##// END OF EJS Templates
copies: no longer cache the ChangedFiles during copy tracing...
copies: no longer cache the ChangedFiles during copy tracing Now that the copies information for both parents are processed all at once, we no longer needs to cache this information, so we simplify the code. The simpler code is also a (tiny) bit faster overall. Repo Case Source-Rev Dest-Rev # of revisions old time new time Difference Factor time per rev --------------------------------------------------------------------------------------------------------------------------------------------------------------- mercurial x_revs_x_added_0_copies ad6b123de1c7 39cfcef4f463 : 1 revs, 0.000041 s, 0.000041 s, +0.000000 s, × 1.0000, 41 µs/rev mercurial x_revs_x_added_x_copies 2b1c78674230 0c1d10351869 : 6 revs, 0.000102 s, 0.000096 s, -0.000006 s, × 0.9412, 16 µs/rev mercurial x000_revs_x000_added_x_copies 81f8ff2a9bf2 dd3267698d84 : 1032 revs, 0.004254 s, 0.004039 s, -0.000215 s, × 0.9495, 3 µs/rev pypy x_revs_x_added_0_copies aed021ee8ae8 099ed31b181b : 9 revs, 0.000282 s, 0.000189 s, -0.000093 s, × 0.6702, 21 µs/rev pypy x_revs_x000_added_0_copies 4aa4e1f8e19a 359343b9ac0e : 1 revs, 0.000048 s, 0.000047 s, -0.000001 s, × 0.9792, 47 µs/rev pypy x_revs_x_added_x_copies ac52eb7bbbb0 72e022663155 : 7 revs, 0.000211 s, 0.000103 s, -0.000108 s, × 0.4882, 14 µs/rev pypy x_revs_x00_added_x_copies c3b14617fbd7 ace7255d9a26 : 1 revs, 0.000375 s, 0.000286 s, -0.000089 s, × 0.7627, 286 µs/rev pypy x_revs_x000_added_x000_copies df6f7a526b60 a83dc6a2d56f : 6 revs, 0.010574 s, 0.010436 s, -0.000138 s, × 0.9869, 1739 µs/rev pypy x000_revs_xx00_added_0_copies 89a76aede314 2f22446ff07e : 4785 revs, 0.049974 s, 0.047465 s, -0.002509 s, × 0.9498, 9 µs/rev pypy x000_revs_x000_added_x_copies 8a3b5bfd266e 2c68e87c3efe : 6780 revs, 0.084300 s, 0.082351 s, -0.001949 s, × 0.9769, 12 µs/rev pypy x000_revs_x000_added_x000_copies 89a76aede314 7b3dda341c84 : 5441 revs, 0.060128 s, 0.058757 s, -0.001371 s, × 0.9772, 10 µs/rev pypy x0000_revs_x_added_0_copies d1defd0dc478 c9cb1334cc78 : 43645 revs, 0.686542 s, 0.674129 s, -0.012413 s, × 0.9819, 15 µs/rev pypy x0000_revs_xx000_added_0_copies bf2c629d0071 4ffed77c095c : 2 revs, 0.009277 s, 0.009434 s, +0.000157 s, × 1.0169, 4717 µs/rev pypy x0000_revs_xx000_added_x000_copies 08ea3258278e d9fa043f30c0 : 11316 revs, 0.114733 s, 0.111935 s, -0.002798 s, × 0.9756, 9 µs/rev netbeans x_revs_x_added_0_copies fb0955ffcbcd a01e9239f9e7 : 2 revs, 0.000081 s, 0.000078 s, -0.000003 s, × 0.9630, 39 µs/rev netbeans x_revs_x000_added_0_copies 6f360122949f 20eb231cc7d0 : 2 revs, 0.000107 s, 0.000106 s, -0.000001 s, × 0.9907, 53 µs/rev netbeans x_revs_x_added_x_copies 1ada3faf6fb6 5a39d12eecf4 : 3 revs, 0.000173 s, 0.000162 s, -0.000011 s, × 0.9364, 54 µs/rev netbeans x_revs_x00_added_x_copies 35be93ba1e2c 9eec5e90c05f : 9 revs, 0.000698 s, 0.000695 s, -0.000003 s, × 0.9957, 77 µs/rev netbeans x000_revs_xx00_added_0_copies eac3045b4fdd 51d4ae7f1290 : 1421 revs, 0.009248 s, 0.008901 s, -0.000347 s, × 0.9625, 6 µs/rev netbeans x000_revs_x000_added_x_copies e2063d266acd 6081d72689dc : 1533 revs, 0.015446 s, 0.014333 s, -0.001113 s, × 0.9279, 9 µs/rev netbeans x000_revs_x000_added_x000_copies ff453e9fee32 411350406ec2 : 5750 revs, 0.074373 s, 0.071998 s, -0.002375 s, × 0.9681, 12 µs/rev netbeans x0000_revs_xx000_added_x000_copies 588c2d1ced70 1aad62e59ddd : 66949 revs, 0.639870 s, 0.615346 s, -0.024524 s, × 0.9617, 9 µs/rev mozilla-central x_revs_x_added_0_copies 3697f962bb7b 7015fcdd43a2 : 2 revs, 0.000088 s, 0.000085 s, -0.000003 s, × 0.9659, 42 µs/rev mozilla-central x_revs_x000_added_0_copies dd390860c6c9 40d0c5bed75d : 8 revs, 0.000199 s, 0.000199 s, +0.000000 s, × 1.0000, 24 µs/rev mozilla-central x_revs_x_added_x_copies 8d198483ae3b 14207ffc2b2f : 9 revs, 0.000171 s, 0.000169 s, -0.000002 s, × 0.9883, 18 µs/rev mozilla-central x_revs_x00_added_x_copies 98cbc58cc6bc 446a150332c3 : 7 revs, 0.000592 s, 0.000590 s, -0.000002 s, × 0.9966, 84 µs/rev mozilla-central x_revs_x000_added_x000_copies 3c684b4b8f68 0a5e72d1b479 : 3 revs, 0.003151 s, 0.003122 s, -0.000029 s, × 0.9908, 1040 µs/rev mozilla-central x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 6 revs, 0.061612 s, 0.061192 s, -0.000420 s, × 0.9932, 10198 µs/rev mozilla-central x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 1593 revs, 0.005381 s, 0.005137 s, -0.000244 s, × 0.9547, 3 µs/rev mozilla-central x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 41 revs, 0.003742 s, 0.003585 s, -0.000157 s, × 0.9580, 87 µs/rev mozilla-central x000_revs_x000_added_x000_copies 7c97034feb78 4407bd0c6330 : 7839 revs, 0.061983 s, 0.060592 s, -0.001391 s, × 0.9776, 7 µs/rev mozilla-central x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 615 revs, 0.019861 s, 0.019596 s, -0.000265 s, × 0.9867, 31 µs/rev mozilla-central x0000_revs_xx000_added_x000_copies f78c615a656c 96a38b690156 : 30263 revs, 0.188101 s, 0.183558 s, -0.004543 s, × 0.9758, 6 µs/rev mozilla-central x00000_revs_x0000_added_x0000_copies 6832ae71433c 4c222a1d9a00 : 153721 revs, 1.806696 s, 1.758083 s, -0.048613 s, × 0.9731, 11 µs/rev mozilla-central x00000_revs_x00000_added_x000_copies 76caed42cf7c 1daa622bbe42 : 204976 revs, 2.682987 s, 2.592955 s, -0.090032 s, × 0.9664, 12 µs/rev mozilla-try x_revs_x_added_0_copies aaf6dde0deb8 9790f499805a : 2 revs, 0.000852 s, 0.000844 s, -0.000008 s, × 0.9906, 422 µs/rev mozilla-try x_revs_x000_added_0_copies d8d0222927b4 5bb8ce8c7450 : 2 revs, 0.000859 s, 0.000861 s, +0.000002 s, × 1.0023, 430 µs/rev mozilla-try x_revs_x_added_x_copies 092fcca11bdb 936255a0384a : 4 revs, 0.000150 s, 0.000150 s, +0.000000 s, × 1.0000, 37 µs/rev mozilla-try x_revs_x00_added_x_copies b53d2fadbdb5 017afae788ec : 2 revs, 0.001158 s, 0.001166 s, +0.000008 s, × 1.0069, 583 µs/rev mozilla-try x_revs_x000_added_x000_copies 20408ad61ce5 6f0ee96e21ad : 1 revs, 0.027240 s, 0.027359 s, +0.000119 s, × 1.0044, 27359 µs/rev mozilla-try x_revs_x0000_added_x0000_copies effb563bb7e5 c07a39dc4e80 : 6 revs, 0.062824 s, 0.061848 s, -0.000976 s, × 0.9845, 10308 µs/rev mozilla-try x000_revs_xx00_added_0_copies 6100d773079a 04a55431795e : 1593 revs, 0.005463 s, 0.005110 s, -0.000353 s, × 0.9354, 3 µs/rev mozilla-try x000_revs_x000_added_x_copies 9f17a6fc04f9 2d37b966abed : 41 revs, 0.004238 s, 0.004168 s, -0.000070 s, × 0.9835, 101 µs/rev mozilla-try x000_revs_x000_added_x000_copies 1346fd0130e4 4c65cbdabc1f : 6657 revs, 0.064113 s, 0.063414 s, -0.000699 s, × 0.9891, 9 µs/rev mozilla-try x0000_revs_x_added_0_copies 63519bfd42ee a36a2a865d92 : 40314 revs, 0.294063 s, 0.288301 s, -0.005762 s, × 0.9804, 7 µs/rev mozilla-try x0000_revs_x_added_x_copies 9fe69ff0762d bcabf2a78927 : 38690 revs, 0.281493 s, 0.275798 s, -0.005695 s, × 0.9798, 7 µs/rev mozilla-try x0000_revs_xx000_added_x_copies 156f6e2674f2 4d0f2c178e66 : 8598 revs, 0.076323 s, 0.074640 s, -0.001683 s, × 0.9779, 8 µs/rev mozilla-try x0000_revs_xx000_added_0_copies 9eec5917337d 67118cc6dcad : 615 revs, 0.020390 s, 0.020327 s, -0.000063 s, × 0.9969, 33 µs/rev mozilla-try x0000_revs_xx000_added_x000_copies 89294cd501d9 7ccb2fc7ccb5 : 97052 revs, 3.023879 s, 2.970385 s, -0.053494 s, × 0.9823, 30 µs/rev mozilla-try x0000_revs_x0000_added_x0000_copies e928c65095ed e951f4ad123a : 52031 revs, 0.735549 s, 0.719432 s, -0.016117 s, × 0.9781, 13 µs/rev mozilla-try x00000_revs_x_added_0_copies 6a320851d377 1ebb79acd503 : 363753 revs, 18.568900 s, 18.165143 s, -0.403757 s, × 0.9783, 49 µs/rev mozilla-try x00000_revs_x00000_added_0_copies dc8a3ca7010e d16fde900c9c : 34414 revs, 0.502584 s, 0.486769 s, -0.015815 s, × 0.9685, 14 µs/rev mozilla-try x00000_revs_x_added_x_copies 5173c4b6f97c 95d83ee7242d : 362229 revs, 18.356645 s, 17.913924 s, -0.442721 s, × 0.9759, 49 µs/rev mozilla-try x00000_revs_x000_added_x_copies 9126823d0e9c ca82787bb23c : 359344 revs, 18.250393 s, 17.660113 s, -0.590280 s, × 0.9677, 49 µs/rev mozilla-try x00000_revs_x0000_added_x0000_copies 8d3fafa80d4b eb884023b810 : 192665 revs, 2.792459 s, 2.709446 s, -0.083013 s, × 0.9703, 14 µs/rev mozilla-try x00000_revs_x00000_added_x0000_copies 1b661134e2ca 1ae03d022d6d : 228985 revs, 107.697264 s, 107.796891 s, +0.099627 s, × 1.0009, 470 µs/rev mozilla-try x00000_revs_x00000_added_x000_copies 9b2a99adc05e 8e29777b48e6 : 382065 revs, 63.961040 s, 63.575217 s, -0.385823 s, × 0.9940, 166 µs/rev Differential Revision: https://phab.mercurial-scm.org/D9423

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setdiscovery.py
500 lines | 16.8 KiB | text/x-python | PythonLexer
# 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,
audit=None,
):
"""Return a tuple (common, anyincoming, remoteheads) used to identify
missing nodes from or in remote.
The audit argument is an optional dictionnary that a caller can pass. it
will be updated with extra data about the discovery, this is useful for
debug.
"""
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 audit is not None:
audit[b'total-roundtrips'] = 1
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 audit is not None:
audit[b'total-roundtrips'] = roundtrips
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