Coverage for python/lsst/daf/butler/datastores/fileDatastore.py: 85%

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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/>. 

21from __future__ import annotations 

22 

23"""Generic file-based datastore code.""" 

24 

25__all__ = ("FileDatastore",) 

26 

27import hashlib 

28import logging 

29from collections import defaultdict 

30from dataclasses import dataclass 

31from typing import ( 

32 TYPE_CHECKING, 

33 Any, 

34 ClassVar, 

35 Dict, 

36 Iterable, 

37 List, 

38 Mapping, 

39 Optional, 

40 Sequence, 

41 Set, 

42 Tuple, 

43 Type, 

44 Union, 

45) 

46 

47from lsst.daf.butler import ( 

48 CompositesMap, 

49 Config, 

50 DatasetId, 

51 DatasetRef, 

52 DatasetRefURIs, 

53 DatasetType, 

54 DatasetTypeNotSupportedError, 

55 Datastore, 

56 DatastoreCacheManager, 

57 DatastoreConfig, 

58 DatastoreDisabledCacheManager, 

59 DatastoreRecordData, 

60 DatastoreValidationError, 

61 FileDataset, 

62 FileDescriptor, 

63 FileTemplates, 

64 FileTemplateValidationError, 

65 Formatter, 

66 FormatterFactory, 

67 Location, 

68 LocationFactory, 

69 Progress, 

70 StorageClass, 

71 StoredDatastoreItemInfo, 

72 StoredFileInfo, 

73 ddl, 

74) 

75from lsst.daf.butler.core.repoRelocation import replaceRoot 

76from lsst.daf.butler.core.utils import transactional 

77from lsst.daf.butler.registry.interfaces import DatastoreRegistryBridge, ReadOnlyDatabaseError 

78from lsst.resources import ResourcePath, ResourcePathExpression 

79from lsst.utils.introspection import get_class_of, get_instance_of 

80from lsst.utils.iteration import chunk_iterable 

81 

82# For VERBOSE logging usage. 

83from lsst.utils.logging import VERBOSE, getLogger 

84from lsst.utils.timer import time_this 

85from sqlalchemy import BigInteger, String 

86 

87from ..registry.interfaces import FakeDatasetRef 

88from .genericDatastore import GenericBaseDatastore 

89 

90if TYPE_CHECKING: 90 ↛ 91line 90 didn't jump to line 91, because the condition on line 90 was never true

91 from lsst.daf.butler import AbstractDatastoreCacheManager, LookupKey 

92 from lsst.daf.butler.registry.interfaces import DatasetIdRef, DatastoreRegistryBridgeManager 

93 

94log = getLogger(__name__) 

95 

96 

97class _IngestPrepData(Datastore.IngestPrepData): 

98 """Helper class for FileDatastore ingest implementation. 

99 

100 Parameters 

101 ---------- 

102 datasets : `list` of `FileDataset` 

103 Files to be ingested by this datastore. 

104 """ 

105 

106 def __init__(self, datasets: List[FileDataset]): 

107 super().__init__(ref for dataset in datasets for ref in dataset.refs) 

108 self.datasets = datasets 

109 

110 

111@dataclass(frozen=True) 

112class DatastoreFileGetInformation: 

113 """Collection of useful parameters needed to retrieve a file from 

114 a Datastore. 

115 """ 

116 

117 location: Location 

118 """The location from which to read the dataset.""" 

119 

120 formatter: Formatter 

121 """The `Formatter` to use to deserialize the dataset.""" 

122 

123 info: StoredFileInfo 

124 """Stored information about this file and its formatter.""" 

125 

126 assemblerParams: Mapping[str, Any] 

127 """Parameters to use for post-processing the retrieved dataset.""" 

128 

129 formatterParams: Mapping[str, Any] 

130 """Parameters that were understood by the associated formatter.""" 

131 

132 component: Optional[str] 

133 """The component to be retrieved (can be `None`).""" 

134 

135 readStorageClass: StorageClass 

136 """The `StorageClass` of the dataset being read.""" 

137 

138 

139class FileDatastore(GenericBaseDatastore): 

140 """Generic Datastore for file-based implementations. 

141 

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

143 

144 Parameters 

145 ---------- 

146 config : `DatastoreConfig` or `str` 

147 Configuration as either a `Config` object or URI to file. 

148 bridgeManager : `DatastoreRegistryBridgeManager` 

149 Object that manages the interface between `Registry` and datastores. 

150 butlerRoot : `str`, optional 

151 New datastore root to use to override the configuration value. 

152 

153 Raises 

154 ------ 

155 ValueError 

156 If root location does not exist and ``create`` is `False` in the 

157 configuration. 

158 """ 

159 

160 defaultConfigFile: ClassVar[Optional[str]] = None 

161 """Path to configuration defaults. Accessed within the ``config`` resource 

162 or relative to a search path. Can be None if no defaults specified. 

163 """ 

164 

165 root: ResourcePath 

166 """Root directory URI of this `Datastore`.""" 

167 

168 locationFactory: LocationFactory 

169 """Factory for creating locations relative to the datastore root.""" 

170 

171 formatterFactory: FormatterFactory 

172 """Factory for creating instances of formatters.""" 

173 

174 templates: FileTemplates 

175 """File templates that can be used by this `Datastore`.""" 

176 

177 composites: CompositesMap 

178 """Determines whether a dataset should be disassembled on put.""" 

179 

180 defaultConfigFile = "datastores/fileDatastore.yaml" 

181 """Path to configuration defaults. Accessed within the ``config`` resource 

182 or relative to a search path. Can be None if no defaults specified. 

183 """ 

184 

185 @classmethod 

186 def setConfigRoot(cls, root: str, config: Config, full: Config, overwrite: bool = True) -> None: 

187 """Set any filesystem-dependent config options for this Datastore to 

188 be appropriate for a new empty repository with the given root. 

189 

190 Parameters 

191 ---------- 

192 root : `str` 

193 URI to the root of the data repository. 

194 config : `Config` 

195 A `Config` to update. Only the subset understood by 

196 this component will be updated. Will not expand 

197 defaults. 

198 full : `Config` 

199 A complete config with all defaults expanded that can be 

200 converted to a `DatastoreConfig`. Read-only and will not be 

201 modified by this method. 

202 Repository-specific options that should not be obtained 

203 from defaults when Butler instances are constructed 

204 should be copied from ``full`` to ``config``. 

205 overwrite : `bool`, optional 

206 If `False`, do not modify a value in ``config`` if the value 

207 already exists. Default is always to overwrite with the provided 

208 ``root``. 

209 

210 Notes 

211 ----- 

212 If a keyword is explicitly defined in the supplied ``config`` it 

213 will not be overridden by this method if ``overwrite`` is `False`. 

214 This allows explicit values set in external configs to be retained. 

215 """ 

216 Config.updateParameters( 

217 DatastoreConfig, 

218 config, 

219 full, 

220 toUpdate={"root": root}, 

221 toCopy=("cls", ("records", "table")), 

222 overwrite=overwrite, 

223 ) 

224 

225 @classmethod 

226 def makeTableSpec(cls, datasetIdColumnType: type) -> ddl.TableSpec: 

227 return ddl.TableSpec( 

228 fields=[ 

229 ddl.FieldSpec(name="dataset_id", dtype=datasetIdColumnType, primaryKey=True), 

230 ddl.FieldSpec(name="path", dtype=String, length=256, nullable=False), 

231 ddl.FieldSpec(name="formatter", dtype=String, length=128, nullable=False), 

232 ddl.FieldSpec(name="storage_class", dtype=String, length=64, nullable=False), 

233 # Use empty string to indicate no component 

234 ddl.FieldSpec(name="component", dtype=String, length=32, primaryKey=True), 

235 # TODO: should checksum be Base64Bytes instead? 

236 ddl.FieldSpec(name="checksum", dtype=String, length=128, nullable=True), 

237 ddl.FieldSpec(name="file_size", dtype=BigInteger, nullable=True), 

238 ], 

239 unique=frozenset(), 

240 indexes=[ddl.IndexSpec("path")], 

241 ) 

242 

243 def __init__( 

244 self, 

245 config: Union[DatastoreConfig, str], 

246 bridgeManager: DatastoreRegistryBridgeManager, 

247 butlerRoot: str | None = None, 

248 ): 

249 super().__init__(config, bridgeManager) 

250 if "root" not in self.config: 250 ↛ 251line 250 didn't jump to line 251, because the condition on line 250 was never true

251 raise ValueError("No root directory specified in configuration") 

252 

253 self._bridgeManager = bridgeManager 

254 

255 # Name ourselves either using an explicit name or a name 

256 # derived from the (unexpanded) root 

257 if "name" in self.config: 

258 self.name = self.config["name"] 

259 else: 

260 # We use the unexpanded root in the name to indicate that this 

261 # datastore can be moved without having to update registry. 

262 self.name = "{}@{}".format(type(self).__name__, self.config["root"]) 

263 

264 # Support repository relocation in config 

265 # Existence of self.root is checked in subclass 

266 self.root = ResourcePath( 

267 replaceRoot(self.config["root"], butlerRoot), forceDirectory=True, forceAbsolute=True 

268 ) 

269 

270 self.locationFactory = LocationFactory(self.root) 

271 self.formatterFactory = FormatterFactory() 

272 

273 # Now associate formatters with storage classes 

274 self.formatterFactory.registerFormatters(self.config["formatters"], universe=bridgeManager.universe) 

275 

276 # Read the file naming templates 

277 self.templates = FileTemplates(self.config["templates"], universe=bridgeManager.universe) 

278 

279 # See if composites should be disassembled 

280 self.composites = CompositesMap(self.config["composites"], universe=bridgeManager.universe) 

281 

282 tableName = self.config["records", "table"] 

283 try: 

284 # Storage of paths and formatters, keyed by dataset_id 

285 self._table = bridgeManager.opaque.register( 

286 tableName, self.makeTableSpec(bridgeManager.datasetIdColumnType) 

287 ) 

288 # Interface to Registry. 

289 self._bridge = bridgeManager.register(self.name) 

290 except ReadOnlyDatabaseError: 

291 # If the database is read only and we just tried and failed to 

292 # create a table, it means someone is trying to create a read-only 

293 # butler client for an empty repo. That should be okay, as long 

294 # as they then try to get any datasets before some other client 

295 # creates the table. Chances are they'rejust validating 

296 # configuration. 

297 pass 

298 

299 # Determine whether checksums should be used - default to False 

300 self.useChecksum = self.config.get("checksum", False) 

301 

302 # Determine whether we can fall back to configuration if a 

303 # requested dataset is not known to registry 

304 self.trustGetRequest = self.config.get("trust_get_request", False) 

305 

306 # Create a cache manager 

307 self.cacheManager: AbstractDatastoreCacheManager 

308 if "cached" in self.config: 308 ↛ 311line 308 didn't jump to line 311, because the condition on line 308 was never false

309 self.cacheManager = DatastoreCacheManager(self.config["cached"], universe=bridgeManager.universe) 

310 else: 

311 self.cacheManager = DatastoreDisabledCacheManager("", universe=bridgeManager.universe) 

312 

313 # Check existence and create directory structure if necessary 

314 if not self.root.exists(): 

315 if "create" not in self.config or not self.config["create"]: 315 ↛ 316line 315 didn't jump to line 316, because the condition on line 315 was never true

316 raise ValueError(f"No valid root and not allowed to create one at: {self.root}") 

317 try: 

318 self.root.mkdir() 

319 except Exception as e: 

320 raise ValueError( 

321 f"Can not create datastore root '{self.root}', check permissions. Got error: {e}" 

322 ) from e 

323 

324 def __str__(self) -> str: 

325 return str(self.root) 

326 

327 @property 

328 def bridge(self) -> DatastoreRegistryBridge: 

329 return self._bridge 

330 

331 def _artifact_exists(self, location: Location) -> bool: 

332 """Check that an artifact exists in this datastore at the specified 

333 location. 

334 

335 Parameters 

336 ---------- 

337 location : `Location` 

338 Expected location of the artifact associated with this datastore. 

339 

340 Returns 

341 ------- 

342 exists : `bool` 

343 True if the location can be found, false otherwise. 

344 """ 

345 log.debug("Checking if resource exists: %s", location.uri) 

346 return location.uri.exists() 

347 

348 def _delete_artifact(self, location: Location) -> None: 

349 """Delete the artifact from the datastore. 

350 

351 Parameters 

352 ---------- 

353 location : `Location` 

354 Location of the artifact associated with this datastore. 

355 """ 

356 if location.pathInStore.isabs(): 356 ↛ 357line 356 didn't jump to line 357, because the condition on line 356 was never true

357 raise RuntimeError(f"Cannot delete artifact with absolute uri {location.uri}.") 

358 

359 try: 

360 location.uri.remove() 

361 except FileNotFoundError: 

362 log.debug("File %s did not exist and so could not be deleted.", location.uri) 

363 raise 

364 except Exception as e: 

365 log.critical("Failed to delete file: %s (%s)", location.uri, e) 

366 raise 

367 log.debug("Successfully deleted file: %s", location.uri) 

368 

369 def addStoredItemInfo(self, refs: Iterable[DatasetRef], infos: Iterable[StoredFileInfo]) -> None: 

370 # Docstring inherited from GenericBaseDatastore 

371 records = [info.rebase(ref).to_record() for ref, info in zip(refs, infos)] 

372 self._table.insert(*records) 

373 

374 def getStoredItemsInfo(self, ref: DatasetIdRef) -> List[StoredFileInfo]: 

375 # Docstring inherited from GenericBaseDatastore 

376 

377 # Look for the dataset_id -- there might be multiple matches 

378 # if we have disassembled the dataset. 

379 records = self._table.fetch(dataset_id=ref.id) 

380 return [StoredFileInfo.from_record(record) for record in records] 

381 

382 def _get_stored_records_associated_with_refs( 

383 self, refs: Iterable[DatasetIdRef] 

384 ) -> Dict[DatasetId, List[StoredFileInfo]]: 

385 """Retrieve all records associated with the provided refs. 

386 

387 Parameters 

388 ---------- 

389 refs : iterable of `DatasetIdRef` 

390 The refs for which records are to be retrieved. 

391 

392 Returns 

393 ------- 

394 records : `dict` of [`DatasetId`, `list` of `StoredFileInfo`] 

395 The matching records indexed by the ref ID. The number of entries 

396 in the dict can be smaller than the number of requested refs. 

397 """ 

398 records = self._table.fetch(dataset_id=[ref.id for ref in refs]) 

399 

400 # Uniqueness is dataset_id + component so can have multiple records 

401 # per ref. 

402 records_by_ref = defaultdict(list) 

403 for record in records: 

404 records_by_ref[record["dataset_id"]].append(StoredFileInfo.from_record(record)) 

405 return records_by_ref 

406 

407 def _refs_associated_with_artifacts( 

408 self, paths: List[Union[str, ResourcePath]] 

409 ) -> Dict[str, Set[DatasetId]]: 

410 """Return paths and associated dataset refs. 

411 

412 Parameters 

413 ---------- 

414 paths : `list` of `str` or `lsst.resources.ResourcePath` 

415 All the paths to include in search. 

416 

417 Returns 

418 ------- 

419 mapping : `dict` of [`str`, `set` [`DatasetId`]] 

420 Mapping of each path to a set of associated database IDs. 

421 """ 

422 records = self._table.fetch(path=[str(path) for path in paths]) 

423 result = defaultdict(set) 

424 for row in records: 

425 result[row["path"]].add(row["dataset_id"]) 

426 return result 

427 

428 def _registered_refs_per_artifact(self, pathInStore: ResourcePath) -> Set[DatasetId]: 

429 """Return all dataset refs associated with the supplied path. 

430 

431 Parameters 

432 ---------- 

433 pathInStore : `lsst.resources.ResourcePath` 

434 Path of interest in the data store. 

435 

436 Returns 

437 ------- 

438 ids : `set` of `int` 

439 All `DatasetRef` IDs associated with this path. 

440 """ 

441 records = list(self._table.fetch(path=str(pathInStore))) 

442 ids = {r["dataset_id"] for r in records} 

443 return ids 

444 

445 def removeStoredItemInfo(self, ref: DatasetIdRef) -> None: 

446 # Docstring inherited from GenericBaseDatastore 

447 self._table.delete(["dataset_id"], {"dataset_id": ref.id}) 

448 

449 def _get_dataset_locations_info(self, ref: DatasetIdRef) -> List[Tuple[Location, StoredFileInfo]]: 

450 r"""Find all the `Location`\ s of the requested dataset in the 

451 `Datastore` and the associated stored file information. 

452 

453 Parameters 

454 ---------- 

455 ref : `DatasetRef` 

456 Reference to the required `Dataset`. 

457 

458 Returns 

459 ------- 

460 results : `list` [`tuple` [`Location`, `StoredFileInfo` ]] 

461 Location of the dataset within the datastore and 

462 stored information about each file and its formatter. 

463 """ 

464 # Get the file information (this will fail if no file) 

465 records = self.getStoredItemsInfo(ref) 

466 

467 # Use the path to determine the location -- we need to take 

468 # into account absolute URIs in the datastore record 

469 return [(r.file_location(self.locationFactory), r) for r in records] 

470 

471 def _can_remove_dataset_artifact(self, ref: DatasetIdRef, location: Location) -> bool: 

472 """Check that there is only one dataset associated with the 

473 specified artifact. 

474 

475 Parameters 

476 ---------- 

477 ref : `DatasetRef` or `FakeDatasetRef` 

478 Dataset to be removed. 

479 location : `Location` 

480 The location of the artifact to be removed. 

481 

482 Returns 

483 ------- 

484 can_remove : `Bool` 

485 True if the artifact can be safely removed. 

486 """ 

487 # Can't ever delete absolute URIs. 

488 if location.pathInStore.isabs(): 

489 return False 

490 

491 # Get all entries associated with this path 

492 allRefs = self._registered_refs_per_artifact(location.pathInStore) 

493 if not allRefs: 

494 raise RuntimeError(f"Datastore inconsistency error. {location.pathInStore} not in registry") 

495 

496 # Remove these refs from all the refs and if there is nothing left 

497 # then we can delete 

498 remainingRefs = allRefs - {ref.id} 

499 

500 if remainingRefs: 

501 return False 

502 return True 

503 

504 def _get_expected_dataset_locations_info(self, ref: DatasetRef) -> List[Tuple[Location, StoredFileInfo]]: 

505 """Predict the location and related file information of the requested 

506 dataset in this datastore. 

507 

508 Parameters 

509 ---------- 

510 ref : `DatasetRef` 

511 Reference to the required `Dataset`. 

512 

513 Returns 

514 ------- 

515 results : `list` [`tuple` [`Location`, `StoredFileInfo` ]] 

516 Expected Location of the dataset within the datastore and 

517 placeholder information about each file and its formatter. 

518 

519 Notes 

520 ----- 

521 Uses the current configuration to determine how we would expect the 

522 datastore files to have been written if we couldn't ask registry. 

523 This is safe so long as there has been no change to datastore 

524 configuration between writing the dataset and wanting to read it. 

525 Will not work for files that have been ingested without using the 

526 standard file template or default formatter. 

527 """ 

528 

529 # If we have a component ref we always need to ask the questions 

530 # of the composite. If the composite is disassembled this routine 

531 # should return all components. If the composite was not 

532 # disassembled the composite is what is stored regardless of 

533 # component request. Note that if the caller has disassembled 

534 # a composite there is no way for this guess to know that 

535 # without trying both the composite and component ref and seeing 

536 # if there is something at the component Location even without 

537 # disassembly being enabled. 

538 if ref.datasetType.isComponent(): 

539 ref = ref.makeCompositeRef() 

540 

541 # See if the ref is a composite that should be disassembled 

542 doDisassembly = self.composites.shouldBeDisassembled(ref) 

543 

544 all_info: List[Tuple[Location, Formatter, StorageClass, Optional[str]]] = [] 

545 

546 if doDisassembly: 

547 for component, componentStorage in ref.datasetType.storageClass.components.items(): 

548 compRef = ref.makeComponentRef(component) 

549 location, formatter = self._determine_put_formatter_location(compRef) 

550 all_info.append((location, formatter, componentStorage, component)) 

551 

552 else: 

553 # Always use the composite ref if no disassembly 

554 location, formatter = self._determine_put_formatter_location(ref) 

555 all_info.append((location, formatter, ref.datasetType.storageClass, None)) 

556 

557 # Convert the list of tuples to have StoredFileInfo as second element 

558 return [ 

559 ( 

560 location, 

561 StoredFileInfo( 

562 formatter=formatter, 

563 path=location.pathInStore.path, 

564 storageClass=storageClass, 

565 component=component, 

566 checksum=None, 

567 file_size=-1, 

568 dataset_id=ref.getCheckedId(), 

569 ), 

570 ) 

571 for location, formatter, storageClass, component in all_info 

572 ] 

573 

574 def _prepare_for_get( 

575 self, ref: DatasetRef, parameters: Optional[Mapping[str, Any]] = None 

576 ) -> List[DatastoreFileGetInformation]: 

577 """Check parameters for ``get`` and obtain formatter and 

578 location. 

579 

580 Parameters 

581 ---------- 

582 ref : `DatasetRef` 

583 Reference to the required Dataset. 

584 parameters : `dict` 

585 `StorageClass`-specific parameters that specify, for example, 

586 a slice of the dataset to be loaded. 

587 

588 Returns 

589 ------- 

590 getInfo : `list` [`DatastoreFileGetInformation`] 

591 Parameters needed to retrieve each file. 

592 """ 

593 log.debug("Retrieve %s from %s with parameters %s", ref, self.name, parameters) 

594 

595 # Get file metadata and internal metadata 

596 fileLocations = self._get_dataset_locations_info(ref) 

597 if not fileLocations: 

598 if not self.trustGetRequest: 

599 raise FileNotFoundError(f"Could not retrieve dataset {ref}.") 

600 # Assume the dataset is where we think it should be 

601 fileLocations = self._get_expected_dataset_locations_info(ref) 

602 

603 # The storage class we want to use eventually 

604 refStorageClass = ref.datasetType.storageClass 

605 

606 if len(fileLocations) > 1: 

607 disassembled = True 

608 

609 # If trust is involved it is possible that there will be 

610 # components listed here that do not exist in the datastore. 

611 # Explicitly check for file artifact existence and filter out any 

612 # that are missing. 

613 if self.trustGetRequest: 

614 fileLocations = [loc for loc in fileLocations if loc[0].uri.exists()] 

615 

616 # For now complain only if we have no components at all. One 

617 # component is probably a problem but we can punt that to the 

618 # assembler. 

619 if not fileLocations: 619 ↛ 620line 619 didn't jump to line 620, because the condition on line 619 was never true

620 raise FileNotFoundError(f"None of the component files for dataset {ref} exist.") 

621 

622 else: 

623 disassembled = False 

624 

625 # Is this a component request? 

626 refComponent = ref.datasetType.component() 

627 

628 fileGetInfo = [] 

629 for location, storedFileInfo in fileLocations: 

630 

631 # The storage class used to write the file 

632 writeStorageClass = storedFileInfo.storageClass 

633 

634 # If this has been disassembled we need read to match the write 

635 if disassembled: 

636 readStorageClass = writeStorageClass 

637 else: 

638 readStorageClass = refStorageClass 

639 

640 formatter = get_instance_of( 

641 storedFileInfo.formatter, 

642 FileDescriptor( 

643 location, 

644 readStorageClass=readStorageClass, 

645 storageClass=writeStorageClass, 

646 parameters=parameters, 

647 ), 

648 ref.dataId, 

649 ) 

650 

651 formatterParams, notFormatterParams = formatter.segregateParameters() 

652 

653 # Of the remaining parameters, extract the ones supported by 

654 # this StorageClass (for components not all will be handled) 

655 assemblerParams = readStorageClass.filterParameters(notFormatterParams) 

656 

657 # The ref itself could be a component if the dataset was 

658 # disassembled by butler, or we disassembled in datastore and 

659 # components came from the datastore records 

660 component = storedFileInfo.component if storedFileInfo.component else refComponent 

661 

662 fileGetInfo.append( 

663 DatastoreFileGetInformation( 

664 location, 

665 formatter, 

666 storedFileInfo, 

667 assemblerParams, 

668 formatterParams, 

669 component, 

670 readStorageClass, 

671 ) 

672 ) 

673 

674 return fileGetInfo 

675 

676 def _prepare_for_put(self, inMemoryDataset: Any, ref: DatasetRef) -> Tuple[Location, Formatter]: 

677 """Check the arguments for ``put`` and obtain formatter and 

678 location. 

679 

680 Parameters 

681 ---------- 

682 inMemoryDataset : `object` 

683 The dataset to store. 

684 ref : `DatasetRef` 

685 Reference to the associated Dataset. 

686 

687 Returns 

688 ------- 

689 location : `Location` 

690 The location to write the dataset. 

691 formatter : `Formatter` 

692 The `Formatter` to use to write the dataset. 

693 

694 Raises 

695 ------ 

696 TypeError 

697 Supplied object and storage class are inconsistent. 

698 DatasetTypeNotSupportedError 

699 The associated `DatasetType` is not handled by this datastore. 

700 """ 

701 self._validate_put_parameters(inMemoryDataset, ref) 

702 return self._determine_put_formatter_location(ref) 

703 

704 def _determine_put_formatter_location(self, ref: DatasetRef) -> Tuple[Location, Formatter]: 

705 """Calculate the formatter and output location to use for put. 

706 

707 Parameters 

708 ---------- 

709 ref : `DatasetRef` 

710 Reference to the associated Dataset. 

711 

712 Returns 

713 ------- 

714 location : `Location` 

715 The location to write the dataset. 

716 formatter : `Formatter` 

717 The `Formatter` to use to write the dataset. 

718 """ 

719 # Work out output file name 

720 try: 

721 template = self.templates.getTemplate(ref) 

722 except KeyError as e: 

723 raise DatasetTypeNotSupportedError(f"Unable to find template for {ref}") from e 

724 

725 # Validate the template to protect against filenames from different 

726 # dataIds returning the same and causing overwrite confusion. 

727 template.validateTemplate(ref) 

728 

729 location = self.locationFactory.fromPath(template.format(ref)) 

730 

731 # Get the formatter based on the storage class 

732 storageClass = ref.datasetType.storageClass 

733 try: 

734 formatter = self.formatterFactory.getFormatter( 

735 ref, FileDescriptor(location, storageClass=storageClass), ref.dataId 

736 ) 

737 except KeyError as e: 

738 raise DatasetTypeNotSupportedError( 

739 f"Unable to find formatter for {ref} in datastore {self.name}" 

740 ) from e 

741 

742 # Now that we know the formatter, update the location 

743 location = formatter.makeUpdatedLocation(location) 

744 

745 return location, formatter 

746 

747 def _overrideTransferMode(self, *datasets: FileDataset, transfer: Optional[str] = None) -> Optional[str]: 

748 # Docstring inherited from base class 

749 if transfer != "auto": 

750 return transfer 

751 

752 # See if the paths are within the datastore or not 

753 inside = [self._pathInStore(d.path) is not None for d in datasets] 

754 

755 if all(inside): 

756 transfer = None 

757 elif not any(inside): 757 ↛ 766line 757 didn't jump to line 766, because the condition on line 757 was never false

758 # Allow ResourcePath to use its own knowledge 

759 transfer = "auto" 

760 else: 

761 # This can happen when importing from a datastore that 

762 # has had some datasets ingested using "direct" mode. 

763 # Also allow ResourcePath to sort it out but warn about it. 

764 # This can happen if you are importing from a datastore 

765 # that had some direct transfer datasets. 

766 log.warning( 

767 "Some datasets are inside the datastore and some are outside. Using 'split' " 

768 "transfer mode. This assumes that the files outside the datastore are " 

769 "still accessible to the new butler since they will not be copied into " 

770 "the target datastore." 

771 ) 

772 transfer = "split" 

773 

774 return transfer 

775 

776 def _pathInStore(self, path: ResourcePathExpression) -> Optional[str]: 

777 """Return path relative to datastore root 

778 

779 Parameters 

780 ---------- 

781 path : `lsst.resources.ResourcePathExpression` 

782 Path to dataset. Can be absolute URI. If relative assumed to 

783 be relative to the datastore. Returns path in datastore 

784 or raises an exception if the path it outside. 

785 

786 Returns 

787 ------- 

788 inStore : `str` 

789 Path relative to datastore root. Returns `None` if the file is 

790 outside the root. 

791 """ 

792 # Relative path will always be relative to datastore 

793 pathUri = ResourcePath(path, forceAbsolute=False) 

794 return pathUri.relative_to(self.root) 

795 

796 def _standardizeIngestPath( 

797 self, path: Union[str, ResourcePath], *, transfer: Optional[str] = None 

798 ) -> Union[str, ResourcePath]: 

799 """Standardize the path of a to-be-ingested file. 

800 

801 Parameters 

802 ---------- 

803 path : `str` or `lsst.resources.ResourcePath` 

804 Path of a file to be ingested. This parameter is not expected 

805 to be all the types that can be used to construct a 

806 `~lsst.resources.ResourcePath`. 

807 transfer : `str`, optional 

808 How (and whether) the dataset should be added to the datastore. 

809 See `ingest` for details of transfer modes. 

810 This implementation is provided only so 

811 `NotImplementedError` can be raised if the mode is not supported; 

812 actual transfers are deferred to `_extractIngestInfo`. 

813 

814 Returns 

815 ------- 

816 path : `str` or `lsst.resources.ResourcePath` 

817 New path in what the datastore considers standard form. If an 

818 absolute URI was given that will be returned unchanged. 

819 

820 Notes 

821 ----- 

822 Subclasses of `FileDatastore` can implement this method instead 

823 of `_prepIngest`. It should not modify the data repository or given 

824 file in any way. 

825 

826 Raises 

827 ------ 

828 NotImplementedError 

829 Raised if the datastore does not support the given transfer mode 

830 (including the case where ingest is not supported at all). 

831 FileNotFoundError 

832 Raised if one of the given files does not exist. 

833 """ 

834 if transfer not in (None, "direct", "split") + self.root.transferModes: 834 ↛ 835line 834 didn't jump to line 835, because the condition on line 834 was never true

835 raise NotImplementedError(f"Transfer mode {transfer} not supported.") 

836 

837 # A relative URI indicates relative to datastore root 

838 srcUri = ResourcePath(path, forceAbsolute=False) 

839 if not srcUri.isabs(): 

840 srcUri = self.root.join(path) 

841 

842 if not srcUri.exists(): 

843 raise FileNotFoundError( 

844 f"Resource at {srcUri} does not exist; note that paths to ingest " 

845 f"are assumed to be relative to {self.root} unless they are absolute." 

846 ) 

847 

848 if transfer is None: 

849 relpath = srcUri.relative_to(self.root) 

850 if not relpath: 

851 raise RuntimeError( 

852 f"Transfer is none but source file ({srcUri}) is not within datastore ({self.root})" 

853 ) 

854 

855 # Return the relative path within the datastore for internal 

856 # transfer 

857 path = relpath 

858 

859 return path 

860 

861 def _extractIngestInfo( 

862 self, 

863 path: ResourcePathExpression, 

864 ref: DatasetRef, 

865 *, 

866 formatter: Union[Formatter, Type[Formatter]], 

867 transfer: Optional[str] = None, 

868 record_validation_info: bool = True, 

869 ) -> StoredFileInfo: 

870 """Relocate (if necessary) and extract `StoredFileInfo` from a 

871 to-be-ingested file. 

872 

873 Parameters 

874 ---------- 

875 path : `lsst.resources.ResourcePathExpression` 

876 URI or path of a file to be ingested. 

877 ref : `DatasetRef` 

878 Reference for the dataset being ingested. Guaranteed to have 

879 ``dataset_id not None`. 

880 formatter : `type` or `Formatter` 

881 `Formatter` subclass to use for this dataset or an instance. 

882 transfer : `str`, optional 

883 How (and whether) the dataset should be added to the datastore. 

884 See `ingest` for details of transfer modes. 

885 record_validation_info : `bool`, optional 

886 If `True`, the default, the datastore can record validation 

887 information associated with the file. If `False` the datastore 

888 will not attempt to track any information such as checksums 

889 or file sizes. This can be useful if such information is tracked 

890 in an external system or if the file is to be compressed in place. 

891 It is up to the datastore whether this parameter is relevant. 

892 

893 Returns 

894 ------- 

895 info : `StoredFileInfo` 

896 Internal datastore record for this file. This will be inserted by 

897 the caller; the `_extractIngestInfo` is only responsible for 

898 creating and populating the struct. 

899 

900 Raises 

901 ------ 

902 FileNotFoundError 

903 Raised if one of the given files does not exist. 

904 FileExistsError 

905 Raised if transfer is not `None` but the (internal) location the 

906 file would be moved to is already occupied. 

907 """ 

908 if self._transaction is None: 908 ↛ 909line 908 didn't jump to line 909, because the condition on line 908 was never true

909 raise RuntimeError("Ingest called without transaction enabled") 

910 

911 # Create URI of the source path, do not need to force a relative 

912 # path to absolute. 

913 srcUri = ResourcePath(path, forceAbsolute=False) 

914 

915 # Track whether we have read the size of the source yet 

916 have_sized = False 

917 

918 tgtLocation: Optional[Location] 

919 if transfer is None or transfer == "split": 

920 # A relative path is assumed to be relative to the datastore 

921 # in this context 

922 if not srcUri.isabs(): 

923 tgtLocation = self.locationFactory.fromPath(srcUri.ospath) 

924 else: 

925 # Work out the path in the datastore from an absolute URI 

926 # This is required to be within the datastore. 

927 pathInStore = srcUri.relative_to(self.root) 

928 if pathInStore is None and transfer is None: 928 ↛ 929line 928 didn't jump to line 929, because the condition on line 928 was never true

929 raise RuntimeError( 

930 f"Unexpectedly learned that {srcUri} is not within datastore {self.root}" 

931 ) 

932 if pathInStore: 932 ↛ 934line 932 didn't jump to line 934, because the condition on line 932 was never false

933 tgtLocation = self.locationFactory.fromPath(pathInStore) 

934 elif transfer == "split": 

935 # Outside the datastore but treat that as a direct ingest 

936 # instead. 

937 tgtLocation = None 

938 else: 

939 raise RuntimeError(f"Unexpected transfer mode encountered: {transfer} for URI {srcUri}") 

940 elif transfer == "direct": 940 ↛ 945line 940 didn't jump to line 945, because the condition on line 940 was never true

941 # Want to store the full URI to the resource directly in 

942 # datastore. This is useful for referring to permanent archive 

943 # storage for raw data. 

944 # Trust that people know what they are doing. 

945 tgtLocation = None 

946 else: 

947 # Work out the name we want this ingested file to have 

948 # inside the datastore 

949 tgtLocation = self._calculate_ingested_datastore_name(srcUri, ref, formatter) 

950 if not tgtLocation.uri.dirname().exists(): 

951 log.debug("Folder %s does not exist yet.", tgtLocation.uri.dirname()) 

952 tgtLocation.uri.dirname().mkdir() 

953 

954 # if we are transferring from a local file to a remote location 

955 # it may be more efficient to get the size and checksum of the 

956 # local file rather than the transferred one 

957 if record_validation_info and srcUri.isLocal: 

958 size = srcUri.size() 

959 checksum = self.computeChecksum(srcUri) if self.useChecksum else None 

960 have_sized = True 

961 

962 # Transfer the resource to the destination. 

963 # Allow overwrite of an existing file. This matches the behavior 

964 # of datastore.put() in that it trusts that registry would not 

965 # be asking to overwrite unless registry thought that the 

966 # overwrite was allowed. 

967 tgtLocation.uri.transfer_from( 

968 srcUri, transfer=transfer, transaction=self._transaction, overwrite=True 

969 ) 

970 

971 if tgtLocation is None: 971 ↛ 973line 971 didn't jump to line 973, because the condition on line 971 was never true

972 # This means we are using direct mode 

973 targetUri = srcUri 

974 targetPath = str(srcUri) 

975 else: 

976 targetUri = tgtLocation.uri 

977 targetPath = tgtLocation.pathInStore.path 

978 

979 # the file should exist in the datastore now 

980 if record_validation_info: 

981 if not have_sized: 

982 size = targetUri.size() 

983 checksum = self.computeChecksum(targetUri) if self.useChecksum else None 

984 else: 

985 # Not recording any file information. 

986 size = -1 

987 checksum = None 

988 

989 return StoredFileInfo( 

990 formatter=formatter, 

991 path=targetPath, 

992 storageClass=ref.datasetType.storageClass, 

993 component=ref.datasetType.component(), 

994 file_size=size, 

995 checksum=checksum, 

996 dataset_id=ref.getCheckedId(), 

997 ) 

998 

999 def _prepIngest(self, *datasets: FileDataset, transfer: Optional[str] = None) -> _IngestPrepData: 

1000 # Docstring inherited from Datastore._prepIngest. 

1001 filtered = [] 

1002 for dataset in datasets: 

1003 acceptable = [ref for ref in dataset.refs if self.constraints.isAcceptable(ref)] 

1004 if not acceptable: 

1005 continue 

1006 else: 

1007 dataset.refs = acceptable 

1008 if dataset.formatter is None: 

1009 dataset.formatter = self.formatterFactory.getFormatterClass(dataset.refs[0]) 

1010 else: 

1011 assert isinstance(dataset.formatter, (type, str)) 

1012 formatter_class = get_class_of(dataset.formatter) 

1013 if not issubclass(formatter_class, Formatter): 1013 ↛ 1014line 1013 didn't jump to line 1014, because the condition on line 1013 was never true

1014 raise TypeError(f"Requested formatter {dataset.formatter} is not a Formatter class.") 

1015 dataset.formatter = formatter_class 

1016 dataset.path = self._standardizeIngestPath(dataset.path, transfer=transfer) 

1017 filtered.append(dataset) 

1018 return _IngestPrepData(filtered) 

1019 

1020 @transactional 

1021 def _finishIngest( 

1022 self, 

1023 prepData: Datastore.IngestPrepData, 

1024 *, 

1025 transfer: Optional[str] = None, 

1026 record_validation_info: bool = True, 

1027 ) -> None: 

1028 # Docstring inherited from Datastore._finishIngest. 

1029 refsAndInfos = [] 

1030 progress = Progress("lsst.daf.butler.datastores.FileDatastore.ingest", level=logging.DEBUG) 

1031 for dataset in progress.wrap(prepData.datasets, desc="Ingesting dataset files"): 

1032 # Do ingest as if the first dataset ref is associated with the file 

1033 info = self._extractIngestInfo( 

1034 dataset.path, 

1035 dataset.refs[0], 

1036 formatter=dataset.formatter, 

1037 transfer=transfer, 

1038 record_validation_info=record_validation_info, 

1039 ) 

1040 refsAndInfos.extend([(ref, info) for ref in dataset.refs]) 

1041 self._register_datasets(refsAndInfos) 

1042 

1043 def _calculate_ingested_datastore_name( 

1044 self, srcUri: ResourcePath, ref: DatasetRef, formatter: Union[Formatter, Type[Formatter]] 

1045 ) -> Location: 

1046 """Given a source URI and a DatasetRef, determine the name the 

1047 dataset will have inside datastore. 

1048 

1049 Parameters 

1050 ---------- 

1051 srcUri : `lsst.resources.ResourcePath` 

1052 URI to the source dataset file. 

1053 ref : `DatasetRef` 

1054 Ref associated with the newly-ingested dataset artifact. This 

1055 is used to determine the name within the datastore. 

1056 formatter : `Formatter` or Formatter class. 

1057 Formatter to use for validation. Can be a class or an instance. 

1058 

1059 Returns 

1060 ------- 

1061 location : `Location` 

1062 Target location for the newly-ingested dataset. 

1063 """ 

1064 # Ingesting a file from outside the datastore. 

1065 # This involves a new name. 

1066 template = self.templates.getTemplate(ref) 

1067 location = self.locationFactory.fromPath(template.format(ref)) 

1068 

1069 # Get the extension 

1070 ext = srcUri.getExtension() 

1071 

1072 # Update the destination to include that extension 

1073 location.updateExtension(ext) 

1074 

1075 # Ask the formatter to validate this extension 

1076 formatter.validateExtension(location) 

1077 

1078 return location 

1079 

1080 def _write_in_memory_to_artifact(self, inMemoryDataset: Any, ref: DatasetRef) -> StoredFileInfo: 

1081 """Write out in memory dataset to datastore. 

1082 

1083 Parameters 

1084 ---------- 

1085 inMemoryDataset : `object` 

1086 Dataset to write to datastore. 

1087 ref : `DatasetRef` 

1088 Registry information associated with this dataset. 

1089 

1090 Returns 

1091 ------- 

1092 info : `StoredFileInfo` 

1093 Information describing the artifact written to the datastore. 

1094 """ 

1095 # May need to coerce the in memory dataset to the correct 

1096 # python type. 

1097 inMemoryDataset = ref.datasetType.storageClass.coerce_type(inMemoryDataset) 

1098 

1099 location, formatter = self._prepare_for_put(inMemoryDataset, ref) 

1100 uri = location.uri 

1101 

1102 if not uri.dirname().exists(): 

1103 log.debug("Folder %s does not exist yet so creating it.", uri.dirname()) 

1104 uri.dirname().mkdir() 

1105 

1106 if self._transaction is None: 1106 ↛ 1107line 1106 didn't jump to line 1107, because the condition on line 1106 was never true

1107 raise RuntimeError("Attempting to write artifact without transaction enabled") 

1108 

1109 def _removeFileExists(uri: ResourcePath) -> None: 

1110 """Remove a file and do not complain if it is not there. 

1111 

1112 This is important since a formatter might fail before the file 

1113 is written and we should not confuse people by writing spurious 

1114 error messages to the log. 

1115 """ 

1116 try: 

1117 uri.remove() 

1118 except FileNotFoundError: 

1119 pass 

1120 

1121 # Register a callback to try to delete the uploaded data if 

1122 # something fails below 

1123 self._transaction.registerUndo("artifactWrite", _removeFileExists, uri) 

1124 

1125 data_written = False 

1126 if not uri.isLocal: 

1127 # This is a remote URI. Some datasets can be serialized directly 

1128 # to bytes and sent to the remote datastore without writing a 

1129 # file. If the dataset is intended to be saved to the cache 

1130 # a file is always written and direct write to the remote 

1131 # datastore is bypassed. 

1132 if not self.cacheManager.should_be_cached(ref): 

1133 try: 

1134 serializedDataset = formatter.toBytes(inMemoryDataset) 

1135 except NotImplementedError: 

1136 # Fallback to the file writing option. 

1137 pass 

1138 except Exception as e: 

1139 raise RuntimeError( 

1140 f"Failed to serialize dataset {ref} of type {type(inMemoryDataset)} to bytes." 

1141 ) from e 

1142 else: 

1143 log.debug("Writing bytes directly to %s", uri) 

1144 uri.write(serializedDataset, overwrite=True) 

1145 log.debug("Successfully wrote bytes directly to %s", uri) 

1146 data_written = True 

1147 

1148 if not data_written: 

1149 # Did not write the bytes directly to object store so instead 

1150 # write to temporary file. Always write to a temporary even if 

1151 # using a local file system -- that gives us atomic writes. 

1152 # If a process is killed as the file is being written we do not 

1153 # want it to remain in the correct place but in corrupt state. 

1154 # For local files write to the output directory not temporary dir. 

1155 prefix = uri.dirname() if uri.isLocal else None 

1156 with ResourcePath.temporary_uri(suffix=uri.getExtension(), prefix=prefix) as temporary_uri: 

1157 # Need to configure the formatter to write to a different 

1158 # location and that needs us to overwrite internals 

1159 log.debug("Writing dataset to temporary location at %s", temporary_uri) 

1160 with formatter._updateLocation(Location(None, temporary_uri)): 

1161 try: 

1162 formatter.write(inMemoryDataset) 

1163 except Exception as e: 

1164 raise RuntimeError( 

1165 f"Failed to serialize dataset {ref} of type" 

1166 f" {type(inMemoryDataset)} to " 

1167 f"temporary location {temporary_uri}" 

1168 ) from e 

1169 

1170 # Use move for a local file since that becomes an efficient 

1171 # os.rename. For remote resources we use copy to allow the 

1172 # file to be cached afterwards. 

1173 transfer = "move" if uri.isLocal else "copy" 

1174 

1175 uri.transfer_from(temporary_uri, transfer=transfer, overwrite=True) 

1176 

1177 if transfer == "copy": 

1178 # Cache if required 

1179 self.cacheManager.move_to_cache(temporary_uri, ref) 

1180 

1181 log.debug("Successfully wrote dataset to %s via a temporary file.", uri) 

1182 

1183 # URI is needed to resolve what ingest case are we dealing with 

1184 return self._extractIngestInfo(uri, ref, formatter=formatter) 

1185 

1186 def _read_artifact_into_memory( 

1187 self, 

1188 getInfo: DatastoreFileGetInformation, 

1189 ref: DatasetRef, 

1190 isComponent: bool = False, 

1191 cache_ref: Optional[DatasetRef] = None, 

1192 ) -> Any: 

1193 """Read the artifact from datastore into in memory object. 

1194 

1195 Parameters 

1196 ---------- 

1197 getInfo : `DatastoreFileGetInformation` 

1198 Information about the artifact within the datastore. 

1199 ref : `DatasetRef` 

1200 The registry information associated with this artifact. 

1201 isComponent : `bool` 

1202 Flag to indicate if a component is being read from this artifact. 

1203 cache_ref : `DatasetRef`, optional 

1204 The DatasetRef to use when looking up the file in the cache. 

1205 This ref must have the same ID as the supplied ref but can 

1206 be a parent ref or component ref to indicate to the cache whether 

1207 a composite file is being requested from the cache or a component 

1208 file. Without this the cache will default to the supplied ref but 

1209 it can get confused with read-only derived components for 

1210 disassembled composites. 

1211 

1212 Returns 

1213 ------- 

1214 inMemoryDataset : `object` 

1215 The artifact as a python object. 

1216 """ 

1217 location = getInfo.location 

1218 uri = location.uri 

1219 log.debug("Accessing data from %s", uri) 

1220 

1221 if cache_ref is None: 

1222 cache_ref = ref 

1223 if cache_ref.id != ref.id: 1223 ↛ 1224line 1223 didn't jump to line 1224, because the condition on line 1223 was never true

1224 raise ValueError( 

1225 "The supplied cache dataset ref refers to a different dataset than expected:" 

1226 f" {ref.id} != {cache_ref.id}" 

1227 ) 

1228 

1229 # Cannot recalculate checksum but can compare size as a quick check 

1230 # Do not do this if the size is negative since that indicates 

1231 # we do not know. 

1232 recorded_size = getInfo.info.file_size 

1233 resource_size = uri.size() 

1234 if recorded_size >= 0 and resource_size != recorded_size: 1234 ↛ 1235line 1234 didn't jump to line 1235, because the condition on line 1234 was never true

1235 raise RuntimeError( 

1236 "Integrity failure in Datastore. " 

1237 f"Size of file {uri} ({resource_size}) " 

1238 f"does not match size recorded in registry of {recorded_size}" 

1239 ) 

1240 

1241 # For the general case we have choices for how to proceed. 

1242 # 1. Always use a local file (downloading the remote resource to a 

1243 # temporary file if needed). 

1244 # 2. Use a threshold size and read into memory and use bytes. 

1245 # Use both for now with an arbitrary hand off size. 

1246 # This allows small datasets to be downloaded from remote object 

1247 # stores without requiring a temporary file. 

1248 

1249 formatter = getInfo.formatter 

1250 nbytes_max = 10_000_000 # Arbitrary number that we can tune 

1251 if resource_size <= nbytes_max and formatter.can_read_bytes(): 

1252 with self.cacheManager.find_in_cache(cache_ref, uri.getExtension()) as cached_file: 

1253 if cached_file is not None: 

1254 desired_uri = cached_file 

1255 msg = f" (cached version of {uri})" 

1256 else: 

1257 desired_uri = uri 

1258 msg = "" 

1259 with time_this(log, msg="Reading bytes from %s%s", args=(desired_uri, msg)): 

1260 serializedDataset = desired_uri.read() 

1261 log.debug( 

1262 "Deserializing %s from %d bytes from location %s with formatter %s", 

1263 f"component {getInfo.component}" if isComponent else "", 

1264 len(serializedDataset), 

1265 uri, 

1266 formatter.name(), 

1267 ) 

1268 try: 

1269 result = formatter.fromBytes( 

1270 serializedDataset, component=getInfo.component if isComponent else None 

1271 ) 

1272 except Exception as e: 

1273 raise ValueError( 

1274 f"Failure from formatter '{formatter.name()}' for dataset {ref.id}" 

1275 f" ({ref.datasetType.name} from {uri}): {e}" 

1276 ) from e 

1277 else: 

1278 # Read from file. 

1279 

1280 # Have to update the Location associated with the formatter 

1281 # because formatter.read does not allow an override. 

1282 # This could be improved. 

1283 location_updated = False 

1284 msg = "" 

1285 

1286 # First check in cache for local version. 

1287 # The cache will only be relevant for remote resources but 

1288 # no harm in always asking. Context manager ensures that cache 

1289 # file is not deleted during cache expiration. 

1290 with self.cacheManager.find_in_cache(cache_ref, uri.getExtension()) as cached_file: 

1291 if cached_file is not None: 

1292 msg = f"(via cache read of remote file {uri})" 

1293 uri = cached_file 

1294 location_updated = True 

1295 

1296 with uri.as_local() as local_uri: 

1297 

1298 can_be_cached = False 

1299 if uri != local_uri: 1299 ↛ 1301line 1299 didn't jump to line 1301, because the condition on line 1299 was never true

1300 # URI was remote and file was downloaded 

1301 cache_msg = "" 

1302 location_updated = True 

1303 

1304 if self.cacheManager.should_be_cached(cache_ref): 

1305 # In this scenario we want to ask if the downloaded 

1306 # file should be cached but we should not cache 

1307 # it until after we've used it (to ensure it can't 

1308 # be expired whilst we are using it). 

1309 can_be_cached = True 

1310 

1311 # Say that it is "likely" to be cached because 

1312 # if the formatter read fails we will not be 

1313 # caching this file. 

1314 cache_msg = " and likely cached" 

1315 

1316 msg = f"(via download to local file{cache_msg})" 

1317 

1318 # Calculate the (possibly) new location for the formatter 

1319 # to use. 

1320 newLocation = Location(*local_uri.split()) if location_updated else None 

1321 

1322 log.debug( 

1323 "Reading%s from location %s %s with formatter %s", 

1324 f" component {getInfo.component}" if isComponent else "", 

1325 uri, 

1326 msg, 

1327 formatter.name(), 

1328 ) 

1329 try: 

1330 with formatter._updateLocation(newLocation): 

1331 with time_this( 

1332 log, 

1333 msg="Reading%s from location %s %s with formatter %s", 

1334 args=( 

1335 f" component {getInfo.component}" if isComponent else "", 

1336 uri, 

1337 msg, 

1338 formatter.name(), 

1339 ), 

1340 ): 

1341 result = formatter.read(component=getInfo.component if isComponent else None) 

1342 except Exception as e: 

1343 raise ValueError( 

1344 f"Failure from formatter '{formatter.name()}' for dataset {ref.id}" 

1345 f" ({ref.datasetType.name} from {uri}): {e}" 

1346 ) from e 

1347 

1348 # File was read successfully so can move to cache 

1349 if can_be_cached: 1349 ↛ 1350line 1349 didn't jump to line 1350, because the condition on line 1349 was never true

1350 self.cacheManager.move_to_cache(local_uri, cache_ref) 

1351 

1352 return self._post_process_get( 

1353 result, getInfo.readStorageClass, getInfo.assemblerParams, isComponent=isComponent 

1354 ) 

1355 

1356 def knows(self, ref: DatasetRef) -> bool: 

1357 """Check if the dataset is known to the datastore. 

1358 

1359 Does not check for existence of any artifact. 

1360 

1361 Parameters 

1362 ---------- 

1363 ref : `DatasetRef` 

1364 Reference to the required dataset. 

1365 

1366 Returns 

1367 ------- 

1368 exists : `bool` 

1369 `True` if the dataset is known to the datastore. 

1370 """ 

1371 fileLocations = self._get_dataset_locations_info(ref) 

1372 if fileLocations: 

1373 return True 

1374 return False 

1375 

1376 def knows_these(self, refs: Iterable[DatasetRef]) -> dict[DatasetRef, bool]: 

1377 # Docstring inherited from the base class. 

1378 

1379 # The records themselves. Could be missing some entries. 

1380 records = self._get_stored_records_associated_with_refs(refs) 

1381 

1382 return {ref: ref.id in records for ref in refs} 

1383 

1384 def _process_mexists_records( 

1385 self, 

1386 id_to_ref: Dict[DatasetId, DatasetRef], 

1387 records: Dict[DatasetId, List[StoredFileInfo]], 

1388 all_required: bool, 

1389 artifact_existence: Optional[Dict[ResourcePath, bool]] = None, 

1390 ) -> Dict[DatasetRef, bool]: 

1391 """Helper function for mexists that checks the given records. 

1392 

1393 Parameters 

1394 ---------- 

1395 id_to_ref : `dict` of [`DatasetId`, `DatasetRef`] 

1396 Mapping of the dataset ID to the dataset ref itself. 

1397 records : `dict` of [`DatasetId`, `list` of `StoredFileInfo`] 

1398 Records as generally returned by 

1399 ``_get_stored_records_associated_with_refs``. 

1400 all_required : `bool` 

1401 Flag to indicate whether existence requires all artifacts 

1402 associated with a dataset ID to exist or not for existence. 

1403 artifact_existence : `dict` [`lsst.resources.ResourcePath`, `bool`] 

1404 Optional mapping of datastore artifact to existence. Updated by 

1405 this method with details of all artifacts tested. Can be `None` 

1406 if the caller is not interested. 

1407 

1408 Returns 

1409 ------- 

1410 existence : `dict` of [`DatasetRef`, `bool`] 

1411 Mapping from dataset to boolean indicating existence. 

1412 """ 

1413 # The URIs to be checked and a mapping of those URIs to 

1414 # the dataset ID. 

1415 uris_to_check: List[ResourcePath] = [] 

1416 location_map: Dict[ResourcePath, DatasetId] = {} 

1417 

1418 location_factory = self.locationFactory 

1419 

1420 uri_existence: Dict[ResourcePath, bool] = {} 

1421 for ref_id, infos in records.items(): 

1422 # Key is the dataset Id, value is list of StoredItemInfo 

1423 uris = [info.file_location(location_factory).uri for info in infos] 

1424 location_map.update({uri: ref_id for uri in uris}) 

1425 

1426 # Check the local cache directly for a dataset corresponding 

1427 # to the remote URI. 

1428 if self.cacheManager.file_count > 0: 1428 ↛ 1429line 1428 didn't jump to line 1429, because the condition on line 1428 was never true

1429 ref = id_to_ref[ref_id] 

1430 for uri, storedFileInfo in zip(uris, infos): 

1431 check_ref = ref 

1432 if not ref.datasetType.isComponent() and (component := storedFileInfo.component): 

1433 check_ref = ref.makeComponentRef(component) 

1434 if self.cacheManager.known_to_cache(check_ref, uri.getExtension()): 

1435 # Proxy for URI existence. 

1436 uri_existence[uri] = True 

1437 else: 

1438 uris_to_check.append(uri) 

1439 else: 

1440 # Check all of them. 

1441 uris_to_check.extend(uris) 

1442 

1443 if artifact_existence is not None: 

1444 # If a URI has already been checked remove it from the list 

1445 # and immediately add the status to the output dict. 

1446 filtered_uris_to_check = [] 

1447 for uri in uris_to_check: 

1448 if uri in artifact_existence: 

1449 uri_existence[uri] = artifact_existence[uri] 

1450 else: 

1451 filtered_uris_to_check.append(uri) 

1452 uris_to_check = filtered_uris_to_check 

1453 

1454 # Results. 

1455 dataset_existence: Dict[DatasetRef, bool] = {} 

1456 

1457 uri_existence.update(ResourcePath.mexists(uris_to_check)) 

1458 for uri, exists in uri_existence.items(): 

1459 dataset_id = location_map[uri] 

1460 ref = id_to_ref[dataset_id] 

1461 

1462 # Disassembled composite needs to check all locations. 

1463 # all_required indicates whether all need to exist or not. 

1464 if ref in dataset_existence: 

1465 if all_required: 

1466 exists = dataset_existence[ref] and exists 

1467 else: 

1468 exists = dataset_existence[ref] or exists 

1469 dataset_existence[ref] = exists 

1470 

1471 if artifact_existence is not None: 

1472 artifact_existence.update(uri_existence) 

1473 

1474 return dataset_existence 

1475 

1476 def mexists( 

1477 self, refs: Iterable[DatasetRef], artifact_existence: Optional[Dict[ResourcePath, bool]] = None 

1478 ) -> Dict[DatasetRef, bool]: 

1479 """Check the existence of multiple datasets at once. 

1480 

1481 Parameters 

1482 ---------- 

1483 refs : iterable of `DatasetRef` 

1484 The datasets to be checked. 

1485 artifact_existence : `dict` [`lsst.resources.ResourcePath`, `bool`] 

1486 Optional mapping of datastore artifact to existence. Updated by 

1487 this method with details of all artifacts tested. Can be `None` 

1488 if the caller is not interested. 

1489 

1490 Returns 

1491 ------- 

1492 existence : `dict` of [`DatasetRef`, `bool`] 

1493 Mapping from dataset to boolean indicating existence. 

1494 

1495 Notes 

1496 ----- 

1497 To minimize potentially costly remote existence checks, the local 

1498 cache is checked as a proxy for existence. If a file for this 

1499 `DatasetRef` does exist no check is done for the actual URI. This 

1500 could result in possibly unexpected behavior if the dataset itself 

1501 has been removed from the datastore by another process whilst it is 

1502 still in the cache. 

1503 """ 

1504 chunk_size = 10_000 

1505 dataset_existence: Dict[DatasetRef, bool] = {} 

1506 log.debug("Checking for the existence of multiple artifacts in datastore in chunks of %d", chunk_size) 

1507 n_found_total = 0 

1508 n_checked = 0 

1509 n_chunks = 0 

1510 for chunk in chunk_iterable(refs, chunk_size=chunk_size): 

1511 chunk_result = self._mexists(chunk, artifact_existence) 

1512 if log.isEnabledFor(VERBOSE): 

1513 n_results = len(chunk_result) 

1514 n_checked += n_results 

1515 # Can treat the booleans as 0, 1 integers and sum them. 

1516 n_found = sum(chunk_result.values()) 

1517 n_found_total += n_found 

1518 log.verbose( 

1519 "Number of datasets found in datastore for chunk %d = %d/%d (running total: %d/%d)", 

1520 n_chunks, 

1521 n_found, 

1522 n_results, 

1523 n_found_total, 

1524 n_checked, 

1525 ) 

1526 dataset_existence.update(chunk_result) 

1527 n_chunks += 1 

1528 

1529 return dataset_existence 

1530 

1531 def _mexists( 

1532 self, refs: Iterable[DatasetRef], artifact_existence: Optional[Dict[ResourcePath, bool]] = None 

1533 ) -> Dict[DatasetRef, bool]: 

1534 """Check the existence of multiple datasets at once. 

1535 

1536 Parameters 

1537 ---------- 

1538 refs : iterable of `DatasetRef` 

1539 The datasets to be checked. 

1540 

1541 Returns 

1542 ------- 

1543 existence : `dict` of [`DatasetRef`, `bool`] 

1544 Mapping from dataset to boolean indicating existence. 

1545 """ 

1546 # Need a mapping of dataset_id to dataset ref since the API 

1547 # works with dataset_id 

1548 id_to_ref = {ref.getCheckedId(): ref for ref in refs} 

1549 

1550 # Set of all IDs we are checking for. 

1551 requested_ids = set(id_to_ref.keys()) 

1552 

1553 # The records themselves. Could be missing some entries. 

1554 records = self._get_stored_records_associated_with_refs(refs) 

1555 

1556 dataset_existence = self._process_mexists_records( 

1557 id_to_ref, records, True, artifact_existence=artifact_existence 

1558 ) 

1559 

1560 # Set of IDs that have been handled. 

1561 handled_ids = {ref.id for ref in dataset_existence.keys()} 

1562 

1563 missing_ids = requested_ids - handled_ids 

1564 if missing_ids: 

1565 if not self.trustGetRequest: 

1566 # Must assume these do not exist 

1567 for missing in missing_ids: 

1568 dataset_existence[id_to_ref[missing]] = False 

1569 else: 

1570 log.debug( 

1571 "%d out of %d datasets were not known to datastore during initial existence check.", 

1572 len(missing_ids), 

1573 len(requested_ids), 

1574 ) 

1575 

1576 # Construct data structure identical to that returned 

1577 # by _get_stored_records_associated_with_refs() but using 

1578 # guessed names. 

1579 records = {} 

1580 for missing in missing_ids: 

1581 expected = self._get_expected_dataset_locations_info(id_to_ref[missing]) 

1582 records[missing] = [info for _, info in expected] 

1583 

1584 dataset_existence.update( 

1585 self._process_mexists_records( 

1586 id_to_ref, records, False, artifact_existence=artifact_existence 

1587 ) 

1588 ) 

1589 

1590 return dataset_existence 

1591 

1592 def exists(self, ref: DatasetRef) -> bool: 

1593 """Check if the dataset exists in the datastore. 

1594 

1595 Parameters 

1596 ---------- 

1597 ref : `DatasetRef` 

1598 Reference to the required dataset. 

1599 

1600 Returns 

1601 ------- 

1602 exists : `bool` 

1603 `True` if the entity exists in the `Datastore`. 

1604 

1605 Notes 

1606 ----- 

1607 The local cache is checked as a proxy for existence in the remote 

1608 object store. It is possible that another process on a different 

1609 compute node could remove the file from the object store even 

1610 though it is present in the local cache. 

1611 """ 

1612 fileLocations = self._get_dataset_locations_info(ref) 

1613 

1614 # if we are being asked to trust that registry might not be correct 

1615 # we ask for the expected locations and check them explicitly 

1616 if not fileLocations: 

1617 if not self.trustGetRequest: 

1618 return False 

1619 

1620 # First check the cache. If it is not found we must check 

1621 # the datastore itself. Assume that any component in the cache 

1622 # means that the dataset does exist somewhere. 

1623 if self.cacheManager.known_to_cache(ref): 1623 ↛ 1624line 1623 didn't jump to line 1624, because the condition on line 1623 was never true

1624 return True 

1625 

1626 # When we are guessing a dataset location we can not check 

1627 # for the existence of every component since we can not 

1628 # know if every component was written. Instead we check 

1629 # for the existence of any of the expected locations. 

1630 for location, _ in self._get_expected_dataset_locations_info(ref): 

1631 if self._artifact_exists(location): 

1632 return True 

1633 return False 

1634 

1635 # All listed artifacts must exist. 

1636 for location, storedFileInfo in fileLocations: 

1637 # Checking in cache needs the component ref. 

1638 check_ref = ref 

1639 if not ref.datasetType.isComponent() and (component := storedFileInfo.component): 

1640 check_ref = ref.makeComponentRef(component) 

1641 if self.cacheManager.known_to_cache(check_ref, location.getExtension()): 

1642 continue 

1643 

1644 if not self._artifact_exists(location): 

1645 return False 

1646 

1647 return True 

1648 

1649 def getURIs(self, ref: DatasetRef, predict: bool = False) -> DatasetRefURIs: 

1650 """Return URIs associated with dataset. 

1651 

1652 Parameters 

1653 ---------- 

1654 ref : `DatasetRef` 

1655 Reference to the required dataset. 

1656 predict : `bool`, optional 

1657 If the datastore does not know about the dataset, should it 

1658 return a predicted URI or not? 

1659 

1660 Returns 

1661 ------- 

1662 uris : `DatasetRefURIs` 

1663 The URI to the primary artifact associated with this dataset (if 

1664 the dataset was disassembled within the datastore this may be 

1665 `None`), and the URIs to any components associated with the dataset 

1666 artifact. (can be empty if there are no components). 

1667 """ 

1668 # if this has never been written then we have to guess 

1669 if not self.exists(ref): 

1670 if not predict: 

1671 raise FileNotFoundError("Dataset {} not in this datastore".format(ref)) 

1672 

1673 return self._predict_URIs(ref) 

1674 

1675 # If this is a ref that we have written we can get the path. 

1676 # Get file metadata and internal metadata 

1677 fileLocations = self._get_dataset_locations_info(ref) 

1678 

1679 return self._locations_to_URI(ref, fileLocations) 

1680 

1681 def getURI(self, ref: DatasetRef, predict: bool = False) -> ResourcePath: 

1682 """URI to the Dataset. 

1683 

1684 Parameters 

1685 ---------- 

1686 ref : `DatasetRef` 

1687 Reference to the required Dataset. 

1688 predict : `bool` 

1689 If `True`, allow URIs to be returned of datasets that have not 

1690 been written. 

1691 

1692 Returns 

1693 ------- 

1694 uri : `str` 

1695 URI pointing to the dataset within the datastore. If the 

1696 dataset does not exist in the datastore, and if ``predict`` is 

1697 `True`, the URI will be a prediction and will include a URI 

1698 fragment "#predicted". 

1699 If the datastore does not have entities that relate well 

1700 to the concept of a URI the returned URI will be 

1701 descriptive. The returned URI is not guaranteed to be obtainable. 

1702 

1703 Raises 

1704 ------ 

1705 FileNotFoundError 

1706 Raised if a URI has been requested for a dataset that does not 

1707 exist and guessing is not allowed. 

1708 RuntimeError 

1709 Raised if a request is made for a single URI but multiple URIs 

1710 are associated with this dataset. 

1711 

1712 Notes 

1713 ----- 

1714 When a predicted URI is requested an attempt will be made to form 

1715 a reasonable URI based on file templates and the expected formatter. 

1716 """ 

1717 primary, components = self.getURIs(ref, predict) 

1718 if primary is None or components: 1718 ↛ 1719line 1718 didn't jump to line 1719, because the condition on line 1718 was never true

1719 raise RuntimeError( 

1720 f"Dataset ({ref}) includes distinct URIs for components. Use Datastore.getURIs() instead." 

1721 ) 

1722 return primary 

1723 

1724 def _predict_URIs( 

1725 self, 

1726 ref: DatasetRef, 

1727 ) -> DatasetRefURIs: 

1728 """Predict the URIs of a dataset ref. 

1729 

1730 Parameters 

1731 ---------- 

1732 ref : `DatasetRef` 

1733 Reference to the required Dataset. 

1734 

1735 Returns 

1736 ------- 

1737 URI : DatasetRefUris 

1738 Primary and component URIs. URIs will contain a URI fragment 

1739 "#predicted". 

1740 """ 

1741 uris = DatasetRefURIs() 

1742 

1743 if self.composites.shouldBeDisassembled(ref): 

1744 

1745 for component, _ in ref.datasetType.storageClass.components.items(): 

1746 comp_ref = ref.makeComponentRef(component) 

1747 comp_location, _ = self._determine_put_formatter_location(comp_ref) 

1748 

1749 # Add the "#predicted" URI fragment to indicate this is a 

1750 # guess 

1751 uris.componentURIs[component] = ResourcePath(comp_location.uri.geturl() + "#predicted") 

1752 

1753 else: 

1754 

1755 location, _ = self._determine_put_formatter_location(ref) 

1756 

1757 # Add the "#predicted" URI fragment to indicate this is a guess 

1758 uris.primaryURI = ResourcePath(location.uri.geturl() + "#predicted") 

1759 

1760 return uris 

1761 

1762 def getManyURIs( 

1763 self, 

1764 refs: Iterable[DatasetRef], 

1765 predict: bool = False, 

1766 allow_missing: bool = False, 

1767 ) -> Dict[DatasetRef, DatasetRefURIs]: 

1768 # Docstring inherited 

1769 

1770 uris: Dict[DatasetRef, DatasetRefURIs] = {} 

1771 

1772 records = self._get_stored_records_associated_with_refs(refs) 

1773 records_keys = records.keys() 

1774 

1775 existing_refs = (ref for ref in refs if ref.id in records_keys) 

1776 missing_refs = (ref for ref in refs if ref.id not in records_keys) 

1777 

1778 for ref in missing_refs: 

1779 

1780 # if this has never been written then we have to guess 

1781 if not predict: 

1782 if not allow_missing: 

1783 raise FileNotFoundError("Dataset {} not in this datastore.".format(ref)) 

1784 else: 

1785 uris[ref] = self._predict_URIs(ref) 

1786 

1787 for ref in existing_refs: 

1788 file_infos = records[ref.getCheckedId()] 

1789 file_locations = [(i.file_location(self.locationFactory), i) for i in file_infos] 

1790 uris[ref] = self._locations_to_URI(ref, file_locations) 

1791 

1792 return uris 

1793 

1794 def _locations_to_URI( 

1795 self, 

1796 ref: DatasetRef, 

1797 file_locations: Sequence[Tuple[Location, StoredFileInfo]], 

1798 ) -> DatasetRefURIs: 

1799 """Convert one or more file locations associated with a DatasetRef 

1800 to a DatasetRefURIs. 

1801 

1802 Parameters 

1803 ---------- 

1804 ref : `DatasetRef` 

1805 Reference to the dataset. 

1806 file_locations : Sequence[Tuple[Location, StoredFileInfo]] 

1807 Each item in the sequence is the location of the dataset within the 

1808 datastore and stored information about the file and its formatter. 

1809 If there is only one item in the sequence then it is treated as the 

1810 primary URI. If there is more than one item then they are treated 

1811 as component URIs. If there are no items then an error is raised 

1812 unless ``self.trustGetRequest`` is `True`. 

1813 

1814 Returns 

1815 ------- 

1816 uris: DatasetRefURIs 

1817 Represents the primary URI or component URIs described by the 

1818 inputs. 

1819 

1820 Raises 

1821 ------ 

1822 RuntimeError 

1823 If no file locations are passed in and ``self.trustGetRequest`` is 

1824 `False`. 

1825 FileNotFoundError 

1826 If the a passed-in URI does not exist, and ``self.trustGetRequest`` 

1827 is `False`. 

1828 RuntimeError 

1829 If a passed in `StoredFileInfo`'s ``component`` is `None` (this is 

1830 unexpected). 

1831 """ 

1832 

1833 guessing = False 

1834 uris = DatasetRefURIs() 

1835 

1836 if not file_locations: 

1837 if not self.trustGetRequest: 1837 ↛ 1838line 1837 didn't jump to line 1838, because the condition on line 1837 was never true

1838 raise RuntimeError(f"Unexpectedly got no artifacts for dataset {ref}") 

1839 file_locations = self._get_expected_dataset_locations_info(ref) 

1840 guessing = True 

1841 

1842 if len(file_locations) == 1: 

1843 # No disassembly so this is the primary URI 

1844 uris.primaryURI = file_locations[0][0].uri 

1845 if guessing and not uris.primaryURI.exists(): 1845 ↛ 1846line 1845 didn't jump to line 1846, because the condition on line 1845 was never true

1846 raise FileNotFoundError(f"Expected URI ({uris.primaryURI}) does not exist") 

1847 else: 

1848 for location, file_info in file_locations: 

1849 if file_info.component is None: 1849 ↛ 1850line 1849 didn't jump to line 1850, because the condition on line 1849 was never true

1850 raise RuntimeError(f"Unexpectedly got no component name for a component at {location}") 

1851 if guessing and not location.uri.exists(): 1851 ↛ 1855line 1851 didn't jump to line 1855, because the condition on line 1851 was never true

1852 # If we are trusting then it is entirely possible for 

1853 # some components to be missing. In that case we skip 

1854 # to the next component. 

1855 if self.trustGetRequest: 

1856 continue 

1857 raise FileNotFoundError(f"Expected URI ({location.uri}) does not exist") 

1858 uris.componentURIs[file_info.component] = location.uri 

1859 

1860 return uris 

1861 

1862 def retrieveArtifacts( 

1863 self, 

1864 refs: Iterable[DatasetRef], 

1865 destination: ResourcePath, 

1866 transfer: str = "auto", 

1867 preserve_path: bool = True, 

1868 overwrite: bool = False, 

1869 ) -> List[ResourcePath]: 

1870 """Retrieve the file artifacts associated with the supplied refs. 

1871 

1872 Parameters 

1873 ---------- 

1874 refs : iterable of `DatasetRef` 

1875 The datasets for which file artifacts are to be retrieved. 

1876 A single ref can result in multiple files. The refs must 

1877 be resolved. 

1878 destination : `lsst.resources.ResourcePath` 

1879 Location to write the file artifacts. 

1880 transfer : `str`, optional 

1881 Method to use to transfer the artifacts. Must be one of the options 

1882 supported by `lsst.resources.ResourcePath.transfer_from()`. 

1883 "move" is not allowed. 

1884 preserve_path : `bool`, optional 

1885 If `True` the full path of the file artifact within the datastore 

1886 is preserved. If `False` the final file component of the path 

1887 is used. 

1888 overwrite : `bool`, optional 

1889 If `True` allow transfers to overwrite existing files at the 

1890 destination. 

1891 

1892 Returns 

1893 ------- 

1894 targets : `list` of `lsst.resources.ResourcePath` 

1895 URIs of file artifacts in destination location. Order is not 

1896 preserved. 

1897 """ 

1898 if not destination.isdir(): 1898 ↛ 1899line 1898 didn't jump to line 1899, because the condition on line 1898 was never true

1899 raise ValueError(f"Destination location must refer to a directory. Given {destination}") 

1900 

1901 if transfer == "move": 

1902 raise ValueError("Can not move artifacts out of datastore. Use copy instead.") 

1903 

1904 # Source -> Destination 

1905 # This also helps filter out duplicate DatasetRef in the request 

1906 # that will map to the same underlying file transfer. 

1907 to_transfer: Dict[ResourcePath, ResourcePath] = {} 

1908 

1909 for ref in refs: 

1910 locations = self._get_dataset_locations_info(ref) 

1911 for location, _ in locations: 

1912 source_uri = location.uri 

1913 target_path: ResourcePathExpression 

1914 if preserve_path: 

1915 target_path = location.pathInStore 

1916 if target_path.isabs(): 1916 ↛ 1919line 1916 didn't jump to line 1919, because the condition on line 1916 was never true

1917 # This is an absolute path to an external file. 

1918 # Use the full path. 

1919 target_path = target_path.relativeToPathRoot 

1920 else: 

1921 target_path = source_uri.basename() 

1922 target_uri = destination.join(target_path) 

1923 to_transfer[source_uri] = target_uri 

1924 

1925 # In theory can now parallelize the transfer 

1926 log.debug("Number of artifacts to transfer to %s: %d", str(destination), len(to_transfer)) 

1927 for source_uri, target_uri in to_transfer.items(): 

1928 target_uri.transfer_from(source_uri, transfer=transfer, overwrite=overwrite) 

1929 

1930 return list(to_transfer.values()) 

1931 

1932 def get( 

1933 self, 

1934 ref: DatasetRef, 

1935 parameters: Optional[Mapping[str, Any]] = None, 

1936 storageClass: Optional[Union[StorageClass, str]] = None, 

1937 ) -> Any: 

1938 """Load an InMemoryDataset from the store. 

1939 

1940 Parameters 

1941 ---------- 

1942 ref : `DatasetRef` 

1943 Reference to the required Dataset. 

1944 parameters : `dict` 

1945 `StorageClass`-specific parameters that specify, for example, 

1946 a slice of the dataset to be loaded. 

1947 storageClass : `StorageClass` or `str`, optional 

1948 The storage class to be used to override the Python type 

1949 returned by this method. By default the returned type matches 

1950 the dataset type definition for this dataset. Specifying a 

1951 read `StorageClass` can force a different type to be returned. 

1952 This type must be compatible with the original type. 

1953 

1954 Returns 

1955 ------- 

1956 inMemoryDataset : `object` 

1957 Requested dataset or slice thereof as an InMemoryDataset. 

1958 

1959 Raises 

1960 ------ 

1961 FileNotFoundError 

1962 Requested dataset can not be retrieved. 

1963 TypeError 

1964 Return value from formatter has unexpected type. 

1965 ValueError 

1966 Formatter failed to process the dataset. 

1967 """ 

1968 # Supplied storage class for the component being read is either 

1969 # from the ref itself or some an override if we want to force 

1970 # type conversion. 

1971 if storageClass is not None: 

1972 ref = ref.overrideStorageClass(storageClass) 

1973 refStorageClass = ref.datasetType.storageClass 

1974 

1975 allGetInfo = self._prepare_for_get(ref, parameters) 

1976 refComponent = ref.datasetType.component() 

1977 

1978 # Create mapping from component name to related info 

1979 allComponents = {i.component: i for i in allGetInfo} 

1980 

1981 # By definition the dataset is disassembled if we have more 

1982 # than one record for it. 

1983 isDisassembled = len(allGetInfo) > 1 

1984 

1985 # Look for the special case where we are disassembled but the 

1986 # component is a derived component that was not written during 

1987 # disassembly. For this scenario we need to check that the 

1988 # component requested is listed as a derived component for the 

1989 # composite storage class 

1990 isDisassembledReadOnlyComponent = False 

1991 if isDisassembled and refComponent: 

1992 # The composite storage class should be accessible through 

1993 # the component dataset type 

1994 compositeStorageClass = ref.datasetType.parentStorageClass 

1995 

1996 # In the unlikely scenario where the composite storage 

1997 # class is not known, we can only assume that this is a 

1998 # normal component. If that assumption is wrong then the 

1999 # branch below that reads a persisted component will fail 

2000 # so there is no need to complain here. 

2001 if compositeStorageClass is not None: 2001 ↛ 2004line 2001 didn't jump to line 2004, because the condition on line 2001 was never false

2002 isDisassembledReadOnlyComponent = refComponent in compositeStorageClass.derivedComponents 

2003 

2004 if isDisassembled and not refComponent: 

2005 # This was a disassembled dataset spread over multiple files 

2006 # and we need to put them all back together again. 

2007 # Read into memory and then assemble 

2008 

2009 # Check that the supplied parameters are suitable for the type read 

2010 refStorageClass.validateParameters(parameters) 

2011 

2012 # We want to keep track of all the parameters that were not used 

2013 # by formatters. We assume that if any of the component formatters 

2014 # use a parameter that we do not need to apply it again in the 

2015 # assembler. 

2016 usedParams = set() 

2017 

2018 components: Dict[str, Any] = {} 

2019 for getInfo in allGetInfo: 

2020 # assemblerParams are parameters not understood by the 

2021 # associated formatter. 

2022 usedParams.update(set(getInfo.formatterParams)) 

2023 

2024 component = getInfo.component 

2025 

2026 if component is None: 2026 ↛ 2027line 2026 didn't jump to line 2027, because the condition on line 2026 was never true

2027 raise RuntimeError(f"Internal error in datastore assembly of {ref}") 

2028 

2029 # We do not want the formatter to think it's reading 

2030 # a component though because it is really reading a 

2031 # standalone dataset -- always tell reader it is not a 

2032 # component. 

2033 components[component] = self._read_artifact_into_memory( 

2034 getInfo, ref.makeComponentRef(component), isComponent=False 

2035 ) 

2036 

2037 inMemoryDataset = ref.datasetType.storageClass.delegate().assemble(components) 

2038 

2039 # Any unused parameters will have to be passed to the assembler 

2040 if parameters: 

2041 unusedParams = {k: v for k, v in parameters.items() if k not in usedParams} 

2042 else: 

2043 unusedParams = {} 

2044 

2045 # Process parameters 

2046 return ref.datasetType.storageClass.delegate().handleParameters( 

2047 inMemoryDataset, parameters=unusedParams 

2048 ) 

2049 

2050 elif isDisassembledReadOnlyComponent: 

2051 

2052 compositeStorageClass = ref.datasetType.parentStorageClass 

2053 if compositeStorageClass is None: 2053 ↛ 2054line 2053 didn't jump to line 2054, because the condition on line 2053 was never true

2054 raise RuntimeError( 

2055 f"Unable to retrieve derived component '{refComponent}' since" 

2056 "no composite storage class is available." 

2057 ) 

2058 

2059 if refComponent is None: 2059 ↛ 2061line 2059 didn't jump to line 2061, because the condition on line 2059 was never true

2060 # Mainly for mypy 

2061 raise RuntimeError(f"Internal error in datastore {self.name}: component can not be None here") 

2062 

2063 # Assume that every derived component can be calculated by 

2064 # forwarding the request to a single read/write component. 

2065 # Rather than guessing which rw component is the right one by 

2066 # scanning each for a derived component of the same name, 

2067 # we ask the storage class delegate directly which one is best to 

2068 # use. 

2069 compositeDelegate = compositeStorageClass.delegate() 

2070 forwardedComponent = compositeDelegate.selectResponsibleComponent( 

2071 refComponent, set(allComponents) 

2072 ) 

2073 

2074 # Select the relevant component 

2075 rwInfo = allComponents[forwardedComponent] 

2076 

2077 # For now assume that read parameters are validated against 

2078 # the real component and not the requested component 

2079 forwardedStorageClass = rwInfo.formatter.fileDescriptor.readStorageClass 

2080 forwardedStorageClass.validateParameters(parameters) 

2081 

2082 # The reference to use for the caching must refer to the forwarded 

2083 # component and not the derived component. 

2084 cache_ref = ref.makeCompositeRef().makeComponentRef(forwardedComponent) 

2085 

2086 # Unfortunately the FileDescriptor inside the formatter will have 

2087 # the wrong write storage class so we need to create a new one 

2088 # given the immutability constraint. 

2089 writeStorageClass = rwInfo.info.storageClass 

2090 

2091 # We may need to put some thought into parameters for read 

2092 # components but for now forward them on as is 

2093 readFormatter = type(rwInfo.formatter)( 

2094 FileDescriptor( 

2095 rwInfo.location, 

2096 readStorageClass=refStorageClass, 

2097 storageClass=writeStorageClass, 

2098 parameters=parameters, 

2099 ), 

2100 ref.dataId, 

2101 ) 

2102 

2103 # The assembler can not receive any parameter requests for a 

2104 # derived component at this time since the assembler will 

2105 # see the storage class of the derived component and those 

2106 # parameters will have to be handled by the formatter on the 

2107 # forwarded storage class. 

2108 assemblerParams: Dict[str, Any] = {} 

2109 

2110 # Need to created a new info that specifies the derived 

2111 # component and associated storage class 

2112 readInfo = DatastoreFileGetInformation( 

2113 rwInfo.location, 

2114 readFormatter, 

2115 rwInfo.info, 

2116 assemblerParams, 

2117 {}, 

2118 refComponent, 

2119 refStorageClass, 

2120 ) 

2121 

2122 return self._read_artifact_into_memory(readInfo, ref, isComponent=True, cache_ref=cache_ref) 

2123 

2124 else: 

2125 # Single file request or component from that composite file 

2126 for lookup in (refComponent, None): 2126 ↛ 2131line 2126 didn't jump to line 2131, because the loop on line 2126 didn't complete

2127 if lookup in allComponents: 2127 ↛ 2126line 2127 didn't jump to line 2126, because the condition on line 2127 was never false

2128 getInfo = allComponents[lookup] 

2129 break 

2130 else: 

2131 raise FileNotFoundError( 

2132 f"Component {refComponent} not found for ref {ref} in datastore {self.name}" 

2133 ) 

2134 

2135 # Do not need the component itself if already disassembled 

2136 if isDisassembled: 

2137 isComponent = False 

2138 else: 

2139 isComponent = getInfo.component is not None 

2140 

2141 # For a component read of a composite we want the cache to 

2142 # be looking at the composite ref itself. 

2143 cache_ref = ref.makeCompositeRef() if isComponent else ref 

2144 

2145 # For a disassembled component we can validate parametersagainst 

2146 # the component storage class directly 

2147 if isDisassembled: 

2148 refStorageClass.validateParameters(parameters) 

2149 else: 

2150 # For an assembled composite this could be a derived 

2151 # component derived from a real component. The validity 

2152 # of the parameters is not clear. For now validate against 

2153 # the composite storage class 

2154 getInfo.formatter.fileDescriptor.storageClass.validateParameters(parameters) 

2155 

2156 return self._read_artifact_into_memory(getInfo, ref, isComponent=isComponent, cache_ref=cache_ref) 

2157 

2158 @transactional 

2159 def put(self, inMemoryDataset: Any, ref: DatasetRef) -> None: 

2160 """Write a InMemoryDataset with a given `DatasetRef` to the store. 

2161 

2162 Parameters 

2163 ---------- 

2164 inMemoryDataset : `object` 

2165 The dataset to store. 

2166 ref : `DatasetRef` 

2167 Reference to the associated Dataset. 

2168 

2169 Raises 

2170 ------ 

2171 TypeError 

2172 Supplied object and storage class are inconsistent. 

2173 DatasetTypeNotSupportedError 

2174 The associated `DatasetType` is not handled by this datastore. 

2175 

2176 Notes 

2177 ----- 

2178 If the datastore is configured to reject certain dataset types it 

2179 is possible that the put will fail and raise a 

2180 `DatasetTypeNotSupportedError`. The main use case for this is to 

2181 allow `ChainedDatastore` to put to multiple datastores without 

2182 requiring that every datastore accepts the dataset. 

2183 """ 

2184 

2185 doDisassembly = self.composites.shouldBeDisassembled(ref) 

2186 # doDisassembly = True 

2187 

2188 artifacts = [] 

2189 if doDisassembly: 

2190 components = ref.datasetType.storageClass.delegate().disassemble(inMemoryDataset) 

2191 if components is None: 2191 ↛ 2192line 2191 didn't jump to line 2192, because the condition on line 2191 was never true

2192 raise RuntimeError( 

2193 f"Inconsistent configuration: dataset type {ref.datasetType.name} " 

2194 f"with storage class {ref.datasetType.storageClass.name} " 

2195 "is configured to be disassembled, but cannot be." 

2196 ) 

2197 for component, componentInfo in components.items(): 

2198 # Don't recurse because we want to take advantage of 

2199 # bulk insert -- need a new DatasetRef that refers to the 

2200 # same dataset_id but has the component DatasetType 

2201 # DatasetType does not refer to the types of components 

2202 # So we construct one ourselves. 

2203 compRef = ref.makeComponentRef(component) 

2204 storedInfo = self._write_in_memory_to_artifact(componentInfo.component, compRef) 

2205 artifacts.append((compRef, storedInfo)) 

2206 else: 

2207 # Write the entire thing out 

2208 storedInfo = self._write_in_memory_to_artifact(inMemoryDataset, ref) 

2209 artifacts.append((ref, storedInfo)) 

2210 

2211 self._register_datasets(artifacts) 

2212 

2213 @transactional 

2214 def trash(self, ref: Union[DatasetRef, Iterable[DatasetRef]], ignore_errors: bool = True) -> None: 

2215 # At this point can safely remove these datasets from the cache 

2216 # to avoid confusion later on. If they are not trashed later 

2217 # the cache will simply be refilled. 

2218 self.cacheManager.remove_from_cache(ref) 

2219 

2220 # If we are in trust mode there will be nothing to move to 

2221 # the trash table and we will have to try to delete the file 

2222 # immediately. 

2223 if self.trustGetRequest: 

2224 # Try to keep the logic below for a single file trash. 

2225 if isinstance(ref, DatasetRef): 

2226 refs = {ref} 

2227 else: 

2228 # Will recreate ref at the end of this branch. 

2229 refs = set(ref) 

2230 

2231 # Determine which datasets are known to datastore directly. 

2232 id_to_ref = {ref.getCheckedId(): ref for ref in refs} 

2233 existing_ids = self._get_stored_records_associated_with_refs(refs) 

2234 existing_refs = {id_to_ref[ref_id] for ref_id in existing_ids} 

2235 

2236 missing = refs - existing_refs 

2237 if missing: 

2238 # Do an explicit existence check on these refs. 

2239 # We only care about the artifacts at this point and not 

2240 # the dataset existence. 

2241 artifact_existence: Dict[ResourcePath, bool] = {} 

2242 _ = self.mexists(missing, artifact_existence) 

2243 uris = [uri for uri, exists in artifact_existence.items() if exists] 

2244 

2245 # FUTURE UPGRADE: Implement a parallelized bulk remove. 

2246 log.debug("Removing %d artifacts from datastore that are unknown to datastore", len(uris)) 

2247 for uri in uris: 

2248 try: 

2249 uri.remove() 

2250 except Exception as e: 

2251 if ignore_errors: 

2252 log.debug("Artifact %s could not be removed: %s", uri, e) 

2253 continue 

2254 raise 

2255 

2256 # There is no point asking the code below to remove refs we 

2257 # know are missing so update it with the list of existing 

2258 # records. Try to retain one vs many logic. 

2259 if not existing_refs: 

2260 # Nothing more to do since none of the datasets were 

2261 # known to the datastore record table. 

2262 return 

2263 ref = list(existing_refs) 

2264 if len(ref) == 1: 

2265 ref = ref[0] 

2266 

2267 # Get file metadata and internal metadata 

2268 if not isinstance(ref, DatasetRef): 

2269 log.debug("Doing multi-dataset trash in datastore %s", self.name) 

2270 # Assumed to be an iterable of refs so bulk mode enabled. 

2271 try: 

2272 self.bridge.moveToTrash(ref, transaction=self._transaction) 

2273 except Exception as e: 

2274 if ignore_errors: 

2275 log.warning("Unexpected issue moving multiple datasets to trash: %s", e) 

2276 else: 

2277 raise 

2278 return 

2279 

2280 log.debug("Trashing dataset %s in datastore %s", ref, self.name) 

2281 

2282 fileLocations = self._get_dataset_locations_info(ref) 

2283 

2284 if not fileLocations: 

2285 err_msg = f"Requested dataset to trash ({ref}) is not known to datastore {self.name}" 

2286 if ignore_errors: 

2287 log.warning(err_msg) 

2288 return 

2289 else: 

2290 raise FileNotFoundError(err_msg) 

2291 

2292 for location, storedFileInfo in fileLocations: 

2293 if not self._artifact_exists(location): 2293 ↛ 2294line 2293 didn't jump to line 2294

2294 err_msg = ( 

2295 f"Dataset is known to datastore {self.name} but " 

2296 f"associated artifact ({location.uri}) is missing" 

2297 ) 

2298 if ignore_errors: 

2299 log.warning(err_msg) 

2300 return 

2301 else: 

2302 raise FileNotFoundError(err_msg) 

2303 

2304 # Mark dataset as trashed 

2305 try: 

2306 self.bridge.moveToTrash([ref], transaction=self._transaction) 

2307 except Exception as e: 

2308 if ignore_errors: 

2309 log.warning( 

2310 "Attempted to mark dataset (%s) to be trashed in datastore %s " 

2311 "but encountered an error: %s", 

2312 ref, 

2313 self.name, 

2314 e, 

2315 ) 

2316 pass 

2317 else: 

2318 raise 

2319 

2320 @transactional 

2321 def emptyTrash(self, ignore_errors: bool = True) -> None: 

2322 """Remove all datasets from the trash. 

2323 

2324 Parameters 

2325 ---------- 

2326 ignore_errors : `bool` 

2327 If `True` return without error even if something went wrong. 

2328 Problems could occur if another process is simultaneously trying 

2329 to delete. 

2330 """ 

2331 log.debug("Emptying trash in datastore %s", self.name) 

2332 

2333 # Context manager will empty trash iff we finish it without raising. 

2334 # It will also automatically delete the relevant rows from the 

2335 # trash table and the records table. 

2336 with self.bridge.emptyTrash( 

2337 self._table, record_class=StoredFileInfo, record_column="path" 

2338 ) as trash_data: 

2339 # Removing the artifacts themselves requires that the files are 

2340 # not also associated with refs that are not to be trashed. 

2341 # Therefore need to do a query with the file paths themselves 

2342 # and return all the refs associated with them. Can only delete 

2343 # a file if the refs to be trashed are the only refs associated 

2344 # with the file. 

2345 # This requires multiple copies of the trashed items 

2346 trashed, artifacts_to_keep = trash_data 

2347 

2348 if artifacts_to_keep is None: 

2349 # The bridge is not helping us so have to work it out 

2350 # ourselves. This is not going to be as efficient. 

2351 trashed = list(trashed) 

2352 

2353 # The instance check is for mypy since up to this point it 

2354 # does not know the type of info. 

2355 path_map = self._refs_associated_with_artifacts( 

2356 [info.path for _, info in trashed if isinstance(info, StoredFileInfo)] 

2357 ) 

2358 

2359 for ref, info in trashed: 

2360 

2361 # Mypy needs to know this is not the base class 

2362 assert isinstance(info, StoredFileInfo), f"Unexpectedly got info of class {type(info)}" 

2363 

2364 # Check for mypy 

2365 assert ref.id is not None, f"Internal logic error in emptyTrash with ref {ref}/{info}" 

2366 

2367 path_map[info.path].remove(ref.id) 

2368 if not path_map[info.path]: 2368 ↛ 2359line 2368 didn't jump to line 2359, because the condition on line 2368 was never false

2369 del path_map[info.path] 

2370 

2371 artifacts_to_keep = set(path_map) 

2372 

2373 for ref, info in trashed: 

2374 

2375 # Should not happen for this implementation but need 

2376 # to keep mypy happy. 

2377 assert info is not None, f"Internal logic error in emptyTrash with ref {ref}." 

2378 

2379 # Mypy needs to know this is not the base class 

2380 assert isinstance(info, StoredFileInfo), f"Unexpectedly got info of class {type(info)}" 

2381 

2382 # Check for mypy 

2383 assert ref.id is not None, f"Internal logic error in emptyTrash with ref {ref}/{info}" 

2384 

2385 if info.path in artifacts_to_keep: 

2386 # This is a multi-dataset artifact and we are not 

2387 # removing all associated refs. 

2388 continue 

2389 

2390 # Only trashed refs still known to datastore will be returned. 

2391 location = info.file_location(self.locationFactory) 

2392 

2393 # Point of no return for this artifact 

2394 log.debug("Removing artifact %s from datastore %s", location.uri, self.name) 

2395 try: 

2396 self._delete_artifact(location) 

2397 except FileNotFoundError: 

2398 # If the file itself has been deleted there is nothing 

2399 # we can do about it. It is possible that trash has 

2400 # been run in parallel in another process or someone 

2401 # decided to delete the file. It is unlikely to come 

2402 # back and so we should still continue with the removal 

2403 # of the entry from the trash table. It is also possible 

2404 # we removed it in a previous iteration if it was 

2405 # a multi-dataset artifact. The delete artifact method 

2406 # will log a debug message in this scenario. 

2407 # Distinguishing file missing before trash started and 

2408 # file already removed previously as part of this trash 

2409 # is not worth the distinction with regards to potential 

2410 # memory cost. 

2411 pass 

2412 except Exception as e: 

2413 if ignore_errors: 

2414 # Use a debug message here even though it's not 

2415 # a good situation. In some cases this can be 

2416 # caused by a race between user A and user B 

2417 # and neither of them has permissions for the 

2418 # other's files. Butler does not know about users 

2419 # and trash has no idea what collections these 

2420 # files were in (without guessing from a path). 

2421 log.debug( 

2422 "Encountered error removing artifact %s from datastore %s: %s", 

2423 location.uri, 

2424 self.name, 

2425 e, 

2426 ) 

2427 else: 

2428 raise 

2429 

2430 @transactional 

2431 def transfer_from( 

2432 self, 

2433 source_datastore: Datastore, 

2434 refs: Iterable[DatasetRef], 

2435 local_refs: Optional[Iterable[DatasetRef]] = None, 

2436 transfer: str = "auto", 

2437 artifact_existence: Optional[Dict[ResourcePath, bool]] = None, 

2438 ) -> None: 

2439 # Docstring inherited 

2440 if type(self) is not type(source_datastore): 

2441 raise TypeError( 

2442 f"Datastore mismatch between this datastore ({type(self)}) and the " 

2443 f"source datastore ({type(source_datastore)})." 

2444 ) 

2445 

2446 # Be explicit for mypy 

2447 if not isinstance(source_datastore, FileDatastore): 2447 ↛ 2448line 2447 didn't jump to line 2448, because the condition on line 2447 was never true

2448 raise TypeError( 

2449 "Can only transfer to a FileDatastore from another FileDatastore, not" 

2450 f" {type(source_datastore)}" 

2451 ) 

2452 

2453 # Stop early if "direct" transfer mode is requested. That would 

2454 # require that the URI inside the source datastore should be stored 

2455 # directly in the target datastore, which seems unlikely to be useful 

2456 # since at any moment the source datastore could delete the file. 

2457 if transfer in ("direct", "split"): 

2458 raise ValueError( 

2459 f"Can not transfer from a source datastore using {transfer} mode since" 

2460 " those files are controlled by the other datastore." 

2461 ) 

2462 

2463 # Empty existence lookup if none given. 

2464 if artifact_existence is None: 

2465 artifact_existence = {} 

2466 

2467 # We will go through the list multiple times so must convert 

2468 # generators to lists. 

2469 refs = list(refs) 

2470 

2471 if local_refs is None: 

2472 local_refs = refs 

2473 else: 

2474 local_refs = list(local_refs) 

2475 

2476 # In order to handle disassembled composites the code works 

2477 # at the records level since it can assume that internal APIs 

2478 # can be used. 

2479 # - If the record already exists in the destination this is assumed 

2480 # to be okay. 

2481 # - If there is no record but the source and destination URIs are 

2482 # identical no transfer is done but the record is added. 

2483 # - If the source record refers to an absolute URI currently assume 

2484 # that that URI should remain absolute and will be visible to the 

2485 # destination butler. May need to have a flag to indicate whether 

2486 # the dataset should be transferred. This will only happen if 

2487 # the detached Butler has had a local ingest. 

2488 

2489 # What we really want is all the records in the source datastore 

2490 # associated with these refs. Or derived ones if they don't exist 

2491 # in the source. 

2492 source_records = source_datastore._get_stored_records_associated_with_refs(refs) 

2493 

2494 # The source dataset_ids are the keys in these records 

2495 source_ids = set(source_records) 

2496 log.debug("Number of datastore records found in source: %d", len(source_ids)) 

2497 

2498 # The not None check is to appease mypy 

2499 requested_ids = set(ref.id for ref in refs if ref.id is not None) 

2500 missing_ids = requested_ids - source_ids 

2501 

2502 # Missing IDs can be okay if that datastore has allowed 

2503 # gets based on file existence. Should we transfer what we can 

2504 # or complain about it and warn? 

2505 if missing_ids and not source_datastore.trustGetRequest: 2505 ↛ 2506line 2505 didn't jump to line 2506, because the condition on line 2505 was never true

2506 raise ValueError( 

2507 f"Some datasets are missing from source datastore {source_datastore}: {missing_ids}" 

2508 ) 

2509 

2510 # Need to map these missing IDs to a DatasetRef so we can guess 

2511 # the details. 

2512 if missing_ids: 

2513 log.info( 

2514 "Number of expected datasets missing from source datastore records: %d out of %d", 

2515 len(missing_ids), 

2516 len(requested_ids), 

2517 ) 

2518 id_to_ref = {ref.id: ref for ref in refs if ref.id in missing_ids} 

2519 

2520 # This should be chunked in case we end up having to check 

2521 # the file store since we need some log output to show 

2522 # progress. 

2523 for missing_ids_chunk in chunk_iterable(missing_ids, chunk_size=10_000): 

2524 records = {} 

2525 for missing in missing_ids_chunk: 

2526 # Ask the source datastore where the missing artifacts 

2527 # should be. An execution butler might not know about the 

2528 # artifacts even if they are there. 

2529 expected = source_datastore._get_expected_dataset_locations_info(id_to_ref[missing]) 

2530 records[missing] = [info for _, info in expected] 

2531 

2532 # Call the mexist helper method in case we have not already 

2533 # checked these artifacts such that artifact_existence is 

2534 # empty. This allows us to benefit from parallelism. 

2535 # datastore.mexists() itself does not give us access to the 

2536 # derived datastore record. 

2537 log.verbose("Checking existence of %d datasets unknown to datastore", len(records)) 

2538 ref_exists = source_datastore._process_mexists_records( 

2539 id_to_ref, records, False, artifact_existence=artifact_existence 

2540 ) 

2541 

2542 # Now go through the records and propagate the ones that exist. 

2543 location_factory = source_datastore.locationFactory 

2544 for missing, record_list in records.items(): 

2545 # Skip completely if the ref does not exist. 

2546 ref = id_to_ref[missing] 

2547 if not ref_exists[ref]: 

2548 log.warning("Asked to transfer dataset %s but no file artifacts exist for it.", ref) 

2549 continue 

2550 # Check for file artifact to decide which parts of a 

2551 # disassembled composite do exist. If there is only a 

2552 # single record we don't even need to look because it can't 

2553 # be a composite and must exist. 

2554 if len(record_list) == 1: 

2555 dataset_records = record_list 

2556 else: 

2557 dataset_records = [ 

2558 record 

2559 for record in record_list 

2560 if artifact_existence[record.file_location(location_factory).uri] 

2561 ] 

2562 assert len(dataset_records) > 0, "Disassembled composite should have had some files." 

2563 

2564 # Rely on source_records being a defaultdict. 

2565 source_records[missing].extend(dataset_records) 

2566 

2567 # See if we already have these records 

2568 target_records = self._get_stored_records_associated_with_refs(local_refs) 

2569 

2570 # The artifacts to register 

2571 artifacts = [] 

2572 

2573 # Refs that already exist 

2574 already_present = [] 

2575 

2576 # Now can transfer the artifacts 

2577 for source_ref, target_ref in zip(refs, local_refs): 

2578 if target_ref.id in target_records: 

2579 # Already have an artifact for this. 

2580 already_present.append(target_ref) 

2581 continue 

2582 

2583 # mypy needs to know these are always resolved refs 

2584 for info in source_records[source_ref.getCheckedId()]: 

2585 source_location = info.file_location(source_datastore.locationFactory) 

2586 target_location = info.file_location(self.locationFactory) 

2587 if source_location == target_location: 2587 ↛ 2591line 2587 didn't jump to line 2591, because the condition on line 2587 was never true

2588 # Either the dataset is already in the target datastore 

2589 # (which is how execution butler currently runs) or 

2590 # it is an absolute URI. 

2591 if source_location.pathInStore.isabs(): 

2592 # Just because we can see the artifact when running 

2593 # the transfer doesn't mean it will be generally 

2594 # accessible to a user of this butler. For now warn 

2595 # but assume it will be accessible. 

2596 log.warning( 

2597 "Transfer request for an outside-datastore artifact has been found at %s", 

2598 source_location, 

2599 ) 

2600 else: 

2601 # Need to transfer it to the new location. 

2602 # Assume we should always overwrite. If the artifact 

2603 # is there this might indicate that a previous transfer 

2604 # was interrupted but was not able to be rolled back 

2605 # completely (eg pre-emption) so follow Datastore default 

2606 # and overwrite. 

2607 target_location.uri.transfer_from( 

2608 source_location.uri, transfer=transfer, overwrite=True, transaction=self._transaction 

2609 ) 

2610 

2611 artifacts.append((target_ref, info)) 

2612 

2613 self._register_datasets(artifacts) 

2614 

2615 if already_present: 

2616 n_skipped = len(already_present) 

2617 log.info( 

2618 "Skipped transfer of %d dataset%s already present in datastore", 

2619 n_skipped, 

2620 "" if n_skipped == 1 else "s", 

2621 ) 

2622 

2623 @transactional 

2624 def forget(self, refs: Iterable[DatasetRef]) -> None: 

2625 # Docstring inherited. 

2626 refs = list(refs) 

2627 self.bridge.forget(refs) 

2628 self._table.delete(["dataset_id"], *[{"dataset_id": ref.getCheckedId()} for ref in refs]) 

2629 

2630 def validateConfiguration( 

2631 self, entities: Iterable[Union[DatasetRef, DatasetType, StorageClass]], logFailures: bool = False 

2632 ) -> None: 

2633 """Validate some of the configuration for this datastore. 

2634 

2635 Parameters 

2636 ---------- 

2637 entities : iterable of `DatasetRef`, `DatasetType`, or `StorageClass` 

2638 Entities to test against this configuration. Can be differing 

2639 types. 

2640 logFailures : `bool`, optional 

2641 If `True`, output a log message for every validation error 

2642 detected. 

2643 

2644 Raises 

2645 ------ 

2646 DatastoreValidationError 

2647 Raised if there is a validation problem with a configuration. 

2648 All the problems are reported in a single exception. 

2649 

2650 Notes 

2651 ----- 

2652 This method checks that all the supplied entities have valid file 

2653 templates and also have formatters defined. 

2654 """ 

2655 

2656 templateFailed = None 

2657 try: 

2658 self.templates.validateTemplates(entities, logFailures=logFailures) 

2659 except FileTemplateValidationError as e: 

2660 templateFailed = str(e) 

2661 

2662 formatterFailed = [] 

2663 for entity in entities: 

2664 try: 

2665 self.formatterFactory.getFormatterClass(entity) 

2666 except KeyError as e: 

2667 formatterFailed.append(str(e)) 

2668 if logFailures: 2668 ↛ 2663line 2668 didn't jump to line 2663, because the condition on line 2668 was never false

2669 log.critical("Formatter failure: %s", e) 

2670 

2671 if templateFailed or formatterFailed: 

2672 messages = [] 

2673 if templateFailed: 2673 ↛ 2674line 2673 didn't jump to line 2674, because the condition on line 2673 was never true

2674 messages.append(templateFailed) 

2675 if formatterFailed: 2675 ↛ 2677line 2675 didn't jump to line 2677, because the condition on line 2675 was never false

2676 messages.append(",".join(formatterFailed)) 

2677 msg = ";\n".join(messages) 

2678 raise DatastoreValidationError(msg) 

2679 

2680 def getLookupKeys(self) -> Set[LookupKey]: 

2681 # Docstring is inherited from base class 

2682 return ( 

2683 self.templates.getLookupKeys() 

2684 | self.formatterFactory.getLookupKeys() 

2685 | self.constraints.getLookupKeys() 

2686 ) 

2687 

2688 def validateKey(self, lookupKey: LookupKey, entity: Union[DatasetRef, DatasetType, StorageClass]) -> None: 

2689 # Docstring is inherited from base class 

2690 # The key can be valid in either formatters or templates so we can 

2691 # only check the template if it exists 

2692 if lookupKey in self.templates: 

2693 try: 

2694 self.templates[lookupKey].validateTemplate(entity) 

2695 except FileTemplateValidationError as e: 

2696 raise DatastoreValidationError(e) from e 

2697 

2698 def export( 

2699 self, 

2700 refs: Iterable[DatasetRef], 

2701 *, 

2702 directory: Optional[ResourcePathExpression] = None, 

2703 transfer: Optional[str] = "auto", 

2704 ) -> Iterable[FileDataset]: 

2705 # Docstring inherited from Datastore.export. 

2706 if transfer == "auto" and directory is None: 

2707 transfer = None 

2708 

2709 if transfer is not None and directory is None: 

2710 raise TypeError(f"Cannot export using transfer mode {transfer} with no export directory given") 

2711 

2712 if transfer == "move": 

2713 raise TypeError("Can not export by moving files out of datastore.") 

2714 elif transfer == "direct": 2714 ↛ 2718line 2714 didn't jump to line 2718, because the condition on line 2714 was never true

2715 # For an export, treat this as equivalent to None. We do not 

2716 # want an import to risk using absolute URIs to datasets owned 

2717 # by another datastore. 

2718 log.info("Treating 'direct' transfer mode as in-place export.") 

2719 transfer = None 

2720 

2721 # Force the directory to be a URI object 

2722 directoryUri: Optional[ResourcePath] = None 

2723 if directory is not None: 

2724 directoryUri = ResourcePath(directory, forceDirectory=True) 

2725 

2726 if transfer is not None and directoryUri is not None: 

2727 # mypy needs the second test 

2728 if not directoryUri.exists(): 2728 ↛ 2729line 2728 didn't jump to line 2729, because the condition on line 2728 was never true

2729 raise FileNotFoundError(f"Export location {directory} does not exist") 

2730 

2731 progress = Progress("lsst.daf.butler.datastores.FileDatastore.export", level=logging.DEBUG) 

2732 for ref in progress.wrap(refs, "Exporting dataset files"): 

2733 fileLocations = self._get_dataset_locations_info(ref) 

2734 if not fileLocations: 

2735 raise FileNotFoundError(f"Could not retrieve dataset {ref}.") 

2736 # For now we can not export disassembled datasets 

2737 if len(fileLocations) > 1: 

2738 raise NotImplementedError(f"Can not export disassembled datasets such as {ref}") 

2739 location, storedFileInfo = fileLocations[0] 

2740 

2741 pathInStore = location.pathInStore.path 

2742 if transfer is None: 

2743 # TODO: do we also need to return the readStorageClass somehow? 

2744 # We will use the path in store directly. If this is an 

2745 # absolute URI, preserve it. 

2746 if location.pathInStore.isabs(): 2746 ↛ 2747line 2746 didn't jump to line 2747, because the condition on line 2746 was never true

2747 pathInStore = str(location.uri) 

2748 elif transfer == "direct": 2748 ↛ 2750line 2748 didn't jump to line 2750, because the condition on line 2748 was never true

2749 # Use full URIs to the remote store in the export 

2750 pathInStore = str(location.uri) 

2751 else: 

2752 # mypy needs help 

2753 assert directoryUri is not None, "directoryUri must be defined to get here" 

2754 storeUri = ResourcePath(location.uri) 

2755 

2756 # if the datastore has an absolute URI to a resource, we 

2757 # have two options: 

2758 # 1. Keep the absolute URI in the exported YAML 

2759 # 2. Allocate a new name in the local datastore and transfer 

2760 # it. 

2761 # For now go with option 2 

2762 if location.pathInStore.isabs(): 2762 ↛ 2763line 2762 didn't jump to line 2763, because the condition on line 2762 was never true

2763 template = self.templates.getTemplate(ref) 

2764 newURI = ResourcePath(template.format(ref), forceAbsolute=False) 

2765 pathInStore = str(newURI.updatedExtension(location.pathInStore.getExtension())) 

2766 

2767 exportUri = directoryUri.join(pathInStore) 

2768 exportUri.transfer_from(storeUri, transfer=transfer) 

2769 

2770 yield FileDataset(refs=[ref], path=pathInStore, formatter=storedFileInfo.formatter) 

2771 

2772 @staticmethod 

2773 def computeChecksum( 

2774 uri: ResourcePath, algorithm: str = "blake2b", block_size: int = 8192 

2775 ) -> Optional[str]: 

2776 """Compute the checksum of the supplied file. 

2777 

2778 Parameters 

2779 ---------- 

2780 uri : `lsst.resources.ResourcePath` 

2781 Name of resource to calculate checksum from. 

2782 algorithm : `str`, optional 

2783 Name of algorithm to use. Must be one of the algorithms supported 

2784 by :py:class`hashlib`. 

2785 block_size : `int` 

2786 Number of bytes to read from file at one time. 

2787 

2788 Returns 

2789 ------- 

2790 hexdigest : `str` 

2791 Hex digest of the file. 

2792 

2793 Notes 

2794 ----- 

2795 Currently returns None if the URI is for a remote resource. 

2796 """ 

2797 if algorithm not in hashlib.algorithms_guaranteed: 2797 ↛ 2798line 2797 didn't jump to line 2798, because the condition on line 2797 was never true

2798 raise NameError("The specified algorithm '{}' is not supported by hashlib".format(algorithm)) 

2799 

2800 if not uri.isLocal: 2800 ↛ 2801line 2800 didn't jump to line 2801, because the condition on line 2800 was never true

2801 return None 

2802 

2803 hasher = hashlib.new(algorithm) 

2804 

2805 with uri.as_local() as local_uri: 

2806 with open(local_uri.ospath, "rb") as f: 

2807 for chunk in iter(lambda: f.read(block_size), b""): 

2808 hasher.update(chunk) 

2809 

2810 return hasher.hexdigest() 

2811 

2812 def needs_expanded_data_ids( 

2813 self, 

2814 transfer: Optional[str], 

2815 entity: Optional[Union[DatasetRef, DatasetType, StorageClass]] = None, 

2816 ) -> bool: 

2817 # Docstring inherited. 

2818 # This _could_ also use entity to inspect whether the filename template 

2819 # involves placeholders other than the required dimensions for its 

2820 # dataset type, but that's not necessary for correctness; it just 

2821 # enables more optimizations (perhaps only in theory). 

2822 return transfer not in ("direct", None) 

2823 

2824 def import_records(self, data: Mapping[str, DatastoreRecordData]) -> None: 

2825 # Docstring inherited from the base class. 

2826 record_data = data.get(self.name) 

2827 if not record_data: 2827 ↛ 2828line 2827 didn't jump to line 2828, because the condition on line 2827 was never true

2828 return 

2829 

2830 self._bridge.insert(FakeDatasetRef(dataset_id) for dataset_id in record_data.records.keys()) 

2831 

2832 # TODO: Verify that there are no unexpected table names in the dict? 

2833 unpacked_records = [] 

2834 for dataset_data in record_data.records.values(): 

2835 records = dataset_data.get(self._table.name) 

2836 if records: 2836 ↛ 2834line 2836 didn't jump to line 2834, because the condition on line 2836 was never false

2837 for info in records: 

2838 assert isinstance(info, StoredFileInfo), "Expecting StoredFileInfo records" 

2839 unpacked_records.append(info.to_record()) 

2840 if unpacked_records: 

2841 self._table.insert(*unpacked_records) 

2842 

2843 def export_records(self, refs: Iterable[DatasetIdRef]) -> Mapping[str, DatastoreRecordData]: 

2844 # Docstring inherited from the base class. 

2845 exported_refs = list(self._bridge.check(refs)) 

2846 ids = {ref.getCheckedId() for ref in exported_refs} 

2847 records: defaultdict[DatasetId, defaultdict[str, List[StoredDatastoreItemInfo]]] = defaultdict( 

2848 lambda: defaultdict(list), {id: defaultdict(list) for id in ids} 

2849 ) 

2850 for row in self._table.fetch(dataset_id=ids): 

2851 info: StoredDatastoreItemInfo = StoredFileInfo.from_record(row) 

2852 records[info.dataset_id][self._table.name].append(info) 

2853 

2854 record_data = DatastoreRecordData(records=records) 

2855 return {self.name: record_data}