Coverage for python/lsst/daf/butler/registry/interfaces/_datasets.py : 57%

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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__all__ = ("DatasetRecordStorageManager", "DatasetRecordStorage")
26from abc import ABC, abstractmethod
27from typing import (
28 Any,
29 Iterable,
30 Iterator,
31 Optional,
32 Tuple,
33 TYPE_CHECKING,
34)
36from ...core import (
37 DataCoordinate,
38 DatasetRef,
39 DatasetType,
40 ddl,
41 SimpleQuery,
42 Timespan,
43)
44from ._versioning import VersionedExtension
46if TYPE_CHECKING: 46 ↛ 47line 46 didn't jump to line 47, because the condition on line 46 was never true
47 from ...core import DimensionUniverse
48 from ._database import Database, StaticTablesContext
49 from ._collections import CollectionManager, CollectionRecord, RunRecord
52class DatasetRecordStorage(ABC):
53 """An interface that manages the records associated with a particular
54 `DatasetType`.
56 Parameters
57 ----------
58 datasetType : `DatasetType`
59 Dataset type whose records this object manages.
60 """
61 def __init__(self, datasetType: DatasetType):
62 self.datasetType = datasetType
64 @abstractmethod
65 def insert(self, run: RunRecord, dataIds: Iterable[DataCoordinate]) -> Iterator[DatasetRef]:
66 """Insert one or more dataset entries into the database.
68 Parameters
69 ----------
70 run : `RunRecord`
71 The record object describing the `~CollectionType.RUN` collection
72 this dataset will be associated with.
73 dataIds : `Iterable` [ `DataCoordinate` ]
74 Expanded data IDs (`DataCoordinate` instances) for the
75 datasets to be added. The dimensions of all data IDs must be the
76 same as ``self.datasetType.dimensions``.
78 Returns
79 -------
80 datasets : `Iterable` [ `DatasetRef` ]
81 References to the inserted datasets.
82 """
83 raise NotImplementedError()
85 @abstractmethod
86 def find(self, collection: CollectionRecord, dataId: DataCoordinate,
87 timespan: Optional[Timespan] = None) -> Optional[DatasetRef]:
88 """Search a collection for a dataset with the given data ID.
90 Parameters
91 ----------
92 collection : `CollectionRecord`
93 The record object describing the collection to search for the
94 dataset. May have any `CollectionType`.
95 dataId: `DataCoordinate`
96 Complete (but not necessarily expanded) data ID to search with,
97 with ``dataId.graph == self.datasetType.dimensions``.
98 timespan : `Timespan`, optional
99 A timespan that the validity range of the dataset must overlap.
100 Required if ``collection.type is CollectionType.CALIBRATION``, and
101 ignored otherwise.
103 Returns
104 -------
105 ref : `DatasetRef`
106 A resolved `DatasetRef` (without components populated), or `None`
107 if no matching dataset was found.
108 """
109 raise NotImplementedError()
111 @abstractmethod
112 def delete(self, datasets: Iterable[DatasetRef]) -> None:
113 """Fully delete the given datasets from the registry.
115 Parameters
116 ----------
117 datasets : `Iterable` [ `DatasetRef` ]
118 Datasets to be deleted. All datasets must be resolved and have
119 the same `DatasetType` as ``self``.
121 Raises
122 ------
123 AmbiguousDatasetError
124 Raised if any of the given `DatasetRef` instances is unresolved.
125 """
126 raise NotImplementedError()
128 @abstractmethod
129 def associate(self, collection: CollectionRecord, datasets: Iterable[DatasetRef]) -> None:
130 """Associate one or more datasets with a collection.
132 Parameters
133 ----------
134 collection : `CollectionRecord`
135 The record object describing the collection. ``collection.type``
136 must be `~CollectionType.TAGGED`.
137 datasets : `Iterable` [ `DatasetRef` ]
138 Datasets to be associated. All datasets must be resolved and have
139 the same `DatasetType` as ``self``.
141 Raises
142 ------
143 AmbiguousDatasetError
144 Raised if any of the given `DatasetRef` instances is unresolved.
146 Notes
147 -----
148 Associating a dataset with into collection that already contains a
149 different dataset with the same `DatasetType` and data ID will remove
150 the existing dataset from that collection.
152 Associating the same dataset into a collection multiple times is a
153 no-op, but is still not permitted on read-only databases.
154 """
155 raise NotImplementedError()
157 @abstractmethod
158 def disassociate(self, collection: CollectionRecord, datasets: Iterable[DatasetRef]) -> None:
159 """Remove one or more datasets from a collection.
161 Parameters
162 ----------
163 collection : `CollectionRecord`
164 The record object describing the collection. ``collection.type``
165 must be `~CollectionType.TAGGED`.
166 datasets : `Iterable` [ `DatasetRef` ]
167 Datasets to be disassociated. All datasets must be resolved and
168 have the same `DatasetType` as ``self``.
170 Raises
171 ------
172 AmbiguousDatasetError
173 Raised if any of the given `DatasetRef` instances is unresolved.
174 """
175 raise NotImplementedError()
177 @abstractmethod
178 def certify(self, collection: CollectionRecord, datasets: Iterable[DatasetRef],
179 timespan: Timespan) -> None:
180 """Associate one or more datasets with a calibration collection and a
181 validity range within it.
183 Parameters
184 ----------
185 collection : `CollectionRecord`
186 The record object describing the collection. ``collection.type``
187 must be `~CollectionType.CALIBRATION`.
188 datasets : `Iterable` [ `DatasetRef` ]
189 Datasets to be associated. All datasets must be resolved and have
190 the same `DatasetType` as ``self``.
191 timespan : `Timespan`
192 The validity range for these datasets within the collection.
194 Raises
195 ------
196 AmbiguousDatasetError
197 Raised if any of the given `DatasetRef` instances is unresolved.
198 ConflictingDefinitionError
199 Raised if the collection already contains a different dataset with
200 the same `DatasetType` and data ID and an overlapping validity
201 range.
202 TypeError
203 Raised if
204 ``collection.type is not CollectionType.CALIBRATION`` or if
205 ``self.datasetType.isCalibration() is False``.
206 """
207 raise NotImplementedError()
209 @abstractmethod
210 def decertify(self, collection: CollectionRecord, timespan: Timespan, *,
211 dataIds: Optional[Iterable[DataCoordinate]] = None) -> None:
212 """Remove or adjust datasets to clear a validity range within a
213 calibration collection.
215 Parameters
216 ----------
217 collection : `CollectionRecord`
218 The record object describing the collection. ``collection.type``
219 must be `~CollectionType.CALIBRATION`.
220 timespan : `Timespan`
221 The validity range to remove datasets from within the collection.
222 Datasets that overlap this range but are not contained by it will
223 have their validity ranges adjusted to not overlap it, which may
224 split a single dataset validity range into two.
225 dataIds : `Iterable` [ `DataCoordinate` ], optional
226 Data IDs that should be decertified within the given validity range
227 If `None`, all data IDs for ``self.datasetType`` will be
228 decertified.
230 Raises
231 ------
232 TypeError
233 Raised if ``collection.type is not CollectionType.CALIBRATION``.
234 """
235 raise NotImplementedError()
237 @abstractmethod
238 def select(self, collection: CollectionRecord,
239 dataId: SimpleQuery.Select.Or[DataCoordinate] = SimpleQuery.Select,
240 id: SimpleQuery.Select.Or[Optional[int]] = SimpleQuery.Select,
241 run: SimpleQuery.Select.Or[None] = SimpleQuery.Select,
242 timespan: SimpleQuery.Select.Or[Optional[Timespan]] = SimpleQuery.Select,
243 ) -> Optional[SimpleQuery]:
244 """Return a SQLAlchemy object that represents a ``SELECT`` query for
245 this `DatasetType`.
247 All arguments can either be a value that constrains the query or
248 the `SimpleQuery.Select` tag object to indicate that the value should
249 be returned in the columns in the ``SELECT`` clause. The default is
250 `SimpleQuery.Select`.
252 Parameters
253 ----------
254 collection : `CollectionRecord`
255 The record object describing the collection to query. May not be
256 of type `CollectionType.CHAINED`.
257 dataId : `DataCoordinate` or `Select`
258 The data ID to restrict results with, or an instruction to return
259 the data ID via columns with names
260 ``self.datasetType.dimensions.names``.
261 id : `int`, `Select` or None,
262 The integer primary key value for the dataset, an instruction to
263 return it via a ``id`` column, or `None` to ignore it
264 entirely.
265 run : `None` or `Select`
266 If `Select` (default), include the dataset's run key value (as
267 column labeled with the return value of
268 ``CollectionManager.getRunForiegnKeyName``).
269 If `None`, do not include this column (to constrain the run,
270 pass a `RunRecord` as the ``collection`` argument instead).
271 timespan : `None`, `Select`, or `Timespan`
272 If `Select` (default), include the validity range timespan in the
273 result columns. If a `Timespan` instance, constrain the results to
274 those whose validity ranges overlap that given timespan. Ignored
275 unless ``collection.type is CollectionType.CALIBRATION``.
277 Returns
278 -------
279 query : `SimpleQuery` or `None`
280 A struct containing the SQLAlchemy object that representing a
281 simple ``SELECT`` query, or `None` if it is known that there are
282 no datasets of this `DatasetType` that match the given constraints.
283 """
284 raise NotImplementedError()
286 datasetType: DatasetType
287 """Dataset type whose records this object manages (`DatasetType`).
288 """
291class DatasetRecordStorageManager(VersionedExtension):
292 """An interface that manages the tables that describe datasets.
294 `DatasetRecordStorageManager` primarily serves as a container and factory
295 for `DatasetRecordStorage` instances, which each provide access to the
296 records for a different `DatasetType`.
297 """
299 @classmethod
300 @abstractmethod
301 def initialize(cls, db: Database, context: StaticTablesContext, *, collections: CollectionManager,
302 universe: DimensionUniverse) -> DatasetRecordStorageManager:
303 """Construct an instance of the manager.
305 Parameters
306 ----------
307 db : `Database`
308 Interface to the underlying database engine and namespace.
309 context : `StaticTablesContext`
310 Context object obtained from `Database.declareStaticTables`; used
311 to declare any tables that should always be present.
312 collections: `CollectionManager`
313 Manager object for the collections in this `Registry`.
314 universe : `DimensionUniverse`
315 Universe graph containing all dimensions known to this `Registry`.
317 Returns
318 -------
319 manager : `DatasetRecordStorageManager`
320 An instance of a concrete `DatasetRecordStorageManager` subclass.
321 """
322 raise NotImplementedError()
324 @classmethod
325 @abstractmethod
326 def addDatasetForeignKey(cls, tableSpec: ddl.TableSpec, *,
327 name: str = "dataset", constraint: bool = True, onDelete: Optional[str] = None,
328 **kwargs: Any) -> ddl.FieldSpec:
329 """Add a foreign key (field and constraint) referencing the dataset
330 table.
332 Parameters
333 ----------
334 tableSpec : `ddl.TableSpec`
335 Specification for the table that should reference the dataset
336 table. Will be modified in place.
337 name: `str`, optional
338 A name to use for the prefix of the new field; the full name is
339 ``{name}_id``.
340 onDelete: `str`, optional
341 One of "CASCADE" or "SET NULL", indicating what should happen to
342 the referencing row if the collection row is deleted. `None`
343 indicates that this should be an integrity error.
344 constraint: `bool`, optional
345 If `False` (`True` is default), add a field that can be joined to
346 the dataset primary key, but do not add a foreign key constraint.
347 **kwargs
348 Additional keyword arguments are forwarded to the `ddl.FieldSpec`
349 constructor (only the ``name`` and ``dtype`` arguments are
350 otherwise provided).
352 Returns
353 -------
354 idSpec : `ddl.FieldSpec`
355 Specification for the ID field.
356 """
357 raise NotImplementedError()
359 @abstractmethod
360 def refresh(self, *, universe: DimensionUniverse) -> None:
361 """Ensure all other operations on this manager are aware of any
362 dataset types that may have been registered by other clients since
363 it was initialized or last refreshed.
364 """
365 raise NotImplementedError()
367 def __getitem__(self, name: str) -> DatasetRecordStorage:
368 """Return the object that provides access to the records associated
369 with the given `DatasetType` name.
371 This is simply a convenience wrapper for `find` that raises `KeyError`
372 when the dataset type is not found.
374 Returns
375 -------
376 records : `DatasetRecordStorage`
377 The object representing the records for the given dataset type.
379 Raises
380 ------
381 KeyError
382 Raised if there is no dataset type with the given name.
384 Notes
385 -----
386 Dataset types registered by another client of the same repository since
387 the last call to `initialize` or `refresh` may not be found.
388 """
389 result = self.find(name)
390 if result is None:
391 raise KeyError(f"Dataset type with name '{name}' not found.")
392 return result
394 @abstractmethod
395 def find(self, name: str) -> Optional[DatasetRecordStorage]:
396 """Return an object that provides access to the records associated with
397 the given `DatasetType` name, if one exists.
399 Parameters
400 ----------
401 name : `str`
402 Name of the dataset type.
404 Returns
405 -------
406 records : `DatasetRecordStorage` or `None`
407 The object representing the records for the given dataset type, or
408 `None` if there are no records for that dataset type.
410 Notes
411 -----
412 Dataset types registered by another client of the same repository since
413 the last call to `initialize` or `refresh` may not be found.
414 """
415 raise NotImplementedError()
417 @abstractmethod
418 def register(self, datasetType: DatasetType) -> Tuple[DatasetRecordStorage, bool]:
419 """Ensure that this `Registry` can hold records for the given
420 `DatasetType`, creating new tables as necessary.
422 Parameters
423 ----------
424 datasetType : `DatasetType`
425 Dataset type for which a table should created (as necessary) and
426 an associated `DatasetRecordStorage` returned.
428 Returns
429 -------
430 records : `DatasetRecordStorage`
431 The object representing the records for the given dataset type.
432 inserted : `bool`
433 `True` if the dataset type did not exist in the registry before.
435 Notes
436 -----
437 This operation may not be invoked within a `Database.transaction`
438 context.
439 """
440 raise NotImplementedError()
442 @abstractmethod
443 def remove(self, name: str, *, universe: DimensionUniverse) -> None:
444 """Remove the dataset type.
446 Parameters
447 ----------
448 name : `str`
449 Name of the dataset type.
450 universe : `DimensionUniverse`
451 Universe to use to trigger a refresh following removal.
452 """
453 raise NotImplementedError()
455 @abstractmethod
456 def __iter__(self) -> Iterator[DatasetType]:
457 """Return an iterator over the the dataset types present in this layer.
459 Notes
460 -----
461 Dataset types registered by another client of the same layer since
462 the last call to `initialize` or `refresh` may not be included.
463 """
464 raise NotImplementedError()
466 @abstractmethod
467 def getDatasetRef(self, id: int, *, universe: DimensionUniverse) -> Optional[DatasetRef]:
468 """Return a `DatasetRef` for the given dataset primary key
469 value.
471 Parameters
472 ----------
473 id : `int`
474 Autoincrement primary key value for the dataset.
475 universe : `DimensionUniverse`
476 All known dimensions.
478 Returns
479 -------
480 ref : `DatasetRef` or `None`
481 Object representing the dataset, or `None` if no dataset with the
482 given primary key values exists in this layer.
483 """
484 raise NotImplementedError()