Coverage for python / lsst / daf / butler / registry / interfaces / _datasets.py: 77%
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2#
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6# See the COPYRIGHT file at the top-level directory of this distribution
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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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23# GNU General Public License for more details.
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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 config: Mapping,
123 registry_schema_version: VersionTuple | None = None,
124 ) -> DatasetRecordStorageManager:
125 """Construct an instance of the manager.
127 Parameters
128 ----------
129 db : `Database`
130 Interface to the underlying database engine and namespace.
131 context : `StaticTablesContext`
132 Context object obtained from `Database.declareStaticTables`; used
133 to declare any tables that should always be present.
134 collections : `CollectionManager`
135 Manager object for the collections in this `Registry`.
136 dimensions : `DimensionRecordStorageManager`
137 Manager object for the dimensions in this `Registry`.
138 caching_context : `CachingContext`
139 Object controlling caching of information returned by managers.
140 config
141 Additional configuration for this manager.
142 registry_schema_version : `VersionTuple` or `None`
143 Schema version of this extension as defined in registry.
145 Returns
146 -------
147 manager : `DatasetRecordStorageManager`
148 An instance of a concrete `DatasetRecordStorageManager` subclass.
149 """
150 raise NotImplementedError()
152 @classmethod
153 @abstractmethod
154 def addDatasetForeignKey(
155 cls,
156 tableSpec: ddl.TableSpec,
157 *,
158 name: str = "dataset",
159 constraint: bool = True,
160 onDelete: str | None = None,
161 **kwargs: Any,
162 ) -> ddl.FieldSpec:
163 """Add a foreign key (field and constraint) referencing the dataset
164 table.
166 Parameters
167 ----------
168 tableSpec : `ddl.TableSpec`
169 Specification for the table that should reference the dataset
170 table. Will be modified in place.
171 name : `str`, optional
172 A name to use for the prefix of the new field; the full name is
173 ``{name}_id``.
174 constraint : `bool`, optional
175 If `False` (`True` is default), add a field that can be joined to
176 the dataset primary key, but do not add a foreign key constraint.
177 onDelete : `str`, optional
178 One of "CASCADE" or "SET NULL", indicating what should happen to
179 the referencing row if the collection row is deleted. `None`
180 indicates that this should be an integrity error.
181 **kwargs
182 Additional keyword arguments are forwarded to the `ddl.FieldSpec`
183 constructor (only the ``name`` and ``dtype`` arguments are
184 otherwise provided).
186 Returns
187 -------
188 idSpec : `ddl.FieldSpec`
189 Specification for the ID field.
190 """
191 raise NotImplementedError()
193 @abstractmethod
194 def refresh(self) -> None:
195 """Ensure all other operations on this manager are aware of any
196 dataset types that may have been registered by other clients since
197 it was initialized or last refreshed.
198 """
199 raise NotImplementedError()
201 @abstractmethod
202 def get_dataset_type(self, name: str) -> DatasetType:
203 """Look up a dataset type by name.
205 Parameters
206 ----------
207 name : `str`
208 Name of a parent dataset type.
210 Returns
211 -------
212 dataset_type : `DatasetType`
213 The object representing the records for the given dataset type.
215 Raises
216 ------
217 MissingDatasetTypeError
218 Raised if there is no dataset type with the given name.
219 """
220 raise NotImplementedError()
222 def conform_exact_dataset_type(self, dataset_type: DatasetType | str) -> DatasetType:
223 """Conform a value that may be a dataset type or dataset type name to
224 just the dataset type name, while checking that the dataset type is not
225 a component and (if a `DatasetType` instance is given) has the exact
226 same definition in the registry.
228 Parameters
229 ----------
230 dataset_type : `str` or `DatasetType`
231 Dataset type object or name.
233 Returns
234 -------
235 dataset_type : `DatasetType`
236 The corresponding registered dataset type.
238 Raises
239 ------
240 DatasetTypeError
241 Raised if ``dataset_type`` is a component, or if its definition
242 does not exactly match the registered dataset type.
243 MissingDatasetTypeError
244 Raised if this dataset type is not registered at all.
245 """
246 if isinstance(dataset_type, DatasetType):
247 dataset_type_name = dataset_type.name
248 given_dataset_type = dataset_type
249 else:
250 dataset_type_name = dataset_type
251 given_dataset_type = None
252 parent_name, component = DatasetType.splitDatasetTypeName(dataset_type_name)
253 if component is not None:
254 raise DatasetTypeNotSupportedError(
255 f"Component dataset {dataset_type_name!r} is not supported in this context."
256 )
257 registered_dataset_type = self.get_dataset_type(dataset_type_name)
258 if given_dataset_type is not None and registered_dataset_type != given_dataset_type:
259 raise DatasetTypeError(
260 f"Given dataset type {given_dataset_type} is not identical to the "
261 f"registered one {registered_dataset_type}."
262 )
263 return registered_dataset_type
265 @abstractmethod
266 def register_dataset_type(self, dataset_type: DatasetType) -> bool:
267 """Ensure that this `Registry` can hold records for the given
268 `DatasetType`, creating new tables as necessary.
270 Parameters
271 ----------
272 dataset_type : `DatasetType`
273 Dataset type for which a table should created (as necessary) and
274 an associated `DatasetRecordStorage` returned.
276 Returns
277 -------
278 inserted : `bool`
279 `True` if the dataset type did not exist in the registry before.
281 Notes
282 -----
283 This operation may not be invoked within a `Database.transaction`
284 context.
285 """
286 raise NotImplementedError()
288 @abstractmethod
289 def remove_dataset_type(self, name: str) -> None:
290 """Remove the dataset type.
292 Parameters
293 ----------
294 name : `str`
295 Name of the dataset type.
296 """
297 raise NotImplementedError()
299 @abstractmethod
300 def resolve_wildcard(
301 self,
302 expression: Any,
303 missing: list[str] | None = None,
304 explicit_only: bool = False,
305 ) -> list[DatasetType]:
306 """Resolve a dataset type wildcard expression.
308 Parameters
309 ----------
310 expression : `~typing.Any`
311 Expression to resolve. Will be passed to
312 `DatasetTypeWildcard.from_expression`.
313 missing : `list` of `str`, optional
314 String dataset type names that were explicitly given (i.e. not
315 regular expression patterns) but not found will be appended to this
316 list, if it is provided.
317 explicit_only : `bool`, optional
318 If `True`, require explicit `DatasetType` instances or `str` names,
319 with `re.Pattern` instances deprecated and ``...`` prohibited.
321 Returns
322 -------
323 dataset_types : `list` [ `DatasetType` ]
324 A list of resolved dataset types.
325 """
326 raise NotImplementedError()
328 @abstractmethod
329 def get_dataset_refs(self, ids: list[DatasetId]) -> list[DatasetRef]:
330 """
331 Return a `DatasetRef` for each of the given dataset UUID values.
333 Parameters
334 ----------
335 ids : `list` [ `DatasetId` ]
336 List of UUID instances to look up.
338 Returns
339 -------
340 refs : `list` [ `DatasetRef` ]
341 A list containing a `DatasetRef` for each of the given UUIDs that
342 was found in the database. If a dataset was not found, no error is
343 thrown -- it is just not included in the list. The returned
344 datasets are in no particular order.
345 """
346 raise NotImplementedError()
348 @abstractmethod
349 def getCollectionSummary(self, collection: CollectionRecord) -> CollectionSummary:
350 """Return a summary for the given collection.
352 Parameters
353 ----------
354 collection : `CollectionRecord`
355 Record describing the collection for which a summary is to be
356 retrieved.
358 Returns
359 -------
360 summary : `CollectionSummary`
361 Summary of the dataset types and governor dimension values in
362 this collection.
363 """
364 raise NotImplementedError()
366 @abstractmethod
367 def fetch_summaries(
368 self,
369 collections: Iterable[CollectionRecord],
370 dataset_types: Iterable[DatasetType] | Iterable[str] | None = None,
371 ) -> Mapping[Any, CollectionSummary]:
372 """Fetch collection summaries given their names and dataset types.
374 Parameters
375 ----------
376 collections : `~collections.abc.Iterable` [`CollectionRecord`]
377 Collection records to query.
378 dataset_types : `~collections.abc.Iterable` [`DatasetType`] or `None`
379 Dataset types to include into returned summaries. If `None` then
380 all dataset types will be included.
382 Returns
383 -------
384 summaries : `~collections.abc.Mapping` [`typing.Any`, \
385 `CollectionSummary`]
386 Collection summaries indexed by collection record key. This mapping
387 will also contain all nested non-chained collections of the chained
388 collections.
389 """
390 raise NotImplementedError()
392 @abstractmethod
393 def fetch_run_dataset_ids(self, run: RunRecord) -> list[DatasetId]:
394 """Return the IDs of all datasets in the given ``RUN``
395 collection.
397 Parameters
398 ----------
399 run : `RunRecord`
400 Record describing the collection.
402 Returns
403 -------
404 dataset_ids : `list` [`uuid.UUID`]
405 List of dataset IDs.
406 """
407 raise NotImplementedError()
409 @abstractmethod
410 def ingest_date_dtype(self) -> type:
411 """Return type of the ``ingest_date`` column."""
412 raise NotImplementedError()
414 @abstractmethod
415 def insert(
416 self,
417 dataset_type_name: str,
418 run: RunRecord,
419 data_ids: Iterable[DataCoordinate],
420 id_generation_mode: DatasetIdGenEnum = DatasetIdGenEnum.UNIQUE,
421 ) -> list[DatasetRef]:
422 """Insert one or more dataset entries into the database.
424 Parameters
425 ----------
426 dataset_type_name : `str`
427 Name of the dataset type.
428 run : `RunRecord`
429 The record object describing the `~CollectionType.RUN` collection
430 these datasets will be associated with.
431 data_ids : `~collections.abc.Iterable` [ `DataCoordinate` ]
432 Expanded data IDs (`DataCoordinate` instances) for the
433 datasets to be added. The dimensions of all data IDs must be the
434 same as ``dataset_type.dimensions``.
435 id_generation_mode : `DatasetIdGenEnum`
436 With `~DatasetIdGenEnum.UNIQUE` each new dataset is inserted with
437 its new unique ID. With non-`~DatasetIdGenEnum.UNIQUE` mode the ID
438 is computed from some combination of dataset type, dataId, and run
439 collection name; if the same ID is already in the database then new
440 record is not inserted.
442 Returns
443 -------
444 datasets : `list` [ `DatasetRef` ]
445 References to the inserted datasets.
446 """
447 raise NotImplementedError()
449 @abstractmethod
450 def import_(self, run: RunRecord, refs: list[DatasetRef], assume_new: bool = False) -> None:
451 """Insert one or more dataset entries into the database.
453 Parameters
454 ----------
455 run : `RunRecord`
456 The record object describing the `~CollectionType.RUN` collection
457 these datasets will be associated with.
458 refs : `list` [ `DatasetRef` ]
459 List of datasets to be be inserted. All of the ``DatasetRef``
460 ``run`` attributes must match the ``run`` parameter.
461 assume_new : `bool`, optional
462 If `True`, assume all datasets are new and skip conflict resolution
463 logic.
464 """
465 raise NotImplementedError()
467 @abstractmethod
468 def delete(self, datasets: Iterable[DatasetId | DatasetRef]) -> None:
469 """Fully delete the given datasets from the registry.
471 Parameters
472 ----------
473 datasets : `~collections.abc.Iterable` [ `DatasetId` or `DatasetRef` ]
474 Datasets to be deleted. If `DatasetRef` instances are passed,
475 only the `DatasetRef.id` attribute is used.
476 """
477 raise NotImplementedError()
479 @abstractmethod
480 def associate(
481 self, dataset_type: DatasetType, collection: CollectionRecord, datasets: Iterable[DatasetRef]
482 ) -> None:
483 """Associate one or more datasets with a collection.
485 Parameters
486 ----------
487 dataset_type : `DatasetType`
488 Type of all datasets.
489 collection : `CollectionRecord`
490 The record object describing the collection. ``collection.type``
491 must be `~CollectionType.TAGGED`.
492 datasets : `~collections.abc.Iterable` [ `DatasetRef` ]
493 Datasets to be associated. All datasets must have the same
494 `DatasetType` as ``dataset_type``, but this is not checked.
496 Notes
497 -----
498 Associating a dataset into collection that already contains a
499 different dataset with the same `DatasetType` and data ID will remove
500 the existing dataset from that collection.
502 Associating the same dataset into a collection multiple times is a
503 no-op, but is still not permitted on read-only databases.
504 """
505 raise NotImplementedError()
507 @abstractmethod
508 def disassociate(
509 self, dataset_type: DatasetType, collection: CollectionRecord, datasets: Iterable[DatasetRef]
510 ) -> None:
511 """Remove one or more datasets from a collection.
513 Parameters
514 ----------
515 dataset_type : `DatasetType`
516 Type of all datasets.
517 collection : `CollectionRecord`
518 The record object describing the collection. ``collection.type``
519 must be `~CollectionType.TAGGED`.
520 datasets : `~collections.abc.Iterable` [ `DatasetRef` ]
521 Datasets to be disassociated. All datasets must have the same
522 `DatasetType` as ``dataset_type``, but this is not checked.
523 """
524 raise NotImplementedError()
526 @abstractmethod
527 def certify(
528 self,
529 dataset_type: DatasetType,
530 collection: CollectionRecord,
531 datasets: Iterable[DatasetRef],
532 timespan: Timespan,
533 query_func: QueryFactoryFunction,
534 ) -> None:
535 """Associate one or more datasets with a calibration collection and a
536 validity range within it.
538 Parameters
539 ----------
540 dataset_type : `DatasetType`
541 Type of all datasets.
542 collection : `CollectionRecord`
543 The record object describing the collection. ``collection.type``
544 must be `~CollectionType.CALIBRATION`.
545 datasets : `~collections.abc.Iterable` [ `DatasetRef` ]
546 Datasets to be associated. All datasets must have the same
547 `DatasetType` as ``dataset_type``, but this is not checked.
548 timespan : `Timespan`
549 The validity range for these datasets within the collection.
550 query_func : `QueryFactoryFunction`
551 Function returning a context manager that sets up a `Query` object
552 for querying the registry. (That is, a function equivalent to
553 ``Butler.query()``).
555 Raises
556 ------
557 ConflictingDefinitionError
558 Raised if the collection already contains a different dataset with
559 the same `DatasetType` and data ID and an overlapping validity
560 range.
561 DatasetTypeError
562 Raised if ``dataset_type.isCalibration() is False``.
563 CollectionTypeError
564 Raised if
565 ``collection.type is not CollectionType.CALIBRATION``.
566 """
567 raise NotImplementedError()
569 @abstractmethod
570 def decertify(
571 self,
572 dataset_type: DatasetType,
573 collection: CollectionRecord,
574 timespan: Timespan,
575 *,
576 data_ids: Iterable[DataCoordinate] | None = None,
577 query_func: QueryFactoryFunction,
578 ) -> None:
579 """Remove or adjust datasets to clear a validity range within a
580 calibration collection.
582 Parameters
583 ----------
584 dataset_type : `DatasetType`
585 Type of all datasets.
586 collection : `CollectionRecord`
587 The record object describing the collection. ``collection.type``
588 must be `~CollectionType.CALIBRATION`.
589 timespan : `Timespan`
590 The validity range to remove datasets from within the collection.
591 Datasets that overlap this range but are not contained by it will
592 have their validity ranges adjusted to not overlap it, which may
593 split a single dataset validity range into two.
594 data_ids : `~collections.abc.Iterable` [ `DataCoordinate` ], optional
595 Data IDs that should be decertified within the given validity range
596 If `None`, all data IDs for ``dataset_type`` in ``collection`` will
597 be decertified.
598 query_func : `QueryFactoryFunction`
599 Function returning a context manager that sets up a `Query` object
600 for querying the registry. (That is, a function equivalent to
601 ``Butler.query()``).
603 Raises
604 ------
605 DatasetTypeError
606 Raised if ``dataset_type.isCalibration() is False``.
607 CollectionTypeError
608 Raised if
609 ``collection.type is not CollectionType.CALIBRATION``.
610 """
611 raise NotImplementedError()
613 @abstractmethod
614 def make_joins_builder(
615 self,
616 dataset_type: DatasetType,
617 collections: Sequence[CollectionRecord],
618 fields: Set[AnyDatasetFieldName],
619 is_union: bool = False,
620 ) -> SqlJoinsBuilder:
621 """Make a `lsst.daf.butler.direct_query_driver.SqlJoinsBuilder`
622 that represents a search for datasets of this type.
624 Parameters
625 ----------
626 dataset_type : `DatasetType`
627 Type of dataset to query for.
628 collections : `~collections.abc.Sequence` [ `CollectionRecord` ]
629 Collections to search, in order, after filtering out collections
630 with no datasets of this type via collection summaries.
631 fields : `~collections.abc.Set` [ `str` ]
632 Names of fields to make available in the builder. Options include:
634 - ``dataset_id`` (UUID)
635 - ``run`` (collection name, `str`)
636 - ``collection`` (collection name, `str`)
637 - ``collection_key`` (collection primary key, manager-dependent)
638 - ``timespan`` (validity range, or unbounded for non-calibrations)
639 - ``ingest_date`` (time dataset was ingested into repository)
641 Dimension keys for the dataset type's required dimensions are
642 always included.
643 is_union : `bool`, optional
644 If `True`, this search is being joined in as part of one term in
645 a union over all dataset types. This causes fields to be added to
646 the builder via the special ``...`` instad of the dataset type
647 name.
649 Returns
650 -------
651 builder : `lsst.daf.butler.direct_query_driver.SqlJoinsBuilder`
652 A query-construction object representing a table or subquery.
653 """
654 raise NotImplementedError()
656 @abstractmethod
657 def refresh_collection_summaries(self, dataset_type: DatasetType) -> None:
658 """Make sure that collection summaries for this dataset type are
659 consistent with the contents of the dataset tables.
661 Parameters
662 ----------
663 dataset_type : `DatasetType`
664 Dataset type whose summary entries should be refreshed.
665 """
666 raise NotImplementedError()