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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 

22 

23__all__ = ("QueryBuilder",) 

24 

25from typing import AbstractSet, Any, Iterable, List, Optional 

26 

27import sqlalchemy.sql 

28 

29from ...core import ( 

30 DimensionElement, 

31 SkyPixDimension, 

32 Dimension, 

33 DatasetType, 

34 SimpleQuery, 

35) 

36 

37from ...core.named import NamedKeyDict, NamedValueAbstractSet, NamedValueSet 

38 

39from .._collectionType import CollectionType 

40from ._structs import QuerySummary, QueryColumns, DatasetQueryColumns, RegistryManagers 

41from .expressions import ClauseVisitor 

42from ._query import DirectQuery, DirectQueryUniqueness, EmptyQuery, Query 

43from ..wildcards import CollectionSearch, CollectionQuery 

44 

45 

46class QueryBuilder: 

47 """A builder for potentially complex queries that join tables based 

48 on dimension relationships. 

49 

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 

64 

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 

71 

72 def joinDimensionElement(self, element: DimensionElement) -> None: 

73 """Add the table for a `DimensionElement` to the query. 

74 

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. 

78 

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. 

82 

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 

97 

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. 

101 

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`. 

107 

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`. 

130 

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 # If we are searching all collections with no constraints, loop over 

147 # RUN collections only, because that will include all datasets. 

148 collectionTypes: AbstractSet[CollectionType] 

149 if collections == CollectionQuery(): 

150 collectionTypes = {CollectionType.RUN} 

151 else: 

152 collectionTypes = CollectionType.all() 

153 datasetRecordStorage = self._managers.datasets.find(datasetType.name) 

154 if datasetRecordStorage is None: 

155 # Unrecognized dataset type means no results. It might be better 

156 # to raise here, but this is consistent with previous behavior, 

157 # which is expected by QuantumGraph generation code in pipe_base. 

158 return False 

159 subsubqueries = [] 

160 runKeyName = self._managers.collections.getRunForeignKeyName() 

161 baseColumnNames = {"id", runKeyName} if isResult else set() 

162 baseColumnNames.update(datasetType.dimensions.required.names) 

163 for rank, collectionRecord in enumerate(collections.iter(self._managers.collections, 

164 collectionTypes=collectionTypes)): 

165 if collectionRecord.type is CollectionType.CALIBRATION: 

166 if datasetType.isCalibration(): 

167 raise NotImplementedError( 

168 f"Query for dataset type '{datasetType.name}' in CALIBRATION-type collection " 

169 f"'{collectionRecord.name}' is not yet supported." 

170 ) 

171 else: 

172 # We can never find a non-calibration dataset in a 

173 # CALIBRATION collection. 

174 continue 

175 ssq = datasetRecordStorage.select(collection=collectionRecord, 

176 dataId=SimpleQuery.Select, 

177 id=SimpleQuery.Select if isResult else None, 

178 run=SimpleQuery.Select if isResult else None) 

179 if ssq is None: 

180 continue 

181 assert {c.name for c in ssq.columns} == baseColumnNames 

182 if findFirst: 

183 ssq.columns.append(sqlalchemy.sql.literal(rank).label("rank")) 

184 subsubqueries.append(ssq.combine()) 

185 if not subsubqueries: 

186 return False 

187 subquery = sqlalchemy.sql.union_all(*subsubqueries) 

188 columns: Optional[DatasetQueryColumns] = None 

189 if isResult: 

190 if findFirst: 

191 # Rewrite the subquery (currently a UNION ALL over 

192 # per-collection subsubqueries) to select the rows with the 

193 # lowest rank per data ID. The block below will set subquery 

194 # to something like this: 

195 # 

196 # WITH {dst}_search AS ( 

197 # SELECT {data-id-cols}, id, run_id, 1 AS rank 

198 # FROM <collection1> 

199 # UNION ALL 

200 # SELECT {data-id-cols}, id, run_id, 2 AS rank 

201 # FROM <collection2> 

202 # UNION ALL 

203 # ... 

204 # ) 

205 # SELECT 

206 # {dst}_window.{data-id-cols}, 

207 # {dst}_window.id, 

208 # {dst}_window.run_id 

209 # FROM ( 

210 # SELECT 

211 # {dst}_search.{data-id-cols}, 

212 # {dst}_search.id, 

213 # {dst}_search.run_id, 

214 # ROW_NUMBER() OVER ( 

215 # PARTITION BY {dst_search}.{data-id-cols} 

216 # ORDER BY rank 

217 # ) AS rownum 

218 # ) {dst}_window 

219 # WHERE 

220 # {dst}_window.rownum = 1; 

221 # 

222 search = subquery.cte(f"{datasetType.name}_search") 

223 windowDataIdCols = [ 

224 search.columns[name].label(name) for name in datasetType.dimensions.required.names 

225 ] 

226 windowSelectCols = [ 

227 search.columns["id"].label("id"), 

228 search.columns[runKeyName].label(runKeyName) 

229 ] 

230 windowSelectCols += windowDataIdCols 

231 assert {c.name for c in windowSelectCols} == baseColumnNames 

232 windowSelectCols.append( 

233 sqlalchemy.sql.func.row_number().over( 

234 partition_by=windowDataIdCols, 

235 order_by=search.columns["rank"] 

236 ).label("rownum") 

237 ) 

238 window = sqlalchemy.sql.select( 

239 windowSelectCols 

240 ).select_from(search).alias( 

241 f"{datasetType.name}_window" 

242 ) 

243 subquery = sqlalchemy.sql.select( 

244 [window.columns[name].label(name) for name in baseColumnNames] 

245 ).select_from( 

246 window 

247 ).where( 

248 window.columns["rownum"] == 1 

249 ).alias(datasetType.name) 

250 else: 

251 subquery = subquery.alias(datasetType.name) 

252 columns = DatasetQueryColumns( 

253 datasetType=datasetType, 

254 id=subquery.columns["id"], 

255 runKey=subquery.columns[runKeyName], 

256 ) 

257 else: 

258 subquery = subquery.alias(datasetType.name) 

259 self.joinTable(subquery, datasetType.dimensions.required, datasets=columns) 

260 return True 

261 

262 def joinTable(self, table: sqlalchemy.sql.FromClause, dimensions: NamedValueAbstractSet[Dimension], *, 

263 datasets: Optional[DatasetQueryColumns] = None) -> None: 

264 """Join an arbitrary table to the query via dimension relationships. 

265 

266 External calls to this method should only be necessary for tables whose 

267 records represent neither datasets nor dimension elements. 

268 

269 Parameters 

270 ---------- 

271 table : `sqlalchemy.sql.FromClause` 

272 SQLAlchemy object representing the logical table (which may be a 

273 join or subquery expression) to be joined. 

274 dimensions : iterable of `Dimension` 

275 The dimensions that relate this table to others that may be in the 

276 query. The table must have columns with the names of the 

277 dimensions. 

278 datasets : `DatasetQueryColumns`, optional 

279 Columns that identify a dataset that is part of the query results. 

280 """ 

281 unexpectedDimensions = NamedValueSet(dimensions - self.summary.requested.dimensions) 

282 unexpectedDimensions.discard(self.summary.universe.commonSkyPix) 

283 if unexpectedDimensions: 

284 raise NotImplementedError( 

285 f"QueryBuilder does not yet support joining in dimensions {unexpectedDimensions} that " 

286 f"were not provided originally to the QuerySummary object passed at construction." 

287 ) 

288 joinOn = self.startJoin(table, dimensions, dimensions.names) 

289 self.finishJoin(table, joinOn) 

290 if datasets is not None: 

291 assert self._columns.datasets is None, \ 

292 "At most one result dataset type can be returned by a query." 

293 self._columns.datasets = datasets 

294 

295 def startJoin(self, table: sqlalchemy.sql.FromClause, dimensions: Iterable[Dimension], 

296 columnNames: Iterable[str] 

297 ) -> List[sqlalchemy.sql.ColumnElement]: 

298 """Begin a join on dimensions. 

299 

300 Must be followed by call to `finishJoin`. 

301 

302 Parameters 

303 ---------- 

304 table : `sqlalchemy.sql.FromClause` 

305 SQLAlchemy object representing the logical table (which may be a 

306 join or subquery expression) to be joined. 

307 dimensions : iterable of `Dimension` 

308 The dimensions that relate this table to others that may be in the 

309 query. The table must have columns with the names of the 

310 dimensions. 

311 columnNames : iterable of `str` 

312 Names of the columns that correspond to dimension key values; must 

313 be `zip` iterable with ``dimensions``. 

314 

315 Returns 

316 ------- 

317 joinOn : `list` of `sqlalchemy.sql.ColumnElement` 

318 Sequence of boolean expressions that should be combined with AND 

319 to form (part of) the ON expression for this JOIN. 

320 """ 

321 joinOn = [] 

322 for dimension, columnName in zip(dimensions, columnNames): 

323 columnInTable = table.columns[columnName] 

324 columnsInQuery = self._columns.keys.setdefault(dimension, []) 

325 for columnInQuery in columnsInQuery: 

326 joinOn.append(columnInQuery == columnInTable) 

327 columnsInQuery.append(columnInTable) 

328 return joinOn 

329 

330 def finishJoin(self, table: sqlalchemy.sql.FromClause, joinOn: List[sqlalchemy.sql.ColumnElement] 

331 ) -> None: 

332 """Complete a join on dimensions. 

333 

334 Must be preceded by call to `startJoin`. 

335 

336 Parameters 

337 ---------- 

338 table : `sqlalchemy.sql.FromClause` 

339 SQLAlchemy object representing the logical table (which may be a 

340 join or subquery expression) to be joined. Must be the same object 

341 passed to `startJoin`. 

342 joinOn : `list` of `sqlalchemy.sql.ColumnElement` 

343 Sequence of boolean expressions that should be combined with AND 

344 to form (part of) the ON expression for this JOIN. Should include 

345 at least the elements of the list returned by `startJoin`. 

346 """ 

347 onclause: Optional[sqlalchemy.sql.ColumnElement] 

348 if len(joinOn) == 0: 

349 onclause = None 

350 elif len(joinOn) == 1: 

351 onclause = joinOn[0] 

352 else: 

353 onclause = sqlalchemy.sql.and_(*joinOn) 

354 self._simpleQuery.join(table, onclause=onclause) 

355 

356 def _joinMissingDimensionElements(self) -> None: 

357 """Join all dimension element tables that were identified as necessary 

358 by `QuerySummary` and have not yet been joined. 

359 

360 For internal use by `QueryBuilder` only; will be called (and should 

361 only by called) by `finish`. 

362 """ 

363 # Join all DimensionElement tables that we need for spatial/temporal 

364 # joins/filters or a nontrivial WHERE expression. 

365 # We iterate over these in *reverse* topological order to minimize the 

366 # number of tables joined. For example, the "visit" table provides 

367 # the primary key value for the "instrument" table it depends on, so we 

368 # don't need to join "instrument" as well unless we had a nontrivial 

369 # expression on it (and hence included it already above). 

370 for element in self.summary.universe.sorted(self.summary.mustHaveTableJoined, reverse=True): 

371 self.joinDimensionElement(element) 

372 # Join in any requested Dimension tables that don't already have their 

373 # primary keys identified by the query. 

374 for dimension in self.summary.universe.sorted(self.summary.mustHaveKeysJoined, reverse=True): 

375 if dimension not in self._columns.keys: 

376 self.joinDimensionElement(dimension) 

377 

378 def _addWhereClause(self) -> None: 

379 """Add a WHERE clause to the query under construction, connecting all 

380 joined dimensions to the expression and data ID dimensions from 

381 `QuerySummary`. 

382 

383 For internal use by `QueryBuilder` only; will be called (and should 

384 only by called) by `finish`. 

385 """ 

386 if self.summary.expression.tree is not None: 

387 visitor = ClauseVisitor(self.summary.universe, self._columns, self._elements) 

388 self._simpleQuery.where.append(self.summary.expression.tree.visit(visitor)) 

389 for dimension, columnsInQuery in self._columns.keys.items(): 

390 if dimension in self.summary.dataId.graph: 

391 givenKey = self.summary.dataId[dimension] 

392 # Add a WHERE term for each column that corresponds to each 

393 # key. This is redundant with the JOIN ON clauses that make 

394 # them equal to each other, but more constraints have a chance 

395 # of making things easier on the DB's query optimizer. 

396 for columnInQuery in columnsInQuery: 

397 self._simpleQuery.where.append(columnInQuery == givenKey) 

398 else: 

399 # Dimension is not fully identified, but it might be a skypix 

400 # dimension that's constrained by a given region. 

401 if self.summary.whereRegion is not None and isinstance(dimension, SkyPixDimension): 

402 # We know the region now. 

403 givenSkyPixIds: List[int] = [] 

404 for begin, end in dimension.pixelization.envelope(self.summary.whereRegion): 

405 givenSkyPixIds.extend(range(begin, end)) 

406 for columnInQuery in columnsInQuery: 

407 self._simpleQuery.where.append(columnInQuery.in_(givenSkyPixIds)) 

408 # If we are given an dataId with a timespan, and there are one or more 

409 # timespans in the query that aren't given, add a WHERE expression for 

410 # each of them. 

411 if self.summary.dataId.graph.temporal and self.summary.temporal: 

412 # Timespan is known now. 

413 givenInterval = self.summary.dataId.timespan 

414 assert givenInterval is not None 

415 for element, intervalInQuery in self._columns.timespans.items(): 

416 assert element not in self.summary.dataId.graph.elements 

417 self._simpleQuery.where.append(intervalInQuery.overlaps(givenInterval)) 

418 

419 def finish(self, joinMissing: bool = True) -> Query: 

420 """Finish query constructing, returning a new `Query` instance. 

421 

422 Parameters 

423 ---------- 

424 joinMissing : `bool`, optional 

425 If `True` (default), automatically join any missing dimension 

426 element tables (according to the categorization of the 

427 `QuerySummary` the builder was constructed with). `False` should 

428 only be passed if the caller can independently guarantee that all 

429 dimension relationships are already captured in non-dimension 

430 tables that have been manually included in the query. 

431 

432 Returns 

433 ------- 

434 query : `Query` 

435 A `Query` object that can be executed and used to interpret result 

436 rows. 

437 """ 

438 if joinMissing: 

439 self._joinMissingDimensionElements() 

440 self._addWhereClause() 

441 if self._columns.isEmpty(): 

442 return EmptyQuery(self.summary.requested.universe, managers=self._managers) 

443 return DirectQuery(graph=self.summary.requested, 

444 uniqueness=DirectQueryUniqueness.NOT_UNIQUE, 

445 whereRegion=self.summary.dataId.region, 

446 simpleQuery=self._simpleQuery, 

447 columns=self._columns, 

448 managers=self._managers)