perf: clear vfs audit_cache before each run
When generating a stream clone, we spend a large amount of time auditing path.
Before this changes, the first run was warming the vfs cache for the other
runs, leading to a large runtime difference and a "faulty" reported timing for
the operation.
We now clear this important cache between run to get a more realistic timing.
Below are some example of median time change when clearing these cases. The
maximum time for a run did not changed significantly.
### data-env-vars.name = mozilla-central-2018-08-01-zstd-sparse-revlog
# benchmark.name = hg.perf.exchange.stream.generate
# bin-env-vars.hg.flavor = default
# bin-env-vars.hg.py-re2-module = default
# benchmark.variants.version = latest
no-clearing: 17.289905
cache-clearing: 21.587965 (+24.86%, +4.30)
## data-env-vars.name = mozilla-central-2024-03-22-zstd-sparse-revlog
no-clearing: 32.670748
cache-clearing: 40.467095 (+23.86%, +7.80)
## data-env-vars.name = mozilla-try-2019-02-18-zstd-sparse-revlog
no-clearing: 37.838858
cache-clearing: 46.072749 (+21.76%, +8.23)
## data-env-vars.name = mozilla-unified-2024-03-22-zstd-sparse-revlog
no-clearing: 32.969395
cache-clearing: 39.646209 (+20.25%, +6.68)
In addition, this significantly reduce the timing difference between the
performance command, from the perf extensions and a `real `hg bundle` call
producing a stream bundle. Some significant differences remain especially on
the "mozilla-try" repositories, but they are now smaller.
Note that some of that difference will actually not be
attributable to the stream generation (like maybe phases or branch map
computation).
Below are some benchmarks done on a currently draft changeset fixing some
unrelated slowness in `hg bundle` (34a78972af409d1ff37c29e60f6ca811ad1a457d)
### data-env-vars.name = mozilla-central-2018-08-01-zstd-sparse-revlog
# bin-env-vars.hg.flavor = default
# bin-env-vars.hg.py-re2-module = default
hg.perf.exchange.stream.generate: 21.587965
hg.command.bundle: 24.301799 (+12.57%, +2.71)
## data-env-vars.name = mozilla-central-2024-03-22-zstd-sparse-revlog
hg.perf.exchange.stream.generate: 40.467095
hg.command.bundle: 44.831317 (+10.78%, +4.36)
## data-env-vars.name = mozilla-unified-2024-03-22-zstd-sparse-revlog
hg.perf.exchange.stream.generate: 39.646209
hg.command.bundle: 45.395258 (+14.50%, +5.75)
## data-env-vars.name = mozilla-try-2019-02-18-zstd-sparse-revlog
hg.perf.exchange.stream.generate: 46.072749
hg.command.bundle: 55.882608 (+21.29%, +9.81)
## data-env-vars.name = mozilla-try-2023-03-22-zlib-general-delta
hg.perf.exchange.stream.generate: 334.716708
hg.command.bundle: 377.856767 (+12.89%, +43.14)
## data-env-vars.name = mozilla-try-2023-03-22-zstd-sparse-revlog
hg.perf.exchange.stream.generate: 302.972301
hg.command.bundle: 326.098755 (+7.63%, +23.13)
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()