setdiscovery: extract sample limitation in a `_limitsample` function
We need to reuse this logic for the initial query. We extract it in a function
to unsure sample limiting is applied consistently in all cases.
# 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 node import nullid
from i18n import _
import random
import util, dagutil
def _updatesample(dag, nodes, sample, always, quicksamplesize=0):
# if nodes is empty we scan the entire graph
if nodes:
heads = dag.headsetofconnecteds(nodes)
else:
heads = dag.heads()
dist = {}
visit = util.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:
if curr not in always: # need this check for the early exit below
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 _setupsample(dag, nodes, size):
if len(nodes) <= size:
return set(nodes), None, 0
always = dag.headsetofconnecteds(nodes)
desiredlen = size - len(always)
if desiredlen <= 0:
# This could be bad if there are very many heads, all unknown to the
# server. We're counting on long request support here.
return always, None, desiredlen
return always, set(), desiredlen
def _takequicksample(dag, nodes, size, initial):
always, sample, desiredlen = _setupsample(dag, nodes, size)
if sample is None:
return always
if initial:
fromset = None
else:
fromset = nodes
_updatesample(dag, fromset, sample, always, quicksamplesize=desiredlen)
sample.update(always)
return sample
def _takefullsample(dag, nodes, size):
always, sample, desiredlen = _setupsample(dag, nodes, size)
if sample is None:
return always
# update from heads
_updatesample(dag, nodes, sample, always)
# update from roots
_updatesample(dag.inverse(), nodes, sample, always)
assert sample
sample = _limitsample(sample, desiredlen)
if len(sample) < desiredlen:
more = desiredlen - len(sample)
sample.update(random.sample(list(nodes - sample - always), more))
sample.update(always)
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 = ownheads
if remote.local():
# stopgap until we have a proper localpeer that supports batch()
srvheadhashes = remote.heads()
yesno = remote.known(dag.externalizeall(sample))
elif remote.capable('batch'):
batch = remote.batch()
srvheadhashesref = batch.heads()
yesnoref = batch.known(dag.externalizeall(sample))
batch.submit()
srvheadhashes = srvheadhashesref.value
yesno = yesnoref.value
else:
# compatibility with pre-batch, but post-known remotes during 1.9
# development
srvheadhashes = remote.heads()
sample = []
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 util.all(yesno):
ui.note(_("all local heads known remotely\n"))
ownheadhashes = dag.externalizeall(ownheads)
return (ownheadhashes, True, srvheadhashes,)
# full blown discovery
# own nodes where I don't know if remote knows them
undecided = dag.nodeset()
# own nodes I know we both know
common = set()
# own nodes I know remote lacks
missing = set()
# treat remote heads (and maybe own heads) as a first implicit sample
# response
common.update(dag.ancestorset(srvheads))
undecided.difference_update(common)
full = False
while undecided:
if sample:
commoninsample = set(n for i, n in enumerate(sample) if yesno[i])
common.update(dag.ancestorset(commoninsample, common))
missinginsample = [n for i, n in enumerate(sample) if not yesno[i]]
missing.update(dag.descendantset(missinginsample, missing))
undecided.difference_update(missing)
undecided.difference_update(common)
if not undecided:
break
if full:
ui.note(_("sampling from both directions\n"))
sample = _takefullsample(dag, undecided, size=fullsamplesize)
elif common:
# use cheapish initial sample
ui.debug("taking initial sample\n")
sample = _takefullsample(dag, undecided, size=fullsamplesize)
else:
# use even cheaper initial sample
ui.debug("taking quick initial sample\n")
sample = _takequicksample(dag, undecided, size=initialsamplesize,
initial=True)
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
result = dag.headsetofconnecteds(common)
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