Mercurial > hg
view tests/test-ancestor.py @ 35012:d80380ba8e7d
changegroup: use any node, not min(), in treemanifest's generatemanifests
This is fixing quadratic behavior, which is probably not noticeable in the
common case, but if a very large directory gets added here, it can get pretty
bad. This was noticed because we had some pushes that spent >25s in changegroup
generation calling min() here, according to profiling.
The original reasoning for min() being used in 829d369fc5a8 was that, at that
point in the series, we were adding almost everything to tmfnodes during the
first iteration through the loop , so we needed to avoid sending child
directories before parents. Later changes made it so that the child directories
were added only when we visited the parent directory (not all of them on the
first iteration), so this is no longer necessary - there won't be any child
directories in tmfnodes before the parents have been sent.
This does mean that the manifests are now exchanged unordered, whereas
previously we would essentially do [a, b, b/c, b/c/d, e], we now can send a, b,
and e in any order; b/c must still follow b, and b/c/d must still follow b/c.
Differential Revision: https://phab.mercurial-scm.org/D1351
author | Kyle Lippincott <spectral@google.com> |
---|---|
date | Wed, 08 Nov 2017 18:24:43 -0800 |
parents | f501322512b6 |
children | 6754d0c5e1b5 |
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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 = [ ('+2*2*2/*3/2', {}), ('+3*3/*2*2/*4*4/*4/2*4/2*2', {}), ('+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()