Coverage for python / lsst / daf / butler / datastores / inMemoryDatastore.py: 0%
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28"""In-memory datastore."""
30from __future__ import annotations
32__all__ = ("InMemoryDatastore", "StoredMemoryItemInfo")
34import logging
35import time
36from collections.abc import Collection, Iterable, Mapping
37from dataclasses import dataclass
38from typing import TYPE_CHECKING, Any
39from urllib.parse import urlencode
41from lsst.daf.butler import DatasetId, DatasetRef, StorageClass
42from lsst.daf.butler._exceptions import DatasetTypeNotSupportedError
43from lsst.daf.butler.datastore import DatasetRefURIs, DatastoreConfig
44from lsst.daf.butler.datastore.generic_base import GenericBaseDatastore, post_process_get
45from lsst.daf.butler.datastore.record_data import DatastoreRecordData
46from lsst.daf.butler.datastore.stored_file_info import StoredDatastoreItemInfo
47from lsst.daf.butler.utils import transactional
48from lsst.resources import ResourcePath, ResourcePathExpression
50if TYPE_CHECKING:
51 from lsst.daf.butler import Config, DatasetProvenance, DatasetType, LookupKey
52 from lsst.daf.butler.datastore import DatastoreOpaqueTable
53 from lsst.daf.butler.datastores.file_datastore.retrieve_artifacts import ArtifactIndexInfo
54 from lsst.daf.butler.registry.interfaces import DatasetIdRef, DatastoreRegistryBridgeManager
56log = logging.getLogger(__name__)
59@dataclass(frozen=True, slots=True)
60class StoredMemoryItemInfo(StoredDatastoreItemInfo):
61 """Internal InMemoryDatastore Metadata associated with a stored
62 DatasetRef.
63 """
65 timestamp: float
66 """Unix timestamp indicating the time the dataset was stored."""
68 storageClass: StorageClass
69 """StorageClass associated with the dataset."""
71 parentID: DatasetId
72 """ID of the parent `DatasetRef` if this entry is a concrete
73 composite. Not used if the dataset being stored is not a
74 virtual component of a composite
75 """
78class InMemoryDatastore(GenericBaseDatastore[StoredMemoryItemInfo]):
79 """Basic Datastore for writing to an in memory cache.
81 This datastore is ephemeral in that the contents of the datastore
82 disappear when the Python process completes. This also means that
83 other processes can not access this datastore.
85 Parameters
86 ----------
87 config : `DatastoreConfig` or `str`
88 Configuration.
89 bridgeManager : `DatastoreRegistryBridgeManager`
90 Object that manages the interface between `Registry` and datastores.
92 Notes
93 -----
94 InMemoryDatastore does not support any file-based ingest.
95 """
97 defaultConfigFile = "datastores/inMemoryDatastore.yaml"
98 """Path to configuration defaults. Accessed within the ``configs`` resource
99 or relative to a search path. Can be None if no defaults specified.
100 """
102 isEphemeral = True
103 """A new datastore is created every time and datasets disappear when
104 the process shuts down."""
106 datasets: dict[DatasetId, Any]
107 """Internal storage of datasets indexed by dataset ID."""
109 records: dict[DatasetId, StoredMemoryItemInfo]
110 """Internal records about stored datasets."""
112 def __init__(
113 self,
114 config: DatastoreConfig,
115 bridgeManager: DatastoreRegistryBridgeManager,
116 ):
117 super().__init__(config, bridgeManager)
119 # Name ourselves with the timestamp the datastore
120 # was created.
121 self.name = f"{type(self).__name__}@{time.time()}"
122 log.debug("Creating datastore %s", self.name)
124 # Storage of datasets, keyed by dataset_id
125 self.datasets: dict[DatasetId, Any] = {}
127 # Records is distinct in order to track concrete composite components
128 # where we register multiple components for a single dataset.
129 self.records: dict[DatasetId, StoredMemoryItemInfo] = {}
131 # Related records that share the same parent
132 self.related: dict[DatasetId, set[DatasetId]] = {}
134 self._trashedIds: set[DatasetId] = set()
136 @classmethod
137 def _create_from_config(
138 cls,
139 config: DatastoreConfig,
140 bridgeManager: DatastoreRegistryBridgeManager,
141 butlerRoot: ResourcePathExpression | None,
142 ) -> InMemoryDatastore:
143 return InMemoryDatastore(config, bridgeManager)
145 def clone(self, bridgeManager: DatastoreRegistryBridgeManager) -> InMemoryDatastore:
146 clone = InMemoryDatastore(self.config, bridgeManager)
147 # Sharing these objects is not thread-safe, but this class is only used
148 # in single-threaded test code.
149 clone.datasets = self.datasets
150 clone.records = self.records
151 clone.related = self.related
152 clone._trashedIds = self._trashedIds
153 return clone
155 @classmethod
156 def setConfigRoot(cls, root: str, config: Config, full: Config, overwrite: bool = True) -> None:
157 """Set any filesystem-dependent config options for this Datastore to
158 be appropriate for a new empty repository with the given root.
160 Does nothing in this implementation.
162 Parameters
163 ----------
164 root : `str`
165 Filesystem path to the root of the data repository.
166 config : `Config`
167 A `Config` to update. Only the subset understood by
168 this component will be updated. Will not expand
169 defaults.
170 full : `Config`
171 A complete config with all defaults expanded that can be
172 converted to a `DatastoreConfig`. Read-only and will not be
173 modified by this method.
174 Repository-specific options that should not be obtained
175 from defaults when Butler instances are constructed
176 should be copied from ``full`` to ``config``.
177 overwrite : `bool`, optional
178 If `False`, do not modify a value in ``config`` if the value
179 already exists. Default is always to overwrite with the provided
180 ``root``.
182 Notes
183 -----
184 If a keyword is explicitly defined in the supplied ``config`` it
185 will not be overridden by this method if ``overwrite`` is `False`.
186 This allows explicit values set in external configs to be retained.
187 """
188 return
190 def _get_stored_item_info(self, dataset_id: DatasetId) -> StoredMemoryItemInfo:
191 # Docstring inherited from GenericBaseDatastore.
192 return self.records[dataset_id]
194 def _remove_stored_item_info(self, dataset_id: DatasetId) -> None:
195 # Docstring inherited from GenericBaseDatastore.
196 # If a component has been removed previously then we can sometimes
197 # be asked to remove it again. Other datastores ignore this
198 # so also ignore here
199 if dataset_id not in self.records:
200 return
201 record = self.records[dataset_id]
202 del self.records[dataset_id]
203 self.related[record.parentID].remove(dataset_id)
205 def removeStoredItemInfo(self, ref: DatasetIdRef) -> None:
206 """Remove information about the file associated with this dataset.
208 Parameters
209 ----------
210 ref : `DatasetRef`
211 The dataset that has been removed.
213 Notes
214 -----
215 This method is actually not used by this implementation, but there are
216 some tests that check that this method works, so we keep it for now.
217 """
218 self._remove_stored_item_info(ref.id)
220 def _get_dataset_info(self, dataset_id: DatasetId) -> tuple[DatasetId, StoredMemoryItemInfo]:
221 """Check that the dataset is present and return the real ID and
222 associated information.
224 Parameters
225 ----------
226 dataset_id : `DatasetRef`
227 Target `DatasetRef`
229 Returns
230 -------
231 realID : `int`
232 The dataset ID associated with this ref that should be used. This
233 could either be the ID of the supplied `DatasetRef` or the parent.
234 storageInfo : `StoredMemoryItemInfo`
235 Associated storage information.
237 Raises
238 ------
239 FileNotFoundError
240 Raised if the dataset is not present in this datastore.
241 """
242 try:
243 storedItemInfo = self._get_stored_item_info(dataset_id)
244 except KeyError:
245 raise FileNotFoundError(f"No such file dataset in memory: {dataset_id}") from None
246 realID = dataset_id
247 if storedItemInfo.parentID is not None:
248 realID = storedItemInfo.parentID
250 if realID not in self.datasets:
251 raise FileNotFoundError(f"No such file dataset in memory: {dataset_id}")
253 return realID, storedItemInfo
255 def knows(self, ref: DatasetRef) -> bool:
256 """Check if the dataset is known to the datastore.
258 This datastore does not distinguish dataset existence from knowledge
259 of a dataset.
261 Parameters
262 ----------
263 ref : `DatasetRef`
264 Reference to the required dataset.
266 Returns
267 -------
268 exists : `bool`
269 `True` if the dataset is known to the datastore.
270 """
271 return self.exists(ref)
273 def exists(self, ref: DatasetRef) -> bool:
274 """Check if the dataset exists in the datastore.
276 Parameters
277 ----------
278 ref : `DatasetRef`
279 Reference to the required dataset.
281 Returns
282 -------
283 exists : `bool`
284 `True` if the entity exists in the `Datastore`.
285 """
286 try:
287 self._get_dataset_info(ref.id)
288 except FileNotFoundError:
289 return False
290 return True
292 def get(
293 self,
294 ref: DatasetRef,
295 parameters: Mapping[str, Any] | None = None,
296 storageClass: StorageClass | str | None = None,
297 ) -> Any:
298 """Load an InMemoryDataset from the store.
300 Parameters
301 ----------
302 ref : `DatasetRef`
303 Reference to the required Dataset.
304 parameters : `dict`
305 `StorageClass`-specific parameters that specify, for example,
306 a slice of the dataset to be loaded.
307 storageClass : `StorageClass` or `str`, optional
308 The storage class to be used to override the Python type
309 returned by this method. By default the returned type matches
310 the dataset type definition for this dataset. Specifying a
311 read `StorageClass` can force a different type to be returned.
312 This type must be compatible with the original type.
314 Returns
315 -------
316 inMemoryDataset : `object`
317 Requested dataset or slice thereof as an InMemoryDataset.
319 Raises
320 ------
321 FileNotFoundError
322 Requested dataset can not be retrieved.
323 TypeError
324 Return value from formatter has unexpected type.
325 ValueError
326 Formatter failed to process the dataset.
327 """
328 log.debug("Retrieve %s from %s with parameters %s", ref, self.name, parameters)
330 realID, storedItemInfo = self._get_dataset_info(ref.id)
332 # We have a write storage class and a read storage class and they
333 # can be different for concrete composites or if overridden.
334 if storageClass is not None:
335 ref = ref.overrideStorageClass(storageClass)
336 refStorageClass = ref.datasetType.storageClass
337 writeStorageClass = storedItemInfo.storageClass
339 component = ref.datasetType.component()
341 # Check that the supplied parameters are suitable for the type read
342 # If this is a derived component we validate against the composite
343 isDerivedComponent = False
344 if component in writeStorageClass.derivedComponents:
345 writeStorageClass.validateParameters(parameters)
346 isDerivedComponent = True
347 else:
348 refStorageClass.validateParameters(parameters)
350 inMemoryDataset = self.datasets[realID]
352 # if this is a read only component we need to apply parameters
353 # before we retrieve the component. We assume that the parameters
354 # will affect the data globally, before the derived component
355 # is selected.
356 if isDerivedComponent:
357 inMemoryDataset = writeStorageClass.delegate().handleParameters(inMemoryDataset, parameters)
358 # Then disable parameters for later
359 parameters = {}
361 # Check if we have a component.
362 if component:
363 # In-memory datastore must have stored the dataset as a single
364 # object in the write storage class. We therefore use that
365 # storage class delegate to obtain the component.
366 inMemoryDataset = writeStorageClass.delegate().getComponent(inMemoryDataset, component)
368 # Since there is no formatter to process parameters, they all must be
369 # passed to the assembler.
370 inMemoryDataset = post_process_get(
371 inMemoryDataset, refStorageClass, parameters, isComponent=component is not None
372 )
374 # Last minute type conversion.
375 return refStorageClass.coerce_type(inMemoryDataset)
377 def put(self, inMemoryDataset: Any, ref: DatasetRef, provenance: DatasetProvenance | None = None) -> None:
378 """Write a InMemoryDataset with a given `DatasetRef` to the store.
380 Parameters
381 ----------
382 inMemoryDataset : `object`
383 The dataset to store.
384 ref : `DatasetRef`
385 Reference to the associated Dataset.
386 provenance : `DatasetProvenance` or `None`, optional
387 Any provenance that should be attached to the serialized dataset.
389 Raises
390 ------
391 TypeError
392 Supplied object and storage class are inconsistent.
393 DatasetTypeNotSupportedError
394 The associated `DatasetType` is not handled by this datastore.
396 Notes
397 -----
398 If the datastore is configured to reject certain dataset types it
399 is possible that the put will fail and raise a
400 `DatasetTypeNotSupportedError`. The main use case for this is to
401 allow `ChainedDatastore` to put to multiple datastores without
402 requiring that every datastore accepts the dataset.
403 """
404 if not self.constraints.isAcceptable(ref):
405 # Raise rather than use boolean return value.
406 raise DatasetTypeNotSupportedError(
407 f"Dataset {ref} has been rejected by this datastore via configuration."
408 )
410 # May need to coerce the in memory dataset to the correct
411 # python type, otherwise parameters may not work.
412 try:
413 delegate = ref.datasetType.storageClass.delegate()
414 except TypeError:
415 # TypeError is raised when a storage class doesn't have a delegate.
416 delegate = None
417 if not delegate or not delegate.can_accept(inMemoryDataset):
418 inMemoryDataset = ref.datasetType.storageClass.coerce_type(inMemoryDataset)
420 # Update provenance.
421 if delegate:
422 inMemoryDataset = delegate.add_provenance(inMemoryDataset, ref, provenance=provenance)
424 self.datasets[ref.id] = inMemoryDataset
425 log.debug("Store %s in %s", ref, self.name)
427 # Store time we received this content, to allow us to optionally
428 # expire it. Instead of storing a filename here, we include the
429 # ID of this datasetRef so we can find it from components.
430 itemInfo = StoredMemoryItemInfo(time.time(), ref.datasetType.storageClass, parentID=ref.id)
432 # We have to register this content with registry.
433 # Currently this assumes we have a file so we need to use stub entries
434 self.records[ref.id] = itemInfo
435 self.related.setdefault(itemInfo.parentID, set()).add(ref.id)
437 if self._transaction is not None:
438 self._transaction.registerUndo("put", self.remove, ref)
440 def put_new(self, in_memory_dataset: Any, ref: DatasetRef) -> Mapping[str, DatasetRef]:
441 # It is OK to call put() here because registry is not populating
442 # bridges as we return empty dict from this method.
443 self.put(in_memory_dataset, ref)
444 # As ephemeral we return empty dict.
445 return {}
447 def getURIs(self, ref: DatasetRef, predict: bool = False) -> DatasetRefURIs:
448 """Return URIs associated with dataset.
450 Parameters
451 ----------
452 ref : `DatasetRef`
453 Reference to the required dataset.
454 predict : `bool`, optional
455 If the datastore does not know about the dataset, controls whether
456 it should return a predicted URI or not.
458 Returns
459 -------
460 uris : `DatasetRefURIs`
461 The URI to the primary artifact associated with this dataset (if
462 the dataset was disassembled within the datastore this may be
463 `None`), and the URIs to any components associated with the dataset
464 artifact. (can be empty if there are no components).
466 Notes
467 -----
468 The URIs returned for in-memory datastores are not usable but
469 provide an indication of the associated dataset.
470 """
471 # Include the dataID as a URI query
472 query = urlencode(ref.dataId.required)
474 # if this has never been written then we have to guess
475 if not self.exists(ref):
476 if not predict:
477 raise FileNotFoundError(f"Dataset {ref} not in this datastore")
478 name = f"{ref.datasetType.name}"
479 fragment = "#predicted"
480 else:
481 realID, _ = self._get_dataset_info(ref.id)
482 name = f"{id(self.datasets[realID])}_{ref.id}"
483 fragment = ""
485 return DatasetRefURIs(ResourcePath(f"mem://{name}?{query}{fragment}"), {})
487 def getURI(self, ref: DatasetRef, predict: bool = False) -> ResourcePath:
488 """URI to the Dataset.
490 Always uses "mem://" URI prefix.
492 Parameters
493 ----------
494 ref : `DatasetRef`
495 Reference to the required Dataset.
496 predict : `bool`
497 If `True`, allow URIs to be returned of datasets that have not
498 been written.
500 Returns
501 -------
502 uri : `str`
503 URI pointing to the dataset within the datastore. If the
504 dataset does not exist in the datastore, and if ``predict`` is
505 `True`, the URI will be a prediction and will include a URI
506 fragment "#predicted".
507 If the datastore does not have entities that relate well
508 to the concept of a URI the returned URI string will be
509 descriptive. The returned URI is not guaranteed to be obtainable.
511 Raises
512 ------
513 FileNotFoundError
514 A URI has been requested for a dataset that does not exist and
515 guessing is not allowed.
516 AssertionError
517 Raised if an internal error occurs.
518 """
519 primary, _ = self.getURIs(ref, predict)
520 if primary is None:
521 # This should be impossible since this datastore does
522 # not disassemble. This check also helps mypy.
523 raise AssertionError(f"Unexpectedly got no URI for in-memory datastore for {ref}")
524 return primary
526 def ingest_zip(self, zip_path: ResourcePath, transfer: str | None, *, dry_run: bool = False) -> None:
527 raise NotImplementedError("Can only ingest a Zip into a file datastore.")
529 def retrieveArtifacts(
530 self,
531 refs: Iterable[DatasetRef],
532 destination: ResourcePath,
533 transfer: str = "auto",
534 preserve_path: bool = True,
535 overwrite: bool | None = False,
536 write_index: bool = True,
537 add_prefix: bool = False,
538 ) -> dict[ResourcePath, ArtifactIndexInfo]:
539 """Retrieve the file artifacts associated with the supplied refs.
541 Parameters
542 ----------
543 refs : `~collections.abc.Iterable` of `DatasetRef`
544 The datasets for which artifacts are to be retrieved.
545 A single ref can result in multiple artifacts. The refs must
546 be resolved.
547 destination : `lsst.resources.ResourcePath`
548 Location to write the artifacts.
549 transfer : `str`, optional
550 Method to use to transfer the artifacts. Must be one of the options
551 supported by `lsst.resources.ResourcePath.transfer_from`.
552 "move" is not allowed.
553 preserve_path : `bool`, optional
554 If `True` the full path of the artifact within the datastore
555 is preserved. If `False` the final file component of the path
556 is used.
557 overwrite : `bool`, optional
558 If `True` allow transfers to overwrite existing files at the
559 destination.
560 write_index : `bool`, optional
561 If `True` write a file at the top level containing a serialization
562 of a `ZipIndex` for the downloaded datasets.
563 add_prefix : `bool`, optional
564 If `True` and if ``preserve_path`` is `False`, apply a prefix to
565 the filenames corresponding to some part of the dataset ref ID.
566 This can be used to guarantee uniqueness.
568 Notes
569 -----
570 Not implemented by this datastore.
571 """
572 # Could conceivably launch a FileDatastore to use formatters to write
573 # the data but this is fraught with problems.
574 raise NotImplementedError("Can not write artifacts to disk from in-memory datastore.")
576 def forget(self, refs: Iterable[DatasetRef]) -> None:
577 # Docstring inherited.
578 refs = list(refs)
579 for ref in refs:
580 self._remove_stored_item_info(ref.id)
582 @transactional
583 def trash(self, ref: DatasetRef | Iterable[DatasetRef], ignore_errors: bool = False) -> None:
584 """Indicate to the Datastore that a dataset can be removed.
586 Parameters
587 ----------
588 ref : `DatasetRef` or iterable thereof
589 Reference to the required Dataset(s).
590 ignore_errors : `bool`, optional
591 Indicate that errors should be ignored.
593 Returns
594 -------
595 None
597 Raises
598 ------
599 FileNotFoundError
600 Attempt to remove a dataset that does not exist. Only relevant
601 if a single dataset ref is given.
603 Notes
604 -----
605 Concurrency should not normally be an issue for the in memory datastore
606 since all internal changes are isolated to solely this process and
607 the registry only changes rows associated with this process.
608 """
609 if isinstance(ref, DatasetRef):
610 # Check that this dataset is known to datastore
611 try:
612 self._get_dataset_info(ref.id)
613 except Exception as e:
614 if ignore_errors:
615 log.warning(
616 "Error encountered moving dataset %s to trash in datastore %s: %s", ref, self.name, e
617 )
618 else:
619 raise
620 log.debug("Trash %s in datastore %s", ref, self.name)
621 ref_list = [ref]
622 else:
623 ref_list = list(ref)
624 log.debug("Bulk trashing of datasets in datastore %s", self.name)
626 def _rollbackMoveToTrash(refs: Iterable[DatasetIdRef]) -> None:
627 for ref in refs:
628 self._trashedIds.remove(ref.id)
630 assert self._transaction is not None, "Must be in transaction"
631 with self._transaction.undoWith(f"Trash {len(ref_list)} datasets", _rollbackMoveToTrash, ref_list):
632 self._trashedIds.update(ref.id for ref in ref_list)
634 def emptyTrash(
635 self, ignore_errors: bool = False, refs: Collection[DatasetRef] | None = None, dry_run: bool = False
636 ) -> set[ResourcePath]:
637 """Remove all datasets from the trash.
639 Parameters
640 ----------
641 ignore_errors : `bool`, optional
642 Ignore errors.
643 refs : `collections.abc.Collection` [ `DatasetRef` ] or `None`
644 Explicit list of datasets that can be removed from trash. If listed
645 datasets are not already stored in the trash table they will be
646 ignored. If `None` every entry in the trash table will be
647 processed.
648 dry_run : `bool`, optional
649 If `True`, the trash table will be queried and results reported
650 but no artifacts will be removed.
652 Returns
653 -------
654 removed : `set` [ `lsst.resources.ResourcePath` ]
655 List of artifacts that were removed. Empty for this datastore.
657 Notes
658 -----
659 The internal tracking of datasets is affected by this method and
660 transaction handling is not supported if there is a problem before
661 the datasets themselves are deleted.
663 Concurrency should not normally be an issue for the in memory datastore
664 since all internal changes are isolated to solely this process and
665 the registry only changes rows associated with this process.
666 """
667 log.debug("Emptying trash in datastore %s", self.name)
669 trashed_ids = self._trashedIds
670 if refs:
671 selected_ids = {ref.id for ref in refs}
672 if selected_ids is not None:
673 trashed_ids = {tid for tid in trashed_ids if tid in selected_ids}
675 if dry_run:
676 log.info(
677 "Would attempt remove %s dataset%s.", len(trashed_ids), "s" if len(trashed_ids) != 1 else ""
678 )
679 return set()
681 for dataset_id in trashed_ids:
682 try:
683 realID, _ = self._get_dataset_info(dataset_id)
684 except FileNotFoundError:
685 # Dataset already removed so ignore it
686 continue
687 except Exception as e:
688 if ignore_errors:
689 log.warning(
690 "Emptying trash in datastore %s but encountered an error with dataset %s: %s",
691 self.name,
692 dataset_id,
693 e,
694 )
695 continue
696 else:
697 raise
699 # Determine whether all references to this dataset have been
700 # removed and we can delete the dataset itself
701 allRefs = self.related[realID]
702 remainingRefs = allRefs - {dataset_id}
703 if not remainingRefs:
704 log.debug("Removing artifact %s from datastore %s", realID, self.name)
705 del self.datasets[realID]
707 # Remove this entry
708 self._remove_stored_item_info(dataset_id)
710 # Empty the trash table
711 self._trashedIds = self._trashedIds - trashed_ids
712 return set()
714 def validateConfiguration(
715 self, entities: Iterable[DatasetRef | DatasetType | StorageClass], logFailures: bool = False
716 ) -> None:
717 """Validate some of the configuration for this datastore.
719 Parameters
720 ----------
721 entities : `~collections.abc.Iterable` [`DatasetRef` | `DatasetType`\
722 | `StorageClass`]
723 Entities to test against this configuration. Can be differing
724 types.
725 logFailures : `bool`, optional
726 If `True`, output a log message for every validation error
727 detected.
729 Returns
730 -------
731 None
733 Raises
734 ------
735 DatastoreValidationError
736 Raised if there is a validation problem with a configuration.
737 All the problems are reported in a single exception.
739 Notes
740 -----
741 This method is a no-op.
742 """
743 return
745 def _overrideTransferMode(self, *datasets: Any, transfer: str | None = None) -> str | None:
746 # Docstring is inherited from base class
747 return transfer
749 def validateKey(self, lookupKey: LookupKey, entity: DatasetRef | DatasetType | StorageClass) -> None:
750 # Docstring is inherited from base class
751 return
753 def getLookupKeys(self) -> set[LookupKey]:
754 # Docstring is inherited from base class
755 return self.constraints.getLookupKeys()
757 def needs_expanded_data_ids(
758 self,
759 transfer: str | None,
760 entity: DatasetRef | DatasetType | StorageClass | None = None,
761 ) -> bool:
762 # Docstring inherited.
763 return False
765 def import_records(self, data: Mapping[str, DatastoreRecordData]) -> None:
766 # Docstring inherited from the base class.
767 return
769 def export_records(self, refs: Iterable[DatasetIdRef]) -> Mapping[str, DatastoreRecordData]:
770 # Docstring inherited from the base class.
772 # In-memory Datastore records cannot be exported or imported
773 return {}
775 def export_predicted_records(self, refs: Iterable[DatasetIdRef]) -> dict[str, DatastoreRecordData]:
776 # Docstring inherited from the base class.
778 # In-memory Datastore records cannot be exported or imported
779 return {}
781 def get_opaque_table_definitions(self) -> Mapping[str, DatastoreOpaqueTable]:
782 # Docstring inherited from the base class.
783 return {}