wireprotov2: move response handling out of httppeer
And fix some bugs while we're here.
The code for processing response data from the unified framing
protocol is mostly peer agnostic. The peer-specific bits are the
configuration of the client reactor and how I/O is performed. I
initially implemented things in httppeer for expediency.
This commit establishes a module for holding the peer API level
code for the framing based protocol. Inside this module we have
a class to help coordinate higher-level activities, such as managing
response object.
The client handler bits could be rolled into clientreactor. However,
I want clientreactor to be sans I/O and I want it to only be
concerned with protocol-level details, not higher-level concepts
like how protocol events are converted into peer API concepts. I
want clientreactor to receive a frame and then tell the caller what
should probably be done about it. If we start putting things like
future resolution into clientreactor, we'll constrain how the protocol
can be used (e.g. by requiring futures).
The new code is loosely based on what was in httppeer before. I
changed things a bit around response handling. We now buffer the
entire response "body" and then handle it as one atomic unit. This
fixed a bug around decoding CBOR data that spanned multiple frames.
I also fixed an off-by-one bug where we failed to read a single byte
CBOR value at the end of the stream. That's why tests have changed.
The new state of httppeer is much cleaner. It is largely agnostic
about framing protocol implementation details. That's how it should
be: the framing protocol is designed to be largely transport
agnostic. We want peers merely putting bytes on the wire and telling
the framing protocol where to read response data from.
There's still a bit of work to be done here, especially for
representing responses. But at least we're a step closer to having a
higher-level peer interface that can be plugged into the SSH peer
someday.
I initially added this class to wireprotoframing. However, we'll
eventually need version 2 specific functions to convert CBOR responses
into data structures expected by the code calling commands. This
needs to live somewhere. Since that code would be shared across peers,
we need a common module. We have wireprotov1peer for the equivalent
version 1 code. So I decided to establish wireprotov2peer.
Differential Revision: https://phab.mercurial-scm.org/D3379
from __future__ import absolute_import, print_function
import binascii
import getopt
import math
import os
import random
import sys
import time
from mercurial.node import nullrev
from mercurial import (
ancestor,
debugcommands,
hg,
pycompat,
ui as uimod,
util,
)
if pycompat.ispy3:
long = int
xrange = range
def buildgraph(rng, nodes=100, rootprob=0.05, mergeprob=0.2, prevprob=0.7):
'''nodes: total number of nodes in the graph
rootprob: probability that a new node (not 0) will be a root
mergeprob: probability that, excluding a root a node will be a merge
prevprob: probability that p1 will be the previous node
return value is a graph represented as an adjacency list.
'''
graph = [None] * nodes
for i in xrange(nodes):
if i == 0 or rng.random() < rootprob:
graph[i] = [nullrev]
elif i == 1:
graph[i] = [0]
elif rng.random() < mergeprob:
if i == 2 or rng.random() < prevprob:
# p1 is prev
p1 = i - 1
else:
p1 = rng.randrange(i - 1)
p2 = rng.choice(list(range(0, p1)) + list(range(p1 + 1, i)))
graph[i] = [p1, p2]
elif rng.random() < prevprob:
graph[i] = [i - 1]
else:
graph[i] = [rng.randrange(i - 1)]
return graph
def buildancestorsets(graph):
ancs = [None] * len(graph)
for i in xrange(len(graph)):
ancs[i] = {i}
if graph[i] == [nullrev]:
continue
for p in graph[i]:
ancs[i].update(ancs[p])
return ancs
class naiveincrementalmissingancestors(object):
def __init__(self, ancs, bases):
self.ancs = ancs
self.bases = set(bases)
def addbases(self, newbases):
self.bases.update(newbases)
def removeancestorsfrom(self, revs):
for base in self.bases:
if base != nullrev:
revs.difference_update(self.ancs[base])
revs.discard(nullrev)
def missingancestors(self, revs):
res = set()
for rev in revs:
if rev != nullrev:
res.update(self.ancs[rev])
for base in self.bases:
if base != nullrev:
res.difference_update(self.ancs[base])
return sorted(res)
def test_missingancestors(seed, rng):
# empirically observed to take around 1 second
graphcount = 100
testcount = 10
inccount = 10
nerrs = [0]
# the default mu and sigma give us a nice distribution of mostly
# single-digit counts (including 0) with some higher ones
def lognormrandom(mu, sigma):
return int(math.floor(rng.lognormvariate(mu, sigma)))
def samplerevs(nodes, mu=1.1, sigma=0.8):
count = min(lognormrandom(mu, sigma), len(nodes))
return rng.sample(nodes, count)
def err(seed, graph, bases, seq, output, expected):
if nerrs[0] == 0:
print('seed:', hex(seed)[:-1], file=sys.stderr)
if gerrs[0] == 0:
print('graph:', graph, file=sys.stderr)
print('* bases:', bases, file=sys.stderr)
print('* seq: ', seq, file=sys.stderr)
print('* output: ', output, file=sys.stderr)
print('* expected:', expected, file=sys.stderr)
nerrs[0] += 1
gerrs[0] += 1
for g in xrange(graphcount):
graph = buildgraph(rng)
ancs = buildancestorsets(graph)
gerrs = [0]
for _ in xrange(testcount):
# start from nullrev to include it as a possibility
graphnodes = range(nullrev, len(graph))
bases = samplerevs(graphnodes)
# fast algorithm
inc = ancestor.incrementalmissingancestors(graph.__getitem__, bases)
# reference slow algorithm
naiveinc = naiveincrementalmissingancestors(ancs, bases)
seq = []
revs = []
for _ in xrange(inccount):
if rng.random() < 0.2:
newbases = samplerevs(graphnodes)
seq.append(('addbases', newbases))
inc.addbases(newbases)
naiveinc.addbases(newbases)
if rng.random() < 0.4:
# larger set so that there are more revs to remove from
revs = samplerevs(graphnodes, mu=1.5)
seq.append(('removeancestorsfrom', revs))
hrevs = set(revs)
rrevs = set(revs)
inc.removeancestorsfrom(hrevs)
naiveinc.removeancestorsfrom(rrevs)
if hrevs != rrevs:
err(seed, graph, bases, seq, sorted(hrevs),
sorted(rrevs))
else:
revs = samplerevs(graphnodes)
seq.append(('missingancestors', revs))
h = inc.missingancestors(revs)
r = naiveinc.missingancestors(revs)
if h != r:
err(seed, graph, bases, seq, h, r)
# graph is a dict of child->parent adjacency lists for this graph:
# o 13
# |
# | o 12
# | |
# | | o 11
# | | |\
# | | | | o 10
# | | | | |
# | o---+ | 9
# | | | | |
# o | | | | 8
# / / / /
# | | o | 7
# | | | |
# o---+ | 6
# / / /
# | | o 5
# | |/
# | o 4
# | |
# o | 3
# | |
# | o 2
# |/
# o 1
# |
# o 0
graph = {0: [-1], 1: [0], 2: [1], 3: [1], 4: [2], 5: [4], 6: [4],
7: [4], 8: [-1], 9: [6, 7], 10: [5], 11: [3, 7], 12: [9],
13: [8]}
def genlazyancestors(revs, stoprev=0, inclusive=False):
print(("%% lazy ancestor set for %s, stoprev = %s, inclusive = %s" %
(revs, stoprev, inclusive)))
return ancestor.lazyancestors(graph.get, revs, stoprev=stoprev,
inclusive=inclusive)
def printlazyancestors(s, l):
print('membership: %r' % [n for n in l if n in s])
print('iteration: %r' % list(s))
def test_lazyancestors():
# Empty revs
s = genlazyancestors([])
printlazyancestors(s, [3, 0, -1])
# Standard example
s = genlazyancestors([11, 13])
printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0])
# Standard with ancestry in the initial set (1 is ancestor of 3)
s = genlazyancestors([1, 3])
printlazyancestors(s, [1, -1, 0])
# Including revs
s = genlazyancestors([11, 13], inclusive=True)
printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0])
# Test with stoprev
s = genlazyancestors([11, 13], stoprev=6)
printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0])
s = genlazyancestors([11, 13], stoprev=6, inclusive=True)
printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0])
# The C gca algorithm requires a real repo. These are textual descriptions of
# DAGs that have been known to be problematic, and, optionally, known pairs
# of revisions and their expected ancestor list.
dagtests = [
(b'+2*2*2/*3/2', {}),
(b'+3*3/*2*2/*4*4/*4/2*4/2*2', {}),
(b'+2*2*/2*4*/4*/3*2/4', {(6, 7): [3, 5]}),
]
def test_gca():
u = uimod.ui.load()
for i, (dag, tests) in enumerate(dagtests):
repo = hg.repository(u, b'gca%d' % i, create=1)
cl = repo.changelog
if not util.safehasattr(cl.index, 'ancestors'):
# C version not available
return
debugcommands.debugbuilddag(u, repo, dag)
# Compare the results of the Python and C versions. This does not
# include choosing a winner when more than one gca exists -- we make
# sure both return exactly the same set of gcas.
# Also compare against expected results, if available.
for a in cl:
for b in cl:
cgcas = sorted(cl.index.ancestors(a, b))
pygcas = sorted(ancestor.ancestors(cl.parentrevs, a, b))
expected = None
if (a, b) in tests:
expected = tests[(a, b)]
if cgcas != pygcas or (expected and cgcas != expected):
print("test_gca: for dag %s, gcas for %d, %d:"
% (dag, a, b))
print(" C returned: %s" % cgcas)
print(" Python returned: %s" % pygcas)
if expected:
print(" expected: %s" % expected)
def main():
seed = None
opts, args = getopt.getopt(sys.argv[1:], 's:', ['seed='])
for o, a in opts:
if o in ('-s', '--seed'):
seed = long(a, base=0) # accepts base 10 or 16 strings
if seed is None:
try:
seed = long(binascii.hexlify(os.urandom(16)), 16)
except AttributeError:
seed = long(time.time() * 1000)
rng = random.Random(seed)
test_missingancestors(seed, rng)
test_lazyancestors()
test_gca()
if __name__ == '__main__':
main()