mercurial/pvec.py
author Pierre-Yves David <pierre-yves.david@ens-lyon.org>
Wed, 02 Oct 2013 11:16:03 +0200
branchstable
changeset 19878 21de61bc2ab5
parent 18918 5093d2a87ff6
child 24339 bcc319d936a3
permissions -rw-r--r--
test: make test-propertycache.py python2.4 compatible The subprocess module have not `check_call` method in python2.4. Fall back to calling `check` with return code verification.
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# pvec.py - probabilistic vector clocks for Mercurial
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#
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# Copyright 2012 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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'''
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A "pvec" is a changeset property based on the theory of vector clocks
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that can be compared to discover relatedness without consulting a
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graph. This can be useful for tasks like determining how a
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disconnected patch relates to a repository.
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Currently a pvec consist of 448 bits, of which 24 are 'depth' and the
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remainder are a bit vector. It is represented as a 70-character base85
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string.
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Construction:
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- a root changeset has a depth of 0 and a bit vector based on its hash
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- a normal commit has a changeset where depth is increased by one and
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  one bit vector bit is flipped based on its hash
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- a merge changeset pvec is constructed by copying changes from one pvec into
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  the other to balance its depth
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Properties:
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- for linear changes, difference in depth is always <= hamming distance
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- otherwise, changes are probably divergent
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- when hamming distance is < 200, we can reliably detect when pvecs are near
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Issues:
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- hamming distance ceases to work over distances of ~ 200
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- detecting divergence is less accurate when the common ancestor is very close
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  to either revision or total distance is high
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- this could probably be improved by modeling the relation between
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  delta and hdist
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Uses:
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- a patch pvec can be used to locate the nearest available common ancestor for
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  resolving conflicts
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- ordering of patches can be established without a DAG
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- two head pvecs can be compared to determine whether push/pull/merge is needed
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  and approximately how many changesets are involved
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- can be used to find a heuristic divergence measure between changesets on
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  different branches
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'''
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import base85, util
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from node import nullrev
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_size = 448 # 70 chars b85-encoded
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_bytes = _size / 8
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_depthbits = 24
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_depthbytes = _depthbits / 8
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_vecbytes = _bytes - _depthbytes
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_vecbits = _vecbytes * 8
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_radius = (_vecbits - 30) / 2 # high probability vectors are related
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def _bin(bs):
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    '''convert a bytestring to a long'''
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    v = 0
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    for b in bs:
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        v = v * 256 + ord(b)
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    return v
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def _str(v, l):
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    bs = ""
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    for p in xrange(l):
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        bs = chr(v & 255) + bs
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        v >>= 8
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    return bs
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def _split(b):
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    '''depth and bitvec'''
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    return _bin(b[:_depthbytes]), _bin(b[_depthbytes:])
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def _join(depth, bitvec):
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    return _str(depth, _depthbytes) + _str(bitvec, _vecbytes)
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def _hweight(x):
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    c = 0
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    while x:
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        if x & 1:
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            c += 1
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        x >>= 1
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    return c
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_htab = [_hweight(x) for x in xrange(256)]
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def _hamming(a, b):
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    '''find the hamming distance between two longs'''
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    d = a ^ b
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    c = 0
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    while d:
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        c += _htab[d & 0xff]
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        d >>= 8
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    return c
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def _mergevec(x, y, c):
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    # Ideally, this function would be x ^ y ^ ancestor, but finding
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    # ancestors is a nuisance. So instead we find the minimal number
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    # of changes to balance the depth and hamming distance
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    d1, v1 = x
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    d2, v2 = y
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    if d1 < d2:
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        d1, d2, v1, v2 = d2, d1, v2, v1
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    hdist = _hamming(v1, v2)
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    ddist = d1 - d2
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    v = v1
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    m = v1 ^ v2 # mask of different bits
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    i = 1
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    if hdist > ddist:
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        # if delta = 10 and hdist = 100, then we need to go up 55 steps
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        # to the ancestor and down 45
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        changes = (hdist - ddist + 1) / 2
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    else:
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        # must make at least one change
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        changes = 1
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    depth = d1 + changes
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    # copy changes from v2
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    if m:
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        while changes:
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            if m & i:
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                v ^= i
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                changes -= 1
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            i <<= 1
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    else:
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        v = _flipbit(v, c)
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    return depth, v
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def _flipbit(v, node):
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    # converting bit strings to longs is slow
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    bit = (hash(node) & 0xffffffff) % _vecbits
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    return v ^ (1<<bit)
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def ctxpvec(ctx):
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    '''construct a pvec for ctx while filling in the cache'''
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    r = ctx._repo
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    if not util.safehasattr(r, "_pveccache"):
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        r._pveccache = {}
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    pvc = r._pveccache
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    if ctx.rev() not in pvc:
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        cl = r.changelog
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        for n in xrange(ctx.rev() + 1):
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            if n not in pvc:
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                node = cl.node(n)
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                p1, p2 = cl.parentrevs(n)
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                if p1 == nullrev:
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                    # start with a 'random' vector at root
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                    pvc[n] = (0, _bin((node * 3)[:_vecbytes]))
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                elif p2 == nullrev:
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                    d, v = pvc[p1]
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                    pvc[n] = (d + 1, _flipbit(v, node))
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                else:
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                    pvc[n] = _mergevec(pvc[p1], pvc[p2], node)
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    bs = _join(*pvc[ctx.rev()])
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    return pvec(base85.b85encode(bs))
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class pvec(object):
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    def __init__(self, hashorctx):
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        if isinstance(hashorctx, str):
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            self._bs = hashorctx
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            self._depth, self._vec = _split(base85.b85decode(hashorctx))
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        else:
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            self._vec = ctxpvec(hashorctx)
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    def __str__(self):
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        return self._bs
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    def __eq__(self, b):
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        return self._vec == b._vec and self._depth == b._depth
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    def __lt__(self, b):
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        delta = b._depth - self._depth
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        if delta < 0:
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            return False # always correct
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        if _hamming(self._vec, b._vec) > delta:
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            return False
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        return True
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    def __gt__(self, b):
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        return b < self
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    def __or__(self, b):
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        delta = abs(b._depth - self._depth)
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        if _hamming(self._vec, b._vec) <= delta:
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            return False
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        return True
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    def __sub__(self, b):
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        if self | b:
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            raise ValueError("concurrent pvecs")
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        return self._depth - b._depth
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    def distance(self, b):
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        d = abs(b._depth - self._depth)
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        h = _hamming(self._vec, b._vec)
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        return max(d, h)
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    def near(self, b):
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        dist = abs(b.depth - self._depth)
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        if dist > _radius or _hamming(self._vec, b._vec) > _radius:
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            return False