mercurial/pvec.py
author Pierre-Yves David <pierre-yves.david@octobus.net>
Tue, 13 Dec 2022 09:59:22 +0100
changeset 50057 cf8b9c80d67c
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child 50928 d718eddf01d9
permissions -rw-r--r--
dirstate: issue a developer warning on implicit write on wlock release Our goal is to get rid of all these to clarify the writing pattern, so it is time to warn about this (and later, forbid it).
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# pvec.py - probabilistic vector clocks for Mercurial
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#
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# Copyright 2012 Olivia Mackall <olivia@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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from .node import nullrev
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from . import (
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    pycompat,
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    util,
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)
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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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    # type: (int, int) -> bytes
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    bs = b""
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    for p in range(l):
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        bs = pycompat.bytechr(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 range(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 range(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(util.b85encode(bs))
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class pvec:
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    def __init__(self, hashorctx):
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        if isinstance(hashorctx, bytes):
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            self._bs = hashorctx
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            self._depth, self._vec = _split(util.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(b"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