Coverage for python / lsst / daf / butler / registry / interfaces / _datasets.py: 77%
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2#
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5# (http://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
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8#
9# This software is dual licensed under the GNU General Public License and also
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12# respectively. If you choose the GPL option then the following text applies
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14#
15# This program is free software: you can redistribute it and/or modify
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20# This program is distributed in the hope that it will be useful,
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22# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
23# GNU General Public License for more details.
24#
25# You should have received a copy of the GNU General Public License
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28from __future__ import annotations
30from ... import ddl
32__all__ = ("DatasetRecordStorageManager",)
34from abc import abstractmethod
35from collections.abc import Iterable, Mapping, Sequence, Set
36from typing import TYPE_CHECKING, Any
38from ..._dataset_ref import DatasetId, DatasetIdGenEnum, DatasetRef
39from ..._dataset_type import DatasetType
40from ..._exceptions import DatasetTypeError, DatasetTypeNotSupportedError
41from ..._timespan import Timespan
42from ...dimensions import DataCoordinate
43from ...queries import QueryFactoryFunction
44from ...queries.tree import AnyDatasetFieldName
45from ._versioning import VersionedExtension, VersionTuple
47if TYPE_CHECKING:
48 from ...direct_query_driver import SqlJoinsBuilder # new query system, server+direct only
49 from .._caching_context import CachingContext
50 from .._collection_summary import CollectionSummary
51 from ._collections import CollectionManager, CollectionRecord, RunRecord
52 from ._database import Database, StaticTablesContext
53 from ._dimensions import DimensionRecordStorageManager
56class DatasetRecordStorageManager(VersionedExtension):
57 """An interface that manages the tables that describe datasets.
59 `DatasetRecordStorageManager` primarily serves as a container and factory
60 for `DatasetRecordStorage` instances, which each provide access to the
61 records for a different `DatasetType`.
63 Parameters
64 ----------
65 registry_schema_version : `VersionTuple` or `None`, optional
66 Version of registry schema.
67 """
69 def __init__(self, *, registry_schema_version: VersionTuple | None = None) -> None:
70 super().__init__(registry_schema_version=registry_schema_version)
72 @abstractmethod
73 def clone(
74 self,
75 *,
76 db: Database,
77 collections: CollectionManager,
78 dimensions: DimensionRecordStorageManager,
79 caching_context: CachingContext,
80 ) -> DatasetRecordStorageManager:
81 """Make an independent copy of this manager instance bound to new
82 instances of `Database` and other managers.
84 Parameters
85 ----------
86 db : `Database`
87 New `Database` object to use when instantiating the manager.
88 collections : `CollectionManager`
89 New `CollectionManager` object to use when instantiating the
90 manager.
91 dimensions : `DimensionRecordStorageManager`
92 New `DimensionRecordStorageManager` object to use when
93 instantiating the manager.
94 caching_context : `CachingContext`
95 New `CachingContext` object to use when instantiating the manager.
97 Returns
98 -------
99 instance : `DatasetRecordStorageManager`
100 New manager instance with the same configuration as this instance,
101 but bound to a new Database object.
102 """
103 raise NotImplementedError()
105 @abstractmethod
106 def preload_cache(self) -> None:
107 """Fetch data from the database and use it to pre-populate caches to
108 speed up later operations.
109 """
110 raise NotImplementedError()
112 @classmethod
113 @abstractmethod
114 def initialize(
115 cls,
116 db: Database,
117 context: StaticTablesContext,
118 *,
119 collections: CollectionManager,
120 dimensions: DimensionRecordStorageManager,
121 caching_context: CachingContext,
122 registry_schema_version: VersionTuple | None = None,
123 ) -> DatasetRecordStorageManager:
124 """Construct an instance of the manager.
126 Parameters
127 ----------
128 db : `Database`
129 Interface to the underlying database engine and namespace.
130 context : `StaticTablesContext`
131 Context object obtained from `Database.declareStaticTables`; used
132 to declare any tables that should always be present.
133 collections : `CollectionManager`
134 Manager object for the collections in this `Registry`.
135 dimensions : `DimensionRecordStorageManager`
136 Manager object for the dimensions in this `Registry`.
137 caching_context : `CachingContext`
138 Object controlling caching of information returned by managers.
139 registry_schema_version : `VersionTuple` or `None`
140 Schema version of this extension as defined in registry.
142 Returns
143 -------
144 manager : `DatasetRecordStorageManager`
145 An instance of a concrete `DatasetRecordStorageManager` subclass.
146 """
147 raise NotImplementedError()
149 @classmethod
150 @abstractmethod
151 def addDatasetForeignKey(
152 cls,
153 tableSpec: ddl.TableSpec,
154 *,
155 name: str = "dataset",
156 constraint: bool = True,
157 onDelete: str | None = None,
158 **kwargs: Any,
159 ) -> ddl.FieldSpec:
160 """Add a foreign key (field and constraint) referencing the dataset
161 table.
163 Parameters
164 ----------
165 tableSpec : `ddl.TableSpec`
166 Specification for the table that should reference the dataset
167 table. Will be modified in place.
168 name : `str`, optional
169 A name to use for the prefix of the new field; the full name is
170 ``{name}_id``.
171 constraint : `bool`, optional
172 If `False` (`True` is default), add a field that can be joined to
173 the dataset primary key, but do not add a foreign key constraint.
174 onDelete : `str`, optional
175 One of "CASCADE" or "SET NULL", indicating what should happen to
176 the referencing row if the collection row is deleted. `None`
177 indicates that this should be an integrity error.
178 **kwargs
179 Additional keyword arguments are forwarded to the `ddl.FieldSpec`
180 constructor (only the ``name`` and ``dtype`` arguments are
181 otherwise provided).
183 Returns
184 -------
185 idSpec : `ddl.FieldSpec`
186 Specification for the ID field.
187 """
188 raise NotImplementedError()
190 @abstractmethod
191 def refresh(self) -> None:
192 """Ensure all other operations on this manager are aware of any
193 dataset types that may have been registered by other clients since
194 it was initialized or last refreshed.
195 """
196 raise NotImplementedError()
198 @abstractmethod
199 def get_dataset_type(self, name: str) -> DatasetType:
200 """Look up a dataset type by name.
202 Parameters
203 ----------
204 name : `str`
205 Name of a parent dataset type.
207 Returns
208 -------
209 dataset_type : `DatasetType`
210 The object representing the records for the given dataset type.
212 Raises
213 ------
214 MissingDatasetTypeError
215 Raised if there is no dataset type with the given name.
216 """
217 raise NotImplementedError()
219 def conform_exact_dataset_type(self, dataset_type: DatasetType | str) -> DatasetType:
220 """Conform a value that may be a dataset type or dataset type name to
221 just the dataset type name, while checking that the dataset type is not
222 a component and (if a `DatasetType` instance is given) has the exact
223 same definition in the registry.
225 Parameters
226 ----------
227 dataset_type : `str` or `DatasetType`
228 Dataset type object or name.
230 Returns
231 -------
232 dataset_type : `DatasetType`
233 The corresponding registered dataset type.
235 Raises
236 ------
237 DatasetTypeError
238 Raised if ``dataset_type`` is a component, or if its definition
239 does not exactly match the registered dataset type.
240 MissingDatasetTypeError
241 Raised if this dataset type is not registered at all.
242 """
243 if isinstance(dataset_type, DatasetType):
244 dataset_type_name = dataset_type.name
245 given_dataset_type = dataset_type
246 else:
247 dataset_type_name = dataset_type
248 given_dataset_type = None
249 parent_name, component = DatasetType.splitDatasetTypeName(dataset_type_name)
250 if component is not None:
251 raise DatasetTypeNotSupportedError(
252 f"Component dataset {dataset_type_name!r} is not supported in this context."
253 )
254 registered_dataset_type = self.get_dataset_type(dataset_type_name)
255 if given_dataset_type is not None and registered_dataset_type != given_dataset_type:
256 raise DatasetTypeError(
257 f"Given dataset type {given_dataset_type} is not identical to the "
258 f"registered one {registered_dataset_type}."
259 )
260 return registered_dataset_type
262 @abstractmethod
263 def register_dataset_type(self, dataset_type: DatasetType) -> bool:
264 """Ensure that this `Registry` can hold records for the given
265 `DatasetType`, creating new tables as necessary.
267 Parameters
268 ----------
269 dataset_type : `DatasetType`
270 Dataset type for which a table should created (as necessary) and
271 an associated `DatasetRecordStorage` returned.
273 Returns
274 -------
275 inserted : `bool`
276 `True` if the dataset type did not exist in the registry before.
278 Notes
279 -----
280 This operation may not be invoked within a `Database.transaction`
281 context.
282 """
283 raise NotImplementedError()
285 @abstractmethod
286 def remove_dataset_type(self, name: str) -> None:
287 """Remove the dataset type.
289 Parameters
290 ----------
291 name : `str`
292 Name of the dataset type.
293 """
294 raise NotImplementedError()
296 @abstractmethod
297 def resolve_wildcard(
298 self,
299 expression: Any,
300 missing: list[str] | None = None,
301 explicit_only: bool = False,
302 ) -> list[DatasetType]:
303 """Resolve a dataset type wildcard expression.
305 Parameters
306 ----------
307 expression : `~typing.Any`
308 Expression to resolve. Will be passed to
309 `DatasetTypeWildcard.from_expression`.
310 missing : `list` of `str`, optional
311 String dataset type names that were explicitly given (i.e. not
312 regular expression patterns) but not found will be appended to this
313 list, if it is provided.
314 explicit_only : `bool`, optional
315 If `True`, require explicit `DatasetType` instances or `str` names,
316 with `re.Pattern` instances deprecated and ``...`` prohibited.
318 Returns
319 -------
320 dataset_types : `list` [ `DatasetType` ]
321 A list of resolved dataset types.
322 """
323 raise NotImplementedError()
325 @abstractmethod
326 def get_dataset_refs(self, ids: list[DatasetId]) -> list[DatasetRef]:
327 """
328 Return a `DatasetRef` for each of the given dataset UUID values.
330 Parameters
331 ----------
332 ids : `list` [ `DatasetId` ]
333 List of UUID instances to look up.
335 Returns
336 -------
337 refs : `list` [ `DatasetRef` ]
338 A list containing a `DatasetRef` for each of the given UUIDs that
339 was found in the database. If a dataset was not found, no error is
340 thrown -- it is just not included in the list. The returned
341 datasets are in no particular order.
342 """
343 raise NotImplementedError()
345 @abstractmethod
346 def getCollectionSummary(self, collection: CollectionRecord) -> CollectionSummary:
347 """Return a summary for the given collection.
349 Parameters
350 ----------
351 collection : `CollectionRecord`
352 Record describing the collection for which a summary is to be
353 retrieved.
355 Returns
356 -------
357 summary : `CollectionSummary`
358 Summary of the dataset types and governor dimension values in
359 this collection.
360 """
361 raise NotImplementedError()
363 @abstractmethod
364 def fetch_summaries(
365 self,
366 collections: Iterable[CollectionRecord],
367 dataset_types: Iterable[DatasetType] | Iterable[str] | None = None,
368 ) -> Mapping[Any, CollectionSummary]:
369 """Fetch collection summaries given their names and dataset types.
371 Parameters
372 ----------
373 collections : `~collections.abc.Iterable` [`CollectionRecord`]
374 Collection records to query.
375 dataset_types : `~collections.abc.Iterable` [`DatasetType`] or `None`
376 Dataset types to include into returned summaries. If `None` then
377 all dataset types will be included.
379 Returns
380 -------
381 summaries : `~collections.abc.Mapping` [`Any`, `CollectionSummary`]
382 Collection summaries indexed by collection record key. This mapping
383 will also contain all nested non-chained collections of the chained
384 collections.
385 """
386 raise NotImplementedError()
388 @abstractmethod
389 def fetch_run_dataset_ids(self, run: RunRecord) -> list[DatasetId]:
390 """Return the IDs of all datasets in the given ``RUN``
391 collection.
393 Parameters
394 ----------
395 run : `RunRecord`
396 Record describing the collection.
398 Returns
399 -------
400 dataset_ids : `list` [`uuid.UUID`]
401 List of dataset IDs.
402 """
403 raise NotImplementedError()
405 @abstractmethod
406 def ingest_date_dtype(self) -> type:
407 """Return type of the ``ingest_date`` column."""
408 raise NotImplementedError()
410 @abstractmethod
411 def insert(
412 self,
413 dataset_type_name: str,
414 run: RunRecord,
415 data_ids: Iterable[DataCoordinate],
416 id_generation_mode: DatasetIdGenEnum = DatasetIdGenEnum.UNIQUE,
417 ) -> list[DatasetRef]:
418 """Insert one or more dataset entries into the database.
420 Parameters
421 ----------
422 dataset_type_name : `str`
423 Name of the dataset type.
424 run : `RunRecord`
425 The record object describing the `~CollectionType.RUN` collection
426 these datasets will be associated with.
427 data_ids : `~collections.abc.Iterable` [ `DataCoordinate` ]
428 Expanded data IDs (`DataCoordinate` instances) for the
429 datasets to be added. The dimensions of all data IDs must be the
430 same as ``dataset_type.dimensions``.
431 id_generation_mode : `DatasetIdGenEnum`
432 With `UNIQUE` each new dataset is inserted with its new unique ID.
433 With non-`UNIQUE` mode ID is computed from some combination of
434 dataset type, dataId, and run collection name; if the same ID is
435 already in the database then new record is not inserted.
437 Returns
438 -------
439 datasets : `list` [ `DatasetRef` ]
440 References to the inserted datasets.
441 """
442 raise NotImplementedError()
444 @abstractmethod
445 def import_(self, run: RunRecord, refs: list[DatasetRef], assume_new: bool = False) -> None:
446 """Insert one or more dataset entries into the database.
448 Parameters
449 ----------
450 run : `RunRecord`
451 The record object describing the `~CollectionType.RUN` collection
452 these datasets will be associated with.
453 refs : `list` [ `DatasetRef` ]
454 List of datasets to be be inserted. All of the ``DatasetRef``
455 ``run`` attributes must match the ``run`` parameter.
456 assume_new : `bool`, optional
457 If `True`, assume all datasets are new and skip conflict resolution
458 logic.
459 """
460 raise NotImplementedError()
462 @abstractmethod
463 def delete(self, datasets: Iterable[DatasetId | DatasetRef]) -> None:
464 """Fully delete the given datasets from the registry.
466 Parameters
467 ----------
468 datasets : `~collections.abc.Iterable` [ `DatasetId` or `DatasetRef` ]
469 Datasets to be deleted. If `DatasetRef` instances are passed,
470 only the `DatasetRef.id` attribute is used.
471 """
472 raise NotImplementedError()
474 @abstractmethod
475 def associate(
476 self, dataset_type: DatasetType, collection: CollectionRecord, datasets: Iterable[DatasetRef]
477 ) -> None:
478 """Associate one or more datasets with a collection.
480 Parameters
481 ----------
482 dataset_type : `DatasetType`
483 Type of all datasets.
484 collection : `CollectionRecord`
485 The record object describing the collection. ``collection.type``
486 must be `~CollectionType.TAGGED`.
487 datasets : `~collections.abc.Iterable` [ `DatasetRef` ]
488 Datasets to be associated. All datasets must have the same
489 `DatasetType` as ``dataset_type``, but this is not checked.
491 Notes
492 -----
493 Associating a dataset into collection that already contains a
494 different dataset with the same `DatasetType` and data ID will remove
495 the existing dataset from that collection.
497 Associating the same dataset into a collection multiple times is a
498 no-op, but is still not permitted on read-only databases.
499 """
500 raise NotImplementedError()
502 @abstractmethod
503 def disassociate(
504 self, dataset_type: DatasetType, collection: CollectionRecord, datasets: Iterable[DatasetRef]
505 ) -> None:
506 """Remove one or more datasets from a collection.
508 Parameters
509 ----------
510 dataset_type : `DatasetType`
511 Type of all datasets.
512 collection : `CollectionRecord`
513 The record object describing the collection. ``collection.type``
514 must be `~CollectionType.TAGGED`.
515 datasets : `~collections.abc.Iterable` [ `DatasetRef` ]
516 Datasets to be disassociated. All datasets must have the same
517 `DatasetType` as ``dataset_type``, but this is not checked.
518 """
519 raise NotImplementedError()
521 @abstractmethod
522 def certify(
523 self,
524 dataset_type: DatasetType,
525 collection: CollectionRecord,
526 datasets: Iterable[DatasetRef],
527 timespan: Timespan,
528 query_func: QueryFactoryFunction,
529 ) -> None:
530 """Associate one or more datasets with a calibration collection and a
531 validity range within it.
533 Parameters
534 ----------
535 dataset_type : `DatasetType`
536 Type of all datasets.
537 collection : `CollectionRecord`
538 The record object describing the collection. ``collection.type``
539 must be `~CollectionType.CALIBRATION`.
540 datasets : `~collections.abc.Iterable` [ `DatasetRef` ]
541 Datasets to be associated. All datasets must have the same
542 `DatasetType` as ``dataset_type``, but this is not checked.
543 timespan : `Timespan`
544 The validity range for these datasets within the collection.
545 query_func : `QueryFactoryFunction`
546 Function returning a context manager that sets up a `Query` object
547 for querying the registry. (That is, a function equivalent to
548 ``Butler.query()``).
550 Raises
551 ------
552 ConflictingDefinitionError
553 Raised if the collection already contains a different dataset with
554 the same `DatasetType` and data ID and an overlapping validity
555 range.
556 DatasetTypeError
557 Raised if ``dataset_type.isCalibration() is False``.
558 CollectionTypeError
559 Raised if
560 ``collection.type is not CollectionType.CALIBRATION``.
561 """
562 raise NotImplementedError()
564 @abstractmethod
565 def decertify(
566 self,
567 dataset_type: DatasetType,
568 collection: CollectionRecord,
569 timespan: Timespan,
570 *,
571 data_ids: Iterable[DataCoordinate] | None = None,
572 query_func: QueryFactoryFunction,
573 ) -> None:
574 """Remove or adjust datasets to clear a validity range within a
575 calibration collection.
577 Parameters
578 ----------
579 dataset_type : `DatasetType`
580 Type of all datasets.
581 collection : `CollectionRecord`
582 The record object describing the collection. ``collection.type``
583 must be `~CollectionType.CALIBRATION`.
584 timespan : `Timespan`
585 The validity range to remove datasets from within the collection.
586 Datasets that overlap this range but are not contained by it will
587 have their validity ranges adjusted to not overlap it, which may
588 split a single dataset validity range into two.
589 data_ids : `~collections.abc.Iterable` [ `DataCoordinate` ], optional
590 Data IDs that should be decertified within the given validity range
591 If `None`, all data IDs for ``dataset_type`` in ``collection`` will
592 be decertified.
593 query_func : `QueryFactoryFunction`
594 Function returning a context manager that sets up a `Query` object
595 for querying the registry. (That is, a function equivalent to
596 ``Butler.query()``).
598 Raises
599 ------
600 DatasetTypeError
601 Raised if ``dataset_type.isCalibration() is False``.
602 CollectionTypeError
603 Raised if
604 ``collection.type is not CollectionType.CALIBRATION``.
605 """
606 raise NotImplementedError()
608 @abstractmethod
609 def make_joins_builder(
610 self,
611 dataset_type: DatasetType,
612 collections: Sequence[CollectionRecord],
613 fields: Set[AnyDatasetFieldName],
614 is_union: bool = False,
615 ) -> SqlJoinsBuilder:
616 """Make a `..direct_query_driver.SqlJoinsBuilder` that represents a
617 search for datasets of this type.
619 Parameters
620 ----------
621 dataset_type : `DatasetType`
622 Type of dataset to query for.
623 collections : `~collections.abc.Sequence` [ `CollectionRecord` ]
624 Collections to search, in order, after filtering out collections
625 with no datasets of this type via collection summaries.
626 fields : `~collections.abc.Set` [ `str` ]
627 Names of fields to make available in the builder. Options include:
629 - ``dataset_id`` (UUID)
630 - ``run`` (collection name, `str`)
631 - ``collection`` (collection name, `str`)
632 - ``collection_key`` (collection primary key, manager-dependent)
633 - ``timespan`` (validity range, or unbounded for non-calibrations)
634 - ``ingest_date`` (time dataset was ingested into repository)
636 Dimension keys for the dataset type's required dimensions are
637 always included.
638 is_union : `bool`, optional
639 If `True`, this search is being joined in as part of one term in
640 a union over all dataset types. This causes fields to be added to
641 the builder via the special ``...`` instad of the dataset type
642 name.
644 Returns
645 -------
646 builder : `..direct_query_driver.SqlJoinsBuilder`
647 A query-construction object representing a table or subquery.
648 """
649 raise NotImplementedError()
651 @abstractmethod
652 def refresh_collection_summaries(self, dataset_type: DatasetType) -> None:
653 """Make sure that collection summaries for this dataset type are
654 consistent with the contents of the dataset tables.
656 Parameters
657 ----------
658 dataset_type : `DatasetType`
659 Dataset type whose summary entries should be refreshed.
660 """
661 raise NotImplementedError()