Query¶

class lsst.daf.butler.registry.queries.Query(*, graph: lsst.daf.butler.core.dimensions._graph.DimensionGraph, whereRegion: Optional[lsst.sphgeom._sphgeom.Region], managers: lsst.daf.butler.registry.queries._structs.RegistryManagers)¶

Bases: abc.ABC

An abstract base class for queries that return some combination of DatasetRef and DataCoordinate objects.

Parameters:	graph : `DimensionGraph` Object describing the dimensions included in the query. whereRegion : `lsst.sphgeom.Region`, optional Region that all region columns in all returned rows must overlap. managers : `RegistryManagers` A struct containing the registry manager instances used by the query system.

Notes

The Query hierarchy abstracts over the database/SQL representation of a particular set of data IDs or datasets. It is expected to be used as a backend for other objects that provide more natural interfaces for one or both of these, not as part of a public interface to query results.

Attributes Summary

`datasetType`	The `DatasetType` of datasets returned by this query, or `None` if there are no dataset results (`DatasetType` or `None`).
`spatial`	An iterator over the dimension element columns used in post-query filtering of spatial overlaps (`Iterator` [ `DimensionElement` ]).
`sql`	A SQLAlchemy object representing the full query (`sqlalchemy.sql.FromClause` or `None`).

Methods Summary

`extractDataId`(row, *, graph, records, …)	Extract a data ID from a result row.
`extractDatasetRef`(row, dataId, records, …)	Extract a `DatasetRef` from a result row.
`extractDimensionsTuple`(row, dimensions)	Extract a tuple of data ID values from a result row.
`getDatasetColumns`()	Return the columns for the datasets returned by this query.
`getDimensionColumn`(name)	Return the query column that contains the primary key value for the dimension with the given name.
`getRegionColumn`(name)	Return a region column for one of the dimension elements iterated over by `spatial`.
`isUnique`()	Return `True` if this query’s rows are guaranteed to be unique, and `False` otherwise.
`makeBuilder`(summary)	Return a `QueryBuilder` that can be used to construct a new `Query` that is joined to (and hence constrained by) this one.
`materialize`(db)	Execute this query and insert its results into a temporary table.
`predicate`(region)	Return a callable that can perform extra Python-side filtering of query results.
`rows`(db, *, region)	Execute the query and yield result rows, applying `predicate`.
`subset`(*, graph, datasets, unique)	Return a new `Query` whose columns and/or rows are (mostly) subset of this one’s.

Attributes Documentation

datasetType¶: The DatasetType of datasets returned by this query, or None if there are no dataset results (DatasetType or None).

spatial¶

An iterator over the dimension element columns used in post-query filtering of spatial overlaps (Iterator [ DimensionElement ]).

Notes

This property is intended primarily as a hook for subclasses to implement and the ABC to call in order to provide higher-level functionality; code that uses Query objects (but does not implement one) should usually not have to access this property.

sql¶

A SQLAlchemy object representing the full query (sqlalchemy.sql.FromClause or None).

This is None in the special case where the query has no columns, and only one logical row.

Methods Documentation

extractDataId(row: Optional[sqlalchemy.engine.RowProxy], *, graph: Optional[DimensionGraph] = None, records: Optional[Mapping[str, Mapping[tuple, DimensionRecord]]] = None) → DataCoordinate¶

Extract a data ID from a result row.

Parameters:

row : sqlalchemy.engine.RowProxy or None: A result row from a SQLAlchemy SELECT query, or None to indicate the row from an EmptyQuery.
graph : DimensionGraph, optional: The dimensions the returned data ID should identify. If not provided, this will be all dimensions in QuerySummary.requested.
records : Mapping [ str, Mapping [ tuple, DimensionRecord ] ]: Nested mapping containing records to attach to the returned DataCoordinate, for which hasRecords will return True. If provided, outer keys must include all dimension element names in graph, and inner keys should be tuples of dimension primary key values in the same order as element.graph.required. If not provided, DataCoordinate.hasRecords will return False on the returned object.

Returns:

dataId : DataCoordinate: A data ID that identifies all required and implied dimensions. If records is not None, this is have hasRecords() return True.

extractDatasetRef(row: sqlalchemy.engine.RowProxy, dataId: Optional[DataCoordinate] = None, records: Optional[Mapping[str, Mapping[tuple, DimensionRecord]]] = None) → DatasetRef¶

Extract a DatasetRef from a result row.

Parameters:

row : sqlalchemy.engine.RowProxy: A result row from a SQLAlchemy SELECT query.
dataId : DataCoordinate: Data ID to attach to the DatasetRef. A minimal (i.e. base class) DataCoordinate is constructed from row if None.
records : Mapping [ str, Mapping [ tuple, DimensionRecord ] ]: Records to use to return an ExpandedDataCoordinate. If provided, outer keys must include all dimension element names in graph, and inner keys should be tuples of dimension primary key values in the same order as element.graph.required.

Returns:

ref : DatasetRef: Reference to the dataset; guaranteed to have DatasetRef.id not None.

extractDimensionsTuple(row: Optional[sqlalchemy.engine.RowProxy], dimensions: Iterable[Dimension]) → tuple¶

Extract a tuple of data ID values from a result row.

Parameters:	row : `sqlalchemy.engine.RowProxy` or `None` A result row from a SQLAlchemy SELECT query, or `None` to indicate the row from an `EmptyQuery`. dimensions : `Iterable` [ `Dimension` ] The dimensions to include in the returned tuple, in order.
Returns:	values : `tuple` A tuple of dimension primary key values.

getDatasetColumns() → Optional[lsst.daf.butler.registry.queries._structs.DatasetQueryColumns]¶

Return the columns for the datasets returned by this query.

Returns:	columns : `DatasetQueryColumns` or `None` Struct containing SQLAlchemy representations of the result columns for a dataset.

Notes

This method is intended primarily as a hook for subclasses to implement and the ABC to call in order to provide higher-level functionality; code that uses Query objects (but does not implement one) should usually not have to call this method.

getDimensionColumn(name: str) → sqlalchemy.sql.elements.ColumnElement¶

Return the query column that contains the primary key value for the dimension with the given name.

Parameters:	name : `str` Name of the dimension.
Returns:	column : `sqlalchemy.sql.ColumnElement`. SQLAlchemy object representing a column in the query.

Notes

This method is intended primarily as a hook for subclasses to implement and the ABC to call in order to provide higher-level functionality; code that uses Query objects (but does not implement one) should usually not have to call this method.

getRegionColumn(name: str) → sqlalchemy.sql.elements.ColumnElement¶

Return a region column for one of the dimension elements iterated over by spatial.

Parameters:	name : `str` Name of the element.
Returns:	column : `sqlalchemy.sql.ColumnElement` SQLAlchemy representing a result column in the query.

Notes

This method is intended primarily as a hook for subclasses to implement and the ABC to call in order to provide higher-level functionality; code that uses Query objects (but does not implement one) should usually not have to call this method.

isUnique() → bool¶

Return True if this query’s rows are guaranteed to be unique, and False otherwise.

If this query has dataset results (datasetType is not None), uniqueness applies to the DatasetRef instances returned by extractDatasetRef from the result of rows. If it does not have dataset results, uniqueness applies to the DataCoordinate instances returned by extractDataId.

makeBuilder(summary: Optional[QuerySummary] = None) → QueryBuilder¶

Return a QueryBuilder that can be used to construct a new Query that is joined to (and hence constrained by) this one.

Parameters:	summary : `QuerySummary`, optional A `QuerySummary` instance that specifies the dimensions and any additional constraints to include in the new query being constructed, or `None` to use the dimensions of `self` with no additional constraints.

materialize(db: lsst.daf.butler.registry.interfaces._database.Database) → Iterator[lsst.daf.butler.registry.queries._query.Query]¶

Execute this query and insert its results into a temporary table.

Parameters:

db : Database: Database engine to execute the query against.

Returns:

context : typing.ContextManager [ MaterializedQuery ]: A context manager that ensures the temporary table is created and populated in __enter__ (returning a MaterializedQuery object backed by that table), and dropped in __exit__. If self is already a MaterializedQuery, __enter__ may just return self and __exit__ may do nothing (reflecting the fact that an outer context manager should already take care of everything else).

predicate(region: Optional[Region] = None) → Callable[[sqlalchemy.engine.RowProxy], bool]¶

Return a callable that can perform extra Python-side filtering of query results.

To get the expected results from a query, the returned predicate must be used to ignore rows for which it returns False; this permits the QueryBuilder implementation to move logic from the database to Python without changing the public interface.

Parameters:	region : `sphgeom.Region`, optional A region that any result-row regions must overlap in order for the predicate to return `True`. If not provided, this will be `self.whereRegion`, if that exists.
Returns:	func : `Callable` A callable that takes a single `sqlalchemy.engine.RowProxy` argmument and returns `bool`.

rows(db: Database, *, region: Optional[Region] = None) → Iterator[Optional[sqlalchemy.engine.RowProxy]]¶

Execute the query and yield result rows, applying predicate.

Parameters:	region : `sphgeom.Region`, optional A region that any result-row regions must overlap in order to be yielded. If not provided, this will be `self.whereRegion`, if that exists.
Yields:	row : `sqlalchemy.engine.RowProxy` or `None` Result row from the query. `None` may yielded exactly once instead of any real rows to indicate an empty query (see `EmptyQuery`).

subset(*, graph: Optional[lsst.daf.butler.core.dimensions._graph.DimensionGraph] = None, datasets: bool = True, unique: bool = False) → lsst.daf.butler.registry.queries._query.Query¶

Return a new Query whose columns and/or rows are (mostly) subset of this one’s.

Parameters:

graph : DimensionGraph, optional: Dimensions to include in the new Query being constructed. If None (default), self.graph is used.
datasets : bool, optional: Whether the new Query should include dataset results. Defaults to True, but is ignored if self does not include dataset results.
unique : bool, optional: Whether the new Query should guarantee unique results (this may come with a performance penalty).

Returns:

query : Query: A query object corresponding to the given inputs. May be self if no changes were requested.

Notes

The way spatial overlaps are handled at present makes it impossible to fully guarantee in general that the new query’s rows are a subset of this one’s while also returning unique rows. That’s because the database is only capable of performing approximate, conservative overlaps via the common skypix system; we defer actual region overlap operations to per-result-row Python logic. But including the region columns necessary to do that postprocessing in the query makes it impossible to do a SELECT DISTINCT on the user-visible dimensions of the query. For example, consider starting with a query with dimensions (instrument, skymap, visit, tract). That involves a spatial join between visit and tract, and we include the region columns from both tables in the results in order to only actually yield result rows (see predicate and rows) where the regions in those two columns overlap. If the user then wants to subset to just (skymap, tract) with unique results, we have two unpalatable options:

we can do a SELECT DISTINCT with just the skymap and tract columns in the SELECT clause, dropping all detailed overlap information and including some tracts that did not actually overlap any of the visits in the original query (but were regarded as _possibly_ overlapping via the coarser, common-skypix relationships);

we can include the tract and visit region columns in the query, and continue to filter out the non-overlapping pairs, but completely disregard the user’s request for unique tracts.

This interface specifies that implementations must do the former, as that’s what makes things efficient in our most important use case (QuantumGraph generation in pipe_base). We may be able to improve this situation in the future by putting exact overlap information in the database, either by using built-in (but engine-specific) spatial database functionality or (more likely) switching to a scheme in which pairwise dimension spatial relationships are explicitly precomputed (for e.g. combinations of instruments and skymaps).

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Query¶