Coverage for python/lsst/daf/butler/registry/queries/_builder.py : 11%

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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/>.
21from __future__ import annotations
23__all__ = ("QueryBuilder",)
25from typing import AbstractSet, Any, Iterable, List, Optional
27import sqlalchemy.sql
29from ...core import (
30 DimensionElement,
31 SkyPixDimension,
32 Dimension,
33 DatasetType,
34 SimpleQuery,
35)
37from ...core.named import NamedKeyDict, NamedValueAbstractSet, NamedValueSet
39from .._collectionType import CollectionType
40from ._structs import QuerySummary, QueryColumns, DatasetQueryColumns, RegistryManagers
41from .expressions import convertExpressionToSql
42from ._query import DirectQuery, DirectQueryUniqueness, EmptyQuery, Query
43from ..wildcards import CollectionSearch, CollectionQuery
46class QueryBuilder:
47 """A builder for potentially complex queries that join tables based
48 on dimension relationships.
50 Parameters
51 ----------
52 summary : `QuerySummary`
53 Struct organizing the dimensions involved in the query.
54 managers : `RegistryManagers`
55 A struct containing the registry manager instances used by the query
56 system.
57 """
58 def __init__(self, summary: QuerySummary, managers: RegistryManagers):
59 self.summary = summary
60 self._simpleQuery = SimpleQuery()
61 self._elements: NamedKeyDict[DimensionElement, sqlalchemy.sql.FromClause] = NamedKeyDict()
62 self._columns = QueryColumns()
63 self._managers = managers
65 def hasDimensionKey(self, dimension: Dimension) -> bool:
66 """Return `True` if the given dimension's primary key column has
67 been included in the query (possibly via a foreign key column on some
68 other table).
69 """
70 return dimension in self._columns.keys
72 def joinDimensionElement(self, element: DimensionElement) -> None:
73 """Add the table for a `DimensionElement` to the query.
75 This automatically joins the element table to all other tables in the
76 query with which it is related, via both dimension keys and spatial
77 and temporal relationships.
79 External calls to this method should rarely be necessary; `finish` will
80 automatically call it if the `DimensionElement` has been identified as
81 one that must be included.
83 Parameters
84 ----------
85 element : `DimensionElement`
86 Element for which a table should be added. The element must be
87 associated with a database table (see `DimensionElement.hasTable`).
88 """
89 assert element not in self._elements, "Element already included in query."
90 storage = self._managers.dimensions[element]
91 fromClause = storage.join(
92 self,
93 regions=self._columns.regions if element in self.summary.spatial else None,
94 timespans=self._columns.timespans if element in self.summary.temporal else None,
95 )
96 self._elements[element] = fromClause
98 def joinDataset(self, datasetType: DatasetType, collections: Any, *,
99 isResult: bool = True, findFirst: bool = False) -> bool:
100 """Add a dataset search or constraint to the query.
102 Unlike other `QueryBuilder` join methods, this *must* be called
103 directly to search for datasets of a particular type or constrain the
104 query results based on the exists of datasets. However, all dimensions
105 used to identify the dataset type must have already been included in
106 `QuerySummary.requested` when initializing the `QueryBuilder`.
108 Parameters
109 ----------
110 datasetType : `DatasetType`
111 The type of datasets to search for.
112 collections : `Any`
113 An expression that fully or partially identifies the collections
114 to search for datasets, such as a `str`, `re.Pattern`, or iterable
115 thereof. `...` can be used to return all collections. See
116 :ref:`daf_butler_collection_expressions` for more information.
117 isResult : `bool`, optional
118 If `True` (default), include the dataset ID column in the
119 result columns of the query, allowing complete `DatasetRef`
120 instances to be produced from the query results for this dataset
121 type. If `False`, the existence of datasets of this type is used
122 only to constrain the data IDs returned by the query.
123 `joinDataset` may be called with ``isResult=True`` at most one time
124 on a particular `QueryBuilder` instance.
125 findFirst : `bool`, optional
126 If `True` (`False` is default), only include the first match for
127 each data ID, searching the given collections in order. Requires
128 that all entries in ``collections`` be regular strings, so there is
129 a clear search order. Ignored if ``isResult`` is `False`.
131 Returns
132 -------
133 anyRecords : `bool`
134 If `True`, joining the dataset table was successful and the query
135 should proceed. If `False`, we were able to determine (from the
136 combination of ``datasetType`` and ``collections``) that there
137 would be no results joined in from this dataset, and hence (due to
138 the inner join that would normally be present), the full query will
139 return no results.
140 """
141 assert datasetType.dimensions.issubset(self.summary.requested)
142 if isResult and findFirst:
143 collections = CollectionSearch.fromExpression(collections)
144 else:
145 collections = CollectionQuery.fromExpression(collections)
146 explicitCollections = frozenset(collections.explicitNames())
147 # If we are searching all collections with no constraints, loop over
148 # RUN collections only, because that will include all datasets.
149 collectionTypes: AbstractSet[CollectionType]
150 if collections == CollectionQuery():
151 collectionTypes = {CollectionType.RUN}
152 else:
153 collectionTypes = CollectionType.all()
154 datasetRecordStorage = self._managers.datasets.find(datasetType.name)
155 if datasetRecordStorage is None:
156 # Unrecognized dataset type means no results. It might be better
157 # to raise here, but this is consistent with previous behavior,
158 # which is expected by QuantumGraph generation code in pipe_base.
159 return False
160 subsubqueries = []
161 runKeyName = self._managers.collections.getRunForeignKeyName()
162 baseColumnNames = {"id", runKeyName, "ingest_date"} if isResult else set()
163 baseColumnNames.update(datasetType.dimensions.required.names)
164 for rank, collectionRecord in enumerate(collections.iter(self._managers.collections,
165 collectionTypes=collectionTypes)):
166 if collectionRecord.type is CollectionType.CALIBRATION:
167 # If collection name was provided explicitly then say sorry,
168 # otherwise collection is a part of chained one and we skip it.
169 if datasetType.isCalibration() and collectionRecord.name in explicitCollections:
170 raise NotImplementedError(
171 f"Query for dataset type '{datasetType.name}' in CALIBRATION-type collection "
172 f"'{collectionRecord.name}' is not yet supported."
173 )
174 else:
175 # We can never find a non-calibration dataset in a
176 # CALIBRATION collection.
177 continue
178 ssq = datasetRecordStorage.select(collection=collectionRecord,
179 dataId=SimpleQuery.Select,
180 id=SimpleQuery.Select if isResult else None,
181 run=SimpleQuery.Select if isResult else None,
182 ingestDate=SimpleQuery.Select if isResult else None)
183 if ssq is None:
184 continue
185 assert {c.name for c in ssq.columns} == baseColumnNames
186 if findFirst:
187 ssq.columns.append(sqlalchemy.sql.literal(rank).label("rank"))
188 subsubqueries.append(ssq.combine())
189 if not subsubqueries:
190 return False
191 # Although one would expect that these subqueries can be
192 # UNION ALL instead of UNION because each subquery is already
193 # distinct, it turns out that with many
194 # subqueries this causes catastrophic performance problems
195 # with both sqlite and postgres. Using UNION may require
196 # more table scans, but a much simpler query plan given our
197 # table structures. See DM-31429.
198 subquery = sqlalchemy.sql.union(*subsubqueries)
199 columns: Optional[DatasetQueryColumns] = None
200 if isResult:
201 if findFirst:
202 # Rewrite the subquery (currently a UNION ALL over
203 # per-collection subsubqueries) to select the rows with the
204 # lowest rank per data ID. The block below will set subquery
205 # to something like this:
206 #
207 # WITH {dst}_search AS (
208 # SELECT {data-id-cols}, id, run_id, 1 AS rank
209 # FROM <collection1>
210 # UNION ALL
211 # SELECT {data-id-cols}, id, run_id, 2 AS rank
212 # FROM <collection2>
213 # UNION ALL
214 # ...
215 # )
216 # SELECT
217 # {dst}_window.{data-id-cols},
218 # {dst}_window.id,
219 # {dst}_window.run_id
220 # FROM (
221 # SELECT
222 # {dst}_search.{data-id-cols},
223 # {dst}_search.id,
224 # {dst}_search.run_id,
225 # ROW_NUMBER() OVER (
226 # PARTITION BY {dst_search}.{data-id-cols}
227 # ORDER BY rank
228 # ) AS rownum
229 # ) {dst}_window
230 # WHERE
231 # {dst}_window.rownum = 1;
232 #
233 search = subquery.cte(f"{datasetType.name}_search")
234 windowDataIdCols = [
235 search.columns[name].label(name) for name in datasetType.dimensions.required.names
236 ]
237 windowSelectCols = [
238 search.columns["id"].label("id"),
239 search.columns[runKeyName].label(runKeyName),
240 search.columns["ingest_date"].label("ingest_date"),
241 ]
242 windowSelectCols += windowDataIdCols
243 assert {c.name for c in windowSelectCols} == baseColumnNames
244 windowSelectCols.append(
245 sqlalchemy.sql.func.row_number().over(
246 partition_by=windowDataIdCols,
247 order_by=search.columns["rank"]
248 ).label("rownum")
249 )
250 window = sqlalchemy.sql.select(
251 windowSelectCols
252 ).select_from(search).alias(
253 f"{datasetType.name}_window"
254 )
255 subquery = sqlalchemy.sql.select(
256 [window.columns[name].label(name) for name in baseColumnNames]
257 ).select_from(
258 window
259 ).where(
260 window.columns["rownum"] == 1
261 ).alias(datasetType.name)
262 else:
263 subquery = subquery.alias(datasetType.name)
264 columns = DatasetQueryColumns(
265 datasetType=datasetType,
266 id=subquery.columns["id"],
267 runKey=subquery.columns[runKeyName],
268 ingestDate=subquery.columns["ingest_date"],
269 )
270 else:
271 subquery = subquery.alias(datasetType.name)
272 self.joinTable(subquery, datasetType.dimensions.required, datasets=columns)
273 return True
275 def joinTable(self, table: sqlalchemy.sql.FromClause, dimensions: NamedValueAbstractSet[Dimension], *,
276 datasets: Optional[DatasetQueryColumns] = None) -> None:
277 """Join an arbitrary table to the query via dimension relationships.
279 External calls to this method should only be necessary for tables whose
280 records represent neither datasets nor dimension elements.
282 Parameters
283 ----------
284 table : `sqlalchemy.sql.FromClause`
285 SQLAlchemy object representing the logical table (which may be a
286 join or subquery expression) to be joined.
287 dimensions : iterable of `Dimension`
288 The dimensions that relate this table to others that may be in the
289 query. The table must have columns with the names of the
290 dimensions.
291 datasets : `DatasetQueryColumns`, optional
292 Columns that identify a dataset that is part of the query results.
293 """
294 unexpectedDimensions = NamedValueSet(dimensions - self.summary.mustHaveKeysJoined.dimensions)
295 unexpectedDimensions.discard(self.summary.universe.commonSkyPix)
296 if unexpectedDimensions:
297 raise NotImplementedError(
298 f"QueryBuilder does not yet support joining in dimensions {unexpectedDimensions} that "
299 f"were not provided originally to the QuerySummary object passed at construction."
300 )
301 joinOn = self.startJoin(table, dimensions, dimensions.names)
302 self.finishJoin(table, joinOn)
303 if datasets is not None:
304 assert self._columns.datasets is None, \
305 "At most one result dataset type can be returned by a query."
306 self._columns.datasets = datasets
308 def startJoin(self, table: sqlalchemy.sql.FromClause, dimensions: Iterable[Dimension],
309 columnNames: Iterable[str]
310 ) -> List[sqlalchemy.sql.ColumnElement]:
311 """Begin a join on dimensions.
313 Must be followed by call to `finishJoin`.
315 Parameters
316 ----------
317 table : `sqlalchemy.sql.FromClause`
318 SQLAlchemy object representing the logical table (which may be a
319 join or subquery expression) to be joined.
320 dimensions : iterable of `Dimension`
321 The dimensions that relate this table to others that may be in the
322 query. The table must have columns with the names of the
323 dimensions.
324 columnNames : iterable of `str`
325 Names of the columns that correspond to dimension key values; must
326 be `zip` iterable with ``dimensions``.
328 Returns
329 -------
330 joinOn : `list` of `sqlalchemy.sql.ColumnElement`
331 Sequence of boolean expressions that should be combined with AND
332 to form (part of) the ON expression for this JOIN.
333 """
334 joinOn = []
335 for dimension, columnName in zip(dimensions, columnNames):
336 columnInTable = table.columns[columnName]
337 columnsInQuery = self._columns.keys.setdefault(dimension, [])
338 for columnInQuery in columnsInQuery:
339 joinOn.append(columnInQuery == columnInTable)
340 columnsInQuery.append(columnInTable)
341 return joinOn
343 def finishJoin(self, table: sqlalchemy.sql.FromClause, joinOn: List[sqlalchemy.sql.ColumnElement]
344 ) -> None:
345 """Complete a join on dimensions.
347 Must be preceded by call to `startJoin`.
349 Parameters
350 ----------
351 table : `sqlalchemy.sql.FromClause`
352 SQLAlchemy object representing the logical table (which may be a
353 join or subquery expression) to be joined. Must be the same object
354 passed to `startJoin`.
355 joinOn : `list` of `sqlalchemy.sql.ColumnElement`
356 Sequence of boolean expressions that should be combined with AND
357 to form (part of) the ON expression for this JOIN. Should include
358 at least the elements of the list returned by `startJoin`.
359 """
360 onclause: Optional[sqlalchemy.sql.ColumnElement]
361 if len(joinOn) == 0:
362 onclause = None
363 elif len(joinOn) == 1:
364 onclause = joinOn[0]
365 else:
366 onclause = sqlalchemy.sql.and_(*joinOn)
367 self._simpleQuery.join(table, onclause=onclause)
369 def _joinMissingDimensionElements(self) -> None:
370 """Join all dimension element tables that were identified as necessary
371 by `QuerySummary` and have not yet been joined.
373 For internal use by `QueryBuilder` only; will be called (and should
374 only by called) by `finish`.
375 """
376 # Join all DimensionElement tables that we need for spatial/temporal
377 # joins/filters or a nontrivial WHERE expression.
378 # We iterate over these in *reverse* topological order to minimize the
379 # number of tables joined. For example, the "visit" table provides
380 # the primary key value for the "instrument" table it depends on, so we
381 # don't need to join "instrument" as well unless we had a nontrivial
382 # expression on it (and hence included it already above).
383 for element in self.summary.universe.sorted(self.summary.mustHaveTableJoined, reverse=True):
384 self.joinDimensionElement(element)
385 # Join in any requested Dimension tables that don't already have their
386 # primary keys identified by the query.
387 for dimension in self.summary.universe.sorted(self.summary.mustHaveKeysJoined, reverse=True):
388 if dimension not in self._columns.keys:
389 self.joinDimensionElement(dimension)
391 def _addWhereClause(self) -> None:
392 """Add a WHERE clause to the query under construction, connecting all
393 joined dimensions to the expression and data ID dimensions from
394 `QuerySummary`.
396 For internal use by `QueryBuilder` only; will be called (and should
397 only by called) by `finish`.
398 """
399 if self.summary.where.tree is not None:
400 self._simpleQuery.where.append(
401 convertExpressionToSql(
402 self.summary.where.tree,
403 self.summary.universe,
404 columns=self._columns,
405 elements=self._elements,
406 bind=self.summary.where.bind,
407 TimespanReprClass=self._managers.TimespanReprClass,
408 )
409 )
410 for dimension, columnsInQuery in self._columns.keys.items():
411 if dimension in self.summary.where.dataId.graph:
412 givenKey = self.summary.where.dataId[dimension]
413 # Add a WHERE term for each column that corresponds to each
414 # key. This is redundant with the JOIN ON clauses that make
415 # them equal to each other, but more constraints have a chance
416 # of making things easier on the DB's query optimizer.
417 for columnInQuery in columnsInQuery:
418 self._simpleQuery.where.append(columnInQuery == givenKey)
419 else:
420 # Dimension is not fully identified, but it might be a skypix
421 # dimension that's constrained by a given region.
422 if self.summary.where.region is not None and isinstance(dimension, SkyPixDimension):
423 # We know the region now.
424 givenSkyPixIds: List[int] = []
425 for begin, end in dimension.pixelization.envelope(self.summary.where.region):
426 givenSkyPixIds.extend(range(begin, end))
427 for columnInQuery in columnsInQuery:
428 self._simpleQuery.where.append(columnInQuery.in_(givenSkyPixIds))
429 # If we are given an dataId with a timespan, and there are one or more
430 # timespans in the query that aren't given, add a WHERE expression for
431 # each of them.
432 if self.summary.where.dataId.graph.temporal and self.summary.temporal:
433 # Timespan is known now.
434 givenInterval = self.summary.where.dataId.timespan
435 assert givenInterval is not None
436 for element, intervalInQuery in self._columns.timespans.items():
437 assert element not in self.summary.where.dataId.graph.elements
438 self._simpleQuery.where.append(
439 intervalInQuery.overlaps(self._managers.TimespanReprClass.fromLiteral(givenInterval))
440 )
442 def finish(self, joinMissing: bool = True) -> Query:
443 """Finish query constructing, returning a new `Query` instance.
445 Parameters
446 ----------
447 joinMissing : `bool`, optional
448 If `True` (default), automatically join any missing dimension
449 element tables (according to the categorization of the
450 `QuerySummary` the builder was constructed with). `False` should
451 only be passed if the caller can independently guarantee that all
452 dimension relationships are already captured in non-dimension
453 tables that have been manually included in the query.
455 Returns
456 -------
457 query : `Query`
458 A `Query` object that can be executed and used to interpret result
459 rows.
460 """
461 if joinMissing:
462 self._joinMissingDimensionElements()
463 self._addWhereClause()
464 if self._columns.isEmpty():
465 return EmptyQuery(self.summary.requested.universe, managers=self._managers)
466 return DirectQuery(graph=self.summary.requested,
467 uniqueness=DirectQueryUniqueness.NOT_UNIQUE,
468 whereRegion=self.summary.where.dataId.region,
469 simpleQuery=self._simpleQuery,
470 columns=self._columns,
471 managers=self._managers)