mercurial/setdiscovery.py
author Augie Fackler <raf@durin42.com>
Wed, 12 Mar 2014 13:21:17 -0400
changeset 20685 56b1f39dd0c1
parent 20656 cdecbc5ab504
child 23083 ee45f5c2ffcc
permissions -rw-r--r--
test-url: move from dict() construction to {} literals The latter are both faster and more consistent across Python 2 and 3.
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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 node import nullid
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from i18n import _
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import random
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import util, dagutil
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def _updatesample(dag, nodes, sample, always, quicksamplesize=0):
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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 = util.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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            if curr not in always: # need this check for the early exit below
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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 _setupsample(dag, nodes, size):
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    if len(nodes) <= size:
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        return set(nodes), None, 0
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    always = dag.headsetofconnecteds(nodes)
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    desiredlen = size - len(always)
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    if desiredlen <= 0:
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        # This could be bad if there are very many heads, all unknown to the
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        # server. We're counting on long request support here.
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        return always, None, desiredlen
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    return always, set(), desiredlen
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def _takequicksample(dag, nodes, size, initial):
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    always, sample, desiredlen = _setupsample(dag, nodes, size)
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    if sample is None:
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        return always
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    if initial:
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        fromset = None
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    else:
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        fromset = nodes
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    _updatesample(dag, fromset, sample, always, quicksamplesize=desiredlen)
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    sample.update(always)
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    return sample
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def _takefullsample(dag, nodes, size):
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    always, sample, desiredlen = _setupsample(dag, nodes, size)
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    if sample is None:
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        return always
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    # update from heads
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    _updatesample(dag, nodes, sample, always)
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    # update from roots
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    _updatesample(dag.inverse(), nodes, sample, always)
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    assert sample
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    if len(sample) > desiredlen:
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        sample = set(random.sample(sample, desiredlen))
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    elif len(sample) < desiredlen:
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        more = desiredlen - len(sample)
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        sample.update(random.sample(list(nodes - sample - always), more))
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    sample.update(always)
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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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    '''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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    roundtrips = 0
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    cl = local.changelog
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    dag = dagutil.revlogdag(cl)
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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 = ownheads
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    if remote.local():
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        # stopgap until we have a proper localpeer that supports batch()
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        srvheadhashes = remote.heads()
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        yesno = remote.known(dag.externalizeall(sample))
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    elif remote.capable('batch'):
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        batch = remote.batch()
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        srvheadhashesref = batch.heads()
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        yesnoref = batch.known(dag.externalizeall(sample))
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        batch.submit()
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        srvheadhashes = srvheadhashesref.value
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        yesno = yesnoref.value
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    else:
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        # compatibility with pre-batch, but post-known remotes during 1.9
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        # development
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        srvheadhashes = remote.heads()
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        sample = []
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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 util.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 where I don't know if remote knows them
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    undecided = dag.nodeset()
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    # own nodes I know we both know
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    common = set()
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    # own nodes I know remote lacks
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    missing = set()
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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.update(dag.ancestorset(srvheads))
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    undecided.difference_update(common)
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    full = False
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    while undecided:
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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.update(dag.ancestorset(commoninsample, common))
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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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            undecided.difference_update(common)
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        if not undecided:
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            break
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        if full:
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            ui.note(_("sampling from both directions\n"))
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            sample = _takefullsample(dag, undecided, size=fullsamplesize)
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        elif common:
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            # use cheapish initial sample
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            ui.debug("taking initial sample\n")
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            sample = _takefullsample(dag, undecided, size=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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            sample = _takequicksample(dag, undecided, size=initialsamplesize,
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                                      initial=True)
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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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    result = dag.headsetofconnecteds(common)
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    ui.progress(_('searching'), None)
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    ui.debug("%d total queries\n" % roundtrips)
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   223
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   224
    if not result and srvheadhashes != [nullid]:
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   225
        if abortwhenunrelated:
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            raise util.Abort(_("repository is unrelated"))
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   227
        else:
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            ui.warn(_("warning: repository is unrelated\n"))
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   229
        return (set([nullid]), True, srvheadhashes,)
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   230
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   231
    anyincoming = (srvheadhashes != [nullid])
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   232
    return dag.externalizeall(result), anyincoming, srvheadhashes