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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, nullrev
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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, 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 = 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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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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'''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 = _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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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 len(ownheads) <= initialsamplesize 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 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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ui.progress(_('searching'), None)
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ui.debug("%d total queries\n" % roundtrips)
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if not result and srvheadhashes != [nullid]:
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if abortwhenunrelated:
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raise util.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 (set([nullid]), True, srvheadhashes,)
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anyincoming = (srvheadhashes != [nullid])
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return dag.externalizeall(result), anyincoming, srvheadhashes
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