Coverage for python/lsst/pipe/base/executionButlerBuilder.py: 16%

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1# This file is part of pipe_base. 

2# 

3# Developed for the LSST Data Management System. 

4# This product includes software developed by the LSST Project 

5# (http://www.lsst.org). 

6# See the COPYRIGHT file at the top-level directory of this distribution 

7# for details of code ownership. 

8# 

9# This program is free software: you can redistribute it and/or modify 

10# it under the terms of the GNU General Public License as published by 

11# the Free Software Foundation, either version 3 of the License, or 

12# (at your option) any later version. 

13# 

14# This program is distributed in the hope that it will be useful, 

15# but WITHOUT ANY WARRANTY; without even the implied warranty of 

16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 

17# GNU General Public License for more details. 

18# 

19# You should have received a copy of the GNU General Public License 

20# along with this program. If not, see <http://www.gnu.org/licenses/>. 

21from __future__ import annotations 

22 

23__all__ = ("buildExecutionButler",) 

24 

25import io 

26import itertools 

27from collections import defaultdict 

28from typing import Callable, DefaultDict, Iterable, List, Mapping, Optional, Set, Tuple, Union 

29 

30from lsst.daf.butler import Butler, Config, DataCoordinate, DatasetRef, DatasetType 

31from lsst.daf.butler.core.repoRelocation import BUTLER_ROOT_TAG 

32from lsst.daf.butler.transfers import RepoExportContext 

33from lsst.resources import ResourcePath, ResourcePathExpression 

34from lsst.utils.introspection import get_class_of 

35 

36from .graph import QuantumGraph, QuantumNode 

37from .pipeline import PipelineDatasetTypes 

38 

39DataSetTypeMap = Mapping[DatasetType, Set[DataCoordinate]] 

40 

41 

42def _accumulate( 

43 graph: QuantumGraph, 

44 dataset_types: PipelineDatasetTypes, 

45) -> Tuple[Set[DatasetRef], DataSetTypeMap]: 

46 # accumulate the DatasetRefs that will be transferred to the execution 

47 # registry 

48 

49 # exports holds all the existing data that will be migrated to the 

50 # execution butler 

51 exports: Set[DatasetRef] = set() 

52 

53 # inserts is the mapping of DatasetType to dataIds for what is to be 

54 # inserted into the registry. These are the products that are expected 

55 # to be produced during processing of the QuantumGraph 

56 inserts: DefaultDict[DatasetType, Set[DataCoordinate]] = defaultdict(set) 

57 

58 # Add inserts for initOutputs (including initIntermediates); these are 

59 # defined fully by their DatasetType, because they have no dimensions, and 

60 # they are by definition not resolved. initInputs are part of Quantum and 

61 # that's the only place the graph stores the dataset IDs, so we process 

62 # them there even though each Quantum for a task has the same ones. 

63 for dataset_type in itertools.chain(dataset_types.initIntermediates, dataset_types.initOutputs): 

64 inserts[dataset_type].add(DataCoordinate.makeEmpty(dataset_type.dimensions.universe)) 

65 

66 n: QuantumNode 

67 for quantum in (n.quantum for n in graph): 

68 for attrName in ("initInputs", "inputs", "outputs"): 

69 attr: Mapping[DatasetType, Union[DatasetRef, List[DatasetRef]]] = getattr(quantum, attrName) 

70 

71 for type, refs in attr.items(): 

72 # This if block is because init inputs has a different 

73 # signature for its items 

74 if not isinstance(refs, list): 

75 refs = [refs] 

76 # iterate over all the references, if it has an id, it 

77 # means it exists and should be exported, if not it should 

78 # be inserted into the new registry 

79 for ref in refs: 

80 if ref.id is not None: 

81 exports.add(ref) 

82 else: 

83 if ref.isComponent(): 

84 # We can't insert a component, and a component will 

85 # be part of some other upstream dataset, so it 

86 # should be safe to skip them here 

87 continue 

88 inserts[type].add(ref.dataId) 

89 return exports, inserts 

90 

91 

92def _discoverCollections(butler: Butler, collections: Iterable[str]) -> set[str]: 

93 # Recurse through any discovered collections to make sure all collections 

94 # are exported. This exists because I ran into a situation where some 

95 # collections were not properly being discovered and exported. This 

96 # method may be able to be removed in the future if collection export 

97 # logic changes 

98 collections = set(collections) 

99 while True: 

100 discoveredCollections = set( 

101 butler.registry.queryCollections(collections, flattenChains=True, includeChains=True) 

102 ) 

103 if len(discoveredCollections) > len(collections): 

104 collections = discoveredCollections 

105 else: 

106 break 

107 return collections 

108 

109 

110def _export( 

111 butler: Butler, collections: Optional[Iterable[str]], exports: Set[DatasetRef], inserts: DataSetTypeMap 

112) -> io.StringIO: 

113 # This exports the datasets that exist in the input butler using 

114 # daf butler objects, however it reaches in deep and does not use the 

115 # public methods so that it can export it to a string buffer and skip 

116 # disk access. 

117 yamlBuffer = io.StringIO() 

118 # Yaml is hard coded, since the class controls both ends of the 

119 # export/import 

120 BackendClass = get_class_of(butler._config["repo_transfer_formats", "yaml", "export"]) 

121 backend = BackendClass(yamlBuffer) 

122 exporter = RepoExportContext(butler.registry, butler.datastore, backend, directory=None, transfer=None) 

123 exporter.saveDatasets(exports) 

124 

125 # Need to ensure that the dimension records for outputs are 

126 # transferred. 

127 for _, dataIds in inserts.items(): 

128 exporter.saveDataIds(dataIds) 

129 

130 # Look for any defined collection, if not get the defaults 

131 if collections is None: 

132 collections = butler.registry.defaults.collections 

133 

134 # look up all collections associated with those inputs, this follows 

135 # all chains to make sure everything is properly exported 

136 for c in _discoverCollections(butler, collections): 

137 exporter.saveCollection(c) 

138 exporter._finish() 

139 

140 # reset the string buffer to the beginning so the read operation will 

141 # actually *see* the data that was exported 

142 yamlBuffer.seek(0) 

143 return yamlBuffer 

144 

145 

146def _setupNewButler(butler: Butler, outputLocation: ResourcePath, dirExists: bool) -> Butler: 

147 # Set up the new butler object at the specified location 

148 if dirExists: 

149 # Remove the existing table, if the code got this far and this exists 

150 # clobber must be true 

151 executionRegistry = outputLocation.join("gen3.sqlite3") 

152 if executionRegistry.exists(): 

153 executionRegistry.remove() 

154 else: 

155 outputLocation.mkdir() 

156 

157 # Copy the existing butler config, modifying the location of the 

158 # registry to the specified location. 

159 # Preserve the root path from the existing butler so things like 

160 # file data stores continue to look at the old location. 

161 config = Config(butler._config) 

162 config["root"] = outputLocation.geturl() 

163 config["allow_put_of_predefined_dataset"] = True 

164 config["registry", "db"] = "sqlite:///<butlerRoot>/gen3.sqlite3" 

165 

166 # Remove any namespace that may be set in main registry. 

167 config.pop(("registry", "namespace"), None) 

168 

169 # record the current root of the datastore if it is specified relative 

170 # to the butler root 

171 if config.get(("datastore", "root")) == BUTLER_ROOT_TAG: 

172 config["datastore", "root"] = butler._config.configDir.geturl() 

173 config["datastore", "trust_get_request"] = True 

174 

175 # Requires that we use the dimension configuration from the original 

176 # butler and not use the defaults. 

177 config = Butler.makeRepo( 

178 root=outputLocation, 

179 config=config, 

180 dimensionConfig=butler.registry.dimensions.dimensionConfig, 

181 overwrite=True, 

182 forceConfigRoot=False, 

183 ) 

184 

185 # Return a newly created butler 

186 return Butler(config, writeable=True) 

187 

188 

189def _import( 

190 yamlBuffer: io.StringIO, 

191 newButler: Butler, 

192 inserts: DataSetTypeMap, 

193 run: str, 

194 butlerModifier: Optional[Callable[[Butler], Butler]], 

195) -> Butler: 

196 # This method takes the exports from the existing butler, imports 

197 # them into the newly created butler, and then inserts the datasets 

198 # that are expected to be produced. 

199 

200 # import the existing datasets using "split" mode. "split" is safe 

201 # because execution butler is assumed to be able to see all the file 

202 # locations that the main datastore can see. "split" supports some 

203 # absolute URIs in the datastore. 

204 newButler.import_(filename=yamlBuffer, format="yaml", reuseIds=True, transfer="split") 

205 

206 # If there is modifier callable, run it to make necessary updates 

207 # to the new butler. 

208 if butlerModifier is not None: 

209 newButler = butlerModifier(newButler) 

210 

211 # Register datasets to be produced and insert them into the registry 

212 for dsType, dataIds in inserts.items(): 

213 newButler.registry.registerDatasetType(dsType) 

214 newButler.registry.insertDatasets(dsType, dataIds, run) 

215 

216 return newButler 

217 

218 

219def buildExecutionButler( 

220 butler: Butler, 

221 graph: QuantumGraph, 

222 outputLocation: ResourcePathExpression, 

223 run: str, 

224 *, 

225 clobber: bool = False, 

226 butlerModifier: Optional[Callable[[Butler], Butler]] = None, 

227 collections: Optional[Iterable[str]] = None, 

228) -> Butler: 

229 r"""buildExecutionButler is a function that is responsible for exporting 

230 input `QuantumGraphs` into a new minimal `~lsst.daf.butler.Butler` which 

231 only contains datasets specified by the `QuantumGraph`. These datasets are 

232 both those that already exist in the input `~lsst.daf.butler.Butler`, and 

233 those that are expected to be produced during the execution of the 

234 `QuantumGraph`. 

235 

236 Parameters 

237 ---------- 

238 butler : `lsst.daf.butler.Bulter` 

239 This is the existing `~lsst.daf.butler.Butler` instance from which 

240 existing datasets will be exported. This should be the 

241 `~lsst.daf.butler.Butler` which was used to create any `QuantumGraphs` 

242 that will be converted with this object. 

243 graph : `QuantumGraph` 

244 Graph containing nodes that are to be exported into an execution 

245 butler 

246 outputLocation : convertible to `ResourcePath 

247 URI Location at which the execution butler is to be exported. May be 

248 specified as a string or a `ResourcePath` instance. 

249 run : `str` optional 

250 The run collection that the exported datasets are to be placed in. If 

251 None, the default value in registry.defaults will be used. 

252 clobber : `bool`, Optional 

253 By default a butler will not be created if a file or directory 

254 already exists at the output location. If this is set to `True` 

255 what is at the location will be deleted prior to running the 

256 export. Defaults to `False` 

257 butlerModifier : `~typing.Callable`, Optional 

258 If supplied this should be a callable that accepts a 

259 `~lsst.daf.butler.Butler`, and returns an instantiated 

260 `~lsst.daf.butler.Butler`. This callable may be used to make any 

261 modifications to the `~lsst.daf.butler.Butler` desired. This 

262 will be called after importing all datasets that exist in the input 

263 `~lsst.daf.butler.Butler` but prior to inserting Datasets expected 

264 to be produced. Examples of what this method could do include 

265 things such as creating collections/runs/ etc. 

266 collections : `~typing.Iterable` of `str`, Optional 

267 An iterable of collection names that will be exported from the input 

268 `~lsst.daf.butler.Butler` when creating the execution butler. If not 

269 supplied the `~lsst.daf.butler.Butler`\ 's `~lsst.daf.butler.Registry` 

270 default collections will be used. 

271 

272 Returns 

273 ------- 

274 executionButler : `lsst.daf.butler.Butler` 

275 An instance of the newly created execution butler 

276 

277 Raises 

278 ------ 

279 FileExistsError 

280 Raised if something exists in the filesystem at the specified output 

281 location and clobber is `False` 

282 NotADirectoryError 

283 Raised if specified output URI does not correspond to a directory 

284 """ 

285 # We know this must refer to a directory. 

286 outputLocation = ResourcePath(outputLocation, forceDirectory=True) 

287 

288 # Do this first to Fail Fast if the output exists 

289 if (dirExists := outputLocation.exists()) and not clobber: 

290 raise FileExistsError("Cannot create a butler at specified location, location exists") 

291 if not outputLocation.isdir(): 

292 raise NotADirectoryError("The specified output URI does not appear to correspond to a directory") 

293 

294 # Gather all DatasetTypes from the Python and check any that already exist 

295 # in the registry for consistency. This does not check that all dataset 

296 # types here exist, because they might want to register dataset types 

297 # later. It would be nice to also check that, but to that we would need to 

298 # be told whether they plan to register dataset types later (DM-30845). 

299 dataset_types = PipelineDatasetTypes.fromPipeline(graph.iterTaskGraph(), registry=butler.registry) 

300 

301 exports, inserts = _accumulate(graph, dataset_types) 

302 yamlBuffer = _export(butler, collections, exports, inserts) 

303 

304 newButler = _setupNewButler(butler, outputLocation, dirExists) 

305 

306 return _import(yamlBuffer, newButler, inserts, run, butlerModifier)