SqlQueryContext

class lsst.daf.butler.registry.queries.SqlQueryContext(db: Database, column_types: ColumnTypeInfo, row_chunk_size: int = 1000)

Bases: QueryContext

An implementation of sql.QueryContext for SqlRegistry.

Parameters:

dbDatabase

Object that abstracts the database engine.

sql_engineButlerSqlEngine

Information about column types that can vary with registry configuration.

row_chunk_sizeint, optional

Number of rows to insert into temporary tables at once. If this is lower than db.get_constant_rows_max() it will be set to that value.

Attributes Summary

column_types	Information about column types that depend on registry configuration (ColumnTypeInfo).
is_open	Whether the context manager has been entered (bool).
preferred_engine	Return the relation engine that this context prefers to execute operations in (lsst.daf.relation.Engine).

Methods Summary

any(relation, *[, execute, exact])	Check whether this relation has any result rows at all.
count(relation, *[, exact, discard])	Count the number of rows in the given relation.
drop_invalidated_postprocessing(relation, ...)	Return a modified relation tree without iteration-engine operations that require columns that are not in the given set.
fetch_iterable(relation)	Execute the given relation and return its rows as an iterable of mappings.
make_data_coordinate_predicate(data_coordinate)	Return a Predicate that represents a data ID constraint.
make_data_id_relation(data_ids, dimension_names)	Transform a set of data IDs into a relation.
make_initial_relation([relation])	Construct an initial relation suitable for this context.
make_spatial_region_overlap_predicate(lhs, rhs)	Return a Predicate that tests whether two regions overlap.
make_spatial_region_skypix_predicate(...)	Return a Predicate that tests whether two region columns overlap.
make_timespan_overlap_predicate(tag, timespan)	Return a Predicate that tests whether a timespan column overlaps a timespan literal.
materialize(target, name)	Hook for implementing materialization operations.
process(relation)	Main entry point for processing a relation tree.
restore_columns(relation, columns_required)	Return a modified relation tree that attempts to restore columns that were dropped by a projection operation.
strip_postprocessing(relation)	Return a modified relation tree without any iteration-engine operations and any transfer to the iteration engine at the end.
transfer(source, destination, materialize_as)	Hook for implementing transfers between engines.

Attributes Documentation

column_types

Information about column types that depend on registry configuration (ColumnTypeInfo).

is_open

preferred_engine

Methods Documentation

any(relation: Relation, *, execute: bool = True, exact: bool = True) → bool

Check whether this relation has any result rows at all.

Parameters:

relationRelation

Relation to be checked.

executebool, optional

If True, execute at least a LIMIT 1 query if it cannot be determined prior to execution that the query would return no rows.

exactbool, optional

If True, run the full query and perform post-query filtering if needed, until at least one result row is found. If False, the returned result does not account for post-query filtering, and hence may be True even when all result rows would be filtered out.

Returns:

any_rowsbool

Whether the relation has any rows, or if it may have any rows if exact=False.

Raises:

RuntimeError

Raised if an exact check was requested and could not be obtained without executing the query.

count(relation: Relation, *, exact: bool = True, discard: bool = False) → int

Count the number of rows in the given relation.

Parameters:

relationRelation

Relation whose rows are to be counted.

exactbool, optional

If True (default), return the exact number of rows. If False, returning an upper bound is permitted if it can be done much more efficiently, e.g. by running a SQL SELECT COUNT(*) query but ignoring client-side filtering that would otherwise take place.

discardbool, optional

If True, compute the exact count even if it would require running the full query and then throwing away the result rows after counting them. If False, this is an error, as the user would usually be better off executing the query first to fetch its rows into a new query (or passing exact=False). Ignored if exact=False.

Returns:

n_rowsint

Number of rows in the relation, or an upper bound. This includes duplicates, if there are any.

Raises:

RuntimeError

Raised if an exact count was requested and could not be obtained without fetching and discarding rows.

drop_invalidated_postprocessing(relation: Relation, new_columns: Set[ColumnTag]) → Relation

Return a modified relation tree without iteration-engine operations that require columns that are not in the given set.

Parameters:

relationRelation

Original relation tree.

new_columnsSet [ ColumnTag ]

The only columns that postprocessing operations may require if they are to be retained in the returned relation tree.

Returns:

modifiedRelation

Modified relation tree with postprocessing operations incompatible with new_columns removed.

fetch_iterable(relation: Relation) → RowIterable

Execute the given relation and return its rows as an iterable of mappings.

Parameters:

relationRelation

Relation representing the query to execute.

Returns:

rowsRowIterable

An iterable over rows, with each row a mapping from ColumnTag to column value.

Notes

A transfer to iteration_engine will be added to the root (end) of the relation tree if the root is not already in the iteration engine.

Any transfers from other engines or persistent materializations will be handled by delegating to process before execution in the iteration engine.

To ensure the result is a multi-pass Python collection in memory, ensure the given tree ends with a materialization operation in the iteration engine.

make_data_coordinate_predicate(data_coordinate: DataCoordinate, full: bool | None = None) → Predicate

Return a Predicate that represents a data ID constraint.

Parameters:

data_coordinateDataCoordinate

Data ID whose keys and values should be transformed to predicate equality constraints.

fullbool, optional

Whether to include constraints on implied dimensions (default is to include implied dimensions if data_coordinate has them).

Returns:

predicatelsst.daf.relation.column_expressions.Predicate

New predicate

make_data_id_relation(data_ids: Set[DataCoordinate], dimension_names: Iterable[str]) → Relation

Transform a set of data IDs into a relation.

Parameters:

data_idsSet [ DataCoordinate ]

Data IDs to upload. All must have at least the dimensions given, but may have more.

dimension_namesIterable [ str ]

Names of dimensions that will be the columns of the relation.

Returns:

relationRelation

Relation in the iteration engine.

make_initial_relation(relation: Relation | None = None) → Relation

Construct an initial relation suitable for this context.

Parameters:

relationRelation, optional

A user-provided initial relation. Must be included by implementations when provided, but may be modified (e.g. by adding a transfer to a new engine) when need to satisfy the context’s invariants.

make_spatial_region_overlap_predicate(lhs: ColumnExpression, rhs: ColumnExpression) → Predicate

Return a Predicate that tests whether two regions overlap.

This operation only works with iteration engines; it is usually used to refine the result of a join or constraint on SkyPixDimension columns in SQL.

Parameters:

lhslsst.daf.relation.column_expressions.ColumnExpression

Expression for one spatial region.

rhslsst.daf.relation.column_expressions.ColumnExpression

Expression for the other spatial region.

Returns:

predicatelsst.daf.relation.column_expressions.Predicate

New predicate with lhs and rhs as its required columns.

make_spatial_region_skypix_predicate(dimension: SkyPixDimension, region: Region) → Predicate

Return a Predicate that tests whether two region columns overlap.

This operation only works with iteration engines; it is usually used to refine the result of a join on SkyPixDimension columns in SQL.

Parameters:

dimensionSkyPixDimension

Dimension whose key column is being constrained.

regionlsst.sphgeom.Region

Spatial region constraint to test against.

Returns:

predicatelsst.daf.relation.column_expressions.Predicate

New predicate with the DimensionKeyColumn associated with dimension as its only required column.

make_timespan_overlap_predicate(tag: ColumnTag, timespan: Timespan) → Predicate

Return a Predicate that tests whether a timespan column overlaps a timespan literal.

Parameters:

tagColumnTag

Identifier for a timespan column.

timespanTimespan

Timespan literal selected rows must overlap.

Returns:

predicatelsst.daf.relation.column_expressions.Predicate

New predicate.

materialize(target: Relation, name: str) → Any

Hook for implementing materialization operations.

This method should be called only by the Processor base class.

Parameters:

targetRelation

Relation to be materialized. Any upstream Transfer operations in this tree are guaranteed to already have a payload already attached (or some intervening relation does), so the relation’s own engine should be capable of processing it on its own.

namestr

The name of the Materialization operation, to be used as needed in the engine-specific payload.

Returns:

payload

Payload for this relation that should be cached.

process(relation: Relation) → Relation

Main entry point for processing a relation tree.

Parameters:

relationRelation

Root of the relation tree to process. On return, relations that hold a Materialization relation will have a new payload attached, if they did not have one already.

Returns:

processedRelation

A version of the relation tree in which any relation with a Transfer operation has a copy of the original Transfer that has a payload attached.

restore_columns(relation: Relation, columns_required: Set[ColumnTag]) → tuple[lsst.daf.relation._relation.Relation, set[lsst.daf.relation._columns._tag.ColumnTag]]

Return a modified relation tree that attempts to restore columns that were dropped by a projection operation.

Parameters:

relationRelation

Original relation tree.

columns_requiredSet [ ColumnTag ]

Columns to attempt to restore. May include columns already present in the relation.

Returns:

modifiedRelation

Possibly-modified tree with any projections that had dropped requested columns replaced by projections that do not drop these columns. Care is taken to ensure that join common columns and deduplication behavior is preserved, even if that means some columns are not restored.

columns_foundset [ ColumnTag ]

Columns from those requested that are present in modified.

strip_postprocessing(relation: Relation) → tuple[lsst.daf.relation._relation.Relation, list[lsst.daf.relation._unary_operation.UnaryOperation]]

Return a modified relation tree without any iteration-engine operations and any transfer to the iteration engine at the end.

Parameters:

relationRelation

Original relation tree.

Returns:

modifiedRelation

Stripped relation tree, with engine != iteration_engine.

strippedUnaryOperation

Operations that were stripped, in the same order they should be reapplied (with transfer=True, preferred_engine=iteration_engine) to recover the original tree.

transfer(source: Relation, destination: Engine, materialize_as: str | None) → Any

Hook for implementing transfers between engines.

This method should be called only by the Processor base class.

Parameters:

sourceRelation

Relation to be transferred. Any upstream Transfer operations in this tree are guaranteed to already have a payload already attached (or some intervening relation does), so the relation’s own engine should be capable of processing it on its own.

destinationEngine

Engine the relation is being transferred to.

materialize_asstr or None

If not None, the name of a Materialization operation that immediately follows the transfer being implemented, in which case the returned payload should be appropriate for caching with the Materialization.

Returns:

payload

Payload for this relation in the destination engine.