view tests/test-ancestor.py @ 29859:a1092e2d70a3

help: internals topic for wire protocol

The Mercurial wire protocol is under-documented. This includes a lack
of source docstrings and comments as well as pages on the official
wiki.

This patch adds the beginnings of "internals" documentation on the
wire protocol.

The documentation should have nearly complete coverage on the
lower-level parts of the protocol, such as the different transport
mechanims, how commands and arguments are sent, capabilities, and,
of course, the commands themselves.

As part of writing this documentation, I discovered a number of
deficiencies in the protocol and bugs in the implementation. I've
started sending patches for some of the issues. I hope to send a lot
more.

This patch starts with the scaffolding for a new internals page.

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,
    commands,
    hg,
    ui as uimod,
    util,
)

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(range(0, p1) + 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] = set([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.
dagtests = [
    '+2*2*2/*3/2',
    '+3*3/*2*2/*4*4/*4/2*4/2*2',
]
def test_gca():
    u = uimod.ui()
    for i, dag in enumerate(dagtests):
        repo = hg.repository(u, 'gca%d' % i, create=1)
        cl = repo.changelog
        if not util.safehasattr(cl.index, 'ancestors'):
            # C version not available
            return

        commands.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.
        for a in cl:
            for b in cl:
                cgcas = sorted(cl.index.ancestors(a, b))
                pygcas = sorted(ancestor.ancestors(cl.parentrevs, a, b))
                if cgcas != pygcas:
                    print("test_gca: for dag %s, gcas for %d, %d:"
                          % (dag, a, b))
                    print("  C returned:      %s" % cgcas)
                    print("  Python returned: %s" % pygcas)

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()