Coverage for python/lsst/daf/butler/registry/datasets/byDimensions/_manager.py: 93%
204 statements
« prev ^ index » next coverage.py v6.5.0, created at 2022-12-15 02:03 -0800
« prev ^ index » next coverage.py v6.5.0, created at 2022-12-15 02:03 -0800
1from __future__ import annotations
3__all__ = (
4 "ByDimensionsDatasetRecordStorageManager",
5 "ByDimensionsDatasetRecordStorageManagerUUID",
6)
8import logging
9import warnings
10from collections import defaultdict
11from typing import TYPE_CHECKING, Any
13import sqlalchemy
14from deprecated.sphinx import deprecated
15from lsst.utils.ellipsis import Ellipsis
17from ....core import DatasetId, DatasetRef, DatasetType, DimensionUniverse, ddl
18from ..._collection_summary import CollectionSummary
19from ..._exceptions import ConflictingDefinitionError, DatasetTypeError, OrphanedRecordError
20from ...interfaces import DatasetIdGenEnum, DatasetRecordStorage, DatasetRecordStorageManager, VersionTuple
21from ...wildcards import DatasetTypeWildcard
22from ._storage import (
23 ByDimensionsDatasetRecordStorage,
24 ByDimensionsDatasetRecordStorageInt,
25 ByDimensionsDatasetRecordStorageUUID,
26)
27from .summaries import CollectionSummaryManager
28from .tables import (
29 addDatasetForeignKey,
30 makeCalibTableName,
31 makeCalibTableSpec,
32 makeStaticTableSpecs,
33 makeTagTableName,
34 makeTagTableSpec,
35)
37if TYPE_CHECKING: 37 ↛ 38line 37 didn't jump to line 38, because the condition on line 37 was never true
38 from ...interfaces import (
39 CollectionManager,
40 CollectionRecord,
41 Database,
42 DimensionRecordStorageManager,
43 StaticTablesContext,
44 )
45 from .tables import StaticDatasetTablesTuple
48# This has to be updated on every schema change
49_VERSION_INT = VersionTuple(1, 0, 0)
50_VERSION_UUID = VersionTuple(1, 0, 0)
52_LOG = logging.getLogger(__name__)
55class MissingDatabaseTableError(RuntimeError):
56 """Exception raised when a table is not found in a database."""
59class ByDimensionsDatasetRecordStorageManagerBase(DatasetRecordStorageManager):
60 """A manager class for datasets that uses one dataset-collection table for
61 each group of dataset types that share the same dimensions.
63 In addition to the table organization, this class makes a number of
64 other design choices that would have been cumbersome (to say the least) to
65 try to pack into its name:
67 - It uses a private surrogate integer autoincrement field to identify
68 dataset types, instead of using the name as the primary and foreign key
69 directly.
71 - It aggressively loads all DatasetTypes into memory instead of fetching
72 them from the database only when needed or attempting more clever forms
73 of caching.
75 Alternative implementations that make different choices for these while
76 keeping the same general table organization might be reasonable as well.
78 This class provides complete implementation of manager logic but it is
79 parametrized by few class attributes that have to be defined by
80 sub-classes.
82 Parameters
83 ----------
84 db : `Database`
85 Interface to the underlying database engine and namespace.
86 collections : `CollectionManager`
87 Manager object for the collections in this `Registry`.
88 dimensions : `DimensionRecordStorageManager`
89 Manager object for the dimensions in this `Registry`.
90 static : `StaticDatasetTablesTuple`
91 Named tuple of `sqlalchemy.schema.Table` instances for all static
92 tables used by this class.
93 summaries : `CollectionSummaryManager`
94 Structure containing tables that summarize the contents of collections.
95 """
97 def __init__(
98 self,
99 *,
100 db: Database,
101 collections: CollectionManager,
102 dimensions: DimensionRecordStorageManager,
103 static: StaticDatasetTablesTuple,
104 summaries: CollectionSummaryManager,
105 ):
106 self._db = db
107 self._collections = collections
108 self._dimensions = dimensions
109 self._static = static
110 self._summaries = summaries
111 self._byName: dict[str, ByDimensionsDatasetRecordStorage] = {}
112 self._byId: dict[DatasetId, ByDimensionsDatasetRecordStorage] = {}
114 @classmethod
115 def initialize(
116 cls,
117 db: Database,
118 context: StaticTablesContext,
119 *,
120 collections: CollectionManager,
121 dimensions: DimensionRecordStorageManager,
122 ) -> DatasetRecordStorageManager:
123 # Docstring inherited from DatasetRecordStorageManager.
124 specs = cls.makeStaticTableSpecs(type(collections), universe=dimensions.universe)
125 static: StaticDatasetTablesTuple = context.addTableTuple(specs) # type: ignore
126 summaries = CollectionSummaryManager.initialize(
127 db,
128 context,
129 collections=collections,
130 dimensions=dimensions,
131 )
132 return cls(db=db, collections=collections, dimensions=dimensions, static=static, summaries=summaries)
134 @classmethod
135 def currentVersion(cls) -> VersionTuple | None:
136 # Docstring inherited from VersionedExtension.
137 return cls._version
139 @classmethod
140 def makeStaticTableSpecs(
141 cls, collections: type[CollectionManager], universe: DimensionUniverse
142 ) -> StaticDatasetTablesTuple:
143 """Construct all static tables used by the classes in this package.
145 Static tables are those that are present in all Registries and do not
146 depend on what DatasetTypes have been registered.
148 Parameters
149 ----------
150 collections: `CollectionManager`
151 Manager object for the collections in this `Registry`.
152 universe : `DimensionUniverse`
153 Universe graph containing all dimensions known to this `Registry`.
155 Returns
156 -------
157 specs : `StaticDatasetTablesTuple`
158 A named tuple containing `ddl.TableSpec` instances.
159 """
160 return makeStaticTableSpecs(
161 collections, universe=universe, dtype=cls.getIdColumnType(), autoincrement=cls._autoincrement
162 )
164 @classmethod
165 def getIdColumnType(cls) -> type:
166 # Docstring inherited from base class.
167 return cls._idColumnType
169 @classmethod
170 def addDatasetForeignKey(
171 cls,
172 tableSpec: ddl.TableSpec,
173 *,
174 name: str = "dataset",
175 constraint: bool = True,
176 onDelete: str | None = None,
177 **kwargs: Any,
178 ) -> ddl.FieldSpec:
179 # Docstring inherited from DatasetRecordStorageManager.
180 return addDatasetForeignKey(
181 tableSpec, cls.getIdColumnType(), name=name, onDelete=onDelete, constraint=constraint, **kwargs
182 )
184 def refresh(self) -> None:
185 # Docstring inherited from DatasetRecordStorageManager.
186 byName: dict[str, ByDimensionsDatasetRecordStorage] = {}
187 byId: dict[DatasetId, ByDimensionsDatasetRecordStorage] = {}
188 c = self._static.dataset_type.columns
189 for row in self._db.query(self._static.dataset_type.select()).mappings():
190 name = row[c.name]
191 dimensions = self._dimensions.loadDimensionGraph(row[c.dimensions_key])
192 calibTableName = row[c.calibration_association_table]
193 datasetType = DatasetType(
194 name, dimensions, row[c.storage_class], isCalibration=(calibTableName is not None)
195 )
196 tags = self._db.getExistingTable(
197 row[c.tag_association_table],
198 makeTagTableSpec(datasetType, type(self._collections), self.getIdColumnType()),
199 )
200 if tags is None: 200 ↛ 201line 200 didn't jump to line 201, because the condition on line 200 was never true
201 raise MissingDatabaseTableError(
202 f"Table {row[c.tag_association_table]} is missing from database schema."
203 )
204 if calibTableName is not None:
205 calibs = self._db.getExistingTable(
206 row[c.calibration_association_table],
207 makeCalibTableSpec(
208 datasetType,
209 type(self._collections),
210 self._db.getTimespanRepresentation(),
211 self.getIdColumnType(),
212 ),
213 )
214 if calibs is None: 214 ↛ 215line 214 didn't jump to line 215, because the condition on line 214 was never true
215 raise MissingDatabaseTableError(
216 f"Table {row[c.calibration_association_table]} is missing from database schema."
217 )
218 else:
219 calibs = None
220 storage = self._recordStorageType(
221 db=self._db,
222 datasetType=datasetType,
223 static=self._static,
224 summaries=self._summaries,
225 tags=tags,
226 calibs=calibs,
227 dataset_type_id=row["id"],
228 collections=self._collections,
229 )
230 byName[datasetType.name] = storage
231 byId[storage._dataset_type_id] = storage
232 self._byName = byName
233 self._byId = byId
234 self._summaries.refresh(lambda dataset_type_id: self._byId[dataset_type_id].datasetType)
236 def remove(self, name: str) -> None:
237 # Docstring inherited from DatasetRecordStorageManager.
238 compositeName, componentName = DatasetType.splitDatasetTypeName(name)
239 if componentName is not None:
240 raise ValueError(f"Cannot delete a dataset type of a component of a composite (given {name})")
242 # Delete the row
243 try:
244 self._db.delete(self._static.dataset_type, ["name"], {"name": name})
245 except sqlalchemy.exc.IntegrityError as e:
246 raise OrphanedRecordError(
247 f"Dataset type {name} can not be removed."
248 " It is associated with datasets that must be removed first."
249 ) from e
251 # Now refresh everything -- removal is rare enough that this does
252 # not need to be fast.
253 self.refresh()
255 def find(self, name: str) -> DatasetRecordStorage | None:
256 # Docstring inherited from DatasetRecordStorageManager.
257 return self._byName.get(name)
259 def register(self, datasetType: DatasetType) -> tuple[DatasetRecordStorage, bool]:
260 # Docstring inherited from DatasetRecordStorageManager.
261 if datasetType.isComponent(): 261 ↛ 262line 261 didn't jump to line 262, because the condition on line 261 was never true
262 raise ValueError(
263 f"Component dataset types can not be stored in registry. Rejecting {datasetType.name}"
264 )
265 storage = self._byName.get(datasetType.name)
266 if storage is None:
267 dimensionsKey = self._dimensions.saveDimensionGraph(datasetType.dimensions)
268 tagTableName = makeTagTableName(datasetType, dimensionsKey)
269 calibTableName = (
270 makeCalibTableName(datasetType, dimensionsKey) if datasetType.isCalibration() else None
271 )
272 # The order is important here, we want to create tables first and
273 # only register them if this operation is successful. We cannot
274 # wrap it into a transaction because database class assumes that
275 # DDL is not transaction safe in general.
276 tags = self._db.ensureTableExists(
277 tagTableName,
278 makeTagTableSpec(datasetType, type(self._collections), self.getIdColumnType()),
279 )
280 if calibTableName is not None:
281 calibs = self._db.ensureTableExists(
282 calibTableName,
283 makeCalibTableSpec(
284 datasetType,
285 type(self._collections),
286 self._db.getTimespanRepresentation(),
287 self.getIdColumnType(),
288 ),
289 )
290 else:
291 calibs = None
292 row, inserted = self._db.sync(
293 self._static.dataset_type,
294 keys={"name": datasetType.name},
295 compared={
296 "dimensions_key": dimensionsKey,
297 # Force the storage class to be loaded to ensure it
298 # exists and there is no typo in the name.
299 "storage_class": datasetType.storageClass.name,
300 },
301 extra={
302 "tag_association_table": tagTableName,
303 "calibration_association_table": calibTableName,
304 },
305 returning=["id", "tag_association_table"],
306 )
307 assert row is not None
308 storage = self._recordStorageType(
309 db=self._db,
310 datasetType=datasetType,
311 static=self._static,
312 summaries=self._summaries,
313 tags=tags,
314 calibs=calibs,
315 dataset_type_id=row["id"],
316 collections=self._collections,
317 )
318 self._byName[datasetType.name] = storage
319 self._byId[storage._dataset_type_id] = storage
320 else:
321 if datasetType != storage.datasetType:
322 raise ConflictingDefinitionError(
323 f"Given dataset type {datasetType} is inconsistent "
324 f"with database definition {storage.datasetType}."
325 )
326 inserted = False
327 return storage, bool(inserted)
329 def resolve_wildcard(
330 self,
331 expression: Any,
332 components: bool | None = None,
333 missing: list[str] | None = None,
334 explicit_only: bool = False,
335 ) -> dict[DatasetType, list[str | None]]:
336 wildcard = DatasetTypeWildcard.from_expression(expression)
337 result: defaultdict[DatasetType, set[str | None]] = defaultdict(set)
338 # This message can be transformed into an error on DM-36303 after v26,
339 # and the components argument here (and in all callers) can be removed
340 # entirely on DM-36457 after v27.
341 deprecation_message = (
342 "Querying for component datasets via Registry query methods is deprecated in favor of using "
343 "DatasetRef and DatasetType methods on parent datasets. Only components=False will be supported "
344 "after v26, and the components argument will be removed after v27."
345 )
346 for name, dataset_type in wildcard.values.items():
347 parent_name, component_name = DatasetType.splitDatasetTypeName(name)
348 if component_name is not None:
349 warnings.warn(deprecation_message, FutureWarning)
350 if (found_storage := self.find(parent_name)) is not None:
351 found_parent = found_storage.datasetType
352 if component_name is not None:
353 found = found_parent.makeComponentDatasetType(component_name)
354 else:
355 found = found_parent
356 if dataset_type is not None:
357 if dataset_type.is_compatible_with(found): 357 ↛ 365line 357 didn't jump to line 365, because the condition on line 357 was never false
358 # Prefer the given dataset type to enable storage class
359 # conversions.
360 if component_name is not None: 360 ↛ 361line 360 didn't jump to line 361, because the condition on line 360 was never true
361 found_parent = dataset_type.makeCompositeDatasetType()
362 else:
363 found_parent = dataset_type
364 else:
365 raise DatasetTypeError(
366 f"Dataset type definition in query expression {dataset_type} is "
367 f"not compatible with the registered type {found}."
368 )
369 result[found_parent].add(component_name)
370 elif missing is not None: 370 ↛ 346line 370 didn't jump to line 346, because the condition on line 370 was never false
371 missing.append(name)
372 already_warned = False
373 if wildcard.patterns is Ellipsis:
374 if explicit_only:
375 raise TypeError(
376 "Universal wildcard '...' is not permitted for dataset types in this context."
377 )
378 for storage in self._byName.values():
379 result[storage.datasetType].add(None)
380 if components:
381 try:
382 result[storage.datasetType].update(
383 storage.datasetType.storageClass.allComponents().keys()
384 )
385 if storage.datasetType.storageClass.allComponents() and not already_warned:
386 warnings.warn(deprecation_message, FutureWarning)
387 already_warned = True
388 except KeyError as err:
389 _LOG.warning(
390 f"Could not load storage class {err} for {storage.datasetType.name}; "
391 "if it has components they will not be included in query results.",
392 )
393 elif wildcard.patterns:
394 if explicit_only:
395 # After v26 this should raise DatasetTypeExpressionError, to
396 # be implemented on DM-36303.
397 warnings.warn(
398 "Passing wildcard patterns here is deprecated and will be prohibited after v26.",
399 FutureWarning,
400 )
401 for storage in self._byName.values():
402 if any(p.fullmatch(storage.datasetType.name) for p in wildcard.patterns):
403 result[storage.datasetType].add(None)
404 if components is not False:
405 for storage in self._byName.values():
406 if components is None and storage.datasetType in result:
407 continue
408 try:
409 components_for_parent = storage.datasetType.storageClass.allComponents().keys()
410 except KeyError as err:
411 _LOG.warning(
412 f"Could not load storage class {err} for {storage.datasetType.name}; "
413 "if it has components they will not be included in query results."
414 )
415 continue
416 for component_name in components_for_parent:
417 if any(
418 p.fullmatch(
419 DatasetType.nameWithComponent(storage.datasetType.name, component_name)
420 )
421 for p in wildcard.patterns
422 ):
423 result[storage.datasetType].add(component_name)
424 if not already_warned:
425 warnings.warn(deprecation_message, FutureWarning)
426 already_warned = True
427 return {k: list(v) for k, v in result.items()}
429 def getDatasetRef(self, id: DatasetId) -> DatasetRef | None:
430 # Docstring inherited from DatasetRecordStorageManager.
431 sql = (
432 sqlalchemy.sql.select(
433 self._static.dataset.columns.dataset_type_id,
434 self._static.dataset.columns[self._collections.getRunForeignKeyName()],
435 )
436 .select_from(self._static.dataset)
437 .where(self._static.dataset.columns.id == id)
438 )
439 row = self._db.query(sql).mappings().fetchone()
440 if row is None:
441 return None
442 recordsForType = self._byId.get(row[self._static.dataset.columns.dataset_type_id])
443 if recordsForType is None: 443 ↛ 444line 443 didn't jump to line 444, because the condition on line 443 was never true
444 self.refresh()
445 recordsForType = self._byId.get(row[self._static.dataset.columns.dataset_type_id])
446 assert recordsForType is not None, "Should be guaranteed by foreign key constraints."
447 return DatasetRef(
448 recordsForType.datasetType,
449 dataId=recordsForType.getDataId(id=id),
450 id=id,
451 run=self._collections[row[self._collections.getRunForeignKeyName()]].name,
452 )
454 def getCollectionSummary(self, collection: CollectionRecord) -> CollectionSummary:
455 # Docstring inherited from DatasetRecordStorageManager.
456 return self._summaries.get(collection)
458 def schemaDigest(self) -> str | None:
459 # Docstring inherited from VersionedExtension.
460 return self._defaultSchemaDigest(self._static, self._db.dialect)
462 _version: VersionTuple
463 """Schema version for this class."""
465 _recordStorageType: type[ByDimensionsDatasetRecordStorage]
466 """Type of the storage class returned by this manager."""
468 _autoincrement: bool
469 """If True then PK column of the dataset table is auto-increment."""
471 _idColumnType: type
472 """Type of dataset column used to store dataset ID."""
475@deprecated(
476 "Integer dataset IDs are deprecated in favor of UUIDs; support will be removed after v26. "
477 "Please migrate or re-create this data repository.",
478 version="v25.0",
479 category=FutureWarning,
480)
481class ByDimensionsDatasetRecordStorageManager(ByDimensionsDatasetRecordStorageManagerBase):
482 """Implementation of ByDimensionsDatasetRecordStorageManagerBase which uses
483 auto-incremental integer for dataset primary key.
484 """
486 _version: VersionTuple = _VERSION_INT
487 _recordStorageType: type[ByDimensionsDatasetRecordStorage] = ByDimensionsDatasetRecordStorageInt
488 _autoincrement: bool = True
489 _idColumnType: type = sqlalchemy.BigInteger
491 @classmethod
492 def supportsIdGenerationMode(cls, mode: DatasetIdGenEnum) -> bool:
493 # Docstring inherited from DatasetRecordStorageManager.
494 # MyPy seems confused about enum value types here.
495 return mode is mode.UNIQUE # type: ignore
498class ByDimensionsDatasetRecordStorageManagerUUID(ByDimensionsDatasetRecordStorageManagerBase):
499 """Implementation of ByDimensionsDatasetRecordStorageManagerBase which uses
500 UUID for dataset primary key.
501 """
503 _version: VersionTuple = _VERSION_UUID
504 _recordStorageType: type[ByDimensionsDatasetRecordStorage] = ByDimensionsDatasetRecordStorageUUID
505 _autoincrement: bool = False
506 _idColumnType: type = ddl.GUID
508 @classmethod
509 def supportsIdGenerationMode(cls, mode: DatasetIdGenEnum) -> bool:
510 # Docstring inherited from DatasetRecordStorageManager.
511 return True