##// END OF EJS Templates
revset: inline spanset containment check (fix perf regression)...
revset: inline spanset containment check (fix perf regression) Calling a function is super expensive in python. We inline the trivial range comparison to get back to more sensible performance on common revset operation. Benchmark result below: Revision mapping: 0) 3f83fc5cfe71 2.9.2 release 1) bcfd44abad93 current @ 2) This revision revset #0: public() 0) wall 0.010890 comb 0.010000 user 0.010000 sys 0.000000 (best of 201) 1) wall 0.012109 comb 0.010000 user 0.010000 sys 0.000000 (best of 199) 2) wall 0.012211 comb 0.020000 user 0.020000 sys 0.000000 (best of 197) revset #1: :10000 and public() 0) wall 0.007141 comb 0.010000 user 0.010000 sys 0.000000 (best of 361) 1) wall 0.014139 comb 0.010000 user 0.010000 sys 0.000000 (best of 186) 2) wall 0.008334 comb 0.010000 user 0.010000 sys 0.000000 (best of 308) revset #2: draft() 0) wall 0.009610 comb 0.010000 user 0.010000 sys 0.000000 (best of 279) 1) wall 0.010942 comb 0.010000 user 0.010000 sys 0.000000 (best of 243) 2) wall 0.011036 comb 0.010000 user 0.010000 sys 0.000000 (best of 239) revset #3: :10000 and draft() 0) wall 0.006852 comb 0.010000 user 0.010000 sys 0.000000 (best of 383) 1) wall 0.014641 comb 0.010000 user 0.010000 sys 0.000000 (best of 183) 2) wall 0.008314 comb 0.010000 user 0.010000 sys 0.000000 (best of 299) We can see this changeset gains back the regression for `and` operation on spanset. We are still a bit slowerfor the `public()` and `draft()`. Predicates not touched by this changeset.

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dagutil.py
279 lines | 8.1 KiB | text/x-python | PythonLexer
# dagutil.py - dag utilities for mercurial
#
# Copyright 2010 Benoit Boissinot <bboissin@gmail.com>
# and Peter Arrenbrecht <peter@arrenbrecht.ch>
#
# This software may be used and distributed according to the terms of the
# GNU General Public License version 2 or any later version.
from node import nullrev
from i18n import _
class basedag(object):
'''generic interface for DAGs
terms:
"ix" (short for index) identifies a nodes internally,
"id" identifies one externally.
All params are ixs unless explicitly suffixed otherwise.
Pluralized params are lists or sets.
'''
def __init__(self):
self._inverse = None
def nodeset(self):
'''set of all node idxs'''
raise NotImplementedError
def heads(self):
'''list of head ixs'''
raise NotImplementedError
def parents(self, ix):
'''list of parents ixs of ix'''
raise NotImplementedError
def inverse(self):
'''inverse DAG, where parents becomes children, etc.'''
raise NotImplementedError
def ancestorset(self, starts, stops=None):
'''
set of all ancestors of starts (incl), but stop walk at stops (excl)
'''
raise NotImplementedError
def descendantset(self, starts, stops=None):
'''
set of all descendants of starts (incl), but stop walk at stops (excl)
'''
return self.inverse().ancestorset(starts, stops)
def headsetofconnecteds(self, ixs):
'''
subset of connected list of ixs so that no node has a descendant in it
By "connected list" we mean that if an ancestor and a descendant are in
the list, then so is at least one path connecting them.
'''
raise NotImplementedError
def externalize(self, ix):
'''return a list of (or set if given a set) of node ids'''
return self._externalize(ix)
def externalizeall(self, ixs):
'''return a list of (or set if given a set) of node ids'''
ids = self._externalizeall(ixs)
if isinstance(ixs, set):
return set(ids)
return list(ids)
def internalize(self, id):
'''return a list of (or set if given a set) of node ixs'''
return self._internalize(id)
def internalizeall(self, ids, filterunknown=False):
'''return a list of (or set if given a set) of node ids'''
ixs = self._internalizeall(ids, filterunknown)
if isinstance(ids, set):
return set(ixs)
return list(ixs)
class genericdag(basedag):
'''generic implementations for DAGs'''
def ancestorset(self, starts, stops=None):
stops = stops and set(stops) or set()
seen = set()
pending = list(starts)
while pending:
n = pending.pop()
if n not in seen and n not in stops:
seen.add(n)
pending.extend(self.parents(n))
return seen
def headsetofconnecteds(self, ixs):
hds = set(ixs)
if not hds:
return hds
for n in ixs:
for p in self.parents(n):
hds.discard(p)
assert hds
return hds
class revlogbaseddag(basedag):
'''generic dag interface to a revlog'''
def __init__(self, revlog, nodeset):
basedag.__init__(self)
self._revlog = revlog
self._heads = None
self._nodeset = nodeset
def nodeset(self):
return self._nodeset
def heads(self):
if self._heads is None:
self._heads = self._getheads()
return self._heads
def _externalize(self, ix):
return self._revlog.index[ix][7]
def _externalizeall(self, ixs):
idx = self._revlog.index
return [idx[i][7] for i in ixs]
def _internalize(self, id):
ix = self._revlog.rev(id)
if ix == nullrev:
raise LookupError(id, self._revlog.indexfile, _('nullid'))
return ix
def _internalizeall(self, ids, filterunknown):
rl = self._revlog
if filterunknown:
return [r for r in map(rl.nodemap.get, ids)
if (r is not None
and r != nullrev
and r not in rl.filteredrevs)]
return map(self._internalize, ids)
class revlogdag(revlogbaseddag):
'''dag interface to a revlog'''
def __init__(self, revlog):
revlogbaseddag.__init__(self, revlog, set(revlog))
def _getheads(self):
return [r for r in self._revlog.headrevs() if r != nullrev]
def parents(self, ix):
rlog = self._revlog
idx = rlog.index
revdata = idx[ix]
prev = revdata[5]
if prev != nullrev:
prev2 = revdata[6]
if prev2 == nullrev:
return [prev]
return [prev, prev2]
prev2 = revdata[6]
if prev2 != nullrev:
return [prev2]
return []
def inverse(self):
if self._inverse is None:
self._inverse = inverserevlogdag(self)
return self._inverse
def ancestorset(self, starts, stops=None):
rlog = self._revlog
idx = rlog.index
stops = stops and set(stops) or set()
seen = set()
pending = list(starts)
while pending:
rev = pending.pop()
if rev not in seen and rev not in stops:
seen.add(rev)
revdata = idx[rev]
for i in [5, 6]:
prev = revdata[i]
if prev != nullrev:
pending.append(prev)
return seen
def headsetofconnecteds(self, ixs):
if not ixs:
return set()
rlog = self._revlog
idx = rlog.index
headrevs = set(ixs)
for rev in ixs:
revdata = idx[rev]
for i in [5, 6]:
prev = revdata[i]
if prev != nullrev:
headrevs.discard(prev)
assert headrevs
return headrevs
def linearize(self, ixs):
'''linearize and topologically sort a list of revisions
The linearization process tries to create long runs of revs where
a child rev comes immediately after its first parent. This is done by
visiting the heads of the given revs in inverse topological order,
and for each visited rev, visiting its second parent, then its first
parent, then adding the rev itself to the output list.
'''
sorted = []
visit = list(self.headsetofconnecteds(ixs))
visit.sort(reverse=True)
finished = set()
while visit:
cur = visit.pop()
if cur < 0:
cur = -cur - 1
if cur not in finished:
sorted.append(cur)
finished.add(cur)
else:
visit.append(-cur - 1)
visit += [p for p in self.parents(cur)
if p in ixs and p not in finished]
assert len(sorted) == len(ixs)
return sorted
class inverserevlogdag(revlogbaseddag, genericdag):
'''inverse of an existing revlog dag; see revlogdag.inverse()'''
def __init__(self, orig):
revlogbaseddag.__init__(self, orig._revlog, orig._nodeset)
self._orig = orig
self._children = {}
self._roots = []
self._walkfrom = len(self._revlog) - 1
def _walkto(self, walkto):
rev = self._walkfrom
cs = self._children
roots = self._roots
idx = self._revlog.index
while rev >= walkto:
data = idx[rev]
isroot = True
for prev in [data[5], data[6]]: # parent revs
if prev != nullrev:
cs.setdefault(prev, []).append(rev)
isroot = False
if isroot:
roots.append(rev)
rev -= 1
self._walkfrom = rev
def _getheads(self):
self._walkto(nullrev)
return self._roots
def parents(self, ix):
if ix is None:
return []
if ix <= self._walkfrom:
self._walkto(ix)
return self._children.get(ix, [])
def inverse(self):
return self._orig