strip: introduce a soft strip option
This is the first user-accessible way to use the archived phase introduced in
4.8. This implements a feature discussed during the Stockholm sprint, using
the archived phase for hiding changesets.
The archived phase behaves exactly as stripping: changesets are no longer
visible, but pulling/unbundling them will make then reappear. The only notable
difference is that unlike hard stripping, soft stripping does not affect
obsmarkers.
The next changeset will make use of the archived phase for history rewriting
command. However, having a way to manually trigger the feature first seems a
necessary step before exposing users to this phase; there is a way to
un-archived changesets (unbundling), so there must be a way to archive them
again.
Adding a flag to strip is a good way to provide access to the feature without
taking a too big risk on the final UI we want. The flag is experimental so it
won't be exposed by default.
Using the archived phase is faster and less traumatic for the repository than
actually stripping changesets.
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 = []
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], 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(xrange(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 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_explicit()
test_missingancestors(seed, rng)
test_lazyancestors()
test_gca()
if __name__ == '__main__':
main()