##// END OF EJS Templates
spanset: directly use __contains__ instead of a lambda...
spanset: directly use __contains__ instead of a lambda Spanset are massively used in revset. First because the initial subset itself is a repo wide spanset. We speed up the __and__ operation by getting rid of a gratuitous lambda call. A more long terms solution would be to: 1. speed up operation between spansets, 2. have a special smartset for `all` revisions. In the mean time, this is a very simple fix that buyback some of the performance regression. Below is performance benchmark for trival `and` operation between two spansets. (Run on an unspecified fairly large repository.) revset tip:0 2.9.2) wall 0.282543 comb 0.280000 user 0.260000 sys 0.020000 (best of 35) before) wall 0.819181 comb 0.820000 user 0.820000 sys 0.000000 (best of 12) after) wall 0.645358 comb 0.650000 user 0.650000 sys 0.000000 (best of 16) Proof of concept implementation of an `all` smartset brings this to 0.10 but it's too invasive for stable.

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similar.py
104 lines | 3.6 KiB | text/x-python | PythonLexer
# similar.py - mechanisms for finding similar files
#
# Copyright 2005-2007 Matt Mackall <mpm@selenic.com>
#
# This software may be used and distributed according to the terms of the
# GNU General Public License version 2 or any later version.
from i18n import _
import util
import mdiff
import bdiff
def _findexactmatches(repo, added, removed):
'''find renamed files that have no changes
Takes a list of new filectxs and a list of removed filectxs, and yields
(before, after) tuples of exact matches.
'''
numfiles = len(added) + len(removed)
# Get hashes of removed files.
hashes = {}
for i, fctx in enumerate(removed):
repo.ui.progress(_('searching for exact renames'), i, total=numfiles)
h = util.sha1(fctx.data()).digest()
hashes[h] = fctx
# For each added file, see if it corresponds to a removed file.
for i, fctx in enumerate(added):
repo.ui.progress(_('searching for exact renames'), i + len(removed),
total=numfiles)
h = util.sha1(fctx.data()).digest()
if h in hashes:
yield (hashes[h], fctx)
# Done
repo.ui.progress(_('searching for exact renames'), None)
def _findsimilarmatches(repo, added, removed, threshold):
'''find potentially renamed files based on similar file content
Takes a list of new filectxs and a list of removed filectxs, and yields
(before, after, score) tuples of partial matches.
'''
copies = {}
for i, r in enumerate(removed):
repo.ui.progress(_('searching for similar files'), i,
total=len(removed))
# lazily load text
@util.cachefunc
def data():
orig = r.data()
return orig, mdiff.splitnewlines(orig)
def score(text):
orig, lines = data()
# bdiff.blocks() returns blocks of matching lines
# count the number of bytes in each
equal = 0
matches = bdiff.blocks(text, orig)
for x1, x2, y1, y2 in matches:
for line in lines[y1:y2]:
equal += len(line)
lengths = len(text) + len(orig)
return equal * 2.0 / lengths
for a in added:
bestscore = copies.get(a, (None, threshold))[1]
myscore = score(a.data())
if myscore >= bestscore:
copies[a] = (r, myscore)
repo.ui.progress(_('searching'), None)
for dest, v in copies.iteritems():
source, score = v
yield source, dest, score
def findrenames(repo, added, removed, threshold):
'''find renamed files -- yields (before, after, score) tuples'''
parentctx = repo['.']
workingctx = repo[None]
# Zero length files will be frequently unrelated to each other, and
# tracking the deletion/addition of such a file will probably cause more
# harm than good. We strip them out here to avoid matching them later on.
addedfiles = set([workingctx[fp] for fp in added
if workingctx[fp].size() > 0])
removedfiles = set([parentctx[fp] for fp in removed
if fp in parentctx and parentctx[fp].size() > 0])
# Find exact matches.
for (a, b) in _findexactmatches(repo,
sorted(addedfiles), sorted(removedfiles)):
addedfiles.remove(b)
yield (a.path(), b.path(), 1.0)
# If the user requested similar files to be matched, search for them also.
if threshold < 1.0:
for (a, b, score) in _findsimilarmatches(repo,
sorted(addedfiles), sorted(removedfiles), threshold):
yield (a.path(), b.path(), score)