Mercurial > hg
annotate mercurial/setdiscovery.py @ 26610:f9f82c444ff7
filemerge: only copy to backup during premerge step
The premerge might leave the original file in an unclean state. Therefore it's
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author | Siddharth Agarwal <sid0@fb.com> |
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date | Sun, 11 Oct 2015 20:04:40 -0700 |
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1 # setdiscovery.py - improved discovery of common nodeset for mercurial |
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2 # |
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3 # Copyright 2010 Benoit Boissinot <bboissin@gmail.com> |
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4 # and Peter Arrenbrecht <peter@arrenbrecht.ch> |
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5 # |
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6 # This software may be used and distributed according to the terms of the |
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7 # GNU General Public License version 2 or any later version. |
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8 """ |
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9 Algorithm works in the following way. You have two repository: local and |
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10 remote. They both contains a DAG of changelists. |
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11 |
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12 The goal of the discovery protocol is to find one set of node *common*, |
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13 the set of nodes shared by local and remote. |
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14 |
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15 One of the issue with the original protocol was latency, it could |
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16 potentially require lots of roundtrips to discover that the local repo was a |
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17 subset of remote (which is a very common case, you usually have few changes |
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18 compared to upstream, while upstream probably had lots of development). |
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19 |
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20 The new protocol only requires one interface for the remote repo: `known()`, |
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21 which given a set of changelists tells you if they are present in the DAG. |
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22 |
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23 The algorithm then works as follow: |
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24 |
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25 - We will be using three sets, `common`, `missing`, `unknown`. Originally |
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26 all nodes are in `unknown`. |
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27 - Take a sample from `unknown`, call `remote.known(sample)` |
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28 - For each node that remote knows, move it and all its ancestors to `common` |
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29 - For each node that remote doesn't know, move it and all its descendants |
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30 to `missing` |
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31 - Iterate until `unknown` is empty |
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32 |
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33 There are a couple optimizations, first is instead of starting with a random |
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34 sample of missing, start by sending all heads, in the case where the local |
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35 repo is a subset, you computed the answer in one round trip. |
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36 |
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37 Then you can do something similar to the bisecting strategy used when |
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38 finding faulty changesets. Instead of random samples, you can try picking |
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39 nodes that will maximize the number of nodes that will be |
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40 classified with it (since all ancestors or descendants will be marked as well). |
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41 """ |
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42 |
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43 from __future__ import absolute_import |
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44 |
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45 import collections |
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46 import random |
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47 |
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48 from .i18n import _ |
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49 from .node import ( |
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50 nullid, |
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51 nullrev, |
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52 ) |
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53 from . import ( |
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54 dagutil, |
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55 error, |
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56 ) |
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57 |
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58 def _updatesample(dag, nodes, sample, quicksamplesize=0): |
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59 """update an existing sample to match the expected size |
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60 |
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61 The sample is updated with nodes exponentially distant from each head of the |
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62 <nodes> set. (H~1, H~2, H~4, H~8, etc). |
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63 |
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64 If a target size is specified, the sampling will stop once this size is |
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65 reached. Otherwise sampling will happen until roots of the <nodes> set are |
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66 reached. |
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67 |
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68 :dag: a dag object from dagutil |
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69 :nodes: set of nodes we want to discover (if None, assume the whole dag) |
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70 :sample: a sample to update |
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71 :quicksamplesize: optional target size of the sample""" |
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72 # if nodes is empty we scan the entire graph |
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73 if nodes: |
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74 heads = dag.headsetofconnecteds(nodes) |
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75 else: |
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76 heads = dag.heads() |
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77 dist = {} |
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78 visit = collections.deque(heads) |
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79 seen = set() |
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80 factor = 1 |
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81 while visit: |
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82 curr = visit.popleft() |
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83 if curr in seen: |
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84 continue |
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85 d = dist.setdefault(curr, 1) |
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86 if d > factor: |
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87 factor *= 2 |
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88 if d == factor: |
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89 sample.add(curr) |
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90 if quicksamplesize and (len(sample) >= quicksamplesize): |
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91 return |
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92 seen.add(curr) |
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93 for p in dag.parents(curr): |
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94 if not nodes or p in nodes: |
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95 dist.setdefault(p, d + 1) |
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96 visit.append(p) |
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97 |
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98 def _takequicksample(dag, nodes, size): |
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99 """takes a quick sample of size <size> |
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100 |
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101 It is meant for initial sampling and focuses on querying heads and close |
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102 ancestors of heads. |
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103 |
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104 :dag: a dag object |
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105 :nodes: set of nodes to discover |
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106 :size: the maximum size of the sample""" |
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107 sample = dag.headsetofconnecteds(nodes) |
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108 if size <= len(sample): |
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109 return _limitsample(sample, size) |
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110 _updatesample(dag, None, sample, quicksamplesize=size) |
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111 return sample |
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112 |
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113 def _takefullsample(dag, nodes, size): |
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114 sample = dag.headsetofconnecteds(nodes) |
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115 # update from heads |
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116 _updatesample(dag, nodes, sample) |
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117 # update from roots |
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118 _updatesample(dag.inverse(), nodes, sample) |
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119 assert sample |
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120 sample = _limitsample(sample, size) |
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121 if len(sample) < size: |
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122 more = size - len(sample) |
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123 sample.update(random.sample(list(nodes - sample), more)) |
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124 return sample |
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125 |
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126 def _limitsample(sample, desiredlen): |
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127 """return a random subset of sample of at most desiredlen item""" |
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128 if len(sample) > desiredlen: |
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129 sample = set(random.sample(sample, desiredlen)) |
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130 return sample |
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131 |
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132 def findcommonheads(ui, local, remote, |
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133 initialsamplesize=100, |
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134 fullsamplesize=200, |
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135 abortwhenunrelated=True): |
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136 '''Return a tuple (common, anyincoming, remoteheads) used to identify |
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137 missing nodes from or in remote. |
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138 ''' |
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139 roundtrips = 0 |
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140 cl = local.changelog |
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141 dag = dagutil.revlogdag(cl) |
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142 |
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143 # early exit if we know all the specified remote heads already |
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144 ui.debug("query 1; heads\n") |
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145 roundtrips += 1 |
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146 ownheads = dag.heads() |
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147 sample = _limitsample(ownheads, initialsamplesize) |
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148 # indices between sample and externalized version must match |
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149 sample = list(sample) |
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150 batch = remote.batch() |
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151 srvheadhashesref = batch.heads() |
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152 yesnoref = batch.known(dag.externalizeall(sample)) |
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153 batch.submit() |
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154 srvheadhashes = srvheadhashesref.value |
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155 yesno = yesnoref.value |
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156 |
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157 if cl.tip() == nullid: |
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158 if srvheadhashes != [nullid]: |
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159 return [nullid], True, srvheadhashes |
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160 return [nullid], False, [] |
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161 |
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162 # start actual discovery (we note this before the next "if" for |
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163 # compatibility reasons) |
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164 ui.status(_("searching for changes\n")) |
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165 |
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166 srvheads = dag.internalizeall(srvheadhashes, filterunknown=True) |
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167 if len(srvheads) == len(srvheadhashes): |
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168 ui.debug("all remote heads known locally\n") |
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169 return (srvheadhashes, False, srvheadhashes,) |
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170 |
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171 if sample and len(ownheads) <= initialsamplesize and all(yesno): |
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172 ui.note(_("all local heads known remotely\n")) |
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173 ownheadhashes = dag.externalizeall(ownheads) |
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174 return (ownheadhashes, True, srvheadhashes,) |
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175 |
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176 # full blown discovery |
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177 |
16683 | 178 # own nodes I know we both know |
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179 # treat remote heads (and maybe own heads) as a first implicit sample |
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180 # response |
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181 common = cl.incrementalmissingrevs(srvheads) |
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182 commoninsample = set(n for i, n in enumerate(sample) if yesno[i]) |
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183 common.addbases(commoninsample) |
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184 # own nodes where I don't know if remote knows them |
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185 undecided = set(common.missingancestors(ownheads)) |
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187 missing = set() | |
188 | |
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189 full = False |
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190 while undecided: |
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191 |
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192 if sample: |
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193 missinginsample = [n for i, n in enumerate(sample) if not yesno[i]] |
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194 missing.update(dag.descendantset(missinginsample, missing)) |
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195 |
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196 undecided.difference_update(missing) |
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197 |
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198 if not undecided: |
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199 break |
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200 |
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201 if full or common.hasbases(): |
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202 if full: |
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203 ui.note(_("sampling from both directions\n")) |
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204 else: |
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205 ui.debug("taking initial sample\n") |
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206 samplefunc = _takefullsample |
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207 targetsize = fullsamplesize |
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208 else: |
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209 # use even cheaper initial sample |
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210 ui.debug("taking quick initial sample\n") |
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211 samplefunc = _takequicksample |
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212 targetsize = initialsamplesize |
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213 if len(undecided) < targetsize: |
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214 sample = list(undecided) |
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215 else: |
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216 sample = samplefunc(dag, undecided, targetsize) |
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217 sample = _limitsample(sample, targetsize) |
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218 |
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219 roundtrips += 1 |
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220 ui.progress(_('searching'), roundtrips, unit=_('queries')) |
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221 ui.debug("query %i; still undecided: %i, sample size is: %i\n" |
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222 % (roundtrips, len(undecided), len(sample))) |
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223 # indices between sample and externalized version must match |
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224 sample = list(sample) |
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225 yesno = remote.known(dag.externalizeall(sample)) |
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226 full = True |
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227 |
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228 if sample: |
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229 commoninsample = set(n for i, n in enumerate(sample) if yesno[i]) |
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230 common.addbases(commoninsample) |
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231 common.removeancestorsfrom(undecided) |
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232 |
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233 # heads(common) == heads(common.bases) since common represents common.bases |
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234 # and all its ancestors |
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235 result = dag.headsetofconnecteds(common.bases) |
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236 # common.bases can include nullrev, but our contract requires us to not |
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237 # return any heads in that case, so discard that |
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238 result.discard(nullrev) |
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239 ui.progress(_('searching'), None) |
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240 ui.debug("%d total queries\n" % roundtrips) |
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241 |
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242 if not result and srvheadhashes != [nullid]: |
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243 if abortwhenunrelated: |
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244 raise error.Abort(_("repository is unrelated")) |
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245 else: |
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246 ui.warn(_("warning: repository is unrelated\n")) |
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247 return (set([nullid]), True, srvheadhashes,) |
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248 |
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249 anyincoming = (srvheadhashes != [nullid]) |
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250 return dag.externalizeall(result), anyincoming, srvheadhashes |