discovery: always use batching now that all peers support batching

Some peers will transparently downgrade batched requests to
non-batched ones, but that simplifies code for everyone using
batching.

mercurial/setdiscovery.py

@@ -1,251 +1,241 b''
 # setdiscovery.py - improved discovery of common nodeset for mercurial
 #
 # Copyright 2010 Benoit Boissinot <[e-mail]>
 # and Peter Arrenbrecht <[e-mail]>
 #
 # 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)
-    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 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