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1# This file is part of daf_butler. 

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/>. 

21 

22from __future__ import annotations 

23 

24"""Generic datastore code useful for most datastores.""" 

25 

26__all__ = ("GenericBaseDatastore", ) 

27 

28import logging 

29from abc import abstractmethod 

30from typing import ( 

31 TYPE_CHECKING, 

32 Any, 

33 Iterable, 

34 List, 

35 Mapping, 

36 Optional, 

37 Sequence, 

38 Tuple, 

39) 

40 

41from lsst.daf.butler import Datastore, DatasetTypeNotSupportedError 

42from lsst.daf.butler.registry.interfaces import DatastoreRegistryBridge 

43 

44if TYPE_CHECKING: 44 ↛ 45line 44 didn't jump to line 45, because the condition on line 44 was never true

45 from lsst.daf.butler import DatasetRef, StorageClass, StoredDatastoreItemInfo 

46 

47log = logging.getLogger(__name__) 

48 

49 

50class GenericBaseDatastore(Datastore): 

51 """Methods useful for most implementations of a `Datastore`. 

52 

53 Should always be sub-classed since key abstract methods are missing. 

54 """ 

55 

56 @property 

57 @abstractmethod 

58 def bridge(self) -> DatastoreRegistryBridge: 

59 """Object that manages the interface between this `Datastore` and the 

60 `Registry` (`DatastoreRegistryBridge`). 

61 """ 

62 raise NotImplementedError() 

63 

64 @abstractmethod 

65 def addStoredItemInfo(self, refs: Iterable[DatasetRef], 

66 infos: Iterable[Any]) -> None: 

67 """Record internal storage information associated with one or more 

68 datasets. 

69 

70 Parameters 

71 ---------- 

72 refs : sequence of `DatasetRef` 

73 The datasets that have been stored. 

74 infos : sequence of `StoredDatastoreItemInfo` 

75 Metadata associated with the stored datasets. 

76 """ 

77 raise NotImplementedError() 

78 

79 @abstractmethod 

80 def getStoredItemsInfo(self, ref: DatasetRef) -> Sequence[Any]: 

81 """Retrieve information associated with files stored in this 

82 `Datastore` associated with this dataset ref. 

83 

84 Parameters 

85 ---------- 

86 ref : `DatasetRef` 

87 The dataset that is to be queried. 

88 

89 Returns 

90 ------- 

91 items : `list` [`StoredDatastoreItemInfo`] 

92 Stored information about the files and associated formatters 

93 associated with this dataset. Only one file will be returned 

94 if the dataset has not been disassembled. Can return an empty 

95 list if no matching datasets can be found. 

96 """ 

97 raise NotImplementedError() 

98 

99 @abstractmethod 

100 def removeStoredItemInfo(self, ref: DatasetRef) -> None: 

101 """Remove information about the file associated with this dataset. 

102 

103 Parameters 

104 ---------- 

105 ref : `DatasetRef` 

106 The dataset that has been removed. 

107 """ 

108 raise NotImplementedError() 

109 

110 def _register_datasets(self, refsAndInfos: Iterable[Tuple[DatasetRef, StoredDatastoreItemInfo]]) -> None: 

111 """Update registry to indicate that one or more datasets have been 

112 stored. 

113 

114 Parameters 

115 ---------- 

116 refsAndInfos : sequence `tuple` [`DatasetRef`, 

117 `StoredDatastoreItemInfo`] 

118 Datasets to register and the internal datastore metadata associated 

119 with them. 

120 """ 

121 expandedRefs: List[DatasetRef] = [] 

122 expandedItemInfos = [] 

123 

124 for ref, itemInfo in refsAndInfos: 

125 expandedRefs.append(ref) 

126 expandedItemInfos.append(itemInfo) 

127 

128 # Dataset location only cares about registry ID so if we have 

129 # disassembled in datastore we have to deduplicate. Since they 

130 # will have different datasetTypes we can't use a set 

131 registryRefs = {r.id: r for r in expandedRefs} 

132 self.bridge.insert(registryRefs.values()) 

133 self.addStoredItemInfo(expandedRefs, expandedItemInfos) 

134 

135 def _move_to_trash_in_registry(self, ref: DatasetRef) -> None: 

136 """Tell registry that this dataset and associated components 

137 are to be trashed. 

138 

139 Parameters 

140 ---------- 

141 ref : `DatasetRef` 

142 Dataset to mark for removal from registry. 

143 

144 Notes 

145 ----- 

146 Dataset is not removed from internal stored item info table. 

147 """ 

148 # Note that a ref can point to component dataset refs that 

149 # have been deleted already from registry but are still in 

150 # the python object. moveToTrash will deal with that. 

151 self.bridge.moveToTrash([ref]) 

152 

153 def _post_process_get(self, inMemoryDataset: Any, readStorageClass: StorageClass, 

154 assemblerParams: Optional[Mapping[str, Any]] = None, 

155 isComponent: bool = False) -> Any: 

156 """Given the Python object read from the datastore, manipulate 

157 it based on the supplied parameters and ensure the Python 

158 type is correct. 

159 

160 Parameters 

161 ---------- 

162 inMemoryDataset : `object` 

163 Dataset to check. 

164 readStorageClass: `StorageClass` 

165 The `StorageClass` used to obtain the assembler and to 

166 check the python type. 

167 assemblerParams : `dict`, optional 

168 Parameters to pass to the assembler. Can be `None`. 

169 isComponent : `bool`, optional 

170 If this is a component, allow the inMemoryDataset to be `None`. 

171 """ 

172 # Process any left over parameters 

173 if assemblerParams: 

174 inMemoryDataset = readStorageClass.assembler().handleParameters(inMemoryDataset, assemblerParams) 

175 

176 # Validate the returned data type matches the expected data type 

177 pytype = readStorageClass.pytype 

178 

179 allowedTypes = [] 

180 if pytype: 180 ↛ 184line 180 didn't jump to line 184, because the condition on line 180 was never false

181 allowedTypes.append(pytype) 

182 

183 # Special case components to allow them to be None 

184 if isComponent: 

185 allowedTypes.append(type(None)) 

186 

187 if allowedTypes and not isinstance(inMemoryDataset, tuple(allowedTypes)): 187 ↛ 188line 187 didn't jump to line 188, because the condition on line 187 was never true

188 raise TypeError("Got Python type {} from datastore but expected {}".format(type(inMemoryDataset), 

189 pytype)) 

190 

191 return inMemoryDataset 

192 

193 def _validate_put_parameters(self, inMemoryDataset: Any, ref: DatasetRef) -> None: 

194 """Validate the supplied arguments for put. 

195 

196 Parameters 

197 ---------- 

198 inMemoryDataset : `object` 

199 The dataset to store. 

200 ref : `DatasetRef` 

201 Reference to the associated Dataset. 

202 """ 

203 storageClass = ref.datasetType.storageClass 

204 

205 # Sanity check 

206 if not isinstance(inMemoryDataset, storageClass.pytype): 206 ↛ 207line 206 didn't jump to line 207, because the condition on line 206 was never true

207 raise TypeError("Inconsistency between supplied object ({}) " 

208 "and storage class type ({})".format(type(inMemoryDataset), 

209 storageClass.pytype)) 

210 

211 # Confirm that we can accept this dataset 

212 if not self.constraints.isAcceptable(ref): 

213 # Raise rather than use boolean return value. 

214 raise DatasetTypeNotSupportedError(f"Dataset {ref} has been rejected by this datastore via" 

215 " configuration.") 

216 

217 return 

218 

219 def remove(self, ref: DatasetRef) -> None: 

220 """Indicate to the Datastore that a dataset can be removed. 

221 

222 .. warning:: 

223 

224 This method deletes the artifact associated with this 

225 dataset and can not be reversed. 

226 

227 Parameters 

228 ---------- 

229 ref : `DatasetRef` 

230 Reference to the required Dataset. 

231 

232 Raises 

233 ------ 

234 FileNotFoundError 

235 Attempt to remove a dataset that does not exist. 

236 

237 Notes 

238 ----- 

239 This method is used for immediate removal of a dataset and is 

240 generally reserved for internal testing of datastore APIs. 

241 It is implemented by calling `trash()` and then immediately calling 

242 `emptyTrash()`. This call is meant to be immediate so errors 

243 encountered during removal are not ignored. 

244 """ 

245 self.trash(ref, ignore_errors=False) 

246 self.emptyTrash(ignore_errors=False) 

247 

248 def transfer(self, inputDatastore: Datastore, ref: DatasetRef) -> None: 

249 """Retrieve a dataset from an input `Datastore`, 

250 and store the result in this `Datastore`. 

251 

252 Parameters 

253 ---------- 

254 inputDatastore : `Datastore` 

255 The external `Datastore` from which to retreive the Dataset. 

256 ref : `DatasetRef` 

257 Reference to the required dataset in the input data store. 

258 

259 """ 

260 assert inputDatastore is not self # unless we want it for renames? 

261 inMemoryDataset = inputDatastore.get(ref) 

262 return self.put(inMemoryDataset, ref)