view mercurial/setdiscovery.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 9f70512ae2cf
children 89a2afe31e82
line wrap: on
line source

# setdiscovery.py - improved discovery of common nodeset for mercurial
#
# Copyright 2010 Benoit Boissinot <bboissin@gmail.com>
# and Peter Arrenbrecht <peter@arrenbrecht.ch>
#
# This software may be used and distributed according to the terms of the
# GNU General Public License version 2 or any later version.
"""
Algorithm works in the following way. You have two repository: local and
remote. They both contains a DAG of changelists.

The goal of the discovery protocol is to find one set of node *common*,
the set of nodes shared by local and remote.

One of the issue with the original protocol was latency, it could
potentially require lots of roundtrips to discover that the local repo was a
subset of remote (which is a very common case, you usually have few changes
compared to upstream, while upstream probably had lots of development).

The new protocol only requires one interface for the remote repo: `known()`,
which given a set of changelists tells you if they are present in the DAG.

The algorithm then works as follow:

 - We will be using three sets, `common`, `missing`, `unknown`. Originally
 all nodes are in `unknown`.
 - Take a sample from `unknown`, call `remote.known(sample)`
   - For each node that remote knows, move it and all its ancestors to `common`
   - For each node that remote doesn't know, move it and all its descendants
   to `missing`
 - Iterate until `unknown` is empty

There are a couple optimizations, first is instead of starting with a random
sample of missing, start by sending all heads, in the case where the local
repo is a subset, you computed the answer in one round trip.

Then you can do something similar to the bisecting strategy used when
finding faulty changesets. Instead of random samples, you can try picking
nodes that will maximize the number of nodes that will be
classified with it (since all ancestors or descendants will be marked as well).
"""

from __future__ import absolute_import

import collections
import random

from .i18n import _
from .node import (
    nullid,
    nullrev,
)
from . import (
    error,
    policy,
    util,
)


def _updatesample(revs, heads, sample, parentfn, quicksamplesize=0):
    """update an existing sample to match the expected size

    The sample is updated with revs exponentially distant from each head of the
    <revs> set. (H~1, H~2, H~4, H~8, etc).

    If a target size is specified, the sampling will stop once this size is
    reached. Otherwise sampling will happen until roots of the <revs> set are
    reached.

    :revs:  set of revs we want to discover (if None, assume the whole dag)
    :heads: set of DAG head revs
    :sample: a sample to update
    :parentfn: a callable to resolve parents for a revision
    :quicksamplesize: optional target size of the sample"""
    dist = {}
    visit = collections.deque(heads)
    seen = set()
    factor = 1
    while visit:
        curr = visit.popleft()
        if curr in seen:
            continue
        d = dist.setdefault(curr, 1)
        if d > factor:
            factor *= 2
        if d == factor:
            sample.add(curr)
            if quicksamplesize and (len(sample) >= quicksamplesize):
                return
        seen.add(curr)

        for p in parentfn(curr):
            if p != nullrev and (not revs or p in revs):
                dist.setdefault(p, d + 1)
                visit.append(p)


def _limitsample(sample, desiredlen, randomize=True):
    """return a random subset of sample of at most desiredlen item.

    If randomize is False, though, a deterministic subset is returned.
    This is meant for integration tests.
    """
    if len(sample) <= desiredlen:
        return sample
    if randomize:
        return set(random.sample(sample, desiredlen))
    sample = list(sample)
    sample.sort()
    return set(sample[:desiredlen])


class partialdiscovery(object):
    """an object representing ongoing discovery

    Feed with data from the remote repository, this object keep track of the
    current set of changeset in various states:

    - common:    revs also known remotely
    - undecided: revs we don't have information on yet
    - missing:   revs missing remotely
    (all tracked revisions are known locally)
    """

    def __init__(self, repo, targetheads, respectsize, randomize=True):
        self._repo = repo
        self._targetheads = targetheads
        self._common = repo.changelog.incrementalmissingrevs()
        self._undecided = None
        self.missing = set()
        self._childrenmap = None
        self._respectsize = respectsize
        self.randomize = randomize

    def addcommons(self, commons):
        """register nodes known as common"""
        self._common.addbases(commons)
        if self._undecided is not None:
            self._common.removeancestorsfrom(self._undecided)

    def addmissings(self, missings):
        """register some nodes as missing"""
        newmissing = self._repo.revs(b'%ld::%ld', missings, self.undecided)
        if newmissing:
            self.missing.update(newmissing)
            self.undecided.difference_update(newmissing)

    def addinfo(self, sample):
        """consume an iterable of (rev, known) tuples"""
        common = set()
        missing = set()
        for rev, known in sample:
            if known:
                common.add(rev)
            else:
                missing.add(rev)
        if common:
            self.addcommons(common)
        if missing:
            self.addmissings(missing)

    def hasinfo(self):
        """return True is we have any clue about the remote state"""
        return self._common.hasbases()

    def iscomplete(self):
        """True if all the necessary data have been gathered"""
        return self._undecided is not None and not self._undecided

    @property
    def undecided(self):
        if self._undecided is not None:
            return self._undecided
        self._undecided = set(self._common.missingancestors(self._targetheads))
        return self._undecided

    def stats(self):
        return {
            'undecided': len(self.undecided),
        }

    def commonheads(self):
        """the heads of the known common set"""
        # heads(common) == heads(common.bases) since common represents
        # common.bases and all its ancestors
        return self._common.basesheads()

    def _parentsgetter(self):
        getrev = self._repo.changelog.index.__getitem__

        def getparents(r):
            return getrev(r)[5:7]

        return getparents

    def _childrengetter(self):

        if self._childrenmap is not None:
            # During discovery, the `undecided` set keep shrinking.
            # Therefore, the map computed for an iteration N will be
            # valid for iteration N+1. Instead of computing the same
            # data over and over we cached it the first time.
            return self._childrenmap.__getitem__

        # _updatesample() essentially does interaction over revisions to look
        # up their children. This lookup is expensive and doing it in a loop is
        # quadratic. We precompute the children for all relevant revisions and
        # make the lookup in _updatesample() a simple dict lookup.
        self._childrenmap = children = {}

        parentrevs = self._parentsgetter()
        revs = self.undecided

        for rev in sorted(revs):
            # Always ensure revision has an entry so we don't need to worry
            # about missing keys.
            children[rev] = []
            for prev in parentrevs(rev):
                if prev == nullrev:
                    continue
                c = children.get(prev)
                if c is not None:
                    c.append(rev)
        return children.__getitem__

    def takequicksample(self, headrevs, size):
        """takes a quick sample of size <size>

        It is meant for initial sampling and focuses on querying heads and close
        ancestors of heads.

        :headrevs: set of head revisions in local DAG to consider
        :size: the maximum size of the sample"""
        revs = self.undecided
        if len(revs) <= size:
            return list(revs)
        sample = set(self._repo.revs(b'heads(%ld)', revs))

        if len(sample) >= size:
            return _limitsample(sample, size, randomize=self.randomize)

        _updatesample(
            None, headrevs, sample, self._parentsgetter(), quicksamplesize=size
        )
        return sample

    def takefullsample(self, headrevs, size):
        revs = self.undecided
        if len(revs) <= size:
            return list(revs)
        repo = self._repo
        sample = set(repo.revs(b'heads(%ld)', revs))
        parentrevs = self._parentsgetter()

        # update from heads
        revsheads = sample.copy()
        _updatesample(revs, revsheads, sample, parentrevs)

        # update from roots
        revsroots = set(repo.revs(b'roots(%ld)', revs))
        childrenrevs = self._childrengetter()
        _updatesample(revs, revsroots, sample, childrenrevs)
        assert sample

        if not self._respectsize:
            size = max(size, min(len(revsroots), len(revsheads)))

        sample = _limitsample(sample, size, randomize=self.randomize)
        if len(sample) < size:
            more = size - len(sample)
            takefrom = list(revs - sample)
            if self.randomize:
                sample.update(random.sample(takefrom, more))
            else:
                takefrom.sort()
                sample.update(takefrom[:more])
        return sample


partialdiscovery = policy.importrust(
    'discovery', member='PartialDiscovery', default=partialdiscovery
)


def findcommonheads(
    ui,
    local,
    remote,
    initialsamplesize=100,
    fullsamplesize=200,
    abortwhenunrelated=True,
    ancestorsof=None,
    samplegrowth=1.05,
):
    '''Return a tuple (common, anyincoming, remoteheads) used to identify
    missing nodes from or in remote.
    '''
    start = util.timer()

    roundtrips = 0
    cl = local.changelog
    clnode = cl.node
    clrev = cl.rev

    if ancestorsof is not None:
        ownheads = [clrev(n) for n in ancestorsof]
    else:
        ownheads = [rev for rev in cl.headrevs() if rev != nullrev]

    # early exit if we know all the specified remote heads already
    ui.debug(b"query 1; heads\n")
    roundtrips += 1
    # We also ask remote about all the local heads. That set can be arbitrarily
    # large, so we used to limit it size to `initialsamplesize`. We no longer
    # do as it proved counter productive. The skipped heads could lead to a
    # large "undecided" set, slower to be clarified than if we asked the
    # question for all heads right away.
    #
    # We are already fetching all server heads using the `heads` commands,
    # sending a equivalent number of heads the other way should not have a
    # significant impact.  In addition, it is very likely that we are going to
    # have to issue "known" request for an equivalent amount of revisions in
    # order to decide if theses heads are common or missing.
    #
    # find a detailled analysis below.
    #
    # Case A: local and server both has few heads
    #
    #     Ownheads is below initialsamplesize, limit would not have any effect.
    #
    # Case B: local has few heads and server has many
    #
    #     Ownheads is below initialsamplesize, limit would not have any effect.
    #
    # Case C: local and server both has many heads
    #
    #     We now transfert some more data, but not significantly more than is
    #     already transfered to carry the server heads.
    #
    # Case D: local has many heads, server has few
    #
    #   D.1 local heads are mostly known remotely
    #
    #     All the known head will have be part of a `known` request at some
    #     point for the discovery to finish. Sending them all earlier is
    #     actually helping.
    #
    #     (This case is fairly unlikely, it requires the numerous heads to all
    #     be merged server side in only a few heads)
    #
    #   D.2 local heads are mostly missing remotely
    #
    #     To determine that the heads are missing, we'll have to issue `known`
    #     request for them or one of their ancestors. This amount of `known`
    #     request will likely be in the same order of magnitude than the amount
    #     of local heads.
    #
    #     The only case where we can be more efficient using `known` request on
    #     ancestors are case were all the "missing" local heads are based on a
    #     few changeset, also "missing".  This means we would have a "complex"
    #     graph (with many heads) attached to, but very independant to a the
    #     "simple" graph on the server. This is a fairly usual case and have
    #     not been met in the wild so far.
    if remote.limitedarguments:
        sample = _limitsample(ownheads, initialsamplesize)
        # indices between sample and externalized version must match
        sample = list(sample)
    else:
        sample = ownheads

    with remote.commandexecutor() as e:
        fheads = e.callcommand(b'heads', {})
        fknown = e.callcommand(
            b'known', {b'nodes': [clnode(r) for r in sample],}
        )

    srvheadhashes, yesno = fheads.result(), fknown.result()

    if cl.tip() == nullid:
        if srvheadhashes != [nullid]:
            return [nullid], True, srvheadhashes
        return [nullid], False, []

    # start actual discovery (we note this before the next "if" for
    # compatibility reasons)
    ui.status(_(b"searching for changes\n"))

    knownsrvheads = []  # revnos of remote heads that are known locally
    for node in srvheadhashes:
        if node == nullid:
            continue

        try:
            knownsrvheads.append(clrev(node))
        # Catches unknown and filtered nodes.
        except error.LookupError:
            continue

    if len(knownsrvheads) == len(srvheadhashes):
        ui.debug(b"all remote heads known locally\n")
        return srvheadhashes, False, srvheadhashes

    if len(sample) == len(ownheads) and all(yesno):
        ui.note(_(b"all local changesets known remotely\n"))
        ownheadhashes = [clnode(r) for r in ownheads]
        return ownheadhashes, True, srvheadhashes

    # full blown discovery

    randomize = ui.configbool(b'devel', b'discovery.randomize')
    disco = partialdiscovery(
        local, ownheads, remote.limitedarguments, randomize=randomize
    )
    # treat remote heads (and maybe own heads) as a first implicit sample
    # response
    disco.addcommons(knownsrvheads)
    disco.addinfo(zip(sample, yesno))

    full = False
    progress = ui.makeprogress(_(b'searching'), unit=_(b'queries'))
    while not disco.iscomplete():

        if full or disco.hasinfo():
            if full:
                ui.note(_(b"sampling from both directions\n"))
            else:
                ui.debug(b"taking initial sample\n")
            samplefunc = disco.takefullsample
            targetsize = fullsamplesize
            if not remote.limitedarguments:
                fullsamplesize = int(fullsamplesize * samplegrowth)
        else:
            # use even cheaper initial sample
            ui.debug(b"taking quick initial sample\n")
            samplefunc = disco.takequicksample
            targetsize = initialsamplesize
        sample = samplefunc(ownheads, targetsize)

        roundtrips += 1
        progress.update(roundtrips)
        stats = disco.stats()
        ui.debug(
            b"query %i; still undecided: %i, sample size is: %i\n"
            % (roundtrips, stats['undecided'], len(sample))
        )

        # indices between sample and externalized version must match
        sample = list(sample)

        with remote.commandexecutor() as e:
            yesno = e.callcommand(
                b'known', {b'nodes': [clnode(r) for r in sample],}
            ).result()

        full = True

        disco.addinfo(zip(sample, yesno))

    result = disco.commonheads()
    elapsed = util.timer() - start
    progress.complete()
    ui.debug(b"%d total queries in %.4fs\n" % (roundtrips, elapsed))
    msg = (
        b'found %d common and %d unknown server heads,'
        b' %d roundtrips in %.4fs\n'
    )
    missing = set(result) - set(knownsrvheads)
    ui.log(b'discovery', msg, len(result), len(missing), roundtrips, elapsed)

    if not result and srvheadhashes != [nullid]:
        if abortwhenunrelated:
            raise error.Abort(_(b"repository is unrelated"))
        else:
            ui.warn(_(b"warning: repository is unrelated\n"))
        return (
            {nullid},
            True,
            srvheadhashes,
        )

    anyincoming = srvheadhashes != [nullid]
    result = {clnode(r) for r in result}
    return result, anyincoming, srvheadhashes