Coverage for python/lsst/daf/butler/registry/bridge/monolithic.py: 25%
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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
7# for details of code ownership.
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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
13# (but note that there is still no warranty even if you opt for BSD instead):
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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27from __future__ import annotations
29from ... import ddl
31__all__ = ("MonolithicDatastoreRegistryBridge", "MonolithicDatastoreRegistryBridgeManager")
33from collections import namedtuple
34from collections.abc import Collection, Iterable, Iterator
35from contextlib import contextmanager
36from typing import TYPE_CHECKING, cast
38import sqlalchemy
40from lsst.utils.iteration import chunk_iterable
42from ..._dataset_ref import DatasetId
43from ...datastore.stored_file_info import StoredDatastoreItemInfo
44from ..interfaces import (
45 DatasetIdRef,
46 DatastoreRegistryBridge,
47 DatastoreRegistryBridgeManager,
48 FakeDatasetRef,
49 OpaqueTableStorage,
50 VersionTuple,
51)
52from ..opaque import ByNameOpaqueTableStorage
53from .ephemeral import EphemeralDatastoreRegistryBridge
55if TYPE_CHECKING:
56 from ...datastore import DatastoreTransaction
57 from ...dimensions import DimensionUniverse
58 from ..interfaces import (
59 Database,
60 DatasetRecordStorageManager,
61 OpaqueTableStorageManager,
62 StaticTablesContext,
63 )
65_TablesTuple = namedtuple(
66 "_TablesTuple",
67 [
68 "dataset_location",
69 "dataset_location_trash",
70 ],
71)
73# This has to be updated on every schema change
74_VERSION = VersionTuple(0, 2, 1)
77def _makeTableSpecs(datasets: type[DatasetRecordStorageManager]) -> _TablesTuple:
78 """Construct specifications for tables used by the monolithic datastore
79 bridge classes.
81 Parameters
82 ----------
83 datasets : subclass of `DatasetRecordStorageManager`
84 Manager class for datasets; used only to create foreign key fields.
86 Returns
87 -------
88 specs : `_TablesTuple`
89 A named tuple containing `ddl.TableSpec` instances.
90 """
91 # We want the dataset_location and dataset_location_trash tables
92 # to have the same definition, aside from the behavior of their link
93 # to the dataset table: the trash table has no foreign key constraint.
94 # The order of columns in dataset_location_trash is reversed, it is more
95 # optimal for query planner.
97 datastore_field = ddl.FieldSpec(
98 name="datastore_name",
99 dtype=sqlalchemy.String,
100 length=256,
101 primaryKey=True,
102 nullable=False,
103 doc="Name of the Datastore this entry corresponds to.",
104 )
106 dataset_location = ddl.TableSpec(
107 doc=(
108 "A table that provides information on whether a dataset is stored in "
109 "one or more Datastores. The presence or absence of a record in this "
110 "table itself indicates whether the dataset is present in that "
111 "Datastore. "
112 ),
113 fields=[datastore_field],
114 )
115 datasets.addDatasetForeignKey(dataset_location, primaryKey=True)
117 dataset_location_trash = ddl.TableSpec(
118 doc="A table that keeps iinformation about datasets that are removed from Datastores.",
119 fields=[],
120 )
121 datasets.addDatasetForeignKey(dataset_location_trash, primaryKey=True, constraint=False)
122 dataset_location_trash.fields.add(datastore_field)
124 return _TablesTuple(
125 dataset_location=dataset_location,
126 dataset_location_trash=dataset_location_trash,
127 )
130class MonolithicDatastoreRegistryBridge(DatastoreRegistryBridge):
131 """An implementation of `DatastoreRegistryBridge` that uses the same two
132 tables for all non-ephemeral datastores.
134 Parameters
135 ----------
136 datastoreName : `str`
137 Name of the `Datastore` as it should appear in `Registry` tables
138 referencing it.
139 db : `Database`
140 Object providing a database connection and generic distractions.
141 tables : `_TablesTuple`
142 Named tuple containing `sqlalchemy.schema.Table` instances.
143 """
145 def __init__(self, datastoreName: str, *, db: Database, tables: _TablesTuple):
146 super().__init__(datastoreName)
147 self._db = db
148 self._tables = tables
150 def _refsToRows(self, refs: Iterable[DatasetIdRef]) -> list[dict]:
151 """Transform an iterable of `DatasetRef` or `FakeDatasetRef` objects to
152 a list of dictionaries that match the schema of the tables used by this
153 class.
155 Parameters
156 ----------
157 refs : `~collections.abc.Iterable` [ `DatasetRef` or `FakeDatasetRef` ]
158 Datasets to transform.
160 Returns
161 -------
162 rows : `list` [ `dict` ]
163 List of dictionaries, with "datastoreName" and "dataset_id" keys.
164 """
165 return [{"datastore_name": self.datastoreName, "dataset_id": ref.id} for ref in refs]
167 def ensure(self, refs: Iterable[DatasetIdRef]) -> None:
168 # Docstring inherited from DatastoreRegistryBridge
169 self._db.ensure(self._tables.dataset_location, *self._refsToRows(refs))
171 def insert(self, refs: Iterable[DatasetIdRef]) -> None:
172 # Docstring inherited from DatastoreRegistryBridge
173 self._db.insert(self._tables.dataset_location, *self._refsToRows(refs))
175 def forget(self, refs: Iterable[DatasetIdRef]) -> None:
176 # Docstring inherited from DatastoreRegistryBridge
177 with self._db.transaction():
178 # The list of IDs can be very large, split it into reasonable size
179 # chunks to avoid hitting limits.
180 for refs_chunk in chunk_iterable(refs, 50_000):
181 dataset_ids = [ref.id for ref in refs_chunk]
182 where = sqlalchemy.sql.and_(
183 self._tables.dataset_location.columns.datastore_name == self.datastoreName,
184 self._tables.dataset_location.columns.dataset_id.in_(dataset_ids),
185 )
186 self._db.deleteWhere(self._tables.dataset_location, where)
188 def moveToTrash(self, refs: Iterable[DatasetIdRef], transaction: DatastoreTransaction | None) -> None:
189 # Docstring inherited from DatastoreRegistryBridge
190 location = self._tables.dataset_location
191 location_trash = self._tables.dataset_location_trash
192 with self._db.transaction():
193 for refs_chunk in chunk_iterable(refs, 50_000):
194 # We only want to move IDs that actually exist in the
195 # dataset_location table. Instead of querying for existing IDs,
196 # which would need an extra query, we use INSERT ... SELECT
197 # and DELETE using WHERE clause that limits operations to
198 # existing IDs.
199 dataset_ids = [ref.id for ref in refs_chunk]
201 where = sqlalchemy.sql.and_(
202 location.columns.datastore_name == self.datastoreName,
203 location.columns.dataset_id.in_(dataset_ids),
204 )
206 select = (
207 sqlalchemy.sql.select(location.columns.datastore_name, location.columns.dataset_id)
208 .where(where)
209 .with_for_update()
210 )
211 self._db.insert(location_trash, select=select)
213 self._db.deleteWhere(location, where)
215 def check(self, datasets: Iterable[DatasetId]) -> set[DatasetId]:
216 # Docstring inherited from DatastoreRegistryBridge
217 found: set[DatasetId] = set()
218 with self._db.session():
219 for batch in chunk_iterable(datasets, 50000):
220 sql = (
221 sqlalchemy.sql.select(self._tables.dataset_location.columns.dataset_id)
222 .select_from(self._tables.dataset_location)
223 .where(
224 sqlalchemy.sql.and_(
225 self._tables.dataset_location.columns.datastore_name == self.datastoreName,
226 self._tables.dataset_location.columns.dataset_id.in_(batch),
227 )
228 )
229 )
230 with self._db.query(sql) as sql_result:
231 sql_ids = sql_result.scalars().all()
232 found.update(sql_ids)
234 return found
236 @contextmanager
237 def emptyTrash(
238 self,
239 records_table: OpaqueTableStorage | None = None,
240 record_class: type[StoredDatastoreItemInfo] | None = None,
241 record_column: str | None = None,
242 selected_ids: Collection[DatasetId] | None = None,
243 dry_run: bool = False,
244 ) -> Iterator[tuple[Iterable[tuple[DatasetIdRef, StoredDatastoreItemInfo | None]], set[str] | None]]:
245 # Docstring inherited from DatastoreRegistryBridge
247 if records_table is None:
248 raise ValueError("This implementation requires a records table.")
250 assert isinstance(records_table, ByNameOpaqueTableStorage), (
251 f"Records table must support hidden attributes. Got {type(records_table)}."
252 )
254 if record_class is None:
255 raise ValueError("Record class must be provided if records table is given.")
257 # Helper closure to generate the common join+where clause.
258 def join_records(
259 select: sqlalchemy.sql.Select, location_table: sqlalchemy.schema.Table
260 ) -> sqlalchemy.sql.Select:
261 # mypy needs to be sure
262 assert isinstance(records_table, ByNameOpaqueTableStorage)
263 return select.select_from(
264 records_table._table.join(
265 location_table,
266 onclause=records_table._table.columns.dataset_id == location_table.columns.dataset_id,
267 )
268 ).where(location_table.columns.datastore_name == self.datastoreName)
270 # SELECT records.dataset_id, records.path FROM records
271 # JOIN records on dataset_location.dataset_id == records.dataset_id
272 # WHERE dataset_location.datastore_name = datastoreName
274 # It's possible that we may end up with a ref listed in the trash
275 # table that is not listed in the records table. Such an
276 # inconsistency would be missed by this query.
277 info_in_trash = join_records(records_table._table.select(), self._tables.dataset_location_trash)
278 if selected_ids:
279 info_in_trash = info_in_trash.where(
280 self._tables.dataset_location_trash.columns["dataset_id"].in_(selected_ids)
281 )
282 info_in_trash = info_in_trash.with_for_update(skip_locked=True)
284 # Run query, transform results into a list of dicts that we can later
285 # use to delete.
286 with self._db.query(info_in_trash) as sql_result:
287 rows = [dict(row, datastore_name=self.datastoreName) for row in sql_result.mappings()]
289 # It is possible for trashed refs to be linked to artifacts that
290 # are still associated with refs that are not to be trashed. We
291 # need to be careful to consider those and indicate to the caller
292 # that those artifacts should be retained. Can only do this check
293 # if the caller provides a column name that can map to multiple
294 # refs.
295 preserved: set[str] | None = None
296 if record_column is not None:
297 # Some helper subqueries
298 items_not_in_trash = join_records(
299 sqlalchemy.sql.select(records_table._table.columns[record_column]),
300 self._tables.dataset_location,
301 ).alias("items_not_in_trash")
302 items_in_trash = join_records(
303 sqlalchemy.sql.select(records_table._table.columns[record_column]),
304 self._tables.dataset_location_trash,
305 )
306 if selected_ids:
307 items_in_trash = items_in_trash.where(
308 self._tables.dataset_location_trash.columns["dataset_id"].in_(selected_ids)
309 )
310 items_in_trash_alias = items_in_trash.alias("items_in_trash")
312 # A query for paths that are referenced by datasets in the trash
313 # and datasets not in the trash.
314 items_to_preserve = sqlalchemy.sql.select(
315 items_in_trash_alias.columns[record_column]
316 ).select_from(
317 items_not_in_trash.join(
318 items_in_trash_alias,
319 onclause=items_in_trash_alias.columns[record_column]
320 == items_not_in_trash.columns[record_column],
321 )
322 )
323 with self._db.query(items_to_preserve) as sql_result:
324 preserved = {row[record_column] for row in sql_result.mappings()}
326 # Convert results to a tuple of id+info and a record of the artifacts
327 # that should not be deleted from datastore. The id+info tuple is
328 # solely to allow logging to report the relevant ID.
329 id_info = ((FakeDatasetRef(row["dataset_id"]), record_class.from_record(row)) for row in rows)
331 # Start contextmanager, return results
332 yield ((id_info, preserved))
334 # No exception raised in context manager block.
335 if not rows or dry_run:
336 return
338 # Delete the rows from the records table
339 records_table.delete(["dataset_id"], *[{"dataset_id": row["dataset_id"]} for row in rows])
341 # Delete those rows from the trash table.
342 self._db.delete(
343 self._tables.dataset_location_trash,
344 ["dataset_id", "datastore_name"],
345 *[{"dataset_id": row["dataset_id"], "datastore_name": row["datastore_name"]} for row in rows],
346 )
349class MonolithicDatastoreRegistryBridgeManager(DatastoreRegistryBridgeManager):
350 """An implementation of `DatastoreRegistryBridgeManager` that uses the same
351 two tables for all non-ephemeral datastores.
353 Parameters
354 ----------
355 db : `Database`
356 Object providing a database connection and generic distractions.
357 tables : `_TablesTuple`
358 Named tuple containing `sqlalchemy.schema.Table` instances.
359 opaque : `OpaqueTableStorageManager`
360 Manager object for opaque table storage in the `Registry`.
361 universe : `DimensionUniverse`
362 All dimensions know to the `Registry`.
363 registry_schema_version : `VersionTuple` or `None`, optional
364 The version of the registry schema.
365 """
367 def __init__(
368 self,
369 *,
370 db: Database,
371 tables: _TablesTuple,
372 opaque: OpaqueTableStorageManager,
373 universe: DimensionUniverse,
374 registry_schema_version: VersionTuple | None = None,
375 ):
376 super().__init__(
377 opaque=opaque,
378 universe=universe,
379 registry_schema_version=registry_schema_version,
380 )
381 self._db = db
382 self._tables = tables
383 self._ephemeral: dict[str, EphemeralDatastoreRegistryBridge] = {}
385 def clone(self, *, db: Database, opaque: OpaqueTableStorageManager) -> DatastoreRegistryBridgeManager:
386 return MonolithicDatastoreRegistryBridgeManager(
387 db=db,
388 tables=self._tables,
389 opaque=opaque,
390 universe=self.universe,
391 registry_schema_version=self._registry_schema_version,
392 )
394 @classmethod
395 def initialize(
396 cls,
397 db: Database,
398 context: StaticTablesContext,
399 *,
400 opaque: OpaqueTableStorageManager,
401 datasets: type[DatasetRecordStorageManager],
402 universe: DimensionUniverse,
403 registry_schema_version: VersionTuple | None = None,
404 ) -> DatastoreRegistryBridgeManager:
405 # Docstring inherited from DatastoreRegistryBridge
406 tables = context.addTableTuple(_makeTableSpecs(datasets))
407 return cls(
408 db=db,
409 tables=cast(_TablesTuple, tables),
410 opaque=opaque,
411 universe=universe,
412 registry_schema_version=registry_schema_version,
413 )
415 def refresh(self) -> None:
416 # Docstring inherited from DatastoreRegistryBridge
417 # This implementation has no in-Python state that depends on which
418 # datastores exist, so there's nothing to do.
419 pass
421 def register(self, name: str, *, ephemeral: bool = False) -> DatastoreRegistryBridge:
422 # Docstring inherited from DatastoreRegistryBridge
423 if ephemeral:
424 return self._ephemeral.setdefault(name, EphemeralDatastoreRegistryBridge(name))
425 return MonolithicDatastoreRegistryBridge(name, db=self._db, tables=self._tables)
427 def findDatastores(self, ref: DatasetIdRef) -> Iterable[str]:
428 # Docstring inherited from DatastoreRegistryBridge
429 sql = (
430 sqlalchemy.sql.select(self._tables.dataset_location.columns.datastore_name)
431 .select_from(self._tables.dataset_location)
432 .where(self._tables.dataset_location.columns.dataset_id == ref.id)
433 )
434 with self._db.query(sql) as sql_result:
435 sql_rows = sql_result.mappings().fetchall()
436 for row in sql_rows:
437 yield row[self._tables.dataset_location.columns.datastore_name]
438 for name, bridge in self._ephemeral.items():
439 if ref in bridge:
440 yield name
442 @classmethod
443 def currentVersions(cls) -> list[VersionTuple]:
444 # Docstring inherited from VersionedExtension.
445 return [_VERSION]