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8a64b55504
We just need a hash table {fctx.data(): fctx} which doesn't keep fctx.data() in memory. Let's simply use hash(fctx.data()) to put data out from memory, and manage collided fctx objects by list. This isn't significantly faster than using sha1, but is more correct as we know SHA-1 collision attack is getting practical. Benchmark with 50k added/removed files, on tmpfs: $ hg addremove --dry-run --time -q previous: real 12.420 secs (user 11.120+0.000 sys 1.280+0.000) this patch: real 12.350 secs (user 11.210+0.000 sys 1.140+0.000)
124 lines
4.1 KiB
Python
124 lines
4.1 KiB
Python
# similar.py - mechanisms for finding similar files
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#
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# Copyright 2005-2007 Matt Mackall <mpm@selenic.com>
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#
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# This software may be used and distributed according to the terms of the
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# GNU General Public License version 2 or any later version.
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from __future__ import absolute_import
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from .i18n import _
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from . import (
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bdiff,
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mdiff,
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)
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def _findexactmatches(repo, added, removed):
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'''find renamed files that have no changes
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Takes a list of new filectxs and a list of removed filectxs, and yields
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(before, after) tuples of exact matches.
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'''
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numfiles = len(added) + len(removed)
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# Build table of removed files: {hash(fctx.data()): [fctx, ...]}.
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# We use hash() to discard fctx.data() from memory.
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hashes = {}
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for i, fctx in enumerate(removed):
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repo.ui.progress(_('searching for exact renames'), i, total=numfiles,
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unit=_('files'))
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h = hash(fctx.data())
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if h not in hashes:
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hashes[h] = [fctx]
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else:
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hashes[h].append(fctx)
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# For each added file, see if it corresponds to a removed file.
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for i, fctx in enumerate(added):
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repo.ui.progress(_('searching for exact renames'), i + len(removed),
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total=numfiles, unit=_('files'))
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adata = fctx.data()
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h = hash(adata)
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for rfctx in hashes.get(h, []):
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# compare between actual file contents for exact identity
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if adata == rfctx.data():
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yield (rfctx, fctx)
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break
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# Done
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repo.ui.progress(_('searching for exact renames'), None)
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def _ctxdata(fctx):
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# lazily load text
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orig = fctx.data()
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return orig, mdiff.splitnewlines(orig)
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def _score(fctx, otherdata):
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orig, lines = otherdata
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text = fctx.data()
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# bdiff.blocks() returns blocks of matching lines
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# count the number of bytes in each
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equal = 0
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matches = bdiff.blocks(text, orig)
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for x1, x2, y1, y2 in matches:
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for line in lines[y1:y2]:
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equal += len(line)
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lengths = len(text) + len(orig)
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return equal * 2.0 / lengths
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def score(fctx1, fctx2):
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return _score(fctx1, _ctxdata(fctx2))
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def _findsimilarmatches(repo, added, removed, threshold):
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'''find potentially renamed files based on similar file content
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Takes a list of new filectxs and a list of removed filectxs, and yields
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(before, after, score) tuples of partial matches.
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'''
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copies = {}
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for i, r in enumerate(removed):
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repo.ui.progress(_('searching for similar files'), i,
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total=len(removed), unit=_('files'))
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data = None
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for a in added:
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bestscore = copies.get(a, (None, threshold))[1]
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if data is None:
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data = _ctxdata(r)
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myscore = _score(a, data)
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if myscore > bestscore:
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copies[a] = (r, myscore)
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repo.ui.progress(_('searching'), None)
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for dest, v in copies.iteritems():
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source, bscore = v
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yield source, dest, bscore
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def _dropempty(fctxs):
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return [x for x in fctxs if x.size() > 0]
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def findrenames(repo, added, removed, threshold):
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'''find renamed files -- yields (before, after, score) tuples'''
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wctx = repo[None]
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pctx = wctx.p1()
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# Zero length files will be frequently unrelated to each other, and
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# tracking the deletion/addition of such a file will probably cause more
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# harm than good. We strip them out here to avoid matching them later on.
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addedfiles = _dropempty(wctx[fp] for fp in sorted(added))
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removedfiles = _dropempty(pctx[fp] for fp in sorted(removed) if fp in pctx)
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# Find exact matches.
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matchedfiles = set()
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for (a, b) in _findexactmatches(repo, addedfiles, removedfiles):
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matchedfiles.add(b)
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yield (a.path(), b.path(), 1.0)
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# If the user requested similar files to be matched, search for them also.
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if threshold < 1.0:
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addedfiles = [x for x in addedfiles if x not in matchedfiles]
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for (a, b, score) in _findsimilarmatches(repo, addedfiles,
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removedfiles, threshold):
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yield (a.path(), b.path(), score)
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