view tests/test-ancestor.py @ 30435:b86a448a2965

zstd: vendor python-zstandard 0.5.0

As the commit message for the previous changeset says, we wish
for zstd to be a 1st class citizen in Mercurial. To make that
happen, we need to enable Python to talk to the zstd C API. And
that requires bindings.

This commit vendors a copy of existing Python bindings. Why do we
need to vendor? As the commit message of the previous commit says,
relying on systems in the wild to have the bindings or zstd present
is a losing proposition. By distributing the zstd and bindings with
Mercurial, we significantly increase our chances that zstd will
work. Since zstd will deliver a better end-user experience by
achieving better performance, this benefits our users. Another
reason is that the Python bindings still aren't stable and the
API is somewhat fluid. While Mercurial could be coded to target
multiple versions of the Python bindings, it is safer to bundle
an explicit, known working version.

The added Python bindings are mostly a fully-featured interface
to the zstd C API. They allow one-shot operations, streaming,
reading and writing from objects implements the file object
protocol, dictionary compression, control over low-level compression
parameters, and more. The Python bindings work on Python 2.6,
2.7, and 3.3+ and have been tested on Linux and Windows. There are
CFFI bindings, but they are lacking compared to the C extension.
Upstream work will be needed before we can support zstd with PyPy.
But it will be possible.

The files added in this commit come from Git commit
e637c1b214d5f869cf8116c550dcae23ec13b677 from
https://github.com/indygreg/python-zstandard and are added without
modifications. Some files from the upstream repository have been
omitted, namely files related to continuous integration.

In the spirit of full disclosure, I'm the maintainer of the
"python-zstandard" project and have authored 100% of the code
added in this commit. Unfortunately, the Python bindings have
not been formally code reviewed by anyone. While I've tested
much of the code thoroughly (I even have tests that fuzz APIs),
there's a good chance there are bugs, memory leaks, not well
thought out APIs, etc. If someone wants to review the code and
send feedback to the GitHub project, it would be greatly
appreciated.

Despite my involvement with both projects, my opinions of code
style differ from Mercurial's. The code in this commit introduces
numerous code style violations in Mercurial's linters. So, the code
is excluded from most lints. However, some violations I agree with.
These have been added to the known violations ignore list for now.

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,
    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

        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.
        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()