Mercurial > hg
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
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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()