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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/>.
22from __future__ import annotations
24"""Chained datastore."""
26__all__ = ("ChainedDatastore",)
28import itertools
29import logging
30import time
31import warnings
32from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Mapping, Optional, Sequence, Set, Tuple, Union
34from lsst.daf.butler import (
35 Constraints,
36 DatasetRef,
37 DatasetTypeNotSupportedError,
38 Datastore,
39 DatastoreConfig,
40 DatastoreValidationError,
41 FileDataset,
42)
43from lsst.resources import ResourcePath
44from lsst.utils import doImportType
46if TYPE_CHECKING: 46 ↛ 47line 46 didn't jump to line 47, because the condition on line 46 was never true
47 from lsst.daf.butler import Config, DatasetType, LookupKey, StorageClass
48 from lsst.daf.butler.registry.interfaces import DatastoreRegistryBridgeManager
50log = logging.getLogger(__name__)
53class _IngestPrepData(Datastore.IngestPrepData):
54 """Helper class for ChainedDatastore ingest implementation.
56 Parameters
57 ----------
58 children : `list` of `tuple`
59 Pairs of `Datastore`, `IngestPrepData` for all child datastores.
60 """
62 def __init__(self, children: List[Tuple[Datastore, Datastore.IngestPrepData]]):
63 super().__init__(itertools.chain.from_iterable(data.refs.values() for _, data in children))
64 self.children = children
67class ChainedDatastore(Datastore):
68 """Chained Datastores to allow read and writes from multiple datastores.
70 A ChainedDatastore is configured with multiple datastore configurations.
71 A ``put()`` is always sent to each datastore. A ``get()``
72 operation is sent to each datastore in turn and the first datastore
73 to return a valid dataset is used.
75 Parameters
76 ----------
77 config : `DatastoreConfig` or `str`
78 Configuration. This configuration must include a ``datastores`` field
79 as a sequence of datastore configurations. The order in this sequence
80 indicates the order to use for read operations.
81 bridgeManager : `DatastoreRegistryBridgeManager`
82 Object that manages the interface between `Registry` and datastores.
83 butlerRoot : `str`, optional
84 New datastore root to use to override the configuration value. This
85 root is sent to each child datastore.
87 Notes
88 -----
89 ChainedDatastore never supports `None` or `"move"` as an `ingest` transfer
90 mode. It supports `"copy"`, `"symlink"`, `"relsymlink"`
91 and `"hardlink"` if and only if all its child datastores do.
92 """
94 defaultConfigFile = "datastores/chainedDatastore.yaml"
95 """Path to configuration defaults. Accessed within the ``configs`` resource
96 or relative to a search path. Can be None if no defaults specified.
97 """
99 containerKey = "datastores"
100 """Key to specify where child datastores are configured."""
102 datastores: List[Datastore]
103 """All the child datastores known to this datastore."""
105 datastoreConstraints: Sequence[Optional[Constraints]]
106 """Constraints to be applied to each of the child datastores."""
108 @classmethod
109 def setConfigRoot(cls, root: str, config: Config, full: Config, overwrite: bool = True) -> None:
110 """Set any filesystem-dependent config options for child Datastores to
111 be appropriate for a new empty repository with the given root.
113 Parameters
114 ----------
115 root : `str`
116 Filesystem path to the root of the data repository.
117 config : `Config`
118 A `Config` to update. Only the subset understood by
119 this component will be updated. Will not expand
120 defaults.
121 full : `Config`
122 A complete config with all defaults expanded that can be
123 converted to a `DatastoreConfig`. Read-only and will not be
124 modified by this method.
125 Repository-specific options that should not be obtained
126 from defaults when Butler instances are constructed
127 should be copied from ``full`` to ``config``.
128 overwrite : `bool`, optional
129 If `False`, do not modify a value in ``config`` if the value
130 already exists. Default is always to overwrite with the provided
131 ``root``.
133 Notes
134 -----
135 If a keyword is explicitly defined in the supplied ``config`` it
136 will not be overridden by this method if ``overwrite`` is `False`.
137 This allows explicit values set in external configs to be retained.
138 """
140 # Extract the part of the config we care about updating
141 datastoreConfig = DatastoreConfig(config, mergeDefaults=False)
143 # And the subset of the full config that we can use for reference.
144 # Do not bother with defaults because we are told this already has
145 # them.
146 fullDatastoreConfig = DatastoreConfig(full, mergeDefaults=False)
148 # Loop over each datastore config and pass the subsets to the
149 # child datastores to process.
151 containerKey = cls.containerKey
152 for idx, (child, fullChild) in enumerate(
153 zip(datastoreConfig[containerKey], fullDatastoreConfig[containerKey])
154 ):
155 childConfig = DatastoreConfig(child, mergeDefaults=False)
156 fullChildConfig = DatastoreConfig(fullChild, mergeDefaults=False)
157 datastoreClass = doImportType(fullChildConfig["cls"])
158 if not issubclass(datastoreClass, Datastore): 158 ↛ 159line 158 didn't jump to line 159, because the condition on line 158 was never true
159 raise TypeError(f"Imported child class {fullChildConfig['cls']} is not a Datastore")
160 newroot = "{}/{}_{}".format(root, datastoreClass.__qualname__, idx)
161 datastoreClass.setConfigRoot(newroot, childConfig, fullChildConfig, overwrite=overwrite)
163 # Reattach to parent
164 datastoreConfig[containerKey, idx] = childConfig
166 # Reattach modified datastore config to parent
167 # If this has a datastore key we attach there, otherwise we assume
168 # this information goes at the top of the config hierarchy.
169 if DatastoreConfig.component in config:
170 config[DatastoreConfig.component] = datastoreConfig
171 else:
172 config.update(datastoreConfig)
174 return
176 def __init__(
177 self,
178 config: Union[Config, str],
179 bridgeManager: DatastoreRegistryBridgeManager,
180 butlerRoot: str = None,
181 ):
182 super().__init__(config, bridgeManager)
184 # Scan for child datastores and instantiate them with the same registry
185 self.datastores = []
186 for c in self.config["datastores"]:
187 c = DatastoreConfig(c)
188 datastoreType = doImportType(c["cls"])
189 if not issubclass(datastoreType, Datastore): 189 ↛ 190line 189 didn't jump to line 190, because the condition on line 189 was never true
190 raise TypeError(f"Imported child class {c['cls']} is not a Datastore")
191 datastore = datastoreType(c, bridgeManager, butlerRoot=butlerRoot)
192 log.debug("Creating child datastore %s", datastore.name)
193 self.datastores.append(datastore)
195 # Name ourself based on our children
196 if self.datastores: 196 ↛ 201line 196 didn't jump to line 201, because the condition on line 196 was never false
197 # We must set the names explicitly
198 self._names = [d.name for d in self.datastores]
199 childNames = ",".join(self.names)
200 else:
201 childNames = "(empty@{})".format(time.time())
202 self._names = [childNames]
203 self.name = "{}[{}]".format(type(self).__qualname__, childNames)
205 # We declare we are ephemeral if all our child datastores declare
206 # they are ephemeral
207 isEphemeral = True
208 for d in self.datastores:
209 if not d.isEphemeral:
210 isEphemeral = False
211 break
212 self.isEphemeral = isEphemeral
214 # per-datastore override constraints
215 if "datastore_constraints" in self.config:
216 overrides = self.config["datastore_constraints"]
218 if len(overrides) != len(self.datastores): 218 ↛ 219line 218 didn't jump to line 219, because the condition on line 218 was never true
219 raise DatastoreValidationError(
220 f"Number of registered datastores ({len(self.datastores)})"
221 " differs from number of constraints overrides"
222 f" {len(overrides)}"
223 )
225 self.datastoreConstraints = [
226 Constraints(c.get("constraints"), universe=bridgeManager.universe) for c in overrides
227 ]
229 else:
230 self.datastoreConstraints = (None,) * len(self.datastores)
232 log.debug("Created %s (%s)", self.name, ("ephemeral" if self.isEphemeral else "permanent"))
234 @property
235 def names(self) -> Tuple[str, ...]:
236 return tuple(self._names)
238 def __str__(self) -> str:
239 chainName = ", ".join(str(ds) for ds in self.datastores)
240 return chainName
242 def knows(self, ref: DatasetRef) -> bool:
243 """Check if the dataset is known to any of the datastores.
245 Does not check for existence of any artifact.
247 Parameters
248 ----------
249 ref : `DatasetRef`
250 Reference to the required dataset.
252 Returns
253 -------
254 exists : `bool`
255 `True` if the dataset is known to the datastore.
256 """
257 for datastore in self.datastores:
258 if datastore.knows(ref):
259 log.debug("%s known to datastore %s", ref, datastore.name)
260 return True
261 return False
263 def mexists(
264 self, refs: Iterable[DatasetRef], artifact_existence: Optional[Dict[ResourcePath, bool]] = None
265 ) -> Dict[DatasetRef, bool]:
266 """Check the existence of multiple datasets at once.
268 Parameters
269 ----------
270 refs : iterable of `DatasetRef`
271 The datasets to be checked.
272 artifact_existence : `dict` [`lsst.resources.ResourcePath`, `bool`]
273 Optional mapping of datastore artifact to existence. Updated by
274 this method with details of all artifacts tested. Can be `None`
275 if the caller is not interested.
277 Returns
278 -------
279 existence : `dict` of [`DatasetRef`, `bool`]
280 Mapping from dataset to boolean indicating existence in any
281 of the child datastores.
282 """
283 dataset_existence: Dict[DatasetRef, bool] = {}
284 for datastore in self.datastores:
285 dataset_existence.update(datastore.mexists(refs, artifact_existence=artifact_existence))
287 # For next datastore no point asking about ones we know
288 # exist already. No special exemption for ephemeral datastores.
289 refs = [ref for ref, exists in dataset_existence.items() if not exists]
291 return dataset_existence
293 def exists(self, ref: DatasetRef) -> bool:
294 """Check if the dataset exists in one of the datastores.
296 Parameters
297 ----------
298 ref : `DatasetRef`
299 Reference to the required dataset.
301 Returns
302 -------
303 exists : `bool`
304 `True` if the entity exists in one of the child datastores.
305 """
306 for datastore in self.datastores:
307 if datastore.exists(ref):
308 log.debug("Found %s in datastore %s", ref, datastore.name)
309 return True
310 return False
312 def get(self, ref: DatasetRef, parameters: Optional[Mapping[str, Any]] = None) -> Any:
313 """Load an InMemoryDataset from the store.
315 The dataset is returned from the first datastore that has
316 the dataset.
318 Parameters
319 ----------
320 ref : `DatasetRef`
321 Reference to the required Dataset.
322 parameters : `dict`
323 `StorageClass`-specific parameters that specify, for example,
324 a slice of the dataset to be loaded.
326 Returns
327 -------
328 inMemoryDataset : `object`
329 Requested dataset or slice thereof as an InMemoryDataset.
331 Raises
332 ------
333 FileNotFoundError
334 Requested dataset can not be retrieved.
335 TypeError
336 Return value from formatter has unexpected type.
337 ValueError
338 Formatter failed to process the dataset.
339 """
341 for datastore in self.datastores:
342 try:
343 inMemoryObject = datastore.get(ref, parameters)
344 log.debug("Found dataset %s in datastore %s", ref, datastore.name)
345 return inMemoryObject
346 except FileNotFoundError:
347 pass
349 raise FileNotFoundError("Dataset {} could not be found in any of the datastores".format(ref))
351 def put(self, inMemoryDataset: Any, ref: DatasetRef) -> None:
352 """Write a InMemoryDataset with a given `DatasetRef` to each
353 datastore.
355 The put() to child datastores can fail with
356 `DatasetTypeNotSupportedError`. The put() for this datastore will be
357 deemed to have succeeded so long as at least one child datastore
358 accepted the inMemoryDataset.
360 Parameters
361 ----------
362 inMemoryDataset : `object`
363 The dataset to store.
364 ref : `DatasetRef`
365 Reference to the associated Dataset.
367 Raises
368 ------
369 TypeError
370 Supplied object and storage class are inconsistent.
371 DatasetTypeNotSupportedError
372 All datastores reported `DatasetTypeNotSupportedError`.
373 """
374 log.debug("Put %s", ref)
376 # Confirm that we can accept this dataset
377 if not self.constraints.isAcceptable(ref):
378 # Raise rather than use boolean return value.
379 raise DatasetTypeNotSupportedError(
380 f"Dataset {ref} has been rejected by this datastore via configuration."
381 )
383 isPermanent = False
384 nsuccess = 0
385 npermanent = 0
386 nephemeral = 0
387 for datastore, constraints in zip(self.datastores, self.datastoreConstraints):
388 if constraints is not None and not constraints.isAcceptable(ref):
389 log.debug("Datastore %s skipping put via configuration for ref %s", datastore.name, ref)
390 continue
392 if datastore.isEphemeral:
393 nephemeral += 1
394 else:
395 npermanent += 1
396 try:
397 datastore.put(inMemoryDataset, ref)
398 nsuccess += 1
399 if not datastore.isEphemeral:
400 isPermanent = True
401 except DatasetTypeNotSupportedError:
402 pass
404 if nsuccess == 0:
405 raise DatasetTypeNotSupportedError(f"None of the chained datastores supported ref {ref}")
407 if not isPermanent and npermanent > 0: 407 ↛ 408line 407 didn't jump to line 408, because the condition on line 407 was never true
408 warnings.warn(f"Put of {ref} only succeeded in ephemeral databases", stacklevel=2)
410 if self._transaction is not None:
411 self._transaction.registerUndo("put", self.remove, ref)
413 def _overrideTransferMode(self, *datasets: Any, transfer: Optional[str] = None) -> Optional[str]:
414 # Docstring inherited from base class.
415 if transfer != "auto":
416 return transfer
417 # Ask each datastore what they think auto means
418 transfers = {d._overrideTransferMode(*datasets, transfer=transfer) for d in self.datastores}
420 # Remove any untranslated "auto" values
421 transfers.discard(transfer)
423 if len(transfers) == 1: 423 ↛ 424line 423 didn't jump to line 424, because the condition on line 423 was never true
424 return transfers.pop()
425 if not transfers: 425 ↛ 429line 425 didn't jump to line 429, because the condition on line 425 was never false
426 # Everything reported "auto"
427 return transfer
429 raise RuntimeError(
430 "Chained datastore does not yet support different transfer modes"
431 f" from 'auto' in each child datastore (wanted {transfers})"
432 )
434 def _prepIngest(self, *datasets: FileDataset, transfer: Optional[str] = None) -> _IngestPrepData:
435 # Docstring inherited from Datastore._prepIngest.
436 if transfer is None or transfer == "move":
437 raise NotImplementedError("ChainedDatastore does not support transfer=None or transfer='move'.")
439 def isDatasetAcceptable(dataset: FileDataset, *, name: str, constraints: Constraints) -> bool:
440 acceptable = [ref for ref in dataset.refs if constraints.isAcceptable(ref)]
441 if not acceptable:
442 log.debug(
443 "Datastore %s skipping ingest via configuration for refs %s",
444 name,
445 ", ".join(str(ref) for ref in dataset.refs),
446 )
447 return False
448 else:
449 return True
451 # Filter down to just datasets the chained datastore's own
452 # configuration accepts.
453 okForParent: List[FileDataset] = [
454 dataset
455 for dataset in datasets
456 if isDatasetAcceptable(dataset, name=self.name, constraints=self.constraints)
457 ]
459 # Iterate over nested datastores and call _prepIngest on each.
460 # Save the results to a list:
461 children: List[Tuple[Datastore, Datastore.IngestPrepData]] = []
462 # ...and remember whether all of the failures are due to
463 # NotImplementedError being raised.
464 allFailuresAreNotImplementedError = True
465 for datastore, constraints in zip(self.datastores, self.datastoreConstraints):
466 okForChild: List[FileDataset]
467 if constraints is not None:
468 okForChild = [
469 dataset
470 for dataset in okForParent
471 if isDatasetAcceptable(dataset, name=datastore.name, constraints=constraints)
472 ]
473 else:
474 okForChild = okForParent
475 try:
476 prepDataForChild = datastore._prepIngest(*okForChild, transfer=transfer)
477 except NotImplementedError:
478 log.debug(
479 "Skipping ingest for datastore %s because transfer mode %s is not supported.",
480 datastore.name,
481 transfer,
482 )
483 continue
484 allFailuresAreNotImplementedError = False
485 children.append((datastore, prepDataForChild))
486 if allFailuresAreNotImplementedError:
487 raise NotImplementedError(f"No child datastore supports transfer mode {transfer}.")
488 return _IngestPrepData(children=children)
490 def _finishIngest(
491 self,
492 prepData: _IngestPrepData,
493 *,
494 transfer: Optional[str] = None,
495 record_validation_info: bool = True,
496 ) -> None:
497 # Docstring inherited from Datastore._finishIngest.
498 for datastore, prepDataForChild in prepData.children:
499 datastore._finishIngest(
500 prepDataForChild, transfer=transfer, record_validation_info=record_validation_info
501 )
503 def getURIs(
504 self, ref: DatasetRef, predict: bool = False
505 ) -> Tuple[Optional[ResourcePath], Dict[str, ResourcePath]]:
506 """Return URIs associated with dataset.
508 Parameters
509 ----------
510 ref : `DatasetRef`
511 Reference to the required dataset.
512 predict : `bool`, optional
513 If the datastore does not know about the dataset, should it
514 return a predicted URI or not?
516 Returns
517 -------
518 primary : `lsst.resources.ResourcePath`
519 The URI to the primary artifact associated with this dataset.
520 If the dataset was disassembled within the datastore this
521 may be `None`.
522 components : `dict`
523 URIs to any components associated with the dataset artifact.
524 Can be empty if there are no components.
526 Notes
527 -----
528 The returned URI is from the first datastore in the list that has
529 the dataset with preference given to the first dataset coming from
530 a permanent datastore. If no datastores have the dataset and prediction
531 is allowed, the predicted URI for the first datastore in the list will
532 be returned.
533 """
534 DatastoreURIs = Tuple[Optional[ResourcePath], Dict[str, ResourcePath]]
535 log.debug("Requesting URIs for %s", ref)
536 predictedUri: Optional[DatastoreURIs] = None
537 predictedEphemeralUri: Optional[DatastoreURIs] = None
538 firstEphemeralUri: Optional[DatastoreURIs] = None
539 for datastore in self.datastores:
540 if datastore.exists(ref):
541 if not datastore.isEphemeral:
542 uri = datastore.getURIs(ref)
543 log.debug("Retrieved non-ephemeral URI: %s", uri)
544 return uri
545 elif not firstEphemeralUri:
546 firstEphemeralUri = datastore.getURIs(ref)
547 elif predict:
548 if not predictedUri and not datastore.isEphemeral:
549 predictedUri = datastore.getURIs(ref, predict)
550 elif not predictedEphemeralUri and datastore.isEphemeral:
551 predictedEphemeralUri = datastore.getURIs(ref, predict)
553 if firstEphemeralUri:
554 log.debug("Retrieved ephemeral URI: %s", firstEphemeralUri)
555 return firstEphemeralUri
557 if predictedUri:
558 log.debug("Retrieved predicted URI: %s", predictedUri)
559 return predictedUri
561 if predictedEphemeralUri:
562 log.debug("Retrieved predicted ephemeral URI: %s", predictedEphemeralUri)
563 return predictedEphemeralUri
565 raise FileNotFoundError("Dataset {} not in any datastore".format(ref))
567 def getURI(self, ref: DatasetRef, predict: bool = False) -> ResourcePath:
568 """URI to the Dataset.
570 The returned URI is from the first datastore in the list that has
571 the dataset with preference given to the first dataset coming from
572 a permanent datastore. If no datastores have the dataset and prediction
573 is allowed, the predicted URI for the first datastore in the list will
574 be returned.
576 Parameters
577 ----------
578 ref : `DatasetRef`
579 Reference to the required Dataset.
580 predict : `bool`
581 If `True`, allow URIs to be returned of datasets that have not
582 been written.
584 Returns
585 -------
586 uri : `lsst.resources.ResourcePath`
587 URI pointing to the dataset within the datastore. If the
588 dataset does not exist in the datastore, and if ``predict`` is
589 `True`, the URI will be a prediction and will include a URI
590 fragment "#predicted".
592 Notes
593 -----
594 If the datastore does not have entities that relate well
595 to the concept of a URI the returned URI string will be
596 descriptive. The returned URI is not guaranteed to be obtainable.
598 Raises
599 ------
600 FileNotFoundError
601 A URI has been requested for a dataset that does not exist and
602 guessing is not allowed.
603 RuntimeError
604 Raised if a request is made for a single URI but multiple URIs
605 are associated with this dataset.
606 """
607 log.debug("Requesting URI for %s", ref)
608 primary, components = self.getURIs(ref, predict)
609 if primary is None or components: 609 ↛ 610line 609 didn't jump to line 610, because the condition on line 609 was never true
610 raise RuntimeError(
611 f"Dataset ({ref}) includes distinct URIs for components. Use Datastore.getURIs() instead."
612 )
613 return primary
615 def retrieveArtifacts(
616 self,
617 refs: Iterable[DatasetRef],
618 destination: ResourcePath,
619 transfer: str = "auto",
620 preserve_path: bool = True,
621 overwrite: bool = False,
622 ) -> List[ResourcePath]:
623 """Retrieve the file artifacts associated with the supplied refs.
625 Parameters
626 ----------
627 refs : iterable of `DatasetRef`
628 The datasets for which file artifacts are to be retrieved.
629 A single ref can result in multiple files. The refs must
630 be resolved.
631 destination : `lsst.resources.ResourcePath`
632 Location to write the file artifacts.
633 transfer : `str`, optional
634 Method to use to transfer the artifacts. Must be one of the options
635 supported by `lsst.resources.ResourcePath.transfer_from()`.
636 "move" is not allowed.
637 preserve_path : `bool`, optional
638 If `True` the full path of the file artifact within the datastore
639 is preserved. If `False` the final file component of the path
640 is used.
641 overwrite : `bool`, optional
642 If `True` allow transfers to overwrite existing files at the
643 destination.
645 Returns
646 -------
647 targets : `list` of `lsst.resources.ResourcePath`
648 URIs of file artifacts in destination location. Order is not
649 preserved.
650 """
651 if not destination.isdir(): 651 ↛ 652line 651 didn't jump to line 652, because the condition on line 651 was never true
652 raise ValueError(f"Destination location must refer to a directory. Given {destination}")
654 # Using getURIs is not feasible since it becomes difficult to
655 # determine the path within the datastore later on. For now
656 # follow getURIs implementation approach.
658 pending = set(refs)
660 # There is a question as to whether an exception should be raised
661 # early if some of the refs are missing, or whether files should be
662 # transferred until a problem is hit. Prefer to complain up front.
663 # Use the datastore integer as primary key.
664 grouped_by_datastore: Dict[int, Set[DatasetRef]] = {}
666 for number, datastore in enumerate(self.datastores):
667 if datastore.isEphemeral:
668 # In the future we will want to distinguish in-memory from
669 # caching datastore since using an on-disk local
670 # cache is exactly what we should be doing.
671 continue
672 datastore_refs = {ref for ref in pending if datastore.exists(ref)}
674 if datastore_refs:
675 grouped_by_datastore[number] = datastore_refs
677 # Remove these from the pending list so that we do not bother
678 # looking for them any more.
679 pending = pending - datastore_refs
681 if pending: 681 ↛ 682line 681 didn't jump to line 682, because the condition on line 681 was never true
682 raise RuntimeError(f"Some datasets were not found in any datastores: {pending}")
684 # Now do the transfer.
685 targets: List[ResourcePath] = []
686 for number, datastore_refs in grouped_by_datastore.items():
687 targets.extend(
688 self.datastores[number].retrieveArtifacts(
689 datastore_refs,
690 destination,
691 transfer=transfer,
692 preserve_path=preserve_path,
693 overwrite=overwrite,
694 )
695 )
697 return targets
699 def remove(self, ref: DatasetRef) -> None:
700 """Indicate to the datastore that a dataset can be removed.
702 The dataset will be removed from each datastore. The dataset is
703 not required to exist in every child datastore.
705 Parameters
706 ----------
707 ref : `DatasetRef`
708 Reference to the required dataset.
710 Raises
711 ------
712 FileNotFoundError
713 Attempt to remove a dataset that does not exist. Raised if none
714 of the child datastores removed the dataset.
715 """
716 log.debug("Removing %s", ref)
717 self.trash(ref, ignore_errors=False)
718 self.emptyTrash(ignore_errors=False)
720 def forget(self, refs: Iterable[DatasetRef]) -> None:
721 for datastore in tuple(self.datastores):
722 datastore.forget(refs)
724 def trash(self, ref: Union[DatasetRef, Iterable[DatasetRef]], ignore_errors: bool = True) -> None:
725 if isinstance(ref, DatasetRef):
726 ref_label = str(ref)
727 else:
728 ref_label = "bulk datasets"
730 log.debug("Trashing %s", ref_label)
732 counter = 0
733 for datastore in self.datastores:
734 try:
735 datastore.trash(ref, ignore_errors=ignore_errors)
736 counter += 1
737 except FileNotFoundError:
738 pass
740 if counter == 0:
741 err_msg = f"Could not mark for removal from any child datastore: {ref_label}"
742 if ignore_errors: 742 ↛ 743line 742 didn't jump to line 743, because the condition on line 742 was never true
743 log.warning(err_msg)
744 else:
745 raise FileNotFoundError(err_msg)
747 def emptyTrash(self, ignore_errors: bool = True) -> None:
748 for datastore in self.datastores:
749 datastore.emptyTrash(ignore_errors=ignore_errors)
751 def transfer(self, inputDatastore: Datastore, ref: DatasetRef) -> None:
752 """Retrieve a dataset from an input `Datastore`,
753 and store the result in this `Datastore`.
755 Parameters
756 ----------
757 inputDatastore : `Datastore`
758 The external `Datastore` from which to retreive the Dataset.
759 ref : `DatasetRef`
760 Reference to the required dataset in the input data store.
762 Returns
763 -------
764 results : `list`
765 List containing the return value from the ``put()`` to each
766 child datastore.
767 """
768 assert inputDatastore is not self # unless we want it for renames?
769 inMemoryDataset = inputDatastore.get(ref)
770 self.put(inMemoryDataset, ref)
772 def validateConfiguration(
773 self, entities: Iterable[Union[DatasetRef, DatasetType, StorageClass]], logFailures: bool = False
774 ) -> None:
775 """Validate some of the configuration for this datastore.
777 Parameters
778 ----------
779 entities : iterable of `DatasetRef`, `DatasetType`, or `StorageClass`
780 Entities to test against this configuration. Can be differing
781 types.
782 logFailures : `bool`, optional
783 If `True`, output a log message for every validation error
784 detected.
786 Raises
787 ------
788 DatastoreValidationError
789 Raised if there is a validation problem with a configuration.
790 All the problems are reported in a single exception.
792 Notes
793 -----
794 This method checks each datastore in turn.
795 """
797 # Need to catch each of the datastore outputs and ensure that
798 # all are tested.
799 failures = []
800 for datastore in self.datastores:
801 try:
802 datastore.validateConfiguration(entities, logFailures=logFailures)
803 except DatastoreValidationError as e:
804 if logFailures: 804 ↛ 806line 804 didn't jump to line 806, because the condition on line 804 was never false
805 log.critical("Datastore %s failed validation", datastore.name)
806 failures.append(f"Datastore {self.name}: {e}")
808 if failures:
809 msg = ";\n".join(failures)
810 raise DatastoreValidationError(msg)
812 def validateKey(self, lookupKey: LookupKey, entity: Union[DatasetRef, DatasetType, StorageClass]) -> None:
813 # Docstring is inherited from base class
814 failures = []
815 for datastore in self.datastores:
816 try:
817 datastore.validateKey(lookupKey, entity)
818 except DatastoreValidationError as e:
819 failures.append(f"Datastore {self.name}: {e}")
821 if failures:
822 msg = ";\n".join(failures)
823 raise DatastoreValidationError(msg)
825 def getLookupKeys(self) -> Set[LookupKey]:
826 # Docstring is inherited from base class
827 keys = set()
828 for datastore in self.datastores:
829 keys.update(datastore.getLookupKeys())
831 keys.update(self.constraints.getLookupKeys())
832 for p in self.datastoreConstraints:
833 if p is not None: 833 ↛ 834line 833 didn't jump to line 834, because the condition on line 833 was never true
834 keys.update(p.getLookupKeys())
836 return keys
838 def needs_expanded_data_ids(
839 self,
840 transfer: Optional[str],
841 entity: Optional[Union[DatasetRef, DatasetType, StorageClass]] = None,
842 ) -> bool:
843 # Docstring inherited.
844 # We can't safely use `self.datastoreConstraints` with `entity` to
845 # check whether a child datastore would even want to ingest this
846 # dataset, because we don't want to filter out datastores that might
847 # need an expanded data ID based in incomplete information (e.g. we
848 # pass a StorageClass, but the constraint dispatches on DatasetType).
849 # So we pessimistically check if any datastore would need an expanded
850 # data ID for this transfer mode.
851 return any(datastore.needs_expanded_data_ids(transfer) for datastore in self.datastores) 851 ↛ exitline 851 didn't finish the generator expression on line 851