|
|
# 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).
|
|
|
"""
|
|
|
|
|
|
import collections
|
|
|
from node import nullid, nullrev
|
|
|
from i18n import _
|
|
|
import random
|
|
|
import util, dagutil
|
|
|
|
|
|
def _updatesample(dag, nodes, sample, quicksamplesize=0):
|
|
|
"""update an existing sample to match the expected size
|
|
|
|
|
|
The sample is updated with nodes exponentially distant from each head of the
|
|
|
<nodes> 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 <nodes> set are
|
|
|
reached.
|
|
|
|
|
|
:dag: a dag object from dagutil
|
|
|
:nodes: set of nodes we want to discover (if None, assume the whole dag)
|
|
|
:sample: a sample to update
|
|
|
:quicksamplesize: optional target size of the sample"""
|
|
|
# if nodes is empty we scan the entire graph
|
|
|
if nodes:
|
|
|
heads = dag.headsetofconnecteds(nodes)
|
|
|
else:
|
|
|
heads = dag.heads()
|
|
|
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 dag.parents(curr):
|
|
|
if not nodes or p in nodes:
|
|
|
dist.setdefault(p, d + 1)
|
|
|
visit.append(p)
|
|
|
|
|
|
def _takequicksample(dag, nodes, size):
|
|
|
"""takes a quick sample of size <size>
|
|
|
|
|
|
It is meant for initial sampling and focuses on querying heads and close
|
|
|
ancestors of heads.
|
|
|
|
|
|
:dag: a dag object
|
|
|
:nodes: set of nodes to discover
|
|
|
:size: the maximum size of the sample"""
|
|
|
sample = dag.headsetofconnecteds(nodes)
|
|
|
if size <= len(sample):
|
|
|
return _limitsample(sample, size)
|
|
|
_updatesample(dag, None, sample, quicksamplesize=size)
|
|
|
return sample
|
|
|
|
|
|
def _takefullsample(dag, nodes, size):
|
|
|
sample = dag.headsetofconnecteds(nodes)
|
|
|
# update from heads
|
|
|
_updatesample(dag, nodes, sample)
|
|
|
# update from roots
|
|
|
_updatesample(dag.inverse(), nodes, sample)
|
|
|
assert sample
|
|
|
sample = _limitsample(sample, size)
|
|
|
if len(sample) < size:
|
|
|
more = size - len(sample)
|
|
|
sample.update(random.sample(list(nodes - sample), more))
|
|
|
return sample
|
|
|
|
|
|
def _limitsample(sample, desiredlen):
|
|
|
"""return a random subset of sample of at most desiredlen item"""
|
|
|
if len(sample) > desiredlen:
|
|
|
sample = set(random.sample(sample, desiredlen))
|
|
|
return sample
|
|
|
|
|
|
def findcommonheads(ui, local, remote,
|
|
|
initialsamplesize=100,
|
|
|
fullsamplesize=200,
|
|
|
abortwhenunrelated=True):
|
|
|
'''Return a tuple (common, anyincoming, remoteheads) used to identify
|
|
|
missing nodes from or in remote.
|
|
|
'''
|
|
|
roundtrips = 0
|
|
|
cl = local.changelog
|
|
|
dag = dagutil.revlogdag(cl)
|
|
|
|
|
|
# early exit if we know all the specified remote heads already
|
|
|
ui.debug("query 1; heads\n")
|
|
|
roundtrips += 1
|
|
|
ownheads = dag.heads()
|
|
|
sample = _limitsample(ownheads, initialsamplesize)
|
|
|
# indices between sample and externalized version must match
|
|
|
sample = list(sample)
|
|
|
batch = remote.batch()
|
|
|
srvheadhashesref = batch.heads()
|
|
|
yesnoref = batch.known(dag.externalizeall(sample))
|
|
|
batch.submit()
|
|
|
srvheadhashes = srvheadhashesref.value
|
|
|
yesno = yesnoref.value
|
|
|
|
|
|
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(_("searching for changes\n"))
|
|
|
|
|
|
srvheads = dag.internalizeall(srvheadhashes, filterunknown=True)
|
|
|
if len(srvheads) == len(srvheadhashes):
|
|
|
ui.debug("all remote heads known locally\n")
|
|
|
return (srvheadhashes, False, srvheadhashes,)
|
|
|
|
|
|
if sample and len(ownheads) <= initialsamplesize and all(yesno):
|
|
|
ui.note(_("all local heads known remotely\n"))
|
|
|
ownheadhashes = dag.externalizeall(ownheads)
|
|
|
return (ownheadhashes, True, srvheadhashes,)
|
|
|
|
|
|
# full blown discovery
|
|
|
|
|
|
# own nodes I know we both know
|
|
|
# treat remote heads (and maybe own heads) as a first implicit sample
|
|
|
# response
|
|
|
common = cl.incrementalmissingrevs(srvheads)
|
|
|
commoninsample = set(n for i, n in enumerate(sample) if yesno[i])
|
|
|
common.addbases(commoninsample)
|
|
|
# own nodes where I don't know if remote knows them
|
|
|
undecided = set(common.missingancestors(ownheads))
|
|
|
# own nodes I know remote lacks
|
|
|
missing = set()
|
|
|
|
|
|
full = False
|
|
|
while undecided:
|
|
|
|
|
|
if sample:
|
|
|
missinginsample = [n for i, n in enumerate(sample) if not yesno[i]]
|
|
|
missing.update(dag.descendantset(missinginsample, missing))
|
|
|
|
|
|
undecided.difference_update(missing)
|
|
|
|
|
|
if not undecided:
|
|
|
break
|
|
|
|
|
|
if full or common.hasbases():
|
|
|
if full:
|
|
|
ui.note(_("sampling from both directions\n"))
|
|
|
else:
|
|
|
ui.debug("taking initial sample\n")
|
|
|
samplefunc = _takefullsample
|
|
|
targetsize = fullsamplesize
|
|
|
else:
|
|
|
# use even cheaper initial sample
|
|
|
ui.debug("taking quick initial sample\n")
|
|
|
samplefunc = _takequicksample
|
|
|
targetsize = initialsamplesize
|
|
|
if len(undecided) < targetsize:
|
|
|
sample = list(undecided)
|
|
|
else:
|
|
|
sample = samplefunc(dag, undecided, targetsize)
|
|
|
sample = _limitsample(sample, targetsize)
|
|
|
|
|
|
roundtrips += 1
|
|
|
ui.progress(_('searching'), roundtrips, unit=_('queries'))
|
|
|
ui.debug("query %i; still undecided: %i, sample size is: %i\n"
|
|
|
% (roundtrips, len(undecided), len(sample)))
|
|
|
# indices between sample and externalized version must match
|
|
|
sample = list(sample)
|
|
|
yesno = remote.known(dag.externalizeall(sample))
|
|
|
full = True
|
|
|
|
|
|
if sample:
|
|
|
commoninsample = set(n for i, n in enumerate(sample) if yesno[i])
|
|
|
common.addbases(commoninsample)
|
|
|
common.removeancestorsfrom(undecided)
|
|
|
|
|
|
# heads(common) == heads(common.bases) since common represents common.bases
|
|
|
# and all its ancestors
|
|
|
result = dag.headsetofconnecteds(common.bases)
|
|
|
# common.bases can include nullrev, but our contract requires us to not
|
|
|
# return any heads in that case, so discard that
|
|
|
result.discard(nullrev)
|
|
|
ui.progress(_('searching'), None)
|
|
|
ui.debug("%d total queries\n" % roundtrips)
|
|
|
|
|
|
if not result and srvheadhashes != [nullid]:
|
|
|
if abortwhenunrelated:
|
|
|
raise util.Abort(_("repository is unrelated"))
|
|
|
else:
|
|
|
ui.warn(_("warning: repository is unrelated\n"))
|
|
|
return (set([nullid]), True, srvheadhashes,)
|
|
|
|
|
|
anyincoming = (srvheadhashes != [nullid])
|
|
|
return dag.externalizeall(result), anyincoming, srvheadhashes
|
|
|
|
