=False
    )

    # recover the list of "lost tags" (i.e. those that were found on the base
    # revision but not on one of the revisions being merged)
    basetagset = set(basetags)
    for n, pntags in enumerate((p1tags, p2tags)):
        pntagset = set(pntags)
        pnlosttagset = basetagset - pntagset
        for t in pnlosttagset:
            pntags[t] = basetags[t]
            if pntags[t][-1][0] != hexnullid:
                pntags[t].append([hexnullid, None])

    conflictedtags = []  # for reporting purposes
    mergedtags = util.sortdict(p1tags)
    # sortdict does not implement iteritems()
    for tname, p2nodes in p2tags.items():
        if tname not in mergedtags:
            mergedtags[tname] = p2nodes
            continue
        p1nodes = mergedtags[tname]
        mergednodes = singletagmerge(p1nodes, p2nodes)
        if mergednodes is None:
            conflictedtags.append(tname)
            continue
        mergedtags[tname] = mergednodes

    if conflictedtags:
        numconflicts = len(conflictedtags)
        ui.warn(
            _(
                b'automatic .hgtags merge failed\n'
                b'the following %d tags are in conflict: %s\n'
            )
            % (numconflicts, b', '.join(sorted(conflictedtags)))
        )
        return True, 1

    writemergedtags(fcd, mergedtags)
    ui.note(_(b'.hgtags merged successfully\n'))
    return False, 0