Mercurial > hg
view tests/test-ancestor.py @ 44363:f7459da77f23
nodemap: introduce an option to use mmap to read the nodemap mapping
The performance and memory benefit is much greater if we don't have to copy all
the data in memory for each information. So we introduce an option (on by
default) to read the data using mmap.
This changeset is the last one definition the API for index support nodemap
data. (they have to be able to use the mmaping).
Below are some benchmark comparing the best we currently have in 5.3 with the
final step of this series (using the persistent nodemap implementation in
Rust). The benchmark run `hg perfindex` with various revset and the following
variants:
Before:
* do not use the persistent nodemap
* use the CPython implementation of the index for nodemap
* use mmapping of the changelog index
After:
* use the MixedIndex Rust code, with the NodeTree object for nodemap access
(still in review)
* use the persistent nodemap data from disk
* access the persistent nodemap data through mmap
* use mmapping of the changelog index
The persistent nodemap greatly speed up most operation on very large
repositories. Some of the previously very fast lookup end up a bit slower because
the persistent nodemap has to be setup. However the absolute slowdown is very
small and won't matters in the big picture.
Here are some numbers (in seconds) for the reference copy of mozilla-try:
Revset Before After abs-change speedup
-10000: 0.004622 0.005532 0.000910 × 0.83
-10: 0.000050 0.000132 0.000082 × 0.37
tip 0.000052 0.000085 0.000033 × 0.61
0 + (-10000:) 0.028222 0.005337 -0.022885 × 5.29
0 0.023521 0.000084 -0.023437 × 280.01
(-10000:) + 0 0.235539 0.005308 -0.230231 × 44.37
(-10:) + :9 0.232883 0.000180 -0.232703 ×1293.79
(-10000:) + (:99) 0.238735 0.005358 -0.233377 × 44.55
:99 + (-10000:) 0.317942 0.005593 -0.312349 × 56.84
:9 + (-10:) 0.313372 0.000179 -0.313193 ×1750.68
:9 0.316450 0.000143 -0.316307 ×2212.93
On smaller repositories, the cost of nodemap related operation is not as big, so
the win is much more modest. Yet it helps shaving a handful of millisecond here
and there.
Here are some numbers (in seconds) for the reference copy of mercurial:
Revset Before After abs-change speedup
-10: 0.000065 0.000097 0.000032 × 0.67
tip 0.000063 0.000078 0.000015 × 0.80
0 0.000561 0.000079 -0.000482 × 7.10
-10000: 0.004609 0.003648 -0.000961 × 1.26
0 + (-10000:) 0.005023 0.003715 -0.001307 × 1.35
(-10:) + :9 0.002187 0.000108 -0.002079 ×20.25
(-10000:) + 0 0.006252 0.003716 -0.002536 × 1.68
(-10000:) + (:99) 0.006367 0.003707 -0.002660 × 1.71
:9 + (-10:) 0.003846 0.000110 -0.003736 ×34.96
:9 0.003854 0.000099 -0.003755 ×38.92
:99 + (-10000:) 0.007644 0.003778 -0.003866 × 2.02
Differential Revision: https://phab.mercurial-scm.org/D7894
author | Pierre-Yves David <pierre-yves.david@octobus.net> |
---|---|
date | Tue, 11 Feb 2020 11:18:52 +0100 |
parents | 2372284d9457 |
children | 89a2afe31e82 |
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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 = [] 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()