mercurial/setdiscovery.py
author Yuya Nishihara <yuya@tcha.org>
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# setdiscovery.py - improved discovery of common nodeset for mercurial
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#
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# Copyright 2010 Benoit Boissinot <bboissin@gmail.com>
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# and Peter Arrenbrecht <peter@arrenbrecht.ch>
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#
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# This software may be used and distributed according to the terms of the
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# GNU General Public License version 2 or any later version.
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"""
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Algorithm works in the following way. You have two repository: local and
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remote. They both contains a DAG of changelists.
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The goal of the discovery protocol is to find one set of node *common*,
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the set of nodes shared by local and remote.
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One of the issue with the original protocol was latency, it could
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potentially require lots of roundtrips to discover that the local repo was a
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subset of remote (which is a very common case, you usually have few changes
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compared to upstream, while upstream probably had lots of development).
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The new protocol only requires one interface for the remote repo: `known()`,
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which given a set of changelists tells you if they are present in the DAG.
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The algorithm then works as follow:
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 - We will be using three sets, `common`, `missing`, `unknown`. Originally
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 all nodes are in `unknown`.
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 - Take a sample from `unknown`, call `remote.known(sample)`
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   - For each node that remote knows, move it and all its ancestors to `common`
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   - For each node that remote doesn't know, move it and all its descendants
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   to `missing`
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 - Iterate until `unknown` is empty
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There are a couple optimizations, first is instead of starting with a random
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sample of missing, start by sending all heads, in the case where the local
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repo is a subset, you computed the answer in one round trip.
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Then you can do something similar to the bisecting strategy used when
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finding faulty changesets. Instead of random samples, you can try picking
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nodes that will maximize the number of nodes that will be
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classified with it (since all ancestors or descendants will be marked as well).
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"""
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from __future__ import absolute_import
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import collections
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import random
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from .i18n import _
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from .node import (
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    nullid,
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    nullrev,
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)
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from . import (
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    dagutil,
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    error,
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    util,
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)
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def _updatesample(dag, nodes, sample, quicksamplesize=0):
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    """update an existing sample to match the expected size
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    The sample is updated with nodes exponentially distant from each head of the
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    <nodes> set. (H~1, H~2, H~4, H~8, etc).
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    If a target size is specified, the sampling will stop once this size is
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    reached. Otherwise sampling will happen until roots of the <nodes> set are
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    reached.
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    :dag: a dag object from dagutil
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    :nodes:  set of nodes we want to discover (if None, assume the whole dag)
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    :sample: a sample to update
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    :quicksamplesize: optional target size of the sample"""
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    # if nodes is empty we scan the entire graph
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    if nodes:
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        heads = dag.headsetofconnecteds(nodes)
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    else:
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        heads = dag.heads()
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    dist = {}
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    visit = collections.deque(heads)
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    seen = set()
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    factor = 1
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    while visit:
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        curr = visit.popleft()
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        if curr in seen:
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            continue
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        d = dist.setdefault(curr, 1)
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        if d > factor:
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            factor *= 2
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        if d == factor:
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            sample.add(curr)
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            if quicksamplesize and (len(sample) >= quicksamplesize):
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                return
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        seen.add(curr)
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        for p in dag.parents(curr):
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            if not nodes or p in nodes:
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                dist.setdefault(p, d + 1)
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                visit.append(p)
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def _takequicksample(dag, nodes, size):
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    """takes a quick sample of size <size>
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    It is meant for initial sampling and focuses on querying heads and close
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    ancestors of heads.
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    :dag: a dag object
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    :nodes: set of nodes to discover
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    :size: the maximum size of the sample"""
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    sample = dag.headsetofconnecteds(nodes)
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    if size <= len(sample):
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        return _limitsample(sample, size)
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    _updatesample(dag, None, sample, quicksamplesize=size)
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    return sample
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def _takefullsample(dag, nodes, size):
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    sample = dag.headsetofconnecteds(nodes)
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    # update from heads
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    _updatesample(dag, nodes, sample)
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    # update from roots
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    _updatesample(dag.inverse(), nodes, sample)
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    assert sample
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    sample = _limitsample(sample, size)
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    if len(sample) < size:
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        more = size - len(sample)
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        sample.update(random.sample(list(nodes - sample), more))
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    return sample
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def _limitsample(sample, desiredlen):
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    """return a random subset of sample of at most desiredlen item"""
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    if len(sample) > desiredlen:
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        sample = set(random.sample(sample, desiredlen))
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    return sample
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def findcommonheads(ui, local, remote,
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                    initialsamplesize=100,
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                    fullsamplesize=200,
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                    abortwhenunrelated=True,
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                    ancestorsof=None):
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    '''Return a tuple (common, anyincoming, remoteheads) used to identify
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    missing nodes from or in remote.
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    '''
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    start = util.timer()
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    roundtrips = 0
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    cl = local.changelog
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    localsubset = None
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    if ancestorsof is not None:
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        rev = local.changelog.rev
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        localsubset = [rev(n) for n in ancestorsof]
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    dag = dagutil.revlogdag(cl, localsubset=localsubset)
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    # early exit if we know all the specified remote heads already
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    ui.debug("query 1; heads\n")
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    roundtrips += 1
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    ownheads = dag.heads()
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    sample = _limitsample(ownheads, initialsamplesize)
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    # indices between sample and externalized version must match
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    sample = list(sample)
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    batch = remote.iterbatch()
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    batch.heads()
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    batch.known(dag.externalizeall(sample))
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    batch.submit()
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    srvheadhashes, yesno = batch.results()
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    if cl.tip() == nullid:
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        if srvheadhashes != [nullid]:
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            return [nullid], True, srvheadhashes
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        return [nullid], False, []
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    # start actual discovery (we note this before the next "if" for
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    # compatibility reasons)
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    ui.status(_("searching for changes\n"))
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    srvheads = dag.internalizeall(srvheadhashes, filterunknown=True)
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    if len(srvheads) == len(srvheadhashes):
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        ui.debug("all remote heads known locally\n")
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        return (srvheadhashes, False, srvheadhashes,)
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    if sample and len(ownheads) <= initialsamplesize and all(yesno):
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        ui.note(_("all local heads known remotely\n"))
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        ownheadhashes = dag.externalizeall(ownheads)
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        return (ownheadhashes, True, srvheadhashes,)
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    # full blown discovery
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    # own nodes I know we both know
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    # treat remote heads (and maybe own heads) as a first implicit sample
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    # response
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    common = cl.incrementalmissingrevs(srvheads)
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    commoninsample = set(n for i, n in enumerate(sample) if yesno[i])
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    common.addbases(commoninsample)
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    # own nodes where I don't know if remote knows them
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    undecided = set(common.missingancestors(ownheads))
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    # own nodes I know remote lacks
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    missing = set()
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    full = False
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    while undecided:
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        if sample:
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            missinginsample = [n for i, n in enumerate(sample) if not yesno[i]]
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            missing.update(dag.descendantset(missinginsample, missing))
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            undecided.difference_update(missing)
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        if not undecided:
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            break
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        if full or common.hasbases():
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            if full:
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                ui.note(_("sampling from both directions\n"))
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            else:
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                ui.debug("taking initial sample\n")
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            samplefunc = _takefullsample
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            targetsize = fullsamplesize
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        else:
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            # use even cheaper initial sample
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            ui.debug("taking quick initial sample\n")
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            samplefunc = _takequicksample
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            targetsize = initialsamplesize
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        if len(undecided) < targetsize:
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            sample = list(undecided)
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        else:
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            sample = samplefunc(dag, undecided, targetsize)
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            sample = _limitsample(sample, targetsize)
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        roundtrips += 1
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        ui.progress(_('searching'), roundtrips, unit=_('queries'))
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        ui.debug("query %i; still undecided: %i, sample size is: %i\n"
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                 % (roundtrips, len(undecided), len(sample)))
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        # indices between sample and externalized version must match
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        sample = list(sample)
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        yesno = remote.known(dag.externalizeall(sample))
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        full = True
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        if sample:
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            commoninsample = set(n for i, n in enumerate(sample) if yesno[i])
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            common.addbases(commoninsample)
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            common.removeancestorsfrom(undecided)
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    # heads(common) == heads(common.bases) since common represents common.bases
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    # and all its ancestors
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    result = dag.headsetofconnecteds(common.bases)
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    # common.bases can include nullrev, but our contract requires us to not
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    # return any heads in that case, so discard that
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    result.discard(nullrev)
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    elapsed = util.timer() - start
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    ui.progress(_('searching'), None)
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    ui.debug("%d total queries in %.4fs\n" % (roundtrips, elapsed))
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    msg = ('found %d common and %d unknown server heads,'
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           ' %d roundtrips in %.4fs\n')
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    missing = set(result) - set(srvheads)
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    ui.log('discovery', msg, len(result), len(missing), roundtrips,
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           elapsed)
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    if not result and srvheadhashes != [nullid]:
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        if abortwhenunrelated:
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            raise error.Abort(_("repository is unrelated"))
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        else:
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            ui.warn(_("warning: repository is unrelated\n"))
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        return ({nullid}, True, srvheadhashes,)
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    anyincoming = (srvheadhashes != [nullid])
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    return dag.externalizeall(result), anyincoming, srvheadhashes