view tests/test-ancestor.py @ 51681:522b4d729e89

mmap: populate the mapping by default Without pre-population, accessing all data through a mmap can result in many pagefault, reducing performance significantly. If the mmap is prepopulated, the performance can no longer get slower than a full read. (See benchmark number below) In some cases were very few data is read, prepopulating can be overkill and slower than populating on access (through page fault). So that behavior can be controlled when the caller can pre-determine the best behavior. (See benchmark number below) In addition, testing with populating in a secondary thread yield great result combining the best of each approach. This might be implemented in later changesets. In all cases, using mmap has a great effect on memory usage when many processes run in parallel on the same machine. ### Benchmarks # What did I run A couple of month back I ran a large benchmark campaign to assess the impact of various approach for using mmap with the revlog (and other files), it highlighted a few benchmarks that capture the impact of the changes well. So to validate this change I checked the following: - log command displaying various revisions (read the changelog index) - log command displaying the patch of listed revisions (read the changelog index, the manifest index and a few files indexes) - unbundling a few revisions (read and write changelog, manifest and few files indexes, and walk the graph to update some cache) - pushing a few revisions (read and write changelog, manifest and few files indexes, walk the graph to update some cache, performs various accesses locally and remotely during discovery) Benchmarks were run using the default module policy (c+py) and the rust one. No significant difference were found between the two implementation, so we will present result using the default policy (unless otherwise specified). I ran them on a few repositories : - mercurial: a "public changeset only" copy of mercurial from 2018-08-01 using zstd compression and sparse-revlog - pypy: a copy of pypy from 2018-08-01 using zstd compression and sparse-revlog - netbeans: a copy of netbeans from 2018-08-01 using zstd compression and sparse-revlog - mozilla-try: a copy of mozilla-try from 2019-02-18 using zstd compression and sparse-revlog - mozilla-try persistent-nodemap: Same as the above but with a persistent nodemap. Used for the log --patch benchmark only # Results For the smaller repositories (mercurial, pypy), the impact of mmap is almost imperceptible, other cost dominating the operation. The impact of prepopulating is undiscernible in the benchmark we ran. For larger repositories the benchmark support explanation given above: On netbeans, the log can be about 1% faster without repopulation (for a difference < 100ms) but unbundle becomes a bit slower, even when small. ### data-env-vars.name = netbeans-2018-08-01-zstd-sparse-revlog # benchmark.name = hg.command.unbundle # benchmark.variants.issue6528 = disabled # benchmark.variants.reuse-external-delta-parent = yes # benchmark.variants.revs = any-1-extra-rev # benchmark.variants.source = unbundle # benchmark.variants.verbosity = quiet with-populate: 0.240157 no-populate: 0.265087 (+10.38%, +0.02) # benchmark.variants.revs = any-100-extra-rev with-populate: 1.459518 no-populate: 1.481290 (+1.49%, +0.02) ## benchmark.name = hg.command.push # benchmark.variants.explicit-rev = none # benchmark.variants.issue6528 = disabled # benchmark.variants.protocol = ssh # benchmark.variants.reuse-external-delta-parent = yes # benchmark.variants.revs = any-1-extra-rev with-populate: 0.771919 no-populate: 0.792025 (+2.60%, +0.02) # benchmark.variants.revs = any-100-extra-rev with-populate: 1.459518 no-populate: 1.481290 (+1.49%, +0.02) For mozilla-try, the "slow down" from pre-populate for small `hg log` is more visible, but still small in absolute time. (using rust value for the persistent nodemap value to be relevant). ### data-env-vars.name = mozilla-try-2019-02-18-ds2-pnm # benchmark.name = hg.command.log # bin-env-vars.hg.flavor = rust # benchmark.variants.patch = yes # benchmark.variants.limit-rev = 1 with-populate: 0.237813 no-populate: 0.229452 (-3.52%, -0.01) # benchmark.variants.limit-rev = 10 # benchmark.variants.patch = yes with-populate: 1.213578 no-populate: 1.205189 ### data-env-vars.name = mozilla-try-2019-02-18-zstd-sparse-revlog # benchmark.variants.limit-rev = 1000 # benchmark.variants.patch = no # benchmark.variants.rev = tip with-populate: 0.198607 no-populate: 0.195038 (-1.80%, -0.00) However pre-populating provide a significant boost on more complex operations like unbundle or push: ### data-env-vars.name = mozilla-try-2019-02-18-zstd-sparse-revlog # benchmark.name = hg.command.push # benchmark.variants.explicit-rev = none # benchmark.variants.issue6528 = disabled # benchmark.variants.protocol = ssh # benchmark.variants.reuse-external-delta-parent = yes # benchmark.variants.revs = any-1-extra-rev with-populate: 4.798632 no-populate: 4.953295 (+3.22%, +0.15) # benchmark.variants.revs = any-100-extra-rev with-populate: 4.903618 no-populate: 5.014963 (+2.27%, +0.11) ## benchmark.name = hg.command.unbundle # benchmark.variants.revs = any-1-extra-rev with-populate: 1.423411 no-populate: 1.585365 (+11.38%, +0.16) # benchmark.variants.revs = any-100-extra-rev with-populate: 1.537909 no-populate: 1.688489 (+9.79%, +0.15)
author Pierre-Yves David <pierre-yves.david@octobus.net>
date Thu, 11 Apr 2024 00:02:07 +0200
parents d718eddf01d9
children 493034cc3265
line wrap: on
line source

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,
)


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 range(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 range(len(graph)):
        ancs[i] = {i}
        if graph[i] == [nullrev]:
            continue
        for p in graph[i]:
            ancs[i].update(ancs[p])
    return ancs


class naiveincrementalmissingancestors:
    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 range(graphcount):
        graph = buildgraph(rng)
        ancs = buildancestorsets(graph)
        gerrs = [0]
        for _ in range(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 = []
            for _ in range(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],
    1: [0, -1],
    2: [1, -1],
    3: [1, -1],
    4: [2, -1],
    5: [4, -1],
    6: [4, -1],
    7: [4, -1],
    8: [-1, -1],
    9: [6, 7],
    10: [5, -1],
    11: [3, 7],
    12: [9, -1],
    13: [8, -1],
}


def test_missingancestors_explicit():
    """A few explicit cases, easier to check for catching errors in refactors.

    The bigger graph at the end has been produced by the random generator
    above, and we have some evidence that the other tests don't cover it.
    """
    for i, (bases, revs) in enumerate(
        (
            ({1, 2, 3, 4, 7}, set(range(10))),
            ({10}, set({11, 12, 13, 14})),
            ({7}, set({1, 2, 3, 4, 5})),
        )
    ):
        print("%% removeancestorsfrom(), example %d" % (i + 1))
        missanc = ancestor.incrementalmissingancestors(graph.get, bases)
        missanc.removeancestorsfrom(revs)
        print("remaining (sorted): %s" % sorted(list(revs)))

    for i, (bases, revs) in enumerate(
        (
            ({10}, {11}),
            ({11}, {10}),
            ({7}, {9, 11}),
        )
    ):
        print("%% missingancestors(), example %d" % (i + 1))
        missanc = ancestor.incrementalmissingancestors(graph.get, bases)
        print("return %s" % missanc.missingancestors(revs))

    print("% removeancestorsfrom(), bigger graph")
    vecgraph = [
        [-1, -1],
        [0, -1],
        [1, 0],
        [2, 1],
        [3, -1],
        [4, -1],
        [5, 1],
        [2, -1],
        [7, -1],
        [8, -1],
        [9, -1],
        [10, 1],
        [3, -1],
        [12, -1],
        [13, -1],
        [14, -1],
        [4, -1],
        [16, -1],
        [17, -1],
        [18, -1],
        [19, 11],
        [20, -1],
        [21, -1],
        [22, -1],
        [23, -1],
        [2, -1],
        [3, -1],
        [26, 24],
        [27, -1],
        [28, -1],
        [12, -1],
        [1, -1],
        [1, 9],
        [32, -1],
        [33, -1],
        [34, 31],
        [35, -1],
        [36, 26],
        [37, -1],
        [38, -1],
        [39, -1],
        [40, -1],
        [41, -1],
        [42, 26],
        [0, -1],
        [44, -1],
        [45, 4],
        [40, -1],
        [47, -1],
        [36, 0],
        [49, -1],
        [-1, -1],
        [51, -1],
        [52, -1],
        [53, -1],
        [14, -1],
        [55, -1],
        [15, -1],
        [23, -1],
        [58, -1],
        [59, -1],
        [2, -1],
        [61, 59],
        [62, -1],
        [63, -1],
        [-1, -1],
        [65, -1],
        [66, -1],
        [67, -1],
        [68, -1],
        [37, 28],
        [69, 25],
        [71, -1],
        [72, -1],
        [50, 2],
        [74, -1],
        [12, -1],
        [18, -1],
        [77, -1],
        [78, -1],
        [79, -1],
        [43, 33],
        [81, -1],
        [82, -1],
        [83, -1],
        [84, 45],
        [85, -1],
        [86, -1],
        [-1, -1],
        [88, -1],
        [-1, -1],
        [76, 83],
        [44, -1],
        [92, -1],
        [93, -1],
        [9, -1],
        [95, 67],
        [96, -1],
        [97, -1],
        [-1, -1],
    ]
    problem_rev = 28
    problem_base = 70
    # problem_rev is a parent of problem_base, but a faulty implementation
    # could forget to remove it.
    bases = {60, 26, 70, 3, 96, 19, 98, 49, 97, 47, 1, 6}
    if problem_rev not in vecgraph[problem_base] or problem_base not in bases:
        print("Conditions have changed")
    missanc = ancestor.incrementalmissingancestors(vecgraph.__getitem__, bases)
    revs = {4, 12, 41, 28, 68, 38, 1, 30, 56, 44}
    missanc.removeancestorsfrom(revs)
    if 28 in revs:
        print("Failed!")
    else:
        print("Ok")


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])

    # Test with stoprev >= min(initrevs)
    s = genlazyancestors([11, 13], stoprev=11, inclusive=True)
    printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0])
    s = genlazyancestors([11, 13], stoprev=12, inclusive=True)
    printlazyancestors(s, [11, 13, 7, 9, 8, 3, 6, 4, 1, -1, 0])

    # Contiguous chains: 5->4, 2->1 (where 1 is in seen set), 1->0
    s = genlazyancestors([10, 1], inclusive=True)
    printlazyancestors(s, [2, 10, 4, 5, -1, 0, 1])


# 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 hasattr(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 = int(a, base=0)  # accepts base 10 or 16 strings

    if seed is None:
        try:
            seed = int(binascii.hexlify(os.urandom(16)), 16)
        except AttributeError:
            seed = int(time.time() * 1000)

    rng = random.Random(seed)
    test_missingancestors_explicit()
    test_missingancestors(seed, rng)
    test_lazyancestors()
    test_gca()


if __name__ == '__main__':
    main()